Prediction of blood glucose level based on lipid profile and blood pressure using multiple linear regression model

Type 2 diabetes mellitus (T2DM) refers to the inability to produce or respond to insulin, resulting in an elevated blood glucose level in the human body. Due to concerns over current diabetes screening and diagnostic procedures that require fasting, oral glucose consumption, and invasive nature (finger prick), the number of undiagnosed T2DM increases yearly. The increase is due to the hesitation of individuals to undergo screening tests as their routine check-up. As T2DM is closely related to blood glucose levels, a predictive model is developed to predict blood glucose levels, which can be used as an alternative for screening T2DM. Thus, the present study proposed a multiple linear regression equation for predicting the fasting blood glucose level based on independent parameters of lipid profile and blood pressure. It is widely known that high blood cholesterol and high blood pressure are the risk factors of T2DM. In this study, a set of 302 data was collected from UMP's retrospective data via the data directory of the University Health Centre from 2017 to 2018. The present study used 211 (70%) data to fit the predictive model, whereas another 91 (30%) of the data were used for selfvalidation of the model. Moreover, the overall model performance was observed by refitting the whole data set (n = 302, 100%) into the predictive model equation. The main outcome of the study showed that 46.8% (adjusted R2= 0.468, p-value < 0.05) of the fasting blood glucose level could be predicted using multiple linear regression based on high-density lipoprotein cholesterol, triglycerides, and systolic blood pressure levels without the standard fasting procedure. The prediction made by this model is acceptable with moderate accuracy (MAPE = 9.46%). This predictive model is easily adaptable to data changes (the difference of error metric values between the training data and testing data: MAE = 0.1836 mmol/L, RMSE = 0.1040 mmol/L, and MAPE = 3.93%). Thus, in order to increase the accuracy of the model, future research should consider a bigger and broader cohort from different comorbidities, which can be an alternative method in screening T2DM

Prediction of blood glucose level based on lipid profile and blood pressure using multiple linear regression model

Diabetes mellitus refers to a metabolic disorder that occurs due to insulin resistance and/or inability to produce enough insulin from islet β–cells in pancreas leads to increasing levels of blood glucose. Due to perturbation towards current diabetes screening and diagnosis procedures that require fasting, oral glucose consumption and involve invasive and finger-pricks, numbers of undiagnosed diabetes mellitus kept increasing due to hesitation of these people to take screening tests as their routine check-up. Since diabetes mellitus is closely related to blood glucose level, a multiple linear regression model for predicting the blood glucose level gives the impression as one of the alternatives. Thus, this study proposed a multiple linear regression equation for predicting the fasting blood glucose level based on independent parameters of lipid profile and blood pressure as high blood cholesterol and high blood pressure are known as risk factors for diabetes. There are 302 data collected from UMP’s retrospective data via data directory from University Health Centre in 2017 to 2018. This study shows that the adjusted R2 of 46.8% for multiple linear regression model of fasting blood glucose level was obtained to predict the possibility of pre-screening diabetes without fasting procedures. This model equation was solely based on high density lipoprotein cholesterol, triglyceride and systolic blood pressure levels with the prediction made by the model are acceptable with moderate accuracy (MAPE = 9.46%). In order to increase the accuracy of the model, future research should consider a bigger and wider cohort from different comorbidities background which can be an alternative method in screening diabetes mellitus

Investigation on the relationship between cholesterol and blood glucose levels using decision tree method in healthy subjects

Obesity is known as a main cause of diabetes and cardiovascular disease. However, the relationship between blood glucose and cholesterol levels among the obese subjects who are diagnosed as pre-diabetic is indistinct and still undergoing. Thus, this study is mainly focused on finding the linkage between blood glucose and cholesterol levels in the pre-diabetic subjects as to explain the cause of diabetes and cardiovascular. 90 subjects (42 male and 48 female; age between 22-58 years old) were recruited in Universiti Malaysia Pahang to undergo oral glucose tolerance test which typically being used to diagnose pre-diabetes and diabetes. The blood test results indicate the glucose level and lipid profile (i.e. the cholesterol levels) of the subjects. Results obtained from pathology lab were analyzed using decision tree to show the difference of blood glucose and cholesterol levels along with their age, systolic blood pressure and body mass index. Overall, older people, high systolic blood pressure, cholesterol and BMI levels have higher probability to be detected as pre-diabetic. Thus, further analyses need to be conducted to prevail the relationship of diabetes and cardiovascular disease among aging and obese subjects