Effectiveness of mRNA Booster Vaccine Against Coronavirus Disease 2019 Infection and Severe Outcomes Among Persons With and Without Immune Dysfunction: A Retrospective Cohort Study of National Electronic Medical Record Data in the United States.

Background: Real-world evidence of coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) booster effectiveness among patients with immune dysfunction are limited. Methods: We included data from patients in the United States National COVID Cohort Collaborative (N3C) who completed ≥2 doses of mRNA vaccination between 10 December 2020 and 27 May 2022. Immune dysfunction conditions included human immunodeficiency virus infection, solid organ or bone marrow transplant, autoimmune diseases, and cancer. We defined incident COVID-19 BTI as positive results from laboratory tests or diagnostic codes 14 days after at least 2 doses of mRNA vaccination; and severe COVID-19 BTI as hospitalization, invasive cardiopulmonary support, and/or death. We used propensity scores to match boosted versus nonboosted patients and evaluated hazards of incident and severe COVID-19 BTI using Cox regression after matching. Results: Among patients without immune dysfunction, the relative effectiveness of booster (3 doses) after 6 months from the primary (2 doses) vaccination against BTI ranged from 69% to 81% during the Delta-predominant period and from 33% to 39% during the Omicron-predominant period. Relative effectiveness against BTI was lower among patients with immune dysfunction but remained statistically significant in both periods. Boosted patients had lower risk of COVID-19-related hospitalization (hazard ratios [HR] ranged from 0.5 [95% confidence interval {CI}, .48-.53] to 0.63 [95% CI, .56-.70]), invasive cardiopulmonary support, or death (HRs ranged from 0.46 [95% CI, .41-.52] to 0.63 [95% CI, .50-.79]) during both periods. Conclusions: Booster vaccines remain effective against severe COVID-19 BTI throughout the Delta- and Omicron-predominant periods, regardless of patients' immune status.

COVID-19 outcomes in persons with hemophilia: results from a US-based national COVID-19 surveillance registry.

BACKGROUND: Hypercoagulable state contributing to thrombotic complications worsens COVID-19 severity and outcomes, whereas anticoagulation improves outcomes by alleviating hypercoagulability. OBJECTIVES: To examine whether hemophilia, an inherent hypocoagulable condition, offers protection against COVID-19 severity and reduces venous thromboembolism (VTE) risk in persons with hemophilia (PwH). METHODS: A 1:3 propensity score-matched retrospective cohort study used national COVID-19 registry data (January 2020 through January 2022) to compare outcomes between 300 male PwH and 900 matched controls without hemophilia. RESULTS: Analyses of PwH demonstrated that known risk factors (older age, heart failure, hypertension, cancer/malignancy, dementia, and renal and liver disease) contributed to severe COVID-19 and/or 30-day all-cause mortality. Non-central nervous system bleeding was an additional risk factor for poor outcomes in PwH. Odds of developing VTE with COVID-19 in PwH were associated with pre-COVID VTE diagnosis (odds ratio [OR], 51.9; 95% CI, 12.8-266; p < .001), anticoagulation therapy (OR, 12.7; 95% CI, 3.01-48.6; p < .001), and pulmonary disease (OR, 16.1; 95% CI, 10.4-25.4; p < .001). Thirty-day all-cause mortality (OR, 1.27; 95% CI, 0.75-2.11; p = .3) and VTE events (OR, 1.32; 95% CI, 0.64-2.73; p = .4) were not significantly different between the matched cohorts; however, hospitalizations (OR, 1.58; 95% CI, 1.20-2.10; p = .001) and non-central nervous system bleeding events (OR, 4.78; 95% CI, 2.98-7.48; p < .001) were increased in PwH. In multivariate analyses, hemophilia did not reduce adverse outcomes (OR, 1.32; 95% CI, 0.74-2.31; p = .2) or VTE (OR, 1.14; 95% CI, 0.44-2.67; p = .8) but increased bleeding risk (OR, 4.70; 95% CI, 2.98-7.48; p < .001). CONCLUSION: After adjusting for patient characteristics/comorbidities, hemophilia increased bleeding risk with COVID-19 but did not protect against severe disease and VTE.

Outcomes of SARS-CoV-2 infection among patients with orthopaedic fracture surgery in the National COVID Cohort Collaborative (N3C).

