Complete blood count inflammation derived indexes as predictors of metabolic syndrome in type 2 diabetes mellitus.

ABSTRACT

BACKGROUND: Metabolic syndrome (MetS) is a group of comorbidities related to regulating hyperglycemia and acute cardiovascular incidents and complications. With the increasing prevalence in individuals with type 2 diabetes mellitus (T2DM), MetS represents an increasing public health problem and clinical challenge, and early diagnosis is necessary to avoid the accelerated development of diabetic complications. OBJECTIVE: To investigate the role of Complete Blood Count-derived Inflammation Indexes (CBCIIs) in predicting MetS in T2DM individuals. METHODS: The study was designed as a two-year prospective study and included 80 T2DM individuals divided into MetS and non-MetS groups based on MetS development over two years. The sera samples were analyzed for complete blood count parameters and C-reactive protein (CRP). Based on the laboratory test results, 13 CBCIIs were calculated and analyzed. The receiver operating characteristic (ROC) curve and their corresponding areas under the curve (AUC) were used to determine prognostic accuracy. RESULTS: There were significant differences between T2DM participants with Mets and those without MetS concerning Neutrophil to Platelet Ratio (NPR) values (p< 0.001), Neutrophil to Lymphocyte and Platelet Ratio (NLPR) (p< 0.001), Platelet to Lymphocyte Ratio (PLR) (p< 0.001), Lymphocyte to C-reactive protein Ratio (LCR) (p< 0.001), C-reactive protein to Lymphocyte Ratio (CRP/Ly) (p< 0.001), Systemic immune inflammation index (SII) (< 0.001), and Aggregate Index of Systemic Inflammation (AISI) (p= 0.005). The results of ROC curve analysis have shown that the LCR (AUC of 0.907), CRP/Ly (AUC of 0.907) can serve as excellent predictors, but NPR (AUC of 0.734), NLRP (AUC of 0.755), PLR (AUC of 0.823), SII (AUC of 0.745), and AISI (AUC of 0.688) as good predictors of MetS in T2 DM individuals. CONCLUSION: This study confirms the reliability of the CBCIIs as novel, simple, low cost and valuable predictors of MetS developing in T2DM.