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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] [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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Deng K, Gupta DK, Shu XO, Lipworth L, Zheng W, Thomas VE, Cai H, Cai Q, Wang TJ, Yu D. Metabolite Signature of Life's Essential 8 and Risk of Coronary Heart Disease Among Low-Income Black and White Americans. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e004230. [PMID: 38014580 PMCID: PMC10843634 DOI: 10.1161/circgen.123.004230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/26/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Life's essential 8 (LE8) is a comprehensive construct of cardiovascular health. Yet, little is known about the LE8 score, its metabolic correlates, and their predictive implications among Black Americans and low-income individuals. METHODS In a nested case-control study of coronary heart disease (CHD) among 299 pairs of Black and 298 pairs of White low-income Americans from the Southern Community Cohort Study, we estimated LE8 score and applied untargeted plasma metabolomics and elastic net with leave-one-out cross-validation to identify metabolite signature (MetaSig) of LE8. Associations of LE8 score and MetaSig with incident CHD were examined using conditional logistic regression. The mediation effect of MetaSig on the LE8-CHD association was also examined. The external validity of MetaSig was evaluated in another nested CHD case-control study among 299 pairs of Chinese adults. RESULTS Higher LE8 score was associated with lower CHD risk (standardized odds ratio, 0.61 [95% CI, 0.53-0.69]). The MetaSig, consisting of 133 metabolites, showed significant correlation with LE8 score (r=0.61) and inverse association with CHD (odds ratio, 0.57 [0.49-0.65]), robust to adjustment for LE8 score and across participants with different sociodemographic and health status ([odds ratios, 0.42-0.69]; all P<0.05). MetaSig mediated a large portion of the LE8-CHD association: 53% (32%-80%). Significant associations of MetaSig with LE8 score and CHD risk were found in validation cohort (r=0.49; odds ratio, 0.57 [0.46-0.69]). CONCLUSIONS Higher LE8 score and its MetaSig were associated with lower CHD risk among low-income Black and White Americans. Metabolomics may offer an objective measure of LE8 and its metabolic phenotype relevant to CHD prevention among diverse populations.
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Affiliation(s)
- Kui Deng
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Deepak K. Gupta
- Vanderbilt Translational & Clinical Cardiovascular Research Center & Division of Cardiovascular Medicine, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Xiao-Ou Shu
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Loren Lipworth
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Wei Zheng
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Victoria E. Thomas
- Vanderbilt Translational & Clinical Cardiovascular Research Center & Division of Cardiovascular Medicine, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Hui Cai
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Qiuyin Cai
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Thomas J. Wang
- Dept of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
| | - Danxia Yu
- Vanderbilt Epidemiology Center and Division of Epidemiology, Dept of Medicine, Vanderbilt University Medical Center, Nashville, TN
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da Silva ACR, Yadegari A, Tzaneva V, Vasanthan T, Laketic K, Shearer J, Bainbridge SA, Harris C, Adamo KB. Metabolomics to Understand Alterations Induced by Physical Activity during Pregnancy. Metabolites 2023; 13:1178. [PMID: 38132860 PMCID: PMC10745110 DOI: 10.3390/metabo13121178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
Physical activity (PA) and exercise have been associated with a reduced risk of cancer, obesity, and diabetes. In the context of pregnancy, maintaining an active lifestyle has been shown to decrease gestational weight gain (GWG) and lower the risk of gestational diabetes mellitus (GDM), hypertension, and macrosomia in offspring. The main pathways activated by PA include BCAAs, lipids, and bile acid metabolism, thereby improving insulin resistance in pregnant individuals. Despite these known benefits, the underlying metabolites and biological mechanisms affected by PA remain poorly understood, highlighting the need for further investigation. Metabolomics, a comprehensive study of metabolite classes, offers valuable insights into the widespread metabolic changes induced by PA. This narrative review focuses on PA metabolomics research using different analytical platforms to analyze pregnant individuals. Existing studies support the hypothesis that exercise behaviour can influence the metabolism of different populations, including pregnant individuals and their offspring. While PA has shown considerable promise in maintaining metabolic health in non-pregnant populations, our comprehension of metabolic changes in the context of a healthy pregnancy remains limited. As a result, further investigation is necessary to clarify the metabolic impact of PA within this unique group, often excluded from physiological research.
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Affiliation(s)
- Ana Carolina Rosa da Silva
- School of Human Kinetics, Faculty of Health Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (A.C.R.d.S.)
| | - Anahita Yadegari
- School of Human Kinetics, Faculty of Health Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (A.C.R.d.S.)
| | - Velislava Tzaneva
- School of Human Kinetics, Faculty of Health Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (A.C.R.d.S.)
| | - Tarushika Vasanthan
- Department of Chemistry and Biology, Toronto Metropolitan University, Toronto, ON M5G 2A7, Canada
| | - Katarina Laketic
- Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Jane Shearer
- Department of Biochemistry and Molecular Biology, Faculty of Kinesiology, Cumming School of Medicine and Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Shannon A. Bainbridge
- Interdisciplinary School of Health Sciences, Faculty of Health Sciences, Ottawa, ON K1N 6N5, Canada
- Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - Cory Harris
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
| | - Kristi B. Adamo
- School of Human Kinetics, Faculty of Health Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada; (A.C.R.d.S.)
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Du Y, Li YY, Choi BY, Fernadez R, Su KJ, Sharma K, Qi L, Yin Z, Zhao Q, Shen H, Qiu C, Zhao LJ, Luo Z, Wu L, Tian Q, Deng HW. Metabolomic profiles associated with physical activity in White and African American adult men. PLoS One 2023; 18:e0289077. [PMID: 37943870 PMCID: PMC10635561 DOI: 10.1371/journal.pone.0289077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 07/11/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Physical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations. OBJECTIVE This study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men. METHODS Using the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking. RESULTS Of the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups. CONCLUSIONS Metabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.
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Affiliation(s)
- Yan Du
- School of Nursing, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - Yuan-Yuan Li
- Department of Nutrition, Nutrition Research Institute, University of North Carolina at Chapel Hill School of Public Health, Kannapolis, North Carolina, United States of America
| | - Byeong Yeob Choi
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - Roman Fernadez
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - Kuan-Jui Su
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America
| | - Kumar Sharma
- Center for Precision Medicine, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States of America
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University; New Orleans, LA, United States of America
| | - Zenong Yin
- Department of Public Health, University of Texas at San Antonio, San Antonio, TX, United States of America
| | - Qi Zhao
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, United States of America
| | - Hui Shen
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America
| | - Chuan Qiu
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America
| | - Lan-Juan Zhao
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America
| | - Zhe Luo
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America
| | - Li Wu
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America
| | - Qing Tian
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University; New Orleans, LA, United States of America
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5
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Moon JY, Chai JC, Yu B, Song RJ, Chen GC, Graff M, Daviglus ML, Chan Q, Thyagarajan B, Castaneda SF, Grove ML, Cai J, Xue X, Mossavar-Rahmani Y, Vasan RS, Boerwinkle E, Kaplan R, Qi Q. Metabolomic Signatures of Sedentary Behavior and Cardiometabolic Traits in US Hispanics/Latinos: Results from HCHS/SOL. Med Sci Sports Exerc 2023; 55:1781-1791. [PMID: 37170952 PMCID: PMC10523950 DOI: 10.1249/mss.0000000000003205] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
PURPOSE The aim of this study was to understand the serum metabolomic signatures of moderate-to-vigorous physical activity (MVPA) and sedentary behavior, and further associate their metabolomic signatures with incident cardiometabolic diseases. METHODS This analysis included 2711 US Hispanics/Latinos from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) aged 18-74 yr (2008-2011). An untargeted, liquid chromatography-mass spectrometry was used to profile the serum metabolome. The associations of metabolites with accelerometer-measured MVPA and sedentary time were examined using survey linear regressions adjusting for covariates. The weighted correlation network analysis identified modules of correlated metabolites in relation to sedentary time, and the modules were associated with incident diabetes, dyslipidemia, and hypertension over the 6-yr follow-up. RESULTS Of 624 metabolites, 5 and 102 were associated with MVPA and sedentary behavior at false discovery rate (FDR) <0.05, respectively, after adjusting for socioeconomic and lifestyle factors. The weighted correlation network analysis identified 8 modules from 102 metabolites associated with sedentary time. Four modules (branched-chain amino acids, erythritol, polyunsaturated fatty acid, creatine) were positively, and the other four (acyl choline, plasmalogen glycerol phosphatidyl choline, plasmalogen glycerol phosphatidyl ethanolamine, urea cycle) were negatively correlated with sedentary time. Among these modules, a higher branched-chain amino acid score and a lower plasmalogen glycerol phosphatidyl choline score were associated with increased risks of diabetes and dyslipidemia. A higher erythritol score was associated with an increased risk of diabetes, and a lower acyl choline score was linked to an increased risk of hypertension. CONCLUSIONS In this study of US Hispanics/Latinos, we identified multiple serum metabolomic signatures of sedentary behavior and their associations with risk of incident diabetes, hypertension, and dyslipidemia. These findings suggest a potential role of circulating metabolites in the links between sedentary behavior and cardiometabolic diseases.