BACKGROUND: The objective of this study was to investigate the outcomes of COVID-19-positive patients undergoing orthopaedic fracture surgery using data from a national database of U.S. adults with a COVID-19 test for SARS-CoV-2. METHODS: This is a retrospective cohort study using data from a national database to compare orthopaedic fracture surgery outcomes between COVID-19-positive and COVID-19-negative patients in the United States. Participants aged 18-99 with orthopaedic fracture surgery between March and December 2020 were included. The main exposure was COVID-19 status. Outcomes included perioperative complications, 30-day all-cause mortality, and overall all-cause mortality. Multivariable adjusted models were fitted to determine the association of COVID-positivity with all-cause mortality. RESULTS: The total population of 6.5 million patient records was queried, identifying 76,697 participants with a fracture. There were 7,628 participants in the National COVID Cohort who had a fracture and operative management. The Charlson Comorbidity Index was higher in the COVID-19-positive group (n = 476, 6.2 %) than the COVID-19-negative group (n = 7,152, 93.8 %) (2.2 vs 1.4, p<0.001). The COVID-19-positive group had higher mortality (13.2 % vs 5.2 %, p<0.001) than the COVID-19-negative group with higher odds of death in the fully adjusted model (Odds Ratio=1.59; 95 % Confidence Interval: 1.16-2.18). CONCLUSION: COVID-19-positive participants with a fracture requiring surgery had higher mortality and perioperative complications than COVID-19-negative patients in this national cohort of U.S. adults tested for COVID-19. The risks associated with COVID-19 can guide potential treatment options and counseling of patients and their families. Future studies can be conducted as data accumulates. LEVEL OF EVIDENCE: Level III.

Ankle Fracture and Length of Stay in US Adult Population Using Data From the National COVID Cohort Collaborative.

BACKGROUND: The National COVID Cohort Collaborative (N3C) is an innovative approach to integrate real-world clinical observations into a harmonized database during the time of the COVID-19 pandemic when clinical research on ankle fracture surgery is otherwise mostly limited to expert opinion and research letters. The purpose of this manuscript is to introduce the largest cohort of US ankle fracture surgery patients to date with a comparison between lab-confirmed COVID-19-positive and COVID-19-negative. METHODS: A retrospective cohort of adults with ankle fracture surgery using data from the N3C database with patients undergoing surgery between March 2020 and June 2021. The database is an NIH-funded platform through which the harmonized clinical data from 46 sites is stored. Patient characteristics included body mass index, Charlson Comorbidity Index, and smoking status. Outcomes included 30-day mortality, overall mortality, surgical site infection (SSI), deep SSI, acute kidney injury, pulmonary embolism, deep vein thrombosis, sepsis, time to surgery, and length of stay. COVID-19-positive patients were compared to COVID-19-negative controls to investigate perioperative outcomes during the pandemic. RESULTS: A total population of 8.4 million patient records was queried, identifying 4735 adults with ankle fracture surgery. The COVID-19-positive group (n=158, 3.3%) had significantly longer times to surgery (6.5 ± 6.6 vs 5.1 ± 5.5 days, P = .001) and longer lengths of stay (8.3 ± 23.5 vs 4.3 ± 7.4 days, P < .001), compared to the COVID-19-negative group. The COVID-19-positive group also had a higher rate of 30-day mortality. CONCLUSION: Patients with ankle fracture surgery had longer time to surgery and prolonged hospitalizations in COVID-19-positive patients compared to those who tested negative (average delay was about 1 day and increased length of hospitalization was about 4 days). Few perioperative events were observed in either group. Overall, the risks associated with COVID-19 were measurable but not substantial.Level of Evidence: Level III, retrospective cohort study.

Association Between COVID-19 and Mortality in Hip Fracture Surgery in the National COVID Cohort Collaborative (N3C): A Retrospective Cohort Study.

