Evidence map›Paper›PMID 41019546›Full record

ArticleFrontiers in nutrition2025

Utilizing nutrition-related biomarkers to develop a nutrition-related aging clock for the chinese demographic.

Ya-Qing Ma, Ya-Min Dang, Lv-Tao Zeng, Xin Gao, Si-Jia Li, Li-Qun Zhang, Jin Li, Xiao-Yang Zhou, Shan-Shan Ren, Hong-Lei Liu and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Ya-Qing MaFifth School of Clinical Medicine, Peking University, Beijing, China.
Ya-Min DangThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Lv-Tao ZengFifth School of Clinical Medicine, Peking University, Beijing, China.
Xin GaoThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Si-Jia LiThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Li-Qun ZhangThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Jin LiThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Xiao-Yang ZhouThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Shan-Shan RenDepartment of Clinical Nutrition, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Hong-Lei LiuBeijing Advanced Innovation Center for Big Data-based Precision Medicine, Capital Medical University, Beijing, China.
Ruo-Mei QiThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Jing PangThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Ju CuiThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Tie-Mei ZhangThe Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Jian-Ping CaiFifth School of Clinical Medicine, Peking University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aims to investigate the relationship between nutrition-related biomarkers, body composition, and oxidative stress indicators in the human aging process, so as to provide new insights for understanding individual aging differences and developing targeted intervention strategies. Methods: A total of 100 healthy participants aged 26-85 years were enrolled. Plasma concentrations of 9 amino acids and 13 vitamins were quantitatively analyzed, along with urinary oxidative stress markers 8-oxoGuo and 8-oxodGuo. Body composition was assessed using bioelectrical impedance analysis (BIA). A nutrition-based aging clock model was constructed using the Light Gradient Boosting Machine algorithm, with model performance evaluated by mean absolute error (MAE) and coefficient of determination (R Results: The younger group showed significantly lower levels of oxidative stress markers compared to the older group. Multiple amino acids and vitamins exhibited age-dependent changes in plasma concentrations. The developed aging clock model demonstrated high predictive accuracy, with an MAE of 2.5877 and R Discussion: This study establishes a significant link between nutrition-related biomarkers, oxidative stress, body composition, and aging. The proposed model serves as a reliable tool for predicting biological age and offers a scientific basis for future research on aging mechanisms and personalized interventions.

Indexed as

aging biomarkersbioelectrical impedance analysisinterindividual aging variationoxidative stress markersthe nutrition-related aging clock

Identifiers

PMID41019546
PMCPMC12465629

What Socratic holds

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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 Socratic graph.