Development of an Ensemble Learning-based, Multi-dimensional Sensory Impairment Score to Predict Cognitive Impairment in an Elderly Cohort of Southern Italy

Background / State of the art Hearing, Vision, Olfaction, and Taste represent a complex sensory neural construct biologically linked to age (Gadkaree et al., 2016). Several studies have described their role as predictors in age-related neurodegenerative processes, particularly those related to cognitive decline (Brenowitz et al., 2020; Schubert et al., 2017). Hearing and vision are the most explored, providing robust population-based evidence (Gates et al., 2010; Panza et al., 2019; Sardone, Battista, et al., 2020) and using groundbreaking technologies, such as retinal imaging, in specific clinical settings for the latter. In its peripheral and central forms, hearing impairment has been considered one of the major modifiable risk factors for Alzheimer's dementia(Livingston et al., 2020). Among the sensory inputs related to the perception of physical events in the body, nociception and proprioception represent a set of functions that are often underestimated in their cognitive processing. Studies have shown how they are important not only in the global function of the elderly individual but also and especially in cognitive function. The interactions between aging processes and nociception are poorly investigated, particularly the relationship between chronic pain and global cognitive functions (Nadar et al., 2016). In particular, there are no precise correlates between different objective cognitive function analysis technologies, such as MRI and single or multiple interactions from sensory impairment.

Description and distribution of activities of each operating unit

Principal collaborators will have divided but integrated roles for the aims of the project. In particular, we can highlight two main branches of activities, with a list of the main activities for each research center:

1. Study Role: clinical and instrumental assessment

* ASL BA - Controls enrollment and cognitive-behavioural assessment research expertise * Scientific Clinical Institutes Maugeri (ICS-Maugeri): Cases enrollment and functional and somato-sensorial research expertise 2. Study Role: design, modelling and feature elaboration

* IRCCS - Study Coordination, Design, and formal analysis , sensory impairment research expertise * Polytechnic University of Bari (PoliBa)- data management and engineering, Bio-signal processing (OCT, electrophysiology, MRI), and ensemble learning architectures development Subject assessment will be divided between clinical setting (ICS Maugeri) and population setting (ASL BARI). IRCCS "S. De Bellis" will provide expertise for both design refinement, creation and monitoring of deliverables and milestones, and technological support for sense measurements. In fact, the close collaboration between Dr. Rodolfo Sardone and the PI and Co-PI, shown by numerous publications in the field of sensory impairment and functional decline, ensures a well-established and effective synergy in the proposed area of expertise.

ICS Maugeri, has also already collaborated with the group in other areas, but their role in this project will be truly innovative. They will be responsible for ensuring the recruitment of cases of demented individuals, whether with probable Alzheimer's dementia or vascular dementia. They will also contribute Dr. Pavese's expertise in introducing functional and lifestyle measures to be administered to the entire sample of subjects. Prof. Natoli, on the other hand, will contribute her expertise in the field of chronic pain assessment, both with subjective and functional scales and in the interpretation of biomedical signals from morphological and functional MRI. The technology group, essentially led by Bari Polytechnic, will be responsible for both the creation of the infrastructure for data collection, the processing of physiological signals (both electrophysiological and retinal imaging and MRI) and all variables collected in the study populations. Their expertise and synergistic collaborations with the group are showcased by a series of shared publications.

Specific aim 1 To assess the specific association between different sensory measures (central and peripheral hearing loss, retinal abnormalities measured by OCT, smell and taste objective measures, chronic pain, and proprioception subjective and electrophysiological measures.

Specific aim 2 To develop a multi-dimensional score using sensory features and clinical and lifestyle variables to predict the different types of dementia (Alzheimer's Disease, Fronto-Temporal Dementia, and Vascular Dementia) at different stages (Mild Cognitive impairment and Normal Cognition).

Specific aim 3 To create a connectomic map of the MRI morphologic and dynamic features of the Dementia cases and their relationships with sensorial features, describing the patterns differences in respect to the normal cognition controls.

Experimental design aim 1

Study Population:

This longitudinal study will be conducted using a multi-centric cross-sectional nested case-control design using data from the Casa della Salute project in Castellana Grotte and from the Clinical Neuro-rehabilitation center of the ICS Maugeri - Pavia.

The cognitive impairment cases will be selected from the ICS Maugeri - Pavia, Unit of Neurorehabilitation, and from the Neuro-psychological Outpatients Unit of the Casa della Salute - ASLBA. The cognitive normal control group will be derived from the secondary data of the SALUS in the Apulia Study baseline. SALUS is an ongoing study, started in 2012, on a representative population of older residents in Castellana Grotte (Puglia Region, Southern Italy). The study design and data collection method are described in detail elsewhere (Sardone et al. 2021). The sample included 2038 participants from the elderly (65+) residents in Castellana Grotte at the baseline (2012 to 2014). From this sample will be derived two sub- sample: one of the cognitively normal subjects and one with mild cognitive impairment according to the inclusion\\exclusion criteria.