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Affiliation(s)
- Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Jin Choul Chai
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Rebecca J. Song
- Department of Medicine, Boston University School of Medicine, Boston, MA
| | - Guo-chong Chen
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
- Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, CHINA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, NC
| | - Martha L. Daviglus
- Institute for Minority Health Research, University of Illinois Chicago, Chicago, IL
| | - Queenie Chan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, IL
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN
| | | | - Megan L. Grove
- Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, CHINA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC
| | - Xiaonan Xue
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Yasmin Mossavar-Rahmani
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | | | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Robert Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
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Deng K, Gupta DK, Shu XO, Lipworth L, Zheng W, Thomas VE, Cai H, Cai Q, Wang TJ, Yu D. Metabolite Signature of Life's Essential 8 and Risk of Coronary Heart Disease among Low-Income Black and White Americans. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.24.23289055. [PMID: 37163035 PMCID: PMC10168489 DOI: 10.1101/2023.04.24.23289055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background and Aims Life's Essential 8 (LE8) is a comprehensive construct of cardiovascular health. Yet, little is known about LE8 score, its metabolic correlates, and their predictive implications among Black Americans and low-income individuals. Methods In a nested case-control study of coronary heart disease (CHD) among 598 Black and 596 White low-income Americans, we estimated LE8 score, conducted untargeted plasma metabolites profiling, and used elastic net with leave-one-out cross-validation to identify metabolite signature (MetaSig) of LE8. Associations of LE8 score and MetaSig with incident CHD were examined using conditional logistic regression. Mediation effect of MetaSig on the LE8-CHD association was also examined. The external validity of MetaSig was evaluated in another nested CHD case-control study among 598 Chinese adults. Results Higher LE8 score was associated with lower CHD risk [standardized OR (95% CI)=0.61 (0.53-0.69)]. The identified MetaSig, consisting of 133 metabolites, showed strong correlation with LE8 score ( r =0.61) and inverse association with CHD risk [OR (95% CI)=0.57 (0.49-0.65)], robust to adjustment for LE8 score and across participants with different sociodemographic and health status (ORs: 0.42-0.69; all P <0.05). MetaSig mediated a large portion of the LE8-CHD association: 53% (32%-80%) ( P <0.001). Significant associations of MetaSig with LE8 score and CHD risk were found in validation cohort [ r =0.49; OR (95% CI)=0.57 (0.46-0.69)]. Conclusions Higher LE8 score and its MetaSig were associated with lower CHD risk among low-income Black and White Americans. Metabolomics may offer an objective and comprehensive measure of LE8 score and its metabolic phenotype relevant to CHD prevention among diverse populations.
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Ding M. A Two-stage Linear Mixed Model (TS-LMM) for Summary-data-based Multivariable Mendelian Randomization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.25.23289099. [PMID: 37162968 PMCID: PMC10168515 DOI: 10.1101/2023.04.25.23289099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Multivariable Mendelian randomization (MVMR) methods provide a strategy for applying genome-wide summary statistics to assess simultaneous causal effects of multiple risk factors on a disease outcome. In contrast to univariate MR methods that assumes no horizonal pleiotropy (genetic variants only associate with one risk factor), MVMR allows for genetic variants associate with multiple risk factors and models pleiotropy by including summary statistics with risk factors as multiple variables into the regression model. Here, we propose a two-stage linear mixed model (TS-LMM) for MVMR that accounts for variance of summary statistics not only in outcome, but also in all of the risk factors. In stage I, we apply linear mixed model to treat variance in summary statistics of disease as fixed-/random-effects, while accounting for covariance between genetic variants due to linkage disequilibrium (LD). Particularly, we use an iteratively re-weighted least squares algorithm to obtain estimates for the random-effects. In stage II, we account for variance in summary statistics of multiple risk factors simultaneously by applying measurement error correction methods that take into consideration LD between genetic variants and correlation between summary statistics of risk factors. We compared our MVMR approach to other approaches in a simulation study. When most of the instrumental variables (IVs) were strong, our model generated the highest coverage of true causal associations, the highest power of detecting significant causal associations, and the lowest false positive rate of identifying null causal effect for a range of scenarios that varied correlation (weak, strong) between summary statistics of risk factors and LD among genetic variants (weak LD [γ 2 ≤0.1], moderate LD [0.1< γ 2 ≤0.5]). When the proportion of strong IVs was reduced, our model showed performances comparable to MVMR-Egger and MVMR-IVW. The more accurate inference of our model in the presence of correlation among risk factors supports potential wide application to -omics data that are commonly multi dimensional and correlated, as shown in application to determinants of longevity, where our method nominated a specific significant lipoprotein subfraction for causal association from a panel of 10 lipoprotein cholesterol measures. The robustness of our model to correlation structure suggests that in practice we can allow moderate LD in selection of IVs, thereby potentially leveraging genome-wide summary data in a more effective manner. Our model is implemented in 'TS_LMM' macro in R.
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Muli S, Brachem C, Alexy U, Schmid M, Oluwagbemigun K, Nöthlings U. Exploring the association of physical activity with the plasma and urine metabolome in adolescents and young adults. Nutr Metab (Lond) 2023; 20:23. [PMID: 37020289 PMCID: PMC10074825 DOI: 10.1186/s12986-023-00742-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/29/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Regular physical activity elicits many health benefits. However, the underlying molecular mechanisms through which physical activity influences overall health are less understood. Untargeted metabolomics enables system-wide mapping of molecular perturbations which may lend insights into physiological responses to regular physical activity. In this study, we investigated the associations of habitual physical activity with plasma and urine metabolome in adolescents and young adults. METHODS This cross-sectional study included participants from the DONALD (DOrtmund Nutritional and Anthropometric Longitudinally Designed) study with plasma samples n = 365 (median age: 18.4 (18.1, 25.0) years, 58% females) and 24 h urine samples n = 215 (median age: 18.1 (17.1, 18.2) years, 51% females). Habitual physical activity was assessed using a validated Adolescent Physical Activity Recall Questionnaire. Plasma and urine metabolite concentrations were determined using ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) methods. In a sex-stratified analysis, we conducted principal component analysis (PCA) to reduce the dimensionality of metabolite data and to create metabolite patterns. Multivariable linear regression models were then applied to assess the associations between self-reported physical activity (metabolic equivalent of task (MET)-hours per week) with single metabolites and metabolite patterns, adjusted for potential confounders and controlling the false discovery rate (FDR) at 5% for each set of regressions. RESULTS Habitual physical activity was positively associated with the "lipid, amino acids and xenometabolite" pattern in the plasma samples of male participants only (β = 1.02; 95% CI: 1.01, 1.04, p = 0.001, adjusted p = 0.042). In both sexes, no association of physical activity with single metabolites in plasma and urine and metabolite patterns in urine was found (all adjusted p > 0.05). CONCLUSIONS Our explorative study suggests that habitual physical activity is associated with alterations of a group of metabolites reflected in the plasma metabolite pattern in males. These perturbations may lend insights into some of underlying mechanisms that modulate effects of physical activity.