BACKGROUND: This study investigated the outcomes of coronavirus disease (COVID-19)-positive patients undergoing hip fracture surgery using a national database. METHODS: This is a retrospective cohort study comparing hip fracture surgery outcomes between COVID-19 positive and negative matched cohorts from 46 sites in the United States. Patients aged 65 and older with hip fracture surgery between March 15 and December 31, 2020, were included. The main outcomes were 30-day all-cause mortality and all-cause mortality. RESULTS: In this national study that included 3303 adults with hip fracture surgery, the 30-day mortality was 14.6% with COVID-19-positive versus 3.8% in COVID-19-negative, a notable difference. The all-cause mortality for hip fracture surgery was 27.0% in the COVID-19-positive group during the study period. DICUSSION: We found higher incidence of all-cause mortality in patients with versus without diagnosis of COVID-19 after undergoing hip fracture surgery. The mortality in hip fracture surgery in this national analysis was lower than other local and regional reports. The medical community can use this information to guide the management of hip fracture patients with a diagnosis of COVID-19.

Associations between HIV infection and clinical spectrum of COVID-19: a population level analysis based on US National COVID Cohort Collaborative (N3C) data.

BACKGROUND: Evidence of whether people living with HIV are at elevated risk of adverse COVID-19 outcomes is inconclusive. We aimed to investigate this association using the population-based National COVID Cohort Collaborative (N3C) data in the USA. METHODS: We included all adult (aged ≥18 years) COVID-19 cases with any health-care encounter from 54 clinical sites in the USA, with data being deposited into the N3C. The outcomes were COVID-19 disease severity, hospitalisation, and mortality. Encounters in the same health-care system beginning on or after January 1, 2018, were also included to provide information about pre-existing health conditions (eg, comorbidities). Logistic regression models were employed to estimate the association of HIV infection and HIV markers (CD4 cell count, viral load) with hospitalisation, mortality, and clinical severity of COVID-19 (multinomial). The models were initially adjusted for demographic characteristics, then subsequently adjusted for smoking, obesity, and a broad range of comorbidities. Interaction terms were added to assess moderation effects by demographic characteristics. FINDINGS: In the harmonised N3C data release set from Jan 1, 2020, to May 8, 2021, there were 1 436 622 adult COVID-19 cases, of these, 13 170 individuals had HIV infection. A total of 26 130 COVID-19 related deaths occurred, with 445 among people with HIV. After adjusting for all the covariates, people with HIV had higher odds of COVID-19 death (adjusted odds ratio 1·29, 95% CI 1·16-1·44) and hospitalisation (1·20, 1·15-1·26), but lower odds of mild or moderate COVID-19 (0·61, 0·59-0·64) than people without HIV. Interaction terms revealed that the elevated odds were higher among older age groups, male, Black, African American, Hispanic, or Latinx adults. A lower CD4 cell count (<200 cells per µL) was associated with all the adverse COVID-19 outcomes, while viral suppression was only associated with reduced hospitalisation. INTERPRETATION: Given the COVID-19 pandemic's exacerbating effects on health inequities, public health and clinical communities must strengthen services and support to prevent aggravated COVID-19 outcomes among people with HIV, particularly for those with pronounced immunodeficiency. FUNDING: National Center for Advancing Translational Sciences, National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA.

Use of Hydroxychloroquine, Remdesivir, and Dexamethasone Among Adults Hospitalized With COVID-19 in the United States : A Retrospective Cohort Study.

BACKGROUND: Relatively little is known about the use patterns of potential pharmacologic treatments of COVID-19 in the United States. OBJECTIVE: To use the National COVID Cohort Collaborative (N3C), a large, multicenter, longitudinal cohort, to characterize the use of hydroxychloroquine, remdesivir, and dexamethasone, overall as well as across individuals, health systems, and time. DESIGN: Retrospective cohort study. SETTING: 43 health systems in the United States. PARTICIPANTS: 137 870 adults hospitalized with COVID-19 between 1 February 2020 and 28 February 2021. MEASUREMENTS: Inpatient use of hydroxychloroquine, remdesivir, or dexamethasone. RESULTS: Among 137 870 persons hospitalized with confirmed or suspected COVID-19, 8754 (6.3%) received hydroxychloroquine, 29 272 (21.2%) remdesivir, and 53 909 (39.1%) dexamethasone during the study period. Since the release of results from the RECOVERY (Randomised Evaluation of COVID-19 Therapy) trial in mid-June, approximately 78% to 84% of people who have had invasive mechanical ventilation have received dexamethasone or other glucocorticoids. The use of hydroxychloroquine increased during March 2020, peaking at 42%, and started declining by April 2020. By contrast, remdesivir and dexamethasone use gradually increased over the study period. Dexamethasone and remdesivir use varied substantially across health centers (intraclass correlation coefficient, 14.2% for dexamethasone and 84.6% for remdesivir). LIMITATION: Because most N3C data contributors are academic medical centers, findings may not reflect the experience of community hospitals. CONCLUSION: Dexamethasone, an evidence-based treatment of COVID-19, may be underused among persons who are mechanically ventilated. The use of remdesivir and dexamethasone varied across health systems, suggesting variation in patient case mix, drug access, treatment protocols, and quality of care. PRIMARY FUNDING SOURCE: National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; and National Institute on Aging.

