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Murthy VL, Mosley JD, Perry AS, Jacobs DR, Tanriverdi K, Zhao S, Sawicki KT, Carnethon M, Wilkins JT, Nayor M, Das S, Abel ED, Freedman JE, Clish CB, Shah RV. Metabolic liability for weight gain in early adulthood. Cell Rep Med 2024; 5:101548. [PMID: 38703763 PMCID: PMC11148768 DOI: 10.1016/j.xcrm.2024.101548] [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: 11/16/2022] [Revised: 03/27/2023] [Accepted: 04/10/2024] [Indexed: 05/06/2024]
Abstract
While weight gain is associated with a host of chronic illnesses, efforts in obesity have relied on single "snapshots" of body mass index (BMI) to guide genetic and molecular discovery. Here, we study >2,000 young adults with metabolomics and proteomics to identify a metabolic liability to weight gain in early adulthood. Using longitudinal regression and penalized regression, we identify a metabolic signature for weight liability, associated with a 2.6% (2.0%-3.2%, p = 7.5 × 10-19) gain in BMI over ≈20 years per SD higher score, after comprehensive adjustment. Identified molecules specified mechanisms of weight gain, including hunger and appetite regulation, energy expenditure, gut microbial metabolism, and host interaction with external exposure. Integration of longitudinal and concurrent measures in regression with Mendelian randomization highlights the complexity of metabolic regulation of weight gain, suggesting caution in interpretation of epidemiologic or genetic effect estimates traditionally used in metabolic research.
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Affiliation(s)
- Venkatesh L Murthy
- Division of Cardiovascular Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Jonathan D Mosley
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Andrew S Perry
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - David R Jacobs
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kahraman Tanriverdi
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Shilin Zhao
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | | | | | - Matthew Nayor
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Saumya Das
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - E Dale Abel
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jane E Freedman
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
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Mäkinen VP, Ala-Korpela M. Influence of age and sex on longitudinal metabolic profiles and body weight trajectories in the UK Biobank. Int J Epidemiol 2024; 53:dyae055. [PMID: 38641429 PMCID: PMC11031410 DOI: 10.1093/ije/dyae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/04/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Accurate characterization of how age influences body weight and metabolism at different stages of life is important for understanding ageing processes. Here, we explore observational longitudinal associations between metabolic health and weight from the fifth to the seventh decade of life, using carefully adjusted statistical designs. METHODS Body measures and biochemical data from blood and urine (220 measures) across two visits were available from 10 104 UK Biobank participants. Participants were divided into stable (within ±4% per decade), weight loss and weight gain categories. Final subgroups were metabolically matched at baseline (48% women, follow-up 4.3 years, ages 41-70; n = 3368 per subgroup) and further stratified by the median age of 59.3 years and sex. RESULTS Pulse pressure, haemoglobin A1c and cystatin-C tracked ageing consistently (P < 0.0001). In women under 59, age-associated increases in citrate, pyruvate, alkaline phosphatase and calcium were observed along with adverse changes across lipoprotein measures, fatty acid species and liver enzymes (P < 0.0001). Principal component analysis revealed a qualitative sex difference in the temporal relationship between body weight and metabolism: weight loss was not associated with systemic metabolic improvement in women, whereas both age strata converged consistently towards beneficial (weight loss) or adverse (weight gain) phenotypes in men. CONCLUSIONS We report longitudinal ageing trends for 220 metabolic measures in absolute concentrations, many of which have not been described for older individuals before. Our results also revealed a fundamental dynamic sex divergence that we speculate is caused by menopause-driven metabolic deterioration in women.
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Affiliation(s)
- Ville-Petteri Mäkinen
- Systems Epidemiology, 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
- Biocenter Oulu, University of Oulu, Oulu, Finland
- NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland
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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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Takeshita S, Nishioka Y, Tamaki Y, Kamitani F, Mohri T, Nakajima H, Kurematsu Y, Okada S, Myojin T, Noda T, Imamura T, Takahashi Y. Novel subgroups of obesity and their association with outcomes: a data-driven cluster analysis. BMC Public Health 2024; 24:124. [PMID: 38195492 PMCID: PMC10775568 DOI: 10.1186/s12889-024-17648-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 01/02/2024] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND Obesity is associated with various complications and decreased life expectancy, and substantial heterogeneity in complications and outcomes has been observed. However, the subgroups of obesity have not yet been clearly defined. This study aimed to identify the subgroups of obesity especially those for target of interventions by cluster analysis. METHODS In this study, an unsupervised, data-driven cluster analysis of 9,494 individuals with obesity (body mass index ≥ 35 kg/m2) was performed using the data of ICD-10, drug, and medical procedure from the healthcare claims database. The prevalence and clinical characteristics of the complications such as diabetes in each cluster were evaluated using the prescription records. Additionally, renal and life prognoses were compared among the clusters. RESULTS We identified seven clusters characterised by different combinations of complications and several complications were observed exclusively in each cluster. Notably, the poorest prognosis was observed in individuals who rarely visited a hospital after being diagnosed with obesity, followed by those with cardiovascular complications and diabetes. CONCLUSIONS In this study, we identified seven subgroups of individuals with obesity using population-based data-driven cluster analysis. We clearly demonstrated important target subgroups for intervention as well as a metabolically healthy obesity group.
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Affiliation(s)
- Saki Takeshita
- Department of Public Health, Health Management and Policy, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
- Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Yuichi Nishioka
- Department of Public Health, Health Management and Policy, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
- Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Yuko Tamaki
- Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Fumika Kamitani
- Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Takako Mohri
- Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Hiroki Nakajima
- Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Yukako Kurematsu
- Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Sadanori Okada
- Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Tomoya Myojin
- Department of Public Health, Health Management and Policy, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Tatsuya Noda
- Department of Public Health, Health Management and Policy, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Tomoaki Imamura
- Department of Public Health, Health Management and Policy, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan
| | - Yutaka Takahashi
- Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-Cho, Kashihara, Nara, 634-8521, Japan.
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