Amyloid pathway-based candidate gene analysis of [(11)C]PiB-PET in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.
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ABSTRACT:
SUBMITTER: Swaminathan S
PROVIDER: S-EPMC3256261 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNeuroimaging measures and chemical biomarkers may be important indices of clinical progression in normal aging and mild cognitive impairment (MCI) and need to be evaluated longitudinally.ObjectiveTo characterize cross-sectionally and longitudinally clinical measures in normal controls, subjects with MCI, and subjects with mild Alzheimer disease (AD) to enable the assessment of the utility of neuroimaging and chemical biomarker measures.MethodsA total of 819 subjects (229 cognitively normal, 398 with MCI, and 192 with AD) were enrolled at baseline and followed for 12 months using standard cognitive and functional measures typical of clinical trials.ResultsThe subjects with MCI were more memory impaired than the cognitively normal subjects but not as impaired as the subjects with AD. Nonmemory cognitive measures were only minimally impaired in the subjects with MCI. The subjects with MCI progressed to dementia in 12 months at a rate of 16.5% per year. Approximately 50% of the subjects with MCI were on antidementia therapies. There was minimal movement on the Alzheimer's Disease Assessment Scale-Cognitive Subscale for the normal control subjects, slight movement for the subjects with MCI of 1.1, and a modest change for the subjects with AD of 4.3. Baseline CSF measures of Abeta-42 separated the 3 groups as expected and successfully predicted the 12-month change in cognitive measures.ConclusionThe Alzheimer's Disease Neuroimaging Initiative has successfully recruited cohorts of cognitively normal subjects, subjects with mild cognitive impairment (MCI), and subjects with Alzheimer disease with anticipated baseline characteristics. The 12-month progression rate of MCI was as predicted, and the CSF measures heralded progression of clinical measures over 12 months.
| S-EPMC2809036 | biostudies-literature
Project description:We describe the outcome of the Biomarkers Consortium CSF Proteomics Project (where CSF is cerebral spinal fluid), a public-private partnership of government, academia, nonprofit, and industry. The goal of this study was to evaluate a multiplexed MS-based approach for the qualification of candidate Alzheimer's disease (AD) biomarkers using CSF samples from the AD Neuroimaging Initiative.Reproducibility of sample processing, analytic variability, and ability to detect a variety of analytes of interest were thoroughly investigated. Multiple approaches to statistical analyses assessed whether panel analytes were associated with baseline pathology (mild cognitive impairment (MCI), AD) versus healthy controls or associated with progression for MCI patients, and included (i) univariate association analyses, (ii) univariate prediction models, (iii) exploratory multivariate analyses, and (iv) supervised multivariate analysis.A robust targeted MS-based approach for the qualification of candidate AD biomarkers was developed. The results identified several peptides with potential diagnostic or predictive utility, with the most significant differences observed for the following peptides for differentiating (including peptides from hemoglobin A, hemoglobin B, and superoxide dismutase) or predicting (including peptides from neuronal pentraxin-2, neurosecretory protein VGF (VGF), and secretogranin-2) progression versus nonprogression from MCI to AD.These data provide potential insights into the biology of CSF in AD and MCI progression and provide a novel tool for AD researchers and clinicians working to improve diagnostic accuracy, evaluation of treatment efficacy, and early diagnosis.
| S-EPMC4739636 | biostudies-literature
Project description: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.
| S-EPMC8619902 | biostudies-literature
Project description:BackgroundRecent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD.Methods and findingsWe focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3 × 10(-13)). We applied a multivariate approach based on combinatorial optimization ((?,?)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering "meta-features," representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%.ConclusionsApplying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages.
| S-EPMC3317783 | biostudies-literature
Project description:We sought to develop and evaluate a composite memory score from the neuropsychological battery used in the Alzheimer's Disease (AD) Neuroimaging Initiative (ADNI). We used modern psychometric approaches to analyze longitudinal Rey Auditory Verbal Learning Test (RAVLT, 2 versions), AD Assessment Schedule - Cognition (ADAS-Cog, 3 versions), Mini-Mental State Examination (MMSE), and Logical Memory data to develop ADNI-Mem, a composite memory score. We compared RAVLT and ADAS-Cog versions, and compared ADNI-Mem to RAVLT recall sum scores, four ADAS-Cog-derived scores, the MMSE, and the Clinical Dementia Rating Sum of Boxes. We evaluated rates of decline in normal cognition, mild cognitive impairment (MCI), and AD, ability to predict conversion from MCI to AD, strength of association with selected imaging parameters, and ability to differentiate rates of decline between participants with and without AD cerebrospinal fluid (CSF) signatures. The second version of the RAVLT was harder than the first. The ADAS-Cog versions were of similar difficulty. ADNI-Mem was slightly better at detecting change than total RAVLT recall scores. It was as good as or better than all of the other scores at predicting conversion from MCI to AD. It was associated with all our selected imaging parameters for people with MCI and AD. Participants with MCI with an AD CSF signature had somewhat more rapid decline than did those without. This paper illustrates appropriate methods for addressing the different versions of word lists, and demonstrates the additional power to be gleaned with a psychometrically sound composite memory score.
