Biological Age

Background: Population aging is accelerating rapidly in Israel and worldwide, necessitating adaptation of the healthcare system and considering new approaches that serve the needs of older adult populations. Working hypothesis and aims: We hypothesize that a personalized health and behavior intervention program will decrease the biological age as assessed by several biological aging clocks and improve functional and cognitive performance among older adults. Methods: We propose to conduct a randomized study among healthy community-dwelling elderly subjects (\>50 years old). The study will include an extensive aging assessment and imaging protocol (baseline assessment), including comprehensive physical, functional, sensory, cognitive, and mental assessment. Each participant in the intervention group will receive a personalized intervention program based on an integrative systems approach analysis. In addition, a uniquely developed application will track compliance and monitor physiological data through a provided wearable device. The control group will be assessed at baseline without receiving an intervention program. Each participant will visit the center aging after 6 months for a blood test and after 12 months for a second extensive diagnostic protocol, similar to the baseline assessment protocol. About 1,500 subjects will be recruited to participate in the study. Expected results: Obtaining data at two points will allow us to examine efficiency and compliance with a personalized intervention program based on integrative systems analysis models. We expect biological age, general well-being, and various clinical and psychosocial outcomes in the intervention group will decrease and improve compared to the control group. Study importance and relevance: The obtained results may help establish evidence-based healthy aging diagnostics protocols and an effective personalized intervention program that might be applied, with proper modifications, to national healthcare organizations for the general older adult population. In addition, to provides a scientific basis on which policymakers and intervention programs can rely to develop national guidelines for promoting extended health.

The association between biological aging and type 2 diabetes mellitus (T2DM) incidence in individuals with and without metabolic dysfunction-associated steatotic liver disease (MASLD) remains unclear.We assessed biological age by calculating phenotypic age (PhenoAge), Klemera-Doubal method (KDMAge), and homeostatic dysregulation (HDAge). To examine the association of biological ageing with the risk of T2DM, cox regression models were conducted. Furthermore, we applied survival analysis, restricted cubic spline models and population attributable fraction (PAF) to further evaluate the association between biological ageing and T2DM incidence.

This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.