Multivariate analyses of peripheral blood leukocyte transcripts distinguish Alzheimer's, Parkinson's, control, and those at risk for developing Alzheimer's

Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer's Disease

The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, Friedreich's ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.

A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer's disease risk

Alzheimer's disease (ad) adversely affects the health, quality of life and independence of patients. There is a critical need to identify novel blood gene biomarkers for ad risk assessment. We performed a transcriptome-wide association study to identify biomarker candidates for ad risk. We leveraged two sets of gene expression prediction models of blood developed using different reference panels and modeling strategies. By applying the prediction models to a meta-GWAS including 71 880 (proxy) cases and 383 378 (proxy) controls, we identified significant associations of genetically determined expression of 108 genes in blood with ad risk. Of these, 15 genes were differentially expressed between ad patients and controls with concordant directions in measured expression data. With evidence from the analyses based on both genetic instruments and directly measured expression levels, this study identifies 15 genes with strong support as biomarkers in blood for ad risk, which may enhance ad risk assessment and mechanism-focused studies.

Abnormal amyloid beta metabolism in systemic abnormalities and Alzheimer's pathology: Insights and therapeutic approaches from periphery

Alzheimer's disease (AD) is an age-associated, multifactorial neurodegenerative disorder that is incurable. Despite recent success in treatments that partially improve symptomatic relief, they have failed in most clinical trials. Re-holding AD for accurate diagnosis and treatment is widely known as a challenging task. Lack of knowledge of basic molecular pathogenesis might be a possible reason for ineffective AD treatment. Historically, a majority of therapy-based studies have investigated the role of amyloid-β (Aβ peptide) in the central nervous system (CNS), whereas less is known about Aβ peptide in the periphery in AD. In this review, we provide a comprehensive summary of the current understanding of Aβ peptide metabolism (anabolism and catabolism) in the brain and periphery. We show that the abnormal metabolism of Aβ peptide is significantly linked with central-brain and peripheral abnormalities; the interaction between peripheral Aβ peptide metabolism and peripheral abnormalities affects central-brain Aβ peptide metabolism, suggesting the existence of significant communication between these two pathways of Aβ peptide metabolism. This close interaction between the central brain and periphery in abnormal Aβ peptide metabolism plays a key role in the development and progression of AD. In conclusion, we need to obtain a full understanding of the dynamic roles of Aβ peptide at the molecular level in both the brain and periphery in relation to the pathology of AD. This will not only provide new information regarding the complex disease pathology, but also offer potential new clues to improve therapeutic strategies and diagnostic biomarkers for the successful treatment of AD.

PICALM mRNA Expression in the Blood of Patients with Neurodegenerative Diseases and Geriatric Depression

BACKGROUND

Phosphatidylinositol-binding clathrin assembly protein (PICALM) is a validated genetic risk factor for late-onset Alzheimer's disease (AD) and is associated with other neurodegenerative diseases. However, PICALM expression in the blood of neurodegenerative diseases remains elusive.

OBJECTIVE

This study aimed to assess the usefulness of PICALM expression levels in the blood of patients with AD, Parkinson's disease (PD), dementia with Lewy bodies (DLB), and geriatric major depressive disorder (MDD) as a diagnostic biomarker.

METHODS

In total, 45, 20, 21, and 19 patients with AD, PD, DLB, and geriatric MDD, respectively, and 54 healthy controls (HCs) were enrolled in the study. Expression data from Gene Expression Omnibus database (GSE97760), (GSE133347) and (GSE98793), (GSE48350), and (GSE144459) were used to validate the ability of biomarkers in the blood of patients with AD, PD, geriatric MDD, and a postmortem human AD brain and animal model of AD (3xTg-AD mouse), respectively.

RESULTS

PICALM mRNA expression in human blood was significantly increased in patients with AD compared with that in HCs. PICALM mRNA expression and age were negatively correlated only in patients with AD. PICALM mRNA expression in human blood was significantly lower in patients with PD than in HCs. No changes in PICALM mRNA expression were found in patients with DLB and geriatric MDD.

