Cardiovascular-kidney-metabolic health, genetic susceptibility, and incident cancer risk: a prospective UK biobank cohort and machine learning study.

Authors

Xue He, Junqiao An, Chunjia Yan, Yutong Zhou, Ziyan Wu, Mengyao Xu, Qingyong He

Published in

Frontiers in endocrinology. Volume 17. Pages 1884752. Epub Aug 03, 2026.

Abstract

It remains unclear whether cardiovascular-kidney-metabolic (CKM) syndrome and genetic susceptibility are associated with cancer incidence. This study aims to evaluate the associations between CKM health status, genetic susceptibility, and cancer incidence, and to develop a machine learning-based prediction model for cancer risk in patients with advanced CKM syndrome.
This study included 399,034 UK Biobank participants (389,289 with genetic data). CKM health was categorized into stages 0-4. Genetic susceptibility was assessed via a polygenic risk score (PRS). Associations were evaluated using Cox proportional hazard models. For participants with advanced CKM, candidate predictors were screened via the Boruta algorithm, LASSO regression, and multivariable logistic regression. Eight machine learning models were constructed and validated, and their predictive discrimination, calibration performance, and clinical practical value were further assessed. Additionally, Shapley Additive Explanations (SHAP) were adopted to interpret the internal mechanism of the optimal model. The dose-response relationship was assessed using restricted cubic splines (RCS), and mediation analysis was further performed to explore the underlying mechanism of the observed associations.
During a median follow-up of 13.7 years, 48,247 incident cancer cases were identified. Compared to CKM stage 0, multivariable-adjusted hazard ratios (HRs) were 1.01 (95% CI: 0.91-1.12) for stage 1, 1.21 (1.10-1.33) for stage 2, and 2.17 (1.95-2.42) for advanced CKM (stage 3-4). Participants with high PRS and advanced CKM faced the highest cancer risk (HR 3.24, 95% CI 2.68-3.93). A total of 14 predictors were retained after screening. The GBM model achieved the best predictive performance (test AUC = 0.801). A web-based calculator was developed to realize individualized cancer risk prediction. RCS analyses indicated that diastolic blood pressure (P _nonlinearity<0.05), neutrophil count(P _nonlinearity<0.05), alkaline phosphatase (P _nonlinearity=0.001), and TyG-BMI (P _nonlinearity<0.05) were significantly and nonlinearly associated with cancer risk. Total bilirubin the strongest positive mediating effect, explaining 44.45% of the total association (P < 0.001).
Advanced CKM syndrome independently increases incident cancer risk, and this elevated risk is significantly amplified by high genetic susceptibility. The validated GBM model provides an interpretable tool for individualized risk estimation, which serves as a valuable complementary tool for clinical risk stratification and cancer patients with, cancer.

PMID:
42609333
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.