The goal of this observational study is to use the combined power of the integration of clinical, molecular, proteomic, genomic, care, social, environmental and behavioural data in patients, using advanced artificial intelligence techniques for data processing and analysis, in order to generate predictive models for the preclinical detection of CI in the population aged 55-70 years.

The "Comprehensive Plan for Alzheimer's and other Dementias" shows that more than 50% of cases of cognitive impairment (CI) in population-based studies are undetected. The figure is particularly striking in the case of mild dementias, of which up to 90% are undiagnosed. The aim is to use the combined power of the integration of clinical, molecular, proteomic, genomic, care, social, environmental and behavioural data in patients, using advanced artificial intelligence techniques for data processing and analysis, in order to generate predictive models for the preclinical detection of CI in the population aged 55-70 years.

Multicentre, non-interventional, convergent mixed methods observational study, with a prospective observational design part and a qualitative design part. Sample recruited randomly among users of the public health system in the participating geographical locations. Data will be collected in 6 regions (Andalucia, Castilla-Mancha, Catalonia, Valencia, Madrid and the Basque Country) and their rural and urban Primary Care (PC) networks.

Non-institutionalised subjects, aged between 55 and 70 years, assigned to PC centres in the territories included in the study, with a "living history" (recorded in the last 12 months) and without an established diagnosis of CI.

A descriptive analysis of the characteristics of the population will be carried out using frequencies and percentages or measures of central tendency and dispersion, with their 95% confidence intervals. Baseline socio-demographic and clinical characteristics will be compared in order to study the homogeneity of the sample. For the comparison of qualitative variables, the Chi-square test or Fisher's exact test will be used and for the comparison of quantitative variables, the t-test or Wilcoxon test will be used. Logistic regression models are proposed to analyse health outcome factors associated with mild cognitive impairment. All models will include repeated measures for each individual. All models will adjust for different risk factors, and for those factors that may change over time, the interaction between time and that factor will be studied.

Initially, multivariate linear latent models will be used for the predictive model of cognitive impairment risk. The integration of data from multiple sources of information will be done using multivariate probabilistic models, in order to find a representation of the patient in a feature space influenced by all data sources (visits).

Web tools such as Ingenuity Pathway Analysis will allow the integration of data at different molecular levels (genetic, protein and autoantibody), while artificial intelligence tools will allow the integration of such data, data derived from electrochemical sensors and data related to clinical and behavioural data with cognitive impairment in order to obtain a predictive model of cognitive impairment, neurodegeneration and AD.

Inclusion Criteria:

• Non-institutionalised subjects from the study locations.
• Aged between 55 and 70 years, attached to the PC centres of the territories included in the study
• Living history (at least one record in the last 12 months)
• Without an established diagnosis of CI.

Exclusion Criteria:

• Participants with significant difficulties in completing self-reported questionnaires
• Those in whom genetic or biological testing may be affected by an underlying genetic or health condition.
• Underlying genetic or health condition.
• Patients who are hospitalised or institutionalised during follow-up will be excluded.

Understanding how factors interrelate with sex or gender will be important in determining user needs and in explaining or predicting differences in outcomes.

Where differences in levels of participation and/or acceptance of participation in the project between men and women are identified, these will be explained and measures identified to address them.

The study will be carried out on non-institutionalised subjects in the study locations, aged 55 to 70 years, attached to the PC centres of the territories included in the study, with a living history (at least one record in the last 12 months). The individuals will be selected from lists of patients from the participating practices in the 7 geographical locations, each of them providing 150 patients. The sample will be obtained by stratified by age (5-year wide strata) and sex who do not include any diagnosis of mild CD in their clinical records (MMSE= 24-27 points). Any refusal to participate after screening will be replaced by the next subject on the sampling list from the same centre and stratum. Samples will be collected at baseline and at 16 months.

All data files generated by the project studies, if they can be k-anonymised, may be distributed in open access, accompanied by a "Readme" file in free text format containing the metadata (title, project and funding information, contact information, date of collection, geographical contact information, date of collection, geographical information, keywords, data information, licence, associated Handles/DOIs, method of generation, method of method, method of processing and analysis, list of variables included (definition, description (definition, description, units of measurement)).

The metadata contained in the Readme file shall use a standardised language, using W3C/ISO 8601 date and time formats; taxonomy and nomenclature accepted by the scientific community (CIE10, CIAP2, etc.) and including keywords with MeSH/DeCS terminology.

In addition, publications and datasets that are deposited in repositories. https://zenodo.org/record/8379825

During 2026

They will be published on the Rapisalud platform (https://repisalud.isciii.es).