The Novara Cohort Study (NCS) was established to investigate the biological, psychological and social factors that influence ageing in the general population. The study aims to identify early risk factors for frailty, allostatic load and cognitive decline, and to uncover molecular and functional markers of accelerated biological ageing. NCS addresses the need for detailed life-course data from Southern Europe to support personalised prevention and early diagnosis, and to promote healthy longevity.

NCS is a population-based, longitudinal cohort in the Novara province (Northern Italy), originally enrolling adults aged 35 and older. The inclusion criteria were later expanded to encompass all residents aged 18 and over, facilitating the study of ageing trajectories from early adulthood onward. As of mid-2025, about 1000 participants have been enrolled, and recruitment is ongoing. The cohort’s diversity in age, employment status and health conditions enhances its value for life-course analysis.

Following a pilot phase in 2022–2023, the whole study protocol now includes detailed demographic, clinical, behavioural, cognitive and psychosocial data, along with biological samples stored in the UPO Biobank. The protocol incorporates validated tools, comprehensive physical and cognitive assessments, and over 90 laboratory biomarkers covering inflammation, metabolism, hormonal function and coagulation. Additionally, a subset of participants underwent advanced inflammatory profiling by simultaneous measurement of 92 immune-related proteins and comprehensive genomic profiling using Illumina Single Nucleotide Polymorphism (SNP) arrays, capturing common genetic variation across multiple biological domains. Preliminary results demonstrate the feasibility of integrating deep phenotyping, reveal the roles of frailty in ageing and show initial evidence of age-related changes in inflammatory proteins.

NCS plans to enrol at least 10 000 participants and will conduct long-term follow-up using both passive methods, such as linking with clinical records and administrative health databases, and active in-person reassessments. Future phases will integrate clinical, behavioural and cognitive data with large-scale omics analyses, including genomics, proteomics, metabolomics and transcriptomics. Machine learning techniques will be employed to model biological age, identify early signs of age-related decline and develop personalised prevention strategies. By combining high-resolution phenotyping with multidimensional data, NCS aims to find modifiable risk factors and molecular signatures of ageing, supporting national and European research efforts and encouraging collaborative studies through open data-sharing frameworks.