DETERMINING THE ASSOCIATED FACTORS RELATED TO DIABETES MELLITUS TYPES II BY USING MULTIPLE LOGISTIC REGRESSION IN MALAYSIA IJPHCS

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International Journal of Public Health and Clinical Sciences e-ISSN : 2289-7577. Vol. 6:No. 2 March/April 2019

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

https://doi.org/10.32827/ijphcs.6.2.172 172

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Open Access: e-Journal

DETERMINING THE ASSOCIATED FACTORS RELATED TO DIABETES MELLITUS TYPES II BY USING MULTIPLE

LOGISTIC REGRESSION IN MALAYSIA

Awang Nawi M.A.^1*, Ahmad W. M.A. W.², Mat Yudin Z.³, Abdul Rohim R.A.⁴

1,2,3,4 School of Dental Sciences, Health Campus, Universiti Sains Malaysia (USM),

16150 Kubang Kerian, Kelantan, Malaysia

*Corresponding author: Mohamad Arif Awang Nawi, School of Dental Sciences, Health Campus, Universiti Sains Malaysia (USM), 16150 Kubang Kerian, Kelantan, Malaysia, Email: mohamadarif@usm.my

https://doi.org/10.32827/ijphcs.6.2.172

ABSTRACT

Background: Diabetes Mellitus is a metabolic disorder categorized by an increase in the 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 are defined as age, body mass index, total cholesterol, hypertension, incident CHD, taking lipid lowering medication and smoking status.

Materials and Methods: Binary logistic regression analysis was conducted with the 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: For 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 and above, have probability to get diabetes mellitus type 2 disease (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 will lead to increased cholesterol levels. 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 pressure more than 160 mm/Hg have the chance to suffer from diabetes mellitus type 2. Meanwhile, a person who taking lipid lowering medication has 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. Continuously work to improve

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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

1.0 INTRODUCTION

Diabetes Mellitus is a metabolic disorder categorized by an increase in the individual’s blood glucose level, causing from the body’s inability to produce insulin or opposition to insulin action, or both (American Diabetes Association, 2006). Clinically distinct type 1 that is caused by the destruction of autoimmune B-Cell in the pancreas, type 2 that is affected by increases resistance to the action of insulin and gestational diabetes that is a form of glucose intolerance affecting pregnant women (American Diabetes Association, 2006). Diabetes and its complications have a significant effect on the health, quality of life and life prospect of patients and it contributes substantially to the health here cost. Type 2 diabetes and its worries constitute a major worldwide public health problem (Wu et al., 2014). Based on this study is to identify the associated factors that contribute diabetes mellitus types 2. The associated factors in this study are defined as age, body mass index, total cholesterol, hypertension, incident CHD, taking lipid lowering medication and smoking status.

Type 2 Diabetes is a serious and public chronic disease resulting from a multifarious legacy environment interaction laterally with other associated factors such as obesity and a sedentary lifestyle. Generous epidemiological studies have shown that obesity is the most important associated factors for Type2 Diabetes Mellitus (T2DM), which may encourage the development of insulin resistance and disease evolution (Belkin et al., 2010). Nearly 90% of diabetic patients developed T2DM mostly concerning to excess body weight according to the World Health Organization (WHO, 2011). Additionally, obesity is strongly inherited (Walley et al., 2006). Moreover, diet is reflected as a modifiable associated factor for T2DM. Studies have shown that a low fiber diet with a high glycaemic index is positively associated with a higher risk of TUDM and specific diet dietary fatty acids may affect insulin resistance and the risk of diabetes in fluctuating degrees (Van Dam et al., 2002). Multivariate logistic regression results from Ming et al., 2006 studied showed that overweight/obesity, hypertension, and higher triglycerides were the associated factors for increasing pre-diabetes. There were a positive interaction between overweight/obesity and triglycerides and also between hypertension and triglycerides on the multiplicative scale portentous that they synergistically influenced the development of pre-diabetes (Ming et al., 2006). Overweight and obesity, hypertension, and triglycerides were the most important associated factors (Ming et al., 2006).

