Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands.
Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.
Diabetes Obes Metab. 2024 Dec;26(12):5922-5930. doi: 10.1111/dom.15966. Epub 2024 Oct 2.
Various anthropometric measures capture distinct as well as overlapping characteristics of an individual's body composition. To characterize independent body composition measures, we aimed to reduce easily-obtainable individual measures reflecting adiposity, anthropometrics and energy expenditure into fewer independent constructs, and to assess their potential sex- and age-specific relation with cardiometabolic diseases.
Analyses were performed within European ancestry participants from UK Biobank (N = 418,963, mean age 58.0 years, 56% women). Principal components (PC) analyses were used for the dimension reduction of 11 measures of adiposity, anthropometrics and energy expenditure. PCs were studied in relation to incident type 2 diabetes mellitus (T2D) and coronary artery disease (CAD). Multivariable-adjusted Cox regression analyses, adjusted for confounding factors, were performed in all and stratified by age. Genome-wide association studies were performed in half of the cohort (N = 156,295) to identify genetic variants as instrumental variables. Genetic risk score analyses were performed in the other half of the cohort stratified by age of disease onset (N = 156,295).
We identified two PCs, of which PC1 reflected lower overall adiposity (negatively correlated with all adiposity aspects) and PC2 reflected more central adiposity (mainly correlated with higher waist-hip ratio, but with lower total body fat) and increased height, collectively capturing 87.8% of the total variance. Similar to that observed in the multivariable-adjusted regression analyses, we found associations between the PC1 genetic risk score and lower risks of CAD and T2D [CAD cases <50 years, odds ratio: 0.91 (95% confidence interval 0.87, 0.94) per SD; T2D cases <50 years, odds ratio: 0.76 (0.72, 0.81)], which attenuated with higher age (p-values 8.13E-4 and 2.41E-6, respectively). No associations were found for PC2.
The consistently observed weaker associations of the composite traits with cardiometabolic disease suggests the need for age-specific cardiometabolic disease prevention strategies.
各种人体测量学指标可以捕捉到个体身体成分的独特和重叠特征。为了描述独立的身体成分指标，我们旨在将反映肥胖、人体测量学和能量消耗的容易获得的个体指标减少到更少的独立结构中，并评估它们与心血管代谢疾病的潜在性别和年龄特异性关系。
在英国生物库的欧洲血统参与者中进行了分析（N=418963，平均年龄 58.0 岁，56%为女性）。主成分（PC）分析用于减少 11 种肥胖、人体测量学和能量消耗指标。研究了 PC 与 2 型糖尿病（T2D）和冠状动脉疾病（CAD）的发病关系。在所有参与者中进行了多变量调整的 Cox 回归分析，并按年龄进行了分层。在一半的队列中（N=156295）进行全基因组关联研究，以鉴定作为工具变量的遗传变异。在按疾病发病年龄分层的另一半队列中（N=156295）进行遗传风险评分分析。
我们确定了两个 PC，其中 PC1 反映了较低的总体肥胖水平（与所有肥胖方面呈负相关），PC2 反映了更多的中心肥胖（主要与较高的腰围-臀围比相关，但与较低的总体体脂相关）和增加的身高，共捕获了 87.8%的总方差。与多变量调整的回归分析中观察到的相似，我们发现 PC1 遗传风险评分与 CAD 和 T2D 的风险降低之间存在关联[CAD 病例<50 岁，比值比：0.91（95%置信区间 0.87，0.94）每 SD；T2D 病例<50 岁，比值比：0.76（0.72，0.81）]，随着年龄的增加而减弱（p 值分别为 8.13E-4 和 2.41E-6）。没有发现与 PC2 相关的关联。
复合特征与心血管代谢疾病的关联较弱，这表明需要针对特定年龄的心血管代谢疾病预防策略。
