Changning Center for Disease Control and Prevention, Shanghai, China.
Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands.
Front Endocrinol (Lausanne). 2022 Apr 6;13:828403. doi: 10.3389/fendo.2022.828403. eCollection 2022.
Evidence about recently proposed data-driven clusters of type 2 diabetes (T2D) is mainly about its prognostic effects and Western populations. We tested the applicability of this clustering approach among the Chinese population. We further investigated the cardiorenal risk profiles among different T2D sub-phenotypes cross-sectionally and before diabetes diagnosis.
With the use of data from the China Health and Nutrition Survey (1989-2009), 6,728 participants with available fasting blood samples and completed questionnaires in the 2009 survey were included. Glycemic statuses (normoglycemia, prediabetes, and new-onset T2D) were defined according to the 2020 American Diabetes Association criteria. Data-driven cluster analysis was conducted among new-onset T2D based on five variables: age at onset, body mass index (BMI), hemoglobin A1c, homeostasis model estimates of β-cell function, and insulin resistance. Linear regression models were used to cross-sectionally examine the differences of cardiorenal risk factors (body fat distribution, blood pressure, lipid profiles, and kidney function) between glycemic statuses. Mixed-effects models were used to explore a maximum of 20-year trajectories of cardiovascular risk factors (body fat distribution and blood pressure) before diabetes diagnosis.
Among 557 (8.3%) new-onset T2D, four sub-phenotypes were found, with 57 (10.2%) assigned to the severe insulin-resistant diabetes (SIRD), 72 (12.9%) to the severe insulin-deficient diabetes (SIDD), 167 (30.0%) to the mild obesity-related diabetes (MOD), and 261 (46.9%) to the mild age-related diabetes (MARD). People clustered within different T2D sub-phenotypes had different cardiorenal risk profiles. Three T2D sub-phenotypes (SIRD, SIDD, and MOD) had worse cardiorenal abnormalities, while the risk burden in the MARD sub-phenotype was similar to that in prediabetes. Compared with people with other T2D sub-phenotypes, people in the MOD sub-phenotype had a faster increment in BMI, waist, upper arm circumference, and triceps skinfold up to 10 years before diagnosis. Blood pressure was less distinct in different T2D sub-phenotypes; however, SIDD and MOD clusters had higher blood pressure levels before diabetes diagnosis.
Data-driven T2D sub-phenotyping is applicable in the Chinese population. Certain sub-phenotypes such as MARD only have a minor cardiorenal risk burden, and distinct cardiovascular risk development occurs long before diabetes diagnosis. Our findings can help improve early prevention and targeted treatment for diabetes.
最近提出的 2 型糖尿病（T2D）数据驱动聚类的证据主要是关于其预后影响和西方人群。我们在中国人群中检验了这种聚类方法的适用性。我们还进一步研究了不同 T2D 亚表型在横断面和糖尿病诊断前的心脏肾脏风险特征。
使用中国健康与营养调查（1989-2009 年）的数据，纳入了在 2009 年调查中具有空腹血样和完整问卷的 6728 名参与者。血糖状态（正常血糖、糖尿病前期和新发 T2D）根据 2020 年美国糖尿病协会标准定义。基于五个变量：发病年龄、体重指数（BMI）、糖化血红蛋白、β细胞功能的稳态模型估计和胰岛素抵抗，对新发 T2D 进行数据驱动的聚类分析。线性回归模型用于在横断面研究中比较不同血糖状态之间的心脏肾脏风险因素（体脂分布、血压、血脂谱和肾功能）的差异。混合效应模型用于探索糖尿病诊断前最大 20 年的心血管风险因素（体脂分布和血压）的轨迹。
在 557 例（8.3%）新发 T2D 中，发现了四个亚表型，其中 57 例（10.2%）归为严重胰岛素抵抗性糖尿病（SIRD），72 例（12.9%）归为严重胰岛素缺乏性糖尿病（SIDD），167 例（30.0%）归为轻度肥胖相关性糖尿病（MOD），261 例（46.9%）归为轻度年龄相关性糖尿病（MARD）。聚类在不同 T2D 亚表型中的人具有不同的心脏肾脏风险特征。三个 T2D 亚表型（SIRD、SIDD 和 MOD）的心脏肾脏异常更严重，而 MARD 亚表型的风险负担与糖尿病前期相似。与其他 T2D 亚表型的人相比，MOD 亚表型的人在诊断前 10 年内 BMI、腰围、上臂围和肱三头肌皮褶厚度的增加更快。不同 T2D 亚表型的血压差异不明显；然而，SIDD 和 MOD 聚类在糖尿病诊断前的血压水平更高。
数据驱动的 T2D 亚表型在中国人群中是适用的。某些亚表型，如 MARD，仅具有轻微的心脏肾脏风险负担，并且在糖尿病诊断前很早就出现了明显的心血管风险发展。我们的研究结果有助于改善糖尿病的早期预防和靶向治疗。
