Evidence map›Paper›PMID 37716958›Full record

ArticleBMC geriatrics2023

Plant-based dietary pattern and low muscle mass: a nation-wide cohort analysis of Chinese older adults.

Longbing Ren, Yuhong Tang, Rui Yang, Yang Hu, Jingjing Wang, Shaojie Li, Mingzhi Yu, Yuling Jiang, Zhouwei Liu, Yifei Wu and 4 more

Open access · goldAbstract read
In one paragraph

Article in BMC geriatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
4.4field-weighted citation impact, top 5% of its field
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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

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  17. Exerkines and myokines in aging sarcopenia.Frontiers in endocrinology · 2025
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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

14 authors at 4 institutions in 3 countries.

Longbing Ren *China Center for Health Developments, Peking University, Beijing, China.
Yuhong Tang *School of Medicine, Tongji University, Shanghai, China.
Rui YangChina Center for Health Developments, Peking University, Beijing, China.
Yang HuChina Center for Health Developments, Peking University, Beijing, China.
Jingjing WangChina Center for Health Developments, Peking University, Beijing, China.
Shaojie LiChina Center for Health Developments, Peking University, Beijing, China.
Mingzhi YuChina Center for Health Developments, Peking University, Beijing, China.
Yuling JiangChina Center for Health Developments, Peking University, Beijing, China.
Zhouwei LiuChina Center for Health Developments, Peking University, Beijing, China.
Yifei WuChina Center for Health Developments, Peking University, Beijing, China.
Ziqi DongChina Center for Health Developments, Peking University, Beijing, China.
Yi ZengCenter for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing, China.
Faqin LvUltrasonic Department, The Third Medical Center of Chinese People's Liberation, Army General Hospital, Beijing, China. lvjin8912@163.com.
Yao YaoChina Center for Health Developments, Peking University, Beijing, China. yao.yao@bjmu.edu.cn.
Peking University · CNChinese PLA General Hospital · CNMinistry of Education · KRTongji University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt remains unclear whether plant-based or animal-based dietary patterns are more beneficial for older adults more in maintaining muscle mass. Using a prospective cohort with nationwide sample of China older adults in this study, we aimed to examine the relationship between adhering to plant-based diet patterns or animal-based diet patterns and muscle loss.

methodsWe included 2771 older adults (≥ 65 years) from the Chinese Longitudinal Health Longevity Survey (CLHLS) with normal muscle mass at baseline (2011 and 2014 waves), which followed up into 2018. Plant-based dietary pattern scores and preference subgroups were constructed using 16 common animal-based and plant-based food frequencies. We used the corrected appendicular skeletal muscle mass (ASM) prediction formula to assess muscle mass. We applied the Cox proportional hazard risk regression to explore associations between dietary patterns and low muscle mass (LMM).

resultsDuring a mean of 4.1 years follow-up, 234 (8.4%) participants with normal muscle mass at baseline showed LMM. The plant-based dietary pattern reduced the risk of LMM by 5% (Hazard Ratios [HR]: 0.95, 95% confidence intervals [95%CI]: 0.92-0.97). In addition, a high plant-based food company with a high animal-based food intake pattern reduced the risk of LMM by 60% (HR: 0.40, 95% CI: 0.240-0.661) and 73% (HR: 0.27, 95% CI: 0.11-0.61) in the BADL disability and IADL disability population compared with a low plant-based food and high animal-based food intake, whereas a high plant-based food and low animal-based food intake was more beneficial in reducing the risk of LMM in the normal BADL functioning (HR: 0.57, 95% CI: 0.35-0.90) and IADL functioning (HR: 0.51, 95% CI: 0.28-0.91) population.

conclusionsWhen it comes to maintaining muscle mass in older Chinese people with functional independence, a plant-based diet pattern is more beneficial and effective than the animal-based one. People with functional dependence may profit from a combination of plant-based and animal-based diets to minimize muscle loss.

Indexed as

Diet, VegetarianEast Asian PeopleMuscle, SkeletalMuscular AtrophyAgedDietDiet, High-ProteinFunctional StatusHumansProspective StudiesAnimal-based foodFunctional dependencyMuscle massOlder peoplePlant-based dietary patternSarcopenia prevention

Identifiers

PMID37716958
PMCPMC10505314
OpenAlexW4386799971

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.