Predictive factors of glycosylated hemoglobin using additive regression model

Hamzeh  Zangeneh; Mehdi Omidi; Marzieh Hadavi; Hossein  Seidkhani; Kourosh Sayehmiri

Koomesh

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Predictive factors of glycosylated hemoglobin using additive regression model

Author(s):

Hamzeh Zangeneh,

Mehdi Omidi,

Marzieh Hadavi,

Hossein Seidkhani

,

Kourosh Sayehmiri^,*

Koomesh:Vol. 23, issue 3; 402-408

Published online:Aug 02, 2021

Article type:Research Article

Received:Aug 01, 2020

Accepted:Nov 02, 2020

How to Cite:Hamzeh ZangenehMehdi OmidiMarzieh HadaviHossein SeidkhaniKourosh SayehmiriPredictive factors of glycosylated hemoglobin using additive regression model.koomesh.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

Keywords

Diabetes Mellitus

Glycated Hemoglobin A

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33.
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34.
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[4] Mohammedi K, Woodward M, Hirakawa Y, Zoungas S, Colagiuri S, Hamet P, et al. Microvascular and macrovascular disease and risk for major peripheral arterial disease in patients with type 2 diabetes. Diabetes Care 2016; 39: 1796-1803.
36.
https://doi.org/10.2337/dc16-1594.
37.
https://doi.org/10.2337/dc16-0588.
38.
PMid:27456835.
39.
[5] Ghafari M, Rakhshanderou S, Heidarnia A, Rajab A. The effectiveness of educational interventions on metabolic control in diabetic patients referred to Iranian Diabetes Association. Iran J Diabet Lipid 2009; 57-64. (Persian).
40.
[6] International Diabetes Federation. IDF Diabetes Atlas. Online International Diabetes Federation. 2015. Available from: URL: https://www.idf.org/e-library/epidemiology-research/diabetes-atlas/13-diabetes-atlasseventh-edition.html.
41.
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42.
[8] Peters KE, Chubb SA, Davis WA, Davis TM. The relationship between hypomagnesemia, metformin therapy and cardiovascular disease complicating type 2 diabetes: the fremantle diabetes study. PLoS One 2013; 8: e74355.
43.
https://doi.org/10.1371/journal.pone.0074355.
44.
PMid:24019966 PMCid:PMC3760872.
45.
[9] Friedman JH, Stuetzle W. Projection pursuit regression. J Am Statis Assoc 1981; 76: 817-823.
46.
https://doi.org/10.1080/01621459.1981.10477729.
47.
[10] Vazirinasab H, Salehi M, Khoshgam M, Rafati N. Comparison of generalized additive models and generalized linear models for estimating the retinopathy risk factors for diabetic patients in Tehran. JNKUMS. 2014; 5: 849-858. (Persian).
48.
https://doi.org/10.29252/jnkums.5.4.849.
49.
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50.
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53.
[15] Ghadiri-Anari A, Kheirollahi K, Hazar N, Mohiti Ardekani A, kharazmi S, Namiranian N, et al. Prevalence of risk factors in diabetic patients with oral complications. Koomesh 2019; 21: 477-485: (Persian).
54.
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55.
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56.
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57.
https://doi.org/10.1214/ss/1177013604.
58.
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59.
https://doi.org/10.2307/2347785.
60.
[20] Khatri namani Z, Bakhshi E, Naghipour A, Hossein Zadeh S. Assessment of hemoglobin A1C in patients with type 2 diabetes in the first three years of care and its related factors. JHPM 2017; 6: 34-42 (Persian).
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[21] Oscar Lado Baleato, Bivariate copular regression models in diabetes research, Master Thesis, univeside. De Santiago 2016-2017.

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