ArticleFrontiers in nutrition2026
Integrating epidemiologic modeling and explainable machine learning to evaluate body roundness index for WHO-defined high cardiovascular risk: evidence from the ChinaHEART-Luohe screening cohort.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Efficient identification of individuals at high cardiovascular disease (CVD) risk is essential for prevention in middle-aged and older adults. The body roundness index (BRI), derived from waist circumference and height, may capture body-shape-related risk beyond conventional measures. We examined the association of BRI with World Health Organization (WHO)-defined CVD high-risk status in a community-based screening population. Methods: This cross-sectional study used baseline data from the Luohe branch of the ChinaHEART cohort, a community-based health screening program in Luohe, Henan, China (March 2021 to February 2022), including adults aged 35-75 years. WHO-defined CVD high-risk status was determined using WHO CVD risk charts, with an estimated 10-year risk ≥20% classified as high risk. BRI was analyzed as a continuous variable (per 1-unit increase), quartiles, and a binary variable using a receiver operating characteristic (ROC)-derived threshold. Multivariable logistic regression, restricted cubic splines, ROC analysis with bootstrap confidence intervals, and subgroup/interaction analyses were performed. An explainable machine-learning workflow (LASSO, random forest, and SHAP) was also applied. Results: Among 6,858 participants, 1,489 (22%) were classified as WHO-defined CVD high risk. Higher BRI remained associated with high-risk status in fully adjusted models. ROC analysis showed only modest standalone discrimination, while subgroup analyses suggested heterogeneity by sex and cardiometabolic strata. In machine-learning analyses, BRI was retained among selected predictors and contributed meaningfully within the multivariable model. Conclusion: In this community screening population, BRI was positively associated with WHO-defined CVD high-risk status and may serve as a low-cost adjunct marker to prioritize individuals for comprehensive risk evaluation in primary-care screening settings.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.