Probing the Virtual Proteome to Identify Novel Disease Biomarkers.

BACKGROUND: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a "virtual proteomic" approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. METHODS: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). RESULTS: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-ß predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08-1.58; P=0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66-0.94; P=0.008) per 1-SD increment in platelet-derived growth factor receptor-ß. CONCLUSIONS: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

The Return of Actionable Variants Empirical (RAVE) Study, a Mayo Clinic Genomic Medicine Implementation Study: Design and Initial Results.

OBJECTIVES: To identify clinically actionable genetic variants from targeted sequencing of 68 disease-related genes, estimate their penetrance, and assess the impact of disclosing results to participants and providers. PATIENTS AND METHODS: The Return of Actionable Variants Empirical (RAVE) Study investigates outcomes following the return of pathogenic/likely pathogenic (P/LP) variants in 68 disease-related genes. The study was initiated in December 2016 and is ongoing. Targeted sequencing was performed in 2533 individuals with hyperlipidemia or colon polyps. The electronic health records (EHRs) of participants carrying P/LP variants in 36 cardiovascular disease (CVD) genes were manually reviewed to ascertain the presence of relevant traits. Clinical outcomes, health care utilization, family communication, and ethical and psychosocial implications of disclosure of genomic results are being assessed by surveys, telephone interviews, and EHR review. RESULTS: Of 29,208 variants in the 68 genes, 1915 were rare (frequency <1%) and putatively functional, and 102 of these (60 in 36 CVD genes) were labeled P/LP based on the American College of Medical Genetics and Genomics framework. Manual review of the EHRs of participants (n=73 with P/LP variants in CVD genes) revealed that 33 had the expected trait(s); however, only 6 of 45 participants with non-familial hypercholesterolemia (FH) P/LP variants had the expected traits. CONCLUSION: Expected traits were present in 13% of participants with P/LP variants in non-FH CVD genes, suggesting low penetrance; this estimate may change with additional testing performed as part of the clinical evaluation. Ongoing analyses of the RAVE Study will inform best practices for genomic medicine.

Building a semantic web-based metadata repository for facilitating detailed clinical modeling in cancer genome studies.

BACKGROUND: Detailed Clinical Models (DCMs) have been regarded as the basis for retaining computable meaning when data are exchanged between heterogeneous computer systems. To better support clinical cancer data capturing and reporting, there is an emerging need to develop informatics solutions for standards-based clinical models in cancer study domains. The objective of the study is to develop and evaluate a cancer genome study metadata management system that serves as a key infrastructure in supporting clinical information modeling in cancer genome study domains. METHODS: We leveraged a Semantic Web-based metadata repository enhanced with both ISO11179 metadata standard and Clinical Information Modeling Initiative (CIMI) Reference Model. We used the common data elements (CDEs) defined in The Cancer Genome Atlas (TCGA) data dictionary, and extracted the metadata of the CDEs using the NCI Cancer Data Standards Repository (caDSR) CDE dataset rendered in the Resource Description Framework (RDF). The ITEM/ITEM_GROUP pattern defined in the latest CIMI Reference Model is used to represent reusable model elements (mini-Archetypes). RESULTS: We produced a metadata repository with 38 clinical cancer genome study domains, comprising a rich collection of mini-Archetype pattern instances. We performed a case study of the domain "clinical pharmaceutical" in the TCGA data dictionary and demonstrated enriched data elements in the metadata repository are very useful in support of building detailed clinical models. CONCