Evidence map›Paper›PMID 39972332›Full record

ArticleBMC public health2025

Dietary and health risk behaviors for metabolic diseases in different age groups: a cross-sectional study in Chongqing, China.

Haihang Qin, Mei Zhao, Tingting Wu, Sili Zhu, Yanwen Qiao, Xun Lei, Weiwei Liu, Rong Sun

Abstract read
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

Who cites it

3 citing papers in PubMed.

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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

8 authors.

Haihang Qin *Chongqing Medical University, Chongqing, 401331, China.
Mei Zhao *Chongqing Medical University, Chongqing, 401331, China.
Tingting WuDepartment of Food and Nutrition, Silla University, Busan, Korea.
Sili ZhuChongqing Medical University, Chongqing, 401331, China.
Yanwen QiaoChongqing Medical University, Chongqing, 401331, China.
Xun LeiChongqing Medical University, Chongqing, 401331, China.
Weiwei LiuChongqing Medical University, Chongqing, 401331, China.
Rong SunChongqing Medical University, Chongqing, 401331, China. ent2002@163.com.

Funding

2021 Chongqing Natural Science Foundation Program cstc2021jcyj-msxmX0069Chongqing Medical University Future Medical Youth Innovation Team Support Program Project W0013Chongqing social science planning project, Wuqinxi and eight brocade on comparative study of college students' mental health intervention effect in Chongqing 2022NDYB196Humanities and Social Sciences Research Program of Chongqing Municipal Education Commission 22SKJD054Program for Youth Innovation in Future Medicine, Chongqing Medical University W0020
6 · The paper itself

Abstract

backgroundThe study aimed to investigate the relationship between the frequency of metabolic diseases (MetD) and dietary behaviors as well as health risk behaviors in various age groups.

methodsThis Cross-Sectional study involved 12,215 participants, and the relationship between MetD and health risk behaviors was discussed using Pearson chi-square tests and unordered binary logistics regression. The Apriori algorithm was used to explore and analyze the combination patterns of health risk behaviors.

resultsInsufficient sleep as a risk factor for MetD (for young adults, odds ratio (OR): 1.31, 95% confidence interval (CI): 1.15, 1.50; for middle-aged adults, OR: 1.15, 95% CI: 1.01, 1.33). For young adults, maladaptive emotion was a risk factor (OR: 1.45; 95% CI: 1.18, 1.80), and their risk of MetD was 1.48 times higher with three health risk behaviors compared with those with no or only one health risk behavior (95% CI: 1.27, 1.72). As for dietary behaviors, meat-based diet, greasy and salty taste preferences were associated with MetD.

conclusionsHigh-risk alcohol consumption is a risk factor for MetD in elderly adults. Insufficient sleep was a common risk factor for MetD in young and middle-aged groups. Young adults may also be affected by maladaptive emotion and the number of risk factors. Poor dietary behaviors such as meat-based diet, greasy and salty dietary taste, may increase risk, particularly among young adults. We should consider interventions systematically and comprehensively targeting modern lifestyles of different key age group populations such as young adults.

Indexed as

DietFeeding BehaviorHealth Risk BehaviorsMetabolic DiseasesAdolescentAdultAgedAge FactorsChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedRisk FactorsYoung AdultCross-sectional studyDietary behaviorsHealth risk behaviorsMetabolic disease

Identifiers

PMID39972332
PMCPMC11837718

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LicenceCC BY-NC-ND
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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.