Uncovering the distinct macro-scale anatomy of dysexecutive and behavioural degenerative diseases.

There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer's disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer's disease pathology (behavioural Alzheimer's disease) and 28 had amnestic Alzheimer's disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as 'eigenbrains'. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer's disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer's disease, then amnestic Alzheimer's disease. Dysexecutive Alzheimer's disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer's disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer's disease, temporo-parietal areas in amnestic Alzheimer's disease and frontotemporal areas in bvFTD and behavioural Alzheimer's disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer's disease patient for behavioural Alzheimer's disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Role of GBA variants in Lewy body disease neuropathology.

Rare and common GBA variants are risk factors for both Parkinson's disease (PD) and dementia with Lewy bodies (DLB). However, the degree to which GBA variants are associated with neuropathological features in Lewy body disease (LBD) is unknown. Herein, we assessed 943 LBD cases and examined associations of 15 different neuropathological outcomes with common and rare GBA variants. Neuropathological outcomes included LBD subtype, presence of a high likelihood of clinical DLB (per consensus guidelines), LB counts in five cortical regions, tyrosine hydroxylase immunoreactivity in the dorsolateral and ventromedial putamen, ventrolateral substantia nigra neuronal loss, Braak neurofibrillary tangle (NFT) stage, Thal amyloid phase, phospho-ubiquitin (pS65-Ub) level, TDP-43 pathology, and vascular disease. Sequencing of GBA exons revealed a total of 42 different variants (4 common [MAF > 0.5%], 38 rare [MAF < 0.5%]) in our series, and 165 cases (17.5%) had a copy of the minor allele for ≥ 1 variant. In analysis of common variants, p.L483P was associated with a lower Braak NFT stage (OR = 0.10, P < 0.001). In gene-burden analysis, presence of the minor allele for any GBA variant was associated with increased odds of a high likelihood of DLB (OR = 2.00, P < 0.001), a lower Braak NFT stage (OR = 0.48, P < 0.001), a lower Thal amyloid phase (OR = 0.55, P < 0.001), and a lower pS65-Ub level (ß: -0.37, P < 0.001). Subgroup analysis revealed that GBA variants were most common in LBD cases with a combination of transitional/diffuse LBD and Braak NFT stage 0-II or Thal amyloid phase 0-1, and correspondingly that the aforementioned associations of GBA gene-burden with a decreased Braak NFT stage and Thal amyloid phase were observed only in transitional or diffuse LBD cases. Our results indicate that in LBD, GBA variants occur most frequently in cases with greater LB pathology and low AD pathology, further informing disease-risk associations of GBA in PD, PD dementia, and DLB.

Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis.

Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment and treatment monitoring in Alzheimer's disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer's disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analysing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, amyloid-ß42/40, glial fibrillary acidic protein or neurofilament light chain. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% versus 68%; P = 0.001). A machine learning approach incorporating plasma biomarkers, demographics and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set) and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity, which could non-invasively enhance the interpretation of blood-based AD biomarker profiles in the population.

Predicting amyloid PET and tau PET stages with plasma biomarkers.

Staging the severity of Alzheimer's disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer's Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer's clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1-2, 3-4, 5-6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aß1-42 and Aß1-40 (analysed as the Aß42/Aß40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78-0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72-0.85 versus C = 0.73-0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer's disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.

Comparison of plasma biomarkers and amyloid PET for predicting memory decline in cognitively unimpaired individuals.

BACKGROUND: We compared the ability of several plasma biomarkers versus amyloid positron emission tomography (PET) to predict rates of memory decline among cognitively unimpaired individuals. METHODS: We studied 645 Mayo Clinic Study of Aging participants. Predictor variables were age, sex, education, apolipoprotein E (APOE) ε4 genotype, amyloid PET, and plasma amyloid beta (Aß)42/40, phosphorylated tau (p-tau)181, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and p-tau217. The outcome was a change in a memory composite measure. RESULTS: All plasma biomarkers, except NfL, were associated with mean memory decline in models with individual biomarkers. However, amyloid PET and plasma p-tau217, along with age, were key variables independently associated with mean memory decline in models combining all predictors. Confidence intervals were narrow for estimates of population mean prediction, but person-level prediction intervals were wide. DISCUSSION: Plasma p-tau217 and amyloid PET provide useful information about predicting rates of future cognitive decline in cognitively unimpaired individuals at the population mean level, but not at the individual person level.

Plasma biomarkers of Alzheimer's disease in the continuum of dementia with Lewy bodies.

