Diabetes mellitus which is estimated to continue to increase over time becomes a problem that needs to be immediately addressed by various related stakeholders. Knowing the risk factors associated with diabetes mellitus is considered the right way to minimize the number of diabetes in the world, especially Indonesia. Analysis of secondary data on the Indonesian Family Life Survey is expected to be the basis for taking appropriate policies in Diabetes Mellitus intervention in Indonesia. Methods: Cross sectional design based on secondary data from the Indonesian Family Life Survey V was conducted in this study to find the most influential risk for diabetic mellitus in Indonesia. Binary logistic regression analysis was used to simplify analyzing information from 30133 respondents aged > 18 years spread across 13 Provinces in Indonesia. The selection of respondents was based on the completeness of the information in the dataset provided by RAND as the dataset provider. The variables were analyzed in this study, included diabetes mellitus, cholesterol levels, gender, hypertension, overweight and age. Results: High cholesterol, hypertension and overweight were risks associated with diabetes mellitus in Indonesia. Of the three risks, overweight was the most significant risk (p = 0,000; OR 2,200; 95% CI 1,655 – 2,295). Conclusion: Striving for healthy living behaviours and periodic screening programs can reduce overweight rates because they can be detected quickly.

Introduction: One of the health problems in developing countries, especially in Indonesia, is obesity. Indonesia Basic Health Research 2018 result revealed the prevalence of obesity in Indonesia as much as 21% in the research. It concluded very high when compared to obesity prevalence in Indonesia Basic Health Survey in 2007 and 2013. The aim of this study is to estimating risk factors that contributed to obesity in Indonesia. Methods: The research design in this study was a cross-sectional study by analyzing secondary data from the Global School-based Student Health Survey (GSHS). There were 9803 respondents was selected from 11142 respondents. Binary logistic regression was performed to answer the research objectives. Results: Sex (p-value 0.0001, AOR 1.908, 95% CI 1.672 – 2.014), educational level (p-value 0.034, AOR 1.431, 95% CI 1.026–1.994), age (p-value 0.0001, AOR 0.561, 95% CI 0.309– 0.968), cigarette smoking (p-value 0.002, AOR 1.945, 95% CI 1.278 – 2.958), fruit consumption (p-value 0.002, AOR 1.458, 95% CI 1.153–1.843) and vegetables consumption (p-value 0.023, AOR 0.742, 95% 0.373 – 0.960) were significance variable in the occurrence of obesity in adolescents. Meanwhile, based on sociodemographic adjusted, gender (p-value 0.0001, AOR 1.660, 95% CI 1.366 – 2.019) and age (p-value 0.0001, AOR 1.822, 95% CI 1.500 – 2.213) were significance variable correlate to obesity in adolescents. Conclusion: Adolescent has a risk of obesity caused by their wrong behaviour, especially young females. Indonesian Ministry of Health needs to make health promotion through social media that it can be easily accessed by the adolescent.