Predictive factors of glycosylated hemoglobin using additive regression model

AuthorHamzeh Zangenehen
AuthorMehdi Omidien
AuthorMarzieh Hadavien
AuthorHossein Seidkhanien
AuthorKourosh Sayehmirien
OrcidHossein Seidkhani [0000-0002-6315-1098]en
Issued Date2021-06-30en
AbstractIntroduction: 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 (Pen
DOIhttps://doi.org/en
KeywordDiabetes Mellitusen
KeywordGlycated Hemoglobin Aen
KeywordBody Mass Indexen
KeywordWaist Circumferenceen
KeywordRegression Analysisen
Keywordدیابت شیرینen
Keywordهموگلوبین گلیکوزیلهen
Keywordشاخص توده بدنیen
Keywordدور کمرen
Keywordآنالیز رگرسیونen
PublisherBrieflandsen
TitlePredictive factors of glycosylated hemoglobin using additive regression modelen
TypeResearch Articleen

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