Alzheimer's Biomarkers From Multiple Modalities Selectively Discriminate Clinical Status: Relative Importance of Salivary Metabolomics Panels, Genetic, Lifestyle, Cognitive, Functional Health and Demographic Risk Markers

Transforming neurodegenerative disorder care with machine learning: Strategies and applications

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting in diverse forms such as memory loss and movement disorders, pose significant challenges due to their complex molecular mechanisms and heterogeneous patient presentations. Diagnosis often relies heavily on clinical assessments and neuroimaging, with definitive confirmation frequently requiring post-mortem autopsy. However, the emergence of Artificial Intelligence (AI) and Machine Learning (ML) offers a transformative potential. These technologies can enable the development of non-invasive tools for early diagnosis, biomarker identification, personalized treatment strategies, patient subtyping and stratification, and disease risk prediction. This review aims to provide a starting point for researchers, both with and without clinical backgrounds, who are interested in applying ML to NDs. We will discuss available data resources for key diseases like Alzheimer's and Parkinson's, explore how ML can revolutionize neurodegenerative care, and emphasize the importance of integrating multiple high-dimensional data sources to gain deeper insights and inform effective therapeutic strategies.

An Update of Salivary Biomarkers for the Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is characterized by progressive cognition and behavior impairments. Diagnosing AD early is important for clinicians to slow down AD progression and preserve brain function. Biomarkers such as tau protein and amyloid-β peptide (Aβ) are used to aid diagnosis as clinical diagnosis often lags. Additionally, biomarkers can be used to monitor AD status and evaluate AD treatment. Clinicians detect these AD biomarkers in the brain using positron emission tomography/computed tomography or in the cerebrospinal fluid using a lumbar puncture. However, these methods are expensive and invasive. In contrast, saliva collection is simple, inexpensive, non-invasive, stress-free, and repeatable. Moreover, damage to the brain parenchyma can impact the oral cavity and some pathogenic molecules could travel back and forth from the brain to the mouth. This has prompted researchers to explore biomarkers in the saliva. Therefore, this study provides an overview of the main finding of salivary biomarkers for AD diagnosis. Based on these available studies, Aβ, tau, cholinesterase enzyme activity, lactoferrin, melatonin, cortisol, proteomics, metabolomics, exosomes, and the microbiome were changed in AD patients' saliva when compared to controls. However, well-designed studies are essential to confirm the reliability and validity of these biomarkers in diagnosing and monitoring AD.

A Speech-Based Mobile Screening Tool for Mild Cognitive Impairment: Technical Performance and User Engagement Evaluation

Traditional screening methods for Mild Cognitive Impairment (MCI) face limitations in accessibility and scalability. To address this, we developed and validated a speech-based automatic screening app implementing three speech-language tasks with user-centered design and server-client architecture. The app integrates automated speech processing and SVM classifiers for MCI detection. Functionality validation included comparison with manual assessment and testing in real-world settings (n = 12), with user engagement evaluated separately (n = 22). The app showed comparable performance with manual assessment (F1 = 0.93 vs. 0.95) and maintained reliability in real-world settings (F1 = 0.86). Task engagement significantly influenced speech patterns: users rating tasks as "most interesting" produced more speech content (p < 0.05), though behavioral observations showed consistent cognitive processing across perception groups. User engagement analysis revealed high technology acceptance (86%) across educational backgrounds, with daily cognitive exercise habits significantly predicting task benefit perception (H = 9.385, p < 0.01). Notably, perceived task difficulty showed no significant correlation with cognitive performance (p = 0.119), suggesting the system's accessibility to users of varying abilities. While preliminary, the mobile app demonstrated both robust assessment capabilities and sustained user engagement, suggesting the potential viability of widespread cognitive screening in the geriatric population.

Frontiers and hotspots evolution in mild cognitive impairment: a bibliometric analysis of from 2013 to 2023

Background

Mild cognitive impairment is a heterogeneous syndrome. The heterogeneity of the syndrome and the absence of consensus limited the advancement of MCI. The purpose of our research is to create a visual framework of the last decade, highlight the hotspots of current research, and forecast the most fruitful avenues for future MCI research.

Methods

We collected all the MCI-related literature published between 1 January 2013, and 24 April 2023, on the "Web of Science." The visual graph was created by the CiteSpace and VOSviewer. The current research hotspots and future research directions are summarized through the analysis of keywords and co-cited literature.

