Personalised Medicine in the Identification of Preclinical Cognitive Impairment. Development of a Predictive Risk Model

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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Personalised Medicine in the Early Identification of Preclinical Cognitive Impairment. Development of a Predictive Risk Model.

Conditions
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Cognitive Dysfunction

Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 55 Years
Maximum Age: 70 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Cognitive level | Evaluated with Minimental State Examination (min 0 - max 30, higher scores mean a better outcome) and Montreal Cognitive Assessment (min 0 - max 30, higher scores mean a better outcome) | 16 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| multi-omics biomarkers | this will be performed with the Illumina Infinium Global Screening array, which allows direct analysis of 750,000 SNPs with a design aimed at Personalised Medicine. These data will be used to estimate the polygenic risk score for cognitive impairment, which is a single quantitative value of the genetic load for CD for each sample/individual. | 16 months | 
| Social support network. | The investigators will use the Arizona Social Support Interview Schedule (Barrera 1980) which elicits networks related to material help, physical assistance, intimate interaction, guidance, feedback and positive social interactions. | 16 months | 
| social interactions | The investigators will use several game-theoretic scenarios (prisoner's dilemma, trust game, investor game, risk aversion and dictator game, Cigarini et al 2018) to elicit how participants interact with each other when there may be different interaction outcomes depending on each other's behaviour. This, on the one hand, relates to the stability of social connections and, on the other hand, to their formation. | 16 months | 
| personalised behavioural patterns. | sing mobile applications that allow continuous and passive collection of a person's behavioural data such as daily patterns of steps, distance travelled, time spent using apps, sleep and presence at home. The aim will be to use such a monitoring tool in the cohort of patients under study, using artificial intelligence methods for the extraction of personalised behavioural patterns that can be combined with other sources of information. | 16 months | 
| Gait speed | the time it takes the person to walk a given distance, usually 4 m, expressed in metres/second. | 16 months | 
| The fluency and content of speech | two algorithms are proposed: a) paralinguistic system based on acoustic processing of the recordings with different versions depending on whether the audio comes from the recording of a memory test, or from a description of an image presented to the patient, b) analysis of speech content (obtained through an automatic speech recognition system) using natural language processing algorithms that extract the most relevant feature vector, as well as the calculation of statistics related to the hit/fail ratio of the memory tests. | 16 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Care, Cognitive Dysfunction, Early Diagnosis, Primary Health Care, Biomarkers, Artificial Intelligence, Precision Medicine