| S-EPMC3806057 | biostudies-literature
Project description:Memory deficits are prominent features of mild cognitive impairment (MCI) and Alzheimer's disease (AD). The genetic architecture underlying these memory deficits likely involves the combined effects of multiple genetic variants operative within numerous biological pathways. In order to identify functional pathways associated with memory impairment, we performed a pathway enrichment analysis on genome-wide association data from 742 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. A composite measure of memory was generated as the phenotype for this analysis by applying modern psychometric theory to item-level data from the ADNI neuropsychological test battery. Using the GSA-SNP software tool, we identified 27 canonical, expertly-curated pathways with enrichment (FDR-corrected p-value < 0.05) against this composite memory score. Processes classically understood to be involved in memory consolidation, such as neurotransmitter receptor-mediated calcium signaling and long-term potentiation, were highly represented among the enriched pathways. In addition, pathways related to cell adhesion, neuronal differentiation and guided outgrowth, and glucose- and inflammation-related signaling were also enriched. Among genes that were highly-represented in these enriched pathways, we found indications of coordinated relationships, including one large gene set that is subject to regulation by the SP1 transcription factor, and another set that displays co-localized expression in normal brain tissue along with known AD risk genes. These results 1) demonstrate that psychometrically-derived composite memory scores are an effective phenotype for genetic investigations of memory impairment and 2) highlight the promise of pathway analysis in elucidating key mechanistic targets for future studies and for therapeutic interventions.
| S-EPMC3713637 | biostudies-literature
Project description:BackgroundAlzheimer's disease (AD) is a chronic progressive neurodegenerative disease impacting an estimated 44 million adults worldwide. The causal pathology of AD (accumulation of amyloid-beta and tau), precedes hallmark symptoms of dementia by more than a decade, necessitating development of early diagnostic markers of disease onset, particularly for new drugs that aim to modify disease processes. To evaluate differentially methylated positions (DMPs) as novel blood-based biomarkers of AD, we used a subset of 653 individuals with peripheral blood (PB) samples in the Alzheimer's disease Neuroimaging Initiative (ADNI) consortium. The selected cohort of AD, mild cognitive impairment (MCI), and age-matched healthy controls (CN) all had imaging, genetics, transcriptomics, cerebrospinal protein markers, and comprehensive clinical records, providing a rich resource of concurrent multi-omics and phenotypic information on a well-phenotyped subset of ADNI participants.ResultsIn this manuscript, we report cross-diagnosis differential peripheral DNA methylation in a cohort of AD, MCI, and age-matched CN individuals with longitudinal DNA methylation measurements. Epigenome-wide association studies (EWAS) were performed using a mixed model with repeated measures over time with a P value cutoff of 1 × 10-5 to test contrasts of pairwise differential peripheral methylation in AD vs CN, AD vs MCI, and MCI vs CN. The most highly significant differentially methylated loci also tracked with Mini Mental State Examination (MMSE) scores. Differentially methylated loci were enriched near brain and neurodegeneration-related genes (e.g., BDNF, BIN1, APOC1) validated using the genotype tissue expression project portal (GTex).ConclusionsOur work shows that peripheral differential methylation between age-matched subjects with AD relative to healthy controls will provide opportunities to further investigate and validate differential methylation as a surrogate of disease. Given the inaccessibility of brain tissue, the PB-associated methylation marks may help identify the stage of disease and progression phenotype, information that would be central to bringing forward successful drugs for AD.