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Affiliation(s)
- Samuel Muli
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany.
| | - Christian Brachem
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Ute Alexy
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Matthias Schmid
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Kolade Oluwagbemigun
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Ute Nöthlings
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
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9
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Lehtovirta M, Wu F, Rovio SP, Heinonen OJ, Laitinen TT, Niinikoski H, Lagström H, Viikari JSA, Rönnemaa T, Jula A, Ala-Korpela M, Raitakari OT, Pahkala K. Association of physical activity with metabolic profile from adolescence to adulthood. Scand J Med Sci Sports 2023; 33:307-318. [PMID: 36331352 DOI: 10.1111/sms.14261] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/27/2022] [Accepted: 11/03/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Physical activity benefits cardiometabolic health, but little is known about its detailed links with serum lipoproteins, amino acids, and glucose metabolism at young age. We therefore studied the association of physical activity with a comprehensive metabolic profile measured repeatedly in adolescence. METHODS The cohort is derived from the longitudinal Special Turku Coronary Risk Factor Intervention Project. At ages 13, 15, 17, and 19 years, data on physical activity were collected by a questionnaire, and circulating metabolic measures were quantified by nuclear magnetic resonance metabolomics from repeatedly assessed serum samples (age 13: n = 503, 15: n = 472, 17: n = 466, and 19: n = 361). RESULTS Leisure-time physical activity (LTPA;MET h/wk) was directly associated with concentrations of polyunsaturated fatty acids, and inversely with the ratio of monounsaturated fatty acids to total fatty acids (-0.006SD; [-0.008, -0.003]; p < 0.0001). LTPA was inversely associated with very-low-density lipoprotein (VLDL) particle concentration (-0.003SD; [-0.005, -0.001]; p = 0.002) and VLDL particle size (-0.005SD; [-0.007, -0.003]; p < 0.0001). LTPA showed direct association with the particle concentration and size of high-density lipoprotein (HDL), and HDL cholesterol concentration (0.004SD; [0.002, 0.006]; p < 0.0001). Inverse associations of LTPA with triglyceride and total lipid concentrations in large to small sized VLDL subclasses were found. Weaker associations were seen for other metabolic measures including inverse associations with concentrations of lactate, isoleucine, glycoprotein acetylation, and a direct association with creatinine concentration. The results remained after adjusting for body mass index and proportions of energy intakes from macronutrients. CONCLUSIONS Physical activity during adolescence is beneficially associated with the metabolic profile including novel markers. The results support recommendations on physical activity during adolescence to promote health and possibly reduce future disease risks.
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Affiliation(s)
- Miia Lehtovirta
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Feitong Wu
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Suvi P Rovio
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Olli J Heinonen
- Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Tomi T Laitinen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Harri Niinikoski
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Department of Pediatrics and Adolescent Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Hanna Lagström
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Department of Public Health, University of Turku and Turku University Hospital, Turku, Finland
| | - Jorma S A Viikari
- Department of Medicine, University of Turku, Turku, Finland.,Division of Medicine, Turku University Hospital, Turku, Finland
| | - Tapani Rönnemaa
- Department of Medicine, University of Turku, Turku, Finland.,Division of Medicine, Turku University Hospital, Turku, Finland
| | - Antti Jula
- Department of Chronic Disease Prevention, Institute for Health and Welfare, Turku, Finland
| | - Mika Ala-Korpela
- Computational Medicine, Center for Life Course Health Research, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland.,NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Katja Pahkala
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
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10
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Kojouri M, Pinto R, Mustafa R, Huang J, Gao H, Elliott P, Tzoulaki I, Dehghan A. Metabolome-wide association study on physical activity. Sci Rep 2023; 13:2374. [PMID: 36759570 PMCID: PMC9911764 DOI: 10.1038/s41598-022-26377-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 12/14/2022] [Indexed: 02/11/2023] Open
Abstract
The underlying mechanisms linking physical activity to better health are not fully understood. Here we examined the associations between physical activity and small circulatory molecules, the metabolome, to highlight relevant biological pathways. We examined plasma metabolites associated with self-reported physical activity among 2217 participants from the Airwave Health Monitoring Study. Metabolic profiling was conducted using the mass spectrometry-based Metabolon platform (LC/GC-MS), measuring 828 known metabolites. We replicated our findings in an independent subset of the study (n = 2971) using untargeted LC-MS. Mendelian randomisation was carried out to investigate potential causal associations between physical activity, body mass index, and metabolites. Higher vigorous physical activity was associated (P < 0.05/828 = 6.03 × 10-5) with circulatory levels of 28 metabolites adjusted for age, sex and body mass index. The association was inverse for glutamate and diacylglycerol lipids, and direct for 3-4-hydroxyphenyllactate, phenyl lactate (PLA), alpha-hydroxy isovalerate, tiglylcarnitine, alpha-hydroxyisocaproate, 2-hydroxy-3-methylvalerate, isobutyrylcarnitine, imidazole lactate, methionine sulfone, indole lactate, plasmalogen lipids, pristanate and fumarate. In the replication panel, we found 23 untargeted LC-MS features annotated to the identified metabolites, for which we found nominal associations with the same direction of effect for three features annotated to 1-(1-enyl-palmitoyl)-2-oleoyl-GPC (P-16:0/18:1), 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2), 1-stearoyl-2-dihomo-linolenoyl-GPC (18:0/20:3n3 or 6). Using Mendelian randomisation, we showed a potential causal relationship between body mass index and three identified metabolites. Circulatory metabolites are associated with physical activity and may play a role in mediating its health effects.
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Affiliation(s)
- Maedeh Kojouri
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
| | - Rui Pinto
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
- UK Dementia Research Institute, Imperial College London, London, W2 1PG, UK
| | - Rima Mustafa
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
- UK Dementia Research Institute, Imperial College London, London, W2 1PG, UK
| | - Jian Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
| | - He Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
- UK Dementia Research Institute, Imperial College London, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK
- UK Dementia Research Institute, Imperial College London, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, W2 1PG, UK.
- UK Dementia Research Institute, Imperial College London, London, W2 1PG, UK.
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.
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11
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Kang JH, Zeleznik O, Frueh L, Lasky-Su J, Eliassen AH, Clish C, Rosner BA, Pasquale LR, Wiggs JL. Prediagnostic Plasma Metabolomics and the Risk of Exfoliation Glaucoma. Invest Ophthalmol Vis Sci 2022; 63:15. [PMID: 35951322 PMCID: PMC9386645 DOI: 10.1167/iovs.63.9.15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Purpose The etiology of exfoliation glaucoma (XFG) is poorly understood. We aimed to identify a prediagnostic plasma metabolomic signature associated with XFG. Methods We conducted a 1:1 matched case-control study nested within the Nurses' Health Study and Health Professionals Follow-up Study. We collected blood samples in 1989-1990 (Nurses' Health Study) and 1993-1995 (Health Professionals Follow-up Study). We identified 205 incident XFG cases through 2016 (average time to diagnosis from blood draw = 11.8 years) who self-reported glaucoma and were confirmed as XFG cases with medical records. We profiled plasma metabolites using liquid chromatography-mass spectrometry. We evaluated 379 known metabolites (transformed for normality using probit scores) using multiple conditional logistic models. Metabolite set enrichment analysis was used to identify metabolite classes associated with XFG. To adjust for multiple comparisons, we used number of effective tests (NEF) and the false discovery rate (FDR). Results Mean age of cases (n = 205) at diagnosis was 71 years; 85% were women and more than 99% were Caucasian; controls (n = 205) reported eye examinations as of the matched cases' index date. Thirty-three metabolites were nominally significantly associated with XFG (P < 0.05), and 4 metabolite classes were FDR-significantly associated. We observed positive associations for lysophosphatidylcholines (FDR = 0.02) and phosphatidylethanolamine plasmalogens (FDR = 0.004) and inverse associations for triacylglycerols (FDR < 0.0001) and steroids (FDR = 0.03). In particular, the multivariable-adjusted odds ratio with each 1 standard deviation higher plasma cortisone levels was 0.49 (95% confidence interval, 0.32-0.74; NEF = 0.05). Conclusions In plasma from a decade before diagnosis, lysophosphatidylcholines and phosphatidylethanolamine plasmalogens were positively associated and triacylglycerols and steroids (e.g., cortisone) were inversely associated with XFG risk.
