Rationale: Mental illness singularly is a devastating condition for patients suffering from it which can further get aggravated by various triggers in the form of lifestyle (diet, sleep patterns, activities), comorbid health conditions such as cardiovascular diseases and cancer. With an increasing number in the elderly population, and early cancer screening and treatment, the number of cancers’ cases is rising, while the mortality rate is decreasing. With the increasing numbers of cancer survivors, as one of the adverse effects of anti-tumor therapy, CVD has gained enormous attention. The incidence of cardiovascular events such as cardiac injury or cardiovascular toxicity is higher than malignant tumors’ recurrence rate. Moreover, people experiencing depression, anxiety, stress, and even PTSD over a longer period of time may experience certain physiologic effects on the body, such as increased cardiac reactivity (e.g., increased heart rate and blood pressure), reduced blood flow to the heart, and heightened levels of cortisol. Furthermore, cancer diagnosis impacts mental health and wellbeing, while depression and anxiety may hinder cancer treatment, recovery, quality of life and survival. The links between mental illness, CVDs and cancer in no specific order is not elucidated enough.
Aims: Therefore, this project aims to achieve a thorough understanding of the triggers for mental health issues, effects or causalities of CVDs, cancers that may coexist with mental illness and establish and explain the molecular and physiological links that may exist between these three conditions in no specific order.
Project Duration: We propose to utilize data from UK Biobank over a period of 3 years to conduct the study. The first year will be spent on establishing the clinical and genetic risk factors for each of the traits through association studies. The second year will be used to hypothesize and establish the links between mental illness, CVDs and cancers in no particular order through regression methods and Mendelian randomization and other statistical techniques. Finally, the third year will be spent in proposing AI-based and/or statistical prediction models for risk stratification or early detection purposes.
Public Health Impact: A thorough understanding the causalities and risks towards developing the above mentioned complex traits would allow for better disease management and primary and secondary preventive measures. Moreover, unravelling the underlying genetics can help establish clinical-genetic prediction models that can facilitate disease stratification, and early detection leading to reduced adverse outcomes.
