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Vaisar T, Heinecke J. Quantification of high-density lipoprotein particle number by proton nuclear magnetic resonance: don't believe the numbers. Curr Opin Lipidol 2024; 35:228-233. [PMID: 39162237 DOI: 10.1097/mol.0000000000000948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/21/2024]
Abstract
PURPOSE OF REVIEW Proton nuclear magnetic resonance (NMR) can rapidly assess lipoprotein concentrations and sizes in biological samples. It may be especially useful for quantifying high-density lipoprotein (HDL), which exhibits diverse particle sizes and concentrations. We provide a critical review of the strengths and limitations of NMR for quantifying HDL subclasses. RECENT FINDINGS Recent studies using NMR have shed light on HDL's role in various disorders, ranging from residual cardiovascular risk to host susceptibility to infection. However, accurately quantifying HDL particle number, size, and concentration (HDL-P) remains a challenge. Discrepancies exist between NMR and other methods such as gel electrophoresis, ion mobility analysis and size-exclusion chromatography in estimating the abundance of HDL species and the ratio of apolipoprotein A-I (APOA1) to HDL particles. SUMMARY NMR is a low-cost method for quantifying HDL-P that is readily applicable to clinical and translational studies. However, inconsistencies between the results of NMR quantification of HDL-P and other independent methods hinder the interpretation of NMR results. Because proton NMR apparently fails to accurately quantify the sizes and concentrations of HDL, the relevance of such studies to HDL biology poses challenges. This limits our understanding of pathophysiological implications of HDL-P as determined by NMR, particularly in determining cardiovascular disease (CVD) risk.
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Affiliation(s)
- Tomas Vaisar
- Department of Medicine, University of Washington, Seattle, Washington, USA
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Consonni D, Fustinoni S. Biochemical and haematological effects of serum PFOA, ADV and cC 6O 4 in workers of a chemical company producing fluoropolymers, Italy, 2013-2022. Int J Hyg Environ Health 2024; 262:114440. [PMID: 39106565 DOI: 10.1016/j.ijheh.2024.114440] [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: 02/22/2024] [Revised: 07/11/2024] [Accepted: 08/01/2024] [Indexed: 08/09/2024]
Abstract
INTRODUCTION Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used in the manufacture of fluoropolymers. We evaluated biochemical and haematological effects of three PFAS, serum perfluorooctanoic acid (PFOA), ADV, and cC6O4 in workers of a fluoropolymer company. METHODS Using data (2013-2022), we fitted random intercept regression models adjusted for several covariates and reciprocal adjustment between the three PFAS. RESULTS We analysed data of 814 workers (698 men, 116 women), 607 from the chemical plant, 207 from the research centre, for a total of 4912 blood samples (2065 with all three PFAS measured). Median levels of PFOA and ADV were 21.3 and 120 μg/L. Most (65.5%) cC6O4 measurements were below the limits of quantification (which varied over time from 5 to 0.1 μg/L). For PFOA, we observed positive associations with total cholesterol (+1.1% increase per ln(PFOA) increase) and apolipoprotein B (+1.4%) and negative associations with alkaline phosphatase (-1.5%); suggestive associations were also found with RBC (-0.4%), IgA (-1.5%), IgM (-1.4%). ADV was positively associated with total and LDL cholesterol (+1.0% and +1.6% per ln(ADV) increase), apolipoprotein B (+1.0%), GGT (+2.1%), IgM (+1.4%), and WBC (+1.5%) and negatively associated with direct bilirubin (-2.3%) and alpha-2-globulins (-0.7%); suggestive associations were found for indirect bilirubin (-2.0%), oestradiol (-2.1%), ad CRP (+6.0%). For samples with detectable cC6O4 levels we observed higher values of ALP (+2.3%), proteins (+0.5%), IgG (+0.7%) and platelets (+1.6%) and suggestively increased total bilirubin (+3.9%), RBC (+0.6%), and oestradiol (+5.8%). Some associations (total cholesterol, apolipoprotein B, WBC, total bilirubin, and alkaline phosphatase showed reverse time trends in parallel with the strong decrease of serum PFOA and ADV over the study period. DISCUSSION We found associations of serum PFOA and ADV with lipid metabolism, liver function, and immunoglobulins. The reverse time trends of some endpoints in parallel with decrease of serum PFOA and ADV reinforce causal interpretation of results. cC6O4 showed a different pattern of associations.
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Affiliation(s)
- Dario Consonni
- Epidemiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Silvia Fustinoni
- Toxicology Lab, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
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Gijbels A, Jardon KM, Trouwborst I, Manusama KC, Goossens GH, Blaak EE, Feskens EJ, Afman LA. Fasting and postprandial plasma metabolite responses to a 12-wk dietary intervention in tissue-specific insulin resistance: a secondary analysis of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) randomized trial. Am J Clin Nutr 2024; 120:347-359. [PMID: 38851634 DOI: 10.1016/j.ajcnut.2024.05.027] [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: 02/22/2024] [Revised: 05/06/2024] [Accepted: 05/28/2024] [Indexed: 06/10/2024] Open
Abstract
BACKGROUND We previously showed that dietary intervention effects on cardiometabolic health were driven by tissue-specific insulin resistance (IR) phenotype: individuals with predominant muscle IR (MIR) benefited more from a low-fat, high-protein, and high-fiber (LFHP) diet, whereas individuals with predominant liver insulin resistance (LIR) benefited more from a high-monounsaturated fatty acid (HMUFA) diet. OBJECTIVES To further characterize the effects of LFHP and HMUFA diets and their interaction with tissue-specific IR, we investigated dietary intervention effects on fasting and postprandial plasma metabolite profile. METHODS Adults with MIR or LIR (40-75 y, BMI 25-40 kg/m2) were randomly assigned to a 12-wk HMUFA or LFHP diet (n = 242). After the exclusion of statin use, 214 participants were included in this prespecified secondary analysis. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, and 240 min) a high-fat mixed meal for quantification of 247 metabolite measures using nuclear magnetic resonance spectroscopy. RESULTS A larger reduction in fasting VLDL-triacylglycerol (TAG) and VLDL particle size was observed in individuals with MIR following the LFHP diet and those with LIR following the HMUFA diet, although no longer statistically significant after false discovery rate (FDR) adjustment. No IR phenotype-by-diet interactions were found for postprandial plasma metabolites assessed as total area under the curve (tAUC). Irrespective of IR phenotype, the LFHP diet induced greater reductions in postprandial plasma tAUC of the larger VLDL particles and small HDL particles, and TAG content in most VLDL subclasses and the smaller LDL and HDL subclasses (for example, VLDL-TAG tAUC standardized mean change [95% CI] LFHP = -0.29 [-0.43, -0.16] compared with HMUFA = -0.04 [-0.16, 0.09]; FDR-adjusted P for diet × time = 0.041). CONCLUSIONS Diet effects on plasma metabolite profiles were more pronounced than phenotype-by-diet interactions. An LFHP diet may be more effective than an HMUFA diet for reducing cardiometabolic risk in individuals with tissue-specific IR, irrespective of IR phenotype. Am J Clin Nutr 20xx;x:xx. This trial was registered at the clinicaltrials.gov registration (https://clinicaltrials.gov/study/NCT03708419?term=NCT03708419&rank=1) as NCT03708419 and CCMO registration (https://www.toetsingonline.nl/to/ccmo_search.nsf/fABRpop?readform&unids=3969AABCD9BA27FEC12587F1001BCC65) as NL63768.068.17.
