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Metabolic obesity phenotypes and their transitions as determinants of multimorbidity trajectories: evidence from global aging cohorts

Year: 2025

Type: article

Abstract: Objective: The high prevalence of multimorbidity poses significant challenges to the health burden of the elderly population and healthcare systems, understanding its trajectories is critical for intervention strategies. Metabolic obesity phenotypes are considered key predictors of multimorbidity, yet the specific associations between different metabolic obesity phenotypes and multimorbidity trajectories remain unclear. This study aimed to analyze the associations between metabolic obesity phenotypes and their transitions with multimorbidity trajectories based on global aging cohorts, further explore age, gender, and socioeconomic status (SES) subgroup differences, and assess the nonlinear effects of the components constituting metabolic obesity phenotypes on high-risk multimorbidity trajectories.Methods: Longitudinal data from three national cohort studies (CHARLS, ELSA, and HRS) were used, and trajectories of multimorbidity were identified through trajectory analysis. Baseline metabolic obesity phenotypes were classified into Metabolically Healthy Normal Weight (MHNW), Metabolically Healthy Obesity/Overweight (MHOO), Metabolically Unhealthy Normal Weight (MUNW), and Metabolically Unhealthy Obesity/Overweight (MUOO). The GBTM (Group-Based Trajectory Modeling) method was used to construct multimorbidity trajectories, perform logistic regression analysis on trajectory groups, adjust all covariates, and control multiple testing errors using Bonferroni correction.Findings: In baseline analysis, MU (both MUNW and MUOO) individuals showed a lower proportion of low-risk multimorbidity trajectories and a higher proportion of high-risk multimorbidity trajectories in all cohorts. The stable MUOO phenotype had the highest proportion in all cohorts. Compared with the MH (both MHNW and MHOO) group, the likelihood of MU group individuals in the low-risk trajectory significantly decreased, and the risk in the high-risk trajectory significantly increased, especially in CHARLS (OR=4.03), ELSA (OR=4.73), HRS (OR=3.01). The analysis of changes in metabolic obesity phenotypes showed that individuals with stable metabolic unhealth had the lowest risk in the low-risk trajectory, and the risk of developing high-risk trajectories significantly increased for phenotypes that had been exposed to metabolic unhealth. In particular, in CHARLS, ELSA, and HRS, individuals continuously exposed to metabolic unhealth significantly increased the risk of developing high-risk trajectories. Moreover, nonlinear analysis showed a significant nonlinear association between increased HbA1C and high-risk trajectories in all cohorts; increased BMI and decreased HDL‐C were significant in all cohorts, but no consistent nonlinear trend was observed; increased systolic pressure, diastolic pressure, and triglycerides were only significant in CHARLS. In the HRS cohort, an HbA1C threshold of approximately 8% was identified, marking a potential tipping point where the impact on multimorbidity trajectories became less pronounced. Subgroup analysis showed that individuals under the age of 60 and males had a higher risk of progressing to high-risk trajectories, while SES subgroup analysis indicated that individuals with high SES in CHARLS and medium SES in ELSA and HRS had a higher risk of high-risk trajectories. These results were validated by using the criteria of metabolic syndrome, further enhancing the robustness of the study conclusions.Interpretations: Metabolic obesity phenotypes and

Source: Diabetology & Metabolic Syndrome

DOI: 10.1186/s13098-025-01992-2