Introduction. Against the backdrop of the COVID-19 pandemic, which has made adjustments to long-term trends in life expectancy, the study of age-related characteristics of the development of diseases is of particular relevance. The maximum delay in the development and manifestation of diseases is a promising vector for prolonging active life. Therefore, predicting the age of manifestation of diseases at the population level using available epidemiological indicators seems to be a very relevant direction. Purpose of the study. Development of a regression model for predicting the median age of disease manifestation based on the age-specific incidence index. Materials and methods. The multicenter retrospective study involved municipal polyclinics in St. Petersburg serving the adult population. The median age of newly diagnosed patients was estimated, as well as the age-specific incidence index of a number of nosologies for 2019-2023. Regression analysis was carried out using the least squares method, the developed model was validated using cross-validation. Results. A regression model based on a degree 2 polynomial was developed, which makes it possible to predict the median age of disease manifestation by the value of the age-specific incidence index (determination coefficient 0.944). Cross-validation confirmed the consistency of the prognostic model. The developed model will make it possible to assess the pace of development of age-related processes, the timing of the manifestation of the disease at the population level. Conclusions. Thus, the regression model for predicting the median age of disease manifestation, developed on the basis of the age-specific incidence index, is a promising tool in gerontology and geriatrics as a vector in solving a whole range of problems.