INTRODUCTION: Patients with dementia with Lewy bodies (DLB) may have Alzheimers disease (AD) pathology that can be detected by plasma biomarkers. Our objective was to evaluate plasma biomarkers of AD and their association with positron emission tomography (PET) biomarkers of amyloid and tau deposition in the continuum of DLB, starting from prodromal stages of the disease. METHODS: The cohort included patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD), mild cognitive impairment with Lewy bodies (MCI-LB), or DLB, with a concurrent blood draw and PET scans. RESULTS: Abnormal levels of plasma glial fibrillary acidic protein (GFAP) were found at the prodromal stage of MCI-LB in association with increased amyloid PET. Abnormal levels of plasma phosphorylated tau (p-tau)-181 and neurofilament light (NfL) were found at the DLB stage. Plasma p-tau-181 showed the highest accuracy in detecting abnormal amyloid and tau PET in patients with DLB. DISCUSSION: The range of AD co-pathology can be detected with plasma biomarkers in the DLB continuum, particularly with plasma p-tau-181 and GFAP.

Optimum Differentiation of Frontotemporal Lobar Degeneration from Alzheimer Disease Achieved with Cross-Sectional Tau Positron Emission Tomography.

OBJECTIVE: This study was undertaken to assess cross-sectional and longitudinal [18 F]-flortaucipir positron emission tomography (PET) uptake in pathologically confirmed frontotemporal lobar degeneration (FTLD) and to compare FTLD to cases with high and low levels of Alzheimer disease (AD) neuropathologic changes (ADNC). METHODS: One hundred forty-three participants who had completed at least one flortaucipir PET and had autopsy-confirmed FTLD (n = 52) or high (n = 58) or low ADNC (n = 33) based on Braak neurofibrillary tangle stages 0-IV versus V-VI were included. Flortaucipir standard uptake value ratios (SUVRs) were calculated for 9 regions of interest (ROIs): an FTLD meta-ROI, midbrain, globus pallidum, an AD meta-ROI, entorhinal, inferior temporal, orbitofrontal, precentral, and medial parietal. Linear mixed effects models were used to compare mean baseline SUVRs and annual rate of change in SUVR by group. Sensitivity and specificity to distinguish FTLD from high and low ADNC were calculated. RESULTS: Baseline uptake in the FTLD meta-ROI, midbrain, and globus pallidus was greater in FTLD than high and low ADNC. No region showed a greater rate of flortaucipir accumulation in FTLD. Baseline uptake in the AD-related regions and orbitofrontal and precentral cortices was greater in high ADNC, and all showed greater rates of accumulation compared to FTLD. Baseline differences were superior to longitudinal rates in differentiating FTLD from high and low ADNC. A simple baseline metric of midbrain/inferior temporal ratio of flortaucipir uptake provided good to excellent differentiation between FTLD and high and low ADNC (sensitivities/specificities = 94%/95% and 71%/70%). INTERPRETATION: There are cross-sectional and longitudinal differences in flortaucipir uptake between FTLD and high and low ADNC. However, optimum differentiation between FTLD and ADNC was achieved with baseline uptake rather than longitudinal rates. ANN NEUROL 2022;92:1016-1029.

Risk Factors for Dementia in Patients With Incident Rheumatoid Arthritis: A Population-Based Cohort Study.

OBJECTIVE: Growing evidence suggests that patients with rheumatoid arthritis (RA) have increased risk for dementia. We assessed risk factors for incident dementia in an inception cohort of patients with RA. METHODS: This retrospective population-based cohort study included residents of 8 counties in Minnesota who were ≥ 50 years of age when they met 1987 American College of Rheumatology criteria for incident RA between 1980 and 2014 and were followed until death/migration or December 31, 2019. Patients with dementia before RA incidence were excluded. Incident dementia was defined as 2 relevant International Classification of Diseases, 9th or 10th revision codes at least 30 days apart. Data on sociodemographics, disease characteristics, cardiovascular/cerebrovascular disease (CVD) risk factors, and comorbidities were abstracted from medical records. RESULTS: The study included 886 patients with RA (mean age 65.1 yrs, 65.2% female). During the follow-up period (median 8.5 yrs), 103 patients developed dementia. After adjusting for age, sex, and calendar year of RA incidence, older age at RA incidence (HR 1.14 per 1 year increase, 95% CI 1.12-1.17), rheumatoid nodules (HR 1.76, 95% CI 1.05-2.95), hypertension (HR 1.84, 95% CI 1.19-2.85), presence of large joint swelling (HR 2.03, 95% CI 1.14-3.60), any CVD (HR 2.25, 95% CI 1.38-3.66), particularly ischemic stroke (HR 3.16, 95% CI 1.84-5.43) and heart failure (HR 1.82, 95% CI 1.10-3.00), anxiety (HR 1.86, 95% CI 1.16-2.97), and depression (HR 2.63, 95% CI 1.76-3.93) were associated with increased risk of dementia. After adjusting for CVD risk factors and any CVD, all covariates listed above were still significantly associated with risk of dementia. CONCLUSION: Apart from age, hypertension, depression, and anxiety, all of which are universally recognized risk factors for dementia, clinically active RA and presence of CVD were associated with an elevated risk of dementia incidence among patients with RA.

