Predictive factors of glycosylated hemoglobin using additive regression model

authors:

avatar Hamzeh Zangeneh , avatar Mehdi Omidi , avatar Marzieh Hadavi , avatar Hossein Seidkhani ORCID , avatar Kourosh Sayehmiri , *

Koomesh: Vol.23, issue 3; 402-408
article type: issue_documents_importer
received: August 01, 2020
accepted: November 02, 2020

how to cite: Zangeneh H , Omidi M, Hadavi M, Seidkhani H , Sayehmiri K. Predictive factors of glycosylated hemoglobin using additive regression model. koomesh. 2021;23(3):e149960. 

Abstract

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

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