Year: 2020
Type: Book chapter
Title: Correlating Glucose Regulation with Lipid Profile
Author: Vishinov, Ilija
Author: Gushev, Marjan
Author: Poposka, Lidija
Author: Vavlukis, Marija
Abstract: Objectives: The goal of this research was to detect the glucose regulation class by evaluating the correlation between the lipid profile of patients and their glucose regulation class. Methodology: The methods used in this research are: i) Point Biserial Correlation, ii) Univariate Logistic Regression iii) Multivariate Logistic Regression iv) Pearson Correlation and v) Spearman Rank correlation. Data: The dataset consists of the following features: age, BMI, gender, weight, height, total cholesterol (Chol), HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), triglycerides (TG), glycated hemoglobin (HbA1C), glucose regulation and diabetes classes, history of diabetes, heart and other chronic illnesses, habitual behaviors (smoking, alcohol consumption, physical activity), and medications intake (calcium channel blockers, BETA blockers, anti-arrhythmic, AKE/ARB inhibitors, diuretics, statins anti-aggregation medication and anticoagulants). Conclusion: The methodologies that were worked through with our data in search for correlations of the lipid profile with HbA1c or the glucose regulation classes gave some significant correlations. Regarding the glucose regulation classes W and B the methods showed statistically significant negative correlations with Chol, HDL-C and LDL-C. When it comes to the correlations of the lipid profile with HbA1c, for all patients there were significant negative correlations with Chol (corr = −0.264, p = 0.002), LDL-C (corr = −0.297, p < 0.001) and HDL-C (corr = −0.28, p = 0.001) and a significant positive correlation with TG (corr = 0.178, p = 0.03). The correlations mentioned are the stronger ones that were found for linear relationships. For non-diabetic patients there was a stronger positive non-linear correlation for HbA1c and HDL-C (corr = 0.511, p = 0.006), and a slightly weaker linear correlation (corr = 0.393, p = 0.043). For prediabetic patients there were no significant correlations. For type 2 diabetes stronger significant negative non-linear correlations were found for HbA1c with LDL-C (corr = −0.299, p = 0.023) and HDL-C (corr = −0.438, p = 0.001). The linear relationships were again, slightly weaker with LDL-C (corr = −0.273, p = 0.038) and with HDL-C (corr = −0.391, p = 0.002).
Publisher: Springer International Publishing
Relation: ICT Innovations 2020. Machine Learning and Applications 12th International Conference, ICT Innovations 2020
Identifier: oai:repository.ukim.mk:20.500.12188/23037
Identifier: http://hdl.handle.net/20.500.12188/23037Identifier: 10.1007/978-3-030-62098-1_18
Identifier: https://link.springer.com/content/pdf/10.1007/978-3-030-62098-1_18Identifier: 217
Identifier: 227
