Association of Alzheimer’s disease polygenic risk scores with amyloid accumulation in cognitively intact older adults

Alzheimer's disease genetic pathways impact cerebrospinal fluid biomarkers and imaging endophenotypes in non-demented individuals

INTRODUCTION

Unraveling how Alzheimer's disease (AD) genetic risk is related to neuropathological heterogeneity, and whether this occurs through specific biological pathways, is a key step toward precision medicine.

METHODS

We computed pathway-specific genetic risk scores (GRSs) in non-demented individuals and investigated how AD risk variants predict cerebrospinal fluid (CSF) and imaging biomarkers reflecting AD pathology, cardiovascular, white matter integrity, and brain connectivity.

RESULTS

CSF amyloidbeta and phosphorylated tau were related to most GRSs. Inflammatory pathways were associated with cerebrovascular disease, whereas quantitative measures of white matter lesion and microstructure integrity were predicted by clearance and migration pathways. Functional connectivity alterations were related to genetic variants involved in signal transduction and synaptic communication.

DISCUSSION

This study reveals distinct genetic risk profiles in association with specific pathophysiological aspects in predementia stages of AD, unraveling the biological substrates of the heterogeneity of AD-associated endophenotypes and promoting a step forward in disease understanding and development of personalized therapies.

HIGHLIGHTS

Polygenic risk for Alzheimer's disease encompasses six biological pathways that can be quantified with pathway-specific genetic risk scores, and differentially relate to cerebrospinal fluid and imaging biomarkers. Inflammatory pathways are mostly related to cerebrovascular burden. White matter health is associated with pathways of clearance and membrane integrity, whereas functional connectivity measures are related to signal transduction and synaptic communication pathways.

Plasma pTau181 and pTau217 predict asymptomatic amyloid accumulation equally well as amyloid PET

The dynamic phase of preclinical Alzheimer's disease, as characterized by accumulating cortical amyloid-β, is a window of opportunity for amyloid-β-lowering therapies to have greater efficacy. Biomarkers that accurately predict amyloid-β accumulation may be of critical importance for participant inclusion in secondary prevention trials and thus enhance development of early Alzheimer's disease therapies. We compared the abilities of baseline plasma pTau181, pTau217 and amyloid-β PET load to predict future amyloid-β accumulation in asymptomatic elderly. In this longitudinal cohort study, baseline plasma pTau181 and pTau217 were quantified using single molecule array assays in cognitively unimpaired elderly selected from the community-recruited F-PACK cohort based on the availability of baseline plasma samples and longitudinal amyloid-β PET data (median time interval = 5 years, range 2-10 years). The predictive abilities of pTau181, pTau217 and PET-based amyloid-β measures for PET-based amyloid-β accumulation were investigated using receiver operating characteristic analyses, correlations and stepwise regression analyses. We included 75 F-PACK subjects (mean age = 70 years, 48% female), of which 16 were classified as amyloid-β accumulators [median (interquartile range) Centiloid rate of change = 3.42 (1.60) Centiloids/year). Plasma pTau181 [area under the curve (95% confidence interval) = 0.72 (0.59-0.86)] distinguished amyloid-β accumulators from non-accumulators with similar accuracy as pTau217 [area under the curve (95% confidence interval) = 0.75 (0.62-0.88) and amyloid-β PET [area under the curve (95% confidence interval) = 0.72 (0.56-0.87)]. Plasma pTau181 and pTau217 strongly correlated with each other (r = 0.93, Pfalse discovery rate < 0.001) and, together with amyloid-β PET, similarly correlated with amyloid-β rate of change (r pTau181 = 0.33, r pTau217 = 0.36, r amyloid-β PET = 0.35, all Pfalse discovery rate ≤ 0.01). Addition of plasma pTau181, plasma pTau217 or amyloid-β PET to a linear demographic model including age, sex and APOE-ε4 carriership similarly improved the prediction of amyloid-β accumulation (ΔAkaike information criterion ≤ 4.1). In a multimodal biomarker model including all three biomarkers, each biomarker lost their individual predictive ability. These findings indicate that plasma pTau181, plasma pTau217 and amyloid-β PET convey overlapping information and therefore predict the dynamic phase of asymptomatic amyloid-β accumulation with comparable performances. In clinical trial recruitment, confirmatory PET scans following blood-based prescreening might thus not provide additional value for detecting participants in these early disease stages who are destined to accumulate cortical amyloid-β. Given the moderate performances, future studies should investigate whether integrating plasma pTau species with other factors can improve performance and thus enhance clinical and research utility.

