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Hillesheim E, Liu W, Yin X, Smith T, Brennan L. Association of plant-based diet indexes with the metabolomic profile. Sci Rep 2024; 14:17927. [PMID: 39095501 PMCID: PMC11297169 DOI: 10.1038/s41598-024-68522-4] [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: 04/25/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
Plant-based diets have gained attention for their potential benefits on both human health and environmental sustainability. The objective of this study was to investigate the association of plant-based dietary patterns with the endogenous metabolites of healthy individuals and identify metabolites that may act as mediators of the associations between dietary intake and modifiable disease risk factors. Adherence to plant-based dietary patterns was assessed for 170 healthy adults using plant-based diet indexes (PDI). Individuals with higher healthful PDI had lower BMI and fasting glucose and higher HDL-C, while those with higher unhealthful PDI had higher BMI, triacylglycerol and fasting glucose and lower HDL-C. Unhealthful PDI was associated with higher levels of several amino acids and biogenic amines previously associated with cardiometabolic diseases and an opposite pattern was observed for healthful PDI. Furthermore, healthful PDI was associated with higher levels of glycerophosphocholines containing very long-chain fatty acids. Glutamate, isoleucine, proline, tyrosine, α-aminoadipate and kynurenine had a statistically significant mediation effect on the associations between PDI scores and LDL-C, HDL-C and fasting glucose. These findings contribute to the growing evidence supporting the role of plant-based diets in promoting metabolic health and shed light on the potential mechanisms explaining their beneficial health effects.
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Affiliation(s)
- Elaine Hillesheim
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Wenxuan Liu
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Xiaofei Yin
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland
| | - Thomas Smith
- Department of Clinical Chemistry, St. Vincents University Hospital, Elm Park, Dublin 4, Ireland
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin 4, Ireland.
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin 4, Ireland.
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2
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Martinez GJ, Kipp ZA, Lee WH, Bates EA, Morris AJ, Marino JS, Hinds TD. Glucocorticoid resistance remodels liver lipids and prompts lipogenesis, eicosanoid, and inflammatory pathways. Prostaglandins Other Lipid Mediat 2024; 173:106840. [PMID: 38830399 PMCID: PMC11199073 DOI: 10.1016/j.prostaglandins.2024.106840] [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: 02/29/2024] [Revised: 04/11/2024] [Accepted: 04/26/2024] [Indexed: 06/05/2024]
Abstract
We have previously demonstrated that the glucocorticoid receptor β (GRβ) isoform induces hepatic steatosis in mice fed a normal chow diet. The GRβ isoform inhibits the glucocorticoid-binding isoform GRα, reducing responsiveness and inducing glucocorticoid resistance. We hypothesized that GRβ regulates lipids that cause metabolic dysfunction. To determine the effect of GRβ on hepatic lipid classes and molecular species, we overexpressed GRβ (GRβ-Ad) and vector (Vec-Ad) using adenovirus delivery, as we previously described. We fed the mice a normal chow diet for 5 days and harvested the livers. We utilized liquid chromatography-mass spectrometry (LC-MS) analyses of the livers to determine the lipid species driven by GRβ. The most significant changes in the lipidome were monoacylglycerides and cholesterol esters. There was also increased gene expression in the GRβ-Ad mice for lipogenesis, eicosanoid synthesis, and inflammatory pathways. These indicate that GRβ-induced glucocorticoid resistance may drive hepatic fat accumulation, providing new therapeutic advantages.
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Affiliation(s)
- Genesee J Martinez
- Department of Pharmacology and Nutritional Sciences, University of Kentucky College of Medicine, Lexington, KY, USA; Drug & Disease Discovery D3 Research Center, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Zachary A Kipp
- Department of Pharmacology and Nutritional Sciences, University of Kentucky College of Medicine, Lexington, KY, USA; Drug & Disease Discovery D3 Research Center, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Wang-Hsin Lee
- Department of Pharmacology and Nutritional Sciences, University of Kentucky College of Medicine, Lexington, KY, USA; Drug & Disease Discovery D3 Research Center, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Evelyn A Bates
- Department of Pharmacology and Nutritional Sciences, University of Kentucky College of Medicine, Lexington, KY, USA; Drug & Disease Discovery D3 Research Center, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Andrew J Morris
- Department of Pharmacology and Toxicology, University of Arkansas for Medical Sciences, and Central Arkansas Veterans Affairs Healthcare System, Little Rock, AR 72205, USA
| | - Joseph S Marino
- Department of Applied Physiology, Health, and Clinical Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Terry D Hinds
- Department of Pharmacology and Nutritional Sciences, University of Kentucky College of Medicine, Lexington, KY, USA; Drug & Disease Discovery D3 Research Center, University of Kentucky College of Medicine, Lexington, KY, USA; Markey Cancer Center, University of Kentucky, Lexington, KY, USA; Barnstable Brown Diabetes Center, University of Kentucky, Lexington, KY, USA.
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3
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Wang W, Liu L, Qiu W, Chen C, Huang Y, Cai A, Nie Z, Ou Y, Zhu Y, Feng Y. The Non-Targeted Lipidomic-Based Classifier Reveals Two Candidate Biomarkers for Ischemic Stroke in Hypertensive Individuals. Risk Manag Healthc Policy 2024; 17:1889-1901. [PMID: 39100548 PMCID: PMC11297523 DOI: 10.2147/rmhp.s465135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 07/11/2024] [Indexed: 08/06/2024] Open
Abstract
Introduction Traditional clinical risk factors are insufficient to estimate the residual risk of large-vessel ischemic stroke. Non-targeted lipidomic techniques provide an opportunity to evaluate these risks. Methods Plasma samples were collected from 113 hypertensive individuals, including 55 individuals at high risk of ischemic stroke and 58 matched individuals, in a prospective nested case-control cohort. To identify dysregulated lipid metabolites, we conducted multivariate and univariate analyses. A classifier based on a cross-validated procedure was employed to select the optimal combination of lipid species and their ratios. Results We identified 23 dysregulated lipid species in patients with and without ischemic stroke, including 16 (69.6%) up-regulated and 7 (30.4%) down-regulated lipid species. Through internal cross-validation, the optimal combination of two lipid features (phosphatidylcholine 34:2 and triglyceride 18:1/18:1/22:1 / phosphatidylcholine 34:2, referred to as ischemic stroke-related 2 lipid features - IS2LP) was selected, leading to a more precise prediction probability for ischemic stroke within 3.9 years. In the comparison of different risk factors, the traditional risk score, the IS2LP risk score, and the combination of the traditional risk score with IS2LP yield AUC values of 0.613(95% CI:0.509-0.717), 0.833(95% CI:0.755-0.911), and 0.843(95% CI:0.777-0.916), respectively. The combination of the traditional risk score and IS2LP exhibited significantly improved discriminative performance, with an integrated discrimination improvement (IDI) of 0.31 (p<0.001) and a continuous net reclassification improvement (NRI) of 1.06 (p < 0.001) compared to the traditional risk score. Conclusion We identified new lipidomic biomarkers associated with the futural event of large-vessel ischemic stroke. These lipid species could serve as potential blood biomarkers for assessing the residual risk of ischemic stroke in hypertensive individuals.
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Affiliation(s)
- Wenbin Wang
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Lin Liu
- Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Weida Qiu
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Chaolei Chen
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Yuqing Huang
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Anping Cai
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Zhiqiang Nie
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Yanqiu Ou
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Yicheng Zhu
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
| | - Yingqing Feng
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, People’s Republic of China
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Healy DR, Zarei I, Mikkonen S, Soininen S, Viitasalo A, Haapala EA, Auriola S, Hanhineva K, Kolehmainen M, Lakka TA. Longitudinal associations of an exposome score with serum metabolites from childhood to adolescence. Commun Biol 2024; 7:890. [PMID: 39039257 PMCID: PMC11263428 DOI: 10.1038/s42003-024-06146-0] [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: 05/24/2023] [Accepted: 04/05/2024] [Indexed: 07/24/2024] Open
Abstract
Environmental and lifestyle factors, including air pollution, impaired diet, and low physical activity, have been associated with cardiometabolic risk factors in childhood and adolescence. However, environmental and lifestyle exposures do not exert their physiological effects in isolation. This study investigated associations between an exposome score to measure the impact of multiple exposures, including diet, physical activity, sleep duration, air pollution, and socioeconomic status, and serum metabolites measured using LC-MS and NMR, compared to the individual components of the score. A general population of 504 children aged 6-9 years at baseline was followed up for eight years. Data were analysed with linear mixed-effects models using the R software. The exposome score was associated with 31 metabolites, of which 12 metabolites were not associated with any individual exposure category. These findings highlight the value of a composite score to predict metabolic changes associated with multiple environmental and lifestyle exposures since childhood.
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Affiliation(s)
- Darren R Healy
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, Finland.
| | - Iman Zarei
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, Finland
| | - Santtu Mikkonen
- Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio Campus, Finland
- Department of Technical Physics, University of Eastern Finland, Kuopio Campus, Finland
| | - Sonja Soininen
- Institute of Biomedicine, University of Eastern Finland, Kuopio Campus, Finland
- Physician and Nursing Services, Health and Social Services Centre, Wellbeing Services County of North Savo, Varkaus, Finland
| | - Anna Viitasalo
- Institute of Biomedicine, University of Eastern Finland, Kuopio Campus, Finland
| | - Eero A Haapala
- Institute of Biomedicine, University of Eastern Finland, Kuopio Campus, Finland
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Seppo Auriola
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio Campus, Finland
- LC-MS Metabolomics Center, Biocenter Kuopio, Kuopio, Finland
| | - Kati Hanhineva
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, Finland
- Food Sciences Unit, Department of Life Technologies, University of Turku, Turku, Finland
| | - Marjukka Kolehmainen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, Finland
| | - Timo A Lakka
- Institute of Biomedicine, University of Eastern Finland, Kuopio Campus, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
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Eichelmann F, Prada M, Sellem L, Jackson KG, Salas Salvadó J, Razquin Burillo C, Estruch R, Friedén M, Rosqvist F, Risérus U, Rexrode KM, Guasch-Ferré M, Sun Q, Willett WC, Martinez-Gonzalez MA, Lovegrove JA, Hu FB, Schulze MB, Wittenbecher C. Lipidome changes due to improved dietary fat quality inform cardiometabolic risk reduction and precision nutrition. Nat Med 2024:10.1038/s41591-024-03124-1. [PMID: 38992128 DOI: 10.1038/s41591-024-03124-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 06/11/2024] [Indexed: 07/13/2024]
Abstract
Current cardiometabolic disease prevention guidelines recommend increasing dietary unsaturated fat intake while reducing saturated fats. Here we use lipidomics data from a randomized controlled dietary intervention trial to construct a multilipid score (MLS), summarizing the effects of replacing saturated fat with unsaturated fat on 45 lipid metabolite concentrations. In the EPIC-Potsdam cohort, a difference in the MLS, reflecting better dietary fat quality, was associated with a significant reduction in the incidence of cardiovascular disease (-32%; 95% confidence interval (95% CI): -21% to -42%) and type 2 diabetes (-26%; 95% CI: -15% to -35%). We built a closely correlated simplified score, reduced MLS (rMLS), and observed that beneficial rMLS changes, suggesting improved dietary fat quality over 10 years, were associated with lower diabetes risk (odds ratio per standard deviation of 0.76; 95% CI: 0.59 to 0.98) in the Nurses' Health Study. Furthermore, in the PREDIMED trial, an olive oil-rich Mediterranean diet intervention primarily reduced diabetes incidence among participants with unfavorable preintervention rMLS levels, suggestive of disturbed lipid metabolism before intervention. Our findings indicate that the effects of dietary fat quality on the lipidome can contribute to a more precise understanding and possible prediction of the health outcomes of specific dietary fat modifications.
