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

Abstract

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. 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. 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. 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.