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Affiliation(s)
- Jae H Kang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
| | - Oana Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
| | - Lisa Frueh
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
| | - A Heather Eliassen
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
- Departments of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Clary Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, United States
| | - Bernard A Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Janey L Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States
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12
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Accelerometer-Based Sedentary Time, Physical Activity, and Serum Metabolome in Young Men. Metabolites 2022; 12:metabo12080700. [PMID: 36005572 PMCID: PMC9414649 DOI: 10.3390/metabo12080700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/07/2022] [Accepted: 07/21/2022] [Indexed: 12/10/2022] Open
Abstract
Physical activity (PA) has been shown to associate with many health benefits but studies with metabolome-wide associations with PA are still lacking. Metabolome studies may deepen the mechanistic understanding of PA on the metabolic pathways related to health outcomes. The aim of the present study was to study the association of accelerometer based sedentary time (SB) and PA with metabolome measures. SB and PA were measured by a hip-worn accelerometer in 314 young adult men (age: mean 28, standard deviation 7 years). Metabolome was analyzed from fasting serum samples consisting of 66 metabolome measures (nuclear magnetic resonance-based metabolomics). The associations were analyzed using a single and compositional approach with regression analysis. The compositional analysis revealed that 4 metabolome variables were significantly (γ: 0.32−0.44, p ≤ 0.002), and 13 variables with a trend towards significance (p < 0.05), associated with SB with varying metabolic pathways. Trends towards significant associations (p < 0.05) were observed with 5 variables with moderate-to-vigorous and 1 variable with light intensity PA with varying metabolic pathways. The present study revealed possible mechanistic pathways relevant for the interaction between especially SB but also PA of moderate-to-vigorous intensity with ketone bodies and amino acid concentration related to exercised-induced energy production and lipid metabolism.
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13
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Hoshi RA, Liu Y, Luttmann-Gibson H, Tiwari S, Giulianini F, Andres AM, Watrous JD, Cook NR, Costenbader KH, Okereke OI, Ridker PM, Manson JE, Lee IM, Vinayagamoorthy M, Cheng S, Copeland T, Jain M, Chasman DI, Demler OV, Mora S. Association of Physical Activity With Bioactive Lipids and Cardiovascular Events. Circ Res 2022; 131:e84-e99. [PMID: 35862024 PMCID: PMC9357171 DOI: 10.1161/circresaha.122.320952] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND To clarify the mechanisms underlying physical activity (PA)-related cardioprotection, we examined the association of PA with plasma bioactive lipids (BALs) and cardiovascular disease (CVD) events. We additionally performed genome-wide associations. METHODS PA-bioactive lipid associations were examined in VITAL (VITamin D and OmegA-3 TriaL)-clinical translational science center (REGISTRATION: URL: https://www. CLINICALTRIALS gov; Unique identifier: NCT01169259; N=1032) and validated in JUPITER (Justification for the Use of statins in Prevention: an Intervention Trial Evaluating Rosuvastatin)-NC (NCT00239681; N=589), using linear models adjusted for age, sex, race, low-density lipoprotein-cholesterol, total-C, and smoking. Significant BALs were carried over to examine associations with incident CVD in 2 nested CVD case-control studies: VITAL-CVD (741 case-control pairs) and JUPITER-CVD (415 case-control pairs; validation). RESULTS We detected 145 PA-bioactive lipid validated associations (false discovery rate <0.1). Annotations were found for 6 of these BALs: 12,13-diHOME, 9,10-diHOME, lysoPC(15:0), oxymorphone-3b-D-glucuronide, cortisone, and oleoyl-glycerol. Genetic analysis within JUPITER-NC showed associations of 32 PA-related BALs with 22 single-nucleotide polymorphisms. From PA-related BALs, 12 are associated with CVD. CONCLUSIONS We identified a PA-related bioactive lipidome profile out of which 12 BALs also had opposite associations with incident CVD events.
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Affiliation(s)
- Rosangela A Hoshi
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., P.M.R., O.V.D., S.M.).,Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.)
| | - Yanyan Liu
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., P.M.R., O.V.D., S.M.).,Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.)
| | - Heike Luttmann-Gibson
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., P.M.R., O.V.D., S.M.).,Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.).,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (H.L.-G., O.I.O., J.E.M., I.-M.L., M.J.)
| | - Saumya Tiwari
- Department of Pharmacology, University of California San Diego, La Jolla (S.T., A.M.A., J.D.W.)
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.)
| | - Allen M Andres
- Department of Pharmacology, University of California San Diego, La Jolla (S.T., A.M.A., J.D.W.)
| | - Jeramie D Watrous
- Department of Pharmacology, University of California San Diego, La Jolla (S.T., A.M.A., J.D.W.)
| | - Nancy R Cook
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.)
| | - Karen H Costenbader
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (K.H.C.)
| | - Olivia I Okereke
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (H.L.-G., O.I.O., J.E.M., I.-M.L., M.J.).,Department of Psychiatry, Massachusetts General Hospital, Boston (O.I.O.)
| | - Paul M Ridker
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., P.M.R., O.V.D., S.M.).,Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.)
| | - JoAnn E Manson
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.).,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (H.L.-G., O.I.O., J.E.M., I.-M.L., M.J.)
| | - I-Min Lee
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.).,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (H.L.-G., O.I.O., J.E.M., I.-M.L., M.J.)
| | - Manickavasagar Vinayagamoorthy
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.)
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA (S.C.)
| | - Trisha Copeland
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.)
| | - Mohit Jain
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA (H.L.-G., O.I.O., J.E.M., I.-M.L., M.J.)
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.)
| | - Olga V Demler
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., P.M.R., O.V.D., S.M.).,Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.).,Department of Computer Science, ETH Zurich, Switzerland (O.V.D.)
| | - Samia Mora
- Center for Lipid Metabolomics, Division of Preventive Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., P.M.R., O.V.D., S.M.).,Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA. (R.A.H., Y.L., H.L.-G., F.G., N.R.C., P.M.R., J.E.M., I.-M.L., M.V., T.C., D.I.C., O.V.D., S.M.)
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14
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Huang T, Zeleznik OA, Roberts AL, Balasubramanian R, Clish CB, Eliassen AH, Rexrode KM, Tworoger SS, Hankinson SE, Koenen KC, Kubzansky LD. Plasma Metabolomic Signature of Early Abuse in Middle-Aged Women. Psychosom Med 2022; 84:536-546. [PMID: 35471987 PMCID: PMC9167800 DOI: 10.1097/psy.0000000000001088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
OBJECTIVE Metabolomic profiling may provide insights into biological mechanisms underlying the strong epidemiologic links observed between early abuse and cardiometabolic disorders in later life. METHODS We examined the associations between early abuse and midlife plasma metabolites in two nonoverlapping subsamples from the Nurses' Health Study II, comprising 803 (mean age = 40 years) and 211 women (mean age = 61 years). Liquid chromatography-tandem mass spectrometry assays were used to measure metabolomic profiles, with 283 metabolites consistently measured in both subsamples. Physical and sexual abuse before age 18 years was retrospectively assessed by validated questions integrating type/frequency of abuse. Analyses were conducted in each sample and pooled using meta-analysis, with multiple testing adjustment using the q value approach for controlling the positive false discovery rate. RESULTS After adjusting for age, race, menopausal status, body size at age 5 years, and childhood socioeconomic indicators, more severe early abuse was consistently associated with five metabolites at midlife (q value < 0.20 in both samples), including lower levels of serotonin and C38:3 phosphatidylethanolamine plasmalogen and higher levels of alanine, proline, and C40:6 phosphatidylethanolamine. Other metabolites potentially associated with early abuse (q value < 0.05 in the meta-analysis) included triglycerides, phosphatidylcholine plasmalogens, bile acids, tyrosine, glutamate, and cotinine. The association between early abuse and midlife metabolomic profiles was partly mediated by adulthood body mass index (32% mediated) and psychosocial distress (13%-26% mediated), but not by other life-style factors. CONCLUSIONS Early abuse was associated with distinct metabolomic profiles of multiple amino acids and lipids in middle-aged women. Body mass index and psychosocial factors in adulthood may be important intermediates for the observed association.
