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Wang T, Chai B, Chen WY, Holmes MD, Erdrich J, Hu FB, Rosner BA, Tamimi RM, Willett WC, Kang JH, Eliassen AH. Metformin and other anti-diabetic medication use and breast cancer incidence in the Nurses' Health Studies. Int J Cancer 2024; 155:211-225. [PMID: 38520039 PMCID: PMC11096056 DOI: 10.1002/ijc.34917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 02/13/2024] [Accepted: 02/20/2024] [Indexed: 03/25/2024]
Abstract
We aimed to examine the association between the use of metformin and other anti-diabetic medications and breast cancer incidence within two large prospective cohort studies. We followed 185,181 women who participated in the Nurses' Health Study (NHS; 1994-2016) and the NHSII (1995-2017), with baseline corresponding to the date metformin was approved for type 2 diabetes (T2D) treatment in the US Information on T2D diagnosis, anti-diabetes medications, and other covariates was self-reported at baseline and repeatedly assessed by follow-up questionnaires every 2 years. Breast cancer cases were self-reported and confirmed by medical record review. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between medication use and breast cancer were estimated using Cox proportional hazards regression models, adjusting for breast cancer risk factors. During 3,324,881 person-years of follow-up, we ascertained 9,192 incident invasive breast cancer cases, of which 451 were among women with T2D. Compared with women without T2D (n = 169,263), neither metformin use (HR = 0.97; 95% CI = 0.81-1.15) nor other anti-diabetic medications use (HR = 1.11; 95% CI = 0.90-1.36) associated with significantly lower breast cancer incidence. Among women with T2D (n = 15,918), compared with metformin never users, metformin ever use was not significantly inversely associated with breast cancer (HR = 0.92; 95% CI = 0.74-1.15). Although we observed that past use of metformin was inversely associated with breast cancer in the T2D population (HR = 0.67; 95% CI = 0.48-0.94), current use (HR = 1.01; 95% CI = 0.80-1.27) and longer duration of metformin use were not associated with breast cancer (each 2-year interval: HR = 1.01; 95% CI = 0.95-1.07). Overall, metformin use was not associated with the risk of developing breast cancer among the overall cohort population or among women with T2D.
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Affiliation(s)
- Tengteng Wang
- Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ
- Division of Medical Oncology, Robert Wood Johnson Medical School, New Brunswick, NJ
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, MA
| | - Boyang Chai
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, MA
| | - Wendy Y. Chen
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, MA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Michelle D. Holmes
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, MA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA
| | | | - Frank B. Hu
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, MA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA
| | - Bernard A. Rosner
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, MA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA
| | - Rulla M. Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
| | - Walter C. Willett
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA
| | - Jae H. Kang
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, MA
| | - A. Heather Eliassen
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, MA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA
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Huang T, Zhu Y, Shutta KH, Balasubramanian R, Zeleznik OA, Rexrode KM, Clish CB, Sun Q, Hu FB, Kubzansky LD, Hankinson SE. A Plasma Metabolite Score Related to Psychological Distress and Diabetes Risk: A Nested Case-control Study in US Women. J Clin Endocrinol Metab 2024; 109:e1434-e1441. [PMID: 38092374 DOI: 10.1210/clinem/dgad731] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Indexed: 05/18/2024]
Abstract
CONTEXT Psychological distress has been linked to diabetes risk. Few population-based, epidemiologic studies have investigated the potential molecular mechanisms (eg, metabolic dysregulation) underlying this association. OBJECTIVE To evaluate the association between a metabolomic signature for psychological distress and diabetes risk. METHODS We conducted a nested case-control study of plasma metabolomics and diabetes risk in the Nurses' Health Study, including 728 women (mean age: 55.2 years) with incident diabetes and 728 matched controls. Blood samples were collected between 1989 and 1990 and incident diabetes was diagnosed between 1992 and 2008. Based on our prior work, we calculated a weighted plasma metabolite-based distress score (MDS) comprised of 19 metabolites. We used conditional logistic regression accounting for matching factors and other diabetes risk factors to estimate odds ratios (OR) and 95% confidence intervals (CI) for diabetes risk according to MDS. RESULTS After adjusting for sociodemographic factors, family history of diabetes, and health behaviors, the OR (95% CI) for diabetes risk across quintiles of the MDS was 1.00 (reference) for Q1, 1.16 (0.77, 1.73) for Q2, 1.30 (0.88, 1.91) for Q3, 1.99 (1.36, 2.92) for Q4, and 2.47 (1.66, 3.67) for Q5. Each SD increase in MDS was associated with 36% higher diabetes risk (95% CI: 1.21, 1.54; P-trend <.0001). This association was moderately attenuated after additional adjustment for body mass index (comparable OR: 1.17; 95% CI: 1.02, 1.35; P-trend = .02). The MDS explained 17.6% of the association between self-reported psychological distress (defined as presence of depression or anxiety symptoms) and diabetes risk (P = .04). CONCLUSION MDS was significantly associated with diabetes risk in women. These results suggest that differences in multiple lipid and amino acid metabolites may underlie the observed association between psychological distress and diabetes risk.
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Affiliation(s)
- Tianyi Huang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA 01003, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Kathryn M Rexrode
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02215, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Boston, MA 02142, USA
| | - Qi Sun
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Laura D Kubzansky
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Susan E Hankinson
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA 01003, USA
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Hu Y, Wang M, Willett WC, Stampfer M, Liang L, Hu FB, Rimm E, Brennan L, Sun Q. Calibration of citrus intake assessed by food frequency questionnaires using urinary proline betaine in an observational study setting. Am J Clin Nutr 2024:S0002-9165(24)00474-X. [PMID: 38762186 DOI: 10.1016/j.ajcnut.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND Whether observational study can be employed to establish calibration equations for self-reported dietary intake using food biomarkers is unknown. OBJECTIVE This study aims to demonstrate the feasibility of obtaining calibration equations based on food biomarkers and 7-day diet records (7DDRs) to correct measurement errors of food frequency questionnaires (FFQ) in an observational study setting. METHODS The study population consisted of 669 male and 749 female from the Women and Men's Lifestyle Validation Study. In the training set, the biomarker-predicted intake derived by regressing 7DDR-assessed intake on urinary proline betaine level was regressed on the FFQ-assessed intake to obtain the calibration equations. The regression coefficients were applied to the test set to calculate the calibrated FFQ intake. We examined total citrus as well as individual citrus fruits/beverages. RESULTS Urinary proline betaine was moderately correlated with orange juice intake [Pearson correlation (r): 0.53 for 7DDR and 0.48 for FFQ] but only weakly correlated with intakes of orange (r: 0.12 for 7DDR and 0.15 for FFQ) and grapefruit (r: 0.14 for 7DDR and 0.09 for FFQ). The FFQ-assessed citrus intake was systematically higher than the 7DDR-assessed intake, and after calibrations the mean calibrated FFQ measurements were almost identical to 7DDR assessments. In the test set, the mean intake levels from 7DDRs, FFQ, and calibrated FFQ were 62.5, 75.3, 63.2 g/d for total citrus, 41.6, 42.5, 41.9 g/d for orange juice, 11.8, 24.3, 12.3 g/d for oranges, and 8.3, 9.3, 8.6 g/d for grapefruit. We observed larger differences between calibrated FFQ and 7DDR assessments at the extreme ends of intake, although on average good agreements were observed for all citrus except grapefruit. CONCLUSION Our two-step calibration approach has the potential to be adapted to correct systematic measurement error for other foods/nutrients with established food biomarkers in a cost-effective way.
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Affiliation(s)
- Yang Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA
| | - Molin Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA;; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA;; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA;; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meir Stampfer
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA;; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA;; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA;; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA;; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA;; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Eric Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA;; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA;; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Lorraine Brennan
- Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Dublin, Ireland
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA, USA;; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA;; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA;.
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Zelicha H, Kaplan A, Yaskolka Meir A, Rinott E, Tsaban G, Blüher M, Klöting N, Ceglarek U, Isermann B, Stumvoll M, Chassidim Y, Shelef I, Hu FB, Shai I. Altered proteome profiles related to visceral adiposity may mediate the favorable effect of green Mediterranean diet: the DIRECT-PLUS trial. Obesity (Silver Spring) 2024. [PMID: 38757229 DOI: 10.1002/oby.24036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 03/08/2024] [Accepted: 03/19/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVE The objective of this study was to explore the effects of a green Mediterranean (green-MED) diet, which is high in dietary polyphenols and green plant-based protein and low in red/processed meat, on cardiovascular disease and inflammation-related circulating proteins and their associations with cardiometabolic risk parameters. METHODS In the 18-month weight loss trial Dietary Intervention Randomized Controlled Trial Polyphenols Unprocessed Study (DIRECT-PLUS), 294 participants with abdominal obesity were randomized to basic healthy dietary guidelines, Mediterranean (MED), or green-MED diets. Both isocaloric MED diet groups consumed walnuts (28 g/day), and the green-MED diet group also consumed green tea (3-4 cups/day) and green shakes (Mankai plant shake, 500 mL/day) and avoided red/processed meat. Proteome panels were measured at three time points using Olink CVDII. RESULTS At baseline, a dominant protein cluster was significantly related to higher phenotypic cardiometabolic risk parameters, with the strongest associations attributed to magnetic resonance imaging-assessed visceral adiposity (false discovery rate of 5%). Overall, after 6 months of intervention, both the MED and green-MED diets induced improvements in cardiovascular disease and proinflammatory risk proteins (p < 0.05, vs. healthy dietary guidelines), with the green-MED diet leading to more pronounced beneficial changes, largely driven by dominant proinflammatory proteins (IL-1 receptor antagonist protein, IL-16, IL-18, thrombospondin-2, leptin, prostasin, galectin-9, and fibroblast growth factor 21; adjusted for age, sex, and weight loss; p < 0.05). After 18 months, proteomics cluster changes presented the strongest correlations with visceral adiposity reduction. CONCLUSIONS Proteomics clusters may enhance our understanding of the favorable effect of a green-MED diet that is enriched with polyphenols and low in red/processed meat on visceral adiposity and cardiometabolic risk.
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Affiliation(s)
- Hila Zelicha
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Alon Kaplan
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Anat Yaskolka Meir
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Ehud Rinott
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Gal Tsaban
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Matthias Blüher
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Nora Klöting
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Berend Isermann
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | | | - Yoash Chassidim
- Department of Engineering, Sapir Academic College, Sapir, Israel
| | - Ilan Shelef
- Soroka University Medical Center, Be'er Sheva, Israel
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
- Harvard Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Iris Shai
- The Health and Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
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Hamaya R, Sun Q, Li J, Yun H, Wang F, Curhan GC, Huang T, Manson JE, Willett WC, Rimm EB, Clish C, Liang L, Hu FB, Ma Y. 24-hour urinary sodium and potassium excretions, plasma metabolomic profiles, and cardiometabolic biomarkers in US adults: A cross-sectional study. Am J Clin Nutr 2024:S0002-9165(24)00473-8. [PMID: 38762185 DOI: 10.1016/j.ajcnut.2024.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/23/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND High sodium and low potassium intakes are associated with a higher risk of hypertension and cardiovascular disease (CVD), but there are limited data on the circulating metabolomics profiles of 24-hour urinary sodium and potassium excretions in free-living individuals. OBJECTIVES To characterize metabolomics signatures of a high-sodium and low-potassium diet in a cross-sectional study. METHODS In 1,028 healthy older adults from the Women's and Men's Lifestyle Validation Studies, we investigated the association of habitual sodium and potassium intakes measured by 2-4 24-hour urine samples with plasma metabolites (quantified using liquid chromatography-tandem mass spectrometry) and metabolomic pathways. Our primary exposures were energy-adjusted 24-hour urinary sodium excretion, potassium excretion, and sodium-to-potassium ratio, calculated based on energy expenditure derived from the Doubly Labelled Water method. Then we assessed the partial correlations of their metabolomics scores, derived from elastic net regressions, with cardiometabolic biomarkers. RESULTS Higher sodium excretion was associated with 38 metabolites including higher piperine, phosphatidylethanolamine, and C5:1 carnitine. In pathway analysis, higher sodium excretion was associated with enhanced biotin and propanoate metabolism, and enhanced degradation of lysine and branched-chain amino acids (BCAAs). Metabolites associated with higher potassium and lower sodium-to-potassium ratio included quinic acid and proline-betaine. After adjusting for confounding factors, the metabolomics score for sodium-to-potassium ratio positively correlated with fasting insulin (Spearman's rank correlation coefficient ρ=0.27), C-peptide (ρ=0.30), and triglyceride (ρ=0.46), and negatively with adiponectin (ρ=-0.40), and high-density lipoprotein (HDL) cholesterol (ρ=-0.42). CONCLUSIONS We discovered metabolites and metabolomics pathways associated with a high-sodium diet, including metabolites related to biotin, propanoate, lysine, and BCAA pathways. The metabolomics signature for a higher sodium-low potassium diet is associated with multiple components of elevated cardiometabolic risk.
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Affiliation(s)
- Rikuta Hamaya
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
| | - Qi Sun
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Jun Li
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Huan Yun
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Gary C Curhan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Renal Division, Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | - Tianyi Huang
- Division of Women's Health, Department of Medicine, Connors Center for Women's Health and Gender Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Mary Horrigan Connors Center for Women's Health and Gender Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Walter C Willett
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Eric B Rimm
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Clary Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
| | - Yuan Ma
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
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Li Y, Huang T, Redline S, Willett WC, Manson JE, Schernhammer ES, Hu FB. Use of melatonin supplements and risk of type 2 diabetes and cardiovascular diseases in the USA: insights from three prospective cohort studies. Lancet Diabetes Endocrinol 2024:S2213-8587(24)00096-2. [PMID: 38710189 DOI: 10.1016/s2213-8587(24)00096-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND Use of melatonin supplements has been increasing substantially in both children and adults in the USA; however, their long-term cardiometabolic effects remain unclear. We aimed to assess the associations between regular use of melatonin supplements and the risk of developing type 2 diabetes or cardiovascular disease in adults. METHODS In this study, we included individuals from three US cohorts: the Nurses' Health Study (women only), the Health Professionals Follow-up Study (men only), and the Nurses' Health Study II (women only). Women aged 25-55 years and men aged 45-75 years at baseline, who had no diagnosis of cancer at baseline, and who responded to the question about melatonin supplement use (yes or no) were included. We excluded baseline prevalent cardiovascular disease and baseline prevalent type 2 diabetes for the main analyses. The main outcomes were cardiovascular disease and type 2 diabetes incidence. In secondary analyses, we stratified by duration of rotating night shift work in the Nurses' Health Study and Nurses' Health Study II to examine whether the associations with melatonin supplement use differed by rotating night shift work. FINDINGS For the cardiovascular disease analysis, we included 67 202 women from the Nurses' Health Study (follow-up 1998-2019, mean age at baseline: 63·6 years [SD 7·1]), 26 629 men from the Health Professionals Follow-up Study (1998-2020, 62·9 years [8·8], and 65 241 women from the Nurses' Health Study II (2003-19, 48·2 years [4·7]). Follow-up for incident type 2 diabetes was from 1998 to June 30, 2021, for the Nurses' Health Study; 2003 to Jan 31, 2023, for the Nurses' Health Study II; and from 1998 to Jan 31, 2020, for the Health Professionals' Follow-up Study. Melatonin supplement use in the study cohorts doubled over recent decades from less than 2% in 1998-2007 to 4% or higher in 2014-15 (4·0% in men and 5·3% in women). We documented 16 917 incident cardiovascular disease events during 2 609 068 person-years of follow-up and 12 730 incident cases of type 2 diabetes during 2 701 830 person-years of follow-up. In a pooled analysis of the three cohorts, comparing users with non-users of melatonin supplements, the pooled multivariable-adjusted hazard ratios were 0·94 (95% CI 0·83-1·06, p=0·32) for cardiovascular disease and 0·98 (0·86-1·12, p=0·80) for type 2 diabetes. In secondary analyses, melatonin supplement use appeared to attenuate the positive association between long-term shift work (>5 years) and risk of cardiovascular disease (pinteraction=0·013) among the female nurses. INTERPRETATION With up to 23 years of follow-up of three large prospective cohorts of middle-aged and older men and women, self-reported melatonin supplement use was not associated with risk of type 2 diabetes or cardiovascular disease. Further research is warranted to assess if melatonin supplement use could mitigate the potential risks of type 2 diabetes and cardiovascular disease associated with rotating night shift work. FUNDING US National Institutes of Health.
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Affiliation(s)
- Yanping Li
- Department of Nutrition, Harvard T H Chan School of Public Health, Boston, MA, USA; Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA.
| | - Tianyi Huang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Susan Redline
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA; Division of Sleep and Circadian Disorders, Department of Medicine and Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Walter C Willett
- Department of Nutrition, Harvard T H Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Eva S Schernhammer
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Center for Public Health, Medical University of Vienna, Austria
| | - Frank B Hu
- Department of Nutrition, Harvard T H Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Pacheco LS, Tobias DK, Li Y, Bhupathiraju SN, Willett WC, Ludwig DS, Ebbeling CB, Haslam DE, Drouin-Chartier JP, Hu FB, Guasch-Ferré M. Corrigendum to 'Sugar-sweetened or artificially-sweetened beverage consumption, physical activity, and risk of cardiovascular disease in adults: a prospective cohort study'The American Journal of Clinical Nutrition volume 119 issue 3 (2024) 669-681. Am J Clin Nutr 2024; 119:1376. [PMID: 38522480 DOI: 10.1016/j.ajcnut.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024] Open
Affiliation(s)
- Lorena S Pacheco
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Yanping Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Shilpa N Bhupathiraju
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States
| | - David S Ludwig
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, MA, United States
| | - Cara B Ebbeling
- New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, MA, United States
| | - Danielle E Haslam
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Jean-Philippe Drouin-Chartier
- Faculté de Pharmacie, Université Laval, Quebec City, Quebec, Canada; Centre Nutrition Santé et Societé (NUTRISS), Institut Sur la Nutrition et les Aliments Fonctionnnels (INAF), Université Laval, Quebec City, Quebec, Canada
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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García-Gavilán JF, Atzeni A, Babio N, Liang L, Belzer C, Vioque J, Corella D, Fitó M, Vidal J, Moreno-Indias I, Torres-Collado L, Coltell O, Toledo E, Clish C, Hernando J, Yun H, Hernández-Cacho A, Jeanfavre S, Dennis C, Gómez-Pérez AM, Martínez MA, Ruiz-Canela M, Tinahones FJ, Hu FB, Salas-Salvadó J. Effect of 1-year lifestyle intervention with energy-reduced Mediterranean diet and physical activity promotion on the gut metabolome and microbiota: a randomized clinical trial. Am J Clin Nutr 2024; 119:1143-1154. [PMID: 38428742 DOI: 10.1016/j.ajcnut.2024.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND The health benefits of the Mediterranean diet (MedDiet) have been linked to the presence of beneficial gut microbes and related metabolites. However, its impact on the fecal metabolome remains poorly understood. OBJECTIVES Our goal was to investigate the weight-loss effects of a 1-y lifestyle intervention based on an energy-reduced MedDiet coupled with physical activity (intervention group), compared with an ad libitum MedDiet (control group), on fecal metabolites, fecal microbiota, and their potential association with cardiovascular disease risk factors. METHODS A total of 400 participants (200 from each study group), aged 55-75 y, and at high cardiovascular disease risk, were included. Dietary and lifestyle information, anthropometric measurements, blood biochemical parameters, and stool samples were collected at baseline and after 1 y of follow-up. Liquid chromatography-tandem mass spectrometry was used to profile endogenous fecal metabolites, and 16S amplicon sequencing was employed to profile the fecal microbiota. RESULTS Compared with the control group, the intervention group exhibited greater weight loss and improvement in various cardiovascular disease risk factors. We identified intervention effects on 4 stool metabolites and subnetworks primarily composed of bile acids, ceramides, and sphingosines, fatty acids, carnitines, nucleotides, and metabolites of purine and the Krebs cycle. Some of these were associated with changes in several cardiovascular disease risk factors. In addition, we observed a reduction in the abundance of the genera Eubacterium hallii group and Dorea, and an increase in alpha diversity in the intervention group after 1 y of follow-up. Changes in the intervention-related microbiota profiles were also associated with alterations in different fecal metabolite subnetworks and some cardiovascular disease risk factors. CONCLUSIONS An intervention based on an energy-reduced MedDiet and physical activity promotion, compared with an ad libitum MedDiet, was associated with improvements in cardiometabolic risk factors, potentially through modulation of the fecal microbiota and metabolome. This trial was registered at https://www.isrctn.com/ as ISRCTN89898870 (https://doi.org/10.1186/ISRCTN89898870).