Results

There are 6,075 articles were included in the final analysis. The number of publications shows an upward trend, especially after 2018. The United States and the University of California System are the most prolific countries and institutions, respectively. Petersen is the author who ranks first in terms of publication volume and influence. Journal of Alzheimer's Disease was the most productive journal. "neuroimaging," "fluid markers," and "predictors" are the focus of current research, and "machine learning," "electroencephalogram," "deep learning," and "blood biomarkers" are potential research directions in the future.

Conclusion

The cognition of MCI has been continuously evolved and renewed by multiple countries' joint efforts in the past decade. Hotspots for current research are on diagnostic biomarkers, such as fluid markers, neuroimaging, and so on. Future hotspots might be focused on the best prognostic and diagnostic models generated by machine learning and large-scale screening tools such as EEG and blood biomarkers.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study

BACKGROUND

Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

METHODS

The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

RESULTS

We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

CONCLUSIONS

Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

Deep learning analysis of UPLC-MS/MS-based metabolomics data to predict Alzheimer's disease

OBJECTIVE

Metabolic biomarkers can potentially inform disease progression in Alzheimer's disease (AD). The purpose of this study is to identify and describe a new set of diagnostic biomarkers for developing deep learning (DL) tools to predict AD using Ultra Performance Liquid Chromatography Mass Spectrometry (UPLC-MS/MS)-based metabolomics data.

METHODS

A total of 177 individuals, including 78 with AD and 99 with cognitive normal (CN), were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort along with 150 metabolomic biomarkers. We performed feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO). The H2O DL function was used to build multilayer feedforward neural networks to predict AD.

RESULTS

The LASSO selected 21 metabolic biomarkers. To develop DL models, the 21 biomarkers identified by LASSO were imported into the H2O package. The data was split into 70% for training and 30% for validation. The best DL model with two layers and 18 neurons achieved an accuracy of 0.881, F1-score of 0.892, and AUC of 0.873. Several metabolomic biomarkers involved in glucose and lipid metabolism, in particular bile acid metabolites, were associated with APOE-ε4 allele and clinical biomarkers (Aβ42, tTau, pTau), cognitive assessments [the Alzheimer's Disease Assessment Scale-cognitive subscale 13 (ADAS13), the Mini-Mental State Examination (MMSE)], and hippocampus volume.

CONCLUSIONS

This study identified a new set of diagnostic metabolomic biomarkers for developing DL tools to predict AD. These biomarkers may help with early diagnosis, prognostic risk stratification, and/or early treatment interventions for patients at risk for AD.

Identifying key multi-modal predictors of incipient dementia in Parkinson's disease: a machine learning analysis and Tree SHAP interpretation

Background

Persons with Parkinson's disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.

Method

Participants were 48 well-characterized PD patients (M_{baseline age} = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation.

Results

An excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains.

Conclusion

Our data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains.

Investigation of human β-defensins 1, 2 and 3 in human saliva by molecular dynamics

Human β-defensins present in saliva have a broad spectrum of antimicrobial activities that work against infections in oral cavity. To provide a better understanding of these molecules' properties and functions at the molecular level, we have investigated and compared the important structural properties of human β-defensin-1, -2 and -3 using molecular dynamics simulations. Our results have shown that human β-defensin-3 has a more flexible structure in water than the other two because of its high hydrophilicity, low β-sheet content and high repulsive forces between its charged residues. Moreover, we found that the location of the salt bridges is important in protein's stability in water. Molecular dynamics simulations of human β-defensins 1, 2 and 3 revealed that the hbd-3 is more flexible in water than hbd-1 and hbd-2.

High Resolution Magic Angle Spinning Proton NMR Study of Alzheimer's Disease with Mouse Models

Alzheimer's disease (AD) is a crippling condition that affects millions of elderly adults each year, yet there remains a serious need for improved methods of diagnosis. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease; however, studies of human brain tissue metabolomics are challenging, due to sample limitations and ethical considerations. Comprehensive comparisons of imaging measurements in animal models to identify similarities and differences between aging- and AD-associated metabolic changes should thus be tested and validated for future human non-invasive studies. In this paper, we present the results of our highresolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) studies of AD and wild-type (WT) mouse models, based on animal age, brain regions, including cortex vs. hippocampus, and disease status. Our findings suggest the ability of HRMAS NMR to differentiate between AD and WT mice using brain metabolomics, which potentially can be implemented in in vivo evaluations.

