CohortExplorer: A Generic Application Programming Interface for Entity Attribute Value Database Schemas

The Role of Free/Libre and Open Source Software in Learning Health Systems

Objective: To give an overview of the role of Free/Libre and Open Source Software (FLOSS) in the context of secondary use of patient data to enable Learning Health Systems (LHSs). Methods: We conducted an environmental scan of the academic and grey literature utilising the MedFLOSS database of open source systems in healthcare to inform a discussion of the role of open source in developing LHSs that reuse patient data for research and quality improvement. Results: A wide range of FLOSS is identified that contributes to the information technology (IT) infrastructure of LHSs including operating systems, databases, frameworks, interoperability software, and mobile and web apps. The recent literature around the development and use of key clinical data management tools is also reviewed. Conclusions: FLOSS already plays a critical role in modern health IT infrastructure for the collection, storage, and analysis of patient data. The nature of FLOSS systems to be collaborative, modular, and modifiable may make open source approaches appropriate for building the digital infrastructure for a LHS.

A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer's Disease Diagnosis

Background:

Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer’s disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust.

Objectives:

This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts.

Methods:

Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis.

Results:

Gene and pathway level models performed similarly to each other and to a model based on demographic information only.

Conclusions:

Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

No Evidence to Suggest that the Use of Acetylcholinesterase Inhibitors Confounds the Results of Two Blood-Based Biomarker Studies in Alzheimer's Disease

BACKGROUND

There is an urgent need to discover Alzheimer's disease (AD) biomarkers that are both easily measured and reliable. Research into blood-based biomarkers for AD using transcriptomics and proteomics has been an attractive and promising area of research. However, to date researchers have not looked into the possibility of AD medication being a confounding factor in these studies.

OBJECTIVE

This study explored whether acetylcholinesterase inhibitors (AChEIs), the main class of AD medication, are a confounding factor in AD blood biomarker studies.

METHODS

The most promising blood transcriptomic and proteomic biomarkers from two recent studies were analyzed to determine if they were differentially expressed between AD subjects on AChEIs and subjects that were not.

RESULTS

None of the gene or protein biomarkers analyzed were found to be significantly altered between subjects in either group.

CONCLUSION

This study found no evidence that AChEIs are a confounding factor in these published AD blood biomarker studies. Further work is needed to confirm that this is also the case for other proposed biomarkers.