Behavioral-metabolic phenotypes and differential risks of diabetic kidney disease and peripheral neuropathy in type 2 diabetes: a latent class analysis
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Yue Wang1,2, Zijian Liu2, Zhengshan Zhang2
1Beijing University of Chinese Medicine, Beijing, China.
Background:
Behavioral and metabolic risk factors frequently coexist in type 2 diabetes mellitus (T2DM), but their combined patterns and associations with microvascular complications remain insufficiently characterized.
Methods:
This cohort study included 706 individuals with T2DM. Outcome-specific risk sets were established by excluding participants with prevalent diabetic kidney disease (DKD) or diabetic peripheral neuropathy (DPN) at baseline. Behavioral and metabolic factors were classified using latent class analysis. Multinomial logistic regression and Cox models, alongside socioeconomic status (SES)-stratified analyses, assessed associations among phenotypes, SES, and incident complications.
Results:
Three behavioral-metabolic phenotypes were identified: the Behavioral-Visceral Adiposity (BVA) phenotype (14.3%), characterized by unhealthy lifestyle behaviors, lower BMI, and higher visceral adiposity; the Obesity-Related Metabolic Risk (OMR) phenotype (39.8%), characterized by obesity, physical inactivity, insulin resistance, and suboptimal blood pressure control; and the Low-Risk (LR) phenotype (45.9%). Compared with the LR phenotype, the OMR phenotype showed increased risks of incident DKD (HR = 2.01; 95% CI = 1.32-3.04) and DPN (HR = 2.06; 95% CI = 1.18-3.58). The BVA phenotype was associated with increased risk of DPN (HR = 2.47; 95% CI = 1.19-5.12). SES was independently associated with phenotype membership. Associations between the OMR phenotype and DKD or DPN were mainly observed among individuals with low SES, whereas the association between the BVA phenotype and DPN was mainly observed among individuals with high SES.
Conclusions:
Three distinct behavioral-metabolic phenotypes were identified among individuals with T2DM and showed differential associations with DKD and DPN risks. Integrating behavioral patterns, metabolic profiles, and socioeconomic context into risk stratification strategies may enhance early identification and individualized prevention of microvascular complications.
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