Back

Clinico-Epidemiological Profile of Type II Diabetes Mellitus in India A Multicentric Institutional Based Survey

Kalra, P.; Mohan, G.; Tiewsoh, I.; K.R, R.; M, R. K.; N. Kumar, S.; Ghosh, S.; Dharmalingam, M.; Bhattacharya, P. K.; R, A.; S, C.; Pandit, K.; Lyngdoh, M.; Subram ani, P.; Mukhopadhyay, P.; Thaman, R. G.; Chandey, M.; B. S, R.; Holigi, S.; Jain, S.; R, S.; Srinivas J, V.; Sreejith, V.; M.D, J.; Rahman, S.; Elizabeth, R. M.; Sekhar, T.; P R, S.; Vempadapu, M.; Sharma, A.; Singh, R.; Odedra, K.

2024-11-08 endocrinology
10.1101/2024.11.06.24316048 medRxiv
Show abstract

BackgroundThe epidemic of diabetes mellitus is one of the leading causes of mortality globally. Therefore, the goal of the registry is to create a database on individuals with diabetes mellitus that may be utilized to provide data on the clinico-epidemiological profile of Diabetes Mellitus in the real world. MethodsData for this registry is captured at seven sites across India recognized by the Biotechnology Industry Research Assistance Council (BIRAC). This observational multi-centric study registered around 25077 Diabetic patients over three years (December 2023). ResultsOut of 25077 patients, 12793 (51%) were male and 12284 (49%) females. There were 11443 (46%) rural patients and 13575 (54%) urban patients. Most patients registered were over 50 years old (74.05{+/-}2.42). Diabetes was seen as a burden for 46% of individuals and their families. Less than 40% of patients exercised. Over half of the patients had a family history of diabetes. This explains the exponential rise of diabetes mellitus over generations and the significance of preventing it. ConclusionThis registry revealed the impact of the clinico-epidemiological aspects of Diabetes Mellitus on a larger number of samples. Future healthcare planners, researchers, and government officials will benefit from this diabetes registry in developing primary and secondary preventive initiatives that might minimize the rising healthcare burden of diabetes.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.