Evidence map›Paper›PMID 42799220›Full record

Observational studyGlobal heart2026

Longitudinal Trajectories of Body Roundness Index and Risk of Incident Hypertension in Non-Diabetic Elderly Adults: A Prospective Nationwide Cohort Study Involving Two Cohorts.

Xueping Gao, Yimou Liu, Bo Xiong, Yuan Xu, Jing Huang, Qiaoqiao Li

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in Global heart, 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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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Xueping GaoDepartment of Geriatrics, The Second Affiliated Hospital of Chongqing Medical University, No. 76 Linjiang Road, Chongqing, 400010, China.
Yimou LiuDepartment of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, No. 76 Linjiang Road, Chongqing, 400010, China.
Bo XiongDepartment of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, No. 76 Linjiang Road, Chongqing, 400010, China.
Yuan XuDepartment of Pathology, Zhongshan Hospital Fudan University, Shanghai, 200000, China.
Jing HuangDepartment of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, No. 76 Linjiang Road, Chongqing, 400010, China.
Qiaoqiao LiDepartment of Geriatrics, The Second Affiliated Hospital of Chongqing Medical University, No. 76 Linjiang Road, Chongqing, 400010, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Central adiposity is a key determinant of hypertension, yet most studies rely on single-time-point measures or include individuals with diabetes, potentially confounding associations. The relationship between long-term body roundness index (BRI) trajectories and incident hypertension in non-diabetic populations remains unclear. Methods: We conducted a longitudinal analysis of non-diabetic middle-aged and older adults from the China Health and Retirement Longitudinal Study (CHARLS), with external validation using the English Longitudinal Study of Ageing (ELSA). In CHARLS, BRI measurements from Waves 1-3 (2011-2016) were used to construct trajectories, and incident hypertension was assessed at Wave 4 (2018-2020). In ELSA, trajectories were constructed using BRI measurements from Waves 2, 4, and 6 (2004-2013), and incident hypertension was assessed at Wave 8 (2016-2017). Associations were examined using multivariable logistic regression, with subgroup and sensitivity analyses. Machine learning models were additionally developed for multivariable hypertension risk prediction. Results: Three BRI trajectories were identified in both cohorts: low-level inverted U-shaped, moderate-level inverted U-shaped, and high-level increasing trajectories. Higher BRI trajectories were associated with a graded increase in incident hypertension risk in CHARLS. Compared with the low-level inverted U-shaped trajectory, the adjusted odds ratios (95% confidence intervals) were 1.40 (1.13-1.73) for the moderate-level inverted U-shaped trajectory and 1.76 (1.21-2.56) for the high-level increasing trajectory. Directionally consistent findings were observed in ELSA, particularly for the high-level increasing trajectory. Findings remained robust in subgroup and sensitivity analyses. Machine learning analyses showed that BRI trajectory provided complementary predictive information when combined with clinical and biomarker variables. Conclusions: Longitudinal BRI patterns were associated with incident hypertension in non-diabetic adults. Repeated assessment of BRI may provide complementary information for identifying individuals at elevated hypertension risk, although its causal and clinical implications require further investigation.

Indexed as

AdiposityHypertensionAgedBody Mass IndexChinaFemaleFollow-Up StudiesHumansIncidenceLongitudinal StudiesMaleMiddle AgedProspective StudiesRisk AssessmentRisk Factorsbody roundness indexhypertensionShapley additive explanationsstacking ensemble machine learningtrajectory analysis

Identifiers

PMID42799220
PMCPMC13614250

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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.