Qin Wang1, Antti J Kangas2, Pasi Soininen1, Mika Tiainen1, Tuulia Tynkkynen1, Katri Puukka2, Aimo Ruokonen2, Jorma Viikari2, Mika Kähönen2, Terho Lehtimäki2, Veikko Salomaa2, Markus Perola1, George Davey Smith2, Olli T Raitakari1, Marjo-Riitta Järvelin1, Peter Würtz2, Johannes Kettunen1, Mika Ala-Korpela1. 1. Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland, NordLab Oulu, Oulu University Hospital and Department of Clinical Chemistry, University of Oulu, Oulu, Finland, Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland, Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland, Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere, School of Medicine, Tampere, Finland, National Institute for Health and Welfare, Helsinki, Finland, Public Health Genomics Unit, Department of Chronic Disease Prevention, National 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, Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK, 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, Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK, Institute of Health Sciences & Biocenter Oulu, University of Oulu, Oulu, Finland, Unit of Primary Care, Oulu University Hospital, Oulu, Finland, Department of Children and Young People and Families, National Institute for Health and Welfare, Oulu, Finland, Computational Medicine, School of Social and Community Medicine, University of Bristol, Bristol, UK and Computational Medicine, Oulu University Hospital, Oulu, Finland. Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, F 2. Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland, NordLab Oulu, Oulu University Hospital and Department of Clinical Chemistry, University of Oulu, Oulu, Finland, Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland, Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland, Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere, School of Medicine, Tampere, Finland, National Institute for Health and Welfare, Helsinki, Finland, Public Health Genomics Unit, Department of Chronic Disease Prevention, National 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, Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK, 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, Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, Imperial College London, London, UK, Institute of Health Sciences & Biocenter Oulu, University of Oulu, Oulu, Finland, Unit of Primary Care, Oulu University Hospital, Oulu, Finland, Department of Children and Young People and Families, National Institute for Health and Welfare, Oulu, Finland, Computational Medicine, School of Social and Community Medicine, University of Bristol, Bristol, UK and Computational Medicine, Oulu University Hospital, Oulu, Finland.
Abstract
BACKGROUND: The causal role of circulating sex hormone-binding globulin (SHBG) for type 2 diabetes is controversial. Information on the relations between SHBG and new biomarkers of cardiometabolic risk is scarce. METHODS: We applied quantitative nuclear magnetic resonance metabolomics in three Finnish population-based cohorts to comprehensively profile circulating lipids and metabolites and study their associations with SHBG. Mendelian randomization was used to examine potential causality of SHBG on the metabolic measures and insulin resistance. Prospective associations and causal effect estimates of SHBG on type 2 diabetes were assessed via meta-analysis including summary statistics from the DIAGRAM consortium. RESULTS: In cross-sectional analysis in 6475 young adults (mean age 31, 57% men), higher SHBG was linked with a more favourable cardiometabolic risk profile, including associations with lipoprotein subclasses, fatty acid composition, amino acids, ketone bodies and inflammation-linked glycoproteins. Prospective analysis of 1377 young adults with 6-year follow-up indicated that SHBG is also associated with future insulin resistance. Mendelian randomization suggested only minor, if any, causal effects of SHBG on lipid and metabolite measures and insulin resistance(n = 10,895).Causal effect estimates on type 2 diabetes for 41,439 cases and 103,870 controls indicated a causative protective role of SHBG (OR = 0.83 per 1-SD, 95% CI: 0.76, 0.91); however, effects were considerably weaker than observed in meta-analysis of prospective studies [hazard ratio (HR) = 0.47 per 1-SD, 95% CI: 0.41, 0.53]. CONCLUSION: Circulating SHBG is strongly associated with systemic metabolism and predictive for insulin resistance and diabetes. The weaker causal estimates suggest that the observational associations are partly confounded rather than conferred directly via circulating SHBG.
BACKGROUND: The causal role of circulating sex hormone-binding globulin (SHBG) for type 2 diabetes is controversial. Information on the relations between SHBG and new biomarkers of cardiometabolic risk is scarce. METHODS: We applied quantitative nuclear magnetic resonance metabolomics in three Finnish population-based cohorts to comprehensively profile circulating lipids and metabolites and study their associations with SHBG. Mendelian randomization was used to examine potential causality of SHBG on the metabolic measures and insulin resistance. Prospective associations and causal effect estimates of SHBG on type 2 diabetes were assessed via meta-analysis including summary statistics from the DIAGRAM consortium. RESULTS: In cross-sectional analysis in 6475 young adults (mean age 31, 57% men), higher SHBG was linked with a more favourable cardiometabolic risk profile, including associations with lipoprotein subclasses, fatty acid composition, amino acids, ketone bodies and inflammation-linked glycoproteins. Prospective analysis of 1377 young adults with 6-year follow-up indicated that SHBG is also associated with future insulin resistance. Mendelian randomization suggested only minor, if any, causal effects of SHBG on lipid and metabolite measures and insulin resistance(n = 10,895).Causal effect estimates on type 2 diabetes for 41,439 cases and 103,870 controls indicated a causative protective role of SHBG (OR = 0.83 per 1-SD, 95% CI: 0.76, 0.91); however, effects were considerably weaker than observed in meta-analysis of prospective studies [hazard ratio (HR) = 0.47 per 1-SD, 95% CI: 0.41, 0.53]. CONCLUSION: Circulating SHBG is strongly associated with systemic metabolism and predictive for insulin resistance and diabetes. The weaker causal estimates suggest that the observational associations are partly confounded rather than conferred directly via circulating SHBG.
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