Polygenic Score Models for Alzheimer's Disease: From Research to Clinical Applications

Polygenic hazard score predicts synaptic and axonal degeneration and cognitive decline in Alzheimer's disease continuum

BACKGROUND

Growth associated protein-43 (GAP-43) and neurofilaments light (NFL) are biomarkers of synaptic and axonal injury, and are associated with cognitive decline in Alzheimer's disease (AD) contiuum. We investigated whether Polygenic Hazard Score (PHS) is associated with specific biomarkers and cognitive measures, and if it can predict the relationship between GAP-43, NFL, and cognitive decline in AD.

METHOD

We enrolled 646 subjects: 93 with AD, 350 with mild cognitive impairment (MCI), and 203 cognitively normal controls. Variables included GAP-43, plasma NFL, and PHS. A PHS of 0.21 or higher was considered high risk while a PHS below this threshold was considered low risk. A subsample of 190 patients with MCI with four years of follow-up cognitive assessments were selected for longitudinal analysis . We assessed the association of the PHS with AD biomarkers and cognitive measures, as well as the predictive power of PHS on cognitive decline and the conversion of MCI to AD.

RESULTS

PHS showed high diagnostic accuracy in distinguishing AD, MCI, and controls. At each follow-up point, high risk MCI patients showed higher level of cognitive impairment compared to the low risk group. GAP-43 correlated with all follow-up cognitive tests in high risk MCI patients which was not detected in low risk MCI patients. Moreover, high risk MCI patients progressed to dementia more rapidly compared to low risk patients.

CONCLUSION

PHS can predict cognitive decline and impacts the relationship between neurodegenerative biomarkers and cognitive impairment in AD contiuum. Categorizing patients based on PHS can improve the prediction of cognitive outcomes and disease progression.

Novel Biomarkers for Alzheimer's Disease: Plasma Neurofilament Light and Cerebrospinal Fluid

Neurodegenerative disorders such as Alzheimer's disease (AD) represent an increasingly significant public health concern. As clinical diagnosis faces challenges, biomarkers are becoming increasingly important in research, trials, and patient assessments. While biomarkers like amyloid-β peptide, tau proteins, CSF levels (Aβ, tau, and p-tau), and neuroimaging techniques are commonly used in AD diagnosis, they are often limited and invasive in monitoring and diagnosis. For this reason, blood-based biomarkers are the optimal choice for detecting neurodegeneration in brain diseases due to their noninvasiveness, affordability, reliability, and consistency. This literature review focuses on plasma neurofilament light (NfL) and CSF NfL as blood-based biomarkers used in recent AD diagnosis. The findings revealed that the core CSF biomarkers of neurodegeneration (T-tau, P-tau, and Aβ42), CSF NFL, and plasma T-tau were strongly associated with Alzheimer's disease, and the core biomarkers were strongly associated with mild cognitive impairment due to Alzheimer's disease. Elevated levels of plasma and cerebrospinal fluid NfL were linked to decreased [18F]FDG uptake in corresponding brain areas. In participants with Aβ positivity (Aβ+), NfL correlated with reduced metabolism in regions susceptible to Alzheimer's disease. In addition, CSF NfL levels correlate with brain atrophy and predict cognitive changes, while plasma total tau does not. Plasma P-tau, especially in combination with Aβ42/Aβ40, is promising for symptomatic AD stages. Though not AD-exclusive, blood NfL holds promise for neurodegeneration detection and assessing treatment efficacy. Given the consistent levels of T-tau, P-tau, Aβ42, and NFL in CSF, their incorporation into both clinical practice and research is highly recommended.

Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease

Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.