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Affiliation(s)
- Fabian Eichelmann
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
| | - Marcela Prada
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Laury Sellem
- Hugh Sinclair Unit of Human Nutrition, Institute for Cardiovascular and Metabolic Research and Institute for Food, Nutrition and Health, Reading, UK
| | - Kim G Jackson
- Hugh Sinclair Unit of Human Nutrition, Institute for Cardiovascular and Metabolic Research and Institute for Food, Nutrition and Health, Reading, UK
| | - Jordi Salas Salvadó
- Human Nutrition Unit, Department of Biochemistry and Biotechnology, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Cristina Razquin Burillo
- Human Nutrition Unit, Department of Biochemistry and Biotechnology, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
- Department of Preventive Medicine and Public Health, IdiSNA (Instituto de Investigación Sanitaria de Navarra), University of Navarra, Pamplona, Spain
| | - Ramon Estruch
- Human Nutrition Unit, Department of Biochemistry and Biotechnology, Institut d'Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Michael Friedén
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Frederik Rosqvist
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Ulf Risérus
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Kathryn M Rexrode
- Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Marta Guasch-Ferré
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- 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
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Miguel Angel Martinez-Gonzalez
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine and Public Health, IdiSNA (Instituto de Investigación Sanitaria de Navarra), University of Navarra, Pamplona, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Julie A Lovegrove
- Hugh Sinclair Unit of Human Nutrition, Institute for Cardiovascular and Metabolic Research and Institute for Food, Nutrition and Health, Reading, UK
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Clemens Wittenbecher
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
- Department of Life Sciences, SciLifeLab, Chalmers University of Technology, Gothenburg, Sweden.
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Beyene HB, Huynh K, Wang T, Paul S, Cinel M, Mellett NA, Olshansky G, Meikle TG, Watts GF, Hung J, Hui J, Beilby J, Blangero J, Moses EK, Shaw JE, Magliano DJ, Giles C, Meikle PJ. Development and validation of a plasmalogen score as an independent modifiable marker of metabolic health: population based observational studies and a placebo-controlled cross-over study. EBioMedicine 2024; 105:105187. [PMID: 38861870 PMCID: PMC11215217 DOI: 10.1016/j.ebiom.2024.105187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/21/2024] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Decreased levels of circulating ethanolamine plasmalogens [PE(P)], and a concurrent increase in phosphatidylethanolamine (PE) are consistently reported in various cardiometabolic conditions. Here we devised, a plasmalogen score (Pls Score) that mirrors a metabolic signal that encompasses the levels of PE(P) and PE and captures the natural variation in circulating plasmalogens and perturbations in their metabolism associated with disease, diet, and lifestyle. METHODS We utilised, plasma lipidomes from the Australian Obesity, Diabetes and Lifestyle study (AusDiab; n = 10,339, 55% women) a nationwide cohort, to devise the Pls Score and validated this in the Busselton Health Study (BHS; n = 4,492, 56% women, serum lipidome) and in a placebo-controlled crossover trial involving Shark Liver Oil (SLO) supplementation (n = 10, 100% men). We examined the association of the Pls Score with cardiometabolic risk factors, type 2 diabetes mellitus (T2DM), cardiovascular disease and all-cause mortality (over 17 years). FINDINGS In a model, adjusted for age, sex and BMI, individuals in the top quintile of the Pls Score (Q5) relative to Q1 had an OR of 0.31 (95% CI 0.21-0.43), 0.39 (95% CI 0.25-0.61) and 0.42 (95% CI 0.30-0.57) for prevalent T2DM, incident T2DM and prevalent cardiovascular disease respectively, and a 34% lower mortality risk (HR = 0.66; 95% CI 0.56-0.78). Significant associations between diet and lifestyle habits and Pls Score exist and these were validated through dietary supplementation of SLO that resulted in a marked change in the Pls Score. INTERPRETATION The Pls Score as a measure that captures the natural variation in circulating plasmalogens, was not only inversely related to cardiometabolic risk and all-cause mortality but also associate with diet and lifestyle. Our results support the potential utility of the Pls Score as a biomarker for metabolic health and its responsiveness to dietary interventions. Further research is warranted to explore the underlying mechanisms and optimise the practical implementation of the Pls Score in clinical and population settings. FUNDING National Health and Medical Research Council (NHMRC grant 233200), National Health and Medical Research Council of Australia (Project grant APP1101320), Health Promotion Foundation of Western Australia, and National Health and Medical Research Council of Australia Senior Research Fellowship (#1042095).
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Affiliation(s)
- Habtamu B Beyene
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia
| | - Sudip Paul
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Michelle Cinel
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | | | | | - Thomas G Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia
| | - Gerald F Watts
- Medical School, University of Western Australia, Perth, WA, Australia; Cardiometabolic Service, Department of Cardiology and Internal Medicine, Royal Perth Hospital, Perth, WA, Australia
| | - Joseph Hung
- Medical School, University of Western Australia, Perth, WA, Australia
| | - Jennie Hui
- PathWest Laboratory Medicine of Western Australia, Queen Elizabeth II Medical Centre, Nedlands, WA, Australia; School of Population and Global Health, University of Western Australia, Crawley, WA, Australia
| | - John Beilby
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Eric K Moses
- School of Biomedical Sciences, University of Western Australia, Crawley, WA, Australia; Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Jonathan E Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Dianna J Magliano
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia.
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia; Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Melbourne, VIC, Australia; Baker Department of Cardiometabolic Health, Melbourne University, Melbourne, VIC, Australia.
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7
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Rojo-López MI, Barranco-Altirriba M, Rossell J, Antentas M, Castelblanco E, Yanes O, Weber RJM, Lloyd GR, Winder C, Dunn WB, Julve J, Granado-Casas M, Mauricio D. The Lipidomic Profile Is Associated with the Dietary Pattern in Subjects with and without Diabetes Mellitus from a Mediterranean Area. Nutrients 2024; 16:1805. [PMID: 38931159 PMCID: PMC11206394 DOI: 10.3390/nu16121805] [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: 05/09/2024] [Revised: 05/31/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024] Open
Abstract
Lipid functions can be influenced by genetics, age, disease states, and lifestyle factors, particularly dietary patterns, which are crucial in diabetes management. Lipidomics is an expanding field involving the comprehensive exploration of lipids from biological samples. In this cross-sectional study, 396 participants from a Mediterranean region, including individuals with type 1 diabetes (T1D), type 2 diabetes (T2D), and non-diabetic individuals, underwent lipidomic profiling and dietary assessment. Participants completed validated food frequency questionnaires, and lipid analysis was conducted using ultra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC/MS). Multiple linear regression models were used to determine the association between lipid features and dietary patterns. Across all subjects, acylcarnitines (AcCa) and triglycerides (TG) displayed negative associations with the alternate Healthy Eating Index (aHEI), indicating a link between lipidomic profiles and dietary habits. Various lipid species (LS) showed positive and negative associations with dietary carbohydrates, fats, and proteins. Notably, in the interaction analysis between diabetes and the aHEI, we found some lysophosphatidylcholines (LPC) that showed a similar direction with respect to aHEI in non-diabetic individuals and T2D subjects, while an opposite direction was observed in T1D subjects. The study highlights the significant association between lipidomic profiles and dietary habits in people with and without diabetes, particularly emphasizing the role of healthy dietary choices, as reflected by the aHEI, in modulating lipid concentrations. These findings underscore the importance of dietary interventions to improve metabolic health outcomes, especially in the context of diabetes management.
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Affiliation(s)
- Marina Idalia Rojo-López
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
| | - Maria Barranco-Altirriba
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
- B2SLab, Departament d’Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Barcelona, Spain
| | - Joana Rossell
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Maria Antentas
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
| | - Esmeralda Castelblanco
- Department of Internal Medicine, Endocrinology, Metabolism and Lipid Research Division, Washington University School of Medicine, St. Louis, MO 63110, USA;
| | - Oscar Yanes
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
- Department of Electronic Engineering, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Ralf J. M. Weber
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; (R.J.M.W.); (G.R.L.); (C.W.); (W.B.D.)
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Gavin R. Lloyd
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; (R.J.M.W.); (G.R.L.); (C.W.); (W.B.D.)
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Catherine Winder
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; (R.J.M.W.); (G.R.L.); (C.W.); (W.B.D.)
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Warwick B. Dunn
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; (R.J.M.W.); (G.R.L.); (C.W.); (W.B.D.)
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Josep Julve
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
| | - Minerva Granado-Casas
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
- Department of Nursing and Physiotherapy, University of Lleida, 25198 Lleida, Spain
- Research Group of Health Care (GreCS), IRBLleida, 25198 Lleida, Spain
| | - Dídac Mauricio
- Institut de Recerca Sant Pau (IR SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain; (M.I.R.-L.); (M.B.-A.); (J.R.); (M.A.); (J.J.)
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain;
- Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain
- Faculty of Medicine, University of Vic (UVIC/UCC), 08500 Vic, Spain
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8
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Liu B, Du Z, Zhang W, Guo X, Lu Y, Jiang Y, Tu P. A pseudo-targeted metabolomics for discovery of potential biomarkers of cardiac hypertrophy in rats. J Chromatogr B Analyt Technol Biomed Life Sci 2024; 1240:124133. [PMID: 38733887 DOI: 10.1016/j.jchromb.2024.124133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/07/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024]
Abstract
Cardiac hypertrophy (CH) is one of the stages in the occurrence and development of severe cardiovascular diseases, and exploring its biomarkers is beneficial for delaying the progression of severe cardiovascular diseases. In this research, we established a comprehensive and highly efficient pseudotargeted metabolomics method, which demonstrated a superior capacity to identify differential metabolites when compared to traditionaluntargeted metabolomics. The intra/inter-day precision and reproducibility results proved the method is reliable and precise. The established method was then applied to seek the potential differentiated metabolic biomarkers of cardiac hypertrophy (CH) rats, and oxylipins, phosphorylcholine (PC), lysophosphatidylcholine (LysoPC), lysophosphatidylethanolamine (LysoPE), Krebs cycle intermediates, carnitines, amino acids, and bile acids were disclosed to be the possible differentiate components. Their metabolic pathway analysis revealed that the potential metabolic alterations in CH rats were mainly associated with phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arachidonic acid metabolism, citrate cycle, glyoxylate and dicarboxylate metabolism, and tyrosine metabolism. In sum, this research provided a comprehensiveand reliable LC-MS/MS MRM platform for pseudo-targeted metabolomics investigation of disease condition, and some interesting potential biomarkers were disclosed for CH, which merit further exploration in the future.