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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
| | - Oana A. Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Andrea L. Roberts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA
| | | | - A. Heather Eliassen
- 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, Boston, MA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Kathryn M. Rexrode
- Division of Women’s Health, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Shelley S. Tworoger
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Susan E. Hankinson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA
| | - Karestan C. Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Laura D. Kubzansky
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA
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15
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Rothwell JA, Murphy N, Bešević J, Kliemann N, Jenab M, Ferrari P, Achaintre D, Gicquiau A, Vozar B, Scalbert A, Huybrechts I, Freisling H, Prehn C, Adamski J, Cross AJ, Pala VM, Boutron-Ruault MC, Dahm CC, Overvad K, Gram IT, Sandanger TM, Skeie G, Jakszyn P, Tsilidis KK, Aleksandrova K, Schulze MB, Hughes DJ, van Guelpen B, Bodén S, Sánchez MJ, Schmidt JA, Katzke V, Kühn T, Colorado-Yohar S, Tumino R, Bueno-de-Mesquita B, Vineis P, Masala G, Panico S, Eriksen AK, Tjønneland A, Aune D, Weiderpass E, Severi G, Chajès V, Gunter MJ. Metabolic Signatures of Healthy Lifestyle Patterns and Colorectal Cancer Risk in a European Cohort. Clin Gastroenterol Hepatol 2022; 20:e1061-e1082. [PMID: 33279777 PMCID: PMC9049188 DOI: 10.1016/j.cgh.2020.11.045] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/19/2020] [Accepted: 11/25/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND & AIMS Colorectal cancer risk can be lowered by adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) guidelines. We derived metabolic signatures of adherence to these guidelines and tested their associations with colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition cohort. METHODS Scores reflecting adherence to the WCRF/AICR recommendations (scale, 1-5) were calculated from participant data on weight maintenance, physical activity, diet, and alcohol among a discovery set of 5738 cancer-free European Prospective Investigation into Cancer and Nutrition participants with metabolomics data. Partial least-squares regression was used to derive fatty acid and endogenous metabolite signatures of the WCRF/AICR score in this group. In an independent set of 1608 colorectal cancer cases and matched controls, odds ratios (ORs) and 95% CIs were calculated for colorectal cancer risk per unit increase in WCRF/AICR score and per the corresponding change in metabolic signatures using multivariable conditional logistic regression. RESULTS Higher WCRF/AICR scores were characterized by metabolic signatures of increased odd-chain fatty acids, serine, glycine, and specific phosphatidylcholines. Signatures were inversely associated more strongly with colorectal cancer risk (fatty acids: OR, 0.51 per unit increase; 95% CI, 0.29-0.90; endogenous metabolites: OR, 0.62 per unit change; 95% CI, 0.50-0.78) than the WCRF/AICR score (OR, 0.93 per unit change; 95% CI, 0.86-1.00) overall. Signature associations were stronger in male compared with female participants. CONCLUSIONS Metabolite profiles reflecting adherence to WCRF/AICR guidelines and additional lifestyle or biological risk factors were associated with colorectal cancer. Measuring a specific panel of metabolites representative of a healthy or unhealthy lifestyle may identify strata of the population at higher risk of colorectal cancer.
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Affiliation(s)
- Joseph A Rothwell
- Centre for Epidemiology and Population Health, U1018, Generations and Health Team, Faculté de Médecine, Université Paris-Saclay, INSERM, Villejuif, France; Gustave Roussy, Villejuif, France; International Agency for Research on Cancer, Lyon, France.
| | - Neil Murphy
- International Agency for Research on Cancer, Lyon, France
| | - Jelena Bešević
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | | | - Mazda Jenab
- International Agency for Research on Cancer, Lyon, France
| | - Pietro Ferrari
- International Agency for Research on Cancer, Lyon, France
| | | | | | - Béatrice Vozar
- International Agency for Research on Cancer, Lyon, France
| | | | | | | | - Cornelia Prehn
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Jerzy Adamski
- Research Unit, Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Experimental Genetics, School of Life Science Weihenstephan, Technische Universität München, Neuherberg, Germany
| | - Amanda J Cross
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Valeria Maria Pala
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Marie-Christine Boutron-Ruault
- Centre for Epidemiology and Population Health, U1018, Generations and Health Team, Faculté de Médecine, Université Paris-Saclay, INSERM, Villejuif, France; Gustave Roussy, Villejuif, France
| | - Christina C Dahm
- Department of Public Health, Aarhus University, Aarhus C, Denmark
| | - Kim Overvad
- Department of Public Health, Aarhus University, Aarhus C, Denmark
| | - Inger Torhild Gram
- Faculty of Health Sciences, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Torkjel M Sandanger
- Faculty of Health Sciences, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Guri Skeie
- Faculty of Health Sciences, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
| | - Paula Jakszyn
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Barcelona, Spain; Blanquerna School of Health Sciences, Ramon Llull University, Barcelona, Spain
| | - Kostas K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Krasimira Aleksandrova
- Nutrition, Immunity and Metabolism Group, Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; Institute of Nutritional Science, University of Potsdam, Potsdam, Germany
| | - Matthias B Schulze
- Institute of Nutritional Science, University of Potsdam, Potsdam, Germany; Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - David J Hughes
- School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin, Ireland
| | - Bethany van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umea University, Umea, Sweden
| | - Stina Bodén
- Department of Radiation Sciences, Oncology Unit, Umea University, Umea, Sweden
| | - Maria-José Sánchez
- CIBER Epidemiología y Salud Pública, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain; Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain
| | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Verena Katzke
- Division of Cancer Epidemiology, Deutsches Krebsforschungszentrum, Stiftung des Öffentlichen Rechts, Heidelberg, Germany
| | - Tilman Kühn
- Division of Cancer Epidemiology, Deutsches Krebsforschungszentrum, Stiftung des Öffentlichen Rechts, Heidelberg, Germany
| | - Sandra Colorado-Yohar
- Department of Epidemiology, Murcia Regional Health Council, Instituto Murciano de Investigatión Biomédica (IMIB)-Arrixaca, Murcia, Spain; CIBER Epidemiología y Salud Pública, Spain; Research Group on Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority, Ragusa, Italy
| | - Bas Bueno-de-Mesquita
- Department for Determinants of Chronic Diseases, National Institute for Public Health and the Environment, BA Bilthoven, The Netherlands
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Italian Institute of Technology, Genova, Italy
| | - Giovanna Masala
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network-Istituto per lo Studio, la Prevenzione e la Rete Oncologica (ISPRO), Florence, Italy
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Federico II University, Naples, Italy
| | - Anne Kirstine Eriksen
- Danish Cancer Society Research Center, Diet, Genes and Environment, Copenhagen, Denmark
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Diet, Genes and Environment, Copenhagen, Denmark
| | - Dagfinn Aune
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom; Department of Nutrition, Bjørknes University College, Oslo, Norway; Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | | | - Gianluca Severi
- Centre for Epidemiology and Population Health, U1018, Generations and Health Team, Faculté de Médecine, Université Paris-Saclay, INSERM, Villejuif, France; Gustave Roussy, Villejuif, France
| | | | - Marc J Gunter
- International Agency for Research on Cancer, Lyon, France
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16
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Metabolite Signature of Physical Activity and the Risk of Type 2 Diabetes in 7271 Men. Metabolites 2022; 12:metabo12010069. [PMID: 35050191 PMCID: PMC8779070 DOI: 10.3390/metabo12010069] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/10/2022] [Accepted: 01/10/2022] [Indexed: 12/24/2022] Open
Abstract
Large population-based studies investigating the association of physical activity (PA) with the metabolite signature contribute significantly to the understanding of the effects of PA on metabolic pathways associated with the risk of type 2 diabetes. Our study included 8749 Finnish men without diabetes at baseline recruited from the Metabolic Syndrome in Men (METSIM) cohort. We used a questionnaire to measure leisure-time PA. Metabolites were measured in 7271 men as a part of Metabolon’s untargeted Discovery HD4 platform using ultrahigh-performance liquid chromatography–tandem mass spectrometry. We found 198 metabolites significantly associated with PA. Several of these metabolites were novel including especially steroids, amino acids, imidazoles, carboxylic acids, and hydroxy acids. Increased PA was significantly associated with high levels of choline plasmalogens, lysophosphatidylcholines, polyunsaturated fatty acids, carotenoids, long chain acylcarnitines, imidazoles, bilirubins, aryl sulfates, hydroxy acids, indolepropionate, and indolelactate. Several of these metabolites have been previously associated with a decreased risk of type 2 diabetes and with a healthy diet. Our population-based study shows that the metabolite signature of increased PA includes multiple metabolic pathways and is associated with better adherence to a healthy lifestyle.