Polygenic burden and its association with baseline cognitive function and postoperative cognitive outcome in temporal lobe epilepsy

OBJECTIVE

Demographic and disease factors are associated with cognitive deficits and postoperative cognitive declines in adults with pharmacoresistant temporal lobe epilepsy (TLE), but the role of genetic factors in cognition in TLE is not well understood. Polygenic scores (PGS) for neurological and neuropsychiatric disorders and IQ have been associated with cognition in patient and healthy populations. In this exploratory study, we examined the relationship between PGS for Alzheimer's disease (AD), depression, and IQ and cognitive outcomes in adults with TLE.

METHODS

202 adults with pharmacoresistant TLE had genotyping and completed neuropsychological evaluations as part of a presurgical work-up. A subset (n = 116) underwent temporal lobe resection and returned for postoperative cognitive testing. Logistic regression was used to determine if PGS for AD, depression, and IQ predicted baseline domain-specific cognitive function and cognitive phenotypes as well as postoperative language and memory decline.

RESULTS

No significant findings survived correction for multiple comparisons. Prior to correction, higher PGS for AD and depression (i.e., increased genetic risk for the disorder), but lower PGS for IQ (i.e., decreased genetic likelihood of high IQ) appeared possibly associated with baseline cognitive impairment in TLE. In comparison, higher PGS for AD and IQ appeared as possible risk factors for cognitive decline following temporal lobectomy, while the possible relationship between PGS for depression and post-operative cognitive outcome was mixed.

SIGNIFICANCE

We did not observe any relationships of large effect between PGS and cognitive function or postsurgical outcome; however, results highlight several promising trends in the data that warrant future investigation in larger samples better powered to detect small genetic effects.

Longitudinal associations of serum biomarkers with early cognitive, amyloid and grey matter changes

Blood-based biomarkers have been extensively evaluated for their diagnostic potential in Alzheimer's disease. However, their relative prognostic and monitoring capabilities for cognitive decline, amyloid-β (Aβ) accumulation and grey matter loss in cognitively unimpaired elderly require further investigation over extended time periods. This prospective cohort study in cognitively unimpaired elderly [n = 185, mean age (range) = 69 (53-84) years, 48% female] examined the prognostic and monitoring capabilities of glial fibrillary acidic protein (GFAP), neurofilament light (NfL), Aβ1-42/Aβ1-40 and phosphorylated tau (pTau)181 through their quantification in serum. All participants underwent baseline Aβ-PET, MRI and blood sampling as well as 2-yearly cognitive testing. A subset additionally underwent Aβ-PET (n = 109), MRI (n = 106) and blood sampling (n = 110) during follow-up [median time interval (range) = 6.1 (1.3-11.0) years]. Matching plasma measurements were available for Aβ1-42/Aβ1-40 and pTau181 (both n = 140). Linear mixed-effects models showed that high serum GFAP and NfL predicted future cognitive decline in memory (βGFAP×Time = -0.021, PFDR = 0.007 and βNfL×Time = -0.031, PFDR = 0.002) and language (βGFAP×Time = -0.021, PFDR = 0.002 and βNfL×Time = -0.018, PFDR = 0.03) domains. Low serum Aβ1-42/Aβ1-40 equally but independently predicted memory decline (βAβ1-42/Aβ1-40×Time = -0.024, PFDR = 0.02). Whole-brain voxelwise analyses revealed that low Aβ1-42/Aβ1-40 predicted Aβ accumulation within the precuneus and frontal regions, high GFAP and NfL predicted grey matter loss within hippocampal regions and low Aβ1-42/Aβ1-40 predicted grey matter loss in lateral temporal regions. Serum GFAP, NfL and pTau181 increased over time, while Aβ1-42/Aβ1-40 decreased only in Aβ-PET-negative elderly. NfL increases associated with declining memory (βNfLchange×Time = -0.030, PFDR = 0.006) and language (βNfLchange×Time = -0.021, PFDR = 0.02) function and serum Aβ1-42/Aβ1-40 decreases associated with declining language function (βAβ1-42/Aβ1-40×Time = -0.020, PFDR = 0.04). GFAP increases associated with Aβ accumulation within the precuneus and NfL increases associated with grey matter loss. Baseline and longitudinal serum pTau181 only associated with Aβ accumulation in restricted occipital regions. In head-to-head comparisons, serum outperformed plasma Aβ1-42/Aβ1-40 (ΔAUC = 0.10, PDeLong, FDR = 0.04), while both plasma and serum pTau181 demonstrated poor performance to detect asymptomatic Aβ-PET positivity (AUC = 0.55 and 0.63, respectively). However, when measured with a more phospho-specific assay, plasma pTau181 detected Aβ-positivity with high performance (AUC = 0.82, PDeLong, FDR < 0.007). In conclusion, serum GFAP, NfL and Aβ1-42/Aβ1-40 are valuable prognostic and/or monitoring tools in asymptomatic stages providing complementary information in a time- and pathology-dependent manner.

Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes

Background and Objectives

Alzheimer disease (AD) has a polygenic architecture, for which genome-wide association studies (GWAS) have helped elucidate sequence variants (SVs) influencing susceptibility. Polygenic risk score (PRS) approaches show promise for generating summary measures of inherited risk for clinical AD based on the effects of APOE and other GWAS hits. However, existing PRS approaches, based on traditional regression models, explain only modest variation in AD dementia risk and AD-related endophenotypes. We hypothesized that machine learning (ML) models of polygenic risk (ML-PRS) could outperform standard regression-based PRS methods and therefore have the potential for greater clinical utility.

Methods

We analyzed combined data from the Mayo Clinic Study of Aging (n = 1,791) and the Alzheimer's Disease Neuroimaging Initiative (n = 864). An AD PRS was computed for each participant using the top common SVs obtained from a large AD dementia GWAS. In parallel, ML models were trained using those SV genotypes, with amyloid PET burden as the primary outcome. Secondary outcomes included amyloid PET positivity and clinical diagnosis (cognitively unimpaired vs impaired). We compared performance between ML-PRS and standard PRS across 100 training sessions with different data splits. In each session, data were split into 80% training and 20% testing, and then five-fold cross-validation was used within the training set to ensure the best model was produced for testing. We also applied permutation importance techniques to assess which genetic factors contributed most to outcome prediction.

Results

ML-PRS models outperformed the AD PRS (r2 = 0.28 vs r2 = 0.24 in test set) in explaining variation in amyloid PET burden. Among ML approaches, methods accounting for nonlinear genetic influences were superior to linear methods. ML-PRS models were also more accurate when predicting amyloid PET positivity (area under the curve [AUC] = 0.80 vs AUC = 0.63) and the presence of cognitive impairment (AUC = 0.75 vs AUC = 0.54) compared with the standard PRS.

Discussion

We found that ML-PRS approaches improved upon standard PRS for prediction of AD endophenotypes, partly related to improved accounting for nonlinear effects of genetic susceptibility alleles. Further adaptations of the ML-PRS framework could help to close the gap of remaining unexplained heritability for AD and therefore facilitate more accurate presymptomatic and early-stage risk stratification for clinical decision-making.

Alzheimer's disease, aging, and cannabidiol treatment: a promising path to promote brain health and delay aging

Alzheimer's disease (AD) is the most common neurodegenerative disease characterized by progressive memory loss, neurodegeneration, and cognitive decline. Aging is one of the risk factors for AD. Although the mechanisms underlying aging and the incidence rate of AD are unclear, aging and AD share some hallmarks, such as oxidative stress and chronic inflammation. Cannabidiol (CBD), the major non-psychoactive phytocannabinoid extracted from Cannabis sativa, has recently emerged as a potential candidate for delaying aging and a valuable therapeutic tool for the treatment of aging-related neurodegenerative diseases due to its antioxidant and anti-inflammation properties. This article reviews the relevant literature on AD, CBD treatment for AD, cellular senescence, aging, and CBD treatment for aging in recent years. By analyzing these published data, we attempt to explore the complex correlation between cellular senescence, aging, and Alzheimer's disease, clarify the positive feedback effect between the senescence of neurocytes and Alzheimer's disease, and summarize the role and possible molecular mechanisms of CBD in preventing aging and treating AD. These data may provide new ideas on how to effectively prevent and delay aging, and develop effective treatment strategies for age-related diseases such as Alzheimer's disease.

