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International Journal of Public Health and Clinical Sciences (IJPHCS)
Open Access e-journal ISSN : 2289-7577


Awang Nawi M.A., Mat Yudin Z., Abdul Rohim R.A., Ahmad W. M.A.W.


Background: Diabetes Mellitus is a metabolic disorder categorized by an increase in individual’s blood glucose level causing from the body’s inability to produce insulin or opposition to insulin action, or both. Based on this study is to identify the associated factors that contribute diabetes mellitus types 2. The associated factors in this study is defined as age, body mass index, total cholesterol, hypertention, incident CHD, taking lipid lowering medication and smoking status.

Materials and Methods: Binary logistic regression analysis was conducted with reporting of odds ratio to establish diabetes mellitus types 2 diseases among diabetes patients in Malaysia. To explore the underlying association between diabetes mellitus types 2 and the selected explanatory variables, a set of logistic regression models is fitted in this section. Let define the following dichotomous variables for the diabetes mellitus types 2 diseases. Data were tabulated, cross-tabulated and analyzed statistically using PASW version 18.

Result: From this study, body mass index is one most associated factor that contributes to diabetes mellitus type 2 where the mean of BMI is 25.91 (overweight) (OR = 1.186, 95% CI: 1.089-1.291, p-value <0.001). Blood glucose was positively related to total cholesterol level in the diabetic mellitus type 2 patients (OR = 0.991, 95% CI: 0.982-1.000, p-value <0.042), suggesting that the higher blood glucose level, the higher the total cholesterol level. Hypertension is highly significant with diabetes mellitus type 2 among patient (OR = 2.840, 95% CI: 1.559-5.175, p-value <0.001) where systolic blood presure more than 160 mm/hg. Meanwhile, a person who taking lipid lowering medication have occurred 4.029 the probability of getting diabetes mellitus type 2 (OR = 4.029, 95% CI: 1.097-14.797, p-value <0.036).

Summary and Conclusion: Suitable control of these associated factors may help to decrease the rigorousness of diabetes and its associated complications. Continue work to improve the understanding of type 2 diabetes associated may assist in the development of optimal strategies for type 2 diabetes prevention with a long-term goal of addressing this major public health concern.

Keywords: Diabetes Mellitus Type 2, Logistic Regression, Associated Factors


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