Biomarkers used in Alzheimer's disease diagnosis, treatment, and prevention

Alzheimer's disease (AD), being the number one in terms of dementia burden, is an insidious age-related neurodegenerative disease and is presently considered a global public health threat. Its main histological hallmarks are the Aβ senile plaques and the P-tau neurofibrillary tangles, while clinically it is marked by a progressive cognitive decline that reflects the underlying synaptic loss and neurodegeneration. Many of the drug therapies targeting the two pathological hallmarks namely Aβ and P-tau have been proven futile. This is probably attributed to the initiation of therapy at a stage where cognitive alterations are already obvious. In other words, the underlying neuropathological changes are at a stage where these drugs lack any therapeutic value in reversing the damage. Therefore, there is an urgent need to start treatment in the very early stage where these changes can be reversed, and hence, early diagnosis is of primordial importance. To this aim, the use of robust and informative biomarkers that could provide accurate diagnosis preferably at an earlier phase of the disease is of the essence. To date, several biomarkers have been established that, to a different extent, allow researchers and clinicians to evaluate, diagnose, and more specially exclude other related pathologies. In this study, we extensively reviewed data on the currently explored biomarkers in terms of AD pathology-specific and non-specific biomarkers and highlighted the recent developments in the diagnostic and theragnostic domains. In the end, we have presented a separate elaboration on aspects of future perspectives and concluding remarks.

Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part I: Emerging Platforms and Perspectives

1H NMR-based metabolomics analysis of human saliva, other oral fluids, and/or tissue biopsies serves as a valuable technique for the exploration of metabolic processes, and when associated with ’state-of-the-art’ multivariate (MV) statistical analysis strategies, provides a powerful means of examining the identification of characteristic metabolite patterns, which may serve to differentiate between patients with oral health conditions (e.g., periodontitis, dental caries, and oral cancers) and age-matched heathy controls. This approach may also be employed to explore such discriminatory signatures in the salivary 1H NMR profiles of patients with systemic diseases, and to date, these have included diabetes, Sjörgen’s syndrome, cancers, neurological conditions such as Alzheimer’s disease, and viral infections. However, such investigations are complicated in view of quite a large number of serious inconsistencies between the different studies performed by independent research groups globally; these include differing protocols and routes for saliva sample collection (e.g., stimulated versus unstimulated samples), their timings (particularly the oral activity abstention period involved, which may range from one to 12 h or more), and methods for sample transport, storage, and preparation for NMR analysis, not to mention a very wide variety of demographic variables that may influence salivary metabolite concentrations, notably the age, gender, ethnic origin, salivary flow-rate, lifestyles, diets, and smoking status of participant donors, together with their exposure to any other possible convoluting environmental factors. In view of the explosive increase in reported salivary metabolomics investigations, in this update, we critically review a wide range of critical considerations for the successful performance of such experiments. These include the nature, composite sources, and biomolecular status of human saliva samples; the merits of these samples as media for the screening of disease biomarkers, notably their facile, unsupervised collection; and the different classes of such metabolomics investigations possible. Also encompassed is an account of the history of NMR-based salivary metabolomics; our recommended regimens for the collection, transport, and storage of saliva samples, along with their preparation for NMR analysis; frequently employed pulse sequences for the NMR analysis of these samples; the supreme resonance assignment benefits offered by homo- and heteronuclear two-dimensional NMR techniques; deliberations regarding salivary biomolecule quantification approaches employed for such studies, including the preprocessing and bucketing of multianalyte salivary NMR spectra, and the normalization, transformation, and scaling of datasets therefrom; salivary phenotype analysis, featuring the segregation of a range of different metabolites into ‘pools’ grouped according to their potential physiological sources; and lastly, future prospects afforded by the applications of LF benchtop NMR spectrometers for direct evaluations of the oral or systemic health status of patients at clinical ‘point-of-contact’ sites, e.g., dental surgeries. This commentary is then concluded with appropriate recommendations for the conduct of future salivary metabolomics studies. Also included are two original case studies featuring investigations of (1) the 1H NMR resonance line-widths of selected biomolecules and their possible dependence on biomacromolecular binding equilibria, and (2) the combined univariate (UV) and MV analysis of saliva specimens collected from a large group of healthy control participants in order to potentially delineate the possible origins of biomolecules therein, particularly host- versus oral microbiome-derived sources. In a follow-up publication, Part II of this series, we conduct censorious reviews of reported observations acquired from a diversity of salivary metabolomics investigations performed to evaluate both localized oral and non-oral diseases. Perplexing problems encountered with these again include those arising from sample collection and preparation protocols, along with 1H NMR spectral misassignments.

Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes

BACKGROUND

Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses.

OBJECTIVE

To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers.

METHODS

We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class.

RESULTS

For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index.

CONCLUSION

Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.

Current Understanding of the Physiopathology, Diagnosis and Therapeutic Approach to Alzheimer's Disease

Alzheimer's disease (AD) is the most common cause of dementia. It is characterized by cognitive decline and progressive memory loss. The aim of this review was to update the state of knowledge on the pathophysiological mechanisms, diagnostic methods and therapeutic approach to AD. Currently, the amyloid cascade hypothesis remains the leading theory in the pathophysiology of AD. This hypothesis states that amyloid-β (Aβ) deposition triggers a chemical cascade of events leading to the development of AD dementia. The antemortem diagnosis of AD is still based on clinical parameters. Diagnostic procedures in AD include fluid-based biomarkers such as those present in cerebrospinal fluid and plasma or diagnostic imaging methods. Currently, the therapeutic armory available focuses on symptom control and is based on four pillars: pharmacological treatment where acetylcholinesterase inhibitors stand out; pharmacological treatment under investigation which includes drugs focused on the control of Aβ pathology and tau hyperphosphorylation; treatment focusing on risk factors such as diabetes; or nonpharmacological treatments aimed at preventing development of the disease or treating symptoms through occupational therapy or psychological help. AD remains a largely unknown disease. Further research is needed to identify new biomarkers and therapies that can prevent progression of the pathology.

Differential Cognitive Decline in Alzheimer's Disease Is Predicted by Changes in Ventricular Size but Moderated by Apolipoprotein E and Pulse Pressure

BACKGROUND

Differential cognitive trajectories in Alzheimer's disease (AD) may be predicted by biomarkers from multiple domains.

OBJECTIVE

In a longitudinal sample of AD and AD-related dementias patients (n = 312), we tested whether 1) change in brain morphometry (ventricular enlargement) predicts differential cognitive trajectories, 2) further risk is contributed by genetic (Apolipoprotein E [APOE] ɛ4+) and vascular (pulse pressure [PP]) factors separately, and 3) the genetic + vascular risk moderates this pattern.

METHODS

We applied a dynamic computational approach (parallel process models) to test both concurrent and change-related associations between predictor (ventricular size) and cognition (executive function [EF]/attention). We then tested these associations as stratified by APOE (ɛ4-/ɛ4+), PP (low/high), and APOE+ PP (low/intermediate/high) risk.

RESULTS

First, concurrently, higher ventricular size predicted lower EF/attention performance and, longitudinally, increasing ventricular size predicted steeper EF/attention decline. Second, concurrently, higher ventricular size predicted lower EF/attention performance selectively in APOEɛ4+ carriers, and longitudinally, increasing ventricular size predicted steeper EF/attention decline selectively in the low PP group. Third, ventricular size and EF/attention associations were absent in the high APOE+ PP risk group both concurrently and longitudinally.

CONCLUSION

As AD progresses, a threshold effect may be present in which ventricular enlargement in the context of exacerbated APOE+ PP risk does not produce further cognitive decline.

Data-driven approaches to executive function performance and structure in aging: Integrating person-centered analyses and machine learning risk prediction

Objective: Executive function (EF) performance and structure in nondemented aging are frequently examined with variable-centered approaches. Person-centered analytics can contribute unique information about classes of persons by simultaneously considering EF performance and structure. The risk predictors of these classes can then be determined by machine learning technology. Using data from the Victoria Longitudinal Study we examined two goals: (a) detect different underlying subgroups (or classes) of EF performance and structure and (b) test multiple risk predictors for best discrimination of these detected subgroups. Method: We used a classification sample (n = 778; M_age = 71.42) for the first goal and a prediction subsample (n = 570; M_age = 70.10) for the second goal. Eight neuropsychological measures represented three EF dimensions (inhibition, updating, shifting). Fifteen predictors represented five domains (genetic, functional, lifestyle, mobility, demographic). Results: First, we observed two distinct classes: (a) lower EF performance and unidimensional structure (Class 1) and (b) higher EF performance and multidimensional structure (Class 2). Second, Class 2 was predicted by younger age, more novel cognitive activity, more education, lower body mass index, lower pulse pressure, female sex, faster balance, and more physical activity. Conclusions: Data-driven modeling approaches tested the possibility of an EF aging class that displayed both preserved EF performance levels and sustained multidimensional structure. The two observed classes differed in both performance level (lower, higher) and structure (unidimensional, multidimensional). Machine learning prediction analyses showed that the higher performing and multidimensional class was associated with multiple brain health-related protective factors. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A Multimodal Risk Network Predicts Executive Function Trajectories in Non-demented Aging

