Abstract
Background: Type 2 Diabetes Mellitus (T2DM) is an escalating problem worldwide and is frequently associated with Metabolic Syndrome (MetSyn) which, in turn, is causally associated with heightened cardiometabolic risk. Therefore, investigating the magnitude of MetSyn in T2DM patients is critical for cardiovascular disease prevention or management of specific comorbidities. Methods: This cross-sectional study was conducted among 309 previously diagnosed T2DM patients. Data on specific clinical chemistry and anthropomorphic parameters was collected. MetSyn was defined according to the IDF harmonized criteria. Pearson Chi-Square test (ꭓ2)/or Fisher’s exact test in the CROSSTAB procedure was used to evaluate the relationship between specific variables. Logistic regression models were constructed to assess risk factors associated with MetSyn. Results: According to the data, 58.1% of the patients had MetSyn. The frequency of MetSyn in females was significantly higher compared to that of males (67.8 vs 49.7%). Among individuals with MetSyn, 54.4% had hypertension; 57.9% had abnormal waist circumference; 75.4% had elevated LDL-C (≥100 mg/dL), 72.8% had raised TG (>150 mg/dl) and 61.0% had reduced HDL-C (males: ≤40 mg/dL and females: ≤50 mg/dL in females). Separately, our study demonstrates that number of MetSyn components is associated with higher averages in multiple traditional (BMI, TG, TC, WHtR, WHR, WC, HC) and non-traditional (TG/HDL-C, TC/HDL-C and LDL/HDL) CVD risk indicators. In the fitted multivariable logistic regression model, the following factors were associated with the presence of MetSyn: age (aOR=1.02, 95%CI=1.00–1.05, p=0.040); LDL-C>100 mg/dL (aOR=3.56, 95%CI=1.52–8.54, p=0.003); Non-HDL-C (aOR=1.02, 95%CI=1.02–1.03, p=0.001); BMI (aOR=1.23, 95%CI = 1.13–1.32, p=0.001). Absence of insulin injection was associated with reduced presence of MetSyn (aOR=0.37,95% CI=0.19–0.70, p=0.002). Conclusion: A comparatively high prevalence of the MetSyn was found. Therefore, there is an urgent need for improvements in the management and prevention of multiple CVD risk indicators. This will require evidence-based optimization of pharmacological and non-pharmacological interventions.