Combining the power of genome and epigenome for multi-pre-disease surveillance and prevention

Multi-morbidity (MM) is defined as the occurrence of two or more of the chronic diseases in an individual and it is more common among older people. The study of the pathophysiology of this condition (MM) based on single marker had limited success.  Furthermore up to date there is no comprehensive biomarker identified to predict MM at the population level. New concept of tackling the MM through combining potential DNA methylation biomarkers along with well identified SNPs related to these diseases and the conventional epidemiological measures is suggested in this proposal. The key goal is to set up a comprehensive tool to predict these MM health issues to identify individuals at risk and to inform policy makers on strategies for prevention and early detection. 
DNA methylation is one of the four epigenetic changes: methylation markers, miRNA expression and processing changes, histone modifications, and chromatin condensation [1]. DNA methylation refers to the attachment of methyl group (CH) to cytosine base in the cytosine–guanine dinucleotides (CpGs) island in the promoter region in eukaryotic genes [2]. These methylation alterations consist of hypo-methylation of global genome and hyper-methylation of specific genes [3]. Like other epigenetic modifications, methylation is mitotically hereditable and plays vital role in gene expression regulation [4]. DNA methylation caused by age, environmental exposures (such as diet, smoking, alcohol intake, stress and more) endogenous exposures (such as the circulated hormones). 
Hyper-methylation of the promoter regions of tumour suppressor genes causes gene silencing, while hypo-methylation of whole genome leads to chromosomal instability and higher rate of mutations [5, 6]. Furthermore, methylation accumulates with age and contributes more to disease progression and treatment resistance [7, 8]. Ever though these changes are hereditable yet they are reversible following healthier lifestyles and using pharmacological reagents [1, 9] which provides potentials for cancer early detection and prevention. 
Regardless to their cause, DNA methylation biomarkers considered as reliable marker for MM risk prediction, early detection and prognosis [8]. Moreover, DNA methylation is the only epigenetic markers which can be measured reliably [10] among all these epigenetic changes. Moreover, epigenetic makers considered as makers for environmental exposures during one’s lifetime. Consequently, using these epigenetic markers rather than using the conventional questionnaire can overcome the recall biases. 
Epigenetic changes are thought to capture and mediate the effects and interactions of genetic and environmental risk factors. Understanding the effects of these modifiable factors on the epigenetic profile is a step towards developing interventions for risk reduction. 
We will therefore combine both conventional epidemiological and new methods based on various Artificial intelligence (AI) methods to investigate the methylome and to evaluate methylation panels in strata defined by broad genetic predisposition, taking into account socioeconomic and lifestyle factors. 
We have been working on such stratification measures for over 20 years and as part of our funded work with the Turing Institute we are at the forefront of applying innovations in machine learning and AI. In this study, we will explore if and how socioeconomic factors (e.g. income, education, occupation) and lifestyle factors (e.g. diet, exercise, and smoking) affect DNA methylation and biological age. We will also identify further methylation sites in people with specific genetic predisposition to MM outcomes (in our case: cancers, stroke, heart, liver, respiratory diseases, osteoarthritis, frailty and diabetes). We will focus on changes in methylation over time to investigate the influence of major lifestyle factors. We will also use the information derived within our parallel Patient and Public Involvement and Engagement (PPIE) work funded by Turing and NIHR to assess how best to translate such findings for interventional studies of behavior change. 
The main aim of this proposal is to identify the most significant DNA methylation biomarkers, SNPs and epidemiological risk factors of MM in Caucasian population to develop a comprehensive tool to identify at-risk individuals. The resulting identified biomarkers/factors will be valid for both males and females. 
In the future, the results can be used to identify the best practice to decrease risks of developing MM and could be used to target the individuals at risk for better aging.