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Clinical Prediction of Type 2 Diabetes Mellitus (T2DM) via Anthropometric and Biochemical Variations in Prakriti

Authors :
Shriti Singh
Neeraj Kumar Agrawal
Girish Singh
Sangeeta Gehlot
Santosh Kumar Singh
Rajesh Singh
Source :
Diseases, Vol 10, Iss 1, p 15 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Type 2 Diabetes Mellitus (T2DM) is a complicated multifactorial illness involving hereditary and external environmental variables. The symptoms typically appear gradually over a number of years without realizing it. This viewpoint is further supported by the Ayurvedic constitution concept (Prakriti). Prakriti explains the biological variability that is observed in different individuals. This study was conducted a retrospective investigation to examine if there was a link between type 2 diabetes and an individual’s constitution based on anthropometric and biochemical characteristics. Physical and mental characteristics and anthropometric and biochemical markers were used to determine reported cases’ prevailing Dosha Prakriti (constitution). Based on biochemical and anthropometric data, significant differences in Prakriti were found between the case (T2DM patients) and control (person without diabetes) groups. The incidence of numerous secondary problems linked with T2DM patients was also evaluated according to their Prakriti types, which revealed a positive relationship. The three primary contributing parameters, such as waist-hip ratio, postprandial blood sugar, and serum creatinine, were correctly classified all person with or without diabetes subjects to 90.6% of the time, whereas the constitution-wise study classified person with diabetes and without diabetes individuals of Pitta and Kapha Prakriti to 94.3% and 90%, respectively. A discriminant function was created to predict a person with diabetes and without diabetes based on these three contributing factors. The primary contributing biochemical parameters discovered by Prakriti in the current study could be used as a biochemical disease diagnostic for predicting type 2 diabetes susceptibility.

Details

Language :
English
ISSN :
20799721
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Diseases
Publication Type :
Academic Journal
Accession number :
edsdoj.f2d45b2663d3400daf07f5dca784af90
Document Type :
article
Full Text :
https://doi.org/10.3390/diseases10010015