Evidence map›Paper›PMID 40511588›Full record

ArticleJournal of cachexia, sarcopenia and muscle2025

Delineating Life-Course Percentile Curves and Normative Values of Multi-Systemic Ageing Metrics in the United Kingdom, the United States, and China.

Liming Zhang, Jiening Yu, Xueqing Jia, Zichang Su, Yingying Hu, Jingyun Zhang, Wei Yang, Xi Chen, Emiel O Hoogendijk, Huiqian Huang and 1 more

Abstract read
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Liming ZhangSecond Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, China.
Jiening YuSecond Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, China.
Xueqing JiaSecond Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, China.
Zichang SuZhejiang University School of Medicine, Hangzhou, China.
Yingying HuSchool of Public Affairs, Zhejiang University, Hangzhou, China.
Jingyun ZhangSecond Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, China.
Wei YangDepartment of Global Health and Social Medicine, Institute of Gerontology, King's College London, London, UK.
Xi ChenDepartment of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA.
Emiel O HoogendijkDepartment of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC-Location VU University Medical Center, Amsterdam, the Netherlands.
Huiqian HuangBasic and Translational Research Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Zuyun LiuSecond Affiliated Hospital, School of Public Health, Zhejiang Key Laboratory of Intelligent Preventive Medicine, Zhejiang University School of Medicine, Hangzhou, China.

Funding

China Health and Retirement Longitudinal StudyR01AG037031 · NIA · PEKING UNIVERSITY · PI STRAUSS, JOHN A, WANG, YAFENG · 2010 to 2024
$15.1M
A Life Course Approach to Understanding Racial and Ethnic Disparities in Alzheimer's Disease and Related Dementias and Health CareR01AG077529 · NIA · YALE UNIVERSITY · PI Xi Chen · 2022 to 2026
$3.6M
Fundamental Research Funds for the Central Universities, Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province 2020E10004National Natural Science Foundation of China 72374180National Natural Science Foundation of China 82171584National Natural Science Foundation of China 82401856NIA NIH HHS R01 AG037031NIA NIH HHS R01 AG077529'Pioneer' and 'Leading Goose' R&D Programs of Zhejiang Province 2023C03163'Pioneer' and 'Leading Goose' R&D Programs of Zhejiang Province 2025C02104US National Institute on Aging R01AG037031US National Institute on Aging R01AG077529Zhejiang University Global Partnership FundZhejiang University School of Public Health Interdisciplinary Research Innovation Team Development Project
6 · The paper itself

Abstract

backgroundAgeing is a complex and multi-dimensional process that manifests heterogeneities across different organs/systems, individuals and countries. We aimed to delineate the life-course percentile curves and establish the normative values of multi-systemic (e.g., muscle-skeletal, brain, cardiovascular and pulmonary) ageing metrics for people under distinct sociodemographic contexts (i.e., sex, income and education).

methodsThree national datasets, the UKB (the United Kingdom), the NHANES (the United States) and the CHARLS (China) were utilized for the analyses. We selected 14 ageing metrics (e.g., body mass index, grip strength, fat-free mass index, bone mineral content [BMC], bone mineral density [BMD], diastolic blood pressure, cognitive function and frailty index_Lab) that represent the functions of different organs/systems and plotted their sex-, educational- and income-specific percentile curves utilizing the GMALSS model. We also estimated the age-specific normative values for each ageing metric in distinct sociodemographic contexts.

resultsThe functions of all metrics, except for cognitive function, manifested a progressive decline or maintained stability after adulthood (20s), especially after middle age (40s-50s). The cognitive function showed an evident decline in old age (70s-75s) (e.g., in the CHARLS: the median [IQR] cognitive function scores were 11.6 [9.1, 13.8], 10.3 [7.5, 12.9], 8.3 [5.5, 11.0] at the ages of 60, 70 and 80 for males, respectively). In the stratified analyses, males and females manifested disparities in percentile curves of ageing metrics involving the muscle-skeletal and cardiovascular systems. For instance, BMC and BMD manifested an evident decline after middle age in females, whereas they showed a slow decline after adulthood in males. Notably, we observed substantial income and educational disparities in percentile curves of several ageing metrics within Chinese participants: the 'low-income' and 'low-education' subgroups manifested an evident decline in ageing metrics (e.g., grip strength and frailty index_Lab) representative of multiple systems. By contrast, these income or educational disparities were not observed in the British and American participants.

conclusionsOur investigation delineated the potential heterogeneities and socioeconomic disparities in percentile curves of multi-systemic ageing metrics and provided their age-specific normative values tailored to different sexes and socioeconomic contexts based on three national datasets. This study may serve as a proof-of-concept for understanding the multi-dimensional signature of systemic ageing and calls for policies to promote health equity across nations when facing dramatic global ageing.

Indexed as

AgingAdultAgedAged, 80 and overChinaFemaleHumansMaleMiddle AgedReference ValuesUnited KingdomUnited StatesYoung Adultcohort studyheterogeneitymulti‐dimensional ageing metricnormative valuepercentile curvesociodemographic disparity

Identifiers

PMID40511588
PMCPMC12163542

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.