Background: Multiple modalities of Alzheimer's disease (AD) risk factors may operate through interacting networks to predict differential cognitive trajectories in asymptomatic aging. We test such a network in a series of three analytic steps. First, we test independent associations between three risk scores (functional-health, lifestyle-reserve, and a combined multimodal risk score) and cognitive [executive function (EF)] trajectories. Second, we test whether all three associations are moderated by the most penetrant AD genetic risk [Apolipoprotein E (APOE) ε4+ allele]. Third, we test whether a non-APOE AD genetic risk score further moderates these APOE × multimodal risk score associations. Methods: We assembled a longitudinal data set (spanning a 40-year band of aging, 53-95 years) with non-demented older adults (baseline n = 602; Mage = 70.63(8.70) years; 66% female) from the Victoria Longitudinal Study (VLS). The measures included for each modifiable risk score were: (1) functional-health [pulse pressure (PP), grip strength, and body mass index], (2) lifestyle-reserve (physical, social, cognitive-integrative, cognitive-novel activities, and education), and (3) the combination of functional-health and lifestyle-reserve risk scores. Two AD genetic risk markers included (1) APOE and (2) a combined AD-genetic risk score (AD-GRS) comprised of three single nucleotide polymorphisms (SNPs; Clusterin[rs11136000], Complement receptor 1[rs6656401], Phosphatidylinositol binding clathrin assembly protein[rs3851179]). The analytics included confirmatory factor analysis (CFA), longitudinal invariance testing, and latent growth curve modeling. Structural path analyses were deployed to test and compare prediction models for EF performance and change. Results: First, separate analyses showed that higher functional-health risk scores, lifestyle-reserve risk scores, and the combined score, predicted poorer EF performance and steeper decline. Second, APOE and AD-GRS moderated the association between functional-health risk score and the combined risk score, on EF performance and change. Specifically, only older adults in the APOEε4- group showed steeper EF decline with high risk scores on both functional-health and combined risk score. Both associations were further magnified for adults with high AD-GRS. Conclusion: The present multimodal AD risk network approach incorporated both modifiable and genetic risk scores to predict EF trajectories. The results add an additional degree of precision to risk profile calculations for asymptomatic aging populations.

Biomarkers in Alzheimer's disease

Background

Alzheimer's disease (AD) is a progressive neurodegenerative disease. AD is the main cause of dementia worldwide and aging is the main risk factor for developing the illness. AD classical diagnostic criteria rely on clinical data. However, the development of a biological definition of AD using biomarkers that reflect the underling neuropathology is needed.

Content

The aim of this review is to describe the main outcomes when measuring classical and novel biomarkers in biological fluids or neuroimaging.

Summary

Nowadays, there are three classical biomarkers for the diagnosis of AD: Aβ42, t-Tau and p-Tau. The diagnostic use of cerebrospinal fluid biomarkers is limited due to invasive collection by lumbar puncture with potential side effects. Plasma/serum measurements are the gold standard in clinics, because they are minimally invasive and, in consequence, easily collected and processed. The two main proteins implicated in the pathological process, Aβ and Tau, can be visualized using neuroimaging techniques, such as positron emission tomography.

Outlook

As it is currently accepted that AD starts decades before clinical symptoms could be diagnosed, the opportunity to detect biological alterations prior to clinical symptoms would allow early diagnosis or even perhaps change treatment possibilities.

Integrating Three Characteristics of Executive Function in Non-Demented Aging: Trajectories, Classification, and Biomarker Predictors

OBJECTIVE

With longitudinal executive function (EF) data from the Victoria Longitudinal Study, we investigated three research goals pertaining to key characteristics of EF in non-demented aging: (a) examining variability in EF longitudinal trajectories, (b) establishing trajectory classes, and (c) identifying biomarker predictors discriminating these classes.

