Personalized Medicine in Managing Chronic Disease: A Role for Genetic and Socioeconomic Factors | ||||
| Egyptian Journal of Chemistry | ||||
| Article 2, Volume 68, Issue 12, December 2025, Page 11-20 | ||||
| Document Type: Review Articles | ||||
| DOI: 10.21608/ejchem.2025.362027.11330 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ali Mohammed Ali Al Sheban  1; Zahra Nasser Ali Aljubran2; Norah Abdulmohsen Alsuwailem2; Seham Moteeb Al-Motairi3; Samia Hassan Alfifi3; Amjad Aiaid Almutairi3; Roseline Deline Davids3; Shaden Alzahrani3
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| 1King Saud University, College of Pharmacy, Department of Pharmaceutical Chemistry, Saudi Arbaia | ||||
| 2Eastern Region, Al Nada Health Center, Ministry of Health, Saudi Arabia | ||||
| 3Nurse Specialist, King Abdulaziz Medical City, Saudi Arabia | ||||
| Abstract | ||||
| Chronic noncommunicable diseases (NCDs), including type 2 diabetes, cardiovascular diseases, and cancers, impose a gradually growing global burden and are associated with chronic, low-grade systemic inflammation. Systemic inflammation can arise from both genetic susceptibility and modifiable environmental and lifestyle exposures that interact and contribute to the development of chronic diseases. The exposome—a framework that encompasses every environmental exposure throughout a person's life—is a powerful model through which we can understand how diet, air pollution, physical inactivity, and psychological stress contribute to disease-specific pathology. Additionally, social determinants of health—including food insecurity, urban stress factors, and inequities in health—also drive inflammation. The gut microbiome acts as a liaison between external environmental influence and internal immune responses. Precision medicine also offers a revolutionary paradigm through the use of multi-omics (genomics, epigenomics, metabolomics) and exposomics to establish individual disease risk and guidance for interventions. Artificial intelligence (AI) and machine learning (ML) are essential for applying these large datasets to identify new biomarkers of disease, ultimately predicting disease risk and informing personalized treatment. Wearable technology can further support health monitoring in real time. Although there are challenges to consider related to privacy and bias from algorithms, personalized medicine supplemented by public health can make chronic disease interventions more effective and equitable. From a health systems perspective, this represents a shift toward a preventative and data-based system to improve health outcomes and lessen the probability of a chronic disease occurring worldwide | ||||
| Keywords | ||||
| Personalised Medicine; Chronic Inflammation; Exposome; Multi-Ome; Socioeconomic Factors | ||||
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