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Affiliation(s)
- Bing Liu
- School of Pharmaceutical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China
| | - Zhiyong Du
- National Clinical Research Center for Cardiovascular Diseases, Beijing Institute of Heart Lung and Blood Vessel Disease, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Wenxin Zhang
- School of Pharmaceutical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China
| | - Xiaoyu Guo
- School of Pharmaceutical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China
| | - Yingyuan Lu
- School of Pharmaceutical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China.
| | - Yong Jiang
- School of Pharmaceutical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China.
| | - Pengfei Tu
- School of Pharmaceutical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China.
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9
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Desjardins LC, Brière F, Tremblay AJ, Rancourt-Bouchard M, Drouin-Chartier JP, Corbeil J, Lemelin V, Charest A, Schaefer EJ, Lamarche B, Couture P. Substitution of dietary monounsaturated fatty acids from olive oil for saturated fatty acids from lard increases low-density lipoprotein apolipoprotein B-100 fractional catabolic rate in subjects with dyslipidemia associated with insulin resistance: a randomized controlled trial. Am J Clin Nutr 2024; 119:1270-1279. [PMID: 38518848 PMCID: PMC11130675 DOI: 10.1016/j.ajcnut.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/21/2024] [Accepted: 03/18/2024] [Indexed: 03/24/2024] Open
Abstract
BACKGROUND The substitution of monounsaturated acids (MUFAs) for saturated fatty acids (SFAs) is recommended for cardiovascular disease prevention but its impact on lipoprotein metabolism in subjects with dyslipidemia associated with insulin resistance (IR) remains largely unknown. OBJECTIVES This study aimed to evaluate the impact of substituting MUFAs for SFAs on the in vivo kinetics of apolipoprotein (apo)B-containing lipoproteins and on the plasma lipidomic profile in adults with IR-induced dyslipidemia. METHODS Males and females with dyslipidemia associated with IR (n = 18) were recruited for this crossover double-blind randomized controlled trial. Subjects consumed, in random order, a diet rich in SFAs (SFAs: 13.4%E; MUFAs: 14.4%E) and a diet rich in MUFAs (SFAs: 7.1%E; MUFAs: 20.7%E) in fully controlled feeding conditions for periods of 4 wk each, separated by a 4-wk washout. At the end of each diet, fasting plasma samples were taken together with measurements of the in vivo kinetics of apoB-containing lipoproteins. RESULTS Substituting MUFAs for SFAs had no impact on triglyceride-rich lipoprotein apoB-48 fractional catabolic rate (FCR) (Δ = -8.9%, P = 0.4) and production rate (Δ = 0.0%, P = 0.9), although it decreased very low-density lipoprotein apoB-100 pool size (PS) (Δ = -22.5%; P = 0.01). This substitution also reduced low-density lipoprotein cholesterol (LDL-C) (Δ = -7.0%; P = 0.01), non-high-density lipoprotein cholesterol (Δ = -2.5%; P = 0.04), and LDL apoB-100 PS (Δ = -6.0%; P = 0.05). These differences were partially attributed to an increase in LDL apoB-100 FCR (Δ = +1.6%; P = 0.05). The MUFA diet showed reduced sphingolipid concentrations and elevated glycerophospholipid levels compared with the SFA diet. CONCLUSIONS This study demonstrated that substituting dietary MUFAs for SFAs decreases LDL-C levels and LDL PS by increasing LDL apoB-100 FCR and results in an overall improved plasma lipidomic profile in individuals with IR-induced lipidemia. TRIAL REGISTRATION This trial was registered as clinicaltrials.gov as NCT03872349.
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Affiliation(s)
- Louis-Charles Desjardins
- Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Canada; School of Nutrition, Université Laval, Quebec, Canada
| | - Francis Brière
- Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Canada; Faculty of Medicine, Université Laval, Quebec, Canada
| | - André J Tremblay
- Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Canada
| | - Maryka Rancourt-Bouchard
- Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Canada; School of Nutrition, Université Laval, Quebec, Canada; Faculty of Pharmacy, Université Laval, Quebec, Canada
| | - Jean-Philippe Drouin-Chartier
- Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Canada; Faculty of Pharmacy, Université Laval, Quebec, Canada
| | - Jacques Corbeil
- Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Canada; Faculty of Medicine, Université Laval, Quebec, Canada; Big Data Research Centre, Université Laval, Quebec, Canada
| | - Valéry Lemelin
- CHU de Québec-Université Laval Research Center, Quebec, Canada
| | - Amélie Charest
- Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Canada; School of Nutrition, Université Laval, Quebec, Canada
| | | | - Benoît Lamarche
- Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Canada; School of Nutrition, Université Laval, Quebec, Canada
| | - Patrick Couture
- Centre Nutrition, santé et société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, Canada; Faculty of Medicine, Université Laval, Quebec, Canada; CHU de Québec-Université Laval Research Center, Quebec, Canada.
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10
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Ament Z, Patki A, Bhave VM, Kijpaisalratana N, Jones AC, Couch CA, Stanton RJ, Rist PM, Cushman M, Judd SE, Long DL, Irvin MR, Kimberly WT. Omega-3 Fatty Acids and Risk of Ischemic Stroke in REGARDS. Transl Stroke Res 2024:10.1007/s12975-024-01256-7. [PMID: 38676880 DOI: 10.1007/s12975-024-01256-7] [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: 12/12/2023] [Revised: 04/18/2024] [Accepted: 04/24/2024] [Indexed: 04/29/2024]
Abstract
We examined associations between lipidomic profiles and incident ischemic stroke in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. Plasma lipids (n = 195) were measured from baseline blood samples, and lipids were consolidated into underlying factors using exploratory factor analysis. Cox proportional hazards models were used to test associations between lipid factors and incident stroke, linear regressions to determine associations between dietary intake and lipid factors, and the inverse odds ratio weighting (IORW) approach to test mediation. The study followed participants over a median (IQR) of 7 (3.4-11) years, and the case-cohort substudy included 1075 incident ischemic stroke and 968 non-stroke participants. One lipid factor, enriched for docosahexaenoic acid (DHA, an omega-3 fatty acid), was inversely associated with stroke risk in a base model (HR = 0.84; 95%CI 0.79-0.90; P = 8.33 × 10-8) and fully adjusted model (HR = 0.88; 95%CI 0.83-0.94; P = 2.79 × 10-4). This factor was associated with a healthy diet pattern (β = 0.21; 95%CI 0.12-0.30; P = 2.06 × 10-6), specifically with fish intake (β = 1.96; 95%CI 0.95-2.96; P = 1.36 × 10-4). DHA was a mediator between fish intake and incident ischemic stroke (30% P = 5.78 × 10-3). Taken together, DHA-containing plasma lipids were inversely associated with incident ischemic stroke and mediated the relationship between fish intake and stroke risk.
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Affiliation(s)
- Zsuzsanna Ament
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Amit Patki
- Department of Epidemiology, School of Public Health at the University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Naruchorn Kijpaisalratana
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Division of Neurology, Department of Medicine and Division of Academic Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Alana C Jones
- Medical Scientist Training Program, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Catharine A Couch
- Department of Epidemiology, School of Public Health at the University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert J Stanton
- Department of Neurology, University of Cincinnati Medical Center, Cincinnati, OH, USA
| | - Pamela M Rist
- Division of Preventive 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
| | - Mary Cushman
- Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington, VT, USA
| | - Suzanne E Judd
- Department of Biostatistics, School of Public Health at the University of Alabama at Birmingham, Birmingham, AL, USA
| | - D Leann Long
- Department of Biostatistics, School of Public Health at the University of Alabama at Birmingham, Birmingham, AL, USA
| | - M Ryan Irvin
- Department of Epidemiology, School of Public Health at the University of Alabama at Birmingham, Birmingham, AL, USA
| | - W Taylor Kimberly
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
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11
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Wu Q, Zhang L, Cheng C, Chen X, Bian S, Huang L, Li T, Li Z, Liu H, Yan J, Du Y, Chen Y, Zhang M, Cao L, Li W, Ma F, Huang G. Protocol for evaluating the effects of the Reducing Cardiometabolic Diseases Risk dietary pattern in the Chinese population with dyslipidaemia: a single-centre, open-label, dietary intervention study. BMJ Open 2024; 14:e082957. [PMID: 38580360 PMCID: PMC11002360 DOI: 10.1136/bmjopen-2023-082957] [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] [Received: 12/08/2023] [Accepted: 03/15/2024] [Indexed: 04/07/2024] Open
Abstract
INTRODUCTION Cardiometabolic disease (CMD) is the leading cause of mortality in China. A healthy diet plays an essential role in the occurrence and development of CMD. Although the Chinese heart-healthy diet is the first diet with cardiovascular benefits, a healthy dietary pattern that fits Chinese food culture that can effectively reduce the risk of CMD has not been found. METHODS/DESIGN The study is a single-centre, open-label, randomised controlled trial aimed at evaluating the effect of the Reducing Cardiometabolic Diseases Risk (RCMDR) dietary pattern in reducing the risk of CMDs in people with dyslipidaemia and providing a reference basis for constructing a dietary pattern suitable for the prevention of CMDs in the Chinese population. Participants are men and women aged 35-45 years with dyslipidaemia in Tianjin. The target sample size is 100. After the run-in period, the participants will be randomised to the RCMDR dietary pattern intervention group or the general health education control group with a 1:1 ratio. The intervention phases will last 12 weeks, with a dietary intervention of 5 working days per week for participants in the intervention group. The primary outcome variable is the cardiometabolic risk score. The secondary outcome variables are blood lipid, blood pressure, blood glucose, body composition indices, insulin resistance and 10-year risk of cardiovascular diseases. ETHICS AND DISSEMINATION The study complies with the Measures for Ethical Review of Life Sciences and Medical Research Involving Human Beings and the Declaration of Helsinki. Signed informed consent will be obtained from all participants. The study has been approved by the Medical Ethics Committee of the Second Hospital of Tianjin Medical University (approval number: KY2023020). The results from the study will be disseminated through publications in a peer-reviewed journal. TRIAL REGISTRATION NUMBER Chinese Clinical Trial Registry (ChiCTR2300072472).
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Affiliation(s)
- Qi Wu
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Liyang Zhang
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Cheng Cheng
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Xukun Chen
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Shanshan Bian
- Department of Nutrition, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Li Huang
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Tongtong Li
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Zhenshu Li
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
| | - Huan Liu
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, Tianjin Medical University, Tianjin, China
| | - Jing Yan
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, Tianjin Medical University, Tianjin, China
- Department of Social Medicine and Health Management, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Yue Du
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, Tianjin Medical University, Tianjin, China
- Department of Social Medicine and Health Management, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Yongjie Chen
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, Tianjin Medical University, Tianjin, China
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Meilin Zhang
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, Tianjin Medical University, Tianjin, China
| | - Lichun Cao
- Department of General Practice, Dazhangzhuang Community Medical Service Center, Beichen District, Tianjin, China
| | - Wen Li
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, Tianjin Medical University, Tianjin, China
| | - Fei Ma
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, Tianjin Medical University, Tianjin, China
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Guowei Huang
- Department of Nutrition and Food Science, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China
- Key Laboratory of Prevention and Control of Major Diseases in the Population, Ministry of Education, Tianjin Medical University, Tianjin, China
- The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin, China
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12
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Gu JY, Li XB, Liao GQ, Wang TC, Wang ZS, Jia Q, Qian YZ, Zhang XL, Qiu J. Comprehensive analysis of phospholipid in milk and their biological roles as nutrients and biomarkers. Crit Rev Food Sci Nutr 2024:1-20. [PMID: 38556904 DOI: 10.1080/10408398.2024.2330696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Phospholipids (PL) have garnered significant attention due to their physiological activities. Milk and other dairy products are important dietary sources for humans and have been extensively used to analyze the presence of PL by various analytical techniques. In this paper, the analysis techniques of PL were reviewed with the eight trigrams of phospholipidomics and a comprehensive fingerprint of 1295 PLs covering 8 subclasses in milk and other dairy products, especially. Technology is the primary productive force. Based on phospholipidomics technology, we further review the relationship between the composition of PL and factors that may be involved in processing and experimental operation, and emphasized the significance of the biological role played by PL in dietary supplements and biomarkers (production, processing and clinical research), and providing the future research directions.
