Prameha purvaroopa as diabetes risk predictor - trends from a retrospective cohort study of newly diagnosed type 2 diabetes patients

Sanjeev Rastogi; Neelendra Singh; Manish Gutch; Arindam Bhattacharya

doi:10.1016/j.jaim.2022.100671

Prameha purvaroopa as diabetes risk predictor - trends from a retrospective cohort study of newly diagnosed type 2 diabetes patients

J Ayurveda Integr Med. 2023 Jan-Feb;14(1):100671. doi: 10.1016/j.jaim.2022.100671. Epub 2022 Nov 13.

Authors

Sanjeev Rastogi¹, Neelendra Singh², Manish Gutch³, Arindam Bhattacharya⁴

Affiliations

¹ PG Department of Kaya Chikitsa, State Ayurvedic College and Hospital, Lucknow University, Lucknow, India. Electronic address: rastogisanjeev@rediffmail.com.
² PG Department of Kaya Chikitsa, State Ayurvedic College and Hospital, Lucknow University, Lucknow, India.
³ Endocrinology and Diabetology, Medanta Hospital, Lucknow, India.
⁴ Ministry of External Affairs, Govt of India, New Delhi, India.

Abstract

Study background: Increasing prevalence of type 2 diabetes has shifted the focus of world from its management to prevention. Life style modifications programs related to diabetes prevention are found to delay the progress of prediabetes into diabetes. Reaching out to community with diabetes prevention program however is still a challenge to meet.

Objective: Of the study: This study proposed to retrospectively screen the presence of prediabetes symptoms depicted in Ayurveda literature in a newly diagnosed diabetic population and to compare this prevalence with non-diabetic and healthy population. The idea is to put weightage upon prediabetes symptoms as a disease predictor if these are available early in the course of the disease.

Material and methods: A questionnaire based upon detailed literature survey of three Ayurveda classics from the subject area of prameha (identical to diabetes) identified 22 features under the class of prediabetes. A questionnaire was developed to find the presence of these features in selected diabetic population retrospectively before the onset of diabetes. 141 newly diagnosed diabetics were selected on the basis of a defined inclusion and exclusion criteria and surveyed for chronological presence of prediabetes features as identified through the literature search and validated through a validation process. This feature prevalence was further compared with non-diabetic and healthy population.

Results: A significant difference (p < 0.001) was observed in specific symptom occurrence in diabetic population comparing to non-diabetic and healthy control for at least 11 of the selected items.

Conclusion: The study observes that few prediabetes features show their clear retrospective presence in diabetic population comparing to non-diabetic and healthy population. This observation can help formulating a risk calculator for future diabetes on the basis of available pre diabetic features in an individual. A prospective cohort study however would be essential to prove any such realistic relation between prediabetes symptoms and future diabetes development among high risk individuals.