Urban-rural differences in food consumption and central obesity among middle-aged adults: A cross-sectional population study in Indonesia.
Ahmad SyauqyZulaikhah Atyas PermatasariSiti Andhini MattarahmawatiFitrah ErnawatiAdriyan PramonoA Fahmy Arif TsaniDeny Yudi FitrantiAryu CandraMartha ArdiariaPublished in: Nutrition and health (2024)
Background: The prevalence of central obesity is increasing in many regions, including low- and middle-income countries. It has been identified that a poor diet has become a significant risk factor for central obesity. However, the relationship between diet and the prevalence of central obesity among rural and urban communities remains unclear in low- and middle-income countries. Aim: This study aimed to analyze the association of food consumption with central obesity among middle-aged adults (45-59 years) in urban and rural areas in Indonesia. Methods: This was a cross-sectional study using secondary data from a national survey (Indonesia Basic Health Survey) in Indonesia in 2018. A total of 154,449 subjects were analyzed for the study. We used the International Diabetes Federation to define central obesity. Food consumption was measured using a validated food frequency questionnaire. Multivariable logistic regression was used to explore the association between food consumption and central obesity. Results: Frequent consumption of refined desserts, fried food, processed food, and inadequate consumption of fruit was significantly associated with central obesity in urban and rural areas ( p < 0.05). In contrast, frequent consumption of seasoning and inadequate consumption of vegetables was significantly associated with central obesity only in rural areas ( p < 0.05). Conclusion: Food consumption has a different association with central obesity risk in rural and urban areas in Indonesia.
Keyphrases
- weight loss
- insulin resistance
- metabolic syndrome
- type diabetes
- high fat diet induced
- weight gain
- middle aged
- human health
- adipose tissue
- physical activity
- south africa
- risk factors
- magnetic resonance imaging
- computed tomography
- body mass index
- electronic health record
- risk assessment
- deep learning
- psychometric properties