Title: Dietary macro-nutrients intake and risk of obesity and type 2 diabetes: to compute a model to predict probability of developing hypertrophic obesity and type 2 diabetes based on the macro-nutrient intake levels

Authors: Shalini Pattabiraman; Riddhi Vyas; Shankar Srinivasan

Addresses: Rutgers, The State University of New Jersey, 11190 Apache drive, Apt # 204, Parma heights, Cleveland, OH 44130, USA ' Department of Health Informatics, School of Health Related Professions, Rutgers, The State University of New Jersey, Stanley Bergen Building, Suite 350, 65, Bergen St. Newark, NJ 07107, USA ' Department of Health Informatics, School of Health Related Professions, Rutgers, The State University of New Jersey, Stanley Bergen Building, Suite 350, 65, Bergen St. Newark, NJ 07107, USA

Abstract: Good nutrition and healthy diet are the primary pre-requisites for a balanced lifestyle. Improper proportions of dietary macronutrients in the food lead to implicated risk of chronic diseases. Obesity is the global cause for many chronic diseases, thus diet has an important role towards the risk for occurrence of these diseases. The purpose of this study is to compute a model to predict the risk for obesity and diabetes based on the macronutrient intake levels. The statistical analysis software (SAS) datasets from NHANES (2013-2014) were extracted, then using descriptive statistical analysis and logistic regression analysis, a model is computed with significant predictive macronutrient intake variables. A receiver operating characteristic (ROC) curve and predicted probability plots were analysed for the accuracy of the model. It was found that the significant predictor variables identified for each disease risk corroborates with the literature studies, but further studies may be required with normalisation of the data in order to validate the results.

Keywords: obesity; type 2 diabetes; dietary intake; macro-nutrients; prediction model; National Health and Nutrition Examination Survey; NHANES; statistical analysis software; SAS; receiver operating characteristics; ROCs; descriptive statistical analysis; predictor variables.

DOI: 10.1504/IJMEI.2020.109941

International Journal of Medical Engineering and Informatics, 2020 Vol.12 No.5, pp.457 - 474

Received: 20 Apr 2018
Accepted: 11 Sep 2018
Published online: 30 Sep 2020 *

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