Predictive factors of glycosylated hemoglobin using additive regression model

Introduction: Diabetes is a chronic disease, non-epidemic disease that costs a lot of money in each year. One of the diagnostic criteria for diabetes is Glycosylated Hemoglobin (HBA1C), which in this study the effective factors on it examined by additive regression model. Materials and Methods: In this cross-sectional study, 130 patients with diabetes type-2 were selected based on simple random sampling in Ilam city (Iran). Several variables were examined such as gender, age, weight, height, systolic and diastolic blood pressure, hypertension, smoking, family history of diabetes, daily walking for at least 30 minutes, waist and hip circumferences, HbA1c, fasting blood sugar (FBS), RBC mean corpuscular volume (MCV) and BMI. The data were collected based on Canadian diabetes checklist questionnaire. Results: In simple linear regression, waist and hip circumferences and in multiple regression, hip circumference and BMI had a significant effect on HBA1C (P<0.05). Importantly, in simple additive regression waist, hip circumferences and fasting blood Sugar as well as in multiple additive regression waist, hip circumferences, fasting blood sugar and BMI had significant effects on HbA1C (P<0.05). Conclusion: Additive regression model with 0.878 adjusted R-squared and AIC equal to 603.464 was better model for examining the influential factors on HbA1C compared with the multiple regression model with adjusted R-squared and AIC equal to 0.386 and 844.730, respectively.