Longitudinal APOE4- and amyloid-dependent changes in the blood transcriptome in cognitively intact older adults

BACKGROUND

Gene expression is dysregulated in Alzheimer's disease (AD) patients, both in peripheral blood and post mortem brain. We investigated peripheral whole-blood gene (co)expression to determine molecular changes prior to symptom onset.

METHODS

RNA was extracted and sequenced for 65 cognitively healthy F-PACK participants (65 (56-80) years, 34 APOE4 non-carriers, 31 APOE4 carriers), at baseline and follow-up (interval: 5.0 (3.4-8.6) years). Participants received amyloid PET at both time points and amyloid rate of change derived. Accumulators were defined with rate of change ≥ 2.19 Centiloids. We performed differential gene expression and weighted gene co-expression network analysis to identify differentially expressed genes and networks of co-expressed genes, respectively, with respect to traits of interest (APOE4 status, amyloid accumulation (binary/continuous)), and amyloid positivity status, followed by Gene Ontology annotation.

RESULTS

There were 166 significant differentially expressed genes at follow-up compared to baseline in APOE4 carriers only, whereas 12 significant differentially expressed genes were found only in APOE4 non-carriers, over time. Among the significant genes in APOE4 carriers, several had strong evidence for a pathogenic role in AD based on direct association scores generated from the DISQOVER platform: NGRN, IGF2, GMPR, CLDN5, SMIM24. Top enrichment terms showed upregulated mitochondrial and metabolic pathways, and an exacerbated upregulation of ribosomal pathways in APOE4 carriers compared to non-carriers. Similarly, there were 33 unique significant differentially expressed genes at follow-up compared to baseline in individuals classified as amyloid negative at baseline and positive at follow-up or amyloid positive at both time points and 32 unique significant differentially expressed genes over time in individuals amyloid negative at both time points. Among the significant genes in the first group, the top five with the highest direct association scores were as follows: RPL17-C18orf32, HSP90AA1, MBP, SIRPB1, and GRINA. Top enrichment terms included upregulated metabolism and focal adhesion pathways. Baseline and follow-up gene co-expression networks were separately built. Seventeen baseline co-expression modules were derived, with one significantly negatively associated with amyloid accumulator status (r² =  - 0.25, p = 0.046). This was enriched for proteasomal protein catabolic process and myeloid cell development. Thirty-two follow-up modules were derived, with two significantly associated with APOE4 status: one downregulated (r² =  - 0.27, p = 0.035) and one upregulated (r² = 0.26, p = 0.039) module. Top enrichment processes for the downregulated module included proteasomal protein catabolic process and myeloid cell homeostasis. Top enrichment processes for the upregulated module included cytoplasmic translation and rRNA processing.

CONCLUSIONS

We show that there are longitudinal gene expression changes that implicate a disrupted immune system, protein removal, and metabolism in cognitively intact individuals who carry APOE4 or who accumulate in cortical amyloid. This provides insight into the pathophysiology of AD, whilst providing novel targets for drug and therapeutic development.

Contributions of rare and common variation to early-onset and atypical dementia risk

We collected and analyzed genomic sequencing data from individuals with clinician- diagnosed early-onset or atypical dementia. Thirty-two patients were previously described, with sixty-eight newly described in this report. Of those sixty-eight, sixty-two patients reported Caucasian, non-Hispanic ethnicity and six reported as African American, non-Hispanic. Fifty-three percent of patients had a returnable variant. Five patients harbored a pathogenic variant as defined by the American College of Medical Genetics criteria for pathogenicity. A polygenic risk score was calculated for Alzheimer's patients in the total cohort and compared to the scores of a late-onset Alzheimer's cohort and a control set. Patients with early-onset Alzheimer's had higher non- APOE polygenic risk scores than patients with late onset Alzheimer's, supporting the conclusion that both rare and common genetic variation associate with early-onset neurodegenerative disease risk.