What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

Influence of a Hyperglycemic Microenvironment on a Diabetic Versus Healthy Rat Vascular Endothelium Reveals Distinguishable Mechanistic and Phenotypic Responses.

Hyperglycemia is a critical factor in the development of endothelial dysfunction in type 2 diabetes mellitus (T2DM). Whether hyperglycemic states result in a disruption of similar molecular mechanisms in endothelial cells under both diabetic and non-diabetic states, remains largely unknown. This study aimed to address this gap in knowledge through molecular and functional characterization of primary rat cardiac microvascular endothelial cells (RCMVECs) derived from the T2DM Goto-Kakizaki (GK) rat model in comparison to control Wistar-Kyoto (WKY) in response to a normal (NG) and hyperglycemic (HG) microenvironment. GK and WKY RCMVECs were cultured under NG (4.5 mM) and HG (25 mM) conditions for 3 weeks, followed by tandem mass spectrometry (MS/MS), qPCR, tube formation assay, microplate based fluorimetry, and mitochondrial respiration analyses. Following database matching and filtering (false discovery rate ≤ 5%, scan count ≥ 10), we identified a greater percentage of significantly altered proteins in GK (7.1%, HG versus NG), when compared to WKY (3.5%, HG versus NG) RCMVECs. Further stringent filters (log2ratio of > 2 or < -2, p < 0.05) followed by enrichment and pathway analyses of the MS/MS and quantitative PCR datasets (84 total genes screened), resulted in the identification of several molecular targets involved in angiogenic, redox and metabolic functions that were distinctively altered in GK as compared to WKY RCMVECs following HG exposure. While the expression of thirteen inflammatory and apoptotic genes were significantly increased in GK RCMVECs under HG conditions (p < 0.05), only 2 were significantly elevated in WKY RCMVECs under HG conditions. Several glycolytic enzymes were markedly reduced and pyruvate kinase activity was elevated in GK HG RCMVECs, while in mitochondrial respiratory chain activity was altered. Supporting this, TNFα and phorbol ester (PMA)-induced Reactive Oxygen Species (ROS) production were significantly enhanced in GK HG RCMVECs when compared to baseline levels (p < 0.05). Additionally, PMA mediated increase was the greatest in GK HG RCMVECs (p < 0.05). While HG caused reduction in tube formation assay parameters for WKY RCMVECs, GK RCMVECs exhibited impaired phenotypes under baseline conditions regardless of the glycemic microenvironment. We conclude that hyperglycemic microenvironment caused distinctive changes in the bioenergetics and REDOX pathways in the diabetic endothelium as compared to those observed in a healthy endothelium.

Genome-wide map of proximity linkage to renin proximal promoter in rat.

A challenge to understanding enhancer-gene relationships is that enhancers are not always sequentially close to the gene they regulate. Physical proximity mapping through sequencing can provide an unbiased view of the chromatin close to the proximal promoter of the renin gene ( Ren). Our objective was to determine genomic regions that physically interact with the renin proximal promoter, using two different genetic backgrounds, the Dahl salt sensitive and normotensive SS-13BN, which have been shown to have different regulation of plasma renin in vivo. The chromatin conformation capture method with sequencing focused at the Ren proximal promoter in rat-derived cardiac endothelial cells was used. Cells were fixed, chromatin close to the Ren promoter was captured, and fragments were sequenced. The clustering of mapped reads produced a genome-wide map of chromatin in contact with the Ren promoter. The largest number of contacts was found on chromosome 13, the chromosome with Ren, and contacts were found on all other chromosomes except chromosome X. These contacts were significantly enriched with genes positively correlated with Ren expression and with mapped quantitative trait loci associated with blood pressure, cardiovascular, and renal phenotypes. The results were reproducible in an independent biological replicate. The findings reported here represent the first map between a critical cardiovascular gene and physical interacting loci throughout the genome and will provide the basis for several new directions of research.

Low cost, scalable proteomics data analysis using Amazon's cloud computing services and open source search algorithms.

One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step-by-step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center Web site ( http://proteomics.mcw.edu/vipdac ).