| S-EPMC7294637 | biostudies-literature
Project description:The Alzheimer's Disease Neuroimaging Initiative (ADNI) measures abilities broadly related to executive function (EF), including WAIS-R Digit Symbol Substitution, Digit Span Backwards, Trails A and B, Category Fluency, and Clock Drawing. This study investigates whether a composite executive function measure based on these multiple indicators has better psychometric characteristics than the widely used individual components. We applied item response theory methods to 800 ADNI participants to derive an EF composite score (ADNI-EF) from the above measures. We then compared ADNI-EF with component measures in 390 longitudinally-followed participants with mild cognitive impairment (MCI) with respect to: (1) Ability to detect change over time; (2) Ability to predict conversion to dementia; (3) Strength of cross-sectional association with MRI-derived measures of structures involved in frontal systems, and (4) Strength of baseline association with cerebrospinal fluid (CSF) levels of amyloid β₁₋₄₂, total tau, and phosphorylated tau(181P). ADNI-EF showed the greatest change over time, followed closely by Category Fluency. ADNI-EF needed a 40 % smaller sample size to detect change. ADNI-EF was the strongest predictor of AD conversion. ADNI-EF was the only measure significantly associated with all the MRI regions, though other measures were more strongly associated in a few of the regions. ADNI-EF was associated with all the CSF measures. ADNI-EF appears to be a useful composite measure of EF in MCI, as good as or better than any of its composite parts. This study demonstrates an approach to developing a psychometrically sophisticated composite score from commonly-used tests.
| S-EPMC3684181 | biostudies-literature
Project description:With increasing life expectancy in developed countries, the incidence of Alzheimer's disease (AD) and its socioeconomic impact are growing. Increasing knowledge of the mechanisms of AD facilitates the development of treatment strategies aimed at slowing down or preventing neuronal death. AD treatment trials using clinical outcome measures require long observation times and large patient samples. There is increasing evidence that neuroimaging and cerebrospinal fluid and blood biomarkers may provide information that may reduce sample sizes and observation periods. The Alzheimer's Disease Neuroimaging Initiative will help identify clinical, neuroimaging, and biomarker outcome measures that provide the highest power for measurement of longitudinal changes and for prediction of transitions.
| S-EPMC2376747 | biostudies-literature
Project description:The positron emission tomography (PET) radiotracer Pittsburgh Compound-B (PiB) binds with high affinity to beta-pleated sheet aggregates of the amyloid-beta (Abeta) peptide in vitro. The in vivo retention of PiB in brains of people with Alzheimer's disease shows a regional distribution that is very similar to distribution of Abeta deposits observed post-mortem. However, the basis for regional variations in PiB binding in vivo, and the extent to which it binds to different types of Abeta-containing plaques and tau-containing neurofibrillary tangles (NFT), has not been thoroughly investigated. The present study examined 28 clinically diagnosed and autopsy-confirmed Alzheimer's disease subjects, including one Alzheimer's disease subject who had undergone PiB-PET imaging 10 months prior to death, to evaluate region- and substrate-specific binding of the highly fluorescent PiB derivative 6-CN-PiB. These data were then correlated with region-matched Abeta plaque load and peptide levels, [(3)H]PiB binding in vitro, and in vivo PET retention levels. We found that in Alzheimer's disease brain tissue sections, the preponderance of 6-CN-PiB binding is in plaques immunoreactive to either Abeta42 or Abeta40, and to vascular Abeta deposits. 6-CN-PiB labelling was most robust in compact/cored plaques in the prefrontal and temporal cortices. While diffuse plaques, including those in caudate nucleus and presubiculum, were less prominently labelled, amorphous Abeta plaques in the cerebellum were not detectable with 6-CN-PiB. Only a small subset of NFT were 6-CN-PiB positive; these resembled extracellular 'ghost' NFT. In Alzheimer's disease brain tissue homogenates, there was a direct correlation between [(3)H]PiB binding and insoluble Abeta peptide levels. In the Alzheimer's disease subject who underwent PiB-PET prior to death, in vivo PiB retention levels correlated directly with region-matched post-mortem measures of [(3)H]PiB binding, insoluble Abeta peptide levels, 6-CN-PiB- and Abeta plaque load, but not with measures of NFT. These results demonstrate, in a typical Alzheimer's disease brain, that PiB binding is highly selective for insoluble (fibrillar) Abeta deposits, and not for neurofibrillary pathology. The strong direct correlation of in vivo PiB retention with region-matched quantitative analyses of Abeta plaques in the same subject supports the validity of PiB-PET imaging as a method for in vivo evaluation of Abeta plaque burden.
| S-EPMC2408940 | biostudies-literature