Tau polygenic risk scoring: a cost-effective aid for prognostic counseling in Alzheimer's disease.

Tau deposition is one of two hallmark features of biologically defined Alzheimer's disease (AD) and is more closely related to cognitive decline than amyloidosis. Further, not all amyloid-positive individuals develop tauopathy, resulting in wide heterogeneity in clinical outcomes across the population with AD. We hypothesized that a polygenic risk score (PRS) based on tau PET (tau PRS) would capture the aggregate inherited susceptibility/resistance architecture influencing tau accumulation, beyond solely the measurement of amyloid-ß burden. Leveraging rich multimodal data from a population-based sample of older adults, we found that this novel tau PRS was a strong surrogate of tau PET deposition and captured a significant proportion of the variance in tau PET levels as compared with amyloid PET burden, APOE (apolipoprotein E) ε4 (the most common risk allele for AD), and a non-APOE PRS of clinical case-control AD risk variants. In independent validation samples, the tau PRS was associated with cerebrospinal fluid phosphorylated tau levels in one cohort and with postmortem Braak neurofibrillary tangle stage in another. We also observed an association of the tau PRS with longitudinal cognitive trajectories, including a statistical interaction of the tau PRS with amyloid burden on cognitive decline. Although additional study is warranted, these findings demonstrate the potential utility of a tau PRS for capturing the collective genetic background influencing tau deposition in the general population. In the future, a tau PRS could be leveraged for cost-effective screening and risk stratification to guide trial enrollment and clinical interventions in AD.

Protective variant for hippocampal atrophy identified by whole exome sequencing.

We used whole-exome sequencing to identify variants other than APOE associated with the rate of hippocampal atrophy in amnestic mild cognitive impairment. An in-silico predicted missense variant in REST (rs3796529) was found exclusively in subjects with slow hippocampal volume loss and validated using unbiased whole-brain analysis and meta-analysis across 5 independent cohorts. REST is a master regulator of neurogenesis and neuronal differentiation that has not been previously implicated in Alzheimer's disease. These findings nominate REST and its functional pathways as protective and illustrate the potential of combining next-generation sequencing with neuroimaging to discover novel disease mechanisms and potential therapeutic targets.

GWAS of longitudinal amyloid accumulation on 18F-florbetapir PET in Alzheimer's disease implicates microglial activation gene IL1RAP.

Brain amyloid deposition is thought to be a seminal event in Alzheimer's disease. To identify genes influencing Alzheimer's disease pathogenesis, we performed a genome-wide association study of longitudinal change in brain amyloid burden measured by (18)F-florbetapir PET. A novel association with higher rates of amyloid accumulation independent from APOE (apolipoprotein E) ε4 status was identified in IL1RAP (interleukin-1 receptor accessory protein; rs12053868-G; P = 1.38 × 10(-9)) and was validated by deep sequencing. IL1RAP rs12053868-G carriers were more likely to progress from mild cognitive impairment to Alzheimer's disease and exhibited greater longitudinal temporal cortex atrophy on MRI. In independent cohorts rs12053868-G was associated with accelerated cognitive decline and lower cortical (11)C-PBR28 PET signal, a marker of microglial activation. These results suggest a crucial role of activated microglia in limiting amyloid accumulation and nominate the IL-1/IL1RAP pathway as a potential target for modulating this process.

Pathway analysis of genomic data: concepts, methods, and prospects for future development.

Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources to harness the extensive, rich information related to disease and treatment mechanisms.

Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans.

INTRODUCTION: Genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) have been crucial in advancing the understanding of Alzheimer's disease (AD) pathophysiology. Here, we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. METHODS: Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing data have been obtained and disseminated. RESULTS: ADNI genetic data have been downloaded thousands of times, and >300 publications have resulted, including reports of large-scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies used ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first whole exome and whole genome sequencing data sets and reports in healthy controls, mild cognitive impairment, and AD. DISCUSSION: Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multiomics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.

Genetics of Parkinson's disease heterogeneity: A genome-wide association study of clinical subtypes.

