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Inkyung Baik  (Baik I) 2 Articles
Response: Projection of Diabetes Prevalence in Korean Adults for the Year 2030 Using Risk Factors Identified from National Data (Diabetes Metab J 2019;43:90–6)
Inkyung Baik
Diabetes Metab J. 2019;43(2):244-245.   Published online April 15, 2019
  • 2,555 View
  • 36 Download
  • 1 Web of Science
  • 1 Crossref


Citations to this article as recorded by  
  • Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications
    Umair Muneer Butt, Sukumar Letchmunan, Mubashir Ali, Fadratul Hafinaz Hassan, Anees Baqir, Hafiz Husnain Raza Sherazi, Daniel Espino
    Journal of Healthcare Engineering.2021; 2021: 1.     CrossRef
Projection of Diabetes Prevalence in Korean Adults for the Year 2030 Using Risk Factors Identified from National Data
Inkyung Baik
Diabetes Metab J. 2019;43(1):90-96.   Published online October 31, 2018
  • 3,922 View
  • 44 Download
  • 15 Web of Science
  • 15 Crossref
AbstractAbstract PDFPubReader   

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.


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.


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.


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.


Citations to this article as recorded by  
  • Angiotensin‐converting enzyme inhibitors versus angiotensin receptor blockers: New‐onset diabetes mellitus stratified by statin use
    Juyoung Shin, Hyunah Kim, Hyeon Woo Yim, Ju Han Kim, Suehyun Lee, Hun‐Sung Kim
    Journal of Clinical Pharmacy and Therapeutics.2022; 47(1): 97.     CrossRef
  • Sleep duration and mortality in patients with diabetes: Results from the 2007–2015 Korea national health and nutrition examination survey
    Kang-Mo Gu, Se Hee Min, Jaeyoung Cho
    Diabetes & Metabolism.2022; 48(3): 101312.     CrossRef
  • Advances in the use of electrospinning as a promising technique for obtaining nanofibers to guide epithelial wound healing in diabetics—Mini‐review
    Pamela Roberta Alves da Costa, Lívia Rodrigues de Menezes, Marcos Lopes Dias, Emerson Oliveira da Silva
    Polymers for Advanced Technologies.2022; 33(4): 1031.     CrossRef
  • GLP-1 receptor agonists in diabetic kidney disease: current evidence and future directions
    Ji Hee Yu, So Young Park, Da Young Lee, Nan Hee Kim, Ji A Seo
    Kidney Research and Clinical Practice.2022; 41(2): 136.     CrossRef
  • Associations between obesity, metabolic syndrome, and endometrial cancer risk in East Asian women
    Boyoung Park
    Journal of Gynecologic Oncology.2022;[Epub]     CrossRef
  • Hemoglobin glycation index is associated with incident chronic kidney disease in subjects with impaired glucose metabolism: A 10-year longitudinal cohort study
    Wonjin Kim, Taehwa Go, Dae Ryong Kang, Eun Jig Lee, Ji Hye Huh
    Journal of Diabetes and its Complications.2021; 35(1): 107760.     CrossRef
  • A Novel User Utility Score for Diabetes Management Using Tailored Mobile Coaching: Secondary Analysis of a Randomized Controlled Trial
    Min-Kyung Lee, Da Young Lee, Hong-Yup Ahn, Cheol-Young Park
    JMIR mHealth and uHealth.2021; 9(2): e17573.     CrossRef
  • Development of a clinical risk score for incident diabetes: A 10‐year prospective cohort study
    Tae Jung Oh, Jae Hoon Moon, Sung Hee Choi, Young Min Cho, Kyong Soo Park, Nam H Cho, Hak Chul Jang
    Journal of Diabetes Investigation.2021; 12(4): 610.     CrossRef
  • Antiobesity and Antidiabetic Effects of Portulaca oleracea Powder Intake in High-Fat Diet-Induced Obese C57BL/6 Mice
    Jae Hyun Jung, Su Bin Hwang, Hyeon Ju Park, Guang-Ri Jin, Bog Hieu Lee, Rômulo Dias Novaes
    Evidence-Based Complementary and Alternative Medicine.2021; 2021: 1.     CrossRef
  • Spironolactone Attenuates Methylglyoxal-induced Cellular Dysfunction in MC3T3-E1 Osteoblastic Cells
    So Young Park, Kwang Sik Suh, Woon-Won Jung, Sang Ouk Chin
    Journal of Korean Medical Science.2021;[Epub]     CrossRef
  • Smoking as a Target for Prevention of Diabetes
    Ye Seul Yang, Tae Seo Sohn
    Diabetes & Metabolism Journal.2020; 44(3): 402.     CrossRef
  • Prognosis of Patients with Colorectal Cancer with Diabetes According to Medication Adherence: A Population-Based Cohort Study
    Sunho Choe, Joonki Lee, Ji Won Park, Seung-Yong Jeong, Young Min Cho, Byung-Joo Park, Aesun Shin
    Cancer Epidemiology, Biomarkers & Prevention.2020; 29(6): 1120.     CrossRef
  • Potential Diagnostic Hemorheological Indexes for Chronic Kidney Disease in Patients With Type 2 Diabetes
    Hoyoon Lee, Wonwhi Na, Sang Bae Lee, Chul Woo Ahn, Jun Sung Moon, Kyu Chang Won, Sehyun Shin
    Frontiers in Physiology.2019;[Epub]     CrossRef
  • Response: Projection of Diabetes Prevalence in Korean Adults for the Year 2030 Using Risk Factors Identified from National Data (Diabetes Metab J 2019;43:90–6)
    Inkyung Baik
    Diabetes & Metabolism Journal.2019; 43(2): 244.     CrossRef
  • Letter: Projection of Diabetes Prevalence in Korean Adults for the Year 2030 Using Risk Factors Identified from National Data (Diabetes Metab J 2019;43:90–6)
    Bo Kyung Koo
    Diabetes & Metabolism Journal.2019; 43(2): 242.     CrossRef

Diabetes Metab J : Diabetes & Metabolism Journal