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Machine learning-based evaluation of lipid biomarkers for cardiovascular risk prediction in chronic kidney disease: A

Guixian Wen1,2, Shixia Zhao3, Feifei Zhang2

  • 1Graduate School of Hebei Medical University, Shijiazhuang City, Hebei Province, China.

Medicine
|July 11, 2026
PubMed

Insights

In chronic kidney disease (CKD) patients, specific lipid markers like total cholesterol, LDL-C, and apolipoprotein B are linked to cardiovascular disease (CVD). LDL-C showed the strongest predictive power for CVD risk.

Area of Science:

  • Nephrology
  • Cardiology
  • Biochemistry

Background:

  • Chronic kidney disease (CKD) significantly elevates cardiovascular disease (CVD) risk.
  • Dyslipidemia is common in CKD patients, contributing to atherosclerosis and cardiovascular events.
  • The precise relationship between various lipid markers and CVD in CKD remains unclear.

Purpose of the Study:

  • To investigate the associations between diverse lipid markers and CVD in CKD patients.
  • To identify optimal lipid biomarkers for predicting cardiovascular risk in CKD.
  • To explore linear and nonlinear relationships between lipid profiles and CVD outcomes.

Main Methods:

  • Analysis of 2696 CKD participants from the National Health and Nutrition Examination Survey (2005-2018).
  • Multivariate logistic regression and restricted cubic splines to examine lipid marker associations with CVD.
  • Machine learning models were employed to evaluate the predictive value of lipid markers for cardiovascular risk.

Main Results:

  • Total cholesterol, low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B were independently associated with CVD in CKD patients.
  • Significant L-shaped associations were observed for total cholesterol, LDL-C, and apolipoprotein B with CVD.
  • Remnant cholesterol and triglycerides showed a U-shaped relationship, while HDL-C had an almost linear association with CVD. LDL-C exhibited the strongest discriminative performance.

Conclusions:

  • Lipid markers are significantly associated with CVD in CKD patients.
  • Integrating these lipid markers into predictive models can enhance cardiovascular risk stratification.
  • Further validation in external CKD cohorts is necessary to confirm these findings.

Keywords:
cardiovascular diseasechronic kidney diseaselipid markersmachine learning

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