Aggregation of amyloid β into plaques in the brain is one of the earliest pathological events in Alzheimer's disease (AD). The exact pathophysiology leading to dementia is still uncertain, but the apolipoprotein E (APOE) ε4 genotype plays a major role. We aimed to identify the molecular pathways associated with amyloid β aggregation using cerebrospinal fluid (CSF) proteomics and to study the potential modifying effects of APOE ε4 genotype.

This Movement Disorder Society Genetic mutation database Systematic Review focuses on monogenic atypical parkinsonism with mutations in the ATP13A2, DCTN1, DNAJC6, FBXO7, SYNJ1, and VPS13C genes. We screened 673 citations and extracted genotypic and phenotypic data for 140 patients (73 families) from 77 publications. In an exploratory fashion, we applied an automated classification procedure via an ensemble of bootstrap-aggregated ("bagged") decision trees to distinguish these 6 forms of monogenic atypical parkinsonism and found a high accuracy of 86.5% (95%CI, 86.3%-86.7%) based on the following 10 clinical variables: age at onset, spasticity and pyramidal signs, hypoventilation, decreased body weight, minimyoclonus, vertical gaze palsy, autonomic symptoms, other nonmotor symptoms, levodopa response quantification, and cognitive decline. Comparing monogenic atypical with monogenic typical parkinsonism using 2063 data sets from Movement Disorder Society Genetic mutation database on patients with SNCA, LRRK2, VPS35, Parkin, PINK1, and DJ-1 mutations, the age at onset was earlier in monogenic atypical parkinsonism (24 vs 40 years; P = 1.2647 × 10-12) and levodopa response less favorable than in patients with monogenic typical presentations (49% vs 93%). In addition, we compared monogenic to nonmonogenic atypical parkinsonism using data from 362 patients with progressive supranuclear gaze palsy, corticobasal degeneration, multiple system atrophy, or frontotemporal lobar degeneration. Although these conditions share many clinical features with the monogenic atypical forms, they can typically be distinguished based on their later median age at onset (64 years; IQR, 57-70 years). In conclusion, age at onset, presence of specific signs, and degree of levodopa response inform differential diagnostic considerations and genetic testing indications in atypical forms of parkinsonism. © 2021 International Parkinson and Movement Disorder Society.

Recently, several genome-wide association studies identified PHACTR1 as key locus for five diverse vascular disorders: coronary artery disease, migraine, fibromuscular dysplasia, cervical artery dissection and hypertension. Although these represent significant risk factors or comorbidities for ischemic stroke, PHACTR1 role in brain small vessel ischemic disease and ischemic stroke most important survival mechanism, such as the recruitment of brain collateral arteries like posterior communicating arteries (PcomAs), remains unknown. Therefore, we applied exome and genome sequencing in a multi-ethnic cohort of 180 early-onset independent familial and apparently sporadic brain small vessel ischemic disease and CADASIL-like Caucasian patients from US, Portugal, Finland, Serbia and Turkey and in 2 C57BL/6J stroke mouse models (bilateral common carotid artery stenosis [BCCAS] and middle cerebral artery occlusion [MCAO]), characterized by different degrees of PcomAs patency. We report 3 very rare coding variants in the small vessel ischemic disease-CADASIL-like cohort (p.Glu198Gln, p.Arg204Gly, p.Val251Leu) and a stop-gain mutation (p.Gln273*) in one MCAO mouse. These coding variants do not cluster in PHACTR1 known pathogenic domains and are not likely to play a critical role in small vessel ischemic disease or brain collateral circulation. We also exclude the possibility that copy number variants (CNVs) or a variant enrichment in Phactr1 may be associated with PcomA recruitment in BCCAS mice or linked to diverse vascular traits (cerebral blood flow pre-surgery, PcomA size, leptomeningeal microcollateral length and junction density during brain hypoperfusion) in C57BL/6J mice, respectively. Genetic variability in PHACTR1 is not likely to be a common susceptibility factor influencing small vessel ischemic disease in patients and PcomA recruitment in C57BL/6J mice. Nonetheless, rare variants in PHACTR1 RPEL domains may influence the stroke outcome and are worth investigating in a larger cohort of small vessel ischemic disease patients, different ischemic stroke subtypes and with functional studies.

We recently discovered three distinct pathophysiological subtypes in Alzheimer's disease (AD) using cerebrospinal fluid (CSF) proteomics: one with neuronal hyperplasticity, a second with innate immune system activation, and a third subtype with blood-brain barrier dysfunction. It remains unclear whether AD proteomic subtype profiles are a consequence of amyloid aggregation, or might exist upstream from aggregated amyloid. We studied this question in 127 older individuals with intact cognition and normal AD biomarkers in two independent cohorts (EMIF-AD MBD and ADNI). We clustered 705 proteins measured in CSF that were previously related to AD. We identified in these cognitively intact individuals without AD pathology three subtypes: two subtypes were seen in both cohorts (n = 49 with neuronal hyperplasticity and n = 44 with blood-brain barrier dysfunction), and one only in ADNI (n = 12 with innate immune activation). The proteins specific for these subtypes strongly overlapped with AD subtype protein profiles (overlap coefficients 92%-71%). Longitudinal p181-tau and amyloid β 1-42 (Aβ42) CSF analysis showed that in the hyperplasticity subtype p181-tau increased (β = 2.6 pg/mL per year, p = 0.01) and Aβ42 decreased over time (β = -4.4 pg/mL per year, p = 0.03), in the innate immune activation subtype p181-tau increased (β = 3.1 pg/mL per year, p = 0.01) while in the blood-brain barrier dysfunction subtype Aβ42 decreased (β = -3.7 pg/mL per year, p = 0.009). These findings suggest that AD proteomic subtypes might already manifest in cognitively normal individuals and may predispose for AD before amyloid has reached abnormal levels.

Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers.

physiological differences between males and females could contribute to the development of Alzheimer's Disease (AD). Here, we examined metabolic pathways that may lead to precision medicine initiatives.

Alzheimer's disease is biologically heterogeneous, and detailed understanding of the processes involved in patients is critical for development of treatments. CSF contains hundreds of proteins, with concentrations reflecting ongoing (patho)physiological processes. This provides the opportunity to study many biological processes at the same time in patients. We studied whether Alzheimer's disease biological subtypes can be detected in CSF proteomics using the dual clustering technique non-negative matrix factorization. In two independent cohorts (EMIF-AD MBD and ADNI) we found that 705 (77% of 911 tested) proteins differed between Alzheimer's disease (defined as having abnormal amyloid, n = 425) and controls (defined as having normal CSF amyloid and tau and normal cognition, n = 127). Using these proteins for data-driven clustering, we identified three robust pathophysiological Alzheimer's disease subtypes within each cohort showing (i) hyperplasticity and increased BACE1 levels; (ii) innate immune activation; and (iii) blood-brain barrier dysfunction with low BACE1 levels. In both cohorts, the majority of individuals were labelled as having subtype 1 (80, 36% in EMIF-AD MBD; 117, 59% in ADNI), 71 (32%) in EMIF-AD MBD and 41 (21%) in ADNI were labelled as subtype 2, and 72 (32%) in EMIF-AD MBD and 39 (20%) individuals in ADNI were labelled as subtype 3. Genetic analyses showed that all subtypes had an excess of genetic risk for Alzheimer's disease (all P > 0.01). Additional pathological comparisons that were available for a subset in ADNI suggested that subtypes showed similar severity of Alzheimer's disease pathology, and did not differ in the frequencies of co-pathologies, providing further support that found subtypes truly reflect Alzheimer's disease heterogeneity. Compared to controls, all non-demented Alzheimer's disease individuals had increased risk of showing clinical progression (all P < 0.01). Compared to subtype 1, subtype 2 showed faster clinical progression after correcting for age, sex, level of education and tau levels (hazard ratio = 2.5; 95% confidence interval = 1.2, 5.1; P = 0.01), and subtype 3 at trend level (hazard ratio = 2.1; 95% confidence interval = 1.0, 4.4; P = 0.06). Together, these results demonstrate the value of CSF proteomics in studying the biological heterogeneity in Alzheimer's disease patients, and suggest that subtypes may require tailored therapy.

The decline in episodic memory (EM) performance is a hallmark of cognitive aging and an early clinical sign in Alzheimer’s disease (AD). In this study, we conducted an epigenome-wide association study (EWAS) using DNA methylation (DNAm) profiles from buccal and blood samples for cross-sectional (n = 1019) and longitudinal changes in EM performance (n = 626; average follow-up time 5.4 years) collected under the auspices of the Lifebrain consortium project. The mean age of participants with cross-sectional data was 69 ± 11 years (30−90 years), with 50% being females. We identified 21 loci showing suggestive evidence of association (p < 1 × 10−5) with either or both EM phenotypes. Among these were SNCA, SEPW1 (both cross-sectional EM), ITPK1 (longitudinal EM), and APBA2 (both EM traits), which have been linked to AD or Parkinson’s disease (PD) in previous work. While the EM phenotypes were nominally significantly (p < 0.05) associated with poly-epigenetic scores (PESs) using EWASs on general cognitive function, none remained significant after correction for multiple testing. Likewise, estimating the degree of “epigenetic age acceleration” did not reveal significant associations with either of the two tested EM phenotypes. In summary, our study highlights several interesting candidate loci in which differential DNAm patterns in peripheral tissue are associated with EM performance in humans.

A critical role linking sleep with memory decay and β-amyloid (Aβ) accumulation, two markers of Alzheimer's disease (AD) pathology, may be played by hippocampal integrity. We tested the hypotheses that worse self-reported sleep relates to decline in memory and intra-hippocampal microstructure, including in the presence of Aβ.