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Affiliation(s)
- Anouk Gijbels
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands; Top Institute Food and Nutrition (TiFN), Wageningen, The Netherlands.
| | - Kelly M Jardon
- Top Institute Food and Nutrition (TiFN), Wageningen, The Netherlands; Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Inez Trouwborst
- Top Institute Food and Nutrition (TiFN), Wageningen, The Netherlands; Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Koen Cm Manusama
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Gijs H Goossens
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Ellen E Blaak
- Top Institute Food and Nutrition (TiFN), Wageningen, The Netherlands; Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Edith Jm Feskens
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Lydia A Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
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Costacou T, Vaisar T, Miller RG, Davidson WS, Heinecke JW, Orchard TJ, Bornfeldt KE. High-Density Lipoprotein Particle Concentration and Size Predict Incident Coronary Artery Disease Events in a Cohort With Type 1 Diabetes. J Am Heart Assoc 2024; 13:e034763. [PMID: 38958152 PMCID: PMC11292758 DOI: 10.1161/jaha.123.034763] [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: 02/01/2024] [Accepted: 05/20/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND The cholesterol efflux capacity of high density lipoprotein (HDL) is negatively associated with cardiovascular risk. Small HDL particles account almost quantitatively for cholesterol efflux capacity, perhaps mediated through efflux of cholesterol and outer leaflet plasma membrane phospholipids by ABCA1 (ATP binding cassette subfamily A member 1). People with type 1 diabetes are at increased coronary artery disease (CAD) risk despite normal HDL-cholesterol concentrations. We therefore tested the hypothesis that small HDL particles (HDL-P)-rather than HDL-cholesterol-predict incident CAD in type 1 diabetes. METHODS AND RESULTS Incident CAD (CAD death, myocardial infarction, or coronary revascularization) was determined in 550 individuals with childhood-onset type 1 diabetes. HDL-P was quantified by calibrated ion mobility analysis and cholesterol efflux capacity was quantified with validated assays. During a median follow-up of 26 years, 36.5% of the participants developed incident CAD, for an incidence density of 181.3 per 10 000 person-years. In multivariable Cox models, neither HDL-cholesterol nor apolipoprotein A1 concentration was significantly associated with CAD risk. In contrast, higher extra-small HDL-P concentrations were significantly associated with decreased CAD risk (hazard ratio [HR], 0.26 [95% CI, 0.14-0.50]). Weaker associations were observed for total HDL-P (HR, 0.88 [95% CI, 0.83-0.93]), small HDL (HR, 0.83 [95% CI, 0.68-1.02]), medium HDL (HR, 0.79 [95% CI, 0.71-0.89]), and large HDL (HR, 0.72 [95% CI, 0.59-0.89]). Although cholesterol efflux capacity was negatively associated with incident CAD, this association was no longer significant after adjustment for total HDL-P. CONCLUSIONS Lower concentrations of total HDL-P and HDL subpopulations were positively associated with incident CAD independently of HDL-cholesterol, apolipoprotein A1, and other common CVD risk factors. Extra-small HDL was a much stronger predictor of risk than the other HDLs. Our data are consistent with the proposal that extra-small HDL plays a critical role in cardioprotection in type 1 diabetes, mediated by macrophage cholesterol efflux by the ABCA1 pathway.
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Affiliation(s)
- Tina Costacou
- Department of EpidemiologyUniversity of PittsburghPittsburghPA
| | - Tomas Vaisar
- Department of MedicineUniversity of WashingtonSeattleWA
| | | | - W. Sean Davidson
- Department of Pathology and Laboratory MedicineUniversity of Cincinnati College of MedicineCincinnatiOH
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Deng K, Pan X, Voehler MW, Cai Q, Cai H, Shu X, Gupta DK, Lipworth L, Zheng W, Yu D. Blood Lipids, Lipoproteins, and Apolipoproteins With Risk of Coronary Heart Disease: A Prospective Study Among Racially Diverse Populations. J Am Heart Assoc 2024; 13:e034364. [PMID: 38726919 PMCID: PMC11179824 DOI: 10.1161/jaha.124.034364] [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: 01/08/2024] [Accepted: 04/16/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND Comprehensive blood lipoprotein profiles and their association with incident coronary heart disease (CHD) among racially and geographically diverse populations remain understudied. METHODS AND RESULTS We conducted nested case-control studies of CHD among 3438 individuals (1719 pairs), including 1084 White Americans (542 pairs), 1244 Black Americans (622 pairs), and 1110 Chinese adults (555 pairs). We examined 36 plasma lipids, lipoproteins, and apolipoproteins, measured by nuclear magnetic resonance spectroscopy, with incident CHD among all participants and subgroups by demographics, lifestyle, and metabolic health status using conditional or unconditional logistic regression adjusted for potential confounders. Conventionally measured blood lipids, that is, total cholesterol, triglycerides, low-density lipoprotein-cholesterol, and high-density lipoprotein-cholesterol, were each associated with incident CHD, with odds ratios (ORs) being 1.33, 1.32, 1.24, and 0.79 per 1-SD increase among all participants. Seventeen lipoprotein biomarkers showed numerically stronger associations than conventional lipids, with ORs per 1-SD among all participants ranging from 1.35 to 1.57 and a negative OR of 0.78 (all false discovery rate <0.05), including apolipoprotein B100 to apolipoprotein A1 ratio (OR, 1.57 [95% CI, 1.45-1.7]), low-density lipoprotein-triglycerides (OR, 1.55 [95% CI, 1.43-1.69]), and apolipoprotein B (OR, 1.49 [95% CI, 1.37-1.62]). All these associations were significant and consistent across racial groups and other subgroups defined by age, sex, smoking, obesity, and metabolic health status, including individuals with normal levels of conventionally measured lipids. CONCLUSIONS Our study highlighted several lipoprotein biomarkers, including apolipoprotein B/ apolipoprotein A1 ratio, apolipoprotein B, and low-density lipoprotein-triglycerides, strongly and consistently associated with incident CHD. Our results suggest that comprehensive lipoprotein measures may complement the standard lipid panel to inform CHD risk among diverse populations.
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Affiliation(s)
- Kui Deng
- Vanderbilt Epidemiology Center and Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTNUSA
| | - Xiong‐Fei Pan
- Vanderbilt Epidemiology Center and Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTNUSA
- Section of Epidemiology and Population Health & Department of Gynecology and Obstetrics, Ministry of Education Key Laboratory of Birth Defects and Related Diseases of Women and Children & National Medical Products Administration Key Laboratory for Technical Research on Drug Products In Vitro and In Vivo Correlation, West China Second University HospitalSichuan UniversityChengduSichuanChina
| | - Markus W. Voehler
- Department of Chemistry and Center for Structural BiologyVanderbilt UniversityNashvilleTNUSA
| | - Qiuyin Cai
- Vanderbilt Epidemiology Center and Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTNUSA
| | - Hui Cai
- Vanderbilt Epidemiology Center and Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTNUSA
| | - Xiao‐Ou Shu
- Vanderbilt Epidemiology Center and Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTNUSA
| | - Deepak K. Gupta
- Vanderbilt Translational and Clinical Cardiovascular Research Center and Division of Cardiovascular Medicine, Department of MedicineVanderbilt University Medical CenterNashvilleTNUSA
| | - Loren Lipworth
- Vanderbilt Epidemiology Center and Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTNUSA
| | - Wei Zheng
- Vanderbilt Epidemiology Center and Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTNUSA
| | - Danxia Yu
- Vanderbilt Epidemiology Center and Division of EpidemiologyDepartment of MedicineVanderbilt University Medical CenterNashvilleTNUSA
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Melnes T, Bogsrud MP, Christensen JJ, Rundblad A, Narverud I, Retterstøl K, Aukrust P, Halvorsen B, Ulven SM, Holven KB. Gene expression profiling in elderly patients with familial hypercholesterolemia with and without coronary heart disease. Atherosclerosis 2024; 392:117507. [PMID: 38663317 DOI: 10.1016/j.atherosclerosis.2024.117507] [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: 10/26/2023] [Revised: 02/29/2024] [Accepted: 03/05/2024] [Indexed: 05/14/2024]
Abstract
BACKGROUND AND AIMS Elderly familial hypercholesterolemia (FH) patients are at high risk of coronary heart disease (CHD) due to high cholesterol burden and late onset of effective cholesterol-lowering therapies. A subset of these individuals remains free from any CHD event, indicating the potential presence of protective factors. Identifying possible cardioprotective gene expression profiles could contribute to our understanding of CHD prevention and future preventive treatment. Therefore, this study aimed to investigate gene expression profiles in elderly event-free FH patients. METHODS Expression of 773 genes was analysed using the Nanostring Metabolic Pathways Panel, in peripheral blood mononuclear cells (PBMCs) from FH patients ≥65 years without CHD (FH event-free, n = 44) and with CHD (FH CHD, n = 39), and from healthy controls ≥70 years (n = 39). RESULTS None of the genes were differentially expressed between FH patients with and without CHD after adjusting for multiple testing. However, at nominal p < 0.05, we found 36 (5%) differentially expressed genes (DEGs) between the two FH groups, mainly related to lipid metabolism (e.g. higher expression of ABCA1 and ABCG1 in FH event-free) and immune responses (e.g. lower expression of STAT1 and STAT3 in FH event-free). When comparing FH patients to controls, the event-free group had fewer DEGs than the CHD group; 147 (19%) and 219 (28%) DEGs, respectively. CONCLUSIONS Elderly event-free FH patients displayed a different PBMC gene expression profile compared to FH patients with CHD. Differences in gene expression compared to healthy controls were more pronounced in the CHD group, indicating a less atherogenic gene expression profile in event-free individuals. Overall, identification of cardioprotective factors could lead to future therapeutic targets.