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Affiliation(s)
- Jesús F García-Gavilán
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Alessandro Atzeni
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
| | - Nancy Babio
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Clara Belzer
- Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands
| | - Jesús Vioque
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Instituto de Investigación Sanitaria y Biomédica de Alicante, Universidad Miguel Hernández (ISABIAL-UMH), Alicante, Spain
| | - Dolores Corella
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Montserrat Fitó
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d'Investigació Médica (IMIM), Barcelona, Spain
| | - Josep Vidal
- CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Department of Endocrinology, Institut d'Investigacions Biomédiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Isabel Moreno-Indias
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga, Spain
| | - Laura Torres-Collado
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain; Instituto de Investigación Sanitaria y Biomédica de Alicante, Universidad Miguel Hernández (ISABIAL-UMH), Alicante, Spain
| | - Oscar Coltell
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Computer Languages and Systems, Jaume I University, Castellón, Spain
| | - Estefanía Toledo
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; Epidemiología y Salud Pública, Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Clary Clish
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Boston, MA, United States
| | - Javier Hernando
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d'Investigació Médica (IMIM), Barcelona, Spain
| | - Huan Yun
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Adrián Hernández-Cacho
- Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Sarah Jeanfavre
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Boston, MA, United States
| | - Courtney Dennis
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Boston, MA, United States
| | - Ana M Gómez-Pérez
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga, Spain
| | - Maria Angeles Martínez
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Miguel Ruiz-Canela
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; Epidemiología y Salud Pública, Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
| | - Francisco J Tinahones
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Department of Endocrinology and Nutrition, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Universitario Virgen de la Victoria, Málaga, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Jordi Salas-Salvadó
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain; Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició, Desenvolupament i Salut Mental (ANUT-DSM), Universitat Rovira i Virgili, Reus, Spain; Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
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Hang D, Du M, Wang L, Wang K, Fang Z, Khandpur N, Rossato SL, Steele EM, Chan AT, Hu FB, Meyerhardt JA, Mozaffarian D, Ogino S, Sun Q, Wong JB, Zhang FF, Song M. Ultra-processed food consumption and mortality among patients with stages I-III colorectal cancer: a prospective cohort study. EClinicalMedicine 2024; 71:102572. [PMID: 38572081 PMCID: PMC10990709 DOI: 10.1016/j.eclinm.2024.102572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/24/2024] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
Background Ultra-processed foods (UPFs) are emerging as a risk factor for colorectal cancer (CRC), yet how post-diagnostic UPF intake may impact CRC prognosis remains unexplored. Methods Data collected from food frequency questionnaires were used to estimate intakes of total UPFs and UPF subgroups (serving/d) at least 6 months but less than 4 years post-diagnosis among 2498 patients diagnosed with stages I-III CRC within the Nurses' Health Study and Health Professionals Follow-up Study during 1980-2016. Hazard ratios (HR) and 95% confidence intervals (CIs) of all-cause, CRC- and cardiovascular disease (CVD)-specific mortality in association with UPF consumption were estimated using an inverse probability weighted multivariable Cox proportional hazards regression model, adjusted for confounders. Findings The mean (SD) age of patients at diagnosis was 68.5 (9.4) years. A total of 1661 deaths were documented, including 321 from CRC and 335 from CVD. Compared to those in the lowest quintile (median = 3.6 servings/d), patients in the highest quintile (median = 10 servings/d) of post-diagnostic UPF intake had higher CVD mortality (HR = 1.65, 95% CI = 1.13-2.40) but not CRC or all-cause mortality. Among UPF subgroups, higher consumption of fats/condiments/sauces was associated with a higher risk of CVD-specific mortality (highest vs. lowest quintile of intake, HR = 1.96, 95% CI = 1.41-2.73), and higher intake of ice cream/sherbet was associated with an increased risk of CRC-specific mortality (highest vs. lowest quintile, HR = 1.86, 95% CI: 1.33-2.61). No statistically significant association was found between UPF subgroups and overall mortality. Interpretation Higher post-diagnostic intake of total UPFs and fats/condiments/sauces in CRC survivors is associated with higher CVD mortality, and higher ice cream/sherbet intake is linked to higher CRC mortality. Funding US National Institutes of Health and the American Cancer Society.
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Affiliation(s)
- Dong Hang
- Department of Epidemiology, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mengxi Du
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Lu Wang
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Kai Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zhe Fang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Neha Khandpur
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Human Nutrition and Health, Wageningen University, Netherlands
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Sinara Laurini Rossato
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Institute of Geography, Universidade Federal de Uberlândia, Minas Gerais, Brazil
| | - Eurídice Martínez Steele
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
- Center for Epidemiological Studies in Health and Nutrition (NUPENS), Faculty of Public Health, University of São Paulo, Brazil
| | - Andrew T. Chan
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank B. Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Jeffrey A. Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
- Tufts School of Medicine and Division of Cardiology, Tufts Medical Center, Boston, MA, USA
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Nutrition, Harvard Medical School, Boston, MA, USA
- Tokyo Medical and Dental University (Institute of Science Tokyo), Tokyo, Japan
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - John B. Wong
- Division of Clinical Decision Making, Tufts Medical Center, Boston, MA, USA
| | - Fang Fang Zhang
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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10
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Tessier AJ, Cortese M, Yuan C, Bjornevik K, Ascherio A, Wang DD, Chavarro JE, Stampfer MJ, Hu FB, Willett WC, Guasch-Ferré M. Consumption of Olive Oil and Diet Quality and Risk of Dementia-Related Death. JAMA Netw Open 2024; 7:e2410021. [PMID: 38709531 DOI: 10.1001/jamanetworkopen.2024.10021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/07/2024] Open
Abstract
Importance Age-standardized dementia mortality rates are on the rise. Whether long-term consumption of olive oil and diet quality are associated with dementia-related death is unknown. Objective To examine the association of olive oil intake with the subsequent risk of dementia-related death and assess the joint association with diet quality and substitution for other fats. Design, Setting, and Participants This prospective cohort study examined data from the Nurses' Health Study (NHS; 1990-2018) and Health Professionals Follow-Up Study (HPFS; 1990-2018). The population included women from the NHS and men from the HPFS who were free of cardiovascular disease and cancer at baseline. Data were analyzed from May 2022 to July 2023. Exposures Olive oil intake was assessed every 4 years using a food frequency questionnaire and categorized as (1) never or less than once per month, (2) greater than 0 to less than or equal to 4.5 g/d, (3) greater than 4.5 g/d to less than or equal to 7 g/d, and (4) greater than 7 g/d. Diet quality was based on the Alternative Healthy Eating Index and Mediterranean Diet score. Main Outcome and Measure Dementia death was ascertained from death records. Multivariable Cox proportional hazards regressions were used to estimate hazard ratios (HRs) and 95% CIs adjusted for confounders including genetic, sociodemographic, and lifestyle factors. Results Of 92 383 participants, 60 582 (65.6%) were women and the mean (SD) age was 56.4 (8.0) years. During 28 years of follow-up (2 183 095 person-years), 4751 dementia-related deaths occurred. Individuals who were homozygous for the apolipoprotein ε4 (APOE ε4) allele were 5 to 9 times more likely to die with dementia. Consuming at least 7 g/d of olive oil was associated with a 28% lower risk of dementia-related death (adjusted pooled HR, 0.72 [95% CI, 0.64-0.81]) compared with never or rarely consuming olive oil (P for trend < .001); results were consistent after further adjustment for APOE ε4. No interaction by diet quality scores was found. In modeled substitution analyses, replacing 5 g/d of margarine and mayonnaise with the equivalent amount of olive oil was associated with an 8% (95% CI, 4%-12%) to 14% (95% CI, 7%-20%) lower risk of dementia mortality. Substitutions for other vegetable oils or butter were not significant. Conclusions and Relevance In US adults, higher olive oil intake was associated with a lower risk of dementia-related mortality, irrespective of diet quality. Beyond heart health, the findings extend the current dietary recommendations of choosing olive oil and other vegetable oils for cognitive-related health.
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Affiliation(s)
- Anne-Julie Tessier
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Marianna Cortese
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Changzheng Yuan
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kjetil Bjornevik
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Alberto Ascherio
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Daniel D Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jorge E Chavarro
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Meir J Stampfer
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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11
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Bogl LH, Strohmaier S, Hu FB, Willett WC, Eliassen AH, Hart JE, Sun Q, Chavarro JE, Field AE, Schernhammer ES. Maternal One-Carbon Nutrient Intake and Risk of Being Overweight or Obese in Their Offspring-A Transgenerational Prospective Cohort Study. Nutrients 2024; 16:1210. [PMID: 38674900 PMCID: PMC11054902 DOI: 10.3390/nu16081210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
We aimed to investigate the associations between maternal intake of folate, vitamin B12, B6, B2, methionine, choline, phosphatidylcholine and betaine during the period surrounding pregnancy and offspring weight outcomes from birth to early adulthood. These associations were examined among 2454 mother-child pairs from the Nurses' Health Study II and Growing Up Today Study. Maternal energy-adjusted nutrient intakes were derived from food frequency questionnaires. Birth weight, body size at age 5 and repeated BMI measurements were considered. Overweight/obesity was defined according to the International Obesity Task Force (<18 years) and World Health Organization guidelines (18+ years). Among other estimands, we report relative risks (RRs) for offspring ever being overweight with corresponding 95% confidence intervals across quintiles of dietary factors, with the lowest quintile as the reference. In multivariate-adjusted models, higher maternal intakes of phosphatidylcholine were associated with a higher risk of offspring ever being overweight (RRQ5vsQ1 = 1.16 [1.01-1.33] p-trend: 0.003). The association was stronger among offspring born to mothers with high red meat intake (high red meat RRQ5vsQ1 = 1.50 [1.14-1.98], p-trend: 0.001; low red meat RRQ5vsQ1 = 1.05 [0.87-1.27], p-trend: 0.46; p-interaction = 0.13). Future studies confirming the association between a higher maternal phosphatidylcholine intake during pregnancy and offspring risk of being overweight or obese are needed.
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Affiliation(s)
- Leonie H. Bogl
- Department of Epidemiology, Center for Public Health, Medical University of Vienna, 1090 Wien, Austria; (L.H.B.); (S.S.)
- School of Health Professions, Bern University of Applied Sciences, 3012 Bern, Switzerland
| | - Susanne Strohmaier
- Department of Epidemiology, Center for Public Health, Medical University of Vienna, 1090 Wien, Austria; (L.H.B.); (S.S.)
| | - Frank B. Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA (J.E.C.)
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA;
| | - Walter C. Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA (J.E.C.)
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA;
| | - A. Heather Eliassen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA (J.E.C.)
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA;
| | - Jaime E. Hart
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA;
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA (J.E.C.)
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA;
| | - Jorge E. Chavarro
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA (J.E.C.)
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA;
| | - Alison E. Field
- Department of Epidemiology, Brown University, Providence, RI 02903, USA
| | - Eva S. Schernhammer
- Department of Epidemiology, Center for Public Health, Medical University of Vienna, 1090 Wien, Austria; (L.H.B.); (S.S.)
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA;
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12
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Willett WC, Hu FB, Forouhi NG. A healthy diet should consider environmental impact. Eur Heart J 2024; 45:1375. [PMID: 38447170 DOI: 10.1093/eurheartj/ehae134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/08/2024] Open
Affiliation(s)
- Walter C Willett
- Department of Nutrition, Harvard T. H. Chan School of Public Health, 665 Huntington Ave., Boston, MA 02115, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T. H. Chan School of Public Health, 665 Huntington Ave., Boston, MA 02115, USA
| | - Nita G Forouhi
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
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13
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Hu FB. Diet strategies for promoting healthy aging and longevity: An epidemiological perspective. J Intern Med 2024; 295:508-531. [PMID: 37867396 PMCID: PMC10939982 DOI: 10.1111/joim.13728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
In recent decades, global life expectancies have risen significantly, accompanied by a marked increase in chronic diseases and population aging. This narrative review aims to summarize recent findings on the dietary factors influencing chronic diseases and longevity, primarily from large cohort studies. First, maintaining a healthy weight throughout life is pivotal for healthy aging and longevity, mirroring the benefits of lifelong, moderate calorie restriction in today's obesogenic food environment. Second, the specific types or food sources of dietary fat, protein, and carbohydrates are more important in influencing chronic disease risk and mortality than their quantity. Third, some traditional diets (e.g., the Mediterranean, Nordic, and Okinawa) and contemporary dietary patterns, such as healthy plant-based diet index, the DASH (dietary approaches to stop hypertension) diet, and alternate healthy eating index, have been associated with lower mortality and healthy longevity. These patterns share many common components (e.g., a predominance of nutrient-rich plant foods; limited red and processed meats; culinary herbs and spices prevalent in global cuisines) while embracing distinct elements from different cultures. Fourth, combining a healthy diet with other lifestyle factors could extend disease-free life expectancies by 8-10 years. While adhering to core principles of healthy diets, it is crucial to adapt dietary recommendations to individual preferences and cultures as well as nutritional needs of aging populations. Public health strategies should aim to create a healthier food environment where nutritious options are readily accessible, especially in public institutions and care facilities for the elderly. Although further mechanistic studies and human trials are needed to better understand molecular effects of diet on aging, there is a pressing need to establish and maintain long-term cohorts studying diet and aging in culturally diverse populations.
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Affiliation(s)
- Frank B. Hu
- Departments of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115. USA
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14
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Birukov A, Guasch-Ferré M, Ley SH, Tobias DK, Wang F, Wittenbecher C, Yang J, Manson JE, Chavarro JE, Hu FB, Zhang C. Lifetime Duration of Breastfeeding and Cardiovascular Risk in Women With Type 2 Diabetes or a History of Gestational Diabetes: Findings From Two Large Prospective Cohorts. Diabetes Care 2024; 47:720-728. [PMID: 38377484 PMCID: PMC11065777 DOI: 10.2337/dc23-1494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/23/2024] [Indexed: 02/22/2024]
Abstract
OBJECTIVE Breastfeeding duration is inversely associated with risks of cardiovascular disease (CVD) and type 2 diabetes in parous women. However, the association among women at high risk, including women with type 2 diabetes or gestational diabetes mellitus (GDM) is unclear. RESEARCH DESIGN AND METHODS We included 15,146 parous women with type 2 diabetes from the Nurses' Health Study I and II (NHS, NHS II) and 4,537 women with a history of GDM from NHS II. Participants reported history of breastfeeding via follow-up questionnaires. Incident CVD by 2017 comprised stroke or coronary heart disease (CHD) (myocardial infarction, coronary revascularization). Adjusted hazard ratios (aHRs) and 95% CIs were estimated using Cox models. RESULTS We documented 1,159 incident CVD cases among women with type 2 diabetes in both cohorts during 188,874 person-years of follow-up and 132 incident CVD cases among women with a GDM history during 100,218 person-years of follow-up. Longer lifetime duration of breastfeeding was significantly associated with lower CVD risk among women with type 2 diabetes, with pooled aHR of 0.68 (95% CI 0.54-0.85) for >18 months versus 0 months and 0.94 (0.91-0.98) per 6-month increment in breastfeeding. Similar associations were observed with CHD (pooled aHR 0.93 [0.88-0.97]) but not with stroke (0.96 [0.91-1.02]) per 6-month increment in breastfeeding. Among women with GDM history, >18 months versus 0 months of breastfeeding was associated with an aHR of 0.49 (0.28-0.86) for total CVD. CONCLUSIONS Longer duration of breastfeeding was associated with lower risk of CVD in women with type 2 diabetes or GDM.
