Pairwise Correlation Analysis of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10−13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses

Assessing Genetic Overlap and Causality Between Blood Plasma Proteins and Alzheimer's Disease

BACKGROUND: Blood plasma proteins have been associated with Alzheimer's disease (AD), but understanding which proteins are on the causal pathway remains challenging. OBJECTIVE: Investigate the genetic overlap between candidate proteins and AD using polygenic risk scores (PRS) and interrogate their causal relationship using bi-directional Mendelian randomization (MR). METHODS: Following a literature review, 31 proteins were selected for PRS analysis. PRS were constructed for prioritized proteins with and without the apolipoprotein E region (APOE+/-PRS) and tested for association with AD status across three cohorts (n = 6,244). An AD PRS was also tested for association with protein levels in one cohort (n = 410). Proteins showing association with AD were taken forward for MR. RESULTS: For APOE ɛ3, apolipoprotein B-100, and C-reactive protein (CRP), protein APOE+ PRS were associated with AD below Bonferroni significance (pBonf, p <  0.00017). No protein APOE- PRS or AD PRS (APOE+/-) passed pBonf. However, vitamin D-binding protein (protein PRS APOE-, p = 0.009) and insulin-like growth factor-binding protein 2 (AD APOE- PRS p = 0.025, protein APOE- PRS p = 0.045) displayed suggestive signals and were selected for MR. In bi-directional MR, none of the five proteins demonstrated a causal association (p <  0.05) in either direction. CONCLUSION: Apolipoproteins and CRP PRS are associated with AD and provide a genetic signal linked to a specific, accessible risk factor. While evidence of causality was limited, this study was conducted in a moderate sample size and provides a framework for larger samples with greater statistical power

The PSEN1, p.E318G variant increases the risk of Alzheimer’s disease in APOE-ԑ4 carriers

The primary constituents of plaques (Aβ42/Aβ40) and neurofibrillary tangles (tau and phosphorylated forms of tau [ptau]) are the current leading diagnostic and prognostic cerebrospinal fluid (CSF) biomarkers for AD. In this study, we performed deep sequencing of APP, PSEN1, PSEN2, GRN, APOE and MAPT genes in individuals with extreme CSF Aβ42, tau, or ptau levels. One known pathogenic mutation (PSEN1 p.A426P), four high-risk variants for AD (APOE p.L46P, MAPT p.A152T, PSEN2 p.R62H and p.R71W) and nine novel variants were identified. Surprisingly, a coding variant in PSEN1, p.E318G (rs17125721-G) exhibited a significant association with high CSF tau (p = 9.2 × 10(-4)) and ptau (p = 1.8 × 10(-3)) levels. The association of the p.E318G variant with Aβ deposition was observed in APOE-ε4 allele carriers. Furthermore, we found that in a large case-control series (n = 5,161) individuals who are APOE-ε4 carriers and carry the p.E318G variant are at a risk of developing AD (OR = 10.7, 95% CI = 4.7-24.6) that is similar to APOE-ε4 homozygous (OR = 9.9, 95% CI = 7.2.9-13.6), and double the risk for APOE-ε4 carriers that do not carry p.E318G (OR = 3.9, 95% CI = 3.4-4.4). The p.E318G variant is present in 5.3% (n = 30) of the families from a large clinical series of LOAD families (n = 565) and exhibited a higher frequency in familial LOAD (MAF = 2.5%) than in sporadic LOAD (MAF = 1.6%) (p = 0.02). Additionally, we found that in the presence of at least one APOE-ε4 allele, p.E318G is associated with more Aβ plaques and faster cognitive decline. We demonstrate that the effect of PSEN1, p.E318G on AD susceptibility is largely dependent on an interaction with APOE-ε4 and mediated by an increased burden of Aβ deposition

Disentangling independent and mediated causal relationships between blood metabolites, cognitive factors, and Alzheimer’s Disease

Background: Education and cognition demonstrate consistent inverse associations with Alzheimer’s disease (AD). The biological underpinnings, however, remain unclear. Blood metabolites reflect the end point of biological processes and are accessible and malleable. Identifying metabolites with etiological relevance to AD and disentangling how these relate to cognitive factors along the AD causal pathway could, therefore, offer unique insights into underlying causal mechanisms. // Methods: Using data from the largest metabolomics genome-wide association study (N ≈ 24,925) and three independent AD cohorts (N = 4725), cross-trait polygenic scores were generated and meta-analyzed. Metabolites genetically associated with AD were taken forward for causal analyses. Bidirectional two-sample Mendelian randomization interrogated univariable causal relationships between 1) metabolites and AD; 2) education and cognition; 3) metabolites, education, and cognition; and 4) education, cognition, and AD. Mediating relationships were computed using multivariable Mendelian randomization. // Results: Thirty-four metabolites were genetically associated with AD at p < .05. Of these, glutamine and free cholesterol in extra-large high-density lipoproteins demonstrated a protective causal effect (glutamine: 95% confidence interval [CI], 0.70 to 0.92; free cholesterol in extra-large high-density lipoproteins: 95% CI, 0.75 to 0.92). An AD-protective effect was also observed for education (95% CI, 0.61 to 0.85) and cognition (95% CI, 0.60 to 0.89), with bidirectional mediation evident. Cognition as a mediator of the education-AD relationship was stronger than vice versa, however. No evidence of mediation via any metabolite was found. // Conclusions: Glutamine and free cholesterol in extra-large high-density lipoproteins show protective causal effects on AD. Education and cognition also demonstrate protection, though education’s effect is almost entirely mediated by cognition. These insights provide key pieces of the AD causal puzzle, important for informing future multimodal work and progressing toward effective intervention strategies

Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts

Alzheimer’s disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI

Evidence of a relation between hippocampal volume, white matter hyperintensities, and cognition in subjective cognitive decline and mild cognitive impairment

Objective: The concepts of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) have been proposed to identify individuals in the early stages of Alzheimer’s disease (AD), or other neurodegenerative diseases. One approach to validate these concepts is to investigate the relationship between pathological brain markers and cognition in those individuals. Method: We included 126 participants from the Consortium for the Early Identification of Alzheimer’s disease-Quebec (CIMA-Q) cohort (67 SCD, 29 MCI, and 30 cognitively healthy controls [CH]). All participants underwent a complete cognitive assessment and structural magnetic resonance imaging. Group comparisons were done using cognitive data, and then correlated with hippocampal volumes and white matter hyperintensities (WMHs). Results: Significant differences were found between participants with MCI and CH on episodic and executive tasks, but no differences were found when comparing SCD and CH. Scores on episodic memory tests correlated with hippocampal volumes in both MCI and SCD, whereas performance on executive tests correlated with WMH in all of our groups. Discussion: As expected, the SCD group was shown to be cognitively healthy on tasks where MCI participants showed impairment. However, SCD’s hippocampal volume related to episodic memory performances, and WMH to executive functions. Thus, SCD represents a valid research concept and should be used, alongside MCI, to better understand the preclinical/prodromal phase of AD

Cross-sectional and longitudinal association of sleep and Alzheimer biomarkers in cognitively unimpaired adults

Sleep abnormalities are prevalent in Alzheimer's disease, with sleep quality already impaired at its preclinical stage. Epidemiological and experimental data point to sleep abnormalities contributing to the risk of Alzheimer's disease. However, previous studies are limited by either a lack of Alzheimer's disease biomarkers, reduced sample size or cross-sectional design. Understanding if, when, and how poor sleep contributes to Alzheimer's disease progression is important so that therapies can be targeted to the right phase of the disease. Using the largest cohort to date, the European Prevention of Alzheimer's Dementia Longitudinal Cohort Study, we test the hypotheses that poor sleep is associated with core Alzheimer's disease CSF biomarkers cross-sectionally and predicts future increments of Alzheimer's disease pathology in people without identifiable symptoms of Alzheimer's disease at baseline. This study included 1168 adults aged over 50 years with CSF core Alzheimer's disease biomarkers (total tau, phosphorylated tau and amyloid-beta), cognitive performance, and sleep quality (Pittsburgh sleep quality index questionnaire) data. We used multivariate linear regressions to analyse associations between core Alzheimer's disease biomarkers and the following Pittsburgh sleep quality index measures: total score of sleep quality, binarized score (poor sleep categorized as Pittsburgh sleep quality index > 5), sleep latency, duration, efficiency and disturbance. On a subsample of 332 participants with CSF taken at baseline and after an average period of 1.5 years, we assessed the effect of baseline sleep quality on change in Alzheimer's disease biomarkers over time. Cross-sectional analyses revealed that poor sleep quality (Pittsburgh sleep quality index total > 5) was significantly associated with higher CSF t-tau; shorter sleep duration (9 versus 0) was associated with lower CSF amyloid-beta. Longitudinal analyses showed that greater sleep disturbances (1-9 versus 0 and >9 versus 0) were associated with a decrease in CSF Aβ42 over time. This study demonstrates that self-reported poor sleep quality is associated with greater Alzheimer's disease-related pathology in cognitively unimpaired individuals, with longitudinal results further strengthening the hypothesis that disrupted sleep may represent a risk factor for Alzheimer's disease. This highlights the need for future work to test the efficacy of preventive practices, designed to improve sleep at pre-symptomatic stages of disease, on reducing Alzheimer's disease pathology

