Prognostic value of resting heart rate in predicting undiagnosed diabetes in adults: Korean National Health and Nutrition Examination Survey 2008-2018.

BACKGROUND AND AIM: Although resting heart rate (RHR) is associated with prevalence and incidence of diabetes, whether it is associated with undiagnosed diabetes is still unclear. We aimed to investigate whether the RHR is associated with the prevalence of undiagnosed diabetes in a large Korean national dataset. METHODS AND RESULTS: The Korean National Health and Nutrition Examination Survey data from 2008 to 2018 were used. After screening, 51,637 participants were included in this study. The odds ratios and 95% confidence intervals (CIs) for undiagnosed diabetes were calculated using multivariable-adjusted logistic regression analyses. Analyses showed that participants with a RHR of ≥90 bpm showed a 4.00- (95% CI: 2.77-5.77) and 3.21-times (95% CI: 2.01-5.14) higher prevalence of undiagnosed diabetes for men and women, respectively, than those with a RHR of <60 bpm. The linear dose-response analyses showed that each 10-bpm increment in RHR was associated with a 1.39- (95% CI: 1.32-1.48) and 1.28-times (95% CI: 1.19-1.37) higher prevalence of undiagnosed diabetes for men and women, respectively. In the stratified analyses, the positive association between RHR and the prevalence of undiagnosed diabetes was tended to be stronger among those who were younger (age: <40 years) and lean (BMI: <23 kg/m2). CONCLUSIONS: Elevated RHR was significantly associated with a higher prevalence of undiagnosed diabetes in Korean men and women, independent of demographic, lifestyle, and medical factors. Accordingly, the value of RHR as a clinical indicator and health marker, especially in reducing the prevalence of undiagnosed diabetes, is suggestible.

The predictive value of resting heart rate in identifying undiagnosed diabetes in Korean adults: Korea National Health and Nutrition Examination Survey.

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.

Breath Acetone Measurement-Based Prediction of Exercise-Induced Energy and Substrate Expenditure.

The purpose of our study was to validate a newly developed breath acetone (BrAce) analyzer, and to explore if BrAce could predict aerobic exercise-related substrate use. Six healthy men ran on a treadmill at 70% of maximal oxygen consumption (VO2max) for 1 h after two days of a low-carbohydrate diet. BrAce and blood ketone (acetoacetate (ACAC), beta-hydroxybutyrate (BOHB)) levels were measured at baseline and at different time points of post-exercise. BrAce values were validated against blood ketones and respiratory exchange ratio (RER). Our results showed that BrAce was moderately correlated with BOHB (r = 0.68, p < 0.01), ACAC (r = 0.37, p < 0.01) and blood ketone (r = 0.60, p < 0.01), suggesting that BrAce reflect blood ketone levels, which increase when fat is oxidized. Furthermore, BrAce also negatively correlated with RER (r = 0.67, p < 0.01). In our multiple regression analyses, we found that when BMI and VO2max were added to the prediction model in addition to BrAce, R2 values increased up to 0.972 at rest and 0.917 at 1 h after exercise. In conclusion, BrAce level measurements of our BrAce analyzer reflect blood ketone levels and the device could potentially predict fat oxidation.