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Affiliation(s)
- Anna Birukov
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sylvia H. Ley
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Deirdre K. Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Clemens Wittenbecher
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Division of Food and Nutrition Science, Department of Life Sciences, SciLifeLab, Chalmers University of Technology, Gothenburg, Sweden
| | - Jiaxi Yang
- Global Centre for Asian Women’s Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - JoAnn E. Manson
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA
| | - Jorge E. Chavarro
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA
| | - Frank B. Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA
| | - Cuilin Zhang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Global Centre for Asian Women’s Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Goldberg DT, Yaskolka Meir A, Tsaban G, Rinott E, Kaplan A, Zelicha H, Klöting N, Ceglarek U, Iserman B, Shelef I, Rosen P, Blüher M, Stumvoll M, Etzion O, Stampfer MJ, Hu FB, Shai I. Novel proteomic signatures may indicate MRI-assessed intrahepatic fat state and changes: The DIRECT PLUS clinical trial. Hepatology 2024:01515467-990000000-00821. [PMID: 38537153 DOI: 10.1097/hep.0000000000000867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 03/03/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND AND AIMS We demonstrated in the randomized 18-month DIRECT PLUS trial (n = 294) that a Mediterranean (MED) diet, supplemented with polyphenol-rich Mankai duckweed, green tea, and walnuts and restricted in red/processed meat, caused substantial intrahepatic fat (IHF%) loss compared with 2 other healthy diets, reducing NAFLD by half, regardless of similar weight loss. Here, we investigated the baseline proteomic profile associated with IHF% and the changes in proteomics associated with IHF% changes induced by lifestyle intervention. APPROACH AND RESULTS We calculated IHF% by proton magnetic resonance spectroscopy (normal IHF% <5% and abnormal IHF% ≥5%). We assayed baseline and 18-month samples for 95 proteomic biomarkers.Participants (age = 51.3 ± 10.8 y; 89% men; and body mass index = 31.3 ± 3.9 kg/m 2 ) had an 89.8% 18-month retention rate; 83% had eligible follow-up proteomics measurements, and 78% had follow-up proton magnetic resonance spectroscopy. At baseline, 39 candidate proteins were significantly associated with IHF% (false discovery rate <0.05), mostly related to immune function pathways (eg, hydroxyacid oxidase 1). An IHF% prediction based on the DIRECT PLUS by combined model ( R2 = 0.47, root mean square error = 1.05) successfully predicted IHF% ( R2 = 0.53) during testing and was stronger than separately inputting proteins/traditional markers ( R2 = 0.43/0.44). The 18-month lifestyle intervention induced changes in 18 of the 39 candidate proteins, which were significantly associated with IHF% change, with proteins related to metabolism, extracellular matrix remodeling, and immune function pathways. Thrombospondin-2 protein change was higher in the green-MED compared to the MED group, beyond weight and IHF% loss ( p = 0.01). Protein principal component analysis revealed differences in the third principal component time distinct interactions across abnormal/normal IHF% trajectory combinations; p < 0.05 for all). CONCLUSIONS Our findings suggest novel proteomic signatures that may indicate MRI-assessed IHF state and changes during lifestyle intervention. Specifically, carbonic anhydrase 5A, hydroxyacid oxidase 1, and thrombospondin-2 protein changes are independently associated with IHF% change, and thrombospondin-2 protein change is greater in the green-MED/high polyphenols diet.
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Affiliation(s)
- Dana T Goldberg
- The Health & Nutrition Innovative International Research Center, Department of Epidemiology, Biostatistics and Community Health Sciences, Faculty of Health Sciences, School of Public Health, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Anat Yaskolka Meir
- The Health & Nutrition Innovative International Research Center, Department of Epidemiology, Biostatistics and Community Health Sciences, Faculty of Health Sciences, School of Public Health, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Gal Tsaban
- The Health & Nutrition Innovative International Research Center, Department of Epidemiology, Biostatistics and Community Health Sciences, Faculty of Health Sciences, School of Public Health, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ehud Rinott
- The Health & Nutrition Innovative International Research Center, Department of Epidemiology, Biostatistics and Community Health Sciences, Faculty of Health Sciences, School of Public Health, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Alon Kaplan
- The Health & Nutrition Innovative International Research Center, Department of Epidemiology, Biostatistics and Community Health Sciences, Faculty of Health Sciences, School of Public Health, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Hila Zelicha
- The Health & Nutrition Innovative International Research Center, Department of Epidemiology, Biostatistics and Community Health Sciences, Faculty of Health Sciences, School of Public Health, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Nora Klöting
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry, and Molecular Diagnostics, University of Leipzig Medical Center, Leipzig, Germany
| | - Berend Iserman
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Institute of Laboratory Medicine, Clinical Chemistry, and Molecular Diagnostics, University of Leipzig Medical Center, Leipzig, Germany
| | - Ilan Shelef
- Department of Diagnostic Imaging, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Philip Rosen
- Department of Diagnostic Imaging, Soroka University Medical Center, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Matthias Blüher
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Ohad Etzion
- Department of Gastroenterology and Liver Diseases, Soroka University Medical Center, Beersheba, Israel
| | - Meir J Stampfer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Iris Shai
- The Health & Nutrition Innovative International Research Center, Department of Epidemiology, Biostatistics and Community Health Sciences, Faculty of Health Sciences, School of Public Health, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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16
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Hong X, Nadeau K, Wang G, Larman B, Smith KN, Pearson C, Ji H, Frischmeyer-Guerrerio P, Liang L, Hu FB, Wang X. Metabolomic profiles during early childhood and risk of food allergies and asthma in multiethnic children from a prospective birth cohort. J Allergy Clin Immunol 2024:S0091-6749(24)00295-1. [PMID: 38548091 DOI: 10.1016/j.jaci.2024.02.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/08/2024] [Accepted: 02/22/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND There are increasing numbers of metabolomic studies in food allergy (FA) and asthma, which, however, are predominantly limited by cross-sectional designs, small sample size, and being conducted in European populations. OBJECTIVE We sought to identify metabolites unique to and shared by children with FA and/or asthma in a racially diverse prospective birth cohort, the Boston Birth Cohort. METHODS Mass spectrometry-based untargeted metabolomic profiling was performed using venous plasma collected in early childhood (n = 811). FA was diagnosed according to clinical symptoms consistent with an acute hypersensitivity reaction at food ingestion and food specific-IgE > 0.35 kU/L. Asthma was defined on the basis of physician diagnosis. Generalized estimating equations were applied to analyze metabolomic associations with FA and asthma, adjusting for potential confounders. RESULTS During a mean ± standard deviation follow-up of 11.8 ± 5.2 years from birth, 78 children developed FA and 171 developed asthma. Androgenic and pregnenolone steroids were significantly associated with a lower risk of FA, especially for egg allergy. N,N,N-trimethyl-5-aminovalerate (odds ratio [OR] = 0.65, 95% confidence interval [CI] = 0.48-0.87), and 1-oleoyl-2-arachidonoyl-sn-glycero-3-phosphoinositol (OR = 0.77; 95% CI = 0.66-0.90) were inversely associated with FA risk. Orotidine (OR = 4.73; 95% CI = 2.2-10.2) and 4-cholesten-3-one (OR = 0.52; 95% CI = 0.35-0.77) were the top 2 metabolites associated with risk of asthma, although they had no association with FA. In comparison, children with both FA and asthma exhibited an altered metabolomic profile that aligned with that of FA, including altered levels of lipids and steroids. CONCLUSION In this US multiethnic prospective birth cohort, unique and shared alterations in plasma metabolites during early childhood were associated with risk of developing FA and/or asthma. These findings await further validation.
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Affiliation(s)
- Xiumei Hong
- Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md.
| | - Kari Nadeau
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Mass
| | - Guoying Wang
- Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md
| | - Ben Larman
- Department of Pathology, Division of Immunology, Johns Hopkins University School of Medicine, Baltimore, Md
| | - Kellie N Smith
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, and the Bloomberg-Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Md
| | - Colleen Pearson
- Department of Pediatrics, Boston University School of Medicine and Boston Medical Center, Boston, Mass
| | - Hongkai Ji
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md
| | - Pamela Frischmeyer-Guerrerio
- Laboratory of Allergic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Liming Liang
- Department of Epidemiology and Biostatistics, T. H. Chan School of Public Health, Harvard University, Boston, Mass
| | - Frank B Hu
- Department of Epidemiology and Biostatistics, T. H. Chan School of Public Health, Harvard University, Boston, Mass; Department of Nutrition, T. H. Chan School of Public Health, Harvard University, Boston, Mass; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass
| | - Xiaobin Wang
- Department of Population, Family and Reproductive Health, Center on the Early Life Origins of Disease, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md; Department of Pediatrics, Division of General Pediatrics & Adolescent Medicine, Johns Hopkins University School of Medicine, Baltimore, Md
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17
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Tessier AJ, Wang F, Liang L, Wittenbecher C, Haslam DE, Eliassen AH, Tobias DK, Li J, Zeleznik OA, Ascherio A, Sun Q, Stampfer MJ, Grodstein F, Rexrode KM, Manson JE, Balasubramanian R, Clish CB, Martínez-González MA, Chavarro JE, Hu FB, Guasch-Ferré M. Plasma metabolites of a healthy lifestyle in relation to mortality and longevity: Four prospective US cohort studies. Med 2024; 5:224-238.e5. [PMID: 38366602 PMCID: PMC10940196 DOI: 10.1016/j.medj.2024.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/09/2023] [Accepted: 01/18/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND A healthy lifestyle is associated with a lower premature mortality risk and with longer life expectancy. However, the metabolic pathways of a healthy lifestyle and how they relate to mortality and longevity are unclear. We aimed to identify and replicate a healthy lifestyle metabolomic signature and examine how it is related to total and cause-specific mortality risk and longevity. METHODS In four large cohorts with 13,056 individuals and 28-year follow-up, we assessed five healthy lifestyle factors, used liquid chromatography mass spectrometry to profile plasma metabolites, and ascertained deaths with death certificates. The unique healthy lifestyle metabolomic signature was identified using an elastic regression. Multivariable Cox regressions were used to assess associations of the signature with mortality and longevity. FINDINGS The identified healthy lifestyle metabolomic signature was reflective of lipid metabolism pathways. Shorter and more saturated triacylglycerol and diacylglycerol metabolite sets were inversely associated with the healthy lifestyle score, whereas cholesteryl ester and phosphatidylcholine plasmalogen sets were positively associated. Participants with a higher healthy lifestyle metabolomic signature had a 17% lower risk of all-cause mortality, 19% for cardiovascular disease mortality, and 17% for cancer mortality and were 25% more likely to reach longevity. The healthy lifestyle metabolomic signature explained 38% of the association between the self-reported healthy lifestyle score and total mortality risk and 49% of the association with longevity. CONCLUSIONS This study identifies a metabolomic signature that measures adherence to a healthy lifestyle and shows prediction of total and cause-specific mortality and longevity. FUNDING This work was funded by the NIH, CIHR, AHA, Novo Nordisk Foundation, and SciLifeLab.
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Affiliation(s)
- Anne-Julie Tessier
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Danielle E Haslam
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - A Heather Eliassen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alberto Ascherio
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meir J Stampfer
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Francine Grodstein
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Kathryn M Rexrode
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
| | - Jorge E Chavarro
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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18
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Wang P, Song M, Eliassen AH, Wang M, Fung TT, Clinton SK, Rimm EB, Hu FB, Willett WC, Tabung FK, Giovannucci EL. Author Correction: Optimal dietary patterns for prevention of chronic disease. Nat Med 2024:10.1038/s41591-024-02889-9. [PMID: 38454128 DOI: 10.1038/s41591-024-02889-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Affiliation(s)
- Peilu Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, China.
| | - Mingyang Song
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Molin Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Teresa T Fung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Simmons University, Boston, MA, USA
| | - Steven K Clinton
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Eric B Rimm
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Walter C Willett
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Fred K Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Edward L Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Nguyen XMT, Li Y, Ivey KL, Whitbourne SB, Willett WC, Hu FB, Cho K, Gaziano M, Djousse L. Data resource profile: nutrition data in the VA million veteran program. Int J Popul Data Sci 2024; 8:2366. [PMID: 38476344 PMCID: PMC10930149 DOI: 10.23889/ijpds.v8i6.2366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024] Open
Abstract
Introduction The Department of Veterans Affairs (VA) Million Veteran Program (MVP) nutrition data is derived from dietary food/beverage intake information collected through a semiquantitative food frequency questionnaire (SFFQ). Methods Estimates of dietary energy, nutrient, and non-nutritive food components intakes data were derived from an extensively validated SFFQ, which assessed the habitual frequency of consumption of 61 food items, added sugar, fried food frequency, and 21 nutritional supplements over the 12 months preceding questionnaire administration. Results Complete nutrition data was available for 353,418 MVP participants as of 30th September 2021. Overall, 91.5% of MVP participants with nutrition data were male with an average age of 65.7 years at enrollment. Participants who completed the SFFQ were primarily White (82.5%), and Blacks accounted for 13.2% of the responders. Mean ± SD energy intake for 353, 418 MVP participants was 1428 ± 616 kcal/day, which was 1434 ± 617 kcal/day for males and 1364 ± 601 kcal/day for females. Energy intake and information on 322 nutrients and non-nutritive food components is available through contact with MVP for research collaborations at www.research.va.gov/mvp. Conclusions The energy and nutrient data derived from MVP SFFQ are an invaluable resource for Veteran health and research. In conjunction with the MVP Lifestyle Survey, electronic health records, and genomic data, MVP nutrition data may be used to assess nutritional status and related risk factors, disease prevalence, and determinants of health that can provide scientific support for the development of evidence-based public health policy and health promotion programs and services for Veterans and general population.
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Affiliation(s)
- Xuan-Mai T. Nguyen
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, IL 61820, USA
| | - Yanping Li
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Harvard T.H. Chan School of Public Health, Department of Nutrition, Boston, MA 02115, USA
| | - Kerry L. Ivey
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Harvard T.H. Chan School of Public Health, Department of Nutrition, Boston, MA 02115, USA
| | - Stacey B. Whitbourne
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Walter C. Willett
- Harvard T.H. Chan School of Public Health, Department of Nutrition, Boston, MA 02115, USA
| | - Frank B. Hu
- Harvard T.H. Chan School of Public Health, Department of Nutrition, Boston, MA 02115, USA
| | - Kelly Cho
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Michael Gaziano
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Luc Djousse
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Harvard T.H. Chan School of Public Health, Department of Nutrition, Boston, MA 02115, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA 02115, USA
- New England Geriatric Research, Education, and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
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Pacheco LS, Tobias DK, Li Y, Bhupathiraju SN, Willett WC, Ludwig DS, Ebbeling CB, Haslam DE, Drouin-Chartier JP, Hu FB, Guasch-Ferré M. Sugar-sweetened or artificially-sweetened beverage consumption, physical activity, and risk of cardiovascular disease in adults: a prospective cohort study. Am J Clin Nutr 2024; 119:669-681. [PMID: 38185281 DOI: 10.1016/j.ajcnut.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/18/2023] [Accepted: 01/03/2024] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Whether physical activity could mitigate the adverse impacts of sugar-sweetened beverages (SSBs) or artificially sweetened beverages (ASBs) on incident cardiovascular disease (CVD) remains uncertain. OBJECTIVES This study aimed to examine the independent and joint associations between SSB or ASB consumption and physical activity and risk of CVD, defined as fatal and nonfatal coronary artery disease and stroke, in adults from 2 United States-based prospective cohort studies. METHODS Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% CIs between SSB or ASB intake and physical activity with incident CVD among 65,730 females in the Nurses' Health Study (1980-2016) and 39,418 males in the Health Professional's Follow-up Study (1986-2016), who were free from chronic diseases at baseline. SSBs and ASBs were assessed every 4-y and physical activity biannually. RESULTS A total of 13,269 CVD events were ascertained during 3,001,213 person-years of follow-up. Compared with those who never/rarely consumed SSBs or ASBs, the HR for CVD for participants consuming ≥2 servings/d was 1.21 (95% CI: 1.12, 1.32; P-trend < 0.001) for SSBs and 1.03 (95% CI: 0.97, 1.09; P-trend = 0.06) for those consuming ≥2 servings/d of ASBs. The HR for CVD per 1 serving increment of SSB per day was 1.18 (95% CI: 1.10, 1.26) and 1.12 (95% CI: 1.04, 1.20) for participants meeting and not meeting physical activity guidelines (≥7.5 compared with <7.5 MET h/wk), respectively. Compared with participants who met physical activity guidelines and never/rarely consumed SSBs, the HR for CVD was 1.47 (95% CI: 1.37, 1.57) for participants not meeting physical activity guidelines and consuming ≥2 servings/wk of SSBs. No significant associations were observed for ASB when stratified by physical activity. CONCLUSIONS Higher SSB intake was associated with CVD risk regardless of physical activity levels. These results support current recommendations to limit the intake of SSBs even for physically active individuals.
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Affiliation(s)
- Lorena S Pacheco
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Yanping Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Shilpa N Bhupathiraju
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States
| | - David S Ludwig
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, MA, United States
| | - Cara B Ebbeling
- New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, MA, United States
| | - Danielle E Haslam
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Jean-Philippe Drouin-Chartier
- Faculté de Pharmacie, Université Laval, Quebec City, Quebec, Canada; Centre Nutrition Santé et Societé (NUTRISS), Institut Sur la Nutrition et les Aliments Fonctionnnels (INAF), Université Laval, Quebec City, Quebec, Canada
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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21
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Wang DD, Li Y, Nguyen XM, Ho YL, Hu FB, Willett WC, Wilson PW, Cho K, Gaziano JM, Djoussé L. Red Meat Intake and the Risk of Cardiovascular Diseases: A Prospective Cohort Study in the Million Veteran Program. J Nutr 2024; 154:886-895. [PMID: 38163586 DOI: 10.1016/j.tjnut.2023.12.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/22/2023] [Accepted: 12/28/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Red meat consumption was associated with an increased risk of cardiovascular disease (CVD) in prospective cohort studies and a profile of biomarkers favoring high CVD risk in short-term controlled trials. However, several recent systematic reviews and meta-analyses concluded with no or weak evidence for limiting red meat intake. OBJECTIVES To prospectively examine the associations between red meat intake and incident CVD in an ongoing cohort study with diverse socioeconomic and racial or ethnic backgrounds. METHODS Our study included 148,506 participants [17,804 female (12.0%)] who were free of cancer, diabetes, and CVD at baseline from the Million Veteran Program. A food frequency questionnaire measured red meat intakes at baseline. Nonfatal myocardial infarction and acute ischemic stroke were identified through a high-throughput phenotyping algorithm, and fatal CVD events were identified by searching the National Death Index. RESULTS Comparing the extreme categories of intake, the multivariate-adjusted relative risks of CVD was 1.18 (95% CI: 1.01, 1.38; P-trend < 0.0001) for total red meat, 1.14 (95% CI: 0.96, 1.36; P-trend = 0.01) for unprocessed red meat, and 1.29 (95% CI: 1.04, 1.60; P-trend = 0.003) for processed red meat. We observed a more pronounced positive association between red meat intake and CVD in African American participants than in White participants (P-interaction = 0.01). Replacing 0.5 servings/d of red meat with 0.5 servings/d of nuts, whole grains, and skimmed milk was associated with 14% (RR: 0.86; 95% CI: 0.83, 0.90), 7% (RR: 0.93; 95% CI: 0.89, 0.96), and 4% (RR: 0.96; 95% CI: 0.94, 0.99) lower risks of CVD, respectively. CONCLUSIONS Red meat consumption is associated with an increased risk of CVD. Our findings support lowering red meat intake and replacing red meat with plant-based protein sources or low-fat dairy foods as a key dietary recommendation for the prevention of CVD.