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Affiliation(s)
- Torunn Melnes
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Martin P Bogsrud
- Unit for Cardiac and Cardiovascular Genetics, Department of Medical Genetics, Oslo University Hospital Ullevål, Norway; Norwegian National Advisory Unit on Familial Hypercholesterolemia, Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital Aker, Norway
| | - Jacob J Christensen
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Amanda Rundblad
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Ingunn Narverud
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Norway; Norwegian National Advisory Unit on Familial Hypercholesterolemia, Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital Aker, Norway
| | - Kjetil Retterstøl
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Norway; The Lipid Clinic, Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital Aker, Norway
| | - Pål Aukrust
- Research Institute for Internal Medicine, Oslo University Hospital, Norway; Institute of Clinical Medicine, University of Oslo, Norway
| | - Bente Halvorsen
- Research Institute for Internal Medicine, Oslo University Hospital, Norway; Institute of Clinical Medicine, University of Oslo, Norway
| | - Stine M Ulven
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Kirsten B Holven
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Norway; Norwegian National Advisory Unit on Familial Hypercholesterolemia, Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital Aker, Norway.
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Li T, Ihanus A, Ohukainen P, Järvelin MR, Kähönen M, Kettunen J, Raitakari OT, Lehtimäki T, Mäkinen VP, Tynkkynen T, Ala-Korpela M. Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers. Int J Epidemiol 2024; 53:dyad162. [PMID: 38030573 PMCID: PMC10859141 DOI: 10.1093/ije/dyad162] [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: 09/04/2022] [Accepted: 11/17/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges. METHODS We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking. RESULTS Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate. CONCLUSIONS Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.
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Affiliation(s)
- Tianqi Li
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Andrei Ihanus
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Pauli Ohukainen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Marjo-Riitta Järvelin
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland
| | - Johannes Kettunen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere, Finland
| | - Ville-Petteri Mäkinen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Tuulia Tynkkynen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
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Zhao S, Hörkkö S, Savolainen MJ, Koivukangas V, Mäkinen VP, Ala-Korpela M, Hukkanen J. Short-Term Metabolic Changes and Their Physiological Mediators in the Roux-en-Y Gastric Bypass Bariatric Surgery. Obes Surg 2024; 34:625-634. [PMID: 38191968 PMCID: PMC10810963 DOI: 10.1007/s11695-023-07042-y] [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: 11/08/2023] [Revised: 12/18/2023] [Accepted: 12/28/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND The Roux-en-Y gastric bypass (RYGB) is a common bariatric surgery to treat obesity. Its metabolic consequences are favourable and long-term clinical corollaries beneficial. However, detailed assessments of various affected metabolic pathways and their mediating physiological factors are scarce. METHODS We performed a clinical study with 30 RYGB patients in preoperative and 6-month postoperative visits. NMR metabolomics was applied to profiling of systemic metabolism via 80 molecular traits, representing core cardiometabolic pathways. Glucose, glycated haemoglobin (HbA1c), insulin, and apolipoprotein B-48 were measured with standard assays. Logistic regression models of the surgery effect were used for each metabolic measure and assessed individually for multiple mediating physiological factors. RESULTS Changes in insulin concentrations reflected those of BMI with robust decreases due to the surgery. Six months after the surgery, triglycerides, remnant cholesterol, and apolipoprotein B-100 were decreased -24%, -18%, and -14%, respectively. Lactate and glycoprotein acetyls, a systemic inflammation biomarker, decreased -16% and -9%, respectively. The concentrations of branched-chain (BCAA; leucine, isoleucine, and valine) and aromatic (phenylalanine and tyrosine) amino acids decreased after the surgery between -17% for tyrosine and -23% for leucine. Except for the most prominent metabolic changes observed for the BCAAs, all changes were almost completely mediated by weight change and insulin. Glucose and type 2 diabetes had clearly weaker effects on the metabolic changes. CONCLUSIONS The comprehensive metabolic analyses indicate that weight loss and improved insulin sensitivity during the 6 months after the RYGB surgery are the key physiological outcomes mediating the short-term advantageous metabolic effects of RYGB. The clinical study was registered at ClinicalTrials.gov as NCT01330251.
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Affiliation(s)
- Siyu Zhao
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Sohvi Hörkkö
- Medical Microbiology and Immunology, Research Unit of Biomedicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Markku J Savolainen
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Research Unit of Internal Medicine, University of Oulu, Oulu, Finland
| | - Vesa Koivukangas
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Department of Surgery, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Ville-Petteri Mäkinen
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.
- Biocenter Oulu, University of Oulu, Oulu, Finland.
- NMR Metabolomics Laboratory, School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.
| | - Janne Hukkanen
- Biocenter Oulu, University of Oulu, Oulu, Finland.
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland.
- Research Unit of Internal Medicine, University of Oulu, Oulu, Finland.
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9
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Lehtovirta M, Pahkala K, Rovio SP, Magnussen CG, Laitinen TT, Niinikoski H, Lagström H, Viikari JSA, Rönnemaa T, Jula A, Ala-Korpela M, Raitakari OT. Association of tobacco smoke exposure with metabolic profile from childhood to early adulthood: the Special Turku Coronary Risk Factor Intervention Project. Eur J Prev Cardiol 2024; 31:103-115. [PMID: 37655930 DOI: 10.1093/eurjpc/zwad285] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/04/2023] [Accepted: 08/29/2023] [Indexed: 09/02/2023]
Abstract
AIMS To investigate the associations between passive tobacco smoke exposure and daily smoking with a comprehensive metabolic profile, measured repeatedly from childhood to adulthood. METHODS AND RESULTS Study cohort was derived from the Special Turku Coronary Risk Factor Intervention Project (STRIP). Smoking status was obtained by questionnaire, while serum cotinine concentrations were measured using gas chromatography. Metabolic measures were quantified by nuclear magnetic resonance metabolomics at 9 (n = 539), 11 (n = 536), 13 (n = 525), 15 (n = 488), 17 (n = 455), and 19 (n = 409) years. Association of passive tobacco smoke exposure with metabolic profile compared participants who reported less-than-weekly smoking and had serum cotinine concentration <1 ng/mL (no exposure) with those whose cotinine concentration was ≥10 ng/mL (passive tobacco smoke exposure). Associations of daily smoking with metabolic profile in adolescence were analysed by comparing participants reporting daily smoking with those reporting no tobacco use and having serum cotinine concentrations <1 ng/mL. Passive tobacco smoke exposure was directly associated with the serum ratio of monounsaturated fatty acids to total fatty acids [β = 0.34 standard deviation (SD), (0.17-0.51), P < 0.0001] and inversely associated with the serum ratios of polyunsaturated fatty acids. Exposure to passive tobacco smoke was directly associated with very-low-density lipoprotein particle size [β = 0.28 SD, (0.12-0.45), P = 0.001] and inversely associated with HDL particle size {β = -0.21 SD, [-0.34 to -0.07], P = 0.003}. Daily smokers exhibited a similar metabolic profile to those exposed to passive tobacco smoke. These results persisted after adjusting for body mass index, STRIP study group allocation, dietary target score, pubertal status, and parental socio-economic status. CONCLUSION Both passive and active tobacco smoke exposures during childhood and adolescence are detrimentally associated with circulating metabolic measures indicative of increased cardio-metabolic risk.
