Mubasyiroh, Rofingatul and Kusumawardani, Nunik and Rachmalina, Rika and Arfines, Prisca Petty and Puspita, Tities and Sudikno, Sudikno (2021) How Well Does Body Mass Index (BMI) Predict Undiagnosed Hypertension and Diabetes in Indonesian Adults Community Population? Global Journal of Health Science, 13 (11). p. 25. ISSN 1916-9736
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Abstract
BACKGROUND: Previous studies have reported that Body Mass Index (BMI) cut-off was related to non-communicable diseases. This study aimed to give the latest evidence related to the accuracy of BMI cut-off towards undiagnosed hypertension and diabetes in the Indonesian population.
METHODS: This was A cross-sectional study that involved data of the 2018 national population-based health survey, with the samples were 15,516 male and female populations aged between 19 years old and above. This study only included those claimed to have never been diagnosed as suffering from diabetes and hypertension by health workers. Receiver operating characteristic (ROC) analysis was conducted to assess the optimal BMI cut-off. The logistic regression was performed to assess the association of BMI on undiagnosed hypertension and diabetes controlled by several variables.
RESULTS: The average BMI sample was 24 kg/m2 (SD = 4.6 kg/m2. The proportion of undiagnosed hypertension was 36.9%, and 12.3% for the proportion of undiagnosed diabetes. According to the ROC, the result shows BMI was more sensitive to hypertension conditions compared to diabetes. BMI cut-off points at 23.9 kg/m2 (AUC=0.59;Se=64.3%;Sp=53.4%) was the optimum value to predict hypertension and 24.9 kg/m2 (AUC=0.55;Se=53.1%;Sp=56.4%) was the optimum for diabetes.
CONCLUSIONS: Based on the optimal AUC cut-off points for BMI which is around 0.5, BMI needs to be reconsidered as an anthropometric index in predicting undiagnosed hypertension and diabetes. And an assessment can be made using other anthropometric indices, such as waist circumference to predict undiagnosed hypertension and diabetes.
Item Type: | Article |
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Subjects: | Eprint Open STM Press > Medical Science |
Depositing User: | Unnamed user with email admin@eprint.openstmpress.com |
Date Deposited: | 05 May 2023 11:55 |
Last Modified: | 03 Jan 2024 06:56 |
URI: | http://library.go4manusub.com/id/eprint/183 |