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Affiliation(s)
- Dong D Wang
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, United States; The Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States.
| | - Yanping Li
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, United States; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Xuan-Mai Nguyen
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, United States; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, United States
| | - Frank B Hu
- The Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Walter C Willett
- The Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Peter Wf Wilson
- Atlanta VA Medical Center, Atlanta, GA, United States; Emory Clinical Cardiovascular Research Institute, Atlanta, GA, United States
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, United States; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, United States; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Luc Djoussé
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, United States; Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
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22
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Nguyen M, Jarvis SE, Chiavaroli L, Mejia SB, Zurbau A, Khan TA, Tobias DK, Willett WC, Hu FB, Hanley AJ, Birken CS, Sievenpiper JL, Malik VS. Consumption of 100% Fruit Juice and Body Weight in Children and Adults: A Systematic Review and Meta-Analysis. JAMA Pediatr 2024; 178:237-246. [PMID: 38227336 PMCID: PMC10792499 DOI: 10.1001/jamapediatrics.2023.6124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 11/13/2023] [Indexed: 01/17/2024]
Abstract
Importance Concerns have been raised that frequent consumption of 100% fruit juice may promote weight gain. Current evidence on fruit juice and weight gain has yielded mixed findings from both observational studies and clinical trials. Objective To synthesize the available evidence on 100% fruit juice consumption and body weight in children and adults. Data Sources MEDLINE, Embase, and Cochrane databases were searched through May 18, 2023. Study Selection Prospective cohort studies of at least 6 months and randomized clinical trials (RCTs) of at least 2 weeks assessing the association of 100% fruit juice with body weight change in children and adults were included. In the trials, fruit juices were compared with noncaloric controls. Data Extraction and Synthesis Data were pooled using random-effects models and presented as β coefficients with 95% CIs for cohort studies and mean differences (MDs) with 95% CIs for RCTs. Main Outcomes and Measures Change in body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) was assessed in children and change in body weight in adults. Results A total of 42 eligible studies were included in this analysis, including 17 among children (17 cohorts; 0 RCTs; 45 851 children; median [IQR] age, 8 [1-15] years) and 25 among adults (6 cohorts; 19 RCTs; 268 095 adults; median [IQR] age among cohort studies, 48 [41-61] years; median [IQR] age among RCTs, 42 [25-59]). Among cohort studies in children, each additional serving per day of 100% fruit juice was associated with a 0.03 (95% CI, 0.01-0.05) higher BMI change. Among cohort studies in adults, studies that did not adjust for energy showed greater body weight gain (0.21 kg; 95% CI, 0.15-0.27 kg) than studies that did adjust for energy intake (-0.08 kg; 95% CI, -0.11 to -0.05 kg; P for meta-regression <.001). RCTs in adults found no significant association of assignment to 100% fruit juice with body weight but the CI was wide (MD, -0.53 kg; 95% CI, -1.55 to 0.48 kg). Conclusion and Relevance Based on the available evidence from prospective cohort studies, in this systematic review and meta-analysis, 1 serving per day of 100% fruit juice was associated with BMI gain among children. Findings in adults found a significant association among studies unadjusted for total energy, suggesting potential mediation by calories. Further trials of 100% fruit juice and body weight are desirable. Our findings support guidance to limit consumption of fruit juice to prevent intake of excess calories and weight gain.
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Affiliation(s)
- Michelle Nguyen
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Sarah E. Jarvis
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Laura Chiavaroli
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto 3D Knowledge Synthesis & Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St Michael’s Hospital, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Ontario, Canada
| | - Sonia Blanco Mejia
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto 3D Knowledge Synthesis & Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St Michael’s Hospital, Toronto, Ontario, Canada
| | - Andreea Zurbau
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto 3D Knowledge Synthesis & Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St Michael’s Hospital, Toronto, Ontario, Canada
| | - Tauseef A. Khan
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto 3D Knowledge Synthesis & Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St Michael’s Hospital, Toronto, Ontario, Canada
| | - Deirdre K. Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Walter C. Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Frank B. Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Anthony J. Hanley
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of Endocrinology, University of Toronto, Toronto, Ontario, Canada
- Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Catherine S. Birken
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
- Child Health Evaluative Sciences, SickKids Research Institute, Toronto, Ontario, Canada
| | - John L. Sievenpiper
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto 3D Knowledge Synthesis & Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St Michael’s Hospital, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Ontario, Canada
- Division of Endocrinology & Metabolism, St Michael’s Hospital, Toronto, Ontario, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Vasanti S. Malik
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Ontario, Canada
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Sotos-Prieto M, Rodriguez-Artalejo F, Fung TT, Meyer HE, Hu FB, Willett WC, Bhupathiraju SN. Plant-Based Diets and Risk of Hip Fracture in Postmenopausal Women. JAMA Netw Open 2024; 7:e241107. [PMID: 38421643 PMCID: PMC10905300 DOI: 10.1001/jamanetworkopen.2024.1107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/14/2024] [Indexed: 03/02/2024] Open
Abstract
Importance Previous research has found that vegetarian diets are associated with lower bone mineral density and higher risk of fractures, but these studies did not differentiate the quality of the plant-based foods. Objective To examine the association between the quality of plant-based diets (not necessarily vegan but also omnivorous) and hip fracture risk among postmenopausal women in the Nurses' Health Study. Design, Setting, and Participants This cohort study analyzed data from 70 285 postmenopausal women who participated in the US Nurses' Health Study from 1984 through 2014. Data were analyzed from January 1 to July 31, 2023. Main outcomes and Measures Hip fractures were self-reported on biennial questionnaires. Diet was assessed every 4 years using a validated semiquantitative food frequency questionnaire. Plant-based diet quality was assessed using 2 previously established indices: the healthful Plant-Based Diet Index (hPDI), for which healthy plant foods (whole grains, fruits, vegetables, nuts, legumes, vegetable oils, and tea or coffee) received positive scores, whereas less healthy plant foods (fruit juices, sweetened beverages, refined grains, potatoes, and sweets or desserts) and animal foods received reversed scores; and the unhealthful Plant-Based Diet Index (uPDI), for which positive scores were given to less healthy plant foods and reversed scores to healthy plant and animal foods. Quintile scores of 18 food groups were summed, with a theoretical range for both indices of 18 to 90 (highest adherence). Cox proportional hazards regression with time-varying covariates was used to compute hazard ratios (HRs) and 95% CIs for hip fracture. Results In total, 70 285 participants (mean [SD] age, 54.92 [4.48] years; 100% White women) were included, and 2038 cases of hip fracture were ascertained during the study and for up to 30 years of follow-up. Neither the hPDI (HR for highest vs lowest quintile, 0.97 [95% CI, 0.83-1.14]) nor the uPDI (HR for highest vs lowest quintile, 1.02 [95% CI, 0.87-1.20]) for long-term diet adherence was associated with hip fracture risk. However, when examining recent intake for the highest vs lowest quintiles, the hPDI was associated with 21% lower risk of hip fracture (HR, 0.79 [95% CI, 0.68-0.92]; P = .02 for trend), and the uPDI was associated with 28% higher risk (1.28 [95% CI, 1.09-1.51]; P = .008 for trend). Conclusions and Relevance Findings of this cohort study indicated that long-term adherence to healthful or unhealthful plant-based diets as assessed by hPDI and uPDI scores was not associated with hip fracture risk. Future research should clarify whether the associations observed with recent dietary intake are due to short-term effects of these dietary patterns, reverse causality, or both.
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Affiliation(s)
- Mercedes Sotos-Prieto
- Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
- CIBERESP (CIBER of Epidemiology and Public Health), Madrid, Spain
- IMDEA-Food Institute, Campus of International Excellence and University of Madrid and Spanish National Research Council (CEI UAM-CSIC), Madrid, Spain
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Fernando Rodriguez-Artalejo
- Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain
- CIBERESP (CIBER of Epidemiology and Public Health), Madrid, Spain
- IMDEA-Food Institute, Campus of International Excellence and University of Madrid and Spanish National Research Council (CEI UAM-CSIC), Madrid, Spain
| | - Teresa T. Fung
- Department of Nutrition, Simmons University, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Haakon E. Meyer
- Department of Community Medicine and Global Health, University of Oslo, Oslo, Norway
- Division of Mental and Physical Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Frank B. Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Walter C. Willett
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Shilpa N. Bhupathiraju
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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Semnani-Azad Z, Toledo E, Babio N, Ruiz-Canela M, Wittenbecher C, Razquin C, Wang F, Dennis C, Deik A, Clish CB, Corella D, Fitó M, Estruch R, Arós F, Ros E, García-Gavilan J, Liang L, Salas-Salvadó J, Martínez-González MA, Hu FB, Guasch-Ferré M. Plasma metabolite predictors of metabolic syndrome incidence and reversion. Metabolism 2024; 151:155742. [PMID: 38007148 PMCID: PMC10872312 DOI: 10.1016/j.metabol.2023.155742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 11/19/2023] [Accepted: 11/19/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND Metabolic Syndrome (MetS) is a progressive pathophysiological state defined by a cluster of cardiometabolic traits. However, little is known about metabolites that may be predictors of MetS incidence or reversion. Our objective was to identify plasma metabolites associated with MetS incidence or MetS reversion. METHODS The study included 1468 participants without cardiovascular disease (CVD) but at high CVD risk at enrollment from two case-cohort studies nested within the PREvención con DIeta MEDiterránea (PREDIMED) study with baseline metabolomics data. MetS was defined in accordance with the harmonized International Diabetes Federation and the American Heart Association/National Heart, Lung, and Blood Institute criteria, which include meeting 3 or more thresholds for waist circumference, triglyceride, HDL cholesterol, blood pressure, and fasting blood glucose. MetS incidence was defined by not having MetS at baseline but meeting the MetS criteria at a follow-up visit. MetS reversion was defined by MetS at baseline but not meeting MetS criteria at a follow-up visit. Plasma metabolome was profiled by LC-MS. Multivariable-adjusted Cox regression models and elastic net regularized regressions were used to assess the association of 385 annotated metabolites with MetS incidence and MetS reversion after adjusting for potential risk factors. RESULTS Of the 603 participants without baseline MetS, 298 developed MetS over the median 4.8-year follow-up. Of the 865 participants with baseline MetS, 285 experienced MetS reversion. A total of 103 and 88 individual metabolites were associated with MetS incidence and MetS reversion, respectively, after adjusting for confounders and false discovery rate correction. A metabolomic signature comprised of 77 metabolites was robustly associated with MetS incidence (HR: 1.56 (95 % CI: 1.33-1.83)), and a metabolomic signature of 83 metabolites associated with MetS reversion (HR: 1.44 (95 % CI: 1.25-1.67)), both p < 0.001. The MetS incidence and reversion signatures included several lipids (mainly glycerolipids and glycerophospholipids) and branched-chain amino acids. CONCLUSION We identified unique metabolomic signatures, primarily comprised of lipids (including glycolipids and glycerophospholipids) and branched-chain amino acids robustly associated with MetS incidence; and several amino acids and glycerophospholipids associated with MetS reversion. These signatures provide novel insights on potential distinct mechanisms underlying the conditions leading to the incidence or reversion of MetS.
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Affiliation(s)
- Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Estefanía Toledo
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Nancy Babio
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere i Virgili, Hospital Universitari Sant Joan de Reus, Reus, Spain.
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Clemens Wittenbecher
- Division of Food and Nutrition Sciences, Department of Biology, Chalmers University of Technology, Gothenburg, Sweden.
| | - Cristina Razquin
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Courtney Dennis
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Amy Deik
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Dolores Corella
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain.
| | - Montserrat Fitó
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; IMIM Hospital del Mar Medical Research Institute, Grup de Risc Cardiovascular i Nutrició, Barcelona, Spain.
| | - Ramon Estruch
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain.
| | - Fernando Arós
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, Vitoria-Gasteiz, Spain; University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain.
| | - Emilio Ros
- Lipid Clinic, Department of Endocrinology and Nutrition, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain.
| | - Jesús García-Gavilan
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain.
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Jordi Salas-Salvadó
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Reus, Spain; Institut d'Investigació Sanitària Pere i Virgili, Hospital Universitari Sant Joan de Reus, Reus, Spain.
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), University of Copenhagen, Copenhagen, Denmark.
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25
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Jenkins DJA, Willett WC, Yusuf S, Hu FB, Glenn AJ, Liu S, Mente A, Miller V, Bangdiwala SI, Gerstein HC, Sieri S, Ferrari P, Patel AV, McCullough ML, Le Marchand L, Freedman ND, Loftfield E, Sinha R, Shu XO, Touvier M, Sawada N, Tsugane S, van den Brandt PA, Shuval K, Khan TA, Paquette M, Sahye-Pudaruth S, Patel D, Siu TFY, Srichaikul K, Kendall CWC, Sievenpiper JL. Association of glycaemic index and glycaemic load with type 2 diabetes, cardiovascular disease, cancer, and all-cause mortality: a meta-analysis of mega cohorts of more than 100 000 participants. Lancet Diabetes Endocrinol 2024; 12:107-118. [PMID: 38272606 DOI: 10.1016/s2213-8587(23)00344-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 01/27/2024]
Abstract
BACKGROUND There is debate over whether the glycaemic index of foods relates to chronic disease. We aimed to assess the associations between glycaemic index (GI) and glycaemic load (GL) and type 2 diabetes, cardiovascular disease, diabetes-related cancers, and all-cause mortality. METHODS We did a meta-analysis of large cohorts (≥100 000 participants) identified from the Richard Doll Consortium. We searched the Cochrane Library, MEDLINE, PubMed, Embase, Web of Science, and Scopus for cohorts that prospectively examined associations between GI or GL and chronic disease outcomes published from database inception to Aug 4, 2023. Full-article review and extraction of summary estimates data were conducted by three independent reviewers. Primary outcomes were incident type 2 diabetes, total cardiovascular disease (including mortality), diabetes-related cancers (ie, bladder, breast, colorectal, endometrial, hepatic, pancreatic, and non-Hodgkin lymphoma), and all-cause mortality. We assessed comparisons between the lowest and highest quantiles of GI and GL, adjusting for dietary factors, and pooling their most adjusted relative risk (RR) estimates using a fixed-effects model. We also assessed associations between diets high in fibre and whole grains and the four main outcomes. The study protocol is registered with PROSPERO, CRD42023394689. FINDINGS From ten prospective large cohorts (six from the USA, one from Europe, two from Asia, and one international), we identified a total of 48 studies reporting associations between GI or GL and the outcomes of interest: 34 (71%) on various cancers, nine (19%) on cardiovascular disease, five (10%) on type 2 diabetes, and three (6%) on all-cause mortality. Consumption of high GI foods was associated with an increased incidence of type 2 diabetes (RR 1·27 [95% CI 1·21-1·34]; p<0·0001), total cardiovascular disease (1·15 [1·11-1·19]; p<0·0001), diabetes-related cancer (1·05 [1·02-1·08]; p=0·0010), and all-cause mortality (1·08 [1·05-1·12]; p<0·0001). Similar associations were seen between high GL and diabetes (RR 1·15 [95% CI 1·09-1·21]; p<0·0001) and total cardiovascular disease (1·15 [1·10-1·20]; p<0·0001). Associations between diets high in fibre and whole grains and the four main outcomes were similar to those for low GI diets. INTERPRETATION Dietary recommendations to reduce GI and GL could have effects on health outcomes that are similar to outcomes of recommendations to increase intake of fibre and whole grain. FUNDING Banting and Best and the Karuna Foundation.
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Affiliation(s)
- David J A Jenkins
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada; Clinical Nutrition & Risk Factor Modification Centre, St Michael's Hospital, Toronto, ON, Canada; Division of Endocrinology and Metabolism, St Michael's Hospital, Toronto, ON, Canada; Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada.
| | - Walter C Willett
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Salim Yusuf
- Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada; Department of Medicine, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada
| | - Frank B Hu
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA; Department of Nutrition, Harvard T H Chan School of Public Health, Boston, MA, USA; Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrea J Glenn
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada; Department of Nutrition, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Simin Liu
- Center for Global Cardiometabolic Health, Department of Epidemiology, Department of Medicine, and Department of Surgery, Brown University, Providence, RI, USA
| | - Andrew Mente
- Department of Medicine, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada
| | - Victoria Miller
- Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada; Department of Medicine, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada
| | - Shrikant I Bangdiwala
- Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada; Department of Health Research Methods, Evidence, and Impact, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada
| | - Hertzel C Gerstein
- Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, ON, Canada
| | - Sabina Sieri
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Pietro Ferrari
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Alpa V Patel
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | | | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Erikka Loftfield
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rashmi Sinha
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xiao-Ou Shu
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mathilde Touvier
- Université Sorbonne Paris Nord and Université Paris Cité, INSERM, INRAE, CNAM, Center of Research in Epidemiology and Statistics, Nutritional Epidemiology Research Team, Bobigny, France; French Network for Nutrition and Cancer Research, Jouy-en-Josas, France
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan
| | - Shoichiro Tsugane
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan; International University of Health and Welfare Graduate School of Public Health, Tokyo, Japan
| | - Piet A van den Brandt
- GROW School for Oncology and Developmental Biology, and Department of Epidemiology, Care and Public Health Research Institute-School for Public Health and Primary Care, Maastricht University Medical Centre, Maastricht, Netherlands
| | | | - Tauseef Ahmad Khan
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada
| | - Melanie Paquette
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Clinical Nutrition & Risk Factor Modification Centre, St Michael's Hospital, Toronto, ON, Canada
| | - Sandhya Sahye-Pudaruth
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Clinical Nutrition & Risk Factor Modification Centre, St Michael's Hospital, Toronto, ON, Canada
| | - Darshna Patel
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Clinical Nutrition & Risk Factor Modification Centre, St Michael's Hospital, Toronto, ON, Canada
| | - Teenie Fei Yi Siu
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Clinical Nutrition & Risk Factor Modification Centre, St Michael's Hospital, Toronto, ON, Canada
| | - Korbua Srichaikul
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Clinical Nutrition & Risk Factor Modification Centre, St Michael's Hospital, Toronto, ON, Canada
| | - Cyril W C Kendall
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada; College of Pharmacy and Nutrition, University of Saskatchewan, SK, Canada
| | - John L Sievenpiper
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada; Clinical Nutrition & Risk Factor Modification Centre, St Michael's Hospital, Toronto, ON, Canada; Division of Endocrinology and Metabolism, St Michael's Hospital, Toronto, ON, Canada; Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada
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26
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Wang T, Fu Y, Shuai M, Zheng JS, Zhu L, Chan AT, Sun Q, Hu FB, Weiss ST, Liu YY. Microbiome-based correction for random errors in nutrient profiles derived from self-reported dietary assessments. bioRxiv 2024:2023.11.21.568102. [PMID: 38045337 PMCID: PMC10690180 DOI: 10.1101/2023.11.21.568102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach --- Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.
