Zoī cohort, a prospective cohort with comprehensive phenotyping for preventive medicine in France, first 1000 participants: cohort profile

Purpose The Zoī cohort is a prospective longitudinal cohort study, designed to advance personalised prevention by systematically screening for undiagnosed or asymptomatic conditions, identifying early risk markers and predicting future disease risks. Participants Recruitment takes place in a dedicated prevention-focused health centre. Adults aged 18 years and older are enrolled either as paying customers or through company-sponsored programmes. This manuscript presents the design of the cohort and the characteristics of the first 1000 participants (67.5% male, mean age 51.1 years, high education levels). The cohort exhibits a healthy volunteer bias, with lower smoking and obesity rates and higher educational attainment than the general French population, which limits generalisability. Findings to date Data collection is conducted in a standardised 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. Despite lower obesity and smoking rates than the general population, almost half (45.6%) of those who reported no ongoing diseases had at least one undiagnosed chronic condition, most frequently hypertension and hypercholesterolaemia. 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 in a health-conscious population. Future plans Longitudinal follow-up is collected via yearly re-evaluations and through a dedicated application. The cohort is designed as a deeply phenotyped, longitudinal resource to support interdisciplinary research collaborations, the development and validation of early risk stratification models and the evaluation of preventive interventions.