Observational studies have demonstrated positive associations between bone mineral density and risk of type 2 diabetes (T2D), but its relationship with risk of coronary heart disease (CHD) is unclear. Moreover, the causal relevance of BMD for both T2D and CHD cannot be fully addressed by traditional observational studies which are typically constrained by residual confounding and reverse causality bias. In current study, we conducted a genome-wide association study of estimated heel bone mineral density (eBMD), assessed by quantitative ultrasound, in the interim release of ~150k UK Biobank participants and examined the relationships of eBMD-associated SNPs with T2D and CHD through Mendelian randomization analyses. We identify 235 independent genetic markers or single nucleotide polymorphisms associated at p<5 10-8 with eBMD in ~116k individuals from the UKB. Using the 235 SNPs as genetic instrumental variables, we demonstrate a causal relationship between higher eBMD and lower risk of fracture (Odds Ratio 0.65; 95% CI: 0.62 to 0.68). We provide the evidences showing that higher genetically instrumented eBMD was associated with higher risks of both T2D (OR 1.08; 95%CI 1.02 to 1.14) and CHD (OR 1.05; 95%CI 1.00 to 1.10). Increased eBMD were also causally associated with lower plasma levels of HDL-cholesterol and increased insulin resistance. The findings of the present study suggest that higher bone density, measured by eBMD, may have an adverse effect on risk of cardiometabolic diseases, which may well have implications for patient care. In conclusion, Mendelian randomization provides evidence of a modest casual effect of elevated bone mineral density (assessed by quantitative ultrasound of heel) on risk of both T2D and CHD, which may be partially mediated by insulin resistance. The findings of this study add to the growing evidence-base suggesting a possible role of bone endocrine function in the pathogenesis of both type 2 diabetes and coronary heart disease.
Type 2 diabetes: using genetic discovery to drive biological inference and translational opportunities
This proposal seeks access to UK Biobank data to support efforts to identify genetic variants contributing to predisposition to T2D and related traits (such as the complications of diabetes), and to leverage those discoveries to obtain insights into disease biology and to drive translational developments. The applicants are world-leaders in the genetics of T2D and related traits and have together played leading roles in the major global discovery efforts for these phenotypes. UK Biobank data offers many opportunities to augment, extend and enrich existing research in this area. The research we plan is entirely congruent with the stated aim of UK Biobank to improve `the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses` Diabetes was specifically listed as one of the target conditions for UK Biobank. The mechanistic inferences that can be derived from well-powered and well-designed human genetic discovery efforts underpin efforts to offer improved strategies for prevention, diagnosis and treatment. We will start by searching for genetic differences that are associated with type 2 diabetes and/or related traits (initially at the baseline visit, and subsequently on follow-up). We will integrate the data provided by UK Biobank with other studies that we have conducted to extend the list of these genetic differences. We will then use this information to support efforts to identify pathways implicated in diabetes development, and to identify those which might be most suitable for therapeutic manipulation.
|Lead investigator:||Professor Mark McCarthy|
|Lead institution:||University of Oxford|
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