Dong Hang, Shi Yingzhou, Ma Yicheng, Cheng Yiping, Liu Luna, Xiao Shengyang, Yuan Zinuo, Wang Zhen, Li Tuo, Zhao Jiajun, Fan Xiude
Department of Endocrinology, Shandong Provincial Hospital, Shandong University; Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Jinan, Shandong, China.
Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China.
Diabetes Obes Metab. 2025 May;27(5):2613-2625. doi: 10.1111/dom.16262. Epub 2025 Feb 19.
The growing epidemic of overweight and obesity elevates disease risks, with metabolic disorders and inflammation critically involved in the pathogenic mechanisms. This study refines the subtyping of overweight and obesity using metabolic and inflammatory markers to enhance risk assessment and personalized prevention.
Based on the UK Biobank, this retrospective study included participants classified as overweight or obese (BMI ≥25 kg/m). K-means clustering was performed using metabolic and inflammatory biomarkers. Multivariate Cox regression analysis assessed the risk of complications and mortality over a follow-up period of 13.5 years. Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) explored cluster-specific genetic traits.
Among 126 145 participants (mean [IQR] age: 55.0 [14.0] years; 61 983 males [49.1%]), five clusters were identified: (1) Low Metabolic Risk-related, (2) Hypertension-Related, (3) Mixed Hyperlipidemia-Related, (4) Elevated Lipoprotein(a)-Related and (5) High BMI and Inflammation-Related. Cluster 1 exhibited a lower risk of complications than other clusters. Cluster 2 had the highest incidence of stroke, linked to variants affecting blood circulation. Cluster 3 showed the highest risks for ischaemic heart disease, characterized by variants enriched in cholesterol metabolism pathways. Cluster 4 was associated with high cardiovascular risks. Cluster 5 had the highest risks for diabetes, asthma, chronic obstructive pulmonary disease, osteoarthritis and mortality, linked to obesity-related genetic variants. We also proposed a method for applying this classification in clinical settings.
This classification provides insights into the heterogeneity of individuals with overweight and obesity, aiding in the identification of high-risk patients who may benefit from targeted interventions.
超重和肥胖的流行日益严重,增加了疾病风险,代谢紊乱和炎症在致病机制中起关键作用。本研究利用代谢和炎症标志物对超重和肥胖进行亚型分类,以加强风险评估和个性化预防。
基于英国生物银行,这项回顾性研究纳入了被分类为超重或肥胖(BMI≥25 kg/m)的参与者。使用代谢和炎症生物标志物进行K均值聚类。多变量Cox回归分析评估了13.5年随访期内并发症和死亡的风险。全基因组关联研究(GWAS)和全表型组关联研究(PheWAS)探索了特定聚类的遗传特征。
在126145名参与者中(平均[IQR]年龄:55.0[14.0]岁;61983名男性[49.1%]),确定了五个聚类:(1)低代谢风险相关型,(2)高血压相关型,(3)混合性高脂血症相关型,(4)脂蛋白(a)升高相关型和(5)高BMI和炎症相关型。聚类1的并发症风险低于其他聚类。聚类2的中风发病率最高,与影响血液循环的变异有关。聚类3的缺血性心脏病风险最高,其特征是富含胆固醇代谢途径的变异。聚类4与高心血管风险相关。聚类5患糖尿病、哮喘、慢性阻塞性肺疾病、骨关节炎和死亡的风险最高,与肥胖相关的遗传变异有关。我们还提出了一种在临床环境中应用这种分类的方法。
这种分类为超重和肥胖个体的异质性提供了见解,有助于识别可能从针对性干预中受益的高危患者。