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Affiliation(s)
- Miia Lehtovirta
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Katja Pahkala
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Suvi P Rovio
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Costan G Magnussen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Tomi T Laitinen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Harri Niinikoski
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Department of Pediatrics and Adolescent Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Hanna Lagström
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Department of Public Health, Turku University Hospital, University of Turku, Turku, Finland
| | - Jorma S A Viikari
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Medicine, University of Turku, Turku, Finland
| | - Tapani Rönnemaa
- Division of Medicine, Turku University Hospital, Turku, Finland
- Department of Medicine, University of Turku, Turku, Finland
| | - Antti Jula
- Department of Chronic Disease Prevention, Institute for Health and Welfare, Turku, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Research Unit of Population Health, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Kiinamyllynkatu 10, Turku FI-20520, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
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10
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Costacou T, Vaisar T, Miller RG, Davidson WS, Heinecke JW, Orchard TJ, Bornfeldt KE. HDL Particle Concentration and Size Predict Incident Coronary Artery Disease Events in People with Type 1 Diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.06.23298165. [PMID: 37986833 PMCID: PMC10659494 DOI: 10.1101/2023.11.06.23298165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Background Cholesterol efflux capacity (CEC) negatively correlates with cardiovascular disease risk. Small HDL particles account almost quantitively for CEC, perhaps mediated through efflux of outer leaflet plasma membrane phospholipids by ABCA1. People with type 1 diabetes (T1D) are at increased risk of coronary artery disease (CAD) despite normal levels of HDL-cholesterol (HDL-C). We therefore tested the hypotheses that small HDL particles (HDL-P)-rather than HDL-C levels-predict incident CAD in T1D. Methods Incident CAD (CAD death, myocardial infarction, and/or coronary revascularization) was determined in a cohort of 550 participants with childhood-onset T1D. HDL-P was quantified by calibrated ion mobility analysis. CEC and phospholipid efflux were quantified with validated assays. Results During a median follow-up of 26 years, 36.5% of the participants developed incident CAD. In multivariable Cox models, levels of HDL-C and apolipoprotein A-I (APOA1) did not predict CAD risk. In contrast, extra-small HDL particle levels strongly and negatively predicted risk (hazard ratio [HR]=0.25, 95% confidence interval [CI]=0.13-0.49). An increased concentration of total HDL particles (T-HDL-P) (HR=0.87, CI=0.82-0.92) and three other HDL sizes were weaker predictors of risk: small HDL (HR=0.80, 0.65-0.98), medium HDL (HR=0.78, CI=0.70-0.87) and large HDL (HR=0.72, CI=0.59-0.89). Although CEC negatively associated with incident CAD, that association disappeared after the model was adjusted for T-HDL-P. Isolated small HDLs strongly promoted ABCA1-dependent efflux of membrane outer leaflet phospholipids. Conclusions Low concentrations of T-HDL-P and all four sizes of HDL subpopulations predicted incident CAD independently of HDL-C, APOA1, and other common CVD risk factors. Extra-small HDL was a much stronger predictor of risk than the other HDLs. Our data are consistent with the proposal that small HDLs play a critical role in cardioprotection in T1D, which might be mediated by macrophage plasma membrane outer leaflet phospholipid export and cholesterol efflux by the ABCA1 pathway.
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Affiliation(s)
- Tina Costacou
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15261
| | - Tomas Vaisar
- Department of Medicine, University of Washington, Seattle, WA 98109
| | - Rachel G. Miller
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15261
| | - W. Sean Davidson
- Department of Pathology and Laboratory Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, 45237
| | - Jay W. Heinecke
- Department of Medicine, University of Washington, Seattle, WA 98109
| | - Trevor J. Orchard
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15261
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11
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Ghini V, Meoni G, Vignoli A, Di Cesare F, Tenori L, Turano P, Luchinat C. Fingerprinting and profiling in metabolomics of biosamples. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2023; 138-139:105-135. [PMID: 38065666 DOI: 10.1016/j.pnmrs.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 12/18/2023]
Abstract
This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.
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Affiliation(s)
- Veronica Ghini
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Gaia Meoni
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy.
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy; Giotto Biotech S.r.l., Sesto Fiorentino, Italy.
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12
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Ala-Korpela M, Lehtimäki T, Kähönen M, Viikari J, Perola M, Salomaa V, Kettunen J, Raitakari OT, Mäkinen VP. Cross-sectionally Calculated Metabolic Aging Does Not Relate to Longitudinal Metabolic Changes-Support for Stratified Aging Models. J Clin Endocrinol Metab 2023; 108:2099-2104. [PMID: 36658689 PMCID: PMC10348460 DOI: 10.1210/clinem/dgad032] [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: 08/12/2022] [Revised: 01/11/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
CONTEXT Aging varies between individuals, with profound consequences for chronic diseases and longevity. One hypothesis to explain the diversity is a genetically regulated molecular clock that runs differently between individuals. Large human studies with long enough follow-up to test the hypothesis are rare due to practical challenges, but statistical models of aging are built as proxies for the molecular clock by comparing young and old individuals cross-sectionally. These models remain untested against longitudinal data. OBJECTIVE We applied novel methodology to test if cross-sectional modeling can distinguish slow vs accelerated aging in a human population. METHODS We trained a machine learning model to predict age from 153 clinical and cardiometabolic traits. The model was tested against longitudinal data from another cohort. The training data came from cross-sectional surveys of the Finnish population (n = 9708; ages 25-74 years). The validation data included 3 time points across 10 years in the Young Finns Study (YFS; n = 1009; ages 24-49 years). Predicted metabolic age in 2007 was compared against observed aging rate from the 2001 visit to the 2011 visit in the YFS dataset and correlation between predicted vs observed metabolic aging was determined. RESULTS The cross-sectional proxy failed to predict longitudinal observations (R2 = 0.018%, P = 0.67). CONCLUSION The finding is unexpected under the clock hypothesis that would produce a positive correlation between predicted and observed aging. Our results are better explained by a stratified model where aging rates per se are similar in adulthood but differences in starting points explain diverging metabolic fates.
