Restoring Epigenetic Reprogramming with Diet and Exercise to Improve Health-Related Metabolic Diseases

Epigenetic reprogramming predicts the long-term functional health effects of health-related metabolic disease. This epigenetic reprogramming is activated by exogenous or endogenous insults, leading to altered healthy and different disease states. The epigenetic and environmental changes involve a roadmap of epigenetic networking, such as dietary components and exercise on epigenetic imprinting and restoring epigenome patterns laid down during embryonic development, which are paramount to establishing youthful cell type and health. Nutrition and exercise are among the most well-known environmental epigenetic factors influencing the proper developmental and functional lifestyle, with potential beneficial or detrimental effects on health status. The diet and exercise strategies applied from conception could represent an innovative epigenetic target for preventing and treating human diseases. Here, we describe the potential role of diet and exercise as therapeutic epigenetic strategies for health and diseases, highlighting putative future perspectives in this field

Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review

Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative treatment to forecast postoperative results and assist physicians in selecting surgical interventions. Clinicians can modify their strategy to reduce risk and enhance outcomes using ML algorithms to examine patient data and discover factors that increase the risk of worsened health outcomes. ML can also enhance the precision and effectiveness of screening tests. Healthcare professionals can identify diseases at an early and curable stage by using ML models to examine medical pictures, diagnostic modalities, and spot patterns that may suggest disease or anomalies. Before the onset of symptoms, ML can be used to identify people at an increased risk of developing specific disorders or diseases. ML algorithms can assess patient data such as medical history, genetics, and lifestyle factors to identify those at higher risk. This enables targeted interventions such as lifestyle adjustments or early screening. In general, using ML in primary care offers the potential to enhance patient outcomes, reduce healthcare costs, and boost productivity

Lipoprotein(a) in clinical practice: A guide for the clinician

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Serum lipoprotein(a) (Lp(a)) has been shown to be an independent and causative risk factor for atherosclerotic CVD and calcific aortic valvular disease. Lp(a) continues to be studied, with emerging insights into the epidemiology of CVD with respect to Lp(a), pathogenic mechanisms of Lp(a) and strategies to mitigate disease. There have been novel insights into genetic polymorphisms of the LPA gene, interactions between concomitant risk factors and Lp(a) based on real-world data, and metabolic pathway targets for Lp(a) reduction. This review highlights these recent advances in our understanding of Lp(a) and discusses management strategies as recommended by cardiovascular professional societies, emerging therapies for lowering Lp(a), and future directions in targeting Lp(a) to reduce CVD

Artificial intelligence in preventive cardiology

Artificial intelligence (AI) is a field of study that strives to replicate aspects of human intelligence into machines. Preventive cardiology, a subspeciality of cardiovascular (CV) medicine, aims to target and mitigate known risk factors for CV disease (CVD). AI\u27s integration into preventive cardiology may introduce novel treatment interventions and AI-centered clinician assistive tools to reduce the risk of CVD. AI\u27s role in nutrition, weight loss, physical activity, sleep hygiene, blood pressure, dyslipidemia, smoking, alcohol, recreational drugs, and mental health has been investigated. AI has immense potential to be used for the screening, detection, and monitoring of the mentioned risk factors. However, the current literature must be supplemented with future clinical trials to evaluate the capabilities of AI interventions for preventive cardiology. This review discusses present examples, potentials, and limitations of AI\u27s role for the primary and secondary prevention of CVD