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Affiliation(s)
- Jing-Yi Gu
- Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agri-food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xia-Bing Li
- Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agri-food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Guang-Qin Liao
- Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agri-food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Tian-Cai Wang
- Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agri-food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zi-Shuang Wang
- Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agri-food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Qi Jia
- Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agri-food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Yong-Zhong Qian
- Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agri-food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Xing-Lian Zhang
- Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agri-food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Jing Qiu
- Institute of Quality Standards and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Agri-food Quality and Safety, Ministry of Agriculture and Rural Affairs, Beijing, China
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13
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Sarkar S, Roy D, Chatterjee B, Ghosh R. Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases. Metabolomics 2024; 20:37. [PMID: 38459207 DOI: 10.1007/s11306-024-02100-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/06/2024] [Indexed: 03/10/2024]
Abstract
BACKGROUND Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts. AIM OF THE REVIEW The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies. KEY SCIENTIFIC CONCEPTS OF REVIEW Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.
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Affiliation(s)
- Sutanu Sarkar
- Amity Institute of Biotechnology (AIBNK), Amity University, Rajarhat, Newtown Action Area 2, Kolkata, 700135, West Bengal, India
| | - Deotima Roy
- Amity Institute of Biotechnology (AIBNK), Amity University, Rajarhat, Newtown Action Area 2, Kolkata, 700135, West Bengal, India
| | - Bhaskar Chatterjee
- Amity Institute of Biotechnology (AIBNK), Amity University, Rajarhat, Newtown Action Area 2, Kolkata, 700135, West Bengal, India
| | - Rajgourab Ghosh
- Amity Institute of Biotechnology (AIBNK), Amity University, Rajarhat, Newtown Action Area 2, Kolkata, 700135, West Bengal, India.
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14
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Ratter-Rieck JM, Shi M, Suhre K, Prehn C, Adamski J, Rathmann W, Thorand B, Roden M, Peters A, Wang-Sattler R, Herder C. Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study. BMJ Open Diabetes Res Care 2024; 12:e003865. [PMID: 38442989 PMCID: PMC11148672 DOI: 10.1136/bmjdrc-2023-003865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/01/2024] [Indexed: 03/07/2024] Open
Abstract
INTRODUCTION Circulating omentin levels have been positively associated with insulin sensitivity. Although a role for adiponectin in this relationship has been suggested, underlying mechanisms remain elusive. In order to reveal the relationship between omentin and systemic metabolism, this study aimed to investigate associations of serum concentrations of omentin and metabolites. RESEARCH DESIGN AND METHODS This study is based on 1124 participants aged 61-82 years from the population-based KORA (Cooperative Health Research in the Region of Augsburg) F4 Study, for whom both serum omentin levels and metabolite concentration profiles were available. Associations were assessed with five multivariable regression models, which were stepwise adjusted for multiple potential confounders, including age, sex, body mass index, waist-to-hip ratio, lifestyle markers (physical activity, smoking behavior and alcohol consumption), serum adiponectin levels, high-density lipoprotein cholesterol, use of lipid-lowering or anti-inflammatory medication, history of myocardial infarction and stroke, homeostasis model assessment 2 of insulin resistance, diabetes status, and use of oral glucose-lowering medication and insulin. RESULTS Omentin levels significantly associated with multiple metabolites including amino acids, acylcarnitines, and lipids (eg, sphingomyelins and phosphatidylcholines (PCs)). Positive associations for several PCs, such as diacyl (PC aa C32:1) and alkyl-alkyl (PC ae C32:2), were significant in models 1-4, whereas those with hydroxytetradecenoylcarnitine (C14:1-OH) were significant in all five models. Omentin concentrations were negatively associated with several metabolite ratios, such as the valine-to-PC ae C32:2 and the serine-to-PC ae C32:2 ratios in most models. CONCLUSIONS Our results suggest that omentin may influence insulin sensitivity and diabetes risk by changing systemic lipid metabolism, but further mechanistic studies investigating effects of omentin on metabolism of insulin-sensitive tissues are needed.
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Affiliation(s)
- Jacqueline M Ratter-Rieck
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
| | - Mengya Shi
- TUM School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Partner Neuherberg, Neuherberg, Germany
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Cornelia Prehn
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jerzy Adamski
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Wolfgang Rathmann
- German Center for Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Barbara Thorand
- German Center for Diabetes Research, Partner Neuherberg, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Annette Peters
- German Center for Diabetes Research, Partner Neuherberg, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Rui Wang-Sattler
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research, Partner Neuherberg, Neuherberg, Germany
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, Neuherberg, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
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Kronborg TM, Gao Q, Trošt K, Ytting H, O’Connell MB, Werge MP, Thing M, Gluud LL, Hamberg O, Møller S, Moritz T, Bendtsen F, Kimer N. Low sphingolipid levels predict poor survival in patients with alcohol-related liver disease. JHEP Rep 2024; 6:100953. [PMID: 38283758 PMCID: PMC10820332 DOI: 10.1016/j.jhepr.2023.100953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/08/2023] [Accepted: 10/20/2023] [Indexed: 01/30/2024] Open
Abstract
Background & Aims Alcohol-related hepatitis (AH) and alcohol-related cirrhosis are grave conditions with poor prognoses. Altered hepatic lipid metabolism can impact disease development and varies between different alcohol-related liver diseases. Therefore, we aimed to investigate lipidomics and metabolomics at various stages of alcohol-related liver diseases and their correlation with survival. Methods Patients with newly diagnosed alcohol-related cirrhosis, who currently used alcohol (ALC-A), stable outpatients with decompensated alcohol-related cirrhosis with at least 8 weeks of alcohol abstinence (ALC), and patients with AH, were compared with each other and with healthy controls (HC). Circulating lipids and metabolites were analysed using HPLC and mass spectrometry. Results Forty patients with ALC, 95 with ALC-A, 30 with AH, and 42 HC provided plasma. Lipid levels changed according to disease severity, with generally lower levels in AH and cirrhosis than in the HC group; this was most pronounced for AH, followed by ALC-A. Nine out of 10 free fatty acids differed between cirrhosis groups by relative increases of 0.12-0.66 in ALC compared with the ALC-A group (p <0.0005). For metabolomics, total bile acids increased by 19.7, 31.3, and 80.4 in the ALC, ALC-A, and AH groups, respectively, compared with HC (all p <0.0001). Low sphingolipid ([d42:1] and [d41:1]) levels could not predict 180-day mortality (AUC = 0.73, p = 0.95 and AUC = 0.73, p = 0.95) more accurately than the model for end-stage liver disease score (AUC = 0.71), but did predict 90-day mortality (AUC d42:1 = 0.922, AUC d41:1 = 0.893; pd42:1 = 0.005, pd41:1 = 0.007) more accurately than the MELD score AUCMELD = 0.70, pMELD = 0.19). Conclusions Alcohol-related severe liver disease is characterised by low lipid levels progressing with severity of liver disease, especially low sphingomyelins, which also associate to poor prognoses. Impact and implications Lipidomics has the potential to diagnose and risk stratify patients with liver diseases. Lipidomics differed between patients with alcohol-related hepatitis and alcohol-related cirrhosis with and without recent alcohol use. Furthermore, lipidomics could predict short-term mortality and might be suitable as a prognostic tool in the future. Clinical Trials Registration Scientific Ethics Committee of the Capital Region of Denmark, journal no. H-21013476.
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Affiliation(s)
| | - Qian Gao
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kajetan Trošt
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henriette Ytting
- Gastro Unit, Medical Division, University Hospital Hvidovre, Hvidovre, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Mira Thing
- Gastro Unit, Medical Division, University Hospital Hvidovre, Hvidovre, Denmark
| | - Lise Lotte Gluud
- Gastro Unit, Medical Division, University Hospital Hvidovre, Hvidovre, Denmark
| | - Ole Hamberg
- Medical Department, University Hospital of Zealand, Koege, Denmark
| | - Søren Møller
- Department of Clinical Medicine, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- Centre for Functional and Diagnostic Imaging and Research, Department of Clinical Physiology and Nuclear Medicine, Hvidovre Hospital, Hvidovre, Denmark
| | - Thomas Moritz
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Flemming Bendtsen
- Gastro Unit, Medical Division, University Hospital Hvidovre, Hvidovre, Denmark
| | - Nina Kimer
- Gastro Unit, Medical Division, University Hospital Hvidovre, Hvidovre, Denmark
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Zeng J, Zhang R, Zhao T, Wang H, Han L, Pu L, Jiang Y, Xu S, Ren H, Wang C. Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension. Metabolomics 2024; 20:13. [PMID: 38180633 DOI: 10.1007/s11306-023-02081-z] [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] [Received: 07/28/2023] [Accepted: 12/11/2023] [Indexed: 01/06/2024]
Abstract
INTRODUCTION The burden of stroke in patients with hypertension is very high, and its prediction is critical. OBJECTIVES We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension. METHODS This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension. RESULTS Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957). CONCLUSIONS We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.
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Affiliation(s)
- Jingjing Zeng
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, Ningbo, 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China
- Department of Cardiology, Ningbo No.2 Hospital, Ningbo, 315000, China
| | - Ruijie Zhang
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, Ningbo, 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China
| | - Tian Zhao
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, Ningbo, 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China
| | - Han Wang
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, Ningbo, 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China
| | - Liyuan Han
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, Ningbo, 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China
| | - Liyuan Pu
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, Ningbo, 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China
| | - Yannan Jiang
- Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, Ningbo No.2 Hospital, Ningbo, 315000, China
- Center for Cardiovascular and Cerebrovascular Epidemiology and Translational Medicine, Ningbo Institute of Life and Health Industry, Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China
| | - Shan Xu
- Department of Non-Communicable Disease Prevention and Control, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, 518000, China
| | - Huiming Ren
- Department of Rehabilitation Medicine, Ningbo No.2 Hospital, Ningbo, 315000, China.
| | - Changyi Wang
- Department of Non-Communicable Disease Prevention and Control, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, 518000, China.