DNA methylation (DNAm) is an epigenetic mark with essential roles in disease development and predisposition. Here, we created genome-wide maps of methylation quantitative trait loci (meQTL) in three peripheral tissues and used Mendelian randomization (MR) analyses to assess the potential causal relationships between DNAm and risk for two common neurodegenerative disorders, i.e. Alzheimer's disease (AD) and Parkinson's disease (PD). Genome-wide single nucleotide polymorphism (SNP; ~5.5M sites) and DNAm (~850K CpG sites) data were generated from whole blood (n=1,058), buccal (n=1,527) and saliva (n=837) specimens. We identified between 11 and 15 million genome-wide significant (p<10-14) SNP-CpG associations in each tissue. Combining these meQTL GWAS results with recent AD/PD GWAS summary statistics by MR strongly suggests that the previously described associations between PSMC3, PICALM, and TSPAN14 and AD may be founded on differential DNAm in or near these genes. In addition, there is strong, albeit less unequivocal, support for causal links between DNAm at PRDM7 in AD as well as at KANSL1/MAPT in AD and PD. Our study adds valuable insights on AD/PD pathogenesis by combining two high-resolution "omics" domains, and the meQTL data shared along with this publication will allow like-minded analyses in other diseases.

With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification.

No abstract available

Plasma proteins have been widely studied as candidate biomarkers to predict brain amyloid deposition to increase recruitment efficiency in secondary prevention clinical trials for Alzheimer's disease. Most such biomarker studies are targeted to specific proteins or are biased toward high abundant proteins.

We examine the hypothesized overlap of genetic architecture for Alzheimer disease (AD), schizophrenia (SZ), and Parkinson disease (PD) through the use of polygenic risk scores (PRSs) with the occurrence of hallucinations in PD.

Previous studies suggest that Dickkopf-1 (DKK1), an inhibitor of Wnt signaling, plays a role in amyloid-induced toxicity and hence Alzheimer's disease (AD). However, the effect of DKK1 expression on protein expression, and whether such proteins are altered in disease, is unknown.

Our study investigated the presence of regional differences in hexanucleotide repeat number in postmortem brain tissues of 2 patients with X-linked dystonia-parkinsonism (XDP), a combined dystonia-parkinsonism syndrome modified by a (CCCTCT)n repeat within the causal SINE-VNTR-Alu retrotransposon insertion in the TAF1 gene.

Dysregulation of microRNAs (miRNAs) is involved in the pathogenesis of neurodegenerative diseases, including Alzheimer's disease (AD). Hitherto, sample sizes from differential miRNA expression studies in AD are exceedingly small aggravating any biological inference. To overcome this limitation, we investigated six candidate miRNAs in a large collection of brain samples. Brain tissue was derived from superior temporal gyrus (STG) and entorhinal cortex (EC) from 99 AD patients and 91 controls. MiRNA expression was examined by qPCR (STG) or small RNA sequencing (EC). Brain region-dependent differential miRNA expression was investigated in a transgenic AD mouse model using qPCR and FISH. Total RNA sequencing was used to assess differential expression of miRNA target genes. MiR-129-5p, miR-132-5p, and miR-138-5p were significantly downregulated in AD vs. controls both in STG and EC, while miR-125b-5p and miR-501-3p showed no evidence for differential expression in this dataset. In addition, miR-195-5p was significantly upregulated in EC but not STG in AD patients. The brain region-specific pattern of miR-195-5p expression was corroborated in vivo in transgenic AD mice. Total RNA sequencing identified several novel and functionally interesting target genes of these miRNAs involved in synaptic transmission (GABRB1), the immune-system response (HCFC2) or AD-associated differential methylation (SLC16A3). Using two different methods (qPCR and small RNA-seq) in two separate brain regions in 190 individuals we more than doubled the available sample size for most miRNAs tested. Differential gene expression analyses confirm the likely involvement of miR-129-5p, miR-132-5p, miR-138-5p, and miR-195-5p in AD pathogenesis and highlight several novel potentially relevant target mRNAs.

Alzheimer's disease (AD) is the most common form of dementia. To date, more than 200 mutations in three genes have been identified as cause of early-onset autosomal dominant inherited AD. The aim of this study was to characterize the mutation spectrum and describe genotype-phenotype correlations in Serbian patients with positive family history of AD or/and early-onset AD. We performed a genetic screening for mutations in the coding regions of Presenilins 1 and 2 (PSEN1 and PSEN2), as well as exons 16 and 17 of the Amyloid Precursor Protein gene (APP) in a total of 47 patients from Serbia with a clinical diagnosis of familial and/or early-onset AD (mean age at onset of 60.3 years; range 32-77). We found one novel mutation in PSEN1, one novel variant in PSEN2, and three previously described variants, one in each of the analyzed genes. Interestingly, we identified one patient harboring two heterozygous mutations: one in APP (p.L723P) and one in PSEN1 (p.R108Q).

Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers.

To test the hypothesis that genetic risk for Alzheimer disease (AD) may represent a stable influence on the brain from early in life, rather than being primarily age dependent, we investigated in a lifespan sample of 1,181 persons with a total of 2,690 brain scans, whether higher polygenic risk score (PGS) for AD and presence of APOE ε4 was associated with lower hippocampal volumes to begin with, as an offset effect, or possibly faster decline in older age.