Factors Influencing the Pedigree Function of Gestational Diabetes in Pima Indian Women
Keywords:
Body mass index (BMI), Diabetes pedigree function (DPF), Gamma model, Joint generalized linear models (JGLMs), Likelihood functionAbstract
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.
References
Meigs JB, Cupples A, Wilson PW. Parental transmission of type 2 diabetes: The Framingham Offspring study. Diabetes. 2000; 49: 2201-2207. doi: 10.2337/diabetes.49.12.2201
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2004; 27(Suppl 1): S5-S10. doi: 10.2337/diacare.27.2007.s5
Annis AM, Mark S Caulder, Michelle L Cook, Debra Duquette. Family History, Diabetes, and Other Demographic and Risk Fac-tors Among Participants of the National Health and Nutrition Examination Survey 1999–2002. Prev Chronic Dis. 2005; 2(2): 1-12.
Medici F, Hawa M, Ianari A, Pyke DA, Leslie RD. Concor-dance rate for type II diabetes mellitus in monozygotic twins: Actuarial analysis. Diabetologia. 1999; 42: 146-150. doi: 10.1007/ s001250051132
Silverstein JH, Rosenbloom AL. Type 2 diabetes in children. Curr Diab Rep. 2001; 1: 19-27. doi: 10.1007/s11892-001-0006-x
Das M, Das RN. The impact of the clinical history of diseases on cardiovascular risk factors. Madridge J Intern Emerg Med. 2021; 5(1): 159-161. doi: 10.18689/mjiem-1000136
Alghamdi M, Al-Mallah M, Keteyian S, Brawner C, Ehrman J, Sakr S. Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford Exercise Testing (FIT) project. PLoS One. 2017; 12: e0179805. doi: 10.1371/journal. pone.0179805
Nguyen BP, Pham HN, Tran H, et al. Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records. Comput Methods Programs Biomed. 2019; 182: 105055. doi: 10.1016/j.cmpb.2019.105055
Habibi S, Ahmadi M, Alizadeh S. Type 2 diabetes mellitus screening and risk factors using decision tree: Results of data min-ing. Glob J Health Sci. 2015; 7: 304-310. doi: 10.5539/gjhs.v7n5p304
Ryden L, Standl E, Bartnik M, et al. Guidelines on diabetes, pre-diabetes, and cardiovascular diseases: Executive summary: The Task Force on Diabetes and Cardiovascular Diseases of the Eu-ropean Society of Cardiology (ESC) and of the European Asso-ciation for the Study of Diabetes (EASD). Eur Heart J. 2007; 28: 88-136. doi: 10.1093/eurheartj/ehl260
Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030. Diabetes Care. 2004; 27: 1047-1053. doi: 10.2337/diacare.27.5.1047
Rolka DB, Narayan KV, Thompson TJ, et al. Performance of recommended screening tests for undiagnosed diabetes and dys-glycemia. Diabetes Care. 2001; 24: 1899-1903. doi: 10.2337/dia-care.24.11.1899
Anderson AE, Kerr WT, Thames A, Li T, Xiao J, Cohen MS. Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States popu-lation: A cross-sectional, unselected, retrospective study. J Biomed Inform. 2016; 60: 162-168. doi: 10.1016/j.jbi.2015.12.006
Joshi RD, and Dhakal CK. Predicting type 2 diabetes using lo-gistic regression and machine learning approaches. Int J Environ Res Submit your article to this journal | https://openventio.org/submit-manuscript/ Das RN, et al Public Health. 2021; 18: 7346. doi: 10.3390/ijerph18147346
Myers RH, Montgomery DC, Vining GG. Generalized Linear Models with Applications in Engineering and the Sciences. New York, USA: John Wiley & Sons; 2002.
Lee, Y, Nelder JA, PawitanY. Generalized Linear Models with Ran-dom Effects (Unified Analysis via H–likelihood). 2nd ed. London, UK: Chapman & Hall; 2017.
Das RN. Robust Response Surfaces, Regression and Positive Data Analyses. London, UK: Chapman & Hall; 2014.
Das RN, Lee Y. Log-normal versus gamma models for analyz-ing data from quality-improvement experiments. Quality Engineer-ing. 2009; 21(1): 79-87. doi: 10.1080/08982110802317372
Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Berlin, Germany: Springer-Verlag; 2009: 203-204.
Nelder JA. The statistics of linear models: Back to basics. Stat Comput. 1994; 4: 221-234. doi: 10.1007/BF00156745
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All content published in Internal Medicine (IMOJ) is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows anyone to copy, distribute, transmit, and adapt the work — including for commercial purposes — provided appropriate credit is given to the original author(s) and source.
