Diabetes and Metabolism Journal

Korean Diabetes Association (대한당뇨병학회)
권호 표지
DOI QR코드

DOI QR Code

Projection of Diabetes Prevalence in Korean Adults for the Year 2030 Using Risk Factors Identified from National Data

  • Baik, Inkyung (Department of Foods and Nutrition, College of Science and Technology, Kookmin University)
  • Received : 2018.03.08
  • Accepted : 2018.06.22
  • Published : 2019.02.28
⟨ Previous Next ⟩

Abstract

Background: A number of studies have reported future prevalence estimates for diabetes mellitus (DM), but these studies have been limited for the Korean population. The present study aimed to construct a forecasting model that includes risk factors for type 2 DM using individual- and national-level data for Korean adults to produce prevalence estimates for the year 2030. Methods: Time series data from the Korea National Health and Nutrition Examination Survey and national statistics from 2005 to 2013 were used. The study subjects were 13,908 male and 18,697 female adults aged 30 years or older who were free of liver cirrhosis. Stepwise logistic regression analysis was used to select significant factors associated with DM prevalence. Results: The results showed that survey year, age, sex, marital, educational, or occupational status, the presence of obesity or hypertension, smoking status, alcohol consumption, sleep duration, psychological distress or depression, and fertility rate significantly contributed to the 8-year trend in DM prevalence (P<0.05). Based on sex-specific forecasting models that included the above factors, DM prevalence for the year 2030 was predicted to be 29.2% (95% confidence interval [CI], 27.6% to 30.8%) in men and 19.7% (95% CI, 18.2% to 21.2%) in women. Conclusion: The present study projected a two-fold increase in the prevalence of DM in 2030 compared with that for the years 2013 and 2014 in Korean adults. Modifiable factors contributing to this increase in DM prevalence, such as obesity, smoking, and psychological factors, may require attention in order to reduce national and individual costs associated with DM.

Keywords

Acknowledgement

This work was supported by National Research Foundation of Korea Grant funded by the Korean Government (NRF-2017R1A2A2A05001380). The funders have no role in the study.

