A Comprehensive Prospective Cohort in Preventive Medicine: Protocol and Profile of the First 1,000 Participants in a Health Screening Program

The Zo[i] cohort is a prospective longitudinal cohort study, designed to advance evidence-based personalized prevention, by systematically screening for undiagnosed or asymptomatic conditions, identifying early risk markers, and predicting future disease risks. Conducted within a dedicated prevention-focused health center, data collection takes place in a standardized environment and combines over 500 self-reported items, clinical examinations, extensive biomarker profiling (196 biomarkers), and multimodal imaging (vascular, breast, abdominal, and pelvic ultrasound, full-body composition, retinal scan). For several major diseases, risk is further estimated through established clinical prediction models. Longitudinal follow-up is collected via yearly re-evaluations and through a dedicated application. This manuscript presents the cohort design and the characteristics of the first 1,000 participants. Participants (67.5% male, mean age 51.1 years, high education levels) exhibited a high level of health awareness, lower obesity and smoking rates than the general population, yet almost half (45.6%) of those who reported no known ongoing diseases had at least one undiagnosed chronic condition (i.e., either disease or risk factor), with hypertension and hypercholesterolemia being the most frequent. Male sex and older age were significantly associated with disease unawareness (p<0.05). These findings highlight a discrepancy between self-reported and objectively measured health status, even among a well-educated and health-conscious cohort. This deeply phenotyped, longitudinal cohort will serve as a platform that supports interdisciplinary research collaborations. It will enable development and validation of early risk stratification models, essential for predictive medicine, as well as evaluation of preventive interventions, to advance evidence-based precision prevention in public health settings.