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Affiliation(s)
- Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland
- Biocenter Oulu, University of Oulu, Oulu 90014, Finland
- Faculty of Health Sciences, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio 90014, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Faculty of Medicine and Health Technology, Fimlab Laboratories, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere 33100, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Faculty of Medicine and Health Technology, Tampere University Hospital, and Finnish Cardiovascular Research Center Tampere, Tampere University, Tampere 33100, Finland
| | - Jorma Viikari
- Department of Medicine, University of Turku, Turku 20520, Finland
- Division of Medicine, Turku University Hospital, Turku 20520, Finland
| | - Markus Perola
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki 00271, Finland
- Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki 00271, Finland
| | - Johannes Kettunen
- Systems Epidemiology, Faculty of Medicine, Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland
- Biocenter Oulu, University of Oulu, Oulu 90014, Finland
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki 00271, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku 20520, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku 20520, Finland
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku 20520, Finland
| | - Ville-Petteri Mäkinen
- Systems Epidemiology, Faculty of Medicine, Center for Life Course Health Research, University of Oulu, Oulu 90014, Finland
- Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA 5000, Australia
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13
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Shao B, Afshinnia F, Mathew AV, Ronsein GE, Thornock C, Irwin AD, Kansal M, Rao PS, Dobre M, Al-Kindi S, Weir MR, Go A, He J, Chen J, Feldman H, Bornfeldt KE, Pennathur S. Low concentrations of medium-sized HDL particles predict incident CVD in chronic kidney disease patients. J Lipid Res 2023; 64:100381. [PMID: 37100172 PMCID: PMC10323925 DOI: 10.1016/j.jlr.2023.100381] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 04/28/2023] Open
Abstract
Patients with chronic kidney disease (CKD) are at high risk for CVD. However, traditional CVD risk factors cannot completely explain the increased risk. Altered HDL proteome is linked with incident CVD in CKD patients, but it is unclear whether other HDL metrics are associated with incident CVD in this population. In the current study, we analyzed samples from two independent prospective case-control cohorts of CKD patients, the Clinical Phenotyping and Resource Biobank Core (CPROBE) and the Chronic Renal Insufficiency Cohort (CRIC). We measured HDL particle sizes and concentrations (HDL-P) by calibrated ion mobility analysis and HDL cholesterol efflux capacity (CEC) by cAMP-stimulated J774 macrophages in 92 subjects from the CPROBE cohort (46 CVD and 46 controls) and in 91 subjects from the CRIC cohort (34 CVD and 57 controls). We tested associations of HDL metrics with incident CVD using logistic regression analysis. No significant associations were found for HDL-C or HDL-CEC in either cohort. Total HDL-P was only negatively associated with incident CVD in the CRIC cohort in unadjusted analysis. Among the six sized HDL subspecies, only medium-sized HDL-P was significantly and negatively associated with incident CVD in both cohorts after adjusting for clinical confounders and lipid risk factors with odds ratios (per 1-SD) of 0.45 (0.22-0.93, P = 0.032) and 0.42 (0.20-0.87, P = 0.019) for CPROBE and CRIC cohorts, respectively. Our observations indicate that medium-sized HDL-P-but not other-sized HDL-P or total HDL-P, HDL-C, or HDL-CEC-may be a prognostic cardiovascular risk marker in CKD.
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Affiliation(s)
- Baohai Shao
- Department of Medicine, UW Medicine Diabetes Institute, University of Washington, Seattle, WA, USA.
| | - Farsad Afshinnia
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Anna V Mathew
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Graziella E Ronsein
- Department of Medicine, UW Medicine Diabetes Institute, University of Washington, Seattle, WA, USA
| | - Carissa Thornock
- Department of Medicine, UW Medicine Diabetes Institute, University of Washington, Seattle, WA, USA
| | - Angela D Irwin
- Department of Medicine, UW Medicine Diabetes Institute, University of Washington, Seattle, WA, USA
| | - Mayank Kansal
- Department of Cardiology, University of Illinois at Chicago, Chicago, IL, USA
| | - Panduranga S Rao
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Mirela Dobre
- Division of Nephrology and Hypertension, Case Western Reserve University, Cleveland, OH, USA
| | - Sadeer Al-Kindi
- Division of Nephrology and Hypertension, Case Western Reserve University, Cleveland, OH, USA
| | - Matthew R Weir
- Division of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alan Go
- Department of Health System Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Jing Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA; Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Harold Feldman
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Karin E Bornfeldt
- Department of Medicine, UW Medicine Diabetes Institute, University of Washington, Seattle, WA, USA
| | - Subramaniam Pennathur
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA; Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA.
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14
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Yap CX, Henders AK, Alvares GA, Giles C, Huynh K, Nguyen A, Wallace L, McLaren T, Yang Y, Hernandez LM, Gandal MJ, Hansell NK, Cleary D, Grove R, Hafekost C, Harun A, Holdsworth H, Jellett R, Khan F, Lawson LP, Leslie J, Levis Frenk M, Masi A, Mathew NE, Muniandy M, Nothard M, Miller JL, Nunn L, Strike LT, Cadby G, Moses EK, de Zubicaray GI, Thompson PM, McMahon KL, Wright MJ, Visscher PM, Dawson PA, Dissanayake C, Eapen V, Heussler HS, Whitehouse AJO, Meikle PJ, Wray NR, Gratten J. Interactions between the lipidome and genetic and environmental factors in autism. Nat Med 2023; 29:936-949. [PMID: 37076741 PMCID: PMC10115648 DOI: 10.1038/s41591-023-02271-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 02/22/2023] [Indexed: 04/21/2023]
Abstract
Autism omics research has historically been reductionist and diagnosis centric, with little attention paid to common co-occurring conditions (for example, sleep and feeding disorders) and the complex interplay between molecular profiles and neurodevelopment, genetics, environmental factors and health. Here we explored the plasma lipidome (783 lipid species) in 765 children (485 diagnosed with autism spectrum disorder (ASD)) within the Australian Autism Biobank. We identified lipids associated with ASD diagnosis (n = 8), sleep disturbances (n = 20) and cognitive function (n = 8) and found that long-chain polyunsaturated fatty acids may causally contribute to sleep disturbances mediated by the FADS gene cluster. We explored the interplay of environmental factors with neurodevelopment and the lipidome, finding that sleep disturbances and unhealthy diet have a convergent lipidome profile (with potential mediation by the microbiome) that is also independently associated with poorer adaptive function. In contrast, ASD lipidome differences were accounted for by dietary differences and sleep disturbances. We identified a large chr19p13.2 copy number variant genetic deletion spanning the LDLR gene and two high-confidence ASD genes (ELAVL3 and SMARCA4) in one child with an ASD diagnosis and widespread low-density lipoprotein-related lipidome derangements. Lipidomics captures the complexity of neurodevelopment, as well as the biological effects of conditions that commonly affect quality of life among autistic people.
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Affiliation(s)
- Chloe X Yap
- Mater Research Institute, The University of Queensland, Brisbane, Queensland, Australia.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia.
| | - Anjali K Henders
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
| | - Gail A Alvares
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anh Nguyen
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Leanne Wallace
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
| | - Tiana McLaren
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
| | - Yuanhao Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
| | - Leanna M Hernandez
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael J Gandal
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children's Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Narelle K Hansell
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Dominique Cleary
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Rachel Grove
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Faculty of Health, University of Technology Sydney, Sydney, New South Wales, Australia
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Claire Hafekost
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Alexis Harun
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Helen Holdsworth
- Mater Research Institute, The University of Queensland, Brisbane, Queensland, Australia
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Rachel Jellett
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Olga Tennison Autism Research Centre, La Trobe University, Melbourne, Victoria, Australia
| | - Feroza Khan
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Lauren P Lawson
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Department of Psychology, Counselling and Therapy, La Trobe University, Melbourne, Victoria, Australia
| | - Jodie Leslie
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Mira Levis Frenk
- Mater Research Institute, The University of Queensland, Brisbane, Queensland, Australia
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Anne Masi
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Nisha E Mathew
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Melanie Muniandy
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Olga Tennison Autism Research Centre, La Trobe University, Melbourne, Victoria, Australia
| | - Michaela Nothard
- Mater Research Institute, The University of Queensland, Brisbane, Queensland, Australia
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Olga Tennison Autism Research Centre, La Trobe University, Melbourne, Victoria, Australia
| | - Jessica L Miller
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Lorelle Nunn
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Lachlan T Strike
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Gemma Cadby
- School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
| | - Eric K Moses
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia
- School of Biomedical Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Greig I de Zubicaray
- School of Psychology and Counselling, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Katie L McMahon
- School of Clinical Sciences, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Queensland, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Paul A Dawson
- Mater Research Institute, The University of Queensland, Brisbane, Queensland, Australia
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
| | - Cheryl Dissanayake
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Olga Tennison Autism Research Centre, La Trobe University, Melbourne, Victoria, Australia
| | - Valsamma Eapen
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
- Academic Unit of Child Psychiatry South West Sydney, Ingham Institute for Applied Medical Research, Liverpool Hospital, Sydney, New South Wales, Australia
| | - Helen S Heussler
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
- Child Development Program, Children's Health Queensland, Brisbane, Queensland, Australia
| | - Andrew J O Whitehouse
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Telethon Kids Institute, The University of Western Australia, Perth, Western Australia, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Melbourne, Victoria, Australia
- Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Jacob Gratten
- Mater Research Institute, The University of Queensland, Brisbane, Queensland, Australia.