METHOD

We used a trajectory analyses sample (n = 781; M age = 71.42) for the first and second goals and a prediction analyses sample (n = 570; M age = 70.10) for the third goal. Eight neuropsychological EF measures were used as indicators of three EF dimensions: inhibition, updating, and shifting. Data-driven classification analyses were applied to the full trajectory distribution. Machine learning prediction analyses tested 15 predictors from genetic, functional, lifestyle, mobility, and demographic risk domains.

RESULTS

First, we observed: (a) significant variability in EF trajectories over a 40-year band of aging and (b) significantly variable patterns of EF decline. Second, a four-class EF trajectory model was observed, characterized with classes differentiated by an algorithm of level and slope information. Third, the highest group class was discriminated from lowest by several prediction factors: more education, more novel cognitive activity, lower pulse pressure, younger age, faster gait, lower body mass index, and better balance.

CONCLUSION

First, with longitudinal variability in EF aging, the data-driven approach showed that long-term trajectories can be differentiated into separable classes. Second, prediction analyses discriminated class membership by a combination of multiple biomarkers from demographic, lifestyle, functional, and mobility domains of risk for brain and cognitive aging decline.

A data-driven prospective study of dementia among older adults in the United States

Background

Studies examining risk factors for dementia have typically focused on testing a priori hypotheses within specific risk factor domains, leaving unanswered the question of what risk factors across broad and diverse research fields may be most important to predicting dementia. We examined the relative importance of 65 sociodemographic, early-life, economic, health and behavioral, social, and genetic risk factors across the life course in predicting incident dementia and how these rankings may vary across racial/ethnic (non-Hispanic white and black) and gender (men and women) groups.

Methods and findings

We conducted a prospective analysis of dementia and its association with 65 risk factors in a sample of 7,908 adults aged 51 years and older from the nationally representative US-based Health and Retirement Study. We used traditional survival analysis methods (Fine and Gray models) and a data-driven approach (random survival forests for competing risks) which allowed us to account for the semi-competing risk of death with up to 14 years of follow-up. Overall, the top five predictors across all groups were lower education, loneliness, lower wealth and income, and lower self-reported health. However, we observed variation in the leading predictors of dementia across racial/ethnic and gender groups such that at most four risk factors were consistently observed in the top ten predictors across the four demographic strata (non-Hispanic white men, non-Hispanic white women, non-Hispanic black men, non-Hispanic black women).

Conclusions

We identified leading risk factors across racial/ethnic and gender groups that predict incident dementia over a 14-year period among a nationally representative sample of US aged 51 years and older. Our ranked lists may be useful for guiding future observational and quasi-experimental research that investigates understudied domains of risk and emphasizes life course economic and health conditions as well as disparities therein.

Future Directions for Dementia Risk Reduction and Prevention Research: An International Research Network on Dementia Prevention Consensus

In the past decade a large body of evidence has accumulated on risk factors for dementia, primarily from Europe and North America. Drawing on recent integrative reviews and a consensus workshop, the International Research Network on Dementia Prevention developed a consensus statement on priorities for future research. Significant gaps in geographical location, representativeness, diversity, duration, mechanisms, and research on combinations of risk factors were identified. Future research to inform dementia risk reduction should fill gaps in the evidence base, take a life-course, multi-domain approach, and inform population health approaches that improve the brain-health of whole communities.

Saliva, an easily accessible fluid as diagnostic tool and potent stem cell source for Alzheimer's Disease: Present and future applications

Alzheimer's disease (AD) is a progressive and multifactorial disease. Many scientific advances have advanced our understanding of the pathogenesis of AD. However, the clinical diagnosis of AD remains difficult, with only post-mortem assays confirming its definitive diagnosis. There is a crucial need for an early and accurate detection of AD related symptoms. To date, current diagnosis techniques are costly or invasive. Finding a peripheral biomarker that could provide a sensitive, reproducible, and accurate detection prior to the onset of the AD clinical symptoms will allow identification of "at risk" individuals, thereby facilitating early initiation of treatments that may prove more effective. Salivary glands contain stem cells, which are affected by aging, suggesting that tissue samples from these glands may reveal a stem cell biomarker of AD, but also stem cells may be harvested from these glands, with proper timing and isolation technique, for cell-based regenerative medicine. Alternatively, instead of the salivary glands, saliva may represent an attractive source for biomarkers due to minimal discomfort to the patient, non-invasive collection, and the possibility of cost-effective screening large populations, encouraging greater compliance in clinical trials and frequent testing. In addition, salivary glands contain stem cells, which are likely also present in the saliva, making these cells as potentially sensitive cellular biomarker of and a therapeutic agent for AD. The aim of this review is to critically analyze the use of saliva for the identification of circulating biological markers to help the diagnosis of early cognitive impairment associated with AD and to generate insights into the potential application of stem cells derived from salivary glands or saliva as therapeutics (i.e., stem cell transplantation) for the disease.