Obesity, exercise, family history, and hypertension were significantly increased in patients having blood glucose levels more than the reference series even though of having regular medication. Microvascular complications were almost equal in both groups while macrovascular complications were more common in controlled diabetic’s patients (Rasheed et al., 2015). Observationally, low levels of HDL cholesterol are consistently associated with increased risk of type 2 diabetes. Hence, plasma HDL cholesterol, increasing has been

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recommended as an innovative therapeutic option to reduce the risk of type 2 diabetes. Whether levels of HDL cholesterol are causally associated with type 2 diabetes is unknown (Christine et al., 2015). Besides that, lipid abnormalities are common in diabetics and frequently seen in type-2 diabetics. Dyslipidaemias make diabetics prone to improve CHD and other complications of atherosclerosis. According to the CDC, 97% of adults with diabetes have one or more lipid abnormalities while the prevalence of diabetic dyslipidemia varies from 25% to 60% in other studies (Khursheed et al., 2011).

2.0 MATERIAL AND METHODS 2.1 Statistical Analysis

Logistic regression model for the two-way dependent variables such as illness or health, death or life. This model can be taken into account as the generalized linear model as a link function and its mistake following of the polynomial distribution (Danesh Pour et al., 2006).

This model as

 

^x ^x ⁱ ⁿ

p p p

it _i _k _k, 1, ,

ln 1

log   ₁ ₁_,   



 





    

Is that

 

i k k i

x x

x x i

r i r

e X e

Y P p

, , 1 1

1 1 1









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 

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Logistic regression was used when the dependent variable is two modes, it means that responses have only two modes, presence or absence of a relationship that's mean the dependent variable is nominal. This method is to estimate the probability of occurrence of a specific event and the dependent variable is odds ratio which is another way of expressing possibility. For example, if the probability of the presence or absence of a disease like diabetes is 5.0 for persons who referred to the physician, the rate of patient’s odds ratio in related disease is a ratio of one to one.

Although logistic regression can be used to arranged categories that sorted classify by two or more modes (such as frequently questions in the questionnaire), but if the dependent variable, such as the name of a major city with several classification are irregular, you can use multivariate discriminant analysis.

While in the logistic regression, the probability of a phenomenon is within the range (0) (1) and observing the normal default predictor variables is not necessary. The logistic regression model analysis except in the case of the dependent variable, is similar to linear regression, in which:

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Y= the odd ratio

Ln(y) = natural logarithm

k  x x

x₁, ₂,, Independent variables

k 



₀, ₁, ₂,, Coefficients of independent variables

 e error

Binary logistic regression analysis was conducted with the 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. Table 1 showed that the description of variables in this study.

Table 1: Description of SPSS data

No Variable Name Variable Description and Coding 1 Diabetes Diabetes millitus types-2 status (1=No, 2=Yes)

2 Age Age in years

3 Bmi Body mass index (weigh (kg) /[height (m)] ²) 4 Choltot Total cholesterol (mg/dl)

5 Hyper Hypertension status: 1=No; 2=Yes (systolic bp >

160 or history of hypertension and antihypertensive MEDs)

6 Incchd Incident (new) CHD during 6 years of follow-up (1=no, 2=yes)

7 Lipid Taking lipid lowering medication (0=no, 1=yes) 8 Smoke Smoking status (1=no, 2=yes)

3.0 RESULTS

Logistic regression is a statistical tool used in order to modeling and analyzes the data. Logistic regression has the following general form:

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



 





  x



 log 1

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. For the binary logistic regression approach, we take the following dichotomous variables.

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Y= 0 No Diabetes millitus types-2 Y= 1 Diabetes millitus types-2

X is a vector of independent variables, including age of patients' diabetes, body mass index, total cholesterol, hypertensive, incidence congenital heart disease (CHD), taking lipid-lowering medication and smoking in patients and β is the vector of the estimated coefficients. π is also likely outcomes or events to being there. Then we obtained the following model.

Table 2: Descriptive of Factors in Diabetes Mellitus Type 2

Variables Mean (SD)

Age 73.24 (5.796)

BMI 25.91 (3.693)

Choltot 198.44 (36.234)

Variables N (%)

Hyper

Yes 134 (41.5)

No 189 (58.5)

Inch

Yes 55 (17)

No 268 (83)

Lipid

Yes 12 (3.7)

No 311 (96.3)

Smoke

Yes 74 (22.9)

No 249 (77.1)

Table 3: Logistic regression model of the associated factors for Diabetes Mellitus Types-2 Dependent

Variable

Independent Variable

Std.

