AI-Driven Multi-Omics Integration for Enhanced Prediction and Personalization in Age-Associated Disease Management

In the 21st century, we've made impressive strides in extending human lifespans, but a crucial question remains: are we living healthier? While life expectancy has increased, our healthspan – years lived free from disease and disability – hasn't kept pace. As people live longer, the risk of age-associated diseases (AADs) like heart disease, diabetes, Alzheimer's, and cancer increases. These conditions affect quality of life and burden healthcare systems worldwide. In Norway, for example, the elderly population is expected to double by 2050, potentially overwhelming the healthcare system. There's an urgent need to shift focus from treating diseases to preventing them. This requires moving from a one-size-fits-all approach to personalized medicine, tailoring treatments and preventive measures to each individual's unique biology. Artificial Intelligence (AI) is key to this transformation. AI can revolutionize healthcare by: Integrating multiple data sources (multi-omics data, clinical records, lifestyle indicators, wearable device data) Identifying complex patterns and relationships Enabling more accurate predictions of individual AAD risks Creating personalized prevention plans AI-driven solutions can shift healthcare towards 4P medicine: preventive, predictive, personalized, and participatory. This approach empowers individuals to take control of their health and maintain a higher quality of life for longer. The promise of better health as we age is no longer just a dream—it's within our reach. By leveraging AI and personalized medicine, we can work towards closing the gap between lifespan and healthspan, ensuring that longer lives are also healthier ones.

This project aims to revolutionize the identification and treatment of AADs and ageing commorbidities by harnessing multi-omics datasets and AI to develop a sophisticated computational framework. The core objective is to integrate multilayered biomarkers with AI to gain a deep understanding of the biological mechanisms underlying AADs. This integrated approach is set to be incorporated into Agespan's software platform, enhancing the prediction of AAD risk, providing prognosis insights, and enabling personalized treatment plans tailored to each patient's unique biological profile. The project has several key applications related to ageing and personalised medicine: Firstly, it aims to construct an AI-driven framework for predicting AAD susceptibility by analyzing complex interactions between multi-omic and lifestyle factors, using extensive data from sources like the UK Biobank and Lifeline. Secondly, it seeks to develop a surgery risk index using multi-omics data combined with lifestyle and clinical information from colorectal cancer patients, which will guide surgeons in assessing the benefits of prehabilitation and estimating postoperative risks on vulnerable and frail patients. Thirdly, the project aims to improve the diagnosis and treatment of neuroendocrine carcinoma (NEC) by employing advanced ML techniques to classify NEC subtypes based on molecular phenotypes, ultimately informing treatment decisions and expanding therapy options. Data sources include international biobanks (UK, Lifeline, Estonian), a NEC biobank at the University of Bergen, and clinical trial data from Bærum hospital.