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March 4, 2026Open Access

Prognostic stratification with composite insulin resistance–inflammation biomarkers in patients with chronic kidney disease and coronary artery disease across glycemic statuses

Puntos clave

The study aims to assess the prognostic utility of composite insulin resistance and inflammation biomarkers for predicting mortality in patients with chronic kidney disease and coronary artery disease across different glycemic statuses.
Analyzed 1353 CKD and CAD patients using NHANES data from 1999 to 2018.
Calculated composite biomarkers including TyG-hsCRP, TyG-CRP, and CTI.
Categorized patients by glycemic status: normoglycemia, prediabetes, diabetes.
Applied statistical analyses including Cox regression and ROC curves, adjusted for demographics and comorbidities.
Conducted subgroup and sensitivity analyses to confirm findings.
744 all-cause and 323 cardiovascular deaths occurred during a median follow-up of 63 months.
Higher levels of composite biomarkers associated with increased mortality rates (e.g., CTI HR 1.43 for all-cause death).
CTI demonstrated strong discrimination and reclassification capabilities (AUC 0.700).
The predictive strength of biomarkers was highest in diabetic patients, showing significant associations with mortality.

Resumen

Patients with chronic kidney disease (CKD) and coronary artery diseases (CAD) have a poor long-term prognosis. Although insulin resistance (IR) and systemic inflammation are well-established drivers of cardiovascular risk, the prognostic value of their composite assessment across the glycemic spectrum in patients with CKD and CAD remains undetermined. This study aimed to evaluate the prognostic utility of composite IR-inflammation biomarkers for predicting mortality in patients with CKD and CAD stratified by glycemic status. 1353 patients with CKD and CAD were enrolled from National Health and Nutrition Examination Survey (NHANES) data (1999–2018). Composite biomarkers (TyG-hsCRP, TyG-CRP, and C-reactive Protein–Triglyceride Glucose Index CTI) were calculated. Patients were categorized by glycemic status (normoglycemia, prediabetes, diabetes) based on WHO/IEC criteria. The endpoint was all-cause and cardiovascular disease (CVD) death. Statistical analyses included Cox regression, Nelson-Aalen cumulative hazard plots with Log-rank test, restricted cubic splines, ROC curves, and reclassification metrics, adjusted for demographics, comorbidities, and treatments. Subgroup and sensitivity analyses ensured robustness. Over a median follow-up of 63-months, 744 all-cause and 323 CVD deaths occurred. Adjusted models showed elevated composite indices linked to higher mortality (e.g., CTI HR 1.43 95% CI 1.24–1.65 for all-cause; HR 1.32 1.06–1.64 for CVD). CTI provided good discrimination (AUC 0.700) and reclassification (IDI 0.010; NRI 0.196 for all-cause). The predictive utility of all three composite biomarkers was most pronounced in patients with diabetes, whereas CTI retained the strong association with all-cause mortality in normoglycemic and prediabetic patients. Risk stratification using both CTI and glycemic status identified patients with diabetes and high CTI as having the highest all-cause (HR 1.63 1.22–2.17) and CVD (HR 1.37 0.88–2.14) death risk. Composite biomarkers integrating IR and inflammation, particularly CTI, significantly enhance mortality prediction in patients with CKD and CAD. The predictive utility is modulated by underlying glycemic status, enabling refined risk stratification and potentially guiding tailored management strategies for this complex patient population.

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