Association between toothbrushing and cardiovascular risk factors: a cross-sectional study using Korean National Health and Nutrition Examination Survey 2015-2017.

BACKGROUND: Previous studies have suggested that frequent toothbrushing is associated with a lower risk of future cardiovascular events. We sought to investigate further the relationship between toothbrushing, cardiovascular risk factors, and lifestyle behaviours. METHODS: We analysed a cross-sectional survey including 13,761 adults aged 30 years or older without a history of cardiovascular diseases from the Korean National Health and Nutritional Examination Survey. Conventional cardiovascular risk factors (blood pressure, lipid profiles, and fasting glucose), and inflammatory markers (high-sensitivity C-reactive protein [hsCRP], and white blood cell counts [WBC]) were investigated in relation to the frequency of toothbrushing. RESULTS: The estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk, calculated using the pooled cohort equations was 13.7%, 9.1%, and 7.3% for participants who reported toothbrushing 0-1, 2, and ≥ 3 times a day, respectively. Both conventional risk factors and inflammatory markers were significantly associated with frequent toothbrushing. However, after adjusting potential confounding factors such as age, sex, comorbidities, and lifestyle behaviours, only inflammatory markers were remained as significant factors. CONCLUSIONS: Oral hygiene behaviours are closely linked to cardiovascular risk factors. This study suggests that reduced systemic inflammatory burden may explain the benefit of improved oral hygiene in terms of cardiovascular risk.

History and evolution of blood pressure measurement.

Hypertension is the leading cause of morbidity and mortality worldwide. Hypertension mostly accompanies no symptoms, and therefore blood pressure (BP) measurement is the only way for early recognition and timely treatment. Methods for BP measurement have a long history of development and improvement. Invasive method via arterial cannulation was first proven possible in the 1800's. Subsequent scientific progress led to the development of the auscultatory method, also known as Korotkoff' sound, and the oscillometric method, which enabled clinically available BP measurement. However, hypertension management status is still poor. Globally, less than half of adults are aware of their hypertension diagnosis, and only one-third of them being treated are under control. Novel methods are actively investigated thanks to technological advances such as sensors and machine learning in addition to the clinical needs for easier and more convenient BP measurement. Each method adopts different technologies with its own specific advantages and disadvantages. Promises of novel methods include comprehensive information on out-of-office BP capturing dynamic short-term and long-term fluctuations. However, there are still pitfalls such as the need for regular calibration since most novel methods capture relative BP changes rather than an absolute value. In addition, there is growing concern on their accuracy and precision as conventional validation protocols are inappropriate for cuffless continuous methods. In this article, we provide a comprehensive overview of the past and present of BP measurement methods. Novel and emerging technologies are also introduced with respect to their potential applications and limitations.

AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.