Evidence map›Paper›PMID 40753232›Full record

ArticleJournal of translational medicine2025

The role of irregular eating behaviors in metabolic dysfunction-associated steatotic liver disease: evidence from a multicenter cross-sectional study in China.

Xingxing Ren, Miao Zhang, Xiaoyang Sun, Lili Zheng, Yufang Bi, Qiang Li, Lirong Sun, Fusheng Di, Yushan Xu, Dalong Zhu and 8 more

Abstract readMulticenter Study
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

18 authors.

Xingxing Ren *Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.ORCID 0009-0002-9099-1786
Miao Zhang *Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Xiaoyang Sun *Department of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Lili ZhengDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yufang BiShanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine Tumors of Ministry of Shanghai, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Qiang LiDepartment of Endocrinology and Metabolism, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Lirong SunKey Laboratory of Hormones and Development (Ministry of Health), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Metabolic Diseases Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Fusheng DiDepartment of Endocrinology and Metabolism, The Third Central Hospital of Tianjin, Tianjin, China.
Yushan XuDepartment of Endocrinology and Metabolism, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Dalong ZhuDepartment of Endocrinology and Metabolism, Drum Tower Hospital Affiliated to Nanjing University Medical School, Nanjing, Jiangsu, China.
Yanyan GaoDepartment of Endocrinology and Metabolism, Affiliated Hospital of Medical College, Qingdao University, Qingdao, China.
Yuqian BaoShanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Department of Endocrinology and Metabolism, Shanghai Jiaotong University Affiliated Sixth People's Hospital, Shanghai, China.
Yao WangDepartment of Endocrinology and Metabolism, Zhongda Hospital Affiliated to Southeast University Medical School, Nanjing, China.
Lanjie HeEndocrine Testing Center, General Hospital of Ningxia Medical University, Yinchuan, China.
Xin GaoDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Jian GaoDepartment of Clinical Nutrition, Zhongshan Hospital, Center of Clinical Epidemiology, EBM of Fudan University, Fudan University, Shanghai, China. gao.jian@zs-hospital.sh.cn.
Mingfeng XiaDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. dr_xiamingfeng@163.com.
Hua BianDepartment of Endocrinology and Metabolism, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. zhongshan_bh@126.com.ORCID 0000-0001-8449-0665

Funding

National Natural Science Foundation of China 82000821National Natural Science Foundation of China 82370870Noncommunicable Chronic Diseases-National Science and Technology Major Project 2023ZD0508703Shanghai Municipal Health Commission 202240295Shanghai Sailing Program 20YF1406300the Science and Technology Commission of Shanghai Municipality 22Y31900302
6 · The paper itself

Abstract

backgroundIrregular eating patterns, such as skipping breakfast and late dinner consumption, have been linked to adverse metabolic profiles. However, the impact of these behaviors or their combined effect on metabolic dysfunction-associated steatotic liver disease (MASLD) remains unclear.

methodsIn this multicenter cross-sectional study, we analyzed data from 1965 adults recruited from diabetes, obesity, and metabolic disease clinics in six municipalities/provinces in China. Participants were categorized for analysis based on two irregular eating habits assessed by monthly frequency: skipping breakfast and late dinner consumption. For individual analyses, each habit was treated as a binary variable, dividing participants into two groups (e.g., skippers vs. non-skippers). For the combined analysis, these habits were aggregated to form a three-level ordinal variable, which classified participants based on the number of habits they reported: "No" (zero), "Medium" (one), or "Severe" (both). We employed logistic regression models to calculate the odds ratios (ORs) for MASLD associated with skipping breakfast, late dinner consumption, and their combination, after adjusting for key demographic, lifestyle, and metabolic factors.

resultsThe prevalence of MASLD was higher among breakfast skippers compared to those who ate breakfast regularly (52.9% vs. 47.2%, p = 0.023), and similarly higher among late dinner eaters compared to those with regular dinner times (54.4% vs. 46.8%, p = 0.003). In the combined analysis, a graded relationship was observed across the three-level ordinal variable, with MASLD prevalence increasing from 46.5% in the "No" irregular eating group, to 50.3% in the "Medium" irregular eating group, and 55.5% in the "Severe" irregular eating group (p = 0.01). After multivariable adjustment, skipping breakfast, late dinner consumption, and the combination of these behaviors were each independently associated with higher odds of MASLD. This was highlighted by a clear dose-response relationship for the combined behaviors, where the odds of MASLD systematically increased with the severity of irregular eating (from "No" to "Medium" to "Severe"). Notably, the "Severe" irregular eating group, representing those with both habits, had a 65% higher odds of MASLD compared to those with none (OR = 1.65, 95% CI 1.17-2.34, p = 0.004).

conclusionsOur study indicates that irregular eating behaviors, such as skipping breakfast and late dinner consumption, as well as their combination, are significantly associated with a higher prevalence of MASLD.

Indexed as

Fatty LiverFeeding BehaviorMetabolic DiseasesAdultChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedOdds RatioPrevalenceIrregular eating behaviorsLate dinner consumptionMetabolic dysfunction-associated steatotic liver diseaseSkipping breakfast

Identifiers

PMID40753232
PMCPMC12318419

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.