Prognostic significance and interplay of circulatory PCSK-9–ApoCIII–chemokine axis in type 2 diabetes and its complications: a pilot study in North Indian patients

Abstract Background Proprotein convertase subtilisin kexin/type-9 (PCSK-9) regulates cholesterol homeostasis through lysosomal degradation of LDL-R and also acts as a pleiotropic mediator of inflammation. Emerging evidence links PCSK-9 to diabetes pathogenesis. Given the genetic diversity and distinct lifestyle of the Indian population, investigating role of PCSK-9 in Indian type-2 diabetes mellitus (T2DM) patients is crucial. Notably, its association with ApoCIII and chemokines such as CXCL2 and MCP-1 remains unexplored. Results A total of 187 participants were included: Heathy control (HC; n = 50), T2DM (n = 49), T2DM with nephropathy (T2DM-N; n = 43), and T2DM with dyslipidaemia (T2DM-DL; n = 45). Plasma PCSK-9, apolipoprotein CIII (ApoCIII), C-X-C motif chemokine ligand 2 (CXCL2), and Monocyte chemoattractant protein-1 (MCP-1) levels were quantified using ELISA. PCSK-9, ApoCIII, CXCL2, and MCP-1 were significantly elevated among diabetic groups when compared to HC subjects. A significant correlation was observed between PCSK-9 and higher levels of ApoCIII, CXCL2, and MCP-1, whereas no such association was observed in HC. Moreover, receiver operating characteristics (ROC) curve analysis demonstrated diagnostic accuracy of the studied biomarkers for predicting diabetes. PCSK-9 showed an area under curve (AUC) of 0.9863 (sensitivity 97%, specificity 100%), CXCL-2 an AUC of 0.9841 (sensitivity 92%, specificity 100%), ApoCIII an AUC of 0.9598 (sensitivity 84%, specificity 96%), and MCP-1 an AUC of 1.00 (sensitivity 100%, specificity 100%). Conclusion Elevated PCSK-9 levels showed significant association with ApoCIII, CXCL2, and MCP-1, suggesting a possible interplay between lipid metabolic and inflammatory pathways in T2DM. This study opens new avenues for utilizing PCSK-9 as a non-traditional biomarker in chronic diabetes management, risk stratification, treatment optimization, and personalized medicine. However, to generalize these findings, larger cohorts and multicentric studies are warranted.