Cases: The cases included will be on two different stages of cognitive impairment: MCI and dementia.

The inclusion criteria for the MCI cases group will be: 1) to be at least 65 years old at the moment of enrollment: 2) to have an MCI (Montreal Cognitive Assessment, MOCA test, between 15.5-26) 3) to have a complete examination of the clinical, neuropsychological and sensory evaluations. The exclusion criteria for the case group will be 1) do not have the mental capacity to express consent at the follow-up; 2) have developed major malignancies or undergo major therapies could cause a loss in sensory function or cognitive decline; 3) be diagnosed with depression. The total number of MCI cases will be 45.

The inclusion criteria for the dementia cases group will be: 1) to be at least 65 years old at the moment of enrollment: 2) to have a mild to moderate cognitive impairment (Montreal Cognitive Assessment, MOCA test, below 15.5) to have a complete examination of the clinical, neuropsychological and sensory evaluations. The exclusion criteria for the case group will be 1) do not have the mental capacity to express consent at the follow-up; 2) have developed major malignancies or undergo major therapies could cause a loss in sensory function or cognitive impairment; 3) be diagnosed with depression. The total number of dementia cases will be 22 (among all the study sites).

Control Cohort: The inclusion criteria for the control cohort group will be: 1) to be at least 65 years old at the moment of enrollment: 2) to have a complete examination of the clinical, neuropsychological and sensory evaluations 3) to have no cognitive impairment (measured using MOCA greater 26). The total number of controls will be at least 151 (considering 1:5 allocation).

Methods: Ophthalmologic, audiological, physical, sensorial (olfactory and taste), pain and cognitive data derived from the examination at each assessment time will be recorded using a cloud-based data input form, associating every measure and quantitative variable to a root code for every subject examined. Further details are in section "Methods of data collection".

Experimental design aim 2 All data derived from the examination at each assessment time will be recorded using a cloud-based data input form, associating every measure and quantitative variable to a root code for every subject examined. The personal data (e.g., name, age, sex, address) will be detached from the clinical data using a subdivision matrix with a translation code (unknown to the evaluators) that links the master data to the subject code.

Outcome: The cognitive decline will be considered the primary outcome, notably the transition for every subject from MOCA 15.5-26 to a MOCA score lower than 15.5. In addition, a reduction in MOCA score of 10% points will be considered a secondary outcome of cognitive decline at every assessment.

Multisensory Score: the score will be calculated using different approaches to allow different combinations of sensitivity analyses to select the score with the most accurate prediction power.

Phenotype score will create an ordinal variable that cumulates every global impairment on each sense (e.g., hearing loss + vision loss yes\\no + hypogeusia + hyposmia) with a variable from 0 to 4.

Ensemble Weighted Score: Every sense will be considered an independent weighted category depending on the number of sub-impairments that constitute the impaired sense. For example, the vision loss category will be created using the cut-offs for every OCT-a macro-variable impaired (under the 25th percentile) and the low visual acuity cut-off. Every variable will be implemented in a Random Forest machine learning model with the primary outcome as a dependent variable. The derived ranking (in terms of prediction power for cognitive decline) of variables will be used to weight each variable using the inverted rank as a multiplier for a weighted average using the total number of impairments as the denominator. Unsupervised Score: every continuous variable used to measure each sense (independently from the impaired side for ears and eyes) will be implemented in an autoencoder neural network algorithm, able to reduce the dimensionality of the variables. The encoders will create small coefficients (codes) explaining the total information derived from single variables. The codes will be used as predictors for each subject in further analysis.

Covariates: different covariates will be used as confounders of the association, particularly smoking, occupational and environmental exposure, education\\socio economical status, physical activity, and BMI.

Modelling: A number of adjusted cox proportional models and other non-parametric time-event models will be run to assess the association between the different scores proposed and the cognitive decline. In addition, sensitivity analysis will be used with different methods to determine the model in terms of best fitting and prediction power. Due to the small number of observations in the models and considering the high number of covariates, particular attention to overfitting will be devoted, including regularised regression models.

Experimental design aim 3

Brain MRI Acquisition and Processing: A subsample of the participants will undergo a non-contrast-enhanced MRI scanning on a 3T scanner. Selected subjects already enrolled in the study will be selected according to the following criteria:

n.5 with a central auditory processing disorder diagnosis n.5 with a central auditory processing disorder and cognitive impairment (MOCA lower than 17.5) n. 5 only with cognitive impairment (MOCA lower than 17.5) n. 15 normal hearing and cognitive controls The aim of this sub-study will be the description of morphological differences between the four small groups using both supervised and unsupervised machine learning feature extraction and non-parametric statistical learning methods. The differences should create new k