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Objective <p>The high prevalence of multimorbidity poses significant challenges to the health of the elderly population and to healthcare systems. Understanding its trajectories is critical for developing intervention strategies. Metabolic obesity phenotypes are considered key predictors of multimorbidity. This study aimed to analyze the associations between metabolic obesity phenotypes and their transitions with multimorbidity trajectories.</p> Methods <p>We used data from three population-based cohorts (CHARLS, ELSA, and HRS). Multimorbidity was defined as the co-occurrence of two or more physician-diagnosed chronic conditions, assessed repeatedly during follow-up. Metabolic health was determined according to the NCEP-ATP III criteria, including elevated blood pressure, impaired fasting glucose/HbA1c, high triglycerides, low HDL-C, and abdominal obesity defined by waist circumference. Overweight/obesity was classified using body mass index thresholds specific to each cohort. Combining metabolic health and weight status, participants were categorized into four phenotypes: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy overweight/obesity (MUOO).Group-based trajectory modeling was applied to identify multimorbidity trajectories. Multinomial logistic regression was used to examine associations between metabolic obesity phenotypes and trajectory membership. We further calculated marginal standardized predicted probabilities, fixing exposure levels while averaging over covariates, to quantify the probability of each trajectory for different phenotypes.</p> Findings <p>A total of 21,532 participants were included (CHARLS: 6,503; ELSA: 6,213; HRS: 8,826). Across cohorts, multimorbidity trajectories were classified into very low-, low-, moderate-, and high-risk patterns, with high-risk groups characterized by older age, female, lower wealth, and lower physical activity. Baseline metabolically unhealthy phenotypes, especially MUOO, were consistently associated with markedly increased probabilities of following moderate- or high-risk trajectories, whereas MHNW was most protective. Transition analyses further showed that stable MUOO conferred the highest risk, while stable MHNW remained protective across cohorts.</p> Interpretations <p>Metabolic obesity phenotypes and their changes are strongly associated with multimorbidity trajectories, especially the strong association between metabolic unhealthy status and high-risk multimorbidity trajectories.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月8日

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  • Heng Wang,
  • Jun Li,
  • Shunming Liu,
  • Chingyu Cheng,
  • Moluan Zhang,
  • Jinghua Jiao,
  • Lei Liu,
  • Han Zhang

摘要

Objective

The high prevalence of multimorbidity poses significant challenges to the health of the elderly population and to healthcare systems. Understanding its trajectories is critical for developing intervention strategies. Metabolic obesity phenotypes are considered key predictors of multimorbidity. This study aimed to analyze the associations between metabolic obesity phenotypes and their transitions with multimorbidity trajectories.

Methods

We used data from three population-based cohorts (CHARLS, ELSA, and HRS). Multimorbidity was defined as the co-occurrence of two or more physician-diagnosed chronic conditions, assessed repeatedly during follow-up. Metabolic health was determined according to the NCEP-ATP III criteria, including elevated blood pressure, impaired fasting glucose/HbA1c, high triglycerides, low HDL-C, and abdominal obesity defined by waist circumference. Overweight/obesity was classified using body mass index thresholds specific to each cohort. Combining metabolic health and weight status, participants were categorized into four phenotypes: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy overweight/obesity (MUOO).Group-based trajectory modeling was applied to identify multimorbidity trajectories. Multinomial logistic regression was used to examine associations between metabolic obesity phenotypes and trajectory membership. We further calculated marginal standardized predicted probabilities, fixing exposure levels while averaging over covariates, to quantify the probability of each trajectory for different phenotypes.

Findings

A total of 21,532 participants were included (CHARLS: 6,503; ELSA: 6,213; HRS: 8,826). Across cohorts, multimorbidity trajectories were classified into very low-, low-, moderate-, and high-risk patterns, with high-risk groups characterized by older age, female, lower wealth, and lower physical activity. Baseline metabolically unhealthy phenotypes, especially MUOO, were consistently associated with markedly increased probabilities of following moderate- or high-risk trajectories, whereas MHNW was most protective. Transition analyses further showed that stable MUOO conferred the highest risk, while stable MHNW remained protective across cohorts.

Interpretations

Metabolic obesity phenotypes and their changes are strongly associated with multimorbidity trajectories, especially the strong association between metabolic unhealthy status and high-risk multimorbidity trajectories.