Roger Higdon

Neuropathology of nondemented aging: Presumptive evidence for preclinical Alzheimer disease

OBJECTIVE To determine the frequency and possible cognitive effect of histological Alzheimers disease (AD) in autopsied older nondemented individuals. DESIGN Senile plaques (SPs) and neurofibrillary tangles (NFTs) were assessed quantitatively in 97 cases from 7 Alzheimers Disease Centers (ADCs). Neuropathological diagnoses of AD (npAD) were also made with four sets of criteria. Adjusted linear mixed models tested differences between participants with and without npAD on the quantitative neuropathology measures and psychometric test scores prior to death. Spearman rank-order correlations between AD lesions and psychometric scores at last assessment were calculated for cases with pathology in particular regions. SETTING Washington University Alzheimers Disease Research Center. PARTICIPANTS Ninety-seven nondemented participants who were age 60 years or older at death (mean=84 years). RESULTS About 40% of nondemented individuals met at least some level of criteria for npAD; when strict criteria were used, about 20% of cases had npAD. Substantial overlap of Braak neurofibrillary stages occurred between npAD and no-npAD cases. Although there was no measurable cognitive impairment prior to death for either the no-npAD or npAD groups, cognitive function in nondemented aging appears to be degraded by the presence of NFTs and SPs. CONCLUSIONS Neuropathological processes related to AD in persons without dementia appear to be associated with subtle cognitive dysfunction and may represent a preclinical stage of the illness. By age 80-85 years, many nondemented older adults have substantial AD pathology.

Statin therapy and risk of dementia in the elderly A community-based prospective cohort study

Objective: To assess the association between statin therapy and risk of Alzheimer disease (AD) in a prospective cohort study with documented statin exposure and incident dementia. Methods: This is a prospective, cohort study of statin use and incident dementia and probable AD. A cohort of 2,356 cognitively intact persons, aged 65 and older, were randomly selected from a health maintenance organization (HMO), and were assessed biennially for dementia. Statin use was identified using the HMO pharmacy database. A proportional hazards model with statin use as a time-dependent covariate was used to assess the statin–dementia/AD association. Results: Among 312 participants with incident dementia, 168 had probable AD. The unadjusted hazard ratios (HRs) with statin use were 1.33 (95% CI 0.95 to 1.85) for all-cause dementia and 0.90 (CI 0.54 to 1.51) for probable AD. Adjusted corresponding HRs were 1.19 (CI 0.82 to 1.75) and 0.82 (CI 0.46 to 1.46). A subgroup analysis of participants with at least one APOE-ε4 allele who entered the study before age 80 produced an adjusted HR of 0.33 (CI 0.10 to 1.04). Conclusion: Employing time-dependent proportional hazards modeling, the authors found no significant association between statin use and incident dementia or probable AD. In contrast, when the data were analyzed, inappropriately, as a case-control study, the authors found an OR of 0.55 for probable AD, falsely indicating a protective effect of statins. Study design and analytic methods may explain the discrepancy between the current null findings and earlier findings.

Gene expression correlates of neurofibrillary tangles in Alzheimer's disease.

Neurofibrillary tangles (NFT) constitute one of the cardinal histopathological features of Alzheimers disease (AD). To explore in vivo molecular processes involved in the development of NFTs, we compared gene expression profiles of NFT-bearing entorhinal cortex neurons from 19 AD patients, adjacent non-NFT-bearing entorhinal cortex neurons from the same patients, and non-NFT-bearing entorhinal cortex neurons from 14 non-demented, histopathologically normal controls (ND). Of the differentially expressed genes, 225 showed progressively increased expression (AD NFT neurons > AD non-NFT neurons > ND non-NFT neurons) or progressively decreased expression (AD NFT neurons < AD non-NFT neurons < ND non-NFT neurons), raising the possibility that they may be related to the early stages of NFT formation. Immunohistochemical studies confirmed that many of the implicated proteins are dysregulated and preferentially localized to NFTs, including apolipoprotein J, interleukin-1 receptor-associated kinase 1, tissue inhibitor of metalloproteinase 3, and casein kinase 2, beta. Functional validation studies are underway to determine which candidate genes may be causally related to NFT neuropathology, thus providing therapeutic targets for the treatment of AD.

