Common Aging Signature in the Peripheral Blood of Vascular Dementia and Alzheimer's Disease

Systematic Review and Phenome-Wide Scans of Genetic Associations with Vascular Cognitive Impairment

Vascular cognitive impairment (VCI) is a heterogenous form of cognitive impairment that results from cerebrovascular disease. It is a result of both genetic and non-genetic factors. Although much research has been conducted on the genetic contributors to other forms of cognitive impairment (e.g. Alzheimer's disease), knowledge is lacking on the genetic factors associated with VCI. A better understanding of the genetics of VCI will be critical for prevention and treatment. To begin to fill this gap, the genetic contributors are reviewed with VCI from the literature. Phenome-wide scans of the identified genes are conducted and genetic variants identified in the review in large-scale resources displaying genetic variant-trait association information. Gene set are also carried out enrichment analysis using the genes identified from the review. Thirty one articles are identified meeting the search criteria and filters, from which 107 unique protein-coding genes are noted related to VCI. The phenome-wide scans and gene set enrichment analysis identify pathways associated with a diverse set of biological systems. This results indicate that genes with evidence of involvement in VCI are involved in a diverse set of biological functions. This information can facilitate downstream research to better dissect possible shared biological mechanisms for future therapies.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

Forgetful, sad and old: Do vascular cognitive impairment and depression share a common pre-disease network and how is it impacted by ageing?

Vascular cognitive impairment (VCI) and depression frequently coexist in geriatric populations and reciprocally increase disease risks. We assert that a shared pre-disease state of the psycho-immune-neuroendocrine (PINE) network model mechanistically explains bidirectional associations between VCI and depression. Five pathophysiological sub-networks are identified that are shared by VCI and depression: neuroinflammation, kynurenine pathway imbalance, hypothalamic-pituitary-adrenal (HPA) axis overactivity, impaired neurotrophic support and cerebrovascular dysfunction. These do not act independently, and their complex interactions necessitate a systems biology approach to better define disease pathogenesis. The PINE network is already established in the context of non-communicable diseases (NCDs) such as depression, hypertension, atherosclerosis, coronary heart disease and type 2 diabetes mellitus. We build on previous literature to specifically explore mechanistic links between MDD and VCI in the context of PINE pathways and discuss key mechanistic commonalities linking these comorbid conditions and identify a common pre-disease state which precedes transition to VCI and MDD. We expand the model to incorporate bidirectional interactions with biological ageing. Diathesis factors for both VCI and depression feed into this network and the culmination of shared mechanisms (on an ageing substrate) lead to a critical network transition to one or both disease states. A common pre-disease state underlying VCI and depression can provide clinicians a unique opportunity for early risk assessment and intervention in disease development. Establishing the mechanistic elements and systems biology of this network can reveal early warning or predictive biomarkers together with novel therapeutic targets. Integrative studies are recommended to elucidate the dynamic networked biology of VCI and depression over time.

Screening of Human Circular RNAs as Biomarkers for Early Onset Detection of Alzheimer’s Disease

Circular RNAs (circRNAs) are a distinctive type of endogenous non-coding RNAs, and their regulatory roles in neurological disorders have received immense attention. CircRNAs significantly contribute to the regulation of gene expression and progression of neurodegenerative disorders including Alzheimer’s disease (AD). The current study aimed to identify circRNAs as prognostic and potential biomarkers in AD. The differentially expressed circRNAs among subjective cognitive decline, amnestic mild cognitive impairment, and age-matched normal donors were determined through Arraystar Human circRNA Array V2 analysis. The annotations of circRNAs-microRNA interactions were predicted by employing Arraystar’s homemade microRNAs (miRNA) target prediction tool. Bioinformatics analyses comprising gene ontology enrichment, KEGG pathway, and network analysis were conducted. Microarray analysis revealed the 33 upregulated and 11 downregulated differentially expressed circRNAs (FC ≥ 1.5 and p-values ≤ 0.05). The top 10 differentially expressed upregulated and downregulated circRNAs have been chosen for further expression validation through quantitative real-time PCR and subsequently, hsa-circRNA_001481 and hsa_circRNA_000479 were confirmed experimentally. Bioinformatics analyses determined the circRNA-miRNA-mRNA interactions and microRNA response elements to inhibit the expression of miRNAs and mRNA targets. Gene ontology enrichment and KEGG pathways analysis revealed the functional clustering of target mRNAs suggesting the functional verification of these two promising circRNAs. It is concluded that human circRNA_001481 and circRNA_000479 could be utilized as potential biomarkers for the early onset detection of AD and the development of effective therapeutics.