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
- Cooperative Research Centre for Living with Autism, Long Pocket, Queensland, Australia.
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15
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Lehtovirta M, Wu F, Rovio SP, Heinonen OJ, Laitinen TT, Niinikoski H, Lagström H, Viikari JSA, Rönnemaa T, Jula A, Ala-Korpela M, Raitakari OT, Pahkala K. Association of physical activity with metabolic profile from adolescence to adulthood. Scand J Med Sci Sports 2023; 33:307-318. [PMID: 36331352 DOI: 10.1111/sms.14261] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/27/2022] [Accepted: 11/03/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Physical activity benefits cardiometabolic health, but little is known about its detailed links with serum lipoproteins, amino acids, and glucose metabolism at young age. We therefore studied the association of physical activity with a comprehensive metabolic profile measured repeatedly in adolescence. METHODS The cohort is derived from the longitudinal Special Turku Coronary Risk Factor Intervention Project. At ages 13, 15, 17, and 19 years, data on physical activity were collected by a questionnaire, and circulating metabolic measures were quantified by nuclear magnetic resonance metabolomics from repeatedly assessed serum samples (age 13: n = 503, 15: n = 472, 17: n = 466, and 19: n = 361). RESULTS Leisure-time physical activity (LTPA;MET h/wk) was directly associated with concentrations of polyunsaturated fatty acids, and inversely with the ratio of monounsaturated fatty acids to total fatty acids (-0.006SD; [-0.008, -0.003]; p < 0.0001). LTPA was inversely associated with very-low-density lipoprotein (VLDL) particle concentration (-0.003SD; [-0.005, -0.001]; p = 0.002) and VLDL particle size (-0.005SD; [-0.007, -0.003]; p < 0.0001). LTPA showed direct association with the particle concentration and size of high-density lipoprotein (HDL), and HDL cholesterol concentration (0.004SD; [0.002, 0.006]; p < 0.0001). Inverse associations of LTPA with triglyceride and total lipid concentrations in large to small sized VLDL subclasses were found. Weaker associations were seen for other metabolic measures including inverse associations with concentrations of lactate, isoleucine, glycoprotein acetylation, and a direct association with creatinine concentration. The results remained after adjusting for body mass index and proportions of energy intakes from macronutrients. CONCLUSIONS Physical activity during adolescence is beneficially associated with the metabolic profile including novel markers. The results support recommendations on physical activity during adolescence to promote health and possibly reduce future disease risks.
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Affiliation(s)
- Miia Lehtovirta
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Feitong Wu
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Suvi P Rovio
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
| | - Olli J Heinonen
- Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Tomi T Laitinen
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
| | - Harri Niinikoski
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Department of Pediatrics and Adolescent Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Hanna Lagström
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Department of Public Health, University of Turku and Turku University Hospital, Turku, Finland
| | - Jorma S A Viikari
- Department of Medicine, University of Turku, Turku, Finland.,Division of Medicine, Turku University Hospital, Turku, Finland
| | - Tapani Rönnemaa
- Department of Medicine, University of Turku, Turku, Finland.,Division of Medicine, Turku University Hospital, Turku, Finland
| | - Antti Jula
- Department of Chronic Disease Prevention, Institute for Health and Welfare, Turku, Finland
| | - Mika Ala-Korpela
- Computational Medicine, Center for Life Course Health Research, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland.,NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Katja Pahkala
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland.,Paavo Nurmi Centre, Unit for Health and Physical Activity, University of Turku, Turku, Finland
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16
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Size matters: HDL particle populations and the risk of infection. Nat Rev Cardiol 2023; 20:279-280. [PMID: 36792718 DOI: 10.1038/s41569-023-00844-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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17
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Ritchie SC, Surendran P, Karthikeyan S, Lambert SA, Bolton T, Pennells L, Danesh J, Di Angelantonio E, Butterworth AS, Inouye M. Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants. Sci Data 2023; 10:64. [PMID: 36720882 PMCID: PMC9887579 DOI: 10.1038/s41597-023-01949-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 01/10/2023] [Indexed: 02/01/2023] Open
Abstract
Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy in approximately 121,000 UK Biobank participants has recently been released as a community resource, comprising absolute concentrations and ratios of 249 circulating metabolites, lipids, and lipoprotein sub-fractions. Here we identify and characterise additional sources of unwanted technical variation influencing individual biomarkers in the data available to download from UK Biobank. These included sample preparation time, shipping plate well, spectrometer batch effects, drift over time within spectrometer, and outlier shipping plates. We developed a procedure for removing this unwanted technical variation, and demonstrate that it increases signal for genetic and epidemiological studies of the NMR metabolic biomarker data in UK Biobank. We subsequently developed an R package, ukbnmr, which we make available to the wider research community to enhance the utility of the UK Biobank NMR metabolic biomarker data and to facilitate rapid analysis.
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Affiliation(s)
- Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Praveen Surendran
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Rutherford Fund Fellow, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Savita Karthikeyan
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Thomas Bolton
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Lisa Pennells
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
- The Alan Turing Institute, London, UK
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18
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Association of Advanced Lipoprotein Subpopulation Profiles with Insulin Resistance and Inflammation in Patients with Type 2 Diabetes Mellitus. J Clin Med 2023; 12:jcm12020487. [PMID: 36675414 PMCID: PMC9864672 DOI: 10.3390/jcm12020487] [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: 10/20/2022] [Revised: 11/10/2022] [Accepted: 11/20/2022] [Indexed: 01/11/2023] Open
Abstract
Plasma lipoproteins exist as several subpopulations with distinct particle number and size that are not fully reflected in the conventional lipid panel. In this study, we sought to quantify lipoprotein subpopulations in patients with type 2 diabetes mellitus (T2DM) to determine whether specific lipoprotein subpopulations are associated with insulin resistance and inflammation markers. The study included 57 patients with T2DM (age, 61.14 ± 9.99 years; HbA1c, 8.66 ± 1.60%; mean body mass index, 35.15 ± 6.65 kg/m2). Plasma lipoprotein particles number and size were determined by nuclear magnetic resonance spectroscopy. Associations of different lipoprotein subpopulations with lipoprotein insulin resistance (LPIR) score and glycoprotein acetylation (GlycA) were assessed using multi-regression analysis. In stepwise regression analysis, VLDL and HDL large particle number and size showed the strongest associations with LPIR (R2 = 0.960; p = 0.0001), whereas the concentrations of the small VLDL and HDL particles were associated with GlycA (R2 = 0.190; p = 0.008 and p = 0.049, respectively). In adjusted multi-regression analysis, small and large VLDL particles and all sizes of lipoproteins independently predicted LPIR, whereas only the number of small LDL particles predicted GlycA. Conventional markers HbA1c and Hs-CRP did not exhibit any significant association with lipoprotein subpopulations. Our data suggest that monitoring insulin resistance-induced changes in lipoprotein subpopulations in T2DM might help to identify novel biomarkers that can be useful for effective clinical intervention.
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19
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Jin Q, Lau ESH, Luk AO, Tam CHT, Ozaki R, Lim CKP, Wu H, Chow EYK, Kong APS, Lee HM, Fan B, Ng ACW, Jiang G, Lee KF, Siu SC, Hui G, Tsang CC, Lau KP, Leung JY, Tsang MW, Cheung EYN, Kam G, Lau IT, Li JK, Yeung VT, Lau E, Lo S, Fung S, Cheng YL, Chow CC, Yu W, Tsui SKW, Huang Y, Lan HY, Szeto CC, So WY, Jenkins AJ, Chan JCN, Ma RCW. High-density lipoprotein subclasses and cardiovascular disease and mortality in type 2 diabetes: analysis from the Hong Kong Diabetes Biobank. Cardiovasc Diabetol 2022; 21:293. [PMID: 36587202 PMCID: PMC9805680 DOI: 10.1186/s12933-022-01726-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/13/2022] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE High-density lipoproteins (HDL) comprise particles of different size, density and composition and their vasoprotective functions may differ. Diabetes modifies the composition and function of HDL. We assessed associations of HDL size-based subclasses with incident cardiovascular disease (CVD) and mortality and their prognostic utility. RESEARCH DESIGN AND METHODS HDL subclasses by nuclear magnetic resonance spectroscopy were determined in sera from 1991 fasted adults with type 2 diabetes (T2D) consecutively recruited from March 2014 to February 2015 in Hong Kong. HDL was divided into small, medium, large and very large subclasses. Associations (per SD increment) with outcomes were evaluated using multivariate Cox proportional hazards models. C-statistic, integrated discrimination index (IDI), and categorial and continuous net reclassification improvement (NRI) were used to assess predictive value. RESULTS Over median (IQR) 5.2 (5.0-5.4) years, 125 participants developed incident CVD and 90 participants died. Small HDL particles (HDL-P) were inversely associated with incident CVD [hazard ratio (HR) 0.65 (95% CI 0.52, 0.81)] and all-cause mortality [0.47 (0.38, 0.59)] (false discovery rate < 0.05). Very large HDL-P were positively associated with all-cause mortality [1.75 (1.19, 2.58)]. Small HDL-P improved prediction of mortality [C-statistic 0.034 (0.013, 0.055), IDI 0.052 (0.014, 0.103), categorical NRI 0.156 (0.006, 0.252), and continuous NRI 0.571 (0.246, 0.851)] and CVD [IDI 0.017 (0.003, 0.038) and continuous NRI 0.282 (0.088, 0.486)] over the RECODe model. CONCLUSION Small HDL-P were inversely associated with incident CVD and all-cause mortality and improved risk stratification for adverse outcomes in people with T2D. HDL-P may be used as markers for residual risk in people with T2D.
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Affiliation(s)
- Qiao Jin
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Eric S. H. Lau
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Andrea O. Luk
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Claudia H. T. Tam
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,CUHK-SJTU Joint Research Centre on Diabetes Genomics and Precision Medicine, Shatin, Hong Kong Special Administrative Region China
| | - Risa Ozaki
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Cadmon K. P. Lim
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,CUHK-SJTU Joint Research Centre on Diabetes Genomics and Precision Medicine, Shatin, Hong Kong Special Administrative Region China
| | - Hongjiang Wu
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Elaine Y. K. Chow
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Alice P. S. Kong
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Heung Man Lee
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Baoqi Fan
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,CUHK-SJTU Joint Research Centre on Diabetes Genomics and Precision Medicine, Shatin, Hong Kong Special Administrative Region China
| | - Alex C. W. Ng
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Guozhi Jiang
- grid.12981.330000 0001 2360 039XSchool of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong China
| | - Ka Fai Lee
- grid.415591.d0000 0004 1771 2899Department of Medicine and Geriatrics, Kwong Wah Hospital, Yau Ma Tei, Hong Kong Special Administrative Region China
| | - Shing Chung Siu
- grid.417347.20000 0004 1799 526XDiabetes Centre, Tung Wah Eastern Hospital, Sheung Wan, Hong Kong Special Administrative Region China
| | - Grace Hui
- grid.417347.20000 0004 1799 526XDiabetes Centre, Tung Wah Eastern Hospital, Sheung Wan, Hong Kong Special Administrative Region China
| | - Chiu Chi Tsang
- grid.413608.80000 0004 1772 5868Diabetes and Education Centre, Alice Ho Miu Ling Nethersole Hospital, Tai Po, Hong Kong Special Administrative Region China
| | - Kam Piu Lau
- grid.490321.d0000000417722990North District Hospital, Sheung Shui, Hong Kong Special Administrative Region China
| | - Jenny Y. Leung
- grid.416291.90000 0004 1775 0609Department of Medicine and Geriatrics, Ruttonjee Hospital, Wan Chai, Hong Kong Special Administrative Region China
| | - Man-wo Tsang
- grid.417037.60000 0004 1771 3082Department of Medicine and Geriatrics, United Christian Hospital, Kwun Tong, Hong Kong Special Administrative Region China
| | - Elaine Y. N. Cheung
- grid.417037.60000 0004 1771 3082Department of Medicine and Geriatrics, United Christian Hospital, Kwun Tong, Hong Kong Special Administrative Region China
| | - Grace Kam
- grid.417037.60000 0004 1771 3082Department of Medicine and Geriatrics, United Christian Hospital, Kwun Tong, Hong Kong Special Administrative Region China
| | - Ip Tim Lau
- grid.490601.a0000 0004 1804 0692Tseung Kwan O Hospital, Hang Hau, Hong Kong Special Administrative Region China
| | - June K. Li
- grid.417335.70000 0004 1804 2890Department of Medicine, Yan Chai Hospital, Tsuen Wan, Hong Kong Special Administrative Region China
| | - Vincent T. Yeung
- grid.499546.30000 0000 9690 2842Centre for Diabetes Education and Management, Our Lady of Maryknoll Hospital, Wong Tai Sin, Hong Kong Special Administrative Region China
| | - Emmy Lau
- grid.417134.40000 0004 1771 4093Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong Special Administrative Region China
| | - Stanley Lo
- grid.417134.40000 0004 1771 4093Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong Special Administrative Region China
| | - Samuel Fung
- grid.415229.90000 0004 1799 7070Department of Medicine and Geriatrics, Princess Margaret Hospital, Lai Chi Kok, Hong Kong Special Administrative Region China
| | - Yuk Lun Cheng
- grid.413608.80000 0004 1772 5868Department of Medicine, Alice Ho Miu Ling Nethersole Hospital, Tai Po, Hong Kong Special Administrative Region China
| | - Chun Chung Chow
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Weichuan Yu
- grid.24515.370000 0004 1937 1450Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong Special Administrative Region China
| | - Stephen K. W. Tsui
- grid.10784.3a0000 0004 1937 0482School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Yu Huang
- grid.10784.3a0000 0004 1937 0482School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.35030.350000 0004 1792 6846Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong Special Administrative Region China
| | - Hui-yao Lan
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Cheuk Chun Szeto
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Wing Yee So
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China
| | - Alicia J. Jenkins
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.1013.30000 0004 1936 834XNHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
| | - Juliana C. N. Chan
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,CUHK-SJTU Joint Research Centre on Diabetes Genomics and Precision Medicine, Shatin, Hong Kong Special Administrative Region China
| | - Ronald C. W. Ma
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,grid.10784.3a0000 0004 1937 0482Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region China ,CUHK-SJTU Joint Research Centre on Diabetes Genomics and Precision Medicine, Shatin, Hong Kong Special Administrative Region China
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20
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Mäkinen VP, Karsikas M, Kettunen J, Lehtimäki T, Kähönen M, Viikari J, Perola M, Salomaa V, Järvelin MR, Raitakari OT, Ala-Korpela M. Longitudinal profiling of metabolic ageing trends in two population cohorts of young adults. Int J Epidemiol 2022; 51:1970-1983. [PMID: 35441226 DOI: 10.1093/ije/dyac062] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 03/20/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Quantification of metabolic changes over the human life course is essential to understanding ageing processes. Yet longitudinal metabolomics data are rare and long gaps between visits can introduce biases that mask true trends. We introduce new ways to process quantitative time-series population data and elucidate metabolic ageing trends in two large cohorts. METHODS Eligible participants included 1672 individuals from the Cardiovascular Risk in Young Finns Study and 3117 from the Northern Finland Birth Cohort 1966. Up to three time points (ages 24-49 years) were analysed by nuclear magnetic resonance metabolomics and clinical biochemistry (236 measures). Temporal trends were quantified as median change per decade. Sample quality was verified by consistency of shared biomarkers between metabolomics and clinical assays. Batch effects between visits were mitigated by a new algorithm introduced in this report. The results below satisfy multiple testing threshold of P < 0.0006. RESULTS Women gained more weight than men (+6.5% vs +5.0%) but showed milder metabolic changes overall. Temporal sex differences were observed for C-reactive protein (women +5.1%, men +21.1%), glycine (women +5.2%, men +1.9%) and phenylalanine (women +0.6%, men +3.5%). In 566 individuals with ≥+3% weight gain vs 561 with weight change ≤-3%, divergent patterns were observed for insulin (+24% vs -10%), very-low-density-lipoprotein triglycerides (+32% vs -6%), high-density-lipoprotein2 cholesterol (-6.5% vs +4.7%), isoleucine (+5.7% vs -6.0%) and C-reactive protein (+25% vs -22%). CONCLUSION We report absolute and proportional trends for 236 metabolic measures as new reference material for overall age-associated and specific weight-driven changes in real-world populations.
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Affiliation(s)
- Ville-Petteri Mäkinen
- Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, Australia.,Australian Centre for Precision Health, University of South Australia, Adelaide, Australia.,Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Mari Karsikas
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland.,Biocenter Oulu, Oulu, Finland.,Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Johannes Kettunen
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland.,Biocenter Oulu, Oulu, Finland.,Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jorma Viikari
- Department of Medicine, University of Turku, Turku, Finland.,Division of Medicine, Turku University Hospital, Turku, Finland
| | - Markus Perola
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.,Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland.,Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland.,Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Department of Life Sciences, College of Health and Life Sciences, Brunel University London, UK
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland.,Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.,Centre for Population Health Research, University of Turku and Turku University Hospital
| | - Mika Ala-Korpela
- Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland.,Biocenter Oulu, Oulu, Finland.,Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
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21
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Metabolomic profiles predict individual multidisease outcomes. Nat Med 2022; 28:2309-2320. [PMID: 36138150 PMCID: PMC9671812 DOI: 10.1038/s41591-022-01980-3] [Citation(s) in RCA: 111] [Impact Index Per Article: 55.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 07/28/2022] [Indexed: 02/02/2023]
Abstract
Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.
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22
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Gut Microbiome and Its Cofactors Are Linked to Lipoprotein Distribution Profiles. Microorganisms 2022; 10:microorganisms10112156. [PMID: 36363749 PMCID: PMC9699503 DOI: 10.3390/microorganisms10112156] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/20/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Increasing evidence indicates that the gut microbiome (GM) plays an important role in dyslipidemia. To date, however, no in-depth characterization of the associations between GM with lipoproteins distributions (LPD) among adult individuals with diverse BMI has been conducted. To determine such associations, we studied blood-plasma LPD, fecal short-chain fatty acids (SCFA) and GM of 262 Danes aged 19–89 years. Stratification of LPD segregated subjects into three clusters displaying recommended levels of lipoproteins and explained by age and body-mass-index. Higher levels of HDL2a and HDL2b were associated with a higher abundance of Ruminococcaceae and Christensenellaceae. Increasing levels of total cholesterol and LDL-1 and LDL-2 were positively associated with Lachnospiraceae and Coriobacteriaceae, and negatively with Bacteroidaceae and Bifidobacteriaceae. Metagenome-sequencing showed a higher abundance of biosynthesis of multiple B-vitamins and SCFA metabolism genes among healthier LPD profiles. Metagenomic-assembled genomes (MAGs) affiliated to Eggerthellaceae and Clostridiales were contributors of these genes and their relative abundance correlated positively with larger HDL subfractions. The study demonstrates that differences in composition and metabolic traits of the GM are associated with variations in LPD among the recruited subjects. These findings provide evidence for GM considerations in future research aiming to shed light on mechanisms of the GM–dyslipidemia axis.
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23
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Fang S, Holmes MV, Gaunt TR, Davey Smith G, Richardson TG. Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers. eLife 2022; 11. [PMID: 36219204 DOI: 10.1101/2021.10.14.21265005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 09/12/2022] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease. METHODS We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank. RESULTS As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings. CONCLUSIONS We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas. FUNDING This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.
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Affiliation(s)
- Si Fang
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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24
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Fang S, Holmes MV, Gaunt TR, Davey Smith G, Richardson TG. Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers. eLife 2022; 11:e73951. [PMID: 36219204 PMCID: PMC9553209 DOI: 10.7554/elife.73951] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Background Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease. Methods We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank. Results As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings. Conclusions We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas. Funding This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.
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Affiliation(s)
- Si Fang
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
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25
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Kujala UM, Leskinen T, Rottensteiner M, Aaltonen S, Ala-Korpela M, Waller K, Kaprio J. Physical activity and health: Findings from Finnish monozygotic twin pairs discordant for physical activity. Scand J Med Sci Sports 2022; 32:1316-1323. [PMID: 35770444 PMCID: PMC9378553 DOI: 10.1111/sms.14205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 05/21/2022] [Accepted: 06/15/2022] [Indexed: 11/28/2022]
Abstract
Genetic and early environmental differences including early health habits associate with future health. To provide insight on the causal nature of these associations, monozygotic (MZ) twin pairs discordant for health habits provide an interesting natural experiment. Twin pairs discordant for leisure‐time physical activity (LTPA) in early adult life is thus a powerful study design to investigate the associations between long‐term LTPA and indicators of health and wellbeing. We have identified 17 LTPA discordant twin pairs from two Finnish twin cohorts and summarize key findings of these studies in this paper. The carefully characterized rare long‐term LTPA discordant MZ twin pairs have participated in multi‐dimensional clinical examinations. Key findings highlight that compared with less active twins in such MZ twin pairs, the twins with higher long‐term LTPA have higher physical fitness, reduced body fat, reduced visceral fat, reduced liver fat, increased lumen diameters of conduit arteries to the lower limbs, increased bone mineral density in loaded bone areas, and an increased number of large high‐density lipoprotein particles. The findings increase our understanding on the possible site‐specific and system‐level effects of long‐term LTPA.
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Affiliation(s)
- Urho M Kujala
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Tuija Leskinen
- Department of Public Health, University of Turku and Turku University Hospital, Turku, Finland
| | - Mirva Rottensteiner
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Sari Aaltonen
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - Mika Ala-Korpela
- Systems Epidemiology, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland.,Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland.,NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Katja Waller
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
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26
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Concerns Regarding NMR Lipoprotein Analyses Performed on the Nightingale Heath Platform – Focus on LDL Subclasses. J Clin Lipidol 2022; 16:250-252. [DOI: 10.1016/j.jacl.2022.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 02/16/2022] [Indexed: 11/23/2022]
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