CONCLUSION

PICALM mRNA expression in blood was higher in patients with AD, but lower in patients with PD, which suggests that PICALM mRNA expression in human blood may be a useful biomarker for differentiating neurodegenerative diseases and geriatric MDD.

Single-cell peripheral immunoprofiling of Alzheimer's and Parkinson's diseases

Peripheral blood mononuclear cells (PBMCs) may provide insight into the pathogenesis of Alzheimer's disease (AD) or Parkinson's disease (PD). We investigated PBMC samples from 132 well-characterized research participants using seven canonical immune stimulants, mass cytometric identification of 35 PBMC subsets, and single-cell quantification of 15 intracellular signaling markers, followed by machine learning model development to increase predictive power. From these, three main intracellular signaling pathways were identified specifically in PBMC subsets from people with AD versus controls: reduced activation of PLCγ2 across many cell types and stimulations and selectively variable activation of STAT1 and STAT5, depending on stimulant and cell type. Our findings functionally buttress the now multiply-validated observation that a rare coding variant in PLCG2 is associated with a decreased risk of AD. Together, these data suggest enhanced PLCγ2 activity as a potential new therapeutic target for AD with a readily accessible pharmacodynamic biomarker.

Plasma Aβ42 as a Biomarker of Prodromal Alzheimer's Disease Progression in Patients with Amnestic Mild Cognitive Impairment: Evidence from the PharmaCog/E-ADNI Study

It is an open issue whether blood biomarkers serve to diagnose Alzheimer's disease (AD) or monitor its progression over time from prodromal stages. Here, we addressed this question starting from data of the European FP7 IMI-PharmaCog/E-ADNI longitudinal study in amnesic mild cognitive impairment (aMCI) patients including biological, clinical, neuropsychological (e.g., ADAS-Cog13), neuroimaging, and electroencephalographic measures. PharmaCog/E-ADNI patients were classified as "positive" (i.e., "prodromal AD" n = 76) or "negative" (n = 52) based on a diagnostic cut-off of Aβ42/P-tau in cerebrospinal fluid as well as APOE ε 4 genotype. Blood was sampled at baseline and at two follow-ups (12 and 18 months), when plasma amyloid peptide 42 and 40 (Aβ42, Aβ40) and apolipoprotein J (clusterin, CLU) were assessed. Linear Mixed Models found no significant differences in plasma molecules between the "positive" (i.e., prodromal AD) and "negative" groups at baseline. In contrast, plasma Aβ42 showed a greater reduction over time in the prodromal AD than the "negative" aMCI group (p = 0.048), while CLU and Aβ40 increased, but similarly in the two groups. Furthermore, plasma Aβ42 correlated with the ADAS-Cog13 score both in aMCI patients as a whole and the prodromal AD group alone. Finally, CLU correlated with the ADAS-Cog13 only in the whole aMCI group, and no association with ADAS-Cog13 was found for Aβ40. In conclusion, plasma Aβ42 showed disease progression-related features in aMCI patients with prodromal AD.

Classification algorithms applied to blood-based transcriptome meta-analysis to predict idiopathic Parkinson's disease