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17
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Tian Q, Corkum AE, Moaddel R, Ferrucci L. Metabolomic profiles of being physically active and less sedentary: a critical review. Metabolomics 2021; 17:68. [PMID: 34245373 DOI: 10.1007/s11306-021-01818-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 07/01/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Being physically active has multiple salutary effects on human health, likely mediated by changes in energy metabolism. Recent reviews have summarized metabolomic responses to acute exercise. However, metabolomic profiles of individuals who exercise regularly are heterogeneous. AIM OF REVIEW We conducted a systematic review to identify metabolites associated with physical activity (PA), fitness, and sedentary time in community-dwelling adults and discussed involved pathways. Twenty-two studies were eligible because they (1) focused on community-dwelling adults from observational studies; (2) assessed PA, fitness, and/or sedentary time, (3) assessed metabolomics in biofluid, and (4) reported on relationships of metabolomics with PA, fitness, and/or sedentary time. KEY SCIENTIFIC CONCEPTS OF REVIEW Several metabolic pathways were associated with higher PA and fitness and less sedentary time, including tricarboxylic acid cycle, glycolysis, aminoacyl-tRNA biosynthesis, urea cycle, arginine biosynthesis, branch-chain amino acids, and estrogen metabolism. Lipids were strongly associated with PA. Cholesterol low-density lipoproteins and triglycerides were lower with higher PA, while cholesterol high-density lipoproteins were higher. Metabolomic profiles of being physically active and less sedentary indicate active skeletal muscle biosynthesis supported by enhanced oxidative phosphorylation and glycolysis and associated with profound changes in lipid and estrogen metabolism. Future longitudinal studies are needed to understand whether these metabolomic changes account for health benefits associated with PA.
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Affiliation(s)
- Qu Tian
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute On Aging, Baltimore, MD, USA.
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute On Aging, 251 Bayview Blvd., Suite 100, Rm 04B316, Baltimore, MD, 21224, USA.
| | - Abigail E Corkum
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute On Aging, Baltimore, MD, USA
- School of Population Health, Thomas Jefferson University, Philadelphia, PA, USA
| | - Ruin Moaddel
- Laboratory of Clinical Investigation, National Institute On Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute On Aging, Baltimore, MD, USA
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18
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van Roekel EH, Bours MJL, van Delden L, Breukink SO, Aquarius M, Keulen ETP, Gicquiau A, Viallon V, Rinaldi S, Vineis P, Arts ICW, Gunter MJ, Leitzmann MF, Scalbert A, Weijenberg MP. Longitudinal associations of physical activity with plasma metabolites among colorectal cancer survivors up to 2 years after treatment. Sci Rep 2021; 11:13738. [PMID: 34215757 PMCID: PMC8253824 DOI: 10.1038/s41598-021-92279-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 05/20/2021] [Indexed: 11/09/2022] Open
Abstract
We investigated longitudinal associations of moderate-to-vigorous physical activity (MVPA) and light-intensity physical activity (LPA) with plasma concentrations of 138 metabolites after colorectal cancer (CRC) treatment. Self-reported physical activity data and blood samples were obtained at 6 weeks, and 6, 12 and 24 months post-treatment in stage I-III CRC survivors (n = 252). Metabolite concentrations were measured by tandem mass spectrometry (BIOCRATES AbsoluteIDQp180 kit). Linear mixed models were used to evaluate confounder-adjusted longitudinal associations. Inter-individual (between-participant differences) and intra-individual associations (within-participant changes over time) were assessed as percentage difference in metabolite concentration per 5 h/week of MVPA or LPA. At 6 weeks post-treatment, participants reported a median of 6.5 h/week of MVPA (interquartile range:2.3,13.5) and 7.5 h/week of LPA (2.0,15.8). Inter-individual associations were observed with more MVPA being related (FDR-adjusted q-value < 0.05) to higher concentrations of arginine, citrulline and histidine, eight lysophosphatidylcholines, nine diacylphosphatidylcholines, 13 acyl-alkylphosphatidylcholines, two sphingomyelins, and acylcarnitine C10:1. No intra-individual associations were found. LPA was not associated with any metabolite. More MVPA was associated with higher concentrations of several lipids and three amino acids, which have been linked to anti-inflammatory processes and improved metabolic health. Mechanistic studies are needed to investigate whether these metabolites may affect prognosis.
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Affiliation(s)
- Eline H van Roekel
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
| | - Martijn J L Bours
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - Linda van Delden
- Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands
| | - Stéphanie O Breukink
- Department of Surgery, GROW School for Oncology and Developmental Biology & NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Michèl Aquarius
- Department of Gastroenterology, VieCuri Medical Center, Venlo, the Netherlands
| | - Eric T P Keulen
- Department of Internal Medicine and Gastroenterology, Zuyderland Medical Centre, Sittard-Geleen, the Netherlands
| | - Audrey Gicquiau
- Biomarkers Group, Nutrition and Metabolism Section, International Agency for Research On Cancer (IARC-WHO), Lyon, France
| | - Vivian Viallon
- Nutritional Methodology and Biostatistics Group, Nutrition and Metabolism Section, International Agency for Research On Cancer (IARC-WHO), Lyon, France
| | - Sabina Rinaldi
- Biomarkers Group, Nutrition and Metabolism Section, International Agency for Research On Cancer (IARC-WHO), Lyon, France
| | - Paolo Vineis
- MRC Centre for Environment and Health, School of Public Health, Imperial College, London, UK
- Italian Institute of Technology, Genoa, Italy
| | - Ilja C W Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Department of Epidemiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Marc J Gunter
- Nutritional Epidemiology Group, Nutrition and Metabolism Section, International Agency for Research On Cancer (IARC-WHO), Lyon, France
| | - Michael F Leitzmann
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | - Augustin Scalbert
- Biomarkers Group, Nutrition and Metabolism Section, International Agency for Research On Cancer (IARC-WHO), Lyon, France
| | - Matty P Weijenberg
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
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19
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Qi J, Spinelli JJ, Dummer TJB, Bhatti P, Playdon MC, Levitt JO, Hauner B, Moore SC, Murphy RA. Metabolomics and cancer preventive behaviors in the BC Generations Project. Sci Rep 2021; 11:12094. [PMID: 34103643 PMCID: PMC8187402 DOI: 10.1038/s41598-021-91753-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 05/21/2021] [Indexed: 12/24/2022] Open
Abstract
Metabolomics can detect metabolic shifts resulting from lifestyle behaviors and may provide insight on the relevance of changes to carcinogenesis. We used non-targeted nuclear magnetic resonance to examine associations between metabolic measures and cancer preventive behaviors in 1319 participants (50% male, mean age 54 years) from the BC Generations Project. Behaviors were dichotomized: BMI < 25 kg/m2, ≥ 5 servings of fruits or vegetables/day, ≤ 2 alcoholic drinks/day for men or 1 drink/day for women and ≥ 30 min of moderate or vigorous physical activity/day. Linear regression was used to estimate coefficients and 95% confidence intervals with a false discovery rate (FDR) of 0.10. Of the 218 metabolic measures, 173, 103, 71 and 6 were associated with BMI, fruits and vegetables, alcohol consumption and physical activity. Notable findings included negative associations between glycoprotein acetyls, an inflammation-related metabolite with lower BMI and greater fruit and vegetable consumption, a positive association between polyunsaturated fatty acids and fruit and vegetable consumption and positive associations between high-density lipoprotein subclasses with lower BMI. These findings provide insight into metabolic alterations in the context of cancer prevention and the diverse biological pathways they are involved in. In particular, behaviors related to BMI, fruit and vegetable and alcohol consumption had a large metabolic impact.
