Skip Navigation
Skip to contents

Epidemiol Health : Epidemiology and Health

OPEN ACCESS
SEARCH
Search

Search

Page Path
HOME > Search
2 "Wonhee Cho"
Filter
Filter
Article category
Keywords
Publication year
Authors
Funded articles
Original Articles
Long-term association of pericardial adipose tissue with incident diabetes and prediabetes: the Coronary Artery Risk Development in Young Adults Study
Minsuk Oh, Wonhee Cho, Dong Hoon Lee, Kara M. Whitaker, Pamela J. Schreiner, James G. Terry, Joon Young Kim
Epidemiol Health. 2023;45:e2023001.   Published online December 3, 2022
DOI: https://doi.org/10.4178/epih.e2023001
  • 2,808 View
  • 101 Download
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
We examined whether pericardial adipose tissue (PAT) is predictive of prediabetes and type 2 diabetes over time.
METHODS
In total, 2,570 adults without prediabetes/diabetes from the Coronary Artery Risk Development in Young Adults Study were followed up over 15 years. PAT volume was measured by computed tomography scans, and the new onset of prediabetes/diabetes was examined 5 years, 10 years, and 15 years after the PAT measurements. Multivariable Cox regression models were used to examine the association between the tertile of PAT and incident prediabetes/diabetes up to 15 years later. The predictive ability of PAT (vs. waist circumference [WC], body mass index [BMI], waist-to-height ratio [WHtR]) for prediabetes/diabetes was examined by comparing the area under the receiver operating characteristic curve (AUC).
RESULTS
The highest tertile of PAT was associated with a 1.56 times (95% confidence interval [CI], 1.03 to 2.34) higher rate of diabetes than the lowest tertile; however, no association was found between the highest tertile of PAT and prediabetes in the fully adjusted models, including additional adjustment for BMI or WC. In the fully adjusted models, the AUCs of WC, BMI, WHtR, and PAT for predicting diabetes were not significantly different, whereas the AUC of WC for predicting prediabetes was higher than that of PAT.
CONCLUSIONS
PAT may be a significant predictor of hyperglycemia, but this association might depend on the effect of BMI or WC. Additional work is warranted to examine whether novel adiposity indicators can suggest advanced and optimal information to supplement the established diagnosis for prediabetes/diabetes.
Summary
Korean summary
본 연구는 심장 내 축적되는 내장지방과 5년에서 15년 뒤의 당뇨병 전조 단계 및 당뇨병의 발생률과 연관성이 있음을 제안한다. 또한, 본 연구는 심장 내 내장지방과 당뇨병 발생률의 관계는 체질량 지수 또는 허리둘레의 영향에 따라 상이할 수 있음을 제안한다.
Key Message
Pericardial adipose tissue may be a significant predictor of future hyperglycemia in adults, but this association might depend on the effect of body mass index or waist circumference.
The predictive value of resting heart rate in identifying undiagnosed diabetes in Korean adults: Korea National Health and Nutrition Examination Survey
Dong-Hyuk Park, Wonhee Cho, Yong-Ho Lee, Sun Ha Jee, Justin Y. Jeon
Epidemiol Health. 2022;44:e2022009.   Published online January 3, 2022
DOI: https://doi.org/10.4178/epih.e2022009
  • 9,393 View
  • 404 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
The purpose of this study was (1) to examine whether the addition of resting heart rate (RHR) to the existing undiagnosed diabetes mellitus (UnDM) prediction model would improve predictability, and (2) to develop and validate UnDM prediction models by using only easily assessable variables such as gender, RHR, age, and waist circumference (WC).
METHODS
Korea National Health and Nutrition Examination Survey (KNHANES) 2010, 2012, 2014, 2016 data were used to develop the model (model building set, n=19,675), while the data from 2011, 2013, 2015, 2017 were used to validate the model (validation set, n=19,917). UnDM was defined as a fasting glucose level ≥126 mg/dL or glycated hemoglobin ≥6.5%; however, doctors have not diagnosed it. Statistical package for the social sciences logistic regression analysis was used to determine the predictors of UnDM.
RESULTS
RHR, age, and WC were associated with UnDM. When RHR was added to the existing model, sensitivity was reduced (86 vs. 73%), specificity was increased (49 vs. 65%), and a higher Youden index (35 vs. 38) was expressed. When only gender, RHR, age, and WC were used in the model, a sensitivity, specificity, and Youden index of 70%, 67%, and 37, respectively, were observed.
CONCLUSIONS
Adding RHR to the existing UnDM prediction model improved specificity and the Youden index. Furthermore, when the prediction model only used gender, RHR, age, and WC, the outcomes were not inferior to those of the existing prediction model.
Summary
Korean summary
당뇨병 미인지 또는 미진단은 적절한 치료 시작 시기를 늦추고 당뇨병 합병증 발생의 위험을 높이기 때문에, 각국은 당뇨병 예측 모형을 개발하여 당뇨병을 조기에 예측하고, 치료 시기를 앞당기기 위해 노력하고 있다. 본 연구는 기존의 한국인 당뇨병 예측 모형에 안정시심박수를 추가 변수로 포함시켜, 예측 모형의 성능이 일부개선되는 것을 확인하였고, 더 나아가 나이, 허리 둘레, 그리고 안정시심박수를 포함하여 예측 모형을 개발하고, 그 성능을 확인하였다. 본 연구에서는 간단하게 측정이 가능한 허리 둘레와 안정시심박수 그리고 나이만 포함한 예측 모형이 기존의 예측 모형과 비교해 성능이 열등하지 않은 것을 확인하였다.
Key Message
Higher RHR is associated with increased risk of diabetes. When RHR is added to the Korean undiagnosed diabetes risk score model (Age, Family history of diabetes, Hypertension, Waist circumference, Smoking, Alcohol consumption), the model somewhat increased its predictability of undiagnosed diabetes. Furthermore, the prediction model developed only using age, waist circumference and RHR, which anyone can easily measure or access, had similar predictability to the previous undiagnosed diabetes risk prediction model. The results of this study may help develop future strategies or applications for predicting early undiagnosed diabetes.

Citations

Citations to this article as recorded by  
  • Comparisons of the prediction models for undiagnosed diabetes between machine learning versus traditional statistical methods
    Seong Gyu Choi, Minsuk Oh, Dong–Hyuk Park, Byeongchan Lee, Yong-ho Lee, Sun Ha Jee, Justin Y. Jeon
    Scientific Reports.2023;[Epub]     CrossRef
  • Factors related to undiagnosed diabetes in Korean adults: a secondary data analysis
    Bohyun Kim
    Journal of Korean Biological Nursing Science.2023; 25(4): 295.     CrossRef

Epidemiol Health : Epidemiology and Health