Cardiovascular Prevention and Pharmacotherapy

Volume 2(2); April 2020

Special Articles

Shifting from Pharmacotherapy to Prevention of Hypertension: Bernard Man Yung Cheung, Man-Fung Tsoi; Cardiovasc Prev Pharmacother. 2020;2(2):33-42. Published online April 30, 2021; DOI: https://doi.org/10.36011/cpp.2020.2.e8

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Hypertension is a common chronic disease affecting a large section of the general population. As hypertension is usually asymptomatic, awareness, treatment and control rates are low. Drug side-effects also affect compliance. Hypotension and electrolyte abnormalities in the elderly can be severe. Therefore, prevention is better than cure. As blood pressure rises with age, prevention should be started early. As there are many genes affecting blood pressure, genetic tests are not useful. Good antenatal care and care of preterm infants can help to prevent adult cardiovascular diseases including hypertension. Childhood obesity is an important determinant of blood pressure in childhood and adolescence. This is a window of opportunity for prevention. The current American College of Cardiology/American Heart Association guideline on hypertension defines stage 1 hypertension as a systolic blood pressure of 130–139 mmHg or a diastolic blood pressure of 80–89 mmHg. Although this makes many people in the general population hypertensive, stage 1 hypertension in young adults is already associated with increased cardiovascular and mortality risk. Fortunately, hypertension at this early stage is easy to control and weight loss is easier in young males, who can get exercise from work or exercise after work. Leisure-time physical activity seems more beneficial than occupational physical activity. Cardiovascular risk assessment and promoting a healthy lifestyle in the young are likely to forestall hypertension and future cardiovascular disease. Preventing or reversing hypertension is no longer an impossible dream.

Citations

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The Association Between Dietary Inflammatory Index (DII) and Risk of Hypertension: A Case–Control Study
Niloofar Shoaei, Nafiseh Shokri-Mashhadi, Awat feizi, Reza Ghiasvand
High Blood Pressure & Cardiovascular Prevention.2022; 29(6): 611. CrossRef

Effects of Low-Carbohydrate, High-Fat Diets on Weight Loss, Cardiovascular Health and Mortality: Bo-Yeon Kim; Cardiovasc Prev Pharmacother. 2020;2(2):43-49. Published online April 30, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e7

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Abstract PDF

Obesity is a worldwide health challenge. The clinical consequences of obesity include nonalcoholic fatty liver disease, type 2 diabetes, and coronary heart disease. Numerous diets have been developed to reduce the incidence of cardiovascular diseases and induce weight loss. Low-carbohydrate, high-fat diets (LCHFDs) have become increasingly popular for weight loss. LCHFDs have led to weight loss in some clinical studies. However, the safety of LCHFDs and their long-term effects on the human body are still controversial. In this review, I will discuss the effects of LCHFDs on weight loss, cardiovascular health, and mortality.

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Cholesterol, fatty acids profile and the indices of atherogenicity and thrombogenicity of raw lamb and mutton offal
B. Pretorius, H.C. Schönfeldt
Food Chemistry.2021; 345: 128868. CrossRef

From Traditional Statistical Methods to Machine and Deep Learning for Prediction Models: Jun Hyeok Lee, Dae Ryong Kang; Cardiovasc Prev Pharmacother. 2020;2(2):50-55. Published online April 30, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e6

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Abstract PDF: Traditional statistical methods have low accuracy and predictability in the analysis of large amounts of data. In this method, non-linear models cannot be developed. Moreover, methods used to analyze data for a single time point exhibit lower performance than those used to analyze data for multiple time points, and the difference in performance increases as the amount of data increases. Using deep learning, it is possible to build a model that reflects all information on repeated measures. A recurrent neural network can be built to develop a predictive model using repeated measures. However, there are long-term dependencies and vanishing gradient problems. Meanwhile, long short-term memory method can be applied to solve problems with long-term dependency and vanishing gradient by assigning a fixed weight inside the cell state. Unlike traditional statistical methods, deep learning methods allow researchers to build non-linear models with high accuracy and predictability, using information from multiple time points. However, deep learning models cannot be interpreted; although, recently, many methods have been developed to do so by weighting time points and variables using attention algorithms, such as ReversE Time AttentIoN (RETAIN). In the future, deep learning methods, as well as traditional statistical methods, will become essential methods for big data analysis.

Original Article

CHA₂DS₂-VASc Score Is Correlated with Cardiac Performance in Chronic Atrial Fibrillation: Doo Soo Jeon, Mi-Jeong Kim, Wonjik Lee, Dongjae Lee, Ik Jun Choi; Cardiovasc Prev Pharmacother. 2020;2(2):56-62. Published online April 30, 2020; DOI: https://doi.org/10.36011/cpp.2020.2.e9

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Abstract PDF: Background
The CHA₂DS₂-VASc score is a popular tool for risk prediction of thromboembolism in patients with atrial fibrillation (AF). Each component of the CHA₂DS₂-VASc scheme is an established risk factor for left ventricular diastolic dysfunction and heart failure (HF). In AF patients, HF is often adversely affecting to clinical outcomes including thromboembolism. We hypothesized that the CHA₂DS₂-VASc score reflects cardiac reserve and the risk of HF as well as the risk of stroke in patients with AF.
Methods
A total of 103 patients who had the diagnosis of chronic non-valvular AF patients with preserved ejection fraction (EF) were enrolled consecutively. CHA₂DS₂-VASc score was compared to exercise capacity (peak oxygen uptake, peak VO₂), B-type natriuretic peptide (BNP) and echocardiographic diastolic dysfunction index (early mitral to annular velocity, E/E′) ratio.
Results
Exercise capacity was correlated with age (β=−0.568, p<0.001), CHA₂DS₂-VASc score (β=−0.526, p<0.001), BNP (β=−0.449, p<0.001) and diastolic dysfunction (β=−0.534, p<0.001). Patients with CHA₂DS₂-VASc score ≥2 had a significantly less exercise capacity than those with CHA₂DS₂-VASc score <2 (p<0.001). Higher CHA₂DS₂-VASc score was associated with lower exercise capacity, more diastolic dysfunction and higher BNP (for trend p=0.001).
Conclusions
High CHA₂DS₂-VASc score is associated with poor exercise capacity in patients with AF. Diastolic dysfunction is thought to be the major mechanism of exercise limitation. CHA₂DS₂-VASc score might be useful for predicting overall cardiac reserve as well as stroke risk stratification in AF patients.

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