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Affiliation(s)
- J Qi
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - J J Spinelli
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - T J B Dummer
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - P Bhatti
- Cancer Control Research, BC Cancer, 2-107, 675 W 10th Ave, Vancouver, BC, V5Z 1L3, Canada
| | - M C Playdon
- Department of Nutrition & Integrative Physiology, University of Utah, Salt Lake City, UT, USA.,Cancer Control and Population Sciences Program, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - J Olin Levitt
- Department of Nutrition & Integrative Physiology, University of Utah, Salt Lake City, UT, USA.,Cancer Control and Population Sciences Program, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - B Hauner
- Department of Nutrition & Integrative Physiology, University of Utah, Salt Lake City, UT, USA.,Cancer Control and Population Sciences Program, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - S C Moore
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, MD, USA
| | - R A Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada. .,Cancer Control Research, BC Cancer, 2-107, 675 W 10th Ave, Vancouver, BC, V5Z 1L3, Canada.
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20
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Zeleznik OA, Balasubramanian R, Zhao Y, Frueh L, Jeanfavre S, Avila-Pacheco J, Clish CB, Tworoger SS, Eliassen AH. Circulating amino acids and amino acid-related metabolites and risk of breast cancer among predominantly premenopausal women. NPJ Breast Cancer 2021; 7:54. [PMID: 34006878 PMCID: PMC8131633 DOI: 10.1038/s41523-021-00262-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 04/15/2021] [Indexed: 02/03/2023] Open
Abstract
Known modifiable risk factors account for a small fraction of premenopausal breast cancers. We investigated associations between pre-diagnostic circulating amino acid and amino acid-related metabolites (N = 207) and risk of breast cancer among predominantly premenopausal women of the Nurses' Health Study II using conditional logistic regression (1057 cases, 1057 controls) and multivariable analyses evaluating all metabolites jointly. Eleven metabolites were associated with breast cancer risk (q-value < 0.2). Seven metabolites remained associated after adjustment for established risk factors (p-value < 0.05) and were selected by at least one multivariable modeling approach: higher levels of 2-aminohippuric acid, kynurenic acid, piperine (all three with q-value < 0.2), DMGV and phenylacetylglutamine were associated with lower breast cancer risk (e.g., piperine: ORadjusted (95%CI) = 0.84 (0.77-0.92)) while higher levels of creatine and C40:7 phosphatidylethanolamine (PE) plasmalogen were associated with increased breast cancer risk (e.g., C40:7 PE plasmalogen: ORadjusted (95%CI) = 1.11 (1.01-1.22)). Five amino acids and amino acid-related metabolites (2-aminohippuric acid, DMGV, kynurenic acid, phenylacetylglutamine, and piperine) were inversely associated, while one amino acid and a phospholipid (creatine and C40:7 PE plasmalogen) were positively associated with breast cancer risk among predominately premenopausal women, independent of established breast cancer risk factors.
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Affiliation(s)
- Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Raji Balasubramanian
- Department of Biostatistics & Epidemiology, University of Massachusetts - Amherst, Amherst, MA, USA
| | - Yibai Zhao
- Department of Biostatistics & Epidemiology, University of Massachusetts - Amherst, Amherst, MA, USA
| | - Lisa Frueh
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Jeanfavre
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Julian Avila-Pacheco
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Shelley S Tworoger
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - A Heather Eliassen
- Channing Division of Network 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
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21
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Hong X, Zhang B, Liang L, Zhang Y, Ji Y, Wang G, Ji H, Clish CB, Burd I, Pearson C, Zuckerman B, Hu FB, Wang X. Postpartum plasma metabolomic profile among women with preeclampsia and preterm delivery: implications for long-term health. BMC Med 2020; 18:277. [PMID: 33046083 PMCID: PMC7552364 DOI: 10.1186/s12916-020-01741-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 08/10/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Preeclampsia and preterm delivery (PTD) are believed to affect women's long-term health including cardiovascular disease (CVD), but the biological underpinnings are largely unknown. We aimed to test whether maternal postpartum metabolomic profiles, especially CVD-related metabolites, varied according to PTD subtypes with and without preeclampsia, in a US urban, low-income multi-ethnic population. METHODS This study, from the Boston Birth Cohort, included 980 women with term delivery, 79 with medically indicated PTD (mPTD) and preeclampsia, 52 with mPTD only, and 219 with spontaneous PTD (sPTD). Metabolomic profiling in postpartum plasma was conducted by liquid chromatography-mass spectrometry. Linear regression models were used to assess the associations of each metabolite with mPTD with preeclampsia, mPTD only, and sPTD, respectively, adjusting for pertinent covariates. Weighted gene coexpression network analysis was applied to investigate interconnected metabolites associated with the PTD/preeclampsia subgroups. Bonferroni correction was applied to account for multiple testing. RESULTS A total of 380 known metabolites were analyzed. Compared to term controls, women with mPTD and preeclampsia showed a significant increase in 36 metabolites, mainly representing acylcarnitines and multiple classes of lipids (diacylglycerols, triacylglycerols, phosphocholines, and lysophosphocholines), as well as a decrease in 11 metabolites including nucleotides, steroids, and cholesteryl esters (CEs) (P < 1.3 × 10-4). Alterations of diacylglycerols, triacylglycerols, and CEs in women with mPTD and preeclampsia remained significant when compared to women with mPTD only. In contrast, the metabolite differences between women with mPTD only and term controls were only seen in phosphatidylethanolamine class. Women with sPTD had significantly different levels of 16 metabolites mainly in amino acid, nucleotide, and steroid classes compared to term controls, of which, anthranilic acid, bilirubin, and steroids also had shared associations in women with mPTD and preeclampsia. CONCLUSION In this sample of US high-risk women, PTD/preeclampsia subgroups each showed some unique and shared associations with maternal postpartum plasma metabolites, including those known to be predictors of future CVD. These findings, if validated, may provide new insight into metabolomic alterations underlying clinically observed PTD/preeclampsia subgroups and implications for women's future cardiometabolic health.
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Affiliation(s)
- Xiumei Hong
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, MD, 21205-2179, USA.
| | - Boyang Zhang
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, MD, 21205, 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
| | - Yan Zhang
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, MD, 21205, USA
| | - Yuelong Ji
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, MD, 21205-2179, USA
| | - Guoying Wang
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, MD, 21205-2179, USA
| | - Hongkai Ji
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, MD, 21205, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Irina Burd
- Integrated Research Center for Fetal Medicine, Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Colleen Pearson
- Department of Pediatrics, Boston University School of Medicine and Boston Medical Center, Boston, MA, 02118, USA
| | - Barry Zuckerman
- Department of Pediatrics, Boston University School of Medicine and Boston Medical Center, Boston, MA, 02118, 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
| | - Xiaobin Wang
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St, Baltimore, MD, 21205-2179, USA.,Division of General Pediatrics & Adolescent Medicine, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
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22
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Wang Y, Wang H, Howard AG, Tsilimigras MCB, Avery CL, Meyer KA, Sha W, Sun S, Zhang J, Su C, Wang Z, Zhang B, Fodor AA, Gordon-Larsen P. Associations of sodium and potassium consumption with the gut microbiota and host metabolites in a population-based study in Chinese adults. Am J Clin Nutr 2020; 112:1599-1612. [PMID: 33022700 PMCID: PMC7727480 DOI: 10.1093/ajcn/nqaa263] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Accepted: 08/24/2020] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND There is increasing evidence that sodium consumption alters the gut microbiota and host metabolome in murine models and small studies in humans. However, there is a lack of population-based studies that capture large variations in sodium consumption as well as potassium consumption. OBJECTIVE We examined the associations of energy-adjusted dietary sodium (milligrams/kilocalorie), potassium, and sodium-to-potassium (Na/K) ratio with the microbiota and plasma metabolome in a well-characterized Chinese cohort with habitual excessive sodium and deficient potassium consumption. METHODS We estimated dietary intakes from 3 consecutive validated 24-h recalls and household inventories. In 2833 adults (18-80 y old, 51.2% females), we analyzed microbial (genus-level 16S ribosomal RNA) between-person diversity, using distance-based redundancy analysis (dbRDA), and within-person diversity and taxa abundance using linear regression, accounting for geographic variation in both. In a subsample (n = 392), we analyzed the overall metabolome (dbRDA) and individual metabolites (linear regression). P values for specific taxa and metabolites were false discovery rate adjusted (q-value). RESULTS Sodium, potassium, and Na/K ratio were associated with microbial between-person diversity (dbRDA P < 0.01) and several specific taxa with large geographic variation, including pathogenic Staphylococcus and Moraxellaceae, and SCFA-producing Phascolarctobacterium and Lachnospiraceae (q-value < 0.05). For example, sodium and Na/K ratio were positively associated with Staphylococcus and Moraxellaceae in Liaoning, whereas potassium was positively associated with 2 genera from Lachnospiraceae in Shanghai. Additionally, sodium, potassium, and Na/K ratio were associated with the overall metabolome (dbRDA P ≤ 0.01) and several individual metabolites, including butyrate/isobutyrate and gut-derived phenolics such as 1,2,3-benzenetriol sulfate, which was negatively associated with sodium in Guizhou (q-value < 0.05). CONCLUSIONS Our findings suggest that sodium and potassium consumption is associated with taxa and metabolites that have been implicated in cardiometabolic health, providing insights into the potential roles of gut microbiota and host metabolites in the pathogenesis of sodium- and potassium-associated diseases. More studies are needed to confirm our results.
