Evidence map›Paper›PMID 42903180›Full record

ArticleFrontiers in nutrition2026

The relationship between meat consumption and frailty among community-residing older adults in Nanjing municipality, China.

Zhao Dong, Qing Ye, Caihong Hu, Yunting Xu, Guofeng Ao, Luyan Yang, Fengli Li, Shifen Jia, Yaqing Xiong, Yu Dou

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Zhao DongNanjing Vocational Health College, Nanjing, China.
Qing YeNanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China.
Caihong HuNanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China.
Yunting XuNanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China.
Guofeng AoNanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China.
Luyan YangNanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China.
Fengli LiJiangsu Province Official Hospital, Nanjing, China.
Shifen JiaJiangsu Province Official Hospital, Nanjing, China.
Yaqing XiongJiangsu Province Official Hospital, Nanjing, China.
Yu DouJiangsu Province Official Hospital, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Frailty is a common complaint in older adults. Although the world is experiencing a rapid process of aging, the relationship between meat consumption and frailty was not well-documented. This study aimed to examine the association of meat consumption with frailty among older adults in China. Methods: In this cross-sectional study, participants were randomly chosen from community-dwelling residents aged 60 years or above in Nanjing municipality, China, in 2023. Frailty, assessed using Groningen Frailty Indicator, was the outcome variable. Meat consumption frequency, measured using a validated food frequency questionnaire, was dichotomized as independent variable based on official recommendations by China Nutrition Society. Mixed-effects logistic regression models were introduced to compute odds ratios (ORs) and 95% confidence intervals (CIs) for examining associations between meat consumption and frailty. Results: Among 5,484 participants, frailty prevalence was 16.9% (95% CI = 15.9, 17.9). Significant difference in frailty prevalence was observed among participants by age (the younger-old Conclusions: A inverse relationship may exist between meat intake and frailty among community-dwelling older individuals in regional China. This study has particular significance of public health for building the healthy-aging society, suggesting that inadequate intake of meat may be a risk factor of frailty for community-dwelling older adults.

Indexed as

agingChinesefrailtymeatolder adults

Identifiers

PMID42903180
PMCPMC13680187

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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.