Urbanization and Unfavorable Changes in Metabolic Profiles: A Prospective Cohort Study of Indonesian Young Adults

Abstract

The substantial increase in the prevalence of non-communicable diseases in Indonesia might be driven by rapid socio-economic development through urbanization. Here, we carried out a longitudinal 1-year follow-up study to evaluate the effect of urbanization, an important determinant of health, on metabolic profiles of young Indonesian adults. University freshmen/women in Jakarta, aged 16–25 years, who either had recently migrated from rural areas or originated from urban settings were studied. Anthropometry, dietary intake, and physical activity, as well as fasting blood glucose and insulin, leptin, and adiponectin were measured at baseline and repeated at one year follow-up. At baseline, 106 urban and 83 rural subjects were recruited, of which 81 urban and 66 rural were followed up. At baseline, rural subjects had better adiposity profiles, whole-body insulin resistance, and adipokine levels compared to their urban counterparts. After 1-year, rural subjects experienced an almost twice higher increase in BMI than urban subjects (estimate (95%CI): 1.23 (0.94; 1.52) and 0.69 (0.43; 0.95) for rural and urban subjects, respectively, Pint < 0.01). Fat intake served as the major dietary component, which partially mediates the differences in BMI between urban and rural group at baseline. It also contributed to the changes in BMI over time for both groups, although it does not explain the enhanced gain of BMI in rural subjects. A significantly higher increase of leptin/adiponectin ratio was also seen in rural subjects after 1-year of living in an urban area. In conclusion, urbanization was associated with less favorable changes in adiposity and adipokine profiles in a population of young Indonesian adults.

Lifestyle and clinical risk factors in relation with the prevalence of diabetes in the Indonesian urban and rural populations: The 2018 Indonesian Basic Health Survey

“…The higher BMI, waist circumference, and obesity prevalence in urban compared to rural population observed in the current study, confirmed our previous findings ( Kurniawan et al, 2022 ). This present study also found these adiposity indices and obesity are positively correlated with diabetes in both populations, similar to what had been observed previously ( Bellou et al, 2018 ).…”

“…This may result in obesity and consequent low-grade inflammatory state and insulin resistance, pathways leading to type 2 diabetes (T2D) ( Schuster, 2010 ). Our previous study in Indonesian young adults showed a higher prevalence of obesity in the urban compared to rural population ( Kurniawan et al, 2022 ).…”

Serum Adiponectin and Leptin Among Ghanaian Migrants in Amsterdam and Their Compatriots in Rural and Urban Ghana: The RODAM Study

“…In a prospective, cohort study done in Indonesia, rural to urban migration, was associated with a disproportionately higher increase in BMI and leptin :adiponectin ratio compared to urban-to-urban migration. 35 Migration and urbanization may lead to unhealthy lifestyle modifications such as consumption of more processed food and physical inactivity, which ultimately lead to obesity and significantly impact one’s health. 24,25 More recently, another link between migration and obesity with its ensuing complication has been established.…”

Body mass index as a dominant risk factor for metabolic syndrome among indonesian adults: a 6-year prospective cohort study of non-communicable diseases

“…In order to develop effective prevention programs, several cohort studies have been conducted to better understand the risk factors for MetS and examine MetS components [ 23 – 25 ], yet this prior research has limitations including relatively short follow-up times (one to four years) [ 26 ], variation in diagnostic criteria for MetS, or studies on populations of different ethnicities, which may affect generalisability to the Indonesian context [ 27 , 28 ]. Another study also explored MetS incidence using a similar six-year cohort, but did not rigorously select participants based on complete MetS component profiles and medication usage, which could introduce bias [ 29 ].…”