Diabetes is a significant cause of blindness, kidney failure, heart attacks, stroke, and lower limb amputation (Srinivasan, 2013). In 2016, an estimated 1.6 million deaths were directlycaused by diabetes. Another 2.2 million deaths were attributable to high blood glucose in 2012. This Scatter plot below showing the positive linear correlation between the severity of coronary artery disease (Gensini score) and log of homeostasis model-insulin resistance in type 2 diabetes mellitus (Srinivasan, 2013).
The two variables that are being used in the study are Insulin Resistance and Severity of Coronary Artery Disease. In this article, insulin resistance was known to be a pathogenic cause that can predict the occurrence of coronary artery disease. However, grading severity or assessing the severity of coronary artery disease based on insulin resistance has not been studied in detail (Srinivasan et al., 2013). The evolution of insulin resistance is unique in type 2 diabetes mellitus because it precedes the onset of diabetes and remains relatively constant throughout the disease process from the time of diagnosis, while the severity of coronary artery disease is used as the primary cause of premature death in diabetic patients, both in type 1 or type 2 diabetes Therisk of Coronary Artery Disease is higher in type 2 diabetic patients in comparison to similarly dyslipidemia non-diabetic subjects, even after the correction of several confounders (Srinivasan et al. 2013).
The scatter plot shows in the diagram above that there was a statistically significant correlation between the log of Homeostasis Model Assessment-Insulin Resistance (HOMA-IR) and severity of Coronary Artery Disease (CAD) assessed by Gensini Score in type 2 diabetic patients. Furthermore, there was no significant correlation between the severity of Coronary Artery Disease and other known risk factors of Coronary Artery Disease in type 2 diabetic patients (Srinivasan et al. 2013).
The scatter plot indicates the usage of insulin resistance to predict coronary artery disease. Further, insulin resistance, as measured by Homeostasis Model Assessment, might aid inpredicting the severity of Coronary Artery Disease and its clinical relevance, especially in resource-limited settings (Srinivasan et al., 2013). Since measurement of insulin resistance (IR) by Homeostasis Model Assessment is easier to perform, it has been shown to correlate well with the euglycemic clamp method, a reference standard method for measuring insulin resistance. has the potential to be used in routine clinical practice. Thus, in the future, we might predict the severity of coronary artery disease through Homeostasis Model Assessment (HOMA-IR), and patients with extensive and severe diseases who are not candidates for angioplasty can be identified easily (Srinivasan, 2013).
To verify and sustenance the claims on the correlation of CAD and IR, there may be a need for additional information from the patient such as age, gender, cholesterol level, history of the prevalent disease, family history, and other hospital information (Srinivasan, 2013).Further IR as measured by HOMA can assist in predicting the severity of CAD and its clinical relevance, especially in resource-limited settings. Since measurement of IR by HOMA is easier to perform, it is shown to correlate well with the euglycemic clamp method. A reference standard method for measuring IR and has the potential to be used in routine clinical practice.: There was a significant correlation between log HOMA-IR and severity of CAD (r = 0.303, P = 0.009) in diabetic patients. The correlation of the Gensini Score with other known risk factors was not significant.: The results of the study indicate that we might able to predict the severity of CAD by a measure of IR.
With new studies, they have incorporated more into the findings of IR and CAD; this study is from India; there are no current North American studies. Here is what they looked at and included different parameters more than just IR and CAD. The study’s objective is to find the correlation of severity of coronary artery disease (Gensini score) with insulin resistance (IR) and other clinical parameters. Clinical data set (Gensini score, glycated hemoglobin, fasting insulin (FI), IR, total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, age, body mass index, waist circumference, high-sensitivity c-reactive protein, and fasting plasma glucose) of 100 patients was collected. The individuals included in the data set were classified into four groups based on IR and phenotypic obesity. R programing language was used to find the correlation between the clinical parameters and the Gensini score. The variation of the Gensini score among the four groups was also analyzed. (Samal et al., 2019).
The variation of the Gensini score among the four groups suggests that IR can drastically increase the severity of CAD and has a more pronounced effect on the Gensini score as compared to phenotypic obesity. It was also observed that age, triglyceride levels, glycated hemoglobin, and FI had the highest positive correlation with the Gensini score. In contrast, parameters such as body mass index and high-sensitivity C-reactive protein had a higher negative correlation. Equations correlating various clinical parameters to the Gensini score were generated.