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Affiliation(s)
- Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yuanqing Fu
- School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Menglei Shuai
- School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ju-Sheng Zheng
- School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Lu Zhu
- Department of Epidemiology, University of Iowa College of Public Health, Iowa City, IA 52242, USA
| | - Andrew T. Chan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Qi Sun
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Frank B. Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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27
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Margara-Escudero HJ, Paz-Graniel I, García-Gavilán J, Ruiz-Canela M, Sun Q, Clish CB, Toledo E, Corella D, Estruch R, Ros E, Castañer O, Arós F, Fiol M, Guasch-Ferré M, Lapetra J, Razquin C, Dennis C, Deik A, Li J, Gómez-Gracia E, Babio N, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease. Cardiovasc Diabetol 2024; 23:38. [PMID: 38245716 PMCID: PMC10800064 DOI: 10.1186/s12933-023-02111-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/29/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD. METHODS The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models. RESULTS Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61-0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86-1.19; p = 0.817) over the follow-up period. CONCLUSIONS This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease. TRIAL REGISTRATION ISRCTN35739639.
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Affiliation(s)
- Hernando J Margara-Escudero
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Miguel Ruiz-Canela
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clary B Clish
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Estefania Toledo
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Navarre, Spain
| | - Dolores Corella
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramón Estruch
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Lipid Clinic, Hospital Clínic, Barcelona, Spain
| | - Olga Castañer
- Centro de Investigación Biomédica en Red (CIBERESP) de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular Risk and Nutrition Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Fernando Arós
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Illes Balears Health Research Institute (IdISBa), Hospital Son Espases, Palma, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - José Lapetra
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Seville, Spain
| | - Cristina Razquin
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Courtney Dennis
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Enrique Gómez-Gracia
- Preventive Medicine and Public Health Department, School of Medicine, University of Málaga, 29010, Malaga, Spain
- Biomedical Research Institute of Malaga-IBIMA Plataforma BIONAND, 29010, Malaga, Spain
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Miguel A Martínez-González
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
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Wang Y, Liu B, Han H, Hu Y, Zhu L, Rimm EB, Hu FB, Sun Q. Correction: Associations Between Plant-Based Dietary Patterns and Risks of Type 2 Diabetes, Cardiovascular Disease, Cancer, and Mortality - A Systematic Review and Meta-analysis. Nutr J 2024; 23:6. [PMID: 38178231 PMCID: PMC10765640 DOI: 10.1186/s12937-023-00891-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024] Open
Affiliation(s)
- Yeli Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Binkai Liu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Han Han
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yang Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Lu Zhu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Eric B Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
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Tobias DK, Hamaya R, Clish CB, Liang L, Deik A, Dennis C, Bullock K, Zhang C, Hu FB, Manson JE. Type 2 diabetes metabolomics score and risk of progression to type 2 diabetes among women with a history of gestational diabetes mellitus. Diabetes Metab Res Rev 2024; 40:e3763. [PMID: 38287718 PMCID: PMC10842268 DOI: 10.1002/dmrr.3763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/08/2023] [Accepted: 11/05/2023] [Indexed: 01/31/2024]
Abstract
BACKGROUND Several metabolites are individually related to incident type 2 diabetes (T2D) risk. We prospectively evaluated a novel T2D-metabolite pattern with a risk of progression to T2D among high-risk women with a history of gestational diabetes mellitus (GDM). METHODS The longitudinal Nurses' Health Study II cohort enroled 116,429 women in 1989 and collected blood samples from 1996 to 1999. We profiled plasma metabolites in 175 incident T2D cases and 175 age-matched controls, all with a history of GDM before the blood draw. We derived a metabolomics score from 21 metabolites previously associated with incident T2D in the published literature by scoring according to the participants' quintile (1-5 points) of each metabolite. We modelled the T2D metabolomics score categorically in quartiles and continuously per 1 standard deviation (SD) with the risk of incident T2D using conditional logistic regression models adjusting for body mass index at the blood draw, and other established T2D risk factors. RESULTS The percentage of women progressing to T2D ranged from 10% in the bottom T2D metabolomics score quartile to 78% in the highest score quartile. Adjusting for established T2D risk factors, women in the highest quartile had more than a 20-fold greater diabetes risk than women in the lowest quartile (odds ratios [OR] = 23.1 [95% CI = 8.6, 62.1]; p for trend<0.001). The continuous T2D metabolomics score was strongly and positively associated with incident T2D (adjusted OR = 2.7 per SD [95% CI = 1.9, 3.7], p < 0.0001). CONCLUSIONS A pattern of plasma metabolites among high-risk women is associated with a markedly elevated risk of progression to T2D later in life.
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Affiliation(s)
- Deirdre K. Tobias
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Nutrition Department, Harvard TH Chan School of Public Health, Boston, MA
| | - Rikuta Hamaya
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Epidemiology Department, Harvard TH Chan School of Public Health, Boston, MA
| | | | - Liming Liang
- Biostatistics Department, Harvard TH Chan School of Public Health, Boston, MA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA
| | | | | | - Cuilin Zhang
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD
| | - Frank B. Hu
- Nutrition Department, Harvard TH Chan School of Public Health, Boston, MA
- Epidemiology Department, Harvard TH Chan School of Public Health, Boston, MA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - JoAnn E. Manson
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
- Epidemiology Department, Harvard TH Chan School of Public Health, Boston, MA
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30
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Lee DH, Jin Q, Shi N, Wang F, Bever AM, Liang L, Hu FB, Song M, Zeleznik OA, Zhang X, Joshi A, Wu K, Jeon JY, Meyerhardt JA, Chan AT, Eliassen AH, Clish C, Clinton SK, Giovannucci EL, Li J, Tabung FK. The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes. Diabetologia 2024; 67:88-101. [PMID: 37816982 DOI: 10.1007/s00125-023-06021-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 08/31/2023] [Indexed: 10/12/2023]
Abstract
AIMS/HYPOTHESIS Diets with higher inflammatory and insulinaemic potential have been associated with an increased risk of type 2 diabetes. However, it remains unknown whether plasma metabolomic profiles related to proinflammatory/hyperinsulinaemic diets and to inflammatory/insulin biomarkers are associated with type 2 diabetes risk. METHODS We analysed 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study to identify the plasma metabolome related to empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), four circulating inflammatory biomarkers and C-peptide. Dietary intakes were assessed using validated food frequency questionnaires. Plasma metabolomic profiling was conducted by LC-MS/MS. Metabolomic signatures were derived using elastic net regression. Multivariable Cox regression was used to examine associations of the metabolomic profiles with type 2 diabetes risk. RESULTS We identified 27 metabolites commonly associated with both EDIP and inflammatory biomarker z score and 21 commonly associated with both EDIH and C-peptide. Higher metabolomic dietary inflammatory potential (MDIP), reflecting higher metabolic potential of both an inflammatory dietary pattern and circulating inflammatory biomarkers, was associated with higher type 2 diabetes risk. The HR comparing highest vs lowest quartiles of MDIP was 3.26 (95% CI 2.39, 4.44). We observed a strong positive association with type 2 diabetes risk for the metabolomic signature associated with EDIP-only (HR 3.75; 95% CI 2.71, 5.17) or inflammatory biomarkers-only (HR 4.07; 95% CI 2.91, 5.69). In addition, higher metabolomic dietary index for hyperinsulinaemia (MDIH), reflecting higher metabolic potential of both an insulinaemic dietary pattern and circulating C-peptide, was associated with greater type 2 diabetes risk (HR 3.00; 95% CI 2.22, 4.06); further associations with type 2 diabetes were HR 2.79 (95% CI 2.07, 3.76) for EDIH-only signature and HR 3.89 (95% CI 2.82, 5.35) for C-peptide-only signature. The diet scores were significantly associated with risk, although adjustment for the corresponding metabolomic signature scores attenuated the associations with type 2 diabetes, these remained significant. CONCLUSIONS/INTERPRETATION The metabolomic signatures reflecting proinflammatory or hyperinsulinaemic diets and related biomarkers were positively associated with type 2 diabetes risk, supporting that these dietary patterns may influence type 2 diabetes risk via the regulation of metabolism.
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Affiliation(s)
- Dong Hoon Lee
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qi Jin
- Department of Exercise and Nutrition Sciences, Moyes College of Education, Weber State University, Ogden, UT, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA
| | - Ni Shi
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alaina M Bever
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Amit Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kana Wu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Justin Y Jeon
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
- Cancer Prevention Center, Yonsei Cancer Center, Seoul, Republic of Korea
| | - Jeffrey A Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Andrew T Chan
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - A Heather Eliassen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven K Clinton
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Fred K Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA.
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA.
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA.
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31
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Nguyen XMT, Li Y, Wang DD, Whitbourne SB, Houghton SC, Hu FB, Willett WC, Sun YV, Djousse L, Gaziano JM, Cho K, Wilson PW. Impact of 8 lifestyle factors on mortality and life expectancy among United States veterans: The Million Veteran Program. Am J Clin Nutr 2024; 119:127-135. [PMID: 38065710 DOI: 10.1016/j.ajcnut.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Lifestyle medicine has been proposed as a way to address the root causes of chronic disease and their associated health care costs. OBJECTIVE This study aimed to estimate mortality risk and longevity associated with individual lifestyle factors and comprehensive lifestyle therapy. METHODS Age- and sex-specific mortality rates were calculated on the basis of 719,147 veterans aged 40-99 y enrolled in the Veteran Affairs Million Veteran Program (2011-2019). Hazard ratios and estimated increase in life expectancy were examined among a subgroup of 276,132 veterans with complete data on 8 lifestyle factors at baseline. The 8 lifestyle factors included never smoking, physical activity, no excessive alcohol consumption, restorative sleep, nutrition, stress management, social connections, and no opioid use disorder. RESULTS On the basis of 1.12 million person-years of follow-up, 34,247 deaths were recorded. Among veterans who adopted 1, 2, 3, 4, 5, 6, 7, and 8 lifestyle factors, the adjusted hazard ratios for mortality were 0.74 (0.60-0.90), 0.60 (95% CI: 0.49, 0.73), 0.50 (95% CI: 0.41, 0.61), 0.43 (95% CI: 0.35, 0.52), 0.35 (95% CI: 0.29, 0.43), 0.27 (95% CI: 0.22, 0.33), 0.21 (95% CI: 0.17, 0.26), and 0.13 (95% CI: 0.10, 0.16), respectively, as compared with veterans with no adopted lifestyle factors. The estimated life expectancy at age 40 y was 23.0, 26.5, 28.8, 30.8, 32.7, 35.1, 38.3, 41.3, and 47.0 y among males and 27.0, 28.8, 33.1, 38.0, 39.2, 41.4, 43.8, 46.3, and 47.5 y for females who adopted 0, 1, 2, 3, 4, 5, 6, 7, and 8 lifestyle factors, respectively. The difference in life expectancy at age 40 y was 24.0 y for male veterans and 20.5 y for female veterans when comparing adoption of 8-9 lifestyle factors. CONCLUSIONS A combination of 8 lifestyle factors is associated with a significantly lower risk of premature mortality and an estimated prolonged life expectancy.
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Affiliation(s)
- Xuan-Mai T Nguyen
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, United States; Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, IL, United States
| | - Yanping Li
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, United States; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States.
| | - Dong D Wang
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, United States; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; The Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Stacey B Whitbourne
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, United States; Division of Aging, Brigham and Women's Hospital, Boston, MA, United States; Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Serena C Houghton
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, United States
| | - Frank B Hu
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; The Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Walter C Willett
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Yan V Sun
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, United States; Department of Medicine, Atlanta VA Health Care System, Decatur, GA 30033, United States
| | - Luc Djousse
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, United States; Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Division of Aging, Brigham and Women's Hospital, Boston, MA, United States; Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - John Michael Gaziano
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, United States; Division of Aging, Brigham and Women's Hospital, Boston, MA, United States; Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Kelly Cho
- Million Veteran Program Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, United States; Division of Aging, Brigham and Women's Hospital, Boston, MA, United States; Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Peter Wf Wilson
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, United States; Department of Medicine, Atlanta VA Health Care System, Decatur, GA 30033, United States; Cardiology Division, Emory Clinical Cardiovascular Research Institute, Atlanta, GA 30033, United States
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32
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Kamer O, Rinott E, Tsaban G, Kaplan A, Yaskolka Meir A, Zelicha H, Knights D, Tuohy K, Fava F, Uwe Scholz M, Ziv O, Rubin E, Blüher M, Stumvoll M, Ceglarek U, Clément K, Koren O, Hu FB, Stampfer MJ, Wang DD, Youngster I, Shai I. Successful weight regain attenuation by autologous fecal microbiota transplantation is associated with non-core gut microbiota changes during weight loss; randomized controlled trial. Gut Microbes 2023; 15:2264457. [PMID: 37796016 PMCID: PMC10557561 DOI: 10.1080/19490976.2023.2264457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 09/25/2023] [Indexed: 10/06/2023] Open
Abstract
We previously reported that autologous-fecal-microbiota-transplantation (aFMT), following 6 m of lifestyle intervention, attenuated subsequent weight regain and insulin rebound for participants consuming a high-polyphenol green-Mediterranean diet. Here, we explored whether specific changes in the core (abundant) vs. non-core (low-abundance) gut microbiome taxa fractions during the weight-loss phase (0-6 m) were differentially associated with weight maintenance following aFMT. Eighty-two abdominally obese/dyslipidemic participants (age = 52 years; 6 m weightloss = -8.3 kg) who provided fecal samples (0 m, 6 m) were included. Frozen 6 m's fecal samples were processed into 1 g, opaque and odorless aFMT capsules. Participants were randomly assigned to receive 100 capsules containing their own fecal microbiota or placebo over 8 m-14 m in ten administrations (adherence rate > 90%). Gut microbiome composition was evaluated using shotgun metagenomic sequencing. Non-core taxa were defined as ≤ 66% prevalence across participants. Overall, 450 species were analyzed. At baseline, 13.3% were classified as core, and Firmicutes presented the highest core proportion by phylum. During 6 m weight-loss phase, abundance of non-core species changed more than core species (P < .0001). Subject-specific changes in core and non-core taxa fractions were strongly correlated (Jaccard Index; r = 0.54; P < .001). Following aFMT treatment, only participants with a low 6 m change in core taxa, and a high change in non-core taxa, avoided 8-14 m weight regain (aFMT = -0.58 ± 2.4 kg, corresponding placebo group = 3.18 ± 3.5 kg; P = .02). In a linear regression model, low core/high non-core 6 m change was the only combination that was significantly associated with attenuated 8-14 m weight regain (P = .038; P = .002 for taxa patterns/treatment intervention interaction). High change in non-core, low-abundance taxa during weight-loss might mediate aFMT treatment success for weight loss maintenance.ClinicalTrials.gov: NCT03020186.
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Affiliation(s)
- Omer Kamer
- Faculty of Health Sciences, Ben-Gurion University of the Negev, The International Center of Health, Innovation & Nutrition On the memory of Manya Igel, Beer-Sheva, Israel
| | - Ehud Rinott
- Department of Medicine, Hebrew University and Hadassah Medical Center, Jerusalem, Israel
| | - Gal Tsaban
- Faculty of Health Sciences, Ben-Gurion University of the Negev, The International Center of Health, Innovation & Nutrition On the memory of Manya Igel, Beer-Sheva, Israel
| | - Alon Kaplan
- Faculty of Health Sciences, Ben-Gurion University of the Negev, The International Center of Health, Innovation & Nutrition On the memory of Manya Igel, Beer-Sheva, Israel
| | - Anat Yaskolka Meir
- Faculty of Health Sciences, Ben-Gurion University of the Negev, The International Center of Health, Innovation & Nutrition On the memory of Manya Igel, Beer-Sheva, Israel
| | - Hila Zelicha
- Faculty of Health Sciences, Ben-Gurion University of the Negev, The International Center of Health, Innovation & Nutrition On the memory of Manya Igel, Beer-Sheva, Israel
| | - Dan Knights
- BioTechnology Institute, University of Minnesota, St Paul, USA
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA
| | - Kieran Tuohy
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy
- School of Food Science & Nutrition, University of Leeds, Leeds, UK
| | - Francesca Fava
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy
| | - Matthias Uwe Scholz
- Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach, Trento, Italy
| | - Oren Ziv
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Elad Rubin
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Matthias Blüher
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig, Leipzig, Germany
| | - Uta Ceglarek
- Department of Medicine, University of Leipzig, Leipzig, Germany
| | - Karine Clément
- Inserm, Nutrition and obesities: systemic approaches, nutriOmicsn Research Unit, Nutrition Department, Pitié-Salpêtrière Hospital, Assistance-Publique Hopitaux de Paris, Sorbonne University, Paris, France
| | - Omry Koren
- Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Frank B. Hu
- Harvard T.H. Chan School of Public Health, Boston, USA
- Department of Medicine, Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital, Boston, USA
| | - Meir J. Stampfer
- Harvard T.H. Chan School of Public Health, Boston, USA
- Department of Medicine, Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital, Boston, USA
| | - Dong D. Wang
- Harvard T.H. Chan School of Public Health, Boston, USA
| | - Ilan Youngster
- Pediatric Division and Center for Microbiome Research, Shamir Medical Center, Be’er Ya’akov, Israel
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Iris Shai
- Faculty of Health Sciences, Ben-Gurion University of the Negev, The International Center of Health, Innovation & Nutrition On the memory of Manya Igel, Beer-Sheva, Israel
- Harvard T.H. Chan School of Public Health, Boston, USA
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Atzeni A, Nishi SK, Babio N, Belzer C, Konstanti P, Vioque J, Corella D, Castañer O, Vidal J, Moreno-Indias I, Torres-Collado L, Asensio EM, Fitó M, Gomez-Perez AM, Arias A, Ruiz-Canela M, Hu FB, Tinahones FJ, Salas-Salvadó J. Carbohydrate quality, fecal microbiota and cardiometabolic health in older adults: a cohort study. Gut Microbes 2023; 15:2246185. [PMID: 37610130 PMCID: PMC10449004 DOI: 10.1080/19490976.2023.2246185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/03/2023] [Accepted: 08/04/2023] [Indexed: 08/24/2023] Open
Abstract
The impact of carbohydrate quality, measured by the carbohydrate quality index (CQI), on gut microbiota and health has been scarcely investigated. The aim of this study was to cross-sectionally and longitudinally explore the relationships between CQI, fecal microbiota, and cardiometabolic risk factors in an elderly Mediterranean population at high cardiovascular risk. At baseline and 1-year, CQI was assessed from food frequency questionnaires data, cardiometabolic risk factors were measured, and fecal microbiota profiled from 16S sequencing. Multivariable-adjusted linear regression models were fitted to assess the associations between tertiles of baseline CQI, fecal microbiota, and cardiometabolic risk factors at baseline, and between tertiles of 1-year change in CQI, 1-year change in fecal microbiota and cardiometabolic risk factors. Cross-sectionally, higher CQI was positively associated with Shannon alpha diversity index, and abundance of genera Faecalibacterium and Christensenellaceae R7 group, and negatively associated with the abundance of Odoribacter, and uncultured Rhodospirillales genera. Some of these genera were associated with higher glycated hemoglobin and lower body mass index. In addition, we observed a positive association between CQI, and some pathways related with the metabolism of butyrate precursors and plants-origin molecules. Longitudinally, 1-year improvement in CQI was associated with a concurrent increase in the abundance of genera Butyrivibrio. Increased abundance of this genera was associated with 1-year improvement in insulin status. These observations suggest that a better quality of carbohydrate intake is associated with improved metabolic health, and this improvement could be modulated by greater alpha diversity and abundance of specific genera linked to beneficial metabolic outcomes.