Diagnosis of Parkinson's disease (PD) remains a challenge in clinical practice, mostly due to lack of peripheral blood markers. Transcriptomic analysis of blood samples has emerged as a potential means to identify biomarkers and gene signatures of PD. In this context, classification algorithms can assist in detecting data patterns such as phenotypes and transcriptional signatures with potential diagnostic application. In this study, we performed gene expression meta-analysis of blood transcriptome from PD and control patients in order to identify a gene-set capable of predicting PD using classification algorithms. We examined microarray data from public repositories and, after systematic review, 4 independent cohorts (GSE6613, GSE57475, GSE72267 and GSE99039) comprising 711 samples (388 idiopathic PD and 323 healthy individuals) were selected. Initially, analysis of differentially expressed genes resulted in minimal overlap among datasets. To circumvent this, we carried out meta-analysis of 17,712 genes across datasets, and calculated weighted mean Hedges' g effect sizes. From the top-100- positive and negative gene effect sizes, algorithms of collinearity recognition and recursive feature elimination were used to generate a 59-gene signature of idiopathic PD. This signature was evaluated by 9 classification algorithms and 4 sample size-adjusted training groups to create 36 models. Of these, 33 showed accuracy higher than the non-information rate, and 2 models built on Support Vector Machine Regression bestowed best accuracy to predict PD and healthy control samples. In summary, the gene meta-analysis followed by machine learning methodology employed herein identified a gene-set capable of accurately predicting idiopathic PD in blood samples.

Identifying Blood Transcriptome Biomarkers of Alzheimer's Disease Using Transgenic Mice

The testing of pathological biomarkers of Alzheimer's disease (AD), such as amyloid beta and tau, is time-consuming, expensive, and invasive. Here, we used 3xTg-AD mice to identify and validate putative novel blood transcriptome biomarkers of AD that can potentially be identified in the blood of patients. mRNA was extracted from the blood and hippocampus of 3xTg-AD and control mice at different ages and used for microarray analysis. Network and functional analyses revealed that the differentially expressed genes between AD and control mice modulated the immune and neuroinflammation systems. Five novel gene transcripts (Cdkn2a, Apobec3, Magi2, Parp3, and Cass4) showed significant increases with age, and their expression in the blood was collated with that in the hippocampus only in AD mice. We further assessed previously identified candidate biomarker genes. The expression of Trem1 and Trem2 in both the blood and brain was significantly increased with age. Decreased Tomm40 and increased Pink1 mRNA levels were observed in the mouse blood. The changes in the expression of Snca and Apoe mRNA in the mouse blood and brain were similar to those found in human AD blood. Our results demonstrated that the immune and neuroinflammatory system is involved in the pathophysiologies of aging and AD and that the blood transcriptome might be useful as a biomarker of AD.

Clinical Utility of Plasma Lipid Peroxidation Biomarkers in Alzheimer's Disease Differential Diagnosis

BACKGROUND

Differential diagnosis of Alzheimer's disease (AD) is a complex task due to the clinical similarity among neurodegenerative diseases. Previous studies showed the role of lipid peroxidation in early AD development. However, the clinical validation of potential specific biomarkers in minimally invasive samples constitutes a great challenge in early AD diagnosis.

METHODS

Plasma samples from participants classified into AD (n = 138), non-AD (including MCI and other dementias not due to AD) (n = 70) and healthy (n = 50) were analysed. Lipid peroxidation compounds (isoprostanes, isofurans, neuroprostanes, neurofurans) were determined by ultra-performance liquid chromatography coupled with tandem mass spectrometry. Statistical analysis for biomarkers' clinical validation was based on Elastic Net.

RESULTS

A two-step diagnosis model was developed from plasma lipid peroxidation products to diagnose early AD specifically, and a bootstrap validated AUC of 0.74 was obtained.

CONCLUSION

A promising AD differential diagnosis model was developed. It was clinically validated as a screening test. However, further external validation is required before clinical application.

Changes in Expression Profiles Revealed by Transcriptomic Analysis in Peripheral Blood Mononuclear Cells of Alzheimer's Disease Patients

Alzheimer's disease (AD) is an age-related neurodegenerative pathology associated with accumulation of DNA damage. Inflammation and cell cycle alterations seem to be implicated in the pathogenesis of AD, although the molecular mechanisms have not been thoroughly elucidated to date. The aim of the present study was to evaluate whether peripheral blood mononuclear cells (PBMCs) of AD patients display alterations in gene expression profiles, focusing on finding markers that might improve the diagnosis of AD. Blood samples were collected from 22 AD patients and 13 healthy individuals to perform genome-wide mRNA expression. We found 593 differentially expressed genes in AD compared to controls, from which 428 were upregulated, and 165 were downregulated. By performing a gene set enrichment analysis, we observed pathways involved in inflammation, DNA damage response, cell cycle, and neuronal processes. Moreover, functional annotation analyses indicated that differentially expressed genes are strongly related to pathways associated with the cell cycle and the immune system. The results were compared with those of an independent study on hippocampus samples, and a number of genes in common between both studies were identified as potential peripheral biomarkers for AD, including DUSP1, FOS, SLC7A2, RGS1, GFAP, CCL2, ANGPTL4, and SSPN. Taken together, our results demonstrate that PBMCs of AD patients do present alterations in gene expression profiles, and these results are comparable to those previously reported in the literature for AD neurons, supporting the hypothesis that blood peripheral mononuclear cells express molecular changes that occur in the neurons of AD patients.

Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer's disease: The INSIGHT-preAD study

BACKGROUND

One of the biggest challenge in Alzheimer's disease (AD) is to identify pathways and markers of disease prediction easily accessible, for prevention and treatment. Here we analysed blood samples from the INveStIGation of AlzHeimer's predicTors (INSIGHT-preAD) cohort of elderly asymptomatic individuals with and without brain amyloid load.

METHODS

We performed blood RNAseq, and plasma metabolomics and lipidomics using liquid chromatography-mass spectrometry on 48 individuals amyloid positive and 48 amyloid negative (SUVr cut-off of 0·7918). The three data sets were analysed separately using differential gene expression based on negative binomial distribution, non-parametric (Wilcoxon) and parametric (correlation-adjusted Student't) tests. Data integration was conducted using sparse partial least squares-discriminant and principal component analyses. Bootstrap-selected top-ten features from the three data sets were tested for their discriminant power using Receiver Operating Characteristic curve. Longitudinal metabolomic analysis was carried out on a subset of 22 subjects.

FINDINGS

Univariate analyses identified three medium chain fatty acids, 4-nitrophenol and a set of 64 transcripts enriched for inflammation and fatty acid metabolism differentially quantified in amyloid positive and negative subjects. Importantly, the amounts of the three medium chain fatty acids were correlated over time in a subset of 22 subjects (p < 0·05). Multi-omics integrative analyses showed that metabolites efficiently discriminated between subjects according to their amyloid status while lipids did not and transcripts showed trends. Finally, the ten top metabolites and transcripts represented the most discriminant omics features with 99·4% chance prediction for amyloid positivity.

INTERPRETATION

This study suggests a potential blood omics signature for prediction of amyloid positivity in asymptomatic at-risk subjects, allowing for a less invasive, more accessible, and less expensive risk assessment of AD as compared to PET studies or lumbar puncture. FUND: Institut Hospitalo-Universitaire and Institut du Cerveau et de la Moelle Epiniere (IHU-A-ICM), French Ministry of Research, Fondation Alzheimer, Pfizer, and Avid.

Global expression profiling of cognitive level and decline in middle-aged monozygotic twins

Only few studies have investigated the genomewide transcriptome of normative cognitive aging. We therefore aimed at investigating blood gene expression patterns associated with cognitive aging using a population-based sample of 235 middle-aged monozygotic twin pairs with longitudinal data on cognitive function. This unique setup enabled examination of gene expression differences associated with individual and intrapair differences in cognitive level and change while controlling for underlying genetic variation and shared early environment. Overall, increased expression of several gene sets was found to strongly correlate with a lower cognitive level and cognitive decline. The most significantly correlated gene sets were related to protein metabolism, translation, RNA metabolism, infectious disease, and the immune system, which are all processes previously linked to transcription signatures of pathological and normal brain aging, and aging in blood. The results of our study thus suggest that gene expression patterns of cognitive level and decline in our sample mirror those seen in cognitively impaired individuals, which could point toward a more generic response to cognitive aging and aging in general.