Ethical, legal, and social implications of genetic risk prediction for multifactorial disease: a narrative review identifying concerns about interpretation and use of polygenic scores

Advances in genomics have enabled the development of polygenic scores (PGS), sometimes called polygenic risk scores, in the context of multifactorial diseases and disorders such as cancer, cardiovascular disease, and schizophrenia. PGS estimate an individual's genetic predisposition, as compared to other members of a population, for conditions which are influenced by both genetic and environmental factors. There is significant interest in using genetic risk prediction afforded through PGS in public health, clinical care, and research settings, yet many acknowledge the need to thoughtfully consider and address ethical, legal, and social implications (ELSI). To contribute to this effort, this paper reports on a narrative review of the literature, with the aim of identifying and categorizing ELSI relating to genetic risk prediction in the context of multifactorial disease, which have been raised by scholars in the field. Ninety-two articles, spanning from 1977 to 2021, met the inclusion criteria for this study. Identified ELSI included potential benefits, challenges and risks that focused on concerns about interpretation and use, and ethical obligations to maximize benefits, minimize risks, promote justice, and support autonomy. This research will support geneticists, clinicians, genetic counselors, patients, patient advocates, and policymakers in recognizing and addressing ethical concerns associated with PGS; it will also guide future empirical and normative research.

Evaluation of the Polygenic Risk Score for Alzheimer's Disease in Russian Patients with Dementia Using a Low-Density Hydrogel Oligonucleotide Microarray

The polygenic risk score (PRS), together with the ɛ4 allele of the APOE gene (APOE-ɛ4), has shown high potential for Alzheimer's disease (AD) risk prediction. The aim of this study was to validate the model of polygenic risk in Russian patients with dementia. A microarray-based assay was developed to identify 21 markers of polygenic risk and ɛ alleles of the APOE gene. This case-control study included 348 dementia patients and 519 cognitively normal volunteers. Cerebrospinal fluid (CSF) amyloid-β (Aβ) and tau protein levels were assessed in 57 dementia patients. PRS and APOE-ɛ4 were significant genetic risk factors for dementia. Adjusted for APOE-ɛ4, individuals with PRS corresponding to the fourth quartile had an increased risk of dementia compared to the first quartile (OR 1.85; p-value 0.002). The area under the curve (AUC) was 0.559 for the PRS model only, and the inclusion of APOE-ɛ4 improved the AUC to 0.604. PRS was positively correlated with tTau and pTau181 and inversely correlated with Aβ42/Aβ40 ratio. Carriers of APOE-ɛ4 had higher levels of tTau and pTau181 and lower levels of Aβ42 and Aβ42/Aβ40. The developed assay can be part of a strategy for assessing individuals for AD risk, with the purpose of assisting primary preventive interventions.

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND

The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.

METHODS

We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model.

RESULTS

The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms.

CONCLUSION

Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.

Modern Approaches to the Diagnosis of Cognitive Impairment and Alzheimer's Disease: A Narrative Literature Review

BACKGROUND

The aging of the worlds population leads to an increase in the prevalence of age-related diseases, including cognitive impairment. At the stage of dementia, therapeutic interventions become usually ineffective. Therefore, researchers and clinical practitioners today are looking for methods that allow for early diagnosis of cognitive impairment, including techniques that are based on the use of biological markers.

AIM

The aim of this literature review is to delve into scientific papers that are centered on modern laboratory tests for Alzheimers disease, including tests for biological markers at the early stages of cognitive impairment.

METHODS

The authors have carried out a descriptive review of scientific papers published from 2015 to 2023. Studies that are included in the PubMed and Web of Science electronic databases were analyzed. A descriptive analysis was used to summarized the gleaned information.

RESULTS

Blood and cerebrospinal fluid (CSF) biomarkers, as well as the advantages and disadvantages of their use, are reviewed. The most promising neurotrophic, neuroinflammatory, and genetic markers, including polygenic risk models, are also discussed.

CONCLUSION

The use of biomarkers in clinical practice will contribute to the early diagnosis of cognitive impairment associated with Alzheimers disease. Genetic screening tests can improve the detection threshold of preclinical abnormalities in the absence of obvious symptoms of cognitive decline. The active use of biomarkers in clinical practice, in combination with genetic screening for the early diagnosis of cognitive impairment in Alzheimers disease, can improve the timeliness and effectiveness of medical interventions.

