Scholarlink

Background <p>Individuals experience heterogeneous aging processes closely associated with metabolism. However, it remains unclear whether older adults undergo different metabolic health-related aging patterns over time. We aimed to investigate the long-term metabolic health-related aging trajectories, their associations with cognitive function, and underlying molecular underpinnings among older adults.</p> Methods <p>This study was based on a longitudinal cohort of 1334 middle-aged and elderly Chinese adults (age: 57.5 ± 5.2 years; 30.7% men) with a median follow-up of 12.4 years across four visits. We used the latent class growth mixed model to identify longitudinal metabolic disturbance trajectory groups. We then examined the prospective associations of the identified longitudinal metabolic disturbance subtypes with cognitive function assessed by Addenbrooke’s Cognitive Examination-Revised (ACE-R), and with brain structure measured by magnetic resonance imaging. Leveraging repeated-measured proteome (up to three times; <i>N</i><sub>proteome</sub> = 1334 with 3417 data points) and metabolome data (up to four times; <i>N</i><sub>metabolome</sub> = 584 with 2330 data points), we explored potential mechanisms underlying the longitudinal metabolic disturbance subtypes.</p> Results <p>We identified two distinct longitudinal metabolic disturbance subtypes, i.e., metabolically unhealthy aging group (MUAG) (<i>N</i> = 270) and metabolically healthy aging group (MHAG) (<i>N</i> = 1064), characterized by differences in 13 metabolic traits and four major metabolic diseases (type 2 diabetes, hypertension, obesity, and metabolic syndrome). MUAG was associated with cognitive impairment and smaller volumes of brain grey matter, thalamus, caudate, hippocampus, and amygdala. The identified longitudinal metabolic disturbance subtypes may involve biological pathways of immune regulation, key enzyme activity regulation, branched-chain amino acid biosynthesis, and citrate cycle. We identified 11 serum proteins and 31 serum metabolites associated with the MUAG, and revealed a protein-metabolite network involving 26 protein-metabolite associations.</p> Conclusions <p>We identified two longitudinal metabolic health-related aging subtypes among middle-aged and elderly adults and uncovered their underlying molecular underpinnings. Our findings may help improve health management of aging populations, offer mechanistic insights, and inform new therapeutic targets for metabolically unhealthy aging.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Long-term metabolically unhealthy aging, its underlying molecular underpinnings, and association with cognitive impairment: a 12.4-year longitudinal cohort study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

查看全文
  • Kui Deng,
  • Zheqing Zhang,
  • Ke Zhang,
  • Kejun Zhou,
  • Xinyue Wang,
  • Shize Jia,
  • Dongmei Ru,
  • Zilong Lu,
  • Yan Yan,
  • Fengjie Huang,
  • Tianlu Chen,
  • Ju-Sheng Zheng,
  • Guoxiang Xie,
  • Yujing Huang,
  • Yu-ming Chen

摘要

Background

Individuals experience heterogeneous aging processes closely associated with metabolism. However, it remains unclear whether older adults undergo different metabolic health-related aging patterns over time. We aimed to investigate the long-term metabolic health-related aging trajectories, their associations with cognitive function, and underlying molecular underpinnings among older adults.

Methods

This study was based on a longitudinal cohort of 1334 middle-aged and elderly Chinese adults (age: 57.5 ± 5.2 years; 30.7% men) with a median follow-up of 12.4 years across four visits. We used the latent class growth mixed model to identify longitudinal metabolic disturbance trajectory groups. We then examined the prospective associations of the identified longitudinal metabolic disturbance subtypes with cognitive function assessed by Addenbrooke’s Cognitive Examination-Revised (ACE-R), and with brain structure measured by magnetic resonance imaging. Leveraging repeated-measured proteome (up to three times; Nproteome = 1334 with 3417 data points) and metabolome data (up to four times; Nmetabolome = 584 with 2330 data points), we explored potential mechanisms underlying the longitudinal metabolic disturbance subtypes.

Results

We identified two distinct longitudinal metabolic disturbance subtypes, i.e., metabolically unhealthy aging group (MUAG) (N = 270) and metabolically healthy aging group (MHAG) (N = 1064), characterized by differences in 13 metabolic traits and four major metabolic diseases (type 2 diabetes, hypertension, obesity, and metabolic syndrome). MUAG was associated with cognitive impairment and smaller volumes of brain grey matter, thalamus, caudate, hippocampus, and amygdala. The identified longitudinal metabolic disturbance subtypes may involve biological pathways of immune regulation, key enzyme activity regulation, branched-chain amino acid biosynthesis, and citrate cycle. We identified 11 serum proteins and 31 serum metabolites associated with the MUAG, and revealed a protein-metabolite network involving 26 protein-metabolite associations.

Conclusions

We identified two longitudinal metabolic health-related aging subtypes among middle-aged and elderly adults and uncovered their underlying molecular underpinnings. Our findings may help improve health management of aging populations, offer mechanistic insights, and inform new therapeutic targets for metabolically unhealthy aging.