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ORIGINAL ARTICLE
Year : 2019  |  Volume : 9  |  Issue : 1  |  Page : 7-16

Statistical correlation of severity of coronary artery disease with insulin resistance and other clinical parameters


1 Department of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland
2 Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal, Karnataka, India
3 Department of Cardiology, Kasturba Medical College, Manipal, Karnataka, India

Correspondence Address:
Mr. Jay Rabindra Kumar Samal
Department of Biomedical Sciences and Engineering, Tampere University of Technology, P. O. Box: 527, FI-33101, Tampere
Finland
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/JICC.JICC_1_18

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Objective: The objective of the study is to find the correlation of severity of coronary artery disease (Gensini score) with insulin resistance (IR) and other clinical parameters. Methods: 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 correlation between the clinical parameters and the Gensini score. Further, the data were normalized and data plot on cftools of Matlab was used to find equations to relate the parameters. The variation of Gensini score among the four groups was also analyzed. Results: The variation of 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, while 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. Conclusion: The equations may be used to develop a software model which can predict the Gensini Score of a patient with an acceptable margin of error. As the parameters included in the study can be obtained during a regular check-up of a person, his/her risk of CAD can be predicted during the regular check-up.


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