Testing a polygenic risk score for morphological microglial activation in Alzheimer's disease and aging

Neuroinflammation and the activation of microglial cells are among the earliest events in Alzheimer's disease (AD). However, direct observation of microglia in living people is not currently possible. Here, we indexed the heritable propensity for neuroinflammation with polygenic risk scores (PRS), using results from a recent genome-wide analysis of a validated post-mortem measure of morphological microglial activation. We sought to determine whether a PRS for microglial activation (PRS mic ) could augment the predictive performance of existing AD PRSs for late-life cognitive impairment. First, PRS mic were calculated and optimized in a calibration cohort (Alzheimer's Disease Neuroimaging Initiative (ADNI), n=450), with resampling. Second, predictive performance of optimal PRS mic was assessed in two independent, population-based cohorts (total n=212,237). Our PRS mic showed no significant improvement in predictive power for either AD diagnosis or cognitive performance. Finally, we explored associations of PRS mic with a comprehensive set of imaging and fluid AD biomarkers in ADNI. This revealed some nominal associations, but with inconsistent effect directions. While genetic scores capable of indexing risk for neuroinflammatory processes in aging are highly desirable, more well-powered genome-wide studies of microglial activation are required. Further, biobank-scale studies would benefit from phenotyping of proximal neuroinflammatory processes to improve the PRS development phase.

Regional transcriptional vulnerability to basal forebrain functional dysconnectivity in mild cognitive impairment patients

Nucleus basalis of Meynert (NbM), one of the earliest targets of Alzheimer's disease (AD), may act as a seed for pathological spreading to its connected regions. However, the underlying basis of regional vulnerability to NbM dysconnectivity remains unclear. NbM functional dysconnectivity was assessed using resting-state fMRI data of health controls and mild cognitive impairment (MCI) patients from the Alzheimer's disease Neuroimaging Initiative (ADNI2/GO phase). Transcriptional correlates of NbM dysconnectivity was explored by leveraging public intrinsic and differential post-mortem brain-wide gene expression datasets from Allen Human Brain Atlas (AHBA) and Mount Sinai Brain Bank (MSBB). By constructing an individual-level tissue-specific gene set risk score (TGRS), we evaluated the contribution of NbM dysconnectivity-correlated gene sets to change rate of cerebral spinal fluid (CSF) biomarkers during preclinical stage of AD, as well as to MCI onset age. An independent cohort of health controls and MCI patients from ADNI3 was used to validate our main findings. Between-group comparison revealed significant connectivity reduction between the right NbM and right middle temporal gyrus in MCI. This regional vulnerability to NbM dysconnectivity correlated with intrinsic expression of genes enriched in protein and immune functions, as well as with differential expression of genes enriched in cholinergic receptors, immune, vascular and energy metabolism functions. TGRS of these NbM dysconnectivity-correlated gene sets are associated with longitudinal amyloid-beta change at preclinical stages of AD, and contributed to MCI onset age independent of traditional AD risks. Our findings revealed the transcriptional vulnerability to NbM dysconnectivity and their crucial role in explaining preclinical amyloid-beta change and MCI onset age, which offer new insights into the early AD pathology and encourage more investigation and clinical trials targeting NbM.

Testing a Polygenic Risk Score for Morphological Microglial Activation in Alzheimer's Disease and Aging

BACKGROUND

Neuroinflammation and the activation of microglial cells are among the earliest events in Alzheimer's disease (AD). However, direct observation of microglia in living people is not currently possible. Here, we indexed the heritable propensity for neuroinflammation with polygenic risk scores (PRS), using results from a recent genome-wide analysis of a validated post-mortem measure of morphological microglial activation.

OBJECTIVE

We sought to determine whether a PRS for microglial activation (PRSmic) could augment the predictive performance of existing AD PRSs for late-life cognitive impairment.

METHODS

First, PRSmic were calculated and optimized in a calibration cohort (Alzheimer's Disease Neuroimaging Initiative (ADNI), n = 450), with resampling. Second, predictive performance of optimal PRSmic was assessed in two independent, population-based cohorts (total n = 212,237). Finally, we explored associations of PRSmic with a comprehensive set of imaging and fluid AD biomarkers in ADNI.