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Affiliation(s)
- Alessandro Atzeni
- Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Unitat de Nutrició Humana, Reus, Spain
- Human Nutrition Unit, Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Stephanie K. Nishi
- Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Unitat de Nutrició Humana, Reus, Spain
- Human Nutrition Unit, Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Toronto 3D (Diet Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, Canada
- Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Unity Health Toronto, Toronto, Canada
| | - Nancy Babio
- Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Unitat de Nutrició Humana, Reus, Spain
- Human Nutrition Unit, Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Clara Belzer
- Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands
| | - Prokopis Konstanti
- Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands
| | - Jesús Vioque
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Investigación Sanitaria y Biomédica de Alicante, Universidad Miguel Hernández (ISABIAL-UMH), Alicante, Spain
| | - Dolores Corella
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Olga Castañer
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d’Investigació Médica (IMIM), Barcelona, Spain
| | - Josep Vidal
- CIBER Diabetes y Enfermedades Metabólicas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Endocrinology, Institut d’Investigacions Biomédiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Isabel Moreno-Indias
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, the Biomedical Research Institute of Malaga and Platform in Nanomedicine (IBIMA-BIONAND Platform), University of Malaga, Malaga, Spain
| | - Laura Torres-Collado
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Investigación Sanitaria y Biomédica de Alicante, Universidad Miguel Hernández (ISABIAL-UMH), Alicante, Spain
| | - Eva M. Asensio
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Montserrat Fitó
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d’Investigació Médica (IMIM), Barcelona, Spain
| | - Ana Maria Gomez-Perez
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, the Biomedical Research Institute of Malaga and Platform in Nanomedicine (IBIMA-BIONAND Platform), University of Malaga, Malaga, Spain
| | - Alejandro Arias
- Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Miguel Ruiz-Canela
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Frank B. Hu
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Francisco J. Tinahones
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, the Biomedical Research Institute of Malaga and Platform in Nanomedicine (IBIMA-BIONAND Platform), University of Malaga, Malaga, Spain
| | - Jordi Salas-Salvadó
- Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Unitat de Nutrició Humana, Reus, Spain
- Human Nutrition Unit, Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
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Henn M, Glenn AJ, Willett WC, Martínez-González MA, Sun Q, Hu FB. Changes in Coffee Intake, Added Sugar and Long-Term Weight Gain - Results from Three Large Prospective US Cohort Studies. Am J Clin Nutr 2023; 118:1164-1171. [PMID: 37783371 PMCID: PMC10739774 DOI: 10.1016/j.ajcnut.2023.09.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/18/2023] [Accepted: 09/27/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND Consumption of both caffeinated and decaffeinated coffee has been reported to attenuate long-term weight gain. Whether the association between coffee consumption and weight gain depends on the addition of sugar, cream, or coffee whitener remains unclear. OBJECTIVE We aimed to study the associations between changes in coffee consumption, caffeine intake, and weight changes by considering the addition of sugar, cream, or a nondairy coffee whitener. METHODS We used 3 large prospective cohorts - the Nurses' Health Study (1986 - 2010), Nurses' Health Study II (1991 - 2015) and Health Professional Follow-up Study (1991 - 2014). We applied multivariable linear regression models with robust variance estimators to assess the association of changes in coffee habits within each 4-y interval with concurrent weight changes. Results across the 3 cohorts were pooled using inverse-variance weights. RESULTS After multivariable adjustment, each 1 cup per day increment in unsweetened caffeinated coffee was associated with a reduction in 4-y weight gain of -0.12 kg (95 % CI: -0.18, -0.05 kg) and of -0.12 kg (95 % CI: -0.16, -0.08 kg) for unsweetened decaffeinated coffee. The habits of adding cream or nondairy coffee whitener were not significantly linked to weight changes. Adding a teaspoon of sugar was associated with a 4-y weight gain of +0.09 kg (0.07, 0.12 kg). Stratified analyses suggested stronger magnitude of the observed associations with younger age and higher baseline BMI. Neither caffeine nor coffee modified the association of adding sugar to any food or beverage with weight changes. CONCLUSIONS An increase in intake of unsweetened caffeinated and decaffeinated coffee was inversely associated with weight gain. The addition of sugar to coffee counteracted coffee's benefit for possible weight management. To the contrary, adding cream or coffee whitener was not associated with greater weight gain.
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Affiliation(s)
- Matthias Henn
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; University of Navarra-IdiSNA (Instituto de Investigacion Sanitaria de Navarra), Department of Preventive Medicine and Public Health, Pamplona, Spain
| | - Andrea J Glenn
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Center, St. Michael's Hospital, Toronto, ON, Canada
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; University of Navarra-IdiSNA (Instituto de Investigacion Sanitaria de Navarra), Department of Preventive Medicine and Public Health, Pamplona, Spain; CIBER Fisiopatología de La Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.
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Gu X, Drouin-Chartier JP, Sacks FM, Hu FB, Rosner B, Willett WC. Red meat intake and risk of type 2 diabetes in a prospective cohort study of United States females and males. Am J Clin Nutr 2023; 118:1153-1163. [PMID: 38044023 PMCID: PMC10739777 DOI: 10.1016/j.ajcnut.2023.08.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/24/2023] [Accepted: 08/30/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Studies with methodological advancements are warranted to confirm the relation of red meat consumption to the incidence of type 2 diabetes (T2D). OBJECTIVE We aimed to assess the relationships of intakes of total, processed, and unprocessed red meat to risk of T2D and to estimate the effects of substituting different protein sources for red meats on T2D risk. METHODS Our study included 216,695 participants (81% females) from the Nurses' Health Study (NHS), NHS II, and Health Professionals Follow-up Study (HPFS). Red meat intakes were assessed with semiquantitative food frequency questionnaires (FFQs) every 2 to 4 y since the study baselines. We used multivariable-adjusted proportional hazards models to estimate the associations between red meats and T2D. RESULTS Over 5,483,981 person-years of follow-up, we documented 22,761 T2D cases. Intakes of total, processed, and unprocessed red meat were positively and approximately linearly associated with higher risks of T2D. Comparing the highest to the lowest quintiles, hazard ratios (HR) were 1.62 (95% confidence interval [CI]: 1.53, 1.71) for total red meat, 1.51 (95% CI: 1.44, 1.58) for processed red meat, and 1.40 (95% CI: 1.33, 1.47) for unprocessed red meat. The percentage lower risk of T2D associated with substituting 1 serving/d of nuts and legumes for total red meat was 30% (HR = 0.70, 95% CI: 0.66, 0.74), for processed red meat was 41% (HR = 0.59, 95% CI: 0.55, 0.64), and for unprocessed red meat was 29% (HR = 0.71, 95% CI: 0.67, 0.75); Substituting 1 serving/d of dairy for total, processed, or unprocessed red meat was also associated with significantly lower risk of T2D. The observed associations became stronger after we calibrated dietary intakes to intakes assessed by weighed diet records. CONCLUSIONS Our study supports current dietary recommendations for limiting consumption of red meat intake and emphasizes the importance of different alternative sources of protein for T2D prevention.
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Affiliation(s)
- Xiao Gu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Jean-Philippe Drouin-Chartier
- Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec, Canada; Faculté de Pharmacie, Université Laval, Québec, Canada
| | - Frank M Sacks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Bernard Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
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Li Y, Wang DD, Nguyen XMT, Song RJ, Ho YL, Hu FB, Willett WC, Wilson PWF, Cho K, Gaziano JM, Djousse L. Plant-based diets and the incidence of cardiovascular disease: the Million Veteran Program. BMJ Nutr Prev Health 2023; 6:212-220. [PMID: 38264362 PMCID: PMC10800254 DOI: 10.1136/bmjnph-2021-000401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 09/25/2023] [Indexed: 01/25/2024] Open
Abstract
Background A healthful plant-based diet was associated with lower risks of coronary heart disease and type 2 diabetes, and a favourable profile of adiposity-associated biomarkers, while an unhealthful plant-based diet was associated with elevated risk of cardiometabolic disease in health professional populations. However, little is known about the associations between plant-based dietary patterns and risk of cardiovascular disease (CVD) in US veterans. Methods The study population consisted of 148 506 participants who were free of diabetes, CVD and cancer at baseline in the Veterans Affairs (VA) Million Veteran Program. Diet was assessed using a Food Frequency Questionnaire at baseline. We calculated an overall Plant-Based Diet Index (PDI), a healthful PDI (hPDI) and an unhealthful PDI (uPDI). The CVD endpoints included non-fatal myocardial infarction (MI) and acute ischaemic stroke (AIS) identified through high-throughput phenotyping algorithms approach and fatal CVD events identified by searching the National Death Index. Results With up to 8 years of follow-up, we documented 5025 CVD cases. After adjustment for confounding factors, a higher PDI was significantly associated with a lower risk of CVD (HR comparing extreme quintiles=0.75, 95% CI 0.68 to 0.82, P trend<0.0001). We observed an inverse association between hPDI and the risk of CVD (HR comparing extreme quintiles=0.71, 95% CI 0.64 to 0.78, P trend<0.001), whereas uPDI was positively associated with the risk of CVD (HR comparing extreme quintiles=1.12, 95% CI 1.02 to 1.24, P trend<0.001). We found similar associations of hPDI with subtypes of CVD; a 10-unit increment in hPDI was associated with HRs (95% CI) of 0.81 (0.75 to 0.87) for fatal CVD, 0.86 (0.79 to 0.94) for non-fatal MI and 0.86 (0.78 to 0.95) for non-fatal AIS. Conclusions Plant-based dietary pattern enriched with healthier plant foods was associated with a substantially lower CVD risk in US veterans.
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Affiliation(s)
- Yanping Li
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Dong D Wang
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- The Channing Division for Network Medicine,Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Xuan-Mai T Nguyen
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Carle Illinois College of Medicine, University of Illinois Urbana Champaign, Champaign, Illinois, USA
| | - Rebecca J Song
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- The Channing Division for Network Medicine,Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- The Channing Division for Network Medicine,Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Peter W F Wilson
- Epidemiology and Genomic Medicine, Atlanta VA Medical Center, Atlanta, Massachusetts, USA
- Division of Cardiology, Emory Clinical Cardiovascular Research Institute, Atlanta, Georgia, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - John Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Luc Djousse
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
- Division of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Yuan M, Hu FB, Li Y, Cabral HJ, Das SK, Deeney JT, Zhou X, Paik JM, Moore LL. Types of dairy foods and risk of fragility fracture in the prospective Nurses' Health Study cohort. Am J Clin Nutr 2023; 118:1172-1181. [PMID: 37777015 PMCID: PMC10797505 DOI: 10.1016/j.ajcnut.2023.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 09/17/2023] [Accepted: 09/21/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND Fragility fractures present enormous health challenges for women. Dairy products provide many bone-beneficial nutrients, such as calcium and vitamin D. Individual dairy foods may exert different effects on bone health. OBJECTIVES The aim of this study was to investigate the associations between total dairy, yogurt, milk, and cheese and fragility fracture risk among females in the prospective Nurses' Health Study (NHS) conducted in the United States. METHODS In the current analysis, 103,003 females with mean age of 48 y were followed from 1980-2004. Proportional hazards models were used to estimate risk of first fracture (of the wrist, hip, or vertebrae) by intakes of dairy foods (total dairy, milk, yogurt, or cheese) obtained from a food frequency questionnaire. Fractures that were caused by high-trauma events were not included. We relied on self-reported data for wrist and hip fractures whereas for vertebral fractures, medical records were used to confirm cases. RESULTS A total of 5495 incident fracture cases were documented during follow-up. After controlling for relevant confounding variables, consumption of ≥2 servings/d of total dairy (compared with <1 serving/d) was associated with lower fracture risk (hazard ratio [HR]: 0.74; 95% confidence interval [CI]: 0.61, 0.89). More than 2 servings of milk per day (compared with <1 serving/d) were associated with a lower fracture risk (HR: 0.85; 95% CI: 0.77, 0.94). Intakes of calcium, vitamin D, and protein from nondairy sources did not modify the effects of total dairy or milk on fracture risk. There was no association between yogurt intake and fracture risk. Intake of cheese (≥1 servings/d compared with <1 serving/wk) was weakly associated with lower fracture risk (HR: 0.89; 95% CI: 0.79, 0.99). CONCLUSIONS Higher total dairy, milk, and cheese intakes are associated with lower risks of fracture in females in the NHS.
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Affiliation(s)
- Mengjie Yuan
- Department of Medicine, Preventive Medicine and Epidemiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Frank B Hu
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Yanping Li
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Howard J Cabral
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Sai Krupa Das
- Energy Metabolism, Jean Mayer USDA Human Nutrition Center on Aging, Tufts University, Boston, MA, United States
| | - Jude T Deeney
- Department of Medicine, Endocrinology, Diabetes, Nutrition & Weight Management, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Xinyi Zhou
- Department of Medicine, Preventive Medicine and Epidemiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Julie M Paik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Lynn L Moore
- Department of Medicine, Preventive Medicine and Epidemiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States.
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Yang J, Song Y, Gaskins AJ, Li LJ, Huang Z, Eriksson JG, Hu FB, Chong YS, Zhang C. Mediterranean diet and female reproductive health over lifespan: a systematic review and meta-analysis. Am J Obstet Gynecol 2023; 229:617-631. [PMID: 37506751 DOI: 10.1016/j.ajog.2023.05.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 05/16/2023] [Accepted: 05/21/2023] [Indexed: 07/30/2023]
Abstract
OBJECTIVE We conducted a systematic review and meta-analysis of the effects of Mediterranean diet on female reproductive health outcomes over the life-course. DATA SOURCES We searched PubMed, Embase, MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov to identify eligible studies published till February 2022. Eligible references from identified studies and review articles were also considered. STUDY ELIGIBILITY CRITERIA Randomized controlled trials, prospective cohort studies, or nested case-control studies examining Mediterranean diet and major female reproductive outcomes over the lifespan, including clinical outcomes from childhood to adulthood (menarche, polycystic ovary syndrome, endometriosis, and outcomes related to fertility, pregnancy, and menopause), were included for review. METHODS Two independent reviewers screened and performed data extraction and risk-of-bias assessment. We performed random-effects meta-analysis to obtain summary relative risks and 95% confidence intervals for major female reproductive outcomes. Subgroup analyses were performed for several pregnancy outcomes according to timing of the interventions for randomized controlled trials and timing of the dietary assessment for observational studies. RESULTS Thirty-two studies (9 randomized controlled trials, 22 prospective cohort studies, and 1 nested case-control study) involving 103,204 predominantly White women (>95%) were included. The pooled relative risk (95% confidence interval) comparing randomization to Mediterranean diet vs a control diet based on 7 randomized controlled trials was 0.74 (0.55-0.99) for gestational diabetes mellitus, 0.45 (0.26-0.76) for preterm birth, 0.71 (0.51-1.00) for gestational hypertension, and 0.82 (0.54-1.22) for preeclampsia; the effect sizes for preterm birth were greater in randomized controlled trials that initiated the interventions in first trimester vs after first trimester (P heterogeneity=.02). We observed inverse associations for all the above-mentioned pregnancy outcomes based on 9 cohort studies. There was suggestive evidence of favorable associations between Mediterranean diet adherence with fertility and gestational weight management. Limited studies suggested associations between higher Mediterranean diet adherence and later time to menarche and fewer vasomotor menopausal symptoms, null associations for polycystic ovary syndrome-like phenotype and pregnancy loss, and positive associations for luteal phase deficiency. CONCLUSION Adherence to Mediterranean diet may lower risks of adverse pregnancy outcomes among predominantly White populations. For fertility-related outcomes, available evidence supporting potential beneficial effects is suggestive yet limited. For other reproductive outcomes across the lifespan, data remains sparse.
