Determinants of Gestational Diabetes Pedigree Function for Pima Indian Females

Authors

  • Mahashweta Das, MA Author
  • Gaurab Bhattacharyya, MSc Author
  • Rui Gong, PhD Author
  • Rahul Misra, MSc Author
  • Sunit K. Medda, MBBS Author
  • Shipra Banik, PhD Author
  • Rabindra N. Das, PhD Author

Keywords:

Body mass index (BMI), Diabetes pedigree function (DPF), Gamma model, Joint generalized linear models (JGLMs), Likelihood function

Abstract

Objectives
Diabetes pedigree function (DPF) calculates diabetes likelihood depending on the subject’s age and his/her diabetic family history. 
Very little is known about the determinants of DPF for gestational diabetes mellitus (GDM) and normal women. The article focuses 
on the determinants of DPF for GDM and normal (non-diabetes) women. 
Results
It has been derived that mean DPF is directly linked to age (p=0.0334), subject’s type (p=0.0006), triceps skin-fold thickness (TSFT) 
(p=0.0083), insulin level (p=0.0032), the joint interaction effect of body mass index (BMI) and glucose level (BMI×Glucose) 
(p=0.0624), while it is inversely linked to pregnancy’s number (p=0.0217), glucose level (p=0.0724) and BMI (p=0.1173). Moreover, 
the variance of DPF is partially inversely linked to pregnancy’s number (p=0.1159) and directly to the joint interaction effect of 
diastolic blood pressure (DBP) and pregnancy’s number (i.e., DBP×pregnancy’s number) (p=0.1304).
Conclusion
It concludes that DPF is not only based on age and subject’s diabetic family history, while it depends on many factors as stated above. 
So, for computing DPF, the above factors should be included in it. 

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Published

2022-12-31