RESULTS

Our PRSmic showed no significant improvement in predictive power for either AD diagnosis or cognitive performance in either external cohort. Some nominal associations were found in ADNI, but with inconsistent effect directions.

CONCLUSION

While genetic scores capable of indexing risk for neuroinflammatory processes in aging are highly desirable, more well-powered genome-wide studies of microglial activation are required. Further, biobank-scale studies would benefit from phenotyping of proximal neuroinflammatory processes to improve the PRS development phase.

Chronic neuropsychiatric sequelae of SARS-CoV-2: Protocol and methods from the Alzheimer's Association Global Consortium

Introduction

Coronavirus disease 2019 (COVID-19) has caused >3.5 million deaths worldwide and affected >160 million people. At least twice as many have been infected but remained asymptomatic or minimally symptomatic. COVID-19 includes central nervous system manifestations mediated by inflammation and cerebrovascular, anoxic, and/or viral neurotoxicity mechanisms. More than one third of patients with COVID-19 develop neurologic problems during the acute phase of the illness, including loss of sense of smell or taste, seizures, and stroke. Damage or functional changes to the brain may result in chronic sequelae. The risk of incident cognitive and neuropsychiatric complications appears independent from the severity of the original pulmonary illness. It behooves the scientific and medical community to attempt to understand the molecular and/or systemic factors linking COVID-19 to neurologic illness, both short and long term.

Methods

This article describes what is known so far in terms of links among COVID-19, the brain, neurological symptoms, and Alzheimer's disease (AD) and related dementias. We focus on risk factors and possible molecular, inflammatory, and viral mechanisms underlying neurological injury. We also provide a comprehensive description of the Alzheimer's Association Consortium on Chronic Neuropsychiatric Sequelae of SARS-CoV-2 infection (CNS SC2) harmonized methodology to address these questions using a worldwide network of researchers and institutions.

Results

Successful harmonization of designs and methods was achieved through a consensus process initially fragmented by specific interest groups (epidemiology, clinical assessments, cognitive evaluation, biomarkers, and neuroimaging). Conclusions from subcommittees were presented to the whole group and discussed extensively. Presently data collection is ongoing at 19 sites in 12 countries representing Asia, Africa, the Americas, and Europe.

Discussion

The Alzheimer's Association Global Consortium harmonized methodology is proposed as a model to study long-term neurocognitive sequelae of SARS-CoV-2 infection.

Key Points

The following review describes what is known so far in terms of molecular and epidemiological links among COVID-19, the brain, neurological symptoms, and AD and related dementias (ADRD)The primary objective of this large-scale collaboration is to clarify the pathogenesis of ADRD and to advance our understanding of the impact of a neurotropic virus on the long-term risk of cognitive decline and other CNS sequelae. No available evidence supports the notion that cognitive impairment after SARS-CoV-2 infection is a form of dementia (ADRD or otherwise). The longitudinal methodologies espoused by the consortium are intended to provide data to answer this question as clearly as possible controlling for possible confounders. Our specific hypothesis is that SARS-CoV-2 triggers ADRD-like pathology following the extended olfactory cortical network (EOCN) in older individuals with specific genetic susceptibility.The proposed harmonization strategies and flexible study designs offer the possibility to include large samples of under-represented racial and ethnic groups, creating a rich set of harmonized cohorts for future studies of the pathophysiology, determinants, long-term consequences, and trends in cognitive aging, ADRD, and vascular disease.We provide a framework for current and future studies to be carried out within the Consortium. and offers a "green paper" to the research community with a very broad, global base of support, on tools suitable for low- and middle-income countries aimed to compare and combine future longitudinal data on the topic.The Consortium proposes a combination of design and statistical methods as a means of approaching causal inference of the COVID-19 neuropsychiatric sequelae. We expect that deep phenotyping of neuropsychiatric sequelae may provide a series of candidate syndromes with phenomenological and biological characterization that can be further explored. By generating high-quality harmonized data across sites we aim to capture both descriptive and, where possible, causal associations.

