Qiuling Dong MSc, Yue Xi MSc, Stefan Brandmaier PhD, Markéta Fuchs MSc, Marie-Theres Huemer PhD, Melanie Waldenberger PhD, Jiefei Niu MSc, Christian Herder PhD, Wolfgang Rathmann MD, Michael Roden MD, Wolfgang Koenig MD, Gidon J. Bönhof PhD, Christian Gieger PhD, Barbara Thorand PhD, Annette Peters PhD, Susanne Rospleszcz PhD, Harald Grallert PhD
{"title":"成人型糖尿病的亚型：以人口为基础的 KORA 队列中的数据驱动聚类。","authors":"Qiuling Dong MSc, Yue Xi MSc, Stefan Brandmaier PhD, Markéta Fuchs MSc, Marie-Theres Huemer PhD, Melanie Waldenberger PhD, Jiefei Niu MSc, Christian Herder PhD, Wolfgang Rathmann MD, Michael Roden MD, Wolfgang Koenig MD, Gidon J. Bönhof PhD, Christian Gieger PhD, Barbara Thorand PhD, Annette Peters PhD, Susanne Rospleszcz PhD, Harald Grallert PhD","doi":"10.1111/dom.16022","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aims</h3>\n \n <p>A data-driven cluster analysis in a cohort of European individuals with type 2 diabetes (T2D) has previously identified four subgroups based on clinical characteristics. In the current study, we performed a comprehensive statistical assessment to (1) replicate the above-mentioned original clusters; (2) derive de novo T2D subphenotypes in the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) cohort and (3) describe underlying genetic risk and diabetes complications.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We used data from <i>n</i> = 301 individuals with T2D from KORA FF4 study (Southern Germany). Original cluster replication was assessed forcing <i>k</i> = 4 clusters using three different hyperparameter combinations. De novo clusters were derived by open <i>k</i>-means analysis. Stability of de novo clusters was assessed by assignment congruence over different variable sets and Jaccard indices. Distribution of polygenic risk scores and diabetes complications in the respective clusters were described as an indication of underlying heterogeneity.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Original clusters did not replicate well, indicated by substantially different assignment frequencies and cluster characteristics between the original and current sample. De novo clustering using <i>k</i> = 3 clusters and including high sensitivity C-reactive protein in the variable set showed high stability (all Jaccard indices >0.75). The three de novo clusters (<i>n</i> = 96, <i>n</i> = 172, <i>n</i> = 33, respectively) adequately captured heterogeneity within the sample and showed different distributions of polygenic risk scores and diabetes complications, that is, cluster 1 was characterized by insulin resistance with high neuropathy prevalence, cluster 2 was defined as age-related diabetes and cluster 3 showed highest risk of genetic and obesity-related diabetes.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>T2D subphenotyping based on its sample's own clinical characteristics leads to stable categorization and adequately reflects T2D heterogeneity.</p>\n </section>\n </div>","PeriodicalId":158,"journal":{"name":"Diabetes, Obesity & Metabolism","volume":"27 1","pages":"338-347"},"PeriodicalIF":5.4000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11618242/pdf/","citationCount":"0","resultStr":"{\"title\":\"Subphenotypes of adult-onset diabetes: Data-driven clustering in the population-based KORA cohort\",\"authors\":\"Qiuling Dong MSc, Yue Xi MSc, Stefan Brandmaier PhD, Markéta Fuchs MSc, Marie-Theres Huemer PhD, Melanie Waldenberger PhD, Jiefei Niu MSc, Christian Herder PhD, Wolfgang Rathmann MD, Michael Roden MD, Wolfgang Koenig MD, Gidon J. 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引用次数: 0
摘要
目的：此前，一项针对欧洲 2 型糖尿病（T2D）患者队列的数据驱动聚类分析根据临床特征确定了四个亚组。在本研究中，我们进行了全面的统计评估，以：（1）复制上述原始聚类；（2）在奥格斯堡地区合作健康研究中心（KORA）队列中得出新的 T2D 亚型；（3）描述潜在的遗传风险和糖尿病并发症：我们使用了来自 KORA FF4 研究（德国南部）的 n = 301 名 T2D 患者的数据。使用三种不同的超参数组合对原始聚类复制进行了评估，强迫 k = 4 个聚类。新的聚类是通过开放式 K-均值分析得出的。新聚类的稳定性通过不同变量集的赋值一致性和 Jaccard 指数进行评估。多基因风险评分和糖尿病并发症在相应聚类中的分布情况被描述为潜在异质性的标志：结果：原始聚类的复制效果不佳，原始样本和当前样本的赋值频率和聚类特征存在很大差异。使用 k = 3 个聚类并将高敏 C 反应蛋白纳入变量集的新聚类显示出很高的稳定性（所有 Jaccard 指数均大于 0.75）。三个新聚类（分别为96、172、33）充分体现了样本内部的异质性，并显示出多基因风险评分和糖尿病并发症的不同分布，即聚类1以胰岛素抵抗为特征，神经病变发病率高；聚类2被定义为年龄相关性糖尿病；聚类3显示出遗传和肥胖相关性糖尿病的最高风险：结论：根据样本自身的临床特征对 T2D 进行亚型分型可实现稳定的分类，并能充分反映 T2D 的异质性。
Subphenotypes of adult-onset diabetes: Data-driven clustering in the population-based KORA cohort
Aims
A data-driven cluster analysis in a cohort of European individuals with type 2 diabetes (T2D) has previously identified four subgroups based on clinical characteristics. In the current study, we performed a comprehensive statistical assessment to (1) replicate the above-mentioned original clusters; (2) derive de novo T2D subphenotypes in the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) cohort and (3) describe underlying genetic risk and diabetes complications.
Methods
We used data from n = 301 individuals with T2D from KORA FF4 study (Southern Germany). Original cluster replication was assessed forcing k = 4 clusters using three different hyperparameter combinations. De novo clusters were derived by open k-means analysis. Stability of de novo clusters was assessed by assignment congruence over different variable sets and Jaccard indices. Distribution of polygenic risk scores and diabetes complications in the respective clusters were described as an indication of underlying heterogeneity.
Results
Original clusters did not replicate well, indicated by substantially different assignment frequencies and cluster characteristics between the original and current sample. De novo clustering using k = 3 clusters and including high sensitivity C-reactive protein in the variable set showed high stability (all Jaccard indices >0.75). The three de novo clusters (n = 96, n = 172, n = 33, respectively) adequately captured heterogeneity within the sample and showed different distributions of polygenic risk scores and diabetes complications, that is, cluster 1 was characterized by insulin resistance with high neuropathy prevalence, cluster 2 was defined as age-related diabetes and cluster 3 showed highest risk of genetic and obesity-related diabetes.
Conclusion
T2D subphenotyping based on its sample's own clinical characteristics leads to stable categorization and adequately reflects T2D heterogeneity.
期刊介绍：
Diabetes, Obesity and Metabolism is primarily a journal of clinical and experimental pharmacology and therapeutics covering the interrelated areas of diabetes, obesity and metabolism. The journal prioritises high-quality original research that reports on the effects of new or existing therapies, including dietary, exercise and lifestyle (non-pharmacological) interventions, in any aspect of metabolic and endocrine disease, either in humans or animal and cellular systems. ‘Metabolism’ may relate to lipids, bone and drug metabolism, or broader aspects of endocrine dysfunction. Preclinical pharmacology, pharmacokinetic studies, meta-analyses and those addressing drug safety and tolerability are also highly suitable for publication in this journal. Original research may be published as a main paper or as a research letter.
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