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Affiliation(s)
- Jiaxi Yang
- Global Centre for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Bia-Echo Asia Centre for Reproductive Longevity & Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yiqing Song
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, IN
| | - Audrey J Gaskins
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA
| | - Ling-Jun Li
- Global Centre for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Bia-Echo Asia Centre for Reproductive Longevity & Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Zhongwei Huang
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Bia-Echo Asia Centre for Reproductive Longevity & Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A∗STAR), Singapore Institute for Clinical Sciences (SICS), Singapore
| | - Johan G Eriksson
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland; Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University Singapore, Singapore; Agency for Science, Technology and Research (A∗STAR), Singapore Institute for Clinical Sciences (SICS), Singapore
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Yap Seng Chong
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Cuilin Zhang
- Global Centre for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Bia-Echo Asia Centre for Reproductive Longevity & Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
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Liu B, Hu Y, Rai SK, Wang M, Hu FB, Sun Q. Low-Carbohydrate Diet Macronutrient Quality and Weight Change. JAMA Netw Open 2023; 6:e2349552. [PMID: 38150249 PMCID: PMC10753393 DOI: 10.1001/jamanetworkopen.2023.49552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 11/13/2023] [Indexed: 12/28/2023] Open
Abstract
Importance The associations of low-carbohydrate diets (LCDs) with long-term weight management remains unclear, and the source and quality of macronutrients within LCDs are less explored. Objectives To prospectively examine associations between changes in LCD indices and weight change among US adults. Design, Setting, and Participants This prospective cohort study included initially healthy participants at baseline from the Nurses' Health Study (NHS; 1986-2010), Nurses' Health Study II (NHSII; 1991-2015), and Health Professionals Follow-up Study (HPFS; 1986-2018). Data analysis was performed between November 2022 and April 2023. Exposures Five LCD indices were examined: (1) a total LCD (TLCD) emphasizing overall lower carbohydrate intake; (2) an animal-based LCD (ALCD) that emphasized animal-sourced protein and fat; (3) a vegetable-based LCD (VLCD) that emphasized plant-sourced protein and fat; (4) a healthy LCD (HLCD) emphasizing less refined carbohydrates, more plant protein, and healthy fat; and (5) an unhealthy LCD (ULCD) emphasizing less healthful carbohydrates, more animal protein, and unhealthy fat. Main Outcomes and Measures The outcome of interest was 4-year changes in self-reported body weight. Results A total of 123 332 participants (mean [SD] age, 45.0 [9.7] years; 103 320 [83.8%] female) were included in this study. The median carbohydrate intake (as a percentage of energy) of the highest quintiles of TLCD score at baseline ranged from 38.3% in HPFS to 40.9% in NHSII. Mean weight gain over 4-year intervals among participants varied from 0.8 kg in the HPFS to 1.8 kg in the NHSII. After adjusting for demographics and baseline and concomitant changes of selected lifestyle factors, each 1-SD increase in TLCD score was associated with 0.06 (95% CI, 0.04-0.08) kg more weight gain over the 4-year periods. Similarly, participants gained 0.13 (95% CI, 0.11 to 0.14) kg per each 1-SD increase in ALCD score and 0.39 (95% CI, 0.37 to 0.40) kg per each 1-SD change in ULCD score. In contrast, each 1-SD increase in VLCD score was associated with 0.03 (95% CI, 0.01 to 0.04) kg less weight gain, and each 1-SD increase in HLCD score was associated with 0.36 (95% CI, 0.35 to 0.38) kg less weight gain. The associations were more pronounced among obese individuals (per 1-SD increase in HLCD score: BMI ≥30, 0.88 [95% CI, 0.80, 0.97] kg less weight gain; BMI <25, 0.23 [95% CI, 0.20, 0.26] kg less weight gain; P for interaction < .001). Conclusions and Relevance These findings suggest that the quality of LCDs may play a critical role in modulating long-term weight change. Only LCDs that emphasized high-quality protein, fat, and carbohydrates from whole grains and other plant-based foods were associated with less weight gain.
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Affiliation(s)
- Binkai Liu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Yang Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Sharan K. Rai
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Molin Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Frank B. Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
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40
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Glenn AJ, Guasch-Ferré M, Malik VS, Kendall CWC, Manson JE, Rimm EB, Willett WC, Sun Q, Jenkins DJA, Hu FB, Sievenpiper JL. Portfolio Diet Score and Risk of Cardiovascular Disease: Findings From 3 Prospective Cohort Studies. Circulation 2023; 148:1750-1763. [PMID: 37877288 PMCID: PMC10841173 DOI: 10.1161/circulationaha.123.065551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/05/2023] [Indexed: 10/26/2023]
Abstract
BACKGROUND The plant-based Portfolio dietary pattern includes recognized cholesterol-lowering foods (ie, plant protein, nuts, viscous fiber, phytosterols, and plant monounsaturated fats) shown to improve several cardiovascular disease (CVD) risk factors in randomized controlled trials. However, there is limited evidence on the role of long-term adherence to the diet and CVD risk. The primary objective was to examine the relationship between the Portfolio Diet Score (PDS) and the risk of total CVD, coronary heart disease (CHD), and stroke. METHODS We prospectively followed 73 924 women in the Nurses' Health Study (1984-2016), 92 346 women in the Nurses' Health Study II (1991-2017), and 43 970 men in the Health Professionals Follow-up Study (1986-2016) without CVD or cancer at baseline. Diet was assessed using validated food frequency questionnaires at baseline and every 4 years using a PDS that positively ranks plant protein (legumes), nuts and seeds, viscous fiber sources, phytosterols (mg/day), and plant monounsaturated fat sources, and negatively ranks foods high in saturated fat and cholesterol. RESULTS During up to 30 years of follow-up, 16 917 incident CVD cases, including 10 666 CHD cases and 6473 strokes, were documented. After multivariable adjustment for lifestyle factors and a modified Alternate Healthy Eating Index (excluding overlapping components), comparing the highest with the lowest quintile, participants with a higher PDS had a lower risk of total CVD (pooled hazard ratio [HR], 0.86 [95% CI, 0.81-0.92]; Ptrend<0.001), CHD (pooled HR, 0.86 [95% CI, 0.80-0.93]; Ptrend=0.0001), and stroke (pooled HR, 0.86 [95% CI, 0.78-0.95]; Ptrend=0.0003). In addition, a 25-percentile higher PDS was associated with a lower risk of total CVD (pooled HR, 0.92 [95% CI, 0.89-0.95]), CHD (pooled HR, 0.92 [95% CI, 0.88-0.95]), and stroke (pooled HR, 0.92 [95% CI, 0.87-0.96]). Results remained consistent across sensitivity and most subgroup analyses, and there was no evidence of departure from linearity for CVD, CHD, or stroke. In a subset of participants, a higher PDS was associated with a more favorable blood lipid and inflammatory profile. CONCLUSIONS The PDS was associated with a lower risk of CVD, including CHD and stroke, and a more favorable blood lipid and inflammatory profile, in 3 large prospective cohorts.
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Affiliation(s)
- Andrea J Glenn
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Toronto, ON, Canada
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vasanti S Malik
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Cyril WC Kendall
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Toronto, ON, Canada
- College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada
| | - JoAnn E Manson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Division of Preventive Medicine Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Eric B Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - David JA Jenkins
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada
- Division of Endocrinology and Metabolism, St. Michael’s Hospital, Toronto, ON, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - John L Sievenpiper
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada
- Division of Endocrinology and Metabolism, St. Michael’s Hospital, Toronto, ON, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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Hu Y, Li J, Wang B, Zhu L, Li Y, Ivey KL, Lee KH, Eliassen AH, Chan A, Huttenhower C, Hu FB, Qi Q, Rimm EB, Sun Q. Interplay between diet, circulating indolepropionate concentrations and cardiometabolic health in US populations. Gut 2023; 72:2260-2271. [PMID: 37739776 PMCID: PMC10841831 DOI: 10.1136/gutjnl-2023-330410] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/23/2023] [Indexed: 09/24/2023]
Abstract
OBJECTIVES To identify indolepropionate (IPA)-predicting gut microbiota species, investigate potential diet-microbiota interactions, and examine the prospective associations of circulating IPA concentrations with type 2 diabetes (T2D) and coronary heart disease (CHD) risk in free-living individuals. DESIGN We included 287 men from the Men's Lifestyle Validation Study, a substudy of the Health Professionals Follow-Up Study (HPFS), who provided up to two pairs of faecal samples and two blood samples. Diet was assessed using 7-day diet records. Associations between plasma concentrations of tryptophan metabolites and T2D CHD risk were examined in 13 032 participants from Nurses' Health Study (NHS), NHSII and HPFS. RESULTS We identified 17 microbial species whose abundance was significantly associated with plasma IPA concentrations. A significant association between higher tryptophan intake and higher IPA concentrations was only observed among men who had higher fibre intake and a higher microbial species score consisting of the 17 species (p-interaction<0.01). Dietary and plasma concentrations of tryptophan and most kynurenine pathway metabolites were positively associated with T2D risk (HRQ5 vs Q1 ranged from 1.17 to 1.46) while a significant inverse association was found for IPA (HRQ5 vs Q1 (95% CI) 0.70 (0.56 to 0.88)). No associations were found in CHD for any plasma tryptophan metabolites. CONCLUSIONS Specific microbial species and dietary fibre jointly predicted significantly higher circulating IPA concentrations at higher tryptophan intake. Dietary and plasma tryptophan, as well as its kynurenine pathway metabolites, demonstrated divergent associations from those for IPA, which was significantly predictive of lower risk of T2D.
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Affiliation(s)
- Yang Hu
- Department of Nutrition, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Jun Li
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Biqi Wang
- Department of Medicine, UMASS Medical School, Worcester, Massachusetts, USA
| | - Lu Zhu
- Department of Nutrition, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Yanping Li
- Department of Nutrition, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Kerry L Ivey
- Department of Nutrition, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Kyu Ha Lee
- Department of Nutrition, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew Chan
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Immunology and Infectious Diseases, Harvard University T. H. Chan School of Public Health, Boston, Boston, Massachusetts, USA
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Immunology and Infectious Diseases, Harvard University T. H. Chan School of Public Health, Boston, Boston, Massachusetts, USA
- Eli and Edythe L. Broad Institute of Harvard and MIT, Flinders University College of Nursing and Health Sciences, Cambridge, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Yeshiva University Albert Einstein College of Medicine, Bronx, New York, USA
| | - Eric B Rimm
- Department of Nutrition, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Qi Sun
- Department of Nutrition, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Balasubramanian R, Shutta KH, Guasch-Ferre M, Huang T, Jha SC, Zhu Y, Shadyab AH, Manson JE, Corella D, Fitó M, Hu FB, Rexrode KM, Clish CB, Hankinson SE, Kubzansky LD. Metabolomic profiles of chronic distress are associated with cardiovascular disease risk and inflammation-related risk factors. Brain Behav Immun 2023; 114:262-274. [PMID: 37557964 DOI: 10.1016/j.bbi.2023.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 08/01/2023] [Accepted: 08/06/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Chronic psychological distress is associated with increased risk of cardiovascular disease (CVD) and investigators have posited inflammatory factors may be centrally involved in these relationships. However, mechanistic evidence and molecular underpinnings of these processes remain unclear, and data are particularly sparse among women. This study examined if a metabolite profile linked with distress was associated with increased CVD risk and inflammation-related risk factors. METHODS A plasma metabolite-based distress score (MDS) of twenty chronic psychological distress-related metabolites was developed in cross-sectional, 1:1 matched case-control data comprised of 558 women from the Nurses' Health Study (NHS; 279 women with distress, 279 controls). This MDS was then evaluated in two other cohorts: the Women's Health Initiative Observational Cohort (WHI-OS) and the Prevención con Dieta Mediterránea (PREDIMED) trial. We tested the MDS's association with risk of future CVD in each sample and with levels of C-reactive protein (CRP) in the WHI-OS. The WHI-OS subsample included 944 postmenopausal women (472 CHD cases; mean time to event = 5.8 years); the PREDIMED subsample included 980 men and women (224 CVD cases, mean time to event = 3.1 years). RESULTS In the WHI-OS, a 1-SD increase in the plasma MDS was associated with a 20% increased incident CHD risk (odds ratio [OR] = 1.20, 95% CI: 1.04 - 1.38), adjusting for known CVD risk factors excluding total and HDL cholesterol. This association was attenuated after including total and HDL cholesterol. CRP mediated an average 12.9% (95% CI: 4.9% - 28%, p < 10-15) of the total effect of MDS on CHD risk when adjusting for matching factors. This effect was attenuated after adjusting for known CVD risk factors. Of the metabolites in the MDS, tryptophan and threonine were inversely associated with incident CHD risk in univariate models. In PREDIMED, each one SD increase in the MDS was associated with an OR of 1.19 (95% CI: 1.00 - 1.41) for incident CVD risk, after adjusting all risk factors. Similar associations were observed in men and women. Four metabolites in the MDS were associated with incident CVD risk in PREDIMED in univariate models. Biliverdin and C36:5 phosphatidylcholine (PC) plasmalogen had inverse associations; C16:0 ceramide and C18:0 lysophosphatidylethanolamine(LPE) each had positive associations with CVD risk. CONCLUSIONS Our study points to molecular alterations that may underlie the association between chronic distress and subsequent risk of cardiovascular disease in adults.
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Affiliation(s)
- Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, United States of America
| | - Katherine H Shutta
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, United States of America; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Marta Guasch-Ferre
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Tianyi Huang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America; Harvard Medical School, Boston, MA, United States of America
| | - Shaili C Jha
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States of America
| | - JoAnn E Manson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America; Harvard Medical School, Boston, MA, United States of America; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Dolores Corella
- Department of Preventive Medicine and Public Health. University of Valencia, Valencia Spain and CIBEROBN, Madrid, Spain
| | - Montserrat Fitó
- Epidemiology and Public Health program. Hospital del Mar Research Institute, Barcelona, Spain and CIBEROBN, Madrid, Spain
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States of America; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America; Harvard Medical School, Boston, MA, United States of America; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
| | - Kathryn M Rexrode
- Harvard Medical School, Boston, MA, United States of America; Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, Boston, MA, the United States of America
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, United States of America
| | - Susan E Hankinson
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, United States of America
| | - Laura D Kubzansky
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America
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Abstract
Worldwide dietary guidelines in the late 20th century promoted a low-fat diet, based, in part, on the notion that dietary fat, the most energy dense macronutrient, causes excess weight gain. However, high-quality evidence accumulating since then refute a direct association between dietary fat and adiposity. Moreover, substitution of carbohydrates for unsaturated fat can increase insulin resistance and cardiometabolic disease, especially among populations with highly prevalent insulin resistance. In this context, the recent WHO conditional recommendation to carry forward the guidance to limit dietary fat to ≤30% seems ill advised and should be reconsidered.
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Affiliation(s)
- David S Ludwig
- New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, MA, United States; Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark.
| | - Frank B Hu
- Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Alice H Lichtenstein
- Cardiovascular Research Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States
| | - Walter C Willett
- Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
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44
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Dennis KK, Wang F, Li Y, Manson JE, Rimm EB, Hu FB, Willett WC, Stampfer MJ, Wang DD. Associations of dietary sugar types with coronary heart disease risk: a prospective cohort study. Am J Clin Nutr 2023; 118:1000-1009. [PMID: 37659725 PMCID: PMC10636232 DOI: 10.1016/j.ajcnut.2023.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/26/2023] [Accepted: 08/30/2023] [Indexed: 09/04/2023] Open
Abstract
BACKGROUND Higher intake of total sugar has been linked with coronary heart disease (CHD) risk, but the role of individual sugars, particularly fructose, is uncertain. OBJECTIVES This study aimed to investigate the associations of individual dietary sugars with CHD risk. METHODS In prospective cohort studies, we followed 76,815 women (Nurses' Health Study, 1980-2020) and 38,878 men (Health Professionals Follow-up Study, 1986-2016). Sugar and carbohydrate intake, including total fructose equivalents ([TFE] from fructose monosaccharides and sucrose), total glucose equivalents ([TGE] from glucose monosaccharides, disaccharides, and starch), and other sugar types, was measured every 2 to 4 y by semiquantitative food frequency questionnaires. RESULTS We documented 9,723 incident CHD cases over 40 years. In isocaloric substitution models with total fat as a comparison nutrient, comparing extreme quintiles of intake, hazard ratios (HRs), 95% confidence interval [CI]) for CHD risk were 1.31 (1.20 to 1.42; Ptrend < 0.001) for TGE and 1.03 (0.94 to 1.11; Ptrend = 0.25) for TFE. TFE from fruits and vegetables was not associated with CHD risk (Ptrend = 0.70), but TFE from added sugar and juice was associated with CHD risk (HR: 1.12, 95% CI: 1.04 to 1.20; Ptrend < 0.01). Intakes of total sugars and added sugar were positively associated with CHD risk (HRs: 1.16, 95% CI: 1.07 to 1.26, Ptrend < 0.001; 1.08, 95% CI: 0.99 to 1.16, Ptrend = 0.04). CONCLUSIONS Intakes of TGE, total sugar, added sugar, and fructose from added sugar and juice were associated with higher CHD risk, but TFE and fructose from fruits and vegetables were not.
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Affiliation(s)
- Kristine K Dennis
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Yanping Li
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, United States
| | - JoAnn E Manson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Eric B Rimm
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Frank B Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Meir J Stampfer
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Dong D Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
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Tobias DK, Papatheodorou S, Yamamoto JM, Hu FB. A Primer on Systematic Review and Meta-analysis in Diabetes Research. Diabetes Care 2023; 46:1882-1893. [PMID: 37890100 PMCID: PMC10620547 DOI: 10.2337/dci23-0031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/22/2023] [Indexed: 10/29/2023]
Abstract
A systematic review is a rigorous process that involves identifying, selecting, and synthesizing available evidence pertaining to an a priori-defined research question. The resulting evidence base may be summarized qualitatively or through a quantitative analytic approach known as meta-analysis. Systematic review and meta-analysis (SRMAs) have risen in popularity across the scientific realm including diabetes research. Although well-conducted SRMAs are an indispensable tool in informing evidence-based medicine, the proliferation of SRMAs has led to many reviews of questionable quality and misleading conclusions. The objective of this article is to provide up-to-date knowledge and a comprehensive understanding of strengths and limitations of SRMAs. We first provide an overview of the SRMA process and offer ways to identify common pitfalls at key steps. We then describe best practices as well as evolving approaches to mitigate biases, improve transparency, and enhance rigor. We discuss several recent developments in SRMAs including individual-level meta-analyses, network meta-analyses, umbrella reviews, and prospective meta-analyses. Additionally, we outline several strategies that can be used to enhance quality of SRMAs and present key questions that authors, editors, and readers should consider in preparing or critically reviewing SRMAs.