Alzheimer’s Disease Polygenic Risk Score Is Not Associated With Cognitive Decline Among Older Adults With Type 2 Diabetes

Objectives

Multiple risk loci for late-onset Alzheimer’s disease (LOAD) have been identified. Type 2 diabetes (T2D) is a risk factor for cognitive decline, dementia and Alzheimer’s disease (AD). We investigated the association of polygenic risk score (PRS) for LOAD with overall cognitive functioning and longitudinal decline, among older adults with T2D.

Methods

The study included 1046 Jewish participants from the Israel Diabetes and Cognitive Decline (IDCD) study, aged ≥ 65 years, diagnosed with T2D, and cognitively normal at baseline. The PRS included variants from 26 LOAD associated loci (at genome-wide significance level), and was calculated with and without APOE. Outcome measures, assessed in 18 months intervals, were global cognition and the specific domains of episodic memory, attention/working memory, executive functions, and language/semantic categorization. Random coefficient models were used for analysis, adjusting for demographic variables, T2D-related characteristics, and cardiovascular factors. Additionally, in a subsample of 202 individuals, we analyzed the association of PRS with the volumes of total gray matter, frontal lobe, hippocampus, amygdala, and white matter hyperintensities. Last, the association of PRS with amyloid beta (Aβ) burden was examined in 44 participants who underwent an 18F-flutemetamol PET scan.

Results

The PRS was not significantly associated with overall functioning or decline in global cognition or any of the specific cognitive domains. Similarly, following correction for multiple testing, there was no association with Aβ burden and other brain imaging phenotypes.

Conclusion

Our results suggest that the cumulative effect of LOAD susceptibility loci is not associated with a greater rate of cognitive decline in older adults with T2D, and other pathways may underlie this link.

Phenotype-informed polygenic risk scores are associated with worse outcome in individuals at risk of Alzheimer's disease

Introduction

Patients with predementia Alzheimer's disease (AD) and at-risk subjects are targets for promising disease-modifying treatments, and improved polygenic risk scores (PRSs) could improve early-stage case selection.

Methods

Phenotype-informed PRSs were developed by selecting AD-associated variants conditional on relevant inflammatory or cardiovascular traits. The primary outcome was longitudinal changes in measures of AD pathology, namely development of pathological amyloid deposition, medial temporal lobe atrophy, and cognitive decline in a prospective cohort study including 394 adults without AD dementia.

Results

High-risk groups defined by phenotype-informed AD PRSs had significantly steeper volume decline in medial temporal cortices, and the high-risk group defined by the cardiovascular-informed AD PRS had significantly increased hazard ratio of pathological amyloid deposition, compared to low-risk groups.

Discussion

AD PRSs informed by inflammatory disorders or cardiovascular risk factors and diseases are associated with development of AD pathology markers and may improve identification of subjects at risk for progression of AD.

Markers of kidney function, genetic variation related to cognitive function, and cognitive performance in the UK Biobank

BACKGROUND

Chronic kidney disease has been linked to worse cognition. However, this association may be dependent on the marker of kidney function used, and studies assessing modification by genetics are lacking. This study examined associations between multiple measures of kidney function and assessed effect modification by a polygenic score for general cognitive function.

METHODS

In this cross-sectional study of up to 341,208 European ancestry participants from the UK Biobank study, we examined associations between albuminuria and estimated glomerular filtration rate based on creatinine (eGFRcre) or cystatin C (eGFRcys) with cognitive performance on tests of verbal-numeric reasoning, reaction time and visual memory. Adjustment for confounding factors was performed using multivariate regression and propensity-score matching. Interaction between kidney function markers and a polygenic risk score for general cognitive function was also assessed.

