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Abstract
Objective
This study aimed to explore whether famine exposure during early life are associated
with a high risk of Type 2 Diabetes Mellitus (T2DM) in adulthood and the role of socioeconomic
status (SES) on this effect.
Materials and methods
We conducted a secondary data analysis based on data from a cross-sectional survey,
collected 3,355 participants born between January 1, 1941 and December 31, 1966. Participants
were categorized into four groups based on their date of birth, unexposed (individuals
born in 1963–1966), infant exposed (individuals born in 1959–1962), childhood exposed
(individuals born in 1949–1958), and adolescent exposed (born in 1941–1948). The association
of famine exposure with T2DM risk in adults and conducted separately in plain area
and mountain area was assessed using logistics regression model.
Result
22.35% of participants were diagnosed with T2DM, of which 43.47% were from the childhood
famine-exposed group, representing the highest proportion among all subgroups (
p < 0.001). Participants exposed to famine during childhood and adolescence from the
lower SES mountain areas showed a significantly higher prevalence of T2DM in adulthood
than those from the plain areas (
p < 0.001). The adolescence stage exposed famine will increase the risk of T2DM in
the mountain area (OR 2.46, 95% CI 1.61, 3.77).
Conclusion
No strong evidence demonstrates that exposure to famine during the early life stage
increases the risk of developing T2DM in adulthood. However, populations with lower
SES are likely to be exposed to more risk factors for T2DM.
Summary Background Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Interpretation Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Funding Bill & Melinda Gates Foundation.
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Funded by: Natural Science Foundation of Ningxia, doi 10.13039/501100004772;
Award ID: 2023AAC05055
Funded by: Natural Science Foundation of China, doi 10.13039/501100001809;
Award ID: 82160644
Funded by: Key R&D Program of Ningxia Hui Autonomous Region
Award ID: 2020BEB04032
The author(s) declare financial support was received for the research, authorship,
and/or publication of this article. This work was supported by the Open Research Fund
of Ningxia Clinical Research Institute, People’s Hospital of Ningxia Hui Autonomous
Region (No. 2023KF08), the Natural Science Foundation of Ningxia (No. 2023AAC05055),
the Natural Science Foundation of China (No. 82160644), and the Key R&D Program of
Ningxia Hui Autonomous Region (No. 2020BEB04032).
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