Dahal Chetana, Wawro Nina, Meisinger Christa, Breuninger Taylor A, Thorand Barbara, Rathmann Wolfgang, Koenig Wolfgang, Hauner Hans, Peters Annette, Linseisen Jakob
Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany.
Epidemiology, Faculty of Medicine, University Hospital Augsburg, University of Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany.
Life (Basel). 2022 Sep 20;12(10):1460. doi: 10.3390/life12101460.
The aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters that are regularly determined in clinical practice. K-means cluster analysis was used to group 3001 adults from the KORA F4 cohort into three clusters. We identified the clustering parameters through variable importance methods, without including any specific disease endpoint. Several unique combinations of selected parameters were used to create different metabotype models. Metabotype models were then described and evaluated, based on various metabolic parameters and on the incidence of cardiometabolic diseases. As a result, two optimal models were identified: a model composed of five parameters, which were fasting glucose, HDLc, non-HDLc, uric acid, and BMI (the metabolic disease model) for clustering; and a model that included four parameters, which were fasting glucose, HDLc, non-HDLc, and triglycerides (the cardiovascular disease model). These identified metabotypes are based on a few common parameters that are measured in everyday clinical practice. These metabotypes are cost-effective, and can be easily applied on a large scale in order to identify specific risk groups that can benefit most from measures to prevent cardiometabolic diseases, such as dietary recommendations and lifestyle interventions.
代谢分型的目的是将个体分类为代谢相似的群体。早期探索代谢分型的研究使用了众多参数，这使得其应用的可转移性较差。因此，本研究旨在基于临床实践中定期测定的一组标准实验室参数来识别代谢型。采用K均值聚类分析将KORA F4队列中的3001名成年人分为三个聚类。我们通过变量重要性方法确定聚类参数，未纳入任何特定疾病终点。使用选定参数的几种独特组合来创建不同的代谢型模型。然后根据各种代谢参数和心血管代谢疾病的发病率对代谢型模型进行描述和评估。结果，确定了两个最优模型：一个由五个参数组成的模型，即空腹血糖、高密度脂蛋白胆固醇（HDLc）、非高密度脂蛋白胆固醇、尿酸和体重指数（BMI）（代谢疾病模型）用于聚类；另一个模型包含四个参数，即空腹血糖、HDLc、非HDLc和甘油三酯（心血管疾病模型）。这些确定的代谢型基于日常临床实践中测量的一些常见参数。这些代谢型具有成本效益，并且可以很容易地大规模应用，以识别能够从预防心血管代谢疾病的措施（如饮食建议和生活方式干预）中获益最大的特定风险群体。
