Background: Although idiopathic Parkinson’s disease (IPD) is increasing with the aging population, there is no adequate screening test for early diagnosis of IPD. Cardiac autonomic dysfunction begins in the early stages of IPD, and an electrocardiogram (ECG) contains precise information on the heart. Objective: This study is to develop an ECG deep learning algorithm that can efficiently screen for IPD. Methods: Data were collected from 751 IPD patients (2,138 ECGs), 751 age and sex-matched non-IPD patients (2,673 ECGs) as a control group, and 297 drug-induced Parkinsonism (DPD) patients (875 ECGs) as a disease control group. ECG data were randomly divided into training set, validation set, and test set at a ratio of 6:2:2. We developed a deep-convolutional neural network (CNN) consisting of 16 layers with Bayesian optimization that classified IPD patients by ECG data. The robustness of the deep learning model was verified through 5-fold cross-validation. Results: The AUROC of the model for detection of IPD was 0.924 (95% CI, 0.913–0.936) in the test set. That for detecting DPD was 0.473 (95% CI, 0.453–0.504). The sensitivities of the model according to Unified Parkinson’s Disease Rating Scale III and Hoehn & Yahr scale were also similar. Conclusion: In conclusion, the CNN-based deep learning model using ECG data showed quite good performance in identifying IPD patients. Standardized 12-lead ECG test could be one of the clinically feasible candidate methods for early screening of IPD in the future.

Objective: Metabolic abnormalities such as dyslipidemia, glucose and high blood pressure are common in diabetic patients. Variabilities in these measures have been reported as a potential residual cardiovascular risk factors. This study aimed to analyze the relationship between variabilities of blood pressure, blood glucose, total cholesterol and triglyceride levels (metabolic variability parameters) and their effects on cardiovascular prognosis in diabetic patients. Design and method: A total of 22,310 diabetic patients aged 40 years or older who had their blood pressure, blood glucose, total cholesterol, and triglyceride levels measured three or more times for three years after January 1, 2017 at three tertiary general hospitals were selected. They were divided into high/low groups based on their systolic blood pressure (SBP), blood glucose, total cholesterol (TC) and triglyceride coefficient of variation (CV) over a 3-year period. The incidence of major adverse cardiovascular events (MACE), a composite of cardiovascular death, myocardial infarction, and stroke, was compared between groups, and significant risk factors for MACE were estimated by multivariable Cox regression analysis. Results: The number of measurements for 3 years was 15.1 times for SBP, 7.8 times for TC, 6.9 times for triglyceride, and 8.6 times for glucose. The correlation between the CV of SBP and the CV of glucose was highest, and the correlation between the CV of SBP and the CV of triglycerides was the lowest. All groups with high CV had a higher incidence of MACE than those with low CV (2.5% vs 6.0% for SBP CV groups, 3.0% vs 5.5% for TC CV groups, 3.8% vs 4.7% for triglyceride CV groups, 2.7% vs 5.8% for glucose CV groups). Multivariable Cox regression analysis suggested male, creatinine, prior myocardial infarction, SCORE2, SBP CV, TC CV, mean TC, and mean triglyceride as independent risk predictors for MACE in diabetic patients (HR 1.087 (95% CI 1.068 – 1.107) for SBP CV, 1.023 (1.016 – 1.031) for TC CV). Conclusions: Metabolic variability parameters, especially SBP CV and TC CV are important residual risk factors for cardiovascular events in diabetic patients.

Background: Diabetes patients are known to produce diabetic retinopathy. Pulse wave velocity (PWV) provides arterial stiffness and hemodynamic parameters. We studied the effect of diabetic retinopathy in different stages on arterial stiffness in diabetics. We measured vascular aging with a new system (pOpmètre) that measures PWV, Ankle-Brachial Index (ABI) and Central Blood pressure Methods: 83 type 1 diabetes patients attending the clinic (59% male) aged (42 ± 1.54 years). Insulin dependent diabetic patients with retinopathy (N = 61) or without retinopathy (N = 22) with abnormal albuminuria ratio (17%), mean diabetes duration 26 years. All patients underwent vascular assessment with pOpmètre (Axelife -France). The following parameters were measured in each patient, PWV, ABI and central Blood Pressure, non-invasively and without cuff compression.Results: PWV correlated with the degree of retinopathy (F = 13.80; p < 10^(-4)) and with aging (r 2 = 0.25; p < 10^(-4)), and with diabetes duration (r 2 = 0.12; p < 0.002) independently of gender, HbA1c, smoking, BMI, diabetes duration or lipid profile. Aging and diabetes duration were not associated (ANOVA, p = 0.5) to the degree of retinopathy unlike PWV.Conclusions: there is a high interdependence between microvascular and macrovascular lesions in this population of well treated type I diabetic patients with or without retinopathy.

Objective: Compared with placebo, olmesartan has been linked to numerical imbalances in cardiovascular mortality. There is a paucity of contemporary real-world evidence on this agent for different study populations. This study investigated the clinical outcomes of olmesartan and other antihypertensives in patients with advanced hypertension. Methods: This multicenter retrospective study used data from the Korea University Medical Center database, built from electronic health records. Patients prescribed at least two antihypertensive medications as a combined therapy were followed-up for 3 years. The primary outcome was a composite of all-cause mortality, myocardial infarction (MI), stroke, and hospitalization for heart failure. Adjusted outcomes were compared using propensity score (PS) matching. Results: Among 24 806 patients, 4050 (16.3%) were olmesartan users between January 2017 and December 2018. The average patient age was 64 years, 45% were women, and 41% had diabetes. Olmesartan users were younger and less likely to have diabetes mellitus or chronic kidney disease. In PS-matched cohort, the 3-year cumulative incidences of the primary outcome were similar between the two groups (P = 0.91). The cumulative incidence of MI at 3 years was 1.4% in olmesartan users (4.8 per 1000 person-years) and 1.5% in active comparators (5.2 per 1000 person-years; P = 0.74). Olmesartan also showed similar safety profiles, including acute kidney injury and newly started dialysis. Conclusions: In real-world practice, olmesartan use in combination therapy resulted in similar cardiovascular outcomes when compared with those of active comparators, and our findings did not show any conclusive evidence that olmesartan is harmful in patients with hypertension.