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Niu YY, Aierken A, Feng L. Unraveling the link between dietary factors and cardiovascular metabolic diseases: Insights from a two-sample Mendelian Randomization investigation. Heart Lung 2024; 63:72-77. [PMID: 37826923 DOI: 10.1016/j.hrtlng.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/29/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND When specific nutrients are inadequate, vulnerability to cardiovascular and metabolic illnesses increases. The data linking dietary nutrition with these illnesses, however, has been sparse in the past observational research and randomized controlled trials. OBJECTIVES A Mendelian randomization (MR) analysis was performed to assess the influence of macronutrients (fat, protein, sugar, and carbohydrates) and micronutrients (β-carotene, folate, calcium, iron, copper, magnesium, phosphorus, selenium, zinc, vitamin C, vitamin D, vitamin B, and vitamin B12) on the susceptibility to cardiovascular metabolic disorders, including coronary artery disease, heart failure, ischemic stroke, and type 2 diabetes. METHODS We employed a two-sample Mendelian randomization (MR) analysis, utilizing inverse variance weighting and conducting comprehensive sensitivity assessments. We obtained publicly accessible summary data from separate cohorts comprising individuals of European ancestry. The level of statistical significance was established at a threshold of P < 0. 00074. RESULTS Based on our research findings, we have established a causal association between the consumption of circulating fat and the development of cardiovascular and metabolic diseases. The study found that an increase of one standard deviation in fat consumption was associated with a decreased risk of heart failure, with an odds ratio of 0. 56 (95 % CI: 0. 40-0. 79; p = 0. 0007). Notably, various sensitivity analyses confirmed the robustness of this association. Conversely, we did not find any significant correlation between other dietary components and the risk of cardiovascular and metabolic disorders. CONCLUSION Our research findings demonstrate a conspicuous impact of dietary fat consumption on the susceptibility to heart failure, independent of coronary artery disease, diabetes, and stroke. Consequently, it is indicated that dietary factors are unrelated to the predisposition to cardiovascular metabolic disorders.
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Affiliation(s)
- Yue-Yue Niu
- Beijing University of Chinese Medicine, Beijing 100029, China; Cadres Health Protection Department, China Academy of Chinese Medical Sciences Guang'anmen Hospital, No. 5, beixiange, Xicheng District, Beijing 100053, China
| | - Aikeremu Aierken
- Beijing University of Chinese Medicine, Beijing 100029, China; Cadres Health Protection Department, China Academy of Chinese Medical Sciences Guang'anmen Hospital, No. 5, beixiange, Xicheng District, Beijing 100053, China
| | - Ling Feng
- Cadres Health Protection Department, China Academy of Chinese Medical Sciences Guang'anmen Hospital, No. 5, beixiange, Xicheng District, Beijing 100053, China.
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Xourafa G, Korbmacher M, Roden M. Inter-organ crosstalk during development and progression of type 2 diabetes mellitus. Nat Rev Endocrinol 2024; 20:27-49. [PMID: 37845351 DOI: 10.1038/s41574-023-00898-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/29/2023] [Indexed: 10/18/2023]
Abstract
Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.
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Affiliation(s)
- Georgia Xourafa
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Melis Korbmacher
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany.
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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Jia X, Chen Q, Wu H, Liu H, Jing C, Gong A, Zhang Y. Exploring a novel therapeutic strategy: the interplay between gut microbiota and high-fat diet in the pathogenesis of metabolic disorders. Front Nutr 2023; 10:1291853. [PMID: 38192650 PMCID: PMC10773723 DOI: 10.3389/fnut.2023.1291853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024] Open
Abstract
In the past two decades, the rapid increase in the incidence of metabolic diseases, including obesity, diabetes, dyslipidemia, non-alcoholic fatty liver disease, hypertension, and hyperuricemia, has been attributed to high-fat diets (HFD) and decreased physical activity levels. Although the phenotypes and pathologies of these metabolic diseases vary, patients with these diseases exhibit disease-specific alterations in the composition and function of their gut microbiota. Studies in germ-free mice have shown that both HFD and gut microbiota can promote the development of metabolic diseases, and HFD can disrupt the balance of gut microbiota. Therefore, investigating the interaction between gut microbiota and HFD in the pathogenesis of metabolic diseases is crucial for identifying novel therapeutic strategies for these diseases. This review takes HFD as the starting point, providing a detailed analysis of the pivotal role of HFD in the development of metabolic disorders. It comprehensively elucidates the impact of HFD on the balance of intestinal microbiota, analyzes the mechanisms underlying gut microbiota dysbiosis leading to metabolic disruptions, and explores the associated genetic factors. Finally, the potential of targeting the gut microbiota as a means to address metabolic disturbances induced by HFD is discussed. In summary, this review offers theoretical support and proposes new research avenues for investigating the role of nutrition-related factors in the pathogenesis of metabolic disorders in the organism.
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Affiliation(s)
- Xiaokang Jia
- School of Traditional Chinese Medicine, Hainan Medical University, Haikou, Hainan, China
| | - Qiliang Chen
- School of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Huiwen Wu
- School of Traditional Chinese Medicine, Hainan Medical University, Haikou, Hainan, China
| | - Hongbo Liu
- School of Traditional Chinese Medicine, Hainan Medical University, Haikou, Hainan, China
| | - Chunying Jing
- School of Traditional Chinese Medicine, Hainan Medical University, Haikou, Hainan, China
| | - Aimin Gong
- School of Traditional Chinese Medicine, Hainan Medical University, Haikou, Hainan, China
| | - Yuanyuan Zhang
- The Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
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Kale D, Fatangare A, Phapale P, Sickmann A. Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update. Cells 2023; 12:2796. [PMID: 38132115 PMCID: PMC10741540 DOI: 10.3390/cells12242796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/24/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023] Open
Abstract
The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.
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Affiliation(s)
- Dipali Kale
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44139 Dortmund, Germany; (A.F.); (P.P.)
| | | | | | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., 44139 Dortmund, Germany; (A.F.); (P.P.)
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Chen X, Gu J, Huang Y. High dietary intake of unsaturated fatty acids is associated with improved insulin resistance - a cross-sectional study based on the NHANES database. Lipids Health Dis 2023; 22:216. [PMID: 38053162 DOI: 10.1186/s12944-023-01982-1] [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: 09/03/2023] [Accepted: 11/30/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND A moderate intake of unsaturated fatty acids (UFA) is associated positively with improved insulin resistance. The aim of this study was to investigate the relationship between the dietary intake of unsaturated fatty acids/total fats (UFA/TF) and insulin resistance. METHODS 15,560 participants were selected from the National Health and Nutrition Examination Survey (NHANES) database enrolled between March 2017 and 2020, and excluded those under 20 years of age, pregnant, or with missing data for key research items. Finally, 7,630 participants were included in the study. R software was used for data analysis that included: (1) general descriptive statistics; (2) comparison of differences in baseline information of three UFA/TF groups, namely low, medium, and high ratios; (3) calculation of the correlation between the UFA/TF ratio and markers of insulin resistance: triglyceride-glucose index (TyG) and homeostatic model assessment for insulin resistance (HOMA-IR); (4) stratification of the study subjects into two groups, with or without insulin resistance, using a cut-off value of HOMA-IR ≥ 2, followed by logistic regression analysis to examine the relationship between UFA/TF and insulin resistance status in the two groups; and (5) further stratification of the subjects according to age, gender, body mass index (BMI), race, total energy intake, total protein, total carbohydrate, total sugars, total dietary fiber, total fat, alcohol consumption, diabetes, hypercholesterolemia to analyze the impact of UFA/TF on insulin resistance status in different subgroups. RESULTS (1) A high UFA/TF level was associated with a low TyG index and HOMA-IR [β (vs. TyG index) = -0.559, 95% CI: (-0.821~-0.297), P < 0.001; β (vs. HOMA-IR) = -0.742, 95% CI: (-1.083~-0.402), P < 0.001]. This negative relationship became more pronounced when UFA/TF exceeded 57.9% (i.e., the higher group). (2) Logistic regression analysis showed that a higher UFA/TF level was associated with a lower risk of developing insulin resistance [Q3 vs. Q1: 0.838 (95%CI: 0.709 ~ 0.991); P for trend = 0.038]. After adjusting for covariates such as gender, age, and BMI, this protective effect remained significant (P value < 0.05). (3) Analysis also showed that increased UFA/TF intake reduced the risk of developing insulin resistance (OR = 0.266, 95% CI: (0.075 ~ 0.946), P = 0.041). Subgroup analysis showed that although elevated UFA/TF intake showed no statistically significant difference in its effect in most subgroups, the large study population in this study provides valuable insights on potential changes. Increased UFA/TF intake may confer relatively greater benefits within specific subgroups, particularly among the elderly [Q3 age group, OR = 0.114, 95%CI: (0.012 ~ 1.078), P = 0.058], females [OR = 0.234, 95%CI: (0.041 ~ 1.333), P = 0.102], those with a BMI ≤ 25 kg/m²[OR = 0.191, 95%CI: (0.016 ~ 2.344), P = 0.196], and individuals without hypercholesterolemia [OR = 0.207, 95%CI: (0.042 ~ 1.013), P = 0.0519]. The impact of high UFA/TF levels within subgroups based on the presence or absence of coronary heart disease and stroke displayed contrasting trends. In those without coronary heart disease, there was a significant protective effect against insulin resistance [OR = 0.254, 95% CI: (0.07 ~ 0.929), P = 0.0384], while in the stroke subgroup, a significantly protective effect against insulin resistance was observed [OR = 0.002, 95%CI: (0 ~ 0.695), P = 0.0376]. CONCLUSION A high dietary intake of UFA relative to total fat consumption could be a protective factor against the risk of developing insulin resistance.
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Affiliation(s)
- Xiaonan Chen
- Department of General Medicine, Shanghai Medical College, Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Gu
- Department of General Medicine, Shanghai Medical College, Huashan Hospital, Fudan University, Shanghai, China
| | - Yanyan Huang
- Department of General Medicine, Shanghai Medical College, Huashan Hospital, Fudan University, Shanghai, China.
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Kortesniemi M, Noerman S, Kårlund A, Raita J, Meuronen T, Koistinen V, Landberg R, Hanhineva K. Nutritional metabolomics: Recent developments and future needs. Curr Opin Chem Biol 2023; 77:102400. [PMID: 37804582 DOI: 10.1016/j.cbpa.2023.102400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/21/2023] [Accepted: 09/07/2023] [Indexed: 10/09/2023]
Abstract
Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.
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Affiliation(s)
- Maaria Kortesniemi
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland.
| | - Stefania Noerman
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Anna Kårlund
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Jasmin Raita
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Topi Meuronen
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland
| | - Ville Koistinen
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland; Institute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland
| | - Rikard Landberg
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Kati Hanhineva
- Food Sciences Unit, Department of Life Technologies, University of Turku, FI-20014 Turun yliopisto, Finland; Institute of Public Health and Clinical Nutrition, School of Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland
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23
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Hillesheim E, Brennan L. Distinct patterns of personalised dietary advice delivered by a metabotype framework similarly improve dietary quality and metabolic health parameters: secondary analysis of a randomised controlled trial. Front Nutr 2023; 10:1282741. [PMID: 38035361 PMCID: PMC10684740 DOI: 10.3389/fnut.2023.1282741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/31/2023] [Indexed: 12/02/2023] Open
Abstract
Background In a 12-week randomised controlled trial, personalised nutrition delivered using a metabotype framework improved dietary intake, metabolic health parameters and the metabolomic profile compared to population-level dietary advice. The objective of the present work was to investigate the patterns of dietary advice delivered during the intervention and the alterations in dietary intake and metabolic and metabolomic profiles to obtain further insights into the effectiveness of the metabotype framework. Methods Forty-nine individuals were randomised into the intervention group and subsequently classified into metabotypes using four biomarkers (triacylglycerol, HDL-C, total cholesterol, glucose). These individuals received personalised dietary advice from decision tree algorithms containing metabotypes and individual characteristics. In a secondary analysis of the data, patterns of dietary advice were identified by clustering individuals according to the dietary messages received and clusters were compared for changes in dietary intake and metabolic health parameters. Correlations between changes in blood clinical chemistry and changes in metabolite levels were investigated. Results Two clusters of individuals with distinct patterns of dietary advice were identified. Cluster 1 had the highest percentage of messages delivered to increase the intake of beans and pulses and milk and dairy products. Cluster 2 had the highest percentage of messages delivered to limit the intake of foods high in added sugar, high-fat foods and alcohol. Following the intervention, both patterns improved dietary quality assessed by the Alternate Mediterranean Diet Score and the Alternative Healthy Eating Index, nutrient intakes, blood pressure, triacylglycerol and LDL-C (p ≤ 0.05). Several correlations were identified between changes in total cholesterol, LDL-C, triacylglycerol, insulin and HOMA-IR and changes in metabolites levels, including mostly lipids (sphingomyelins, lysophosphatidylcholines, glycerophosphocholines and fatty acid carnitines). Conclusion The findings indicate that the metabotype framework effectively personalises and delivers dietary advice to improve dietary quality and metabolic health. Clinical trial registration isrctn.com, identifier ISRCTN15305840.