Analysis of Genes (\u3ci\u3eTMEM106B\u3c/i\u3e, \u3ci\u3eGRN\u3c/i\u3e, \u3ci\u3eABCC9\u3c/i\u3e, \u3ci\u3eKCNMB2\u3c/i\u3e, and \u3ci\u3eAPOE\u3c/i\u3e) Implicated in Risk for LATE-NC and Hippocampal Sclerosis Provides Pathogenetic Insights: A Retrospective Genetic Association Study

Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is the most prevalent subtype of TDP-43 proteinopathy, affecting up to 1/3rd of aged persons. LATE-NC often co-occurs with hippocampal sclerosis (HS) pathology. It is currently unknown why some individuals with LATE-NC develop HS while others do not, but genetics may play a role. Previous studies found associations between LATE-NC phenotypes and specific genes: TMEM106B, GRN, ABCC9, KCNMB2, and APOE. Data from research participants with genomic and autopsy measures from the National Alzheimer’s Coordinating Center (NACC; n = 631 subjects included) and the Religious Orders Study and Memory and the Rush Aging Project (ROSMAP; n = 780 included) were analyzed in the current study. Our goals were to reevaluate disease-associated genetic variants using newly collected data and to query whether the specific genotype/phenotype associations could provide new insights into disease-driving pathways. Research subjects included in prior LATE/HS genome-wide association studies (GWAS) were excluded. Single nucleotide variants (SNVs) within 10 kb of TMEM106B, GRN, ABCC9, KCNMB2, and APOE were tested for association with HS and LATE-NC, and separately for Alzheimer’s pathologies, i.e. amyloid plaques and neurofibrillary tangles. Significantly associated SNVs were identified. When results were meta-analyzed, TMEM106B, GRN, and APOE had significant gene-based associations with both LATE and HS, whereas ABCC9 had significant associations with HS only. In a sensitivity analysis limited to LATE-NC + cases, ABCC9 variants were again associated with HS. By contrast, the associations of TMEM106B, GRN, and APOE with HS were attenuated when adjusting for TDP-43 proteinopathy, indicating that these genes may be associated primarily with TDP-43 proteinopathy. None of these genes except APOE appeared to be associated with Alzheimer’s-type pathology. In summary, using data not included in prior studies of LATE or HS genomics, we replicated several previously reported gene-based associations and found novel evidence that specific risk alleles can differentially affect LATE-NC and HS

Peripheral Blood Cell-Stratified Subgroups of Inflamed Depression.

BACKGROUND: Depression has been associated with increased inflammatory proteins, but changes in circulating immune cells are less well defined. METHODS: We used multiparametric flow cytometry to count 14 subsets of peripheral blood cells in 206 depression cases and 77 age- and sex-matched controls (N = 283). We used univariate and multivariate analyses to investigate the immunophenotypes associated with depression and depression severity. RESULTS: Depression cases, compared with controls, had significantly increased immune cell counts, especially neutrophils, CD4+ T cells, and monocytes, and increased inflammatory proteins (C-reactive protein and interleukin-6). Within-group analysis of cases demonstrated significant associations between the severity of depressive symptoms and increased myeloid and CD4+ T-cell counts. Depression cases were partitioned into 2 subgroups by forced binary clustering of cell counts: the inflamed depression subgroup (n = 81 out of 206; 39%) had increased monocyte, CD4+, and neutrophil counts; increased C-reactive protein and interleukin-6; and more severe depression than the uninflamed majority of cases. Relaxing the presumption of a binary classification, data-driven analysis identified 4 subgroups of depression cases, 2 of which (n = 38 and n = 100; 67% collectively) were associated with increased inflammatory proteins and more severe depression but differed in terms of myeloid and lymphoid cell counts. Results were robust to potentially confounding effects of age, sex, body mass index, recent infection, and tobacco use. CONCLUSIONS: Peripheral immune cell counts were used to distinguish inflamed and uninflamed subgroups of depression and to indicate that there may be mechanistically distinct subgroups of inflamed depression.This work was supported by the Wellcome Trust [104025]. M Lynall was supported by a fellowship and grant from Addenbrooke’s Charitable Trust, Cambridge and a fellowship from the Medical Research Council (MR/S006257/1). M. R. Clatworthy is supported by the NIHR Cambridge Biomedical Research Centre (Transplant and Regenerative Medicine), NIHR Blood and Transplant Research Unit, MRC New Investigator Research Grant, MR/N024907/1; Arthritis Research UK Cure Challenge Research Grant, 21777), and an NIHR Research Professorship (RP-2017-08-ST2-002). E. T. Bullmore and C. M. Pariante are each supported by a NIHR Senior Investigator award. This work was also supported by the NIHR Cambridge Biomedical Research Centre (Mental Health) and the Cambridge NIHR BRC Cell Phenotyping Hub, as well as the NIHR BRC at the South London and Maudsley NHS Foundation Trust and King's College London, London