MOPED: Model Organism Protein Expression Database

Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share, integrate, query and compare the studies. The Model Organism Protein Expression Database (MOPED, htttp://moped.proteinspire.org) is a new and expanding proteomics resource that enables rapid browsing of protein expression information from publicly available studies on humans and model organisms. MOPED is designed to simplify the comparison and sharing of proteomics data for the greater research community. MOPED uniquely provides protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis. Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression. MOPED empowers users to visualize their own expression data and compare it with existing studies. Further, MOPED links to various protein and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED contains over 43 000 proteins with at least one spectral match and more than 11 million high certainty spectra.

Temporoparietal Hypometabolism in Frontotemporal Lobar Degeneration and Associated Imaging Diagnostic Errors

OBJECTIVE To evaluate the cause of diagnostic errors in the visual interpretation of positron emission tomographic scans with fludeoxyglucose F 18 (FDG-PET) in patients with frontotemporal lobar degeneration (FTLD) and patients with Alzheimer disease (AD). DESIGN Twelve trained raters unaware of clinical and autopsy information independently reviewed FDG-PET scans and provided their diagnostic impression and confidence of either FTLD or AD. Six of these raters also recorded whether metabolism appeared normal or abnormal in 5 predefined brain regions in each hemisphere-frontal cortex, anterior cingulate cortex, anterior temporal cortex, temporoparietal cortex, and posterior cingulate cortex. Results were compared with neuropathological diagnoses. SETTING Academic medical centers. PATIENTS Forty-five patients with pathologically confirmed FTLD (n=14) or AD (n=31). RESULTS Raters had a high degree of diagnostic accuracy in the interpretation of FDG-PET scans; however, raters consistently found some scans more difficult to interpret than others. Unanimity of diagnosis among the raters was more frequent in patients with AD (27 of 31 patients [87%]) than in patients with FTLD (7 of 14 patients [50%]) (P=.02). Disagreements in interpretation of scans in patients with FTLD largely occurred when there was temporoparietal hypometabolism, which was present in 7 of the 14 FTLD scans and 6 of the 7 scans lacking unanimity. Hypometabolism of anterior cingulate and anterior temporal regions had higher specificities and positive likelihood ratios for FTLD than temporoparietal hypometabolism had for AD. CONCLUSIONS Temporoparietal hypometabolism in FTLD is common and may cause inaccurate interpretation of FDG-PET scans. An interpretation paradigm that focuses on the absence of hypometabolism in regions typically affected in AD before considering FTLD is likely to misclassify a significant portion of FTLD scans. Anterior cingulate and/or anterior temporal hypometabolism indicates a high likelihood of FTLD, even when temporoparietal hypometabolism is present. Ultimately, the accurate interpretation of FDG-PET scans in patients with dementia cannot rest on the presence or absence of a single region of hypometabolism but rather must take into account the relative hypometabolism of all brain regions.

A predictive model for identifying proteins by a single peptide match

MOTIVATION Tandem mass-spectrometry of trypsin digests, followed by database searching, is one of the most popular approaches in high-throughput proteomics studies. Peptides are considered identified if they pass certain scoring thresholds. To avoid false positive protein identification, > or = 2 unique peptides identified within a single protein are generally recommended. Still, in a typical high-throughput experiment, hundreds of proteins are identified only by a single peptide. We introduce here a method for distinguishing between true and false identifications among single-hit proteins. The approach is based on randomized database searching and usage of logistic regression models with cross-validation. This approach is implemented to analyze three bacterial samples enabling recovery 68-98% of the correct single-hit proteins with an error rate of < 2%. This results in a 22-65% increase in number of identified proteins. Identifying true single-hit proteins will lead to discovering many crucial regulators, biomarkers and other low abundance proteins. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Differential Expression Analysis for Pathways

Life science technologies generate a deluge of data that hold the keys to unlocking the secrets of important biological functions and disease mechanisms. We present DEAP, Differential Expression Analysis for Pathways, which capitalizes on information about biological pathways to identify important regulatory patterns from differential expression data. DEAP makes significant improvements over existing approaches by including information about pathway structure and discovering the most differentially expressed portion of the pathway. On simulated data, DEAP significantly outperformed traditional methods: with high differential expression, DEAP increased power by two orders of magnitude; with very low differential expression, DEAP doubled the power. DEAP performance was illustrated on two different gene and protein expression studies. DEAP discovered fourteen important pathways related to chronic obstructive pulmonary disease and interferon treatment that existing approaches omitted. On the interferon study, DEAP guided focus towards a four protein path within the 26 protein Notch signalling pathway.

Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.

Toward more transparent and reproducible omics studies through a common metadata checklist and data publications.

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

The Promise of Multi-Omics and Clinical Data Integration to Identify and Target Personalized Healthcare Approaches in Autism Spectrum Disorders

Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.