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Affiliation(s)
- Elaine Hillesheim
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Lorraine Brennan
- UCD School of Agriculture and Food Science, Institute of Food and Health, University College Dublin, Dublin, Ireland
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
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Lai KZH, Semnani-Azad Z, Boucher BA, Retnakaran R, Harris SB, Malik V, Bazinet RP, Hanley AJ. Association of Serum Very-Long-Chain Saturated Fatty Acids With Changes in Insulin Sensitivity and β-Cell Function: The Prospective Metabolism and Islet Cell Evaluation (PROMISE) Cohort. Diabetes 2023; 72:1664-1670. [PMID: 37586083 DOI: 10.2337/db22-1050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 08/03/2023] [Indexed: 08/18/2023]
Abstract
A unique group of circulating very-long-chain saturated fatty acids (VLCSFAs), including arachidic acid (20:0), behenic acid (22:0), and lignoceric acid (24:0), have been associated with a lower risk of type 2 diabetes, although associations with early metabolic risk phenotypes preceding type 2 diabetes have received limited study. We aimed to examine the associations of VLCSFAs with longitudinal changes in insulin sensitivity and β-cell function in a cohort at risk for type 2 diabetes. VLCSFAs in the four main serum pools (phospholipid, triacylglycerol, cholesteryl ester, and nonesterified fatty acid) were extracted from fasting baseline samples (n = 467). Generalized estimating equations were used to determine the associations between VLCSFAs and changes over 9 years in validated indices of insulin sensitivity (HOMA2-%S [insulin sensitivity as percentage of normal population and ISI) and β-cell function (insulinogenic index [IGI], IGI divided by HOMA-insulin resistance [IGI/IR], and insulin secretion sensitivity index 2 [ISSI-2]). Associations of VLCSFAs with outcomes were strongest in the triacylglycerol lipid pool: 20:0 was positively associated with both insulin sensitivity and β-cell function (5.01% increase in HOMA2-%S and 4.01-6.28% increase in IGI/IR and ISSI-2 per SD increase in 20:0); 22:0 was positively associated with insulin sensitivity, with a 6.55% increase in HOMA2-%S and a 5.80% increase in ISI per SD increase in 22:0. Lastly, 24:0 was positively associated with insulin sensitivity and β-cell function (7.94-8.45% increase in HOMA2-%S and ISI, and a 4.61-6.93% increase in IGI/IR and ISSI-2 per SD increase in 24:0). Fewer significant associations were observed in the cholesteryl ester and nonesterified pools. Overall, our results indicate positive longitudinal associations of VLCSFAs with insulin sensitivity and β-cell function, especially within the triacylglycerol pool. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Kira Zhi Hua Lai
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Zhila Semnani-Azad
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Beatrice A Boucher
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ravi Retnakaran
- Division of Endocrinology and Metabolism, University of Toronto, Toronto, Ontario, Canada
- Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Stewart B Harris
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Vasanti Malik
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Richard P Bazinet
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Anthony J Hanley
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of Endocrinology and Metabolism, University of Toronto, Toronto, Ontario, Canada
- Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Ontario, Canada
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25
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Liu D, Aziz NA, Landstra EN, Breteler MMB. The lipidomic correlates of epigenetic aging across the adult lifespan: A population-based study. Aging Cell 2023; 22:e13934. [PMID: 37496173 PMCID: PMC10497837 DOI: 10.1111/acel.13934] [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: 04/03/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/28/2023] Open
Abstract
Lipid signaling is involved in longevity regulation, but which specific lipid molecular species affect human biological aging remains largely unknown. We investigated the relation between complex lipids and DNA methylation-based metrics of biological aging among 4181 participants (mean age 55.1 years (range 30.0-95.0)) from the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany. The absolute concentration of 14 lipid classes, covering 964 molecular species and 267 fatty acid composites, was measured by Metabolon Complex Lipid Panel. DNA methylation-based metrics of biological aging (AgeAccelPheno and AgeAccelGrim) were calculated based on published algorithms. Epigenome-wide association analyses (EWAS) of biological aging-associated lipids and pathway analysis were performed to gain biological insights into the mechanisms underlying the effects of lipidomics on biological aging. We found that higher levels of molecular species belonging to neutral lipids, phosphatidylethanolamines, phosphatidylinositols, and dihydroceramides were associated with faster biological aging, whereas higher levels of lysophosphatidylcholine, hexosylceramide, and lactosylceramide species were associated with slower biological aging. Ceramide, phosphatidylcholine, and lysophosphatidylethanolamine species with odd-numbered fatty acid tail lengths were associated with slower biological aging, whereas those with even-numbered chain lengths were associated with faster biological aging. EWAS combined with functional pathway analysis revealed several complex lipids associated with biological aging as important regulators of known longevity and aging-related pathways.
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Affiliation(s)
- Dan Liu
- Population Health SciencesGerman Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | - N. Ahmad Aziz
- Population Health SciencesGerman Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of Neurology, Faculty of MedicineUniversity of BonnBonnGermany
| | - Elvire Nadieh Landstra
- Population Health SciencesGerman Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | - Monique M. B. Breteler
- Population Health SciencesGerman Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of MedicineUniversity of BonnBonnGermany
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Anastasilakis AD, Polyzos SA, Vorkas PA, Gkiomisi A, Yavropoulou MP, Rauner M, Nikolakopoulos P, Papachatzopoulos S, Makras P, Gerou S, Hofbauer LC, Palermo A, Tsourdi E. Lipid Profile after Pharmacologic Discontinuation and Restoration of Menstruation in Women with Endometriosis: A 12-Month Observational Prospective Study. J Clin Med 2023; 12:5430. [PMID: 37629472 PMCID: PMC10455875 DOI: 10.3390/jcm12165430] [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: 07/07/2023] [Revised: 08/01/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
The lipid profile is affected following menstrual cessation (MC). We aimed to evaluate the effects of goserelin-induced MC and subsequent menstrual restoration (MR) on lipid metabolism. Premenopausal women with histologically verified endometriosis (n = 15) received goserelin monthly for 6 months (6mο), resulting in MC, and were followed-up for another 6 months after MR (12mο). Serum total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein a ([Lp(a)] and lipidomics were measured at baseline, 6mo and 12mo. Shotgun quantitative deep lipidomics were determined at the level of lipid class category, subclass, species, and fatty acyl chain lengths and degree of saturation. TC (p = 0.006), LDL-C (p = 0.028), HDL-C (p = 0.002), and apoA1 (p = 0.013) increased during goserelin-induced MC and remained practically unchanged during MR. TG, apoB, and Lp(a) did not change. From the deep lipidomics analysis, multivariate statistical analysis demonstrated profound alterations in lipid species with MC, whereas no statistically valid models could be fitted for the restoration period. In conclusion, GnRH-analog-induced MC alters lipid profiles at various levels, from standard blood lipid and lipoprotein profiles to several lipid species as detected by lipidomics analysis. Changes largely persist for at least 6 m after MR.
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Affiliation(s)
| | - Stergios A. Polyzos
- First Laboratory of Pharmacology, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Panagiotis A. Vorkas
- School of Cardiovascular and Metabolic Medicine & Sciences, King’s College London, London SE5 9RJ, UK;
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;
- Institute of Applied Biosciences, Centre for Research and Technology Hellas (INAB|CERTH), 57001 Thessaloniki, Greece
| | - Athina Gkiomisi
- Department of Obstetrics and Gynaecology, 424 General Military Hospital, 56429 Thessaloniki, Greece; (A.G.); (S.P.)
| | - Maria P. Yavropoulou
- Endocrinology Unit, First Department of Propaedeutic and Internal Medicine, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Martina Rauner
- Department of Medicine III and Center for Healthy Aging, Technische Universität Dresden, 01307 Dresden, Germany; (M.R.); (L.C.H.); (E.T.)
| | - Panagiotis Nikolakopoulos
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK;
| | - Stergios Papachatzopoulos
- Department of Obstetrics and Gynaecology, 424 General Military Hospital, 56429 Thessaloniki, Greece; (A.G.); (S.P.)
| | - Polyzois Makras
- Department of Endocrinology and Diabetes and Department of Medical Research, 251 Hellenic Air Force & VA General Hospital, 11525 Athens, Greece
| | | | - Lorenz C. Hofbauer
- Department of Medicine III and Center for Healthy Aging, Technische Universität Dresden, 01307 Dresden, Germany; (M.R.); (L.C.H.); (E.T.)
| | - Andrea Palermo
- Unit of Endocrinology and Diabetes, Campus Bio-Medico University, 00128 Rome, Italy;
| | - Elena Tsourdi
- Department of Medicine III and Center for Healthy Aging, Technische Universität Dresden, 01307 Dresden, Germany; (M.R.); (L.C.H.); (E.T.)
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27
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Villasanta-Gonzalez A, Mora-Ortiz M, Alcala-Diaz JF, Rivas-Garcia L, Torres-Peña JD, Lopez-Bascon A, Calderon-Santiago M, Arenas-Larriva AP, Priego-Capote F, Malagon MM, Eichelmann F, Perez-Martinez P, Delgado-Lista J, Schulze MB, Camargo A, Lopez-Miranda J. Plasma lipidic fingerprint associated with type 2 diabetes in patients with coronary heart disease: CORDIOPREV study. Cardiovasc Diabetol 2023; 22:199. [PMID: 37537576 PMCID: PMC10401778 DOI: 10.1186/s12933-023-01933-1] [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] [Received: 05/21/2023] [Accepted: 07/21/2023] [Indexed: 08/05/2023] Open
Abstract
OBJECTIVE We aimed to identify a lipidic profile associated with type 2 diabetes mellitus (T2DM) development in coronary heart disease (CHD) patients, to provide a new, highly sensitive model which could be used in clinical practice to identify patients at T2DM risk. METHODS This study considered the 462 patients of the CORDIOPREV study (CHD patients) who were not diabetic at the beginning of the intervention. In total, 107 of them developed T2DM after a median follow-up of 60 months. They were diagnosed using the American Diabetes Association criteria. A novel lipidomic methodology employing liquid chromatography (LC) separation followed by HESI, and detection by mass spectrometry (MS) was used to annotate the lipids at the isomer level. The patients were then classified into a Training and a Validation Set (60-40). Next, a Random Survival Forest (RSF) was carried out to detect the lipidic isomers with the lowest prediction error, these lipids were then used to build a Lipidomic Risk (LR) score which was evaluated through a Cox. Finally, a production model combining the clinical variables of interest, and the lipidic species was carried out. RESULTS LC-tandem MS annotated 440 lipid species. From those, the RSF identified 15 lipid species with the lowest prediction error. These lipids were combined in an LR score which showed association with the development of T2DM. The LR hazard ratio per unit standard deviation was 2.87 and 1.43, in the Training and Validation Set respectively. Likewise, patients with higher LR Score values had lower insulin sensitivity (P = 0.006) and higher liver insulin resistance (P = 0.005). The receiver operating characteristic (ROC) curve obtained by combining clinical variables and the selected lipidic isomers using a generalised lineal model had an area under the curve (AUC) of 81.3%. CONCLUSION Our study showed the potential of comprehensive lipidomic analysis in identifying patients at risk of developing T2DM. In addition, the lipid species combined with clinical variables provided a new, highly sensitive model which can be used in clinical practice to identify patients at T2DM risk. Moreover, these results also indicate that we need to look closely at isomers to understand the role of this specific compound in T2DM development. Trials registration NCT00924937.