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Affiliation(s)
- Deirdre K. Tobias
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Nutrition Department, Harvard T.H. Chan School of Public Health, Boston, MA
| | | | - Jennifer M. Yamamoto
- Department of Internal Medicine, Faculty of Health Sciences, University of Manitoba, and Children’s Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
| | - Frank B. Hu
- Nutrition Department, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
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Chen Z, Hu Y, Hu FB, Manson JE, Rimm EB, Doria A, Sun Q. Dietary Glutamine and Glutamate in Relation to Cardiovascular Disease Incidence and Mortality in the United States Men and Women with Diabetes Mellitus. J Nutr 2023; 153:3247-3258. [PMID: 37660951 PMCID: PMC10687617 DOI: 10.1016/j.tjnut.2023.08.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 08/03/2023] [Accepted: 08/24/2023] [Indexed: 09/05/2023] Open
Abstract
BACKGROUND Evidence regarding the potential health effects of dietary amino acids glutamine and glutamate among individuals with type 2 diabetes (T2D) is limited. OBJECTIVES The aim was to examine dietary glutamine and glutamate in relation to subsequent risk of cardiovascular disease (CVD) and mortality among individuals with T2D. METHODS We prospectively followed 15,040 men and women with T2D at baseline or diagnosed during follow-up (Nurses' Health Study: 1980-2014 and Health Professionals Follow-Up Study: 1986-2018). Diet was repeatedly assessed using validated food frequency questionnaires every 2-4 y. Associations of energy-adjusted glutamine and glutamate intake, as well as their ratio, with CVD risk and mortality, were assessed using Cox proportional-hazards models with adjustments for demographics, dietary and lifestyle factors, and medical history. RESULTS During 196,955 and 225,371 person-years of follow-up in participants with T2D, there were 2927 incident CVD cases and 4898 deaths, respectively. Higher intake of glutamine was associated with lower risk of CVD incidence, CVD mortality, and total mortality: comparing extreme quintiles, the hazard ratios (HRs) (95% confidence intervals [CIs]) were 0.88 (0.77, 0.99), 0.78 (0.65, 0.92), and 0.84 (0.76, 0.92), respectively (all P-trend < 0.05). In contrast, higher intake of glutamate was associated with a higher risk of CVD incidence, CVD mortality, and total mortality; the HRs were 1.30 (1.15, 1.46), 1.46 (1.24, 1.72), and 1.20 (1.09, 1.32), respectively (all P-trend < 0.05). Furthermore, comparing extreme quintiles, a higher dietary glutamine-to-glutamate ratio was associated with a lower risk of CVD incidence (0.84 [0.75, 0.95]), CVD mortality (0.66 [0.57, 0.77]), and total mortality (0.82 [0.75, 0.90]). In addition, compared with participants with stable or decreased consumption of glutamine-to-glutamate ratio from prediabetes to postdiabetes diagnosis, those who increased the ratio had a 17% (5%, 27%) lower CVD mortality. CONCLUSIONS In adults with T2D, dietary glutamine was associated with a lower risk of CVD incidence and mortality, whereas the opposite was observed for glutamate intake.
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Affiliation(s)
- Zhangling Chen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Yang Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Eric B Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Alessandro Doria
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Joslin Diabetes Center, Boston, MA, United States
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Joslin Diabetes Center, Boston, MA, United States.
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Wang Y, Liu B, Han H, Hu Y, Zhu L, Rimm EB, Hu FB, Sun Q. Associations between plant-based dietary patterns and risks of type 2 diabetes, cardiovascular disease, cancer, and mortality - a systematic review and meta-analysis. Nutr J 2023; 22:46. [PMID: 37789346 PMCID: PMC10548756 DOI: 10.1186/s12937-023-00877-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 09/21/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Plant-based dietary patterns are gaining more attention due to their potential in reducing the risk of developing major chronic diseases, including type 2 diabetes (T2D), cardiovascular disease (CVD), cancer, and mortality, while an up-to-date comprehensive quantitative review is lacking. This study aimed to summarize the existing prospective observational evidence on associations between adherence to plant-based dietary patterns and chronic disease outcomes. METHODS We conducted a systematic review and meta-analysis of evidence across prospective observational studies. The data sources used were PubMed and MEDLINE, Embase, Web of Science, and screening of references. We included all prospective observational studies that evaluated the association between adherence to plant-based dietary patterns and incidence of T2D, CVD, cancer, and mortality among adults (≥ 18 years). RESULTS A total of 76 publications were identified, including 2,230,443 participants with 60,718 cases of incident T2D, 157,335 CVD cases, 57,759 cancer cases, and 174,435 deaths. An inverse association was observed between higher adherence to a plant-based dietary pattern and risks of T2D (RR, 0.82 [95% CI: 0.77-0.86]), CVD (0.90 [0.85-0.94]), cancer (0.91 [0.87-0.96]), and all-cause mortality (0.84 [0.78-0.92]) with moderate to high heterogeneity across studies (I2 ranged: 47.8-95.4%). The inverse associations with T2D, CVD and cancer were strengthened when healthy plant-based foods, such as vegetables, fruits, whole grains, and legumes, were emphasized in the definition of plant-based dietary patterns (T2D: 0.79 [0.72-0.87]; CVD: 0.85 [0.80-0.92]; cancer: 0.86 [0.80-0.92]; I2 ranged: 53.1-84.1%). Association for mortality was largely similar when the analyses were restricted to healthy plant-based diets (0.86 [0.80-0.92], I2 = 91.9%). In contrast, unhealthy plant-based diets were positively associated with these disease outcomes. Among four studies that examined changes in dietary patterns, increased adherence to plant-based dietary patterns was associated with a significantly reduced risk of T2D (0.83 [0.71-0.96]; I2 = 71.5%) and a marginally lower risk of mortality (0.95 [0.91-1.00]; I2 = 0%). CONCLUSIONS Better adherence to plant-based dietary patterns, especially those emphasizing healthy plant-based foods, is beneficial for lowering the risks of major chronic conditions, including T2D, CVD, cancer, as well as premature deaths. REGISTRATION OF REVIEW PROTOCOL This review was registered at the PROSPERO International Prospective Register of Systematic Reviews ( https://www.crd.york.ac.uk/PROSPERO/ ) with the registration number CRD42022290202.
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Affiliation(s)
- Yeli Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Binkai Liu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Han Han
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yang Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Lu Zhu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Eric B Rimm
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Konieczna J, Ruiz-Canela M, Galmes-Panades AM, Abete I, Babio N, Fiol M, Martín-Sánchez V, Estruch R, Vidal J, Buil-Cosiales P, García-Gavilán JF, Moñino M, Marcos-Delgado A, Casas R, Olbeyra R, Fitó M, Hu FB, Martínez-Gonzalez MÁ, Martínez JA, Romaguera D, Salas-Salvadó J. An Energy-Reduced Mediterranean Diet, Physical Activity, and Body Composition: An Interim Subgroup Analysis of the PREDIMED-Plus Randomized Clinical Trial. JAMA Netw Open 2023; 6:e2337994. [PMID: 37851444 PMCID: PMC10585413 DOI: 10.1001/jamanetworkopen.2023.37994] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/01/2023] [Indexed: 10/19/2023] Open
Abstract
Importance Strategies targeting body composition may help prevent chronic diseases in persons with excess weight, but randomized clinical trials evaluating lifestyle interventions have rarely reported effects on directly quantified body composition. Objective To evaluate the effects of a lifestyle weight-loss intervention on changes in overall and regional body composition. Design, Setting, and Participants The ongoing Prevención con Dieta Mediterránea-Plus (PREDIMED-Plus) randomized clinical trial is designed to test the effect of the intervention on cardiovascular disease prevention after 8 years of follow-up. The trial is being conducted in 23 Spanish research centers and includes men and women (age 55-75 years) with body mass index between 27 and 40 and metabolic syndrome. The trial reported herein is an interim subgroup analysis of the intermediate outcome body composition after 3-year follow-up, and data analysis was conducted from February 1 to November 30, 2022. Of 6874 total PREDIMED-Plus participants, a subsample of 1521 individuals, coming from centers with access to a dual energy x-ray absorptiometry device, underwent body composition measurements at 3 time points. Intervention Participants were randomly allocated to a multifactorial intervention based on an energy-reduced Mediterranean diet (MedDiet) and increased physical activity (PA) or to a control group based on usual care, with advice to follow an ad libitum MedDiet, but no physical activity promotion. Main Outcomes and Measures The outcomes (continuous) were 3-year changes in total fat and lean mass (expressed as percentages of body mass) and visceral fat (in grams), tested using multivariable linear mixed-effects models. Clinical relevance of changes in body components (dichotomous) was assessed based on 5% or more improvements in baseline values, using logistic regression. Main analyses were performed in the evaluable population (completers only) and in sensitivity analyses, multiple imputation was performed to include data of participants lost to follow-up (intention-to-treat analyses). Results A total of 1521 individuals were included (mean [SD] age, 65.3 [5.0] years; 52.1% men). In comparison with the control group (n=761), participants in the intervention arm (n=760) showed greater reductions in the percentage of total fat (between group differences after 1-year, -0.94% [95% CI, -1.19 to -0.69]; 3 years, -0.38% [95% CI, -0.64 to -0.12] and visceral fat storage after 1 year, -126 g [95% CI, -179 to -73.3 g]; 3 years, -70.4 g [95% CI, -126 to -15.2 g] and greater increases in the percentage of total lean mass at 1 year, 0.88% [95% CI, 0.63%-1.12%]; 3-years 0.34% [95% CI, 0.09%-0.60%]). The intervention group was more likely to show improvements of 5% or more in baseline body components (absolute risk reduction after 1 year, 13% for total fat mass, 11% for total lean mass, and 14% for visceral fat mass; after 3-years: 6% for total fat mass, 6% for total lean mass, and 8% for visceral fat mass). The number of participants needed to treat was between 12 and 17 to attain at least 1 individual with possibly clinically meaningful improvements in body composition. Conclusions and Relevance The findings of this trial suggest a weight-loss lifestyle intervention based on an energy-reduced MedDiet and physical activity significantly reduced total and visceral fat and attenuated age-related losses of lean mass in older adults with overweight or obesity and metabolic syndrome. Continued follow-up is warranted to confirm the long-term consequences of these changes on cardiovascular clinical end points. Trial Registration isrctn.org Identifier: ISRCTN89898870.
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Affiliation(s)
- Jadwiga Konieczna
- Research Group on Nutritional Epidemiology & Cardiovascular Physiopathology, Health Research Institute of the Balearic Islands, University Hospital Son Espases, Palma de Mallorca, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
| | - Miguel Ruiz-Canela
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, IDISNA, University of Navarra, Pamplona, Spain
| | - Aina M. Galmes-Panades
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Global Health Research Group, Health Research Institute of the Balearic Islands, University of the Balearic Islands, Palma de Mallorca, Spain
| | - Itziar Abete
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Department of Nutrition, Food Sciences, and Physiology, University of Navarra, Pamplona, Spain
| | - Nancy Babio
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Institut d’Investigació Pere Virgili, Reus, Spain
| | - Miquel Fiol
- Research Group on Nutritional Epidemiology & Cardiovascular Physiopathology, Health Research Institute of the Balearic Islands, University Hospital Son Espases, Palma de Mallorca, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
| | - Vicente Martín-Sánchez
- CIBER de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Institute of Biomedicine, University of León, León, Spain
| | - Ramón Estruch
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain
- Institut de Recerca en Nutrició i Seguretat Alimentaria, University of Barcelona, Barcelona, Spain
| | - Josep Vidal
- CIBER Diabetes y Enfermedades Metabólicas, Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology, Institut d’Investigacions Biomédiques August Pi Sunyer, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Pilar Buil-Cosiales
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, IDISNA, University of Navarra, Pamplona, Spain
- Primary Care Services, Navarra Regional Health Service, Pamplona, Spain
| | - Jesús F. García-Gavilán
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Institut d’Investigació Pere Virgili, Reus, Spain
| | - Manuel Moñino
- Research Group on Nutritional Epidemiology & Cardiovascular Physiopathology, Health Research Institute of the Balearic Islands, University Hospital Son Espases, Palma de Mallorca, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
| | | | - Rosa Casas
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain
- Institut de Recerca en Nutrició i Seguretat Alimentaria, University of Barcelona, Barcelona, Spain
| | - Romina Olbeyra
- Department of Endocrinology, Institut d’Investigacions Biomédiques August Pi Sunyer, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Cardiovascular Risk and Nutrition Research Group, Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - Frank B. Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Miguel Ángel Martínez-Gonzalez
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, IDISNA, University of Navarra, Pamplona, Spain
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - J. Alfredo Martínez
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Department of Nutrition, Food Sciences, and Physiology, University of Navarra, Pamplona, Spain
- Precision Nutrition and Cardiometabolic Health Program, IMDEA Food, CEI UAM + CSIC, Madrid, Spain
| | - Dora Romaguera
- Research Group on Nutritional Epidemiology & Cardiovascular Physiopathology, Health Research Institute of the Balearic Islands, University Hospital Son Espases, Palma de Mallorca, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
| | - Jordi Salas-Salvadó
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Institute of Health Carlos III, Madrid, Spain
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Institut d’Investigació Pere Virgili, Reus, Spain
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Yaskolka Meir A, Yun H, Stampfer MJ, Liang L, Hu FB. Nutrition, DNA methylation and obesity across life stages and generations. Epigenomics 2023; 15:991-1015. [PMID: 37933548 DOI: 10.2217/epi-2023-0172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023] Open
Abstract
Obesity is a complex multifactorial condition that often manifests in early life with a lifelong burden on metabolic health. Diet, including pre-pregnancy maternal diet, in utero nutrition and dietary patterns in early and late life, can shape obesity development. Growing evidence suggests that epigenetic modifications, specifically DNA methylation, might mediate or accompany these effects across life stages and generations. By reviewing human observational and intervention studies conducted over the past 10 years, this work provides a comprehensive overview of the evidence linking nutrition to DNA methylation and its association with obesity across different age periods, spanning from preconception to adulthood and identify future research directions in the field.
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Affiliation(s)
- Anat Yaskolka Meir
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Huan Yun
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Meir J Stampfer
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Medicine, Channing Division of Network Medicine, Brigham & Women's Hospital & Harvard Medical School, Boston, MA 02115, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Medicine, Channing Division of Network Medicine, Brigham & Women's Hospital & Harvard Medical School, Boston, MA 02115, USA
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Kaptoge S, Seshasai SRK, Sun L, Walker M, Bolton T, Spackman S, Ataklte F, Willeit P, Bell S, Burgess S, Pennells L, Altay S, Assmann G, Ben-Shlomo Y, Best LG, Björkelund C, Blazer DG, Brenner H, Brunner EJ, Dagenais GR, Cooper JA, Cooper C, Crespo CJ, Cushman M, D'Agostino RB, Daimon M, Daniels LB, Danker R, Davidson KW, de Jongh RT, Donfrancesco C, Ducimetiere P, Elders PJM, Engström G, Ford I, Gallacher I, Bakker SJL, Goldbourt U, de La Cámara G, Grimsgaard S, Gudnason V, Hansson PO, Imano H, Jukema JW, Kabrhel C, Kauhanen J, Kavousi M, Kiechl S, Knuiman MW, Kromhout D, Krumholz HM, Kuller LH, Laatikainen T, Lowler DA, Meyer HE, Mukamal K, Nietert PJ, Ninomiya T, Nitsch D, Nordestgaard BG, Palmieri L, Price JF, Ridker PM, Sun Q, Rosengren A, Roussel R, Sakurai M, Salomaa V, Schöttker B, Shaw JE, Strandberg TE, Sundström J, Tolonen H, Tverdal A, Verschuren WMM, Völzke H, Wagenknecht L, Wallace RB, Wannamethee SG, Wareham NJ, Wassertheil-Smoller S, Yamagishi K, Yeap BB, Harrison S, Inouye M, Griffin S, Butterworth AS, Wood AM, Thompson SG, Sattar N, Danesh J, Di Angelantonio E, Tipping RW, Russell S, Johansen M, Bancks MP, Mongraw-Chaffin M, Magliano D, Barr ELM, Zimmet PZ, Knuiman MW, Whincup PH, Willeit J, Willeit P, Leitner C, Lawlor DA, Ben-Shlomo Y, Elwood P, Sutherland SE, Hunt KJ, Cushman M, Selmer RM, Haheim LL, Ariansen I, Tybjaer-Hansen A, Frikkle-Schmidt R, Langsted A, Donfrancesco C, Lo Noce C, Balkau B, Bonnet F, Fumeron F, Pablos DL, Ferro CR, Morales TG, Mclachlan S, Guralnik J, Khaw KT, Brenner H, Holleczek B, Stocker H, Nissinen A, Palmieri L, Vartiainen E, Jousilahti P, Harald K, Massaro JM, Pencina M, Lyass A, Susa S, Oizumi T, Kayama T, Chetrit A, Roth J, Orenstein L, Welin L, Svärdsudd K, Lissner L, Hange D, Mehlig K, Salomaa V, Tilvis RS, Dennison E, Cooper C, Westbury L, Norman PE, Almeida OP, Hankey GJ, Hata J, Shibata M, Furuta Y, Bom MT, Rutters F, Muilwijk M, Kraft P, Lindstrom S, Turman C, Kiyama M, Kitamura A, Yamagishi K, Gerber Y, Laatikainen T, Salonen JT, van Schoor LN, van Zutphen EM, Verschuren WMM, Engström G, Melander O, Psaty BM, Blaha M, de Boer IH, Kronmal RA, Sattar N, Rosengren A, Nitsch D, Grandits G, Tverdal A, Shin HC, Albertorio JR, Gillum RF, Hu FB, Cooper JA, Humphries S, Hill- Briggs F, Vrany E, Butler M, Schwartz JE, Kiyama M, Kitamura A, Iso H, Amouyel P, Arveiler D, Ferrieres J, Gansevoort RT, de Boer R, Kieneker L, Crespo CJ, Assmann G, Trompet S, Kearney P, Cantin B, Després JP, Lamarche B, Laughlin G, McEvoy L, Aspelund T, Thorsson B, Sigurdsson G, Tilly M, Ikram MA, Dorr M, Schipf S, Völzke H, Fretts AM, Umans JG, Ali T, Shara N, Davey-Smith G, Can G, Yüksel H, Özkan U, Nakagawa H, Morikawa Y, Ishizaki M, Njølstad I, Wilsgaard T, Mathiesen E, Sundström J, Buring J, Cook N, Arndt V, Rothenbacher D, Manson J, Tinker L, Shipley M, Tabak AG, Kivimaki M, Packard C, Robertson M, Feskens E, Geleijnse M, Kromhout D. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries: 23 million person-years of observation. Lancet Diabetes Endocrinol 2023; 11:731-742. [PMID: 37708900 PMCID: PMC7615299 DOI: 10.1016/s2213-8587(23)00223-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/14/2023] [Accepted: 07/14/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. METHODS For this observational study, we conducted a combined analysis of individual-participant data from 19 high-income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23·1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. FINDINGS For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2·69 (95% CI 2·43-2·97) when diagnosed at 30-39 years, 2·26 (2·08-2·45) at 40-49 years, 1·84 (1·72-1·97) at 50-59 years, 1·57 (1·47-1·67) at 60-69 years, and 1·39 (1·29-1·51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier when diagnosed aged 50 years than an individual without diabetes. Using EU death rates, the corresponding estimates were 13, 9, or 5 years earlier. INTERPRETATION Every decade of earlier diagnosis of diabetes was associated with about 3-4 years of lower life expectancy, highlighting the need to develop and implement interventions that prevent or delay the onset of diabetes and to intensify the treatment of risk factors among young adults diagnosed with diabetes. FUNDING British Heart Foundation, Medical Research Council, National Institute for Health and Care Research, and Health Data Research UK.
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