Metabolic syndrome is a complex clinical condition characterized by the simultaneous presence of multiple metabolic risk factors and represents a major public health concern. The syndrome develops silently and may remain undiagnosed for long periods, highlighting the importance of investigating early metabolic alterations before overt disease onset. Longitudinal monitoring of predominantly healthy individuals may help identify metabolic risk early. The paper proposes a Bayesian statistical model to estimate the probability of metabolic syndrome among blood donors during pre-donation screening, incorporating information collected at previous visits. Using longitudinal data from one of the main blood donor associations in Italy, AVIS Milan, we analyze repeated clinical and lifestyle measurements from a predominantly healthy population of donors. In particular, we fit a Bayesian multivariate model that jointly represents the logarithm of the five diagnostic components of metabolic syndrome. The model accounts for within-donor dependence across repeated visits and provides probabilistic estimates of individual risk. Our framework aims to provide clinicians at AVIS Milan with an interpretable traffic-light warning system (low, intermediate, high risk) during pre-donation screening to facilitate the identification of individuals at risk of metabolic syndrome at future visits and to support targeted preventive interventions during routine donor assessment, ultimately contributing to a long-term reduction in healthcare costs for the Italian national healthcare system.

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