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Affiliation(s)
- Alejandro Villasanta-Gonzalez
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain
| | - Marina Mora-Ortiz
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain
| | - Juan F Alcala-Diaz
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Lorenzo Rivas-Garcia
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain
| | - Jose D Torres-Peña
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Asuncion Lopez-Bascon
- Department of Analytical Chemistry and Nanochemistry University Institute, University of Cordoba, Cordoba, Spain
- CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Monica Calderon-Santiago
- Department of Analytical Chemistry and Nanochemistry University Institute, University of Cordoba, Cordoba, Spain
- CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Antonio P Arenas-Larriva
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain
| | - Feliciano Priego-Capote
- Department of Analytical Chemistry and Nanochemistry University Institute, University of Cordoba, Cordoba, Spain
- CIBER de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain
| | - Maria M Malagon
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, Cordoba, Spain
| | - Fabian Eichelmann
- German Center for Diabetes Research, Munich-Neuherberg, Germany
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
| | - Pablo Perez-Martinez
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Javier Delgado-Lista
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Matthias B Schulze
- German Center for Diabetes Research, Munich-Neuherberg, Germany
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
- Germany Institute of Nutrition Science, University of Potsdam, Nuthetal, Germany
| | - Antonio Camargo
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain.
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain.
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Jose Lopez-Miranda
- Lipids and Atherosclerosis Unit, Department of Internal Medicine, Reina Sofia University Hospital, University of Cordoba, Cordoba, Spain.
- Department of Medical and Surgical Sciences, University of Cordoba, Cordoba, Spain.
- Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Córdoba, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
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Zhang T, Naudin S, Hong HG, Albanes D, Männistö S, Weinstein SJ, Moore SC, Stolzenberg-Solomon RZ. Dietary Quality and Circulating Lipidomic Profiles in 2 Cohorts of Middle-Aged and Older Male Finnish Smokers and American Populations. J Nutr 2023; 153:2389-2400. [PMID: 37328109 PMCID: PMC10493471 DOI: 10.1016/j.tjnut.2023.06.010] [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] [Received: 04/21/2023] [Revised: 06/02/2023] [Accepted: 06/07/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Higher dietary quality is associated with lower disease risks and has not been examined extensively with lipidomic profiles. OBJECTIVES Our goal was to examine associations of the Healthy Eating Index (HEI)-2015, Alternate HEI-2010 (AHEI-2010), and alternate Mediterranean Diet Index (aMED) diet quality indices with serum lipidomic profiles. METHODS We conducted a cross-sectional analysis of HEI-2015, AHEI-2010, and aMED with lipidomic profiles from 2 nested case-control studies within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (n = 627) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (n = 711). We used multivariable linear regression to determine associations of the indices, derived from baseline food-frequency questionnaires (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial: 1993-2001, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: 1985-1988) with serum concentrations of 904 lipid species and 252 fatty acids (FAs) across 15 lipid classes and 28 total FAs, within each cohort and meta-analyzed results using fixed-effect models for lipids significant at Bonferroni-corrected threshold in common in both cohorts. RESULTS Adherence to HEI-2015, AHEI-2010, or aMED was associated positively with 31, 41, and 54 lipid species and 8, 6, and 10 class-specific FAs and inversely with 2, 8, and 34 lipid species and 1, 3, and 5 class-specific FAs, respectively. Twenty-five lipid species and 5 class-specific FAs were common to all indices, predominantly triacylglycerols, FA22:6 [docosahexaenoic acid (DHA)]-containing species, and DHA. All indices were positively associated with total FA22:6. AHEI-2010 and aMED were inversely associated with total FA18:1 (oleic acid) and total FA17:0 (margaric acid), respectively. The identified lipids were most associated with components of seafood and plant proteins and unsaturated:saturated fat ratio in HEI-2015; eicosapentaenoic acid plus DHA in AHEI-2010; and fish and monounsaturated:saturated fat ratio in aMED. CONCLUSIONS Adherence to HEI-2015, AHEI-2010, and aMED is associated with serum lipidomic profiles, mostly triacylglycerols or FA22:6-containing species, which are related to seafood and plant proteins, eicosapentaenoic acid-DHA, fish, or fat ratio index components.
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Affiliation(s)
- Ting Zhang
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Sabine Naudin
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States; Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Hyokyoung G Hong
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Demetrius Albanes
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Satu Männistö
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Stephanie J Weinstein
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Steven C Moore
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States
| | - Rachael Z Stolzenberg-Solomon
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, United States.
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Bellot PENR, Braga ES, Omage FB, da Silva Nunes FL, Lima SCVC, Lyra CO, Marchioni DML, Pedrosa LFC, Barbosa F, Tasic L, Sena-Evangelista KCM. Plasma lipid metabolites as potential biomarkers for identifying individuals at risk of obesity-induced metabolic complications. Sci Rep 2023; 13:11729. [PMID: 37474543 PMCID: PMC10359283 DOI: 10.1038/s41598-023-38703-8] [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: 12/28/2022] [Accepted: 07/13/2023] [Indexed: 07/22/2023] Open
Abstract
Lipidomics studies have indicated an association between obesity and lipid metabolism dysfunction. This study aimed to evaluate and compare cardiometabolic risk factors, and the lipidomic profile in adults and older people. A cross-sectional study was conducted with 72 individuals, divided into two sex and age-matched groups: obese (body mass index-BMI ≥ 30 kg/m2; n = 36) and non-obese (BMI < 30 kg/m2; n = 36). The lipidomic profiles were evaluated in plasma using 1H nuclear magnetic resonance (1H-NMR) spectroscopy. Obese individuals had higher waist circumference (p < 0.001), visceral adiposity index (p = 0.029), homeostatic model assessment insulin resistance (HOMA-IR) (p = 0.010), and triacylglycerols (TAG) levels (p = 0.018). 1H-NMR analysis identified higher amounts of saturated lipid metabolite fragments, lower levels of unsaturated lipids, and some phosphatidylcholine species in the obese group. Two powerful machine learning (ML) models-k-nearest neighbors (kNN) and XGBoost (XGB) were employed to characterize the lipidomic profile of obese individuals. The results revealed metabolic alterations associated with obesity in the NMR signals. The models achieved high accuracy of 86% and 81%, respectively. The feature importance analysis identified signal at 1.50-1.60 ppm (-CO-CH2-CH2-, Cholesterol and fatty acid in TAG, Phospholipids) to have the highest importance in the two models.
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Affiliation(s)
- Paula Emília Nunes Ribeiro Bellot
- Postgraduate Program in Nutrition, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Erik Sobrinho Braga
- Biological Chemistry Laboratory, Department of Organic Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
| | - Folorunsho Bright Omage
- Biological Chemistry Laboratory, Department of Organic Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
- Computational Biology Research Group, Embrapa Agricultural Informatics, Campinas, São Paulo, Brazil
| | - Francisca Leide da Silva Nunes
- Postgraduate Program in Nutrition, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | | | - Clélia Oliveira Lyra
- Department of Nutrition, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Dirce Maria Lobo Marchioni
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo Campus, São Paulo, SP, Brazil
| | | | - Fernando Barbosa
- Department of Clinical Analyses, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirão Preto of the University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Ljubica Tasic
- Biological Chemistry Laboratory, Department of Organic Chemistry, Institute of Chemistry, University of Campinas (UNICAMP), Campinas, São Paulo, Brazil
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Naudin S, Sampson JN, Moore SC, Albanes D, Freedman ND, Weinstein SJ, Stolzenberg-Solomon R. Lipidomics and pancreatic cancer risk in two prospective studies. Eur J Epidemiol 2023; 38:783-793. [PMID: 37169992 PMCID: PMC11152614 DOI: 10.1007/s10654-023-01014-3] [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: 01/07/2023] [Accepted: 04/27/2023] [Indexed: 05/13/2023]
Abstract
Pancreatic ductal carcinoma (PDAC) is highly fatal with limited understanding of mechanisms underlying its carcinogenesis. We comprehensively investigated whether lipidomic measures were associated with PDAC in two prospective studies. We measured 904 lipid species and 252 fatty acids across 15 lipid classes in pre-diagnostic serum (up to 24 years) in a PDAC nested-case control study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, NCT00002540) with 332 matched case-control sets including 272 having serial blood samples and Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC, NCT00342992) with 374 matched case-control sets. Controls were matched to cases by cohort, age, sex, race, and date at blood draw. We used conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) per one-standard deviation increase in log-lipid concentrations within each cohort, and combined ORs using fixed-effects meta-analyses. Forty-three lipid species were associated with PDAC (false discovery rate, FDR ≤ 0.10), including lysophosphatidylcholines (LPC, n = 2), phosphatidylethanolamines (PE, n = 17), triacylglycerols (n = 13), phosphatidylcholines (PC, n = 3), diacylglycerols (n = 4), monoacylglycerols (MAG, n = 2), cholesteryl esters (CE, n = 1), and sphingomyelins (n = 1). LPC(18:2) and PE(O-16:0/18:2) showed significant inverse associations with PDAC at the Bonferroni threshold (P value < 5.5 × 10-5). The fatty acids LPC[18:2], LPC[16:0], PC[15:0], MAG[18:1] and CE[22:0] were significantly associated with PDAC (FDR < 0.10). Similar associations were observed in both cohorts. There was no significant association for the differences between PLCO serial lipidomic measures or heterogeneity by follow-up time overall. Results support that the pre-diagnostic serum lipidome, including 43 lipid species from 8 lipid classes and 5 fatty acids, is associated with PDAC.