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Affiliation(s)
- Yiqing Wang
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill (UNC-Chapel Hill), Chapel Hill, NC, USA
| | - Huijun Wang
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Annie Green Howard
- Department of Biostatistics, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA,Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC, USA
| | - Matthew C B Tsilimigras
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill (UNC-Chapel Hill), Chapel Hill, NC, USA,Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC, USA,Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA
| | - Christy L Avery
- Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC, USA,Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA
| | - Katie A Meyer
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill (UNC-Chapel Hill), Chapel Hill, NC, USA,Nutrition Research Institute, UNC-Chapel Hill, Kannapolis, NC, USA
| | - Wei Sha
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA,Department of Cancer Biostatistics, Levine Cancer Institute, Atrium Health, Charlotte, NC, USA
| | - Shan Sun
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Jiguo Zhang
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chang Su
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhihong Wang
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Bing Zhang
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Anthony A Fodor
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
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23
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Wang Y, Sha W, Wang H, Howard AG, Tsilimigras MCB, Zhang J, Su C, Wang Z, Zhang B, Fodor AA, Gordon-Larsen P. Urbanization in China is associated with pronounced perturbation of plasma metabolites. Metabolomics 2020; 16:103. [PMID: 32951074 PMCID: PMC7707273 DOI: 10.1007/s11306-020-01724-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 09/12/2020] [Indexed: 12/16/2022]
Abstract
INTRODUCTION Urbanization is associated with major changes in environmental and lifestyle exposures that may influence metabolic signatures. OBJECTIVES We investigated cross-sectional urban and rural differences in plasma metabolome analyzed by liquid chromatography/mass spectrometry platform in 500 Chinese adults aged 25-68 years from two neighboring southern Chinese provinces. METHODS We first examined the overall metabolome differences by urban and rural residential location, using Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) and random forest classification. We then tested the association between urbanization status and individual metabolites using a linear regression adjusting for age, sex, and province and conducted pathway analysis (Fisher's exact test) to identify metabolic pathways differed by urbanization status. RESULTS We observed distinct overall metabolome by urbanization status in OPLS-DA and random forest classification. Using linear regression, out of a total of 1108 unique metabolite features identified in this sample, we found that 266 metabolites were differed by urbanization status (positive false discovery rate-adjusted p-value, q-value < 0.05). For example, the following metabolites were positively associated with urbanization status: caffeine metabolites from xanthine metabolism, hazardous pollutants like 4-hydroxychlorothalonil and perfluorooctanesulfonate, and metabolites implicated in cardiometabolic diseases, such as branched-chain amino acids. In pathway analysis, we found that xanthine metabolism pathways differed by urbanization status (q-value = 1.64E-04). CONCLUSION We detected profound differences in host metabolites by urbanization status. Urban residents were characterized by metabolites signaling caffeine metabolism and toxic pollutants and metabolites on known pathways to cardiometabolic disease risks, compared to their rural counterparts. Our findings highlight the importance of considering urbanization in metabolomics analysis.
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Affiliation(s)
- Yiqing Wang
- Department of Nutrition, Gillings School of Global Public Health & School of Medicine, University of North Carolina at Chapel Hill (UNC-Chapel Hill), Chapel Hill, NC, USA
| | - Wei Sha
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
- Department of Cancer Biostatistics, Levine Cancer Institute, Atrium Health, Charlotte, NC, USA
| | - Huijun Wang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Annie Green Howard
- Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA
| | - Matthew C B Tsilimigras
- Department of Nutrition, Gillings School of Global Public Health & School of Medicine, University of North Carolina at Chapel Hill (UNC-Chapel Hill), Chapel Hill, NC, USA
- Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC, USA
- Department of Epidemiology, Gillings School of Global Public Health, UNC-Chapel Hill, Chapel Hill, NC, USA
| | - Jiguo Zhang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Chang Su
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Zhihong Wang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Bing Zhang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
| | - Anthony A Fodor
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health & School of Medicine, University of North Carolina at Chapel Hill (UNC-Chapel Hill), Chapel Hill, NC, USA.
- Carolina Population Center, UNC-Chapel Hill, Chapel Hill, NC, USA.
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24
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Kelly RS, Kelly MP, Kelly P. Metabolomics, physical activity, exercise and health: A review of the current evidence. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165936. [PMID: 32827647 DOI: 10.1016/j.bbadis.2020.165936] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 08/12/2020] [Accepted: 08/14/2020] [Indexed: 01/09/2023]
Abstract
Physical activity (PA) and exercise are among the most important determinants of health. However, PA is a complex and heterogeneous behavior and the biological mechanisms through which it impacts individuals and populations in different ways are not well understood. Genetics and environment likely play pivotal roles but further work is needed to understand their relative contributions and how they may be mediated. Metabolomics offers a promising approach to explore these relationships. In this review, we provide a comprehensive appraisal of the PA-metabolomics literature to date. This overwhelmingly supports the hypothesis of a metabolomic response to PA, which can differ between groups and individuals. It also suggests a biological gradient in this response based on PA intensity, with some evidence for global longer-term changes in the metabolome of highly active individuals. However, many questions remain and we conclude by highlighting future critical research avenues to help elucidate the role of PA in the maintenance of health and the development of disease.
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Affiliation(s)
- Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Michael P Kelly
- Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Forvie Site, Cambridge CB2 0SR. UK.
| | - Paul Kelly
- Physical Activity for Health Research Center (PAHRC), University of Edinburgh, St Leonard's Land, Edinburgh EH8 8AQ, UK.
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25
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Circulating Short-Chain Fatty Acids Are Positively Associated with Adiposity Measures in Chinese Adults. Nutrients 2020; 12:nu12072127. [PMID: 32708978 PMCID: PMC7400849 DOI: 10.3390/nu12072127] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/14/2020] [Accepted: 07/15/2020] [Indexed: 12/17/2022] Open
Abstract
Epidemiological studies suggest a positive association between obesity and fecal short-chain fatty acids (SCFAs) produced by microbial fermentation of dietary carbohydrates, while animal models suggest increased energy harvest through colonic SCFA production in obesity. However, there is a lack of human population-based studies with dietary intake data, plasma SCFAs, gut microbial, and anthropometric data. In 490 Chinese adults aged 30–68 years, we examined the associations between key plasma SCFAs (butyrate/isobutyrate, isovalerate, and valerate measured by non-targeted plasma metabolomics) with body mass index (BMI) using multivariable-adjusted linear regression. We then assessed whether overweight (BMI ≥ 24 kg/m2) modified the association between dietary-precursors of SCFAs (insoluble fiber, total carbohydrates, and high-fiber foods) with plasma SCFAs. In a sub-sample (n = 209) with gut metagenome data, we examined the association between gut microbial SCFA-producers with BMI. We found positive associations between butyrate/isobutyrate and BMI (p-value < 0.05). The associations between insoluble fiber and butyrate/isobutyrate differed by overweight (p-value < 0.10). There was no statistical evidence for an association between microbial SCFA-producers and BMI. In sum, plasma SCFAs were positively associated with BMI and that the colonic fermentation of fiber may differ for adults with versus without overweight.
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