Inflammation markers in the saliva of infants born from Zika-infected mothers: exploring potential mechanisms of microcephaly during fetal development

Zika virus (ZIKV) has emerged as one of the most medically relevant viral infections of the past decades; the devastating effects of this virus over the developing brain are a major matter of concern during pregnancy. Although the connection with congenital malformations are well documented, the mechanisms by which ZIKV reach the central nervous system (CNS) and the causes of impaired cortical growth in affected fetuses need to be better addressed. We performed a non-invasive, metabolomics-based screening of saliva from infants with congenital Zika syndrome (CZS), born from mothers that were infected with ZIKV during pregnancy. We were able to identify three biomarkers that suggest that this population suffered from an important inflammatory process; with the detection of mediators associated with glial activation, we propose that microcephaly is a product of immune response to the virus, as well as excitotoxicity mechanisms, which remain ongoing even after birth.

Integration of Machine Learning Methods to Dissect Genetically Imputed Transcriptomic Profiles in Alzheimer's Disease

The genetic component of many common traits is associated with the gene expression and several variants act as expression quantitative loci, regulating the gene expression in a tissue specific manner. In this work, we applied tissue-specific cis-eQTL gene expression prediction models on the genotype of 808 samples including controls, subjects with mild cognitive impairment, and patients with Alzheimer's Disease. We then dissected the imputed transcriptomic profiles by means of different unsupervised and supervised machine learning approaches to identify potential biological associations. Our analysis suggests that unsupervised and supervised methods can provide complementary information, which can be integrated for a better characterization of the underlying biological system. In particular, a variational autoencoder representation of the transcriptomic profiles, followed by a support vector machine classification, has been used for tissue-specific gene prioritizations. Interestingly, the achieved gene prioritizations can be efficiently integrated as a feature selection step for improving the accuracy of deep learning classifier networks. The identified gene-tissue information suggests a potential role for inflammatory and regulatory processes in gut-brain axis related tissues. In line with the expected low heritability that can be apportioned to eQTL variants, we were able to achieve only relatively low prediction capability with deep learning classification models. However, our analysis revealed that the classification power strongly depends on the network structure, with recurrent neural networks being the best performing network class. Interestingly, cross-tissue analysis suggests a potentially greater role of models trained in brain tissues also by considering dementia-related endophenotypes. Overall, the present analysis suggests that the combination of supervised and unsupervised machine learning techniques can be used for the evaluation of high dimensional omics data.

The current status of blood epigenetic biomarkers for dementia

Dementia is an overarching term which describes a group of symptoms that result in long-term decline in cognitive functioning that is significant enough to affect daily function. It is caused by a number of different diseases, the most common of which is Alzheimer's disease. Currently, there are no definitive biomarkers for preclinical or diagnostic use, or which differentiate between underlying disease types. The purpose of this review is to highlight several important areas of research on blood-based biomarkers of dementia, with a specific focus on epigenetic biomarkers. A systematic search of the literature identified 77 studies that compared blood DNA methylation between individuals with dementia and controls and 45 studies that measured microRNA. Very few studies were identified that focused on histone modifications. There were many promising findings from studies in the field of blood-based epigenetic biomarkers of dementia, however, a lack of consistency in study design, technologies, and platforms used for the biomarker measurement, as well as statistical analysis methods, have hampered progress. To date, there are very few findings that have been independently replicated across more than one study, indicating a preponderance of false-positive findings and the field has likely been plagued by positive publication bias. Here, we highlight and discuss several of the limitations of existing studies and provide recommendations for how these could be overcome in future research. A robust framework should be followed to enable development of the most valid and reproducible biomarkers with the strongest clinical utility. Defining a series of biomarkers that may be complimentary to each other could permit a stronger multifactorial biomarker to be developed that would allow for not only accurate dementia diagnosis but preclinical detection.