Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Commun Biol. 2022 Nov 19;5(1):1271. doi: 10.1038/s42003-022-04237-4.
Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h= .25 vs. .13, p = 1.8x10). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r= .49, p = 2.7x10). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.
肥胖症及相关的病态，包括代谢相关脂肪性肝病（MAFLD），是全球最大的公共健康威胁之一。人们已经知道身体成分和相关的风险因素是可以遗传的，确定其遗传决定因素可能有助于制定更好的预防和治疗策略。最近，大规模的全身 MRI 数据已经可用，这些数据提供了比人体测量学（如体重指数）更具体的身体成分测量值。在这里，我们旨在通过对这些 MRI 衍生测量值进行全基因组关联研究（GWAS）来阐明身体成分的遗传结构。我们对 33588 名白种欧洲 UK Biobank 参与者的 14 项 MRI 衍生的脂肪和肌肉组织分布测量值进行了单变量和多变量 GWAS（平均年龄 64.5 岁，51.4%为女性）。通过多变量分析，我们在身体成分测量值中发现了 100 个具有分布式效应的位点和 241 个主要参与免疫系统功能的显著基因。肝脏脂肪突出，具有高度可发现的和寡基因结构以及最强的遗传关联。与 21 种常见的心血管代谢特征进行比较，发现了共享和特定的遗传影响，MRI 测量值的平均遗传力更高（h=0.25 比.13，p=1.8x10）。我们发现身体成分测量值与一系列心血管代谢疾病之间存在实质性的遗传相关性，其中肝脏脂肪与 2 型糖尿病之间的相关性最强（r=0.49，p=2.7x10）。这些发现表明，MRI 衍生的身体成分测量值补充了传统的身体人体测量学和其他心血管代谢健康的生物标志物，突出了肝脏脂肪的核心作用，并提高了我们对身体成分和相关疾病遗传结构的认识。