INTRODUCTION: Substantial heterogeneity between individual patients in the clinical presentation of Parkinson's disease (PD) has led to the classification of distinct PD subtypes. However, genetic susceptibility factors for specific PD subtypes are not well understood. Therefore, the present study aimed to investigate the genetics of PD heterogeneity by performing a genome-wide association study (GWAS) of PD subtypes. METHODS: A total of 799 PD patients were included and classified into tremor-dominant (TD) (N = 345), akinetic-rigid (AR) (N = 227), gait-difficulty (GD) (N = 82), and mixed (MX) (N = 145) phenotypic subtypes. After array genotyping and subsequent imputation, a total of 7,918,344 variants were assessed for association with each PD subtype using logistic regression models that were adjusted for age, sex, and the top five principal components of GWAS data. RESULTS: We identified one genome-wide significant association (P < 5 × 10-8), which was between the MIR3976HG rs7504760 variant and the AR subtype (Odds ratio [OR] = 6.12, P = 2.57 × 10-8). Suggestive associations (P < 1 × 10-6) were observed regarding TD for RP11-497G19.3/RP11-497G19.1 rs7304254 (OR = 3.33, P = 3.89 × 10-7), regarding GD for HES2 rs111473931 (OR = 3.18, P = 6.85 × 10-7), RP11-400D2.3/CTD-2012I17.1 rs149082205 (OR = 8.96, P = 9.08 × 10-7), and RN7SL408P/SGK1 rs56161738 (OR = 2.97, P = 6.19 × 10-7), and regarding MX for MMRN2 rs112991171 (OR = 4.98, P = 1.02 × 10-7). CONCLUSION: Our findings indicate that genetic variation may account for part of the clinical heterogeneity of PD. In particular, we found a novel genome-wide significant association between MIR3976HG variation and the AR PD subtype. Replication of these findings will be important in order to better define the genetic architecture of clinical variability in PD disease presentation.

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 Disease Cerebrospinal Fluid Biomarkers in a Tertiary Neurology Practice.

OBJECTIVE: To evaluate the performance of Alzheimer disease (AD) cerebrospinal fluid (CSF) biomarkers in a tertiary neurology clinic setting with high frequency of non-AD cases, including normal pressure hydrocephalus (NPH). METHODS: There were 534 patients who underwent AD CSF biomarkers (Roche Elecsys Aß42, p-Tau181, total-Tau) from April 1, 2020, through April 23, 2021. A behavioral neurologist blinded to CSF results assigned a clinical diagnosis retrospectively on the basis of consensus criteria, and a neuroradiologist blinded to the diagnosis and CSF studies graded brain magnetic resonance images for indicators of CSF dynamics disorders. Associations between biomarkers, diagnoses, and imaging were assessed by χ2, analysis of covariance, and linear regression methods. RESULTS: Median age at time of testing was 67 years (range, 19 to 96 years), median symptom duration was 2 years (range, 0.4 to 28 years), and median Short Test of Mental Status score was 30 (range, 0 to 38). Clinical diagnoses significantly correlated with different CSF biomarker values (χ2=208.3; P=10e-4). p-Tau181/Aß42 ratios above 0.023 positively correlated with Alzheimer dementia (more than individual measures). This ratio also had the best performance for differentiating Alzheimer dementia from NPH (area under the curve, 0.869). Imaging markers supportive of CSF dynamics disorders correlated with low Aß42, p-Tau181, and total-Tau. CONCLUSION: In a heterogeneous clinical population, abnormal p-Tau181/Aß42 ratios (>0.023) have the strongest association with Alzheimer dementia and probably represent a comorbid AD pathologic component in persons clearly matching non-AD neurodegenerative syndromes. Altered CSF dynamics were associated with lower concentrations of AD CSF biomarkers regardless of clinical diagnosis, but the ratio compensates for these changes. In the appropriate clinical setting, an isolated abnormal Aß42 should prompt consideration of NPH.

Assessing network degeneration and phenotypic heterogeneity in genetic frontotemporal lobar degeneration by decoding FDG-PET.

Genetic mutations causative of frontotemporal lobar degeneration (FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter-individual variability in their patterns of network degeneration and clinical manifestations. We collected clinical and 18Fluorodeoxyglucose-positron emission tomography (FDG-PET) data from 39 patients with genetic FTLD, including 11 carrying the C9orf72 hexanucleotide expansion, 16 carrying a MAPT mutation and 12 carrying a GRN mutation. We performed a spectral covariance decomposition analysis between FDG-PET images to yield unbiased latent patterns reflective of whole brain patterns of metabolism ("eigenbrains" or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB-based predictions of genetic mutation and predominant clinical phenotype (i.e., behavior/personality, language, asymptomatic). Five EBs were significant and explained 58.52 % of the covariance between FDG-PET images. EBs indicative of hypometabolism in left frontotemporal and temporo-parietal areas distinguished GRN mutation carriers from other genetic mutations and were associated with predominant language phenotypes. EBs indicative of hypometabolism in prefrontal and temporopolar areas with a right hemispheric predominance were mostly associated with predominant behavioral phenotypes and distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 79.5 % and 76.9 % in predicting genetic status and predominant clinical phenotype, respectively. A small number of EBs explained a high proportion of covariance in patterns of network degeneration across FTLD-related genetic mutations. These EBs contained biological information relevant to the variability in the pathophysiological and clinical aspects of genetic FTLD, and for offering valuable guidance in complex clinical decision-making, such as decisions related to genetic testing.