Coefficient B

S.E p-

value

Adjusted OR

95% C.I. for Adjusted OR Lower Upper Diabetes

millitus types 2

Age 0.005 0.028 0.854 1.005 0.952 1.061

BMI 0.171 0.043 0.000 1.186** 1.089 1.291

Choltot -0.009 0.005 0.042 0.991* 0.982 1.000

Hyper 1.044 0.306 0.001 2.840* 1.559 5.175

Incchd 0.516 0.380 0.174 1.675 0.796 3.527

Lipid 1.393 0.664 0.036 4.029* 1.097 14.797

Smoke -0.306 0.398 0.442 0.737 0.338 1.606

Hosmer and Lemeshow Test Overall percentage The area under the Curve

p-value = 0.344 81.4%

0.726 (95% CI: 0.648, 0.805)

**Significant level < 0.001 *Significant level < 0.05

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Based on Table 3, we can make a statement on the occurrence of associated factors diabetes mellitus types 2 with respect to the risk. Age, inched and smoke are not the associated factors of diabetes Mellitus types 2 in Malaysia. The person who is overweight and inactive is much more likely to develop type 2 diabetes mellitus because certain lifestyle choices greatly influence how well your body uses insulin. This study shows that higher mean of BMI (25.91 and above) may be the most important pathogenic factor and associated with type 2 DM (OR

= 1.186, 95% CI: 1.089-1.291, p-value <0.001). These findings were consistent with conclusions from European elderly and Asian population studies (Olafsdottir et al. 2009).

Blood glucose was positively related to total cholesterol level in the type 2 diabetic mellitus patients (OR = 0.991, 95% CI: 0.982-1.000, p-value <0.042). A high level of glucose in the blood can lead to 'bad' cholesterol that lives longer in the bloodstream. People with type 2 DM tend to have high cholesterol levels, both increasing the risk of heart disease. Based on Table 3, 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 pressure reading more than 160 mm/Hg. Meanwhile, for lipid lowering medication, 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) compare to a pearson who not taking lipid lowering medication.

Hosmer and Lemeshow Test are based on grouping cases into deciles of risk. It compares the observed probability with the expected probability within each decile. The p-value is checked.

If it is > 0.05, there is no significant difference between the observed probability and the expected probability. Based on Table 3 above, p-value = 0.344 obtained is greater than 0.05 (p-value> 0.05) and suggesting that the model was fit to the data well.

Figure 1: ROC Curve

The ROC curve is a fundamental tool for diagnostic test evaluation. It’s a graphical plot of the sensitivity which measures of the overall performance of a diagnostic test. ROC Curve take on any value between 0 and 1 since both the x and y-axes have values ranging from 0 to 1. If the area is 1.0, we have an ideal test, because it achieves both 100% sensitivity and 100%

specificity. If the area is 0.5, then we have a test which has effectively 50% sensitivity and 50%

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specificity (Ahmad et al., 2010). In practice, a diagnostic test is going to have an area somewhere between these two extremes. Area under the ROC curve is 0.726 (95% CI: 0.648, 0.805). It is significantly different from 0.05 (0.000). The model can accurately discriminate 71.8% of the cases.

4.0 SUMMARY AND CONCLUSION

Type 2 Diabetes Mellitus are connected complications execute heavy health burdens worldwide and there have been no effective measures to fully cope with the diseases. All studied associated factors had a significant association with control of blood sugar level. Based on this study, BMI, cut-out, hypertension, and lipid are major health problems associated with increased associated with diabetes. Along with prescriptions, patients should be encouraged to decrease weight, do regular exercise and control blood pressure. Suitable control of these associated factors may help to decrease the rigorousness of diabetes and its associated complications. Continuously 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.

Declaration

Author(s) declare that there is no conflict of interest with the publication of this article.

Authors' contribution

Author 1: initiation of idea, final manuscript review and editing Author 2: manuscript review and editing

Author 3: literature searching and drafting the manuscript Author 4: literature searching and drafting the manuscript

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American Diabetes Association. (2006). Diagnosis and Classification of Diabetes Mellitus.

Diabetes Care. 29:43-8.

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