Epigenetic Alterations Induced by Smoking and Their Intersection with Artificial Intelligence: A Narrative Review

Introduction: Cigarette smoking is unquestionably associated with an increase in morbidity and mortality worldwide, exerting significant adverse effects on respiratory health. The impact of tobacco persists in the epigenome long after smoking cessation. Furthermore, the offspring of smokers may also be affected by the detrimental effects of smoking. Material and methods: The modifications made to the body, such as DNA methylation, histone modification, and regulation by non-coding RNAs, do not change the DNA sequence but can influence gene expression. In respiratory disease, multigenerational effects have been reported in humans, with an increased risk of asthma or COPD and decreased lung function in offspring, despite them not being exposed to smoke. Prenatal nicotine exposure leads to pulmonary pathology that persists across three consecutive generations, supported by animal studies conducted by Rehan et al. Significant advances in high-throughput genomic and epigenomic technologies have enabled the discovery of molecular phenotypes. These either reflect or are influenced by them. Due to the hidden environmental effects and the rise of artificial intelligence (AI) in biomedical research, new predictive models are emerging that not only explain complex data but also enable earlier detection and prevention of smoking-related diseases. In this narrative review, we synthesise the latest research on how smoking affects gene regulation and chromatin structure, emphasising how tobacco can increase vulnerability to multiple diseases. Discussion: For many years, it was widely believed that diseases are solely inherited through genetics. However, recent research in epigenetics has led to a significant realisation: environmental factors play a crucial role in an individual’s life. External influences leave a mark on DNA that can influence future health and offer insights into potential illnesses. In this context, it is possible that in the future, doctors might treat people not as a whole but as individual beings, with personalised medication, tests, and other approaches. Conclusions: The accumulated evidence suggests that exposure to various environmental factors is associated with multigenerational changes in gene expression patterns, which may contribute to increased disease risk. The application of artificial intelligence in this domain is currently a crucial tool for researching potential future health issues in individuals, and it holds a powerful prospect that could transform current medical and scientific practice.