References

  1. World Health Organization. Global report on diabetes. Geneva: World Health Organization; 2016.
  2. Ministry of Health and Welfare, Korea Centers for Disease Control and Prevention. Korea health statistics 2015: Korea National Health and Nutrition Examination Survey (KNHANES VI-3). Sejong: Korea Centers for Disease Control and Prevention; 2016.
  3. National Statistics Office. Population projections for Korea: 2015-2065. Seoul: Government of Korea; 2016.
  4. Whiting DR, Guariguata L, Weil C, Shaw J. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract 2011;94:311-21. https://doi.org/10.1016/j.diabres.2011.10.029
  5. Brinks R, Tamayo T, Kowall B, Rathmann W. Prevalence of type 2 diabetes in Germany in 2040: estimates from an epidemiological model. Eur J Epidemiol 2012;27:791-7. https://doi.org/10.1007/s10654-012-9726-2
  6. Javanbakht M, Mashayekhi A, Baradaran HR, Haghdoost A, Afshin A. Projection of diabetes population size and associated economic burden through 2030 in Iran: evidence from micro-simulation Markov model and Bayesian meta-analysis. PLoS One 2015;10:e0132505. https://doi.org/10.1371/journal.pone.0132505
  7. Andersson T, Ahlbom A, Carlsson S. Diabetes prevalence in Sweden at present and projections for year 2050. PLoS One 2015;10:e0143084. https://doi.org/10.1371/journal.pone.0143084
  8. Sozmen K, Unal B, Capewell S, Critchley J, O'Flaherty M. Estimating diabetes prevalence in Turkey in 2025 with and without possible interventions to reduce obesity and smoking prevalence, using a modelling approach. Int J Public Health 2015;60 Suppl 1:S13-21.
  9. Holman N, Forouhi NG, Goyder E, Wild SH. The Association of Public Health Observatories (APHO) diabetes prevalence model: estimates of total diabetes prevalence for England, 2010-2030. Diabet Med 2011;28:575-82. https://doi.org/10.1111/j.1464-5491.2010.03216.x
  10. Honeycutt AA, Boyle JP, Broglio KR, Thompson TJ, Hoerger TJ, Geiss LS, Narayan KM. A dynamic Markov model for forecasting diabetes prevalence in the United States through 2050. Health Care Manag Sci 2003;6:155-64. https://doi.org/10.1023/A:1024467522972
  11. Gregg EW, Boyle JP, Thompson TJ, Barker LE, Albright AL, Williamson DF. Modeling the impact of prevention policies on future diabetes prevalence in the United States: 2010-2030. Popul Health Metr 2013;11:18. https://doi.org/10.1186/1478-7954-11-18
  12. Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metr 2010;8:29. https://doi.org/10.1186/1478-7954-8-29
  13. Wang Y, Beydoun MA, Liang L, Caballero B, Kumanyika SK. Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity (Silver Spring) 2008;16:2323-30. https://doi.org/10.1038/oby.2008.351
  14. Maddams J, Utley M, Moller H. Projections of cancer prevalence in the United Kingdom, 2010-2040. Br J Cancer 2012; 107:1195-202. https://doi.org/10.1038/bjc.2012.366
  15. Brookmeyer R, Abdalla N, Kawas CH, Corrada MM. Forecasting the prevalence of preclinical and clinical Alzheimer's disease in the United States. Alzheimers Dement 2018;14:121-9. https://doi.org/10.1016/j.jalz.2017.10.009
  16. Finkelstein EA, Khavjou OA, Thompson H, Trogdon JG, Pan L, Sherry B, Dietz W. Obesity and severe obesity forecasts through 2030. Am J Prev Med 2012;42:563-70. https://doi.org/10.1016/j.amepre.2011.10.026
  17. National Statistics Office. Population projections for Korea: 2010-2060. Seoul: Government of Korea; 2011.
  18. Korean Diabetes Association. Diabetes fact sheet in Korea 2016. Seoul: Korean Diabetes Association; 2016.
  19. Koska J, Stefan N, Votruba SB, Smith SR, Krakoff J, Bunt JC. Distribution of subcutaneous fat predicts insulin action in obesity in sex-specific manner. Obesity (Silver Spring) 2008;16:2003-9. https://doi.org/10.1038/oby.2008.292
  20. Asia Pacific Cohort Studies Collaboration, Ni Mhurchu C, Parag V, Nakamura M, Patel A, Rodgers A, Lam TH. Body mass index and risk of diabetes mellitus in the Asia-Pacific region. Asia Pac J Clin Nutr 2006;15:127-33.
  21. Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J. Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis. JAMA 2007;298:2654-64. https://doi.org/10.1001/jama.298.22.2654
  22. Cappuccio FP, D'Elia L, Strazzullo P, Miller MA. Quantity and quality of sleep and incidence of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care 2010;33:414-20. https://doi.org/10.2337/dc09-1124
  23. Yu M, Zhang X, Lu F, Fang L. Depression and risk for diabetes: a meta-analysis. Can J Diabetes 2015;39:266-72. https://doi.org/10.1016/j.jcjd.2014.11.006
  24. Sui H, Sun N, Zhan L, Lu X, Chen T, Mao X. Association between work-related stress and risk for type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies. PLoS One 2016;11:e0159978. https://doi.org/10.1371/journal.pone.0159978
  25. Koppes LL, Dekker JM, Hendriks HF, Bouter LM, Heine RJ. Moderate alcohol consumption lowers the risk of type 2 diabetes: a meta-analysis of prospective observational studies. Diabetes Care 2005;28:719-25. https://doi.org/10.2337/diacare.28.3.719

Cited by

  1. Potential Diagnostic Hemorheological Indexes for Chronic Kidney Disease in Patients With Type 2 Diabetes vol.10, 2019, https://doi.org/10.3389/fphys.2019.01062
  2. Smoking as a Target for Prevention of Diabetes vol.44, pp.3, 2019, https://doi.org/10.4093/dmj.2020.0126
  3. Prognosis of Patients with Colorectal Cancer with Diabetes According to Medication Adherence: A Population-Based Cohort Study vol.29, pp.6, 2019, https://doi.org/10.1158/1055-9965.epi-19-1455
  4. A Novel User Utility Score for Diabetes Management Using Tailored Mobile Coaching: Secondary Analysis of a Randomized Controlled Trial vol.9, pp.2, 2019, https://doi.org/10.2196/17573
  5. Spironolactone Attenuates Methylglyoxal-induced Cellular Dysfunction in MC3T3-E1 Osteoblastic Cells vol.36, pp.38, 2019, https://doi.org/10.3346/jkms.2021.36.e265
  6. Antiobesity and Antidiabetic Effects of Portulaca oleracea Powder Intake in High-Fat Diet-Induced Obese C57BL/6 Mice vol.2021, 2019, https://doi.org/10.1155/2021/5587848
  7. Development of a clinical risk score for incident diabetes: A 10‐year prospective cohort study vol.12, pp.4, 2021, https://doi.org/10.1111/jdi.13382
  8. Sleep duration and mortality in patients with diabetes: Results from the 2007-2015 Korea national health and nutrition examination survey vol.48, pp.3, 2019, https://doi.org/10.1016/j.diabet.2021.101312