Evidence map›Paper›PMID 42250232›Full record

ArticleAnnals of medicine2026

A body roundness index (BRI)-based predictive model for metabolic syndrome in perimenopausal and postmenopausal women-from a cross-sectional machine learning study to a longitudinal dynamic assessment.

Yue Xi, Qiyue Sun, Yining Han, Pengxiang Zhu, Jiaxin Guo, Beining Zhang, Jiacheng Fan, Zhijun Hong, Xiaofeng Li

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Article in Annals of 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.

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5 · Who and what money

Authors and funding

9 authors.

Yue XiDepartment of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
Qiyue SunDepartment of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
Yining HanDepartment of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
Pengxiang ZhuDepartment of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
Jiaxin GuoDepartment of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
Beining ZhangDepartment of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
Jiacheng FanDepartment of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
Zhijun HongThe Health Management Center, The First Affiliated Hospital of Dalian Medical University, Dalian, China.ORCID 0000-0002-7707-7570
Xiaofeng LiDepartment of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.ORCID 0000-0002-1786-9154

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsMetabolic syndrome (MetS) is highly prevalent among perimenopausal and postmenopausal women and poses a major public health challenge because of its association with cardiovascular disease, type 2 diabetes, and premature mortality. However, prediction tools for this population remain limited. Therefore, this study aimed to develop a Body Roundness Index (BRI)-based prediction model for MetS by integrating cross-sectional machine learning and longitudinal assessment. METHODS AND

resultsCross-sectional models were trained using NHANES 2007-2020 and validated in the Affiliated Hospital of Dalian University (2023-2024). Sixteen predictors were selected

conclusionBRI is significantly associated with MetS in perimenopausal and postmenopausal women. The ANN model provides an efficient cross-sectional screening tool, while incorporating longitudinal trajectories of BRI and key laboratory indicators enhances long-term MetS risk prediction.

Indexed as

Machine LearningMetabolic SyndromePerimenopausePostmenopausePredictive Learning ModelsCross-Sectional StudiesFemaleHumansLongitudinal StudiesMiddle AgedNeural Networks, ComputerNutrition SurveysRisk Factorsbody roundness indexmachine learningMetabolic syndromemulticenterwomen during the perimenopausal and postmenopausal periods

Identifiers

PMID42250232
PMCPMC13244513

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LicenceCC BY-NC
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