Evidence map›Paper›PMID 42359062›Full record

ArticleFrontiers in medicine2026

Association between predicted body composition and metabolic-associated fatty liver disease: a case-control study in a Chinese population.

Shuang Liang, Mingli Li, Jiali Lin, Shengzhu Huang, Yulan Bai, Zefeng Chen, Xinyang Long, Yuanfan Li, Qianqian Luo, Zheng Wen and 1 more

Abstract read
In one paragraph

Article in Frontiers in medicine, 2026. 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
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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

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

11 authors.

Shuang LiangDepartment of Postgraduate and Continuing Education, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Mingli LiCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.
Jiali LinResearch Center of Health Management, Guangxi Zhuang Autonomous Region People's Hospital, Guangxi Academy of Medical Sciences, Nanning, Guangxi, China.
Shengzhu HuangCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.
Yulan BaiDepartment of Hospital Infection Management, Guangxi Medical University College of Stomatology, Nanning, Guangxi, China.
Zefeng ChenDepartment of Scientific Research and Discipline Construction, Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nanning, Guangxi, China.
Xinyang LongCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.
Yuanfan LiDepartment of Hospital Infection Management, Wuming Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Qianqian LuoDepartment of Hospital Infection Management, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
Zheng WenCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.
Zengnan MoCenter for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Metabolic-associated fatty liver disease (MASLD) is a growing global health concern. Few studies have examined the independent associations of fat mass (FM) and lean body mass (LBM) with MASLD. This study investigated the associations of predicted FM, body fat percentage (BF%), LBM, and the FM-to-LBM ratio with MASLD risk in a physical examination population from China. Methods: This 1:1 age- and sex-matched case-control study included 1,683 MASLD patients and 1,683 controls. Predicted FM, BF%, LBM, and FM/LBM were calculated using equations derived from the National Health and Nutrition Examination Survey (NHANES). Conditional logistic regression was used to analyze associations between body composition indices and MASLD risk after adjusting for confounders. Results: Levels of BMI, blood pressure, blood lipids, fasting glucose, liver enzymes, and uric acid were significantly higher in the MASLD group (all Conclusion: Elevated levels of predicted FM, BF%, and FM/LBM were independently associated with increased MASLD risk, showing non-linear dose-response relationships. Higher predicted LBM was also associated with increased MASLD risk; however, this finding should be interpreted with caution. These associations were more pronounced in younger, non-obese, and normotensive individuals.

Indexed as

body compositionfat masslean body massmetabolic-associated fatty liver diseaserisk factor

Identifiers

PMID42359062
PMCPMC13290577

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