Clustering of lifestyle behaviours and analysis of their associations with MAFLD: a cross-sectional study of 196,515 individuals in China.

Adult Female Humans Male Cross-Sectional Studies Life Style Cluster Analysis Multivariate Analysis China / epidemiology Non-alcoholic Fatty Liver Disease
Cluster analysis Lifestyle pattern Lifestyle scoring system MAFLD Multivariate analysis of variance Principal component analysis

Journal

BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562

Informations de publication

Date de publication:
21 Nov 2023
Historique:
received: 25 10 2022
accepted: 07 11 2023
medline: 27 11 2023
pubmed: 22 11 2023
entrez: 22 11 2023
Statut: epublish

Résumé

The aggregation of lifestyle behaviours and their association with metabolic-associated fatty liver disease (MAFLD) remain unclear. We identified lifestyle patterns and investigated their association with the risk of developing MAFLD in a sample of Chinese adults who underwent annual physical examinations. Annual physical examination data of Chinese adults from January 2016 to December 2020 were used in this study. We created a scoring system for lifestyle items combining a statistical method (multivariate analysis of variance) and clinical expertise (Delphi method). Subsequently, principal component analysis and two-step cluster analysis were implemented to derive the lifestyle patterns of men and women. Binary logistic regression analysis was used to explore the prevalence risk of MAFLD among lifestyle patterns stratified by sex. A total of 196,515 subjects were included in the analysis. Based on the defined lifestyle scoring system, nine and four lifestyle patterns were identified for men and women, respectively, which included "healthy or unhealthy" patterns and mixed patterns containing a combination of healthy and risky lifestyle behaviours. This study showed that subjects with an unhealthy or mixed pattern had a significantly higher risk of developing MAFLD than subjects with a relatively healthy pattern, especially among men. Clusters of unfavourable behaviours are more prominent in men than in women. Lifestyle patterns, as important factors influencing the development of MAFLD, show significant sex differences in the risk of MAFLD. There is a strong need for future research to develop targeted MAFLD interventions based on the identified behavioural clusters by sex stratification.

Sections du résumé

BACKGROUND BACKGROUND
The aggregation of lifestyle behaviours and their association with metabolic-associated fatty liver disease (MAFLD) remain unclear. We identified lifestyle patterns and investigated their association with the risk of developing MAFLD in a sample of Chinese adults who underwent annual physical examinations.
METHODS METHODS
Annual physical examination data of Chinese adults from January 2016 to December 2020 were used in this study. We created a scoring system for lifestyle items combining a statistical method (multivariate analysis of variance) and clinical expertise (Delphi method). Subsequently, principal component analysis and two-step cluster analysis were implemented to derive the lifestyle patterns of men and women. Binary logistic regression analysis was used to explore the prevalence risk of MAFLD among lifestyle patterns stratified by sex.
RESULTS RESULTS
A total of 196,515 subjects were included in the analysis. Based on the defined lifestyle scoring system, nine and four lifestyle patterns were identified for men and women, respectively, which included "healthy or unhealthy" patterns and mixed patterns containing a combination of healthy and risky lifestyle behaviours. This study showed that subjects with an unhealthy or mixed pattern had a significantly higher risk of developing MAFLD than subjects with a relatively healthy pattern, especially among men.
CONCLUSIONS CONCLUSIONS
Clusters of unfavourable behaviours are more prominent in men than in women. Lifestyle patterns, as important factors influencing the development of MAFLD, show significant sex differences in the risk of MAFLD. There is a strong need for future research to develop targeted MAFLD interventions based on the identified behavioural clusters by sex stratification.

Identifiants

DOI: 10.1186/s12889-023-17177-3 PMID: 37990228 PMC: PMC10664514
pubmed: 37990228
doi: 10.1186/s12889-023-17177-3
pii: 10.1186/s12889-023-17177-3
pmc: PMC10664514
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

2303

Subventions

Organisme : Fundamental Research Funds for Central Universities of the Central South University
ID : 2021zzts1016
Organisme : Natural Science Foundation of Hunan Province
ID : 2022JJ70066
Organisme : Key Research and Development Program of Hunan Province of China
ID : 2020SK2089
Organisme : Key Research and Development Program of Hunan Province of China
ID : 2021SK2024
Organisme : National Natural Science Foundation of China
ID : 72074225

Informations de copyright

© 2023. The Author(s).

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Auteurs

Bingqian Zhou (B)

Department of Health Management Center, The Third Xiangya Hospital, Central South University, No.138, Tongzipo Road, Changsha, 410013, China.
Xiangya Nursing School, Central South University, Changsha, 410013, China.

Ni Gong (N)

Department of Health Management Center, The Third Xiangya Hospital, Central South University, No.138, Tongzipo Road, Changsha, 410013, China.
Xiangya Nursing School, Central South University, Changsha, 410013, China.

Qingnan He (Q)

Department of Health Management Center, The Third Xiangya Hospital, Central South University, No.138, Tongzipo Road, Changsha, 410013, China.

Xinjuan Huang (X)

Xiangya Nursing School, Central South University, Changsha, 410013, China.

Jingchi Zhu (J)

Jishou University School of Medicine, Jishou, 416000, China.

Lijun Zhang (L)

State Key Laboratory of Ultrasound in Medicine and Engineering, School of Public Health and Management, Chongqing Medical University, No.1, Medical College Road , Chongqing, 400016, China.

Yanyan Huang (Y)

State Key Laboratory of Ultrasound in Medicine and Engineering, School of Public Health and Management, Chongqing Medical University, No.1, Medical College Road , Chongqing, 400016, China.

Xinyun Tan (X)

Xiangya Nursing School, Central South University, Changsha, 410013, China.

Yuanqin Xia (Y)

Jishou University School of Medicine, Jishou, 416000, China.

Yu Zheng (Y)

Xiangya Nursing School, Central South University, Changsha, 410013, China.

Qiuling Shi (Q)

State Key Laboratory of Ultrasound in Medicine and Engineering, School of Public Health and Management, Chongqing Medical University, No.1, Medical College Road , Chongqing, 400016, China. qshi@cqmu.edu.cn.

Chunxiang Qin (C)

Department of Health Management Center, The Third Xiangya Hospital, Central South University, No.138, Tongzipo Road, Changsha, 410013, China. chunxiangqin@csu.edu.cn.
Xiangya Nursing School, Central South University, Changsha, 410013, China. chunxiangqin@csu.edu.cn.

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