Leader: Massimo Montalto (UNICATT); Other collaborator(s): RICCARDO CALVANI (UCSC)
Based on the available databases from longitudinal cohorts (identified in task 1.1), multivariate/multivariable models will be built to unveil the bidirectional relationships between clinical, functional and biological parameters. Statistical approaches will be based on variable selection methods coupled with classification algorithms
Brief description of the activities and of the intermediate results: The identification of variables of interest to be used as input variables in the predictive models has been completed from pilot analyses. Multimorbidity, defined as the presence of two or more diseases in a same individual has been identified as the condition ‘at risk’ of adverse health outcomes. Outcomes include functional and cognitive decline, disability, burden of multimorbidity, other health adverse outcomes such as hospitalization and death. Frailty, polypharmacy, clinical and biological parameters related to muscle mass, hormone and metabolic parameters, inflammatory markers, nutritional and environmental factors have been selected to be explored in predictive models. Cohorts available include Ospedale San Raffaele (Milano) and UCSC (Policlinico A. Gemelli- Roma) memory clinic datasets, CHROnOS, SPRINT-T, FRASNET, IlSirente, CASSIOPEA.
Main policy, industrial and scientific implications: The integration of multiple predictors using a multidimensional approach is expected to produce tools that may adequately capture the complexity of older adults in clinical practice.
A secondary analysis from the SPRINTT dataset has been completed to identify predictors of incident mobility disability in older adults with physical frailty and sarcopenia. Classification models based on partial least squares discriminant analysis (PLS-DA) allowed correct classification of participants with approximately 70% accuracy.
Findings from the analysis on the identification of predictors of incident disability have been shared with Spoke 3 researchers and reported during the July internal Spoke meeting. Manuscript is under preparation.
Analysis of plasmatic biomarkers of cognitive disability has been planned and preliminary associations have been identified using a cohort of 100 individuals with mild cognitive impairment. Additional cohorts, such as the SPRINTT cohort, will serve to further develop the identification of predictors of incident cognitive disability and to validate the observed associations.
In the past quarter, longitudinal analyses were conducted to identify predictors of incident cognitive disability. Specifically, the predictive power of circulating biomarkers on the risk of dementia was studied in a population with MCI. The analysis on the effectiveness of the circulating neutrophil-to-lymphocyte ratio in predicting the risk of conversion to dementia has been completed. Several classification models have been constructed using multiple chemometric and machine learning strategies (i.e., PLS-DA, Random Forest, K-Nearest Neighbours) to identify predictors of incident motor disability in subjects enrolled in the SPRINTT study. All models were subjected to stringent double repeated cross-validation and permutation testing, which are regarded as the most effective means of ensuring robust model validation and mitigating the risks of overfitting and spurious feature selection. In general, the best performing models in classification were those built using only continuous variables, regardless of the analytical method used.
The manuscript reporting evidence on the longitudinal association between the neutrophil-to-lymphocyte ratio is currently in preparation. The manuscript describing the entire model building process, the different classification performance of the models developed, together with the identification of predictors of incident motor disability, is currently being drafted and will be submitted in a high impact journal in the coming months.
Presentation at EUGMS meeting, Valencia 2024 : “Predictors of incident mobility disability in older adults with physical frailty and sarcopenia: a secondary analysis from the SPRINTT clinical trial”