Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans

Growth hormone in inflammatory bowel disease

Crohn's disease and ulcerative colitis are inflammatory diseases of the gastrointestinal tract characterized by chronic relapsing inflammation and catabolism. Growth hormone/insulin-like growth factor-I axis is important in inflammatory bowel disease, because of the effects on epithelial cell kinetics, collagen deposition and immunomodulation. The potential of growth hormone as a therapeutic option in inflammatory bowel disease has been proven in various clinical settings. Acquired growth hormone resistance in inflammatory bowel disease seems to be mediated by a combination of undernutrition and active inflammation. In particular, proinflammatory cytokines, such as TNF-a and interleukin-6, have been implicated as potential mediators of growth hormone resistance. The introduction of anti-TNF-alpha monoclonal antibodies has proven very efficacious in patients with inflammatory bowel disease. By reducing cytokines levels in inflammatory cells of intestinal mucosa, infliximab could interfere with cytokine-induced growth hormone resistance. Recent in vivo data have shown that acquired growth hormone resistance in patients with inflammatory bowel disease may be reversed after the administration of anti-TNF-alpha therapy

Characterization of Sarcopenia in an IBD Population Attending an Italian Gastroenterology Tertiary Center

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