ArticleFrontiers in endocrinology2026
Atherogenic, cardiometabolic, and adiposity composite indices for identifying prehypertension and hypertension: conventional regression and explainable machine learning.
Article in Frontiers in endocrinology, 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Prehypertension and hypertension are cardiovascular risk states. We examined whether biomarker-derived atherogenic, cardiometabolic, and adiposity indices were associated with blood pressure status in adults undergoing health examinations. Methods: This hospital-based cross-sectional study included 2, 136 adults from the Health Examination Center of the Second Affiliated Hospital of Shandong First Medical University between 2024 and 2025. Blood pressure was classified as normal, prehypertension, or hypertension. Composite indices were reconstructed from anthropometric, biochemical, and questionnaire variables. Analyses included multivariable regression, quartiles, 3-knot restricted cubic splines, mixture analysis, incremental evaluation, explainable machine learning, subgroup analyses, and sensitivity analyses. Results: The cohort included 983 participants with normal blood pressure (46.02%), 422 with prehypertension (19.76%), and 731 with hypertension (34.22%). In fully adjusted analyses, CVAI (OR, 2.17 (1.80-2.62); P<0.01), TyG-WC (OR, 1.94 (1.64-2.28); P<0.01), TyG-BMI (OR, 1.93 (1.66-2.26); P<0.01), and METS-IR (OR, 1.91 (1.63-2.24); P<0.01) showed the strongest standardized associations with abnormal blood pressure. The highest quartiles of CVAI, METS-IR, and TyG-WHtR had higher odds than the lowest quartiles (all P for trend <0.01). Cross-validated AUROC improved from 0.80 to 0.83 after adding clinical and index information. SHAP highlighted CVAI, age, fasting glucose, waist circumference, eGFR, and uric acid. Conclusions: Composite indices integrating visceral adiposity, insulin resistance, and lipid burden were associated with prehypertension and hypertension. They may help characterize cardiometabolic risk clustering but are not substitutes for direct blood pressure measurement or diagnostic evaluation. These findings support association and screening-value inference, not causal or diagnostic claims.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.