Prediction of cardiovascular risk factors and metabolic syndrome in adults from Saudi Arabia using the logarithm of triglyceride/HDL-cholesterol ratio - Althwab, Sami A.; Al Abdulmonem, Waleed; Allemailem, Khalid S.; Alarifi, Saud A.; Hamad, Essam M. (International Journal of Health Sciences, 2024)
Objective: Cardiovascular diseases (CVD) are the leading cause of death globally. Metabolic syndrome (MtS) is a risk factor that increases the likelihood of CVD. The atherogenic index (AIP), calculated as the logarithm of the ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL) cholesterol in plasma, is a valuable marker for highly atherogenic small dense low-density lipoprotein cholesterol particles. This study aimed to explore MtS prevalence and investigate the potential of using the AIP as a predictor for CVD risk factors in adults from the Qassim region of Saudi Arabia. Methods: The cross-sectional study enrolled 589 participants from public hospitals in nine major cities who completed a detailed questionnaire on health, diet, and lifestyle. Anthropometric measurements and some clinical parameters were measured. Results: The findings indicated a significant prevalence of MtS (37.5%) among participants from the Qassim Area, which was higher in males (39.9%) than females (34.9%). Nevertheless, a significant prevalence was shown for CVD risk factors among participants, with hyperglycemia (78.1%), hypertriglyceridemia (39.0%), hypo-HDL-cholesterolemia (38.9%), and hypertension (21.6%) being common. The AIP’s performance in identifying CVD risk factors showed a receiver operating characteristic value of 0.909 (P < 0.001). The optimal cutoff value for the AIP was determined to be 0.468, demonstrating high sensitivity (84.8%) and specificity (78.6%). Conclusion: Incorporating AIP into clinical practice could enhance CVD risk prediction compared to using lipid profiles alone. These findings suggest that there is a high prevalence of MtS among adults in the Qassim region of Saudi Arabia. Further longitudinal studies are needed to recommend AIP as a robust tool for predicting CVD in clinical settings.