Project Details
Abstract
Understanding how genetics and the environment combine to influence human health is still a work in progress. Identifying the role of genetics in phenotypic alteration and disease causation and decoding the contribution of environmental factors in this causal web can lead to more personalized treatments through targeted and more effective drug design. Aberrant metabolism is an important driver of many serious health issues, resulting in reduced life expectancy. Maladaptive metabolism leads to the release of many molecules that in turn activates the inflammatory pathway. Inflammation is the immune system's normal response to a range of stimuli, including injured cells, pathogens, and harmful substances. The synergistic interaction between a host and an exposure, a mix of environmental drivers like diet, lifestyle, pollutants, and others over an individual's lifetime, results in metabolically induced inflammation, also known as meta-inflammation or low-grade chronic inflammation. This pathophysiologic status is linked to many diseases related to lifestyles including diabetes, obesity, nonalcoholic fatty liver disease (NAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Therefore, the underlying causes of chronic diseases are complex which varies geographically, as well as by gender and societal-economic status. Chronic diseases are a growing concern worldwide including in Qatar yet, there has been no straightforward way to estimate the incidence of chronic diseases in an accurate manner. Understanding the burden of disease is the starting point for developing health interventions targeting individuals and communities. One such intervention is the application of precision medicine at the population level. Quantifying an individual’s risk for chronic diseases is an essential objective of precision medicine. It offers a structure for lifestyle modification that centers on the complex relationship between the individual’s biology, lifestyle, and environment. The goal of precision medicine is to enhance diagnostic, therapeutic and prognostic capacity using the individual’s variability in gene, lifestyle, and environment. Artificial intelligence (AI), machine learning (ML), and big data analytics are rapidly emerging as critical components of precision medicine. Additionally, wearable sensors: health-enabled technologies in particular, communication technologies in general, are achieving the desired level of precision in health care. The potential to mine large stores of unstructured and structured data for better insights using intelligent algorithms has given providers the resources to devise customized treatments for individual patients. It also takes the health care system from a “treating conditions as they appear” to a “more preventive” approach in disease control. The Qatar Precision Medicine Institute's (QPMI) (Qatar biobank-QBB and Qatar genome programme-QGP) broad data sets currently available from 17,065 individuals covering individuals exposome to whole-genome sequencing provide the rich source to fulfill the aims of applying and advancing precision medicine in Qatar. Despite the richness of the QPMI’s data sets, limitations exist in that there is a lack of data pertaining to the gut microbiome and the longitudinal follow-up of individual's diet and lifestyle. Since this information is critically needed to gain a full understanding of chronic diseases “deep phenotyping” provides an opportunity to close the information gap. The "deep phenotyping research" strategy of integrating various aspects of behavioral data with physiological, multi-omics, and imaging data have evolved as a blueprint for precision medicine. Deep and precise phenotyping allows the use of data from a limited number of individuals to gain clinically relevant and translational insights into disease etiology. Such methods are especially powerful when considering the possible modifiers of disease risk (e.g., microenvironment, family history, lifestyle, microbiome, longitudinal follow-up, etc.) as relevant to everyday life scenarios. In this proposal, the “deep-phenotyping” study is designed to contribute to what is observed and to advance precision medicine to investigate the biological heterogeneity of healthy individuals or metabolically unhealthy individuals over time and in-depth. For instance, Switzerland has one of the lowest prevalence of overweight and obesity in the world. In contrast to Switzerland, the prevalence of adult obesity and chronic lifestyle-related diseases in Qatar remains high and continues to be a major contributor of disability and death. Therefore, the reference health status that combines several dimensions of health from the population of two countries (Qatar and Switzerland) will be beneficial to characterize the exposome factors, genotype, and phenotype associations according to geography. Taken together, our primary objectives of the current proposal are to: (1) Identification of specific exposome factors by implementing the machine learning approaches to the available large-scale data from the Qatar Precision Medicine Institute. (2) Implementation of "deep-phenotyping" and longitudinal follow-up to decode complexities of health from healthy and metabolically unhealthy individuals living in Qatar and Switzerland. (3) Identify actionable targets, pathways, and networks to improve the health of an individual living in Qatar and develop diagnostics to detect the early transition from health to disease. The uniqueness of this study is its experimental and comparative nature poised to provide an all-around understanding of individual's health and their exposomes. Findings from this research will have major potential implications for health policymakers in designing strategies and supporting decision-making to control non-communicable diseases at the population level.
Submitting Institute Name
Ministry of Public Health
Sponsor's Award Number | PPM 05-0506-210017 |
---|---|
Proposal ID | EX-QNRF-PPM-7 |
Status | Active |
Effective start/end date | 7/01/24 → 7/01/28 |
Collaborative partners
- Hamad Bin Khalifa University (lead)
- Ministry of Public Health
Primary Theme
- Precision Health
Primary Subtheme
- PH - Preventative health
Secondary Theme
- Precision Health
Secondary Subtheme
- PH - Diagnosis Treatment
Keywords
- Artificial intelligence, Machine learning
- Precision lifestyle medicine,
- Chronic disesaes of lifestyle, Gene-environment interaction
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