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Affiliation(s)
- Sabine Naudin
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, DHHS, 9609 Medical Center Drive, NCI Shady Grove, Room 6E420, Rockville, MD, 20850, USA
| | - Joshua N Sampson
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, Rockville, MD, USA
| | - Steven C Moore
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, DHHS, 9609 Medical Center Drive, NCI Shady Grove, Room 6E420, Rockville, MD, 20850, USA
| | - Demetrius Albanes
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, DHHS, 9609 Medical Center Drive, NCI Shady Grove, Room 6E420, Rockville, MD, 20850, USA
| | - Neal D Freedman
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, DHHS, 9609 Medical Center Drive, NCI Shady Grove, Room 6E420, Rockville, MD, 20850, USA
| | - Stephanie J Weinstein
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, DHHS, 9609 Medical Center Drive, NCI Shady Grove, Room 6E420, Rockville, MD, 20850, USA
| | - Rachael Stolzenberg-Solomon
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institute of Health, DHHS, 9609 Medical Center Drive, NCI Shady Grove, Room 6E420, Rockville, MD, 20850, USA.
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Liu J, Wu Y, Cai Y, Tan Z, Deng N. Long-term consumption of different doses of Grifola frondosa affects immunity and metabolism: correlation with intestinal mucosal microbiota and blood lipids. 3 Biotech 2023; 13:189. [PMID: 37193332 PMCID: PMC10183060 DOI: 10.1007/s13205-023-03617-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/06/2023] [Indexed: 05/18/2023] Open
Abstract
Grifola frondosa (GF) is an edible mushroom with hypoglycemic and hypolipidemic effects. In this study, the specific pathogen-free male mice were randomized into the normal (NM), low-dose GF (LGF), medium-dose GF (MGF), and high-dose GF (HGF) groups. The LGF, MGF, and HGF groups were fed with 1.425 g/(kg d), 2.85 g/(kg d), and 5.735 g/(kg d) of GF solution for 8 weeks. After feeding with GF solution, compared with the NM group, the thymus index was significantly increased in the LGF group, and TC, TG, and LDL of mice were significantly increased in the HGF group, while HDL was significantly decreased. Compared with the NM group, the uncultured Bacteroidales bacterium, Ligilactobacillus increased in the LGF group, and Candidatus Arthromitus increased in the MGF group. The characteristic bacteria of the HGF group included Christensenellaceae R7, unclassified Clostridia UCG 014, unclassified Eubacteria coprostanoligenes, and Prevotellaceae Ga6A1. Among them, Ligilactobacillus showed a negative correlation with HDL. Unclassified Eubacterium coprostanoligenes group and Ligilactobacillus showed a positive correlation with TG. In summary, our experiments evidenced that GF improves lipid metabolism disorders by regulating the intestinal microbiota, providing a new pathway for hypolipidemic using GF dietary.
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Affiliation(s)
- Jing Liu
- College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208 Hunan Province China
| | - Yi Wu
- College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208 Hunan Province China
| | - Ying Cai
- College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208 Hunan Province China
| | - Zhoujin Tan
- College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208 Hunan Province China
| | - Na Deng
- College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410208 Hunan Province China
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Sellem L, Eichelmann F, Jackson KG, Wittenbecher C, Schulze MB, Lovegrove JA. Replacement of dietary saturated with unsaturated fatty acids is associated with beneficial effects on lipidome metabolites: a secondary analysis of a randomized trial. Am J Clin Nutr 2023:S0002-9165(23)46314-9. [PMID: 37062359 DOI: 10.1016/j.ajcnut.2023.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 04/18/2023] Open
Abstract
BACKGROUND The effects of replacing dietary saturated fatty acids (SFAs) with monounsaturated fatty acids (MUFAs) and/or polyunsaturated fatty acids (PUFAs) on the plasma lipidome in relation to the cardiometabolic disease (CMD) risk are poorly understood. OBJECTIVES We aimed to assess the impact of substituting dietary SFAs with unsaturated fatty acids (UFAs) on the plasma lipidome and examine the relationship between lipid metabolites modulated by diet and CMD risk. METHODS Plasma fatty acid (FA) concentrations among 16 lipid classes (within-class FAs) were measured in a subgroup from the Dietary Intervention and VAScular function (DIVAS) parallel randomized controlled trial (n = 113/195), which consisted of three 16-wk diets enriched in SFAs (target SFA:MUFA:n-6PUFA ratio = 17:11:4% total energy [TE]), MUFAs (9:19:4% TE), or a MUFA/PUFA mixture (9:13:10% TE). Similar lipidomics analyses were conducted in the European investigation into Cancer and Nutrition (EPIC)-Potsdam prospective cohort study (specific case/cohorts: n = 775/1886 for type 2 diabetes [T2D], n = 551/1671 for cardiovascular disease [CVD]). Multiple linear regression and multivariable Cox models identified within-class FAs sensitive to replacement of dietary SFA with UFA in DIVAS and their association with CMD risk in EPIC-Potsdam. Elastic-net regression models identified within-class FAs associated with changes in CMD risk markers post-DIVAS interventions. RESULTS DIVAS high-UFA interventions reduced plasma within-class FAs associated with a higher CVD risk in EPIC-Potsdam, especially SFA-containing glycerolipids and sphingolipids (e.g., diacylglycerol (20:0) z-score = -1.08; SE = 0.17; P value < 10-8), whereas they increased those inversely associated with CVD risk. The results on T2D were less clear. Specific sphingolipids and phospholipids were associated with changes in markers of endothelial function and ambulatory blood pressure, whereas higher low-density lipoprotein cholesterol concentrations were characterized by higher plasma glycerolipids containing lauric and stearic acids. CONCLUSIONS These results suggest a mediating role of plasma lipid metabolites in the association between dietary fat and CMD risk. Future research combining interventional and observational findings will further our understanding of the role of dietary fat in CMD etiology. This trial was registered in ClinicalTrials.gov as NCT01478958.
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Affiliation(s)
- Laury Sellem
- Hugh Sinclair Unit of Human Nutrition, and Institute for Cardiovascular and Metabolic Research, Department of Food and Nutritional Science, University of Reading, Whiteknights, Pepper Lane, Harry Nursten Building, Reading, UK
| | - Fabian Eichelmann
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Kim G Jackson
- Hugh Sinclair Unit of Human Nutrition, and Institute for Cardiovascular and Metabolic Research, Department of Food and Nutritional Science, University of Reading, Whiteknights, Pepper Lane, Harry Nursten Building, Reading, UK
| | - Clemens Wittenbecher
- Division of Food Science and Nutrition, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany; German Center for Diabetes Research (DZD), München-Neuherberg, Germany; Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Julie A Lovegrove
- Hugh Sinclair Unit of Human Nutrition, and Institute for Cardiovascular and Metabolic Research, Department of Food and Nutritional Science, University of Reading, Whiteknights, Pepper Lane, Harry Nursten Building, Reading, UK.
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Lin K, Cheng W, Shen Q, Wang H, Wang R, Guo S, Wu X, Wu W, Chen P, Wang Y, Ye H, Zhang Q, Wang R. Lipid Profiling Reveals Lipidomic Signatures of Weight Loss Interventions. Nutrients 2023; 15:nu15071784. [PMID: 37049623 PMCID: PMC10097218 DOI: 10.3390/nu15071784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/01/2023] [Accepted: 04/04/2023] [Indexed: 04/09/2023] Open
Abstract
Obesity is an epidemic all around the world. Weight loss interventions that are effective differ from each other with regard to various lipidomic responses. Here, we aimed to find lipidomic biomarkers that are related to beneficial changes in weight loss. We adopted an untargeted liquid chromatography with tandem mass spectrometry (LC-MS/MS) method to measure 953 lipid species for Exercise (exercise intervention cohort, N = 25), 1388 lipid species for LSG (laparoscopic sleeve gastrectomy cohort, N = 36), and 886 lipid species for Cushing (surgical removal of the ACTH-secreting pituitary adenomas cohort, N = 25). Overall, the total diacylglycerol (DG), triacylglycerol (TG), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine (PS), and sphingomyelin (SM) levels were associated with changes in BMI, glycated hemoglobin (HbA1c), triglyceride, and total cholesterol according to weight loss interventions. We found that 73 lipid species changed among the three weight loss interventions. We screened 13 lipid species with better predictive accuracy in diagnosing weight loss situations in either Exercise, LSG, or Cushing cohorts (AUROC > 0.7). More importantly, we identified three phosphatidylcholine (PC) lipid species, PC (14:0_18:3), PC (31:1), and PC (32:2) that were significantly associated with weight change in three studies. Our results highlight potential lipidomic biomarkers that, in the future, could be used in personalized approaches involving weight loss interventions.
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Affiliation(s)
- Kaiqing Lin
- School of Exercise and Health, Shanghai Frontiers Science Research Base of Exercise and Metabolic Health, Shanghai University of Sport, Shanghai 200438, China
| | - Wei Cheng
- Department of Endocrinology, Yangpu Hospital, School of Medicine, Tongji University, Shanghai 200090, China
| | - Qiwei Shen
- Department of General Surgery, Huashan Hospital, Fudan University, Shanghai 200433, China
| | - Hui Wang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism & Integrative Biology, Fudan University, Shanghai 200433, China
| | - Ruwen Wang
- School of Exercise and Health, Shanghai Frontiers Science Research Base of Exercise and Metabolic Health, Shanghai University of Sport, Shanghai 200438, China
| | - Shanshan Guo
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Xianmin Wu
- School of Exercise and Health, Shanghai Frontiers Science Research Base of Exercise and Metabolic Health, Shanghai University of Sport, Shanghai 200438, China
| | - Wei Wu
- Department of Endocrinology and Metabolism, Huashan Hospital, Fudan University, Shanghai 200433, China
| | - Peijie Chen
- School of Exercise and Health, Shanghai Frontiers Science Research Base of Exercise and Metabolic Health, Shanghai University of Sport, Shanghai 200438, China
| | - Yongfei Wang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200433, China
| | - Hongying Ye
- Department of Endocrinology and Metabolism, Huashan Hospital, Fudan University, Shanghai 200433, China
| | - Qiongyue Zhang
- Department of Endocrinology and Metabolism, Huashan Hospital, Fudan University, Shanghai 200433, China
| | - Ru Wang
- School of Exercise and Health, Shanghai Frontiers Science Research Base of Exercise and Metabolic Health, Shanghai University of Sport, Shanghai 200438, China
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Abstract
Metabolites produced by commensal gut microbes impact host health through their recognition by the immune system and their influence on numerous metabolic pathways. Notably, the gut microbiota can both transform and synthesize lipids as well as break down dietary lipids to generate secondary metabolites with host modulatory properties. Although lipids have largely been consigned to structural roles, particularly in cell membranes, recent research has led to an increased appreciation of their signaling activities, with potential impacts on host health and physiology. This review focuses on studies that highlight the functions of bioactive lipids in mammalian physiology, with a special emphasis on immunity and metabolism.
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Affiliation(s)
- Eric M Brown
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
| | - Jon Clardy
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Blavatnik Institute, Boston, MA 02115, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
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35
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Zhou H, Ren C, Yang Z. Letter by Zhou et al Regarding Article, "Deep Lipidomics in Human Plasma: Cardiometabolic Disease Risk and Effect of Dietary Fat Modulation". Circulation 2023; 147:e70-e71. [PMID: 36716256 DOI: 10.1161/circulationaha.122.062095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Hong Zhou
- Department of Laboratory Medicine, Clinical Medical College of Yangzhou University, China (H.Z., C.R.)
| | - Chuanli Ren
- Department of Laboratory Medicine, Clinical Medical College of Yangzhou University, China (H.Z., C.R.)
| | - Zhanjun Yang
- Department of Chemistry, Yangzhou University, China (Z.Y.)
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