RESULTS

Albuminuria was associated with worse performance on tasks of verbal-numeric reasoning (β(points) = -0.09, p < 0.001), reaction time (β(milliseconds) = 7.06, p < 0.001) and visual memory (β(log errors) = 0.013, p = 0.01). A polygenic score for cognitive function modified the association between albuminuria and verbal-numeric reasoning with significantly lower scores in those with albuminuria and a lower polygenic score (p = 0.009). Compared to participants with eGFRcre ≥ 60 ml/min, those with eGFRcre < 60 ml/min had lower verbal-numeric reasoning scores and slower mean reaction times (verbal numeric reasoning β = -0.11, p < 0.001 and reaction time β = 6.08, p < 0.001 for eGFRcre < 60 vs eGFRcre ≥ 60). Associations were stronger using cystatin C-based eGFR than creatinine-based eGFR (verbal numeric reasoning β = -0.21, p < 0.001 and reaction time β = 11.21, p < 0.001 for eGFRcys < 60 vs eGFRcys ≥ 60).

CONCLUSIONS

Increased urine albumin is associated with worse cognition, but this may depend on genetic risk. Cystatin C-based eGFR may better predict cognitive performance than creatinine-based estimates.

Blood biomarkers for Alzheimer's disease and related disorders

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the single commonest cause of dementia. Many other diseases can, however, cause dementia, and differential diagnosis can be challenging, especially in early disease stages. For most neurodegenerative dementias, accumulation of brain pathologies starts many years before clinical onset; the ability to detect these pathologies paves the way for targeted disease-modifying prevention trials. AD is associated with β-amyloid and tau pathologies, which can be quantified using cerebrospinal fluid and imaging biomarkers and, more recently, using highly sensitive blood tests. While for the most part, specific biomarkers of non-AD neurodegenerative dementias are lacking, non-specific biomarkers of neurodegeneration are available. This review summarizes recent advances in the neurodegenerative dementia blood biomarker research and discusses the next steps required for clinical implementation.

Stability of polygenic scores across discovery genome-wide association studies

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer´S Disease Biomarkers – Update

Background:

Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.

Objective:

To update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.

Methods:

Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (L) of samples for analysis.

Results:

Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-b or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.

Conclusion:

The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.

Polygenic Risk Scores in Alzheimer's Disease Genetics: Methodology, Applications, Inclusion, and Diversity

The success of genome-wide association studies (GWAS) completed in the last 15 years has reinforced a key fact: polygenic architecture makes a substantial contribution to variation of susceptibility to complex disease, including Alzheimer's disease. One straight-forward way to capture this architecture and predict which individuals in a population are most at risk is to calculate a polygenic risk score (PRS). This score aggregates the risk conferred across multiple genetic variants, ultimately representing an individual's predicted genetic susceptibility for a disease. PRS have received increasing attention after having been successfully used in complex traits. This has brought with it renewed attention on new methods which improve the accuracy of risk prediction. While these applications are initially informative, their utility is far from equitable: the majority of PRS models use samples heavily if not entirely of individuals of European descent. This basic approach opens concerns of health equity if applied inaccurately to other population groups, or health disparity if we fail to use them at all. In this review we will examine the methods of calculating PRS and some of their previous uses in disease prediction. We also advocate for, with supporting scientific evidence, inclusion of data from diverse populations in these existing and future studies of population risk via PRS.

Resting-state brain connectivity in healthy young and middle-aged adults at risk of progressive Alzheimer's disease

Functional brain connectivity of the resting-state networks has gained recent attention as a possible biomarker of Alzheimer's Disease (AD). In this paper, we review the literature of functional connectivity differences in young adults and middle-aged cognitively intact individuals with non-modifiable risk factors of AD (n = 17). We focus on three main intrinsic resting-state networks: The Default Mode network, Executive network, and the Salience network. Overall, the evidence from the literature indicated early vulnerability of functional connectivity across different at-risk groups, particularly in the Default Mode Network. While there was little consensus on the interpretation on directionality, the topography of the findings showed frequent overlap across studies, especially in regions that are characteristic of AD (i.e., precuneus, posterior cingulate cortex, and medial prefrontal cortex areas). We conclude that while resting-state functional connectivity markers have great potential to identify at-risk individuals, implementing more data-driven approaches, further longitudinal and cross-validation studies, and the analysis of greater sample sizes are likely to be necessary to fully establish the effectivity and utility of resting-state network-based analyses.