Addition of Aβ42 to Total Cerebral Small Vessel Disease Score Improves the Prediction for Cognitive Impairment in Cerebral Small Vessel Disease Patients

PURPOSE

To investigate the associations between concentrations of Aβ₄₀ and Aβ₄₂ and vascular cognitive impairment (VCI) in cerebral small vessel disease (CSVD) patients and evaluate the value of combination of levels of Aβ₄₀ or Aβ₄₂ and the total CSVD score in predicting VCI.

PATIENTS AND METHODS

A total of 199 CSVD patients were divided into VCI group and non-VCI group according to the criteria of VCI. Demographic data, MRI markers of CSVD, blood pressure, vascular risk factors, laboratory markers, and serum Aβ₄₀ and Aβ₄₂ concentration were collected. Univariate analysis was performed with the Student's t-test, Mann-Whitney U-test or Chi-square test. Variables with P<0.10 in univariate analysis were then included in multivariate analysis that used a backward stepwise logistic regression model. The predictive values were assessed with receiver operating characteristic (ROC) curve.

RESULTS

VCI was determined in 112 CSVD patients (56.3%). Hyperlipidemia (OR: 1.618, 95% CI: 1.265-3.049), the total CSVD score (OR: 1.414, 95% CI: 1.213-2.278) and serum Aβ₄₂ concentration (OR: 1.401, 95% CI: 1.212-1.946) were independent risk factors for VCI in CSVD patients with adjustment for age, education years, diabetes and fasting blood-glucose (FBG). The area under curves (AUCs) were 0.640 (SE: 0.040, 95% CI: 0.563-0.718), 0.733 (SE: 0.035, 95% CI: 0.664-0.802) and 0.827 (SE: 0.030, 95% CI: 0.768-0.887), respectively, for the total CSVD score, serum Aβ₄₂ concentration and their combination applied in predicting VCI in CSVD patients. Z test demonstrated that the AUC of combination prediction was significantly higher than individual prediction (0.827 vs 0.640, Z=3.740, P<0.001; 0.827 vs 0.733, Z=2.039, P=0.021).

CONCLUSION

Combination of Aβ₄₂ and total CSVD score could significantly elevate the predictive value of cognitive impairment in CSVD patients.

Evaluation of Peripheral Immune Dysregulation in Alzheimer's Disease and Vascular Dementia

Immune dysregulation has been observed in the brain and blood of patients with Alzheimer's disease (AD). However, a convenient assay to evaluate peripheral immune dysregulation in AD has not been developed, partly due to the inconsistent observations from different studies. We hypothesized that peripheral immune dysregulation may only exist in a subpopulation of AD patients; therefore it may be valuable to identify this subpopulation with a convenient assay. Along this line, we selected 14 candidate genes based on our analysis of microarray data on peripheral blood of AD and other diseases. We used RT-qPCR to examine the expression of these 14 genes in a cohort of 288 subjects, including 74 patients with AD, 64 patients with mild cognitive impairment (MCI), 51 patients with vascular dementia (VaD), and 99 elderly controls with no cognitive dysfunction/impairment. Seven of these 14 genes displayed significant difference in group comparison. Switching from group comparison to individualized evaluation revealed more in-depth information. First, there existed a wide dynamic range for the expression of these immune genes in peripheral blood even within the control group. Second, for the vast majority of the patients (AD, VaD, and MCI patients), the expression of these genes fell within the dynamic range of the control group. Third, a small portion of outliers were observed in the patient groups, more so in the VaD group than that in the AD or MCI groups. This is our first attempt to conduct personalized evaluation of peripheral immune dysregulation in AD and VaD. These findings may be applicable to the identification of peripheral immune dysregulation in AD and VaD patients which may lead to tailored treatment toward those 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.

Genetic heterogeneity of Alzheimer's disease in subjects with and without hypertension

Alzheimer's disease (AD) is a progressive neurodegenerative disorder caused by the interplay of multiple genetic and non-genetic factors. Hypertension is one of the AD risk factors that has been linked to underlying pathological changes like senile plaques and neurofibrillary tangles formation as well as hippocampal atrophy. In this study, we investigated the differences in the genetic architecture of AD between hypertensive and non-hypertensive subjects in four independent cohorts. Our genome-wide association analyses revealed significant associations of 15 novel potentially AD-associated polymorphisms (P < 5E-06) that were located outside the chromosome 19q13 region and were significant either in hypertensive or non-hypertensive groups. The closest genes to 14 polymorphisms were not associated with AD at P < 5E-06 in previous genome-wide association studies (GWAS). Also, four of them were located within two chromosomal regions (i.e., 3q13.11 and 17q21.2) that were not associated with AD at P < 5E-06 before. In addition, 30 genes demonstrated evidence of group-specific associations with AD at the false discovery rates (FDR) < 0.05 in our gene-based and transcriptome-wide association analyses. The chromosomal regions corresponding to four genes (i.e., 2p13.1, 9p13.3, 17q12, and 18q21.1) were not associated with AD at P < 5E-06 in previous GWAS. These genes may serve as a list of prioritized candidates for future functional studies. Our pathway-enrichment analyses revealed the associations of 11 non-group-specific and four group-specific pathways with AD at FDR < 0.05. These findings provided novel insights into the potential genetic heterogeneity of AD among subjects with and without hypertension.

The frontline of immune response in peripheral blood

Peripheral blood is an attractive source for the discovery of disease biomarkers. Gene expression profiling of whole blood or its components has been widely conducted for various diseases. However, due to population heterogeneity and the dynamic nature of gene expression, certain biomarkers discovered from blood transcriptome studies could not be replicated in independent studies. In the meantime, it's also important to know whether a reliable biomarker is shared by several diseases or specific to certain health conditions. We hypothesized that common mechanism of immune response in blood may be shared by different diseases. Under this hypothesis, we surveyed publicly available transcriptome data on infectious and autoimmune diseases derived from peripheral blood. We examined to which extent common gene dys-regulation existed in different diseases. We also investigated whether the commonly dys-regulated genes could serve as reliable biomarkers. First, we found that a limited number of genes are frequently dys-regulated in infectious and autoimmune diseases, from which we selected 10 genes co-dysregulated in viral infections and another set of 10 genes co-dysregulated in bacterial infections. In addition to its ability to distinguish viral infections from bacterial infections, these 20 genes could assist in disease classification and monitoring of treatment effect for several infectious and autoimmune diseases. In some cases, a single gene is sufficient to serve this purpose. It was interesting that dys-regulation of these 20 genes were also observed in other types of diseases including cancer and stroke where certain genes could also serve as biomarkers for diagnosis or prognosis. Furthermore, we demonstrated that this set of 20 genes could also be used in continuous monitoring of personal health. The rich information from these commonly dys-regulated genes may find its wide application in clinical practice and personal healthcare. More validation studies and in-depth investigations are warranted in the future.

Towards Personalized Intervention for Alzheimer's Disease

Alzheimer's disease (AD) remains to be a grand challenge for the international community despite over a century of exploration. A key factor likely accounting for such a situation is the vast heterogeneity in the disease etiology, which involves very complex and divergent pathways. Therefore, intervention strategies shall be tailored for subgroups of AD patients. Both demographic and in-depth information is needed for patient stratification. The demographic information includes primarily APOE genotype, age, gender, education, environmental exposure, life style, and medical history, whereas in-depth information stems from genome sequencing, brain imaging, peripheral biomarkers, and even functional assays on neurons derived from patient-specific induced pluripotent cells (iPSCs). Comprehensive information collection, better understanding of the disease mechanisms, and diversified strategies of drug development would help with more effective intervention in the foreseeable future.