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Lim AMW, Lim EU, Chen PL, Fann CSJ. Unsupervised clustering identified clinically relevant metabolic syndrome endotypes in UK and Taiwan Biobanks. iScience 2024; 27:109815. [PMID: 39040048 PMCID: PMC11260869 DOI: 10.1016/j.isci.2024.109815] [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: 09/29/2023] [Revised: 02/02/2024] [Accepted: 04/23/2024] [Indexed: 07/24/2024] Open
Abstract
Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on individuals from UK Biobank to reveal endotypes. Five MetS subgroups were identified: Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycemic. For all of the endotypes, we identified the corresponding cardiometabolic traits and their associations with clinical outcomes. Genome-wide association studies (GWASs) were conducted to identify associated genotypic traits. We then determined endotype-specific genotypic traits and constructed polygenic risk score (PRS) models specific to each endotype. GWAS of each MetS clusters revealed different genotypic traits. C1 GWAS revealed novel findings of TRIM63, MYBPC3, MYLPF, and RAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues. MetS clusters with comparable phenotypic and genotypic traits were identified in Taiwan Biobank.
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Affiliation(s)
- Aylwin Ming Wee Lim
- Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei 112304, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- ASUS Intelligent Cloud Services (AICS), Taipei 112, Taiwan
| | - Evan Unit Lim
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Pei-Lung Chen
- Graduate Institute of Medical Genomics and Proteomics, College of Medicine, National Taiwan University, Taipei 10617, Taiwan
- Department of Medical Genetics, National Taiwan University Hospital, Taipei 100, Taiwan
| | - Cathy Shen Jang Fann
- Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei 112304, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
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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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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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Mäkinen VP, Kettunen J, Lehtimäki T, Kähönen M, Viikari J, Perola M, Salomaa V, Järvelin MR, Raitakari OT, Ala-Korpela M. Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity pandemic. Int J Obes (Lond) 2023; 47:453-462. [PMID: 36823293 DOI: 10.1038/s41366-023-01281-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 02/07/2023] [Accepted: 02/13/2023] [Indexed: 02/25/2023]
Abstract
BACKGROUND/OBJECTIVE This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools. SUBJECTS/METHODS Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression. RESULTS The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile. CONCLUSIONS Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.
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Affiliation(s)
- 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. .,Computational and Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia. .,Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia.
| | - 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, Oulu, Finland.,Department of 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 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 Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, 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, School of Public Health, Imperial College London, London, UK.,Department of Life Sciences, College of Health and Life Sciences, Brunel University London, 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, Turku, 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, Oulu, Finland. .,NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland.
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Ahmed M, Mäkinen VP, Lumsden A, Boyle T, Mulugeta A, Lee SH, Olver I, Hyppönen E. Metabolic profile predicts incident cancer: A large-scale population study in the UK Biobank. Metabolism 2023; 138:155342. [PMID: 36377121 DOI: 10.1016/j.metabol.2022.155342] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/24/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND AND AIMS Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction. METHODS We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 individual biomarkers and cancer risk. RESULTS In total, 21,973 individuals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03-5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as "high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II ("high triglycerides & liver enzymes") was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of individual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were individually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65). CONCLUSIONS Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.
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Affiliation(s)
- Muktar Ahmed
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia; Department of Epidemiology, Faculty of Public Health, Jimma University Institute of Health, Jimma, Ethiopia; UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Ville-Petteri Mäkinen
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia; Computational Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Amanda Lumsden
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia; UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Terry Boyle
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia; UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA, Australia
| | - Anwar Mulugeta
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia; UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Sang Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia; UniSA Allied Health & Human Performance, University of South Australia, Adelaide, SA, Australia
| | - Ian Olver
- School of Psychology, Faculty of Health and Medical Sciences, University of Adelaide, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, SA, Australia; UniSA Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
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