Non-conventional lipid parameters predict the risk of rapid kidney function decline in patients with cardiovascular-kidney-metabolic syndrome: The first cohort study evidence from CHARLS

Abstract

Background

This study evaluates the predictive value of three novel lipid-derived biomarkers: non-HDL-to-HDL cholesterol ratio (NHHR); natural logarithm of remnant cholesterol (lnRC); and cholesterol, high-density lipoprotein, and glucose index (CHG) for rapid kidney function decline (RKFD) in patients with cardiovascular-kidney-metabolic (CKM) syndrome.

Methods

Using data from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2015), we evaluated 2734 CKM patients (stages 1–4) using RKFD as the primary endpoint. We assessed the associations of NHHR, lnRC, and CHG with renal outcomes using Logistic regression models. Restricted cubic spline and subgroup analyses were used to explore nonlinear relationships and consistency across population strata. Receiver operating characteristic (ROC) curve analysis compared the predictive performance of these indices.

Results

Fully adjusted Logistic models showed that a unit increase in NHHR was associated with a 19 % increased RKFD risk (p < 0.001). LnRC and CHG demonstrated more pronounced effects, with risks increasing by 112 % and 125 % per unit, respectively (both p < 0.001). Restricted cubic splines analysis revealed a linear relationship for NHHR and “J-shaped” associations for lnRC and CHG. ROC analysis showed improved discriminative capacity, with receiver operating characteristic curve values of 0.694 (NHHR), 0.734 (lnRC), and 0.695 (CHG).

Conclusions

NHHR, lnRC, and CHG are significantly associated with RKFD in CKM patients and are robust predictors of renal function deterioration.

Key learning points

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  What was known: The existing rapid kidney function decline (RKFD) models for patients with cardiovascular-kidney-metabolic (CKM) syndrome lack sensitivity and often overlook the synergistic effect of glucose and lipid metabolism disorders on renal deterioration.
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  This study adds: The non-HDL-to-HDL cholesterol ratio; Natural Logarithm of Remnant Cholesterol (lnRC); and Cholesterol High-Density Lipoprotein, and Glucose index are strongly associated with RKFD, with lnRC demonstrating superior predictive accuracy over traditional risk factors, with identified thresholds for high-risk stratification.
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  Potential impact: Integrating these indices into standard clinical practice enhances the early detection of CKM syndrome patients at high risk of RKFD, thus permitting prompt intervention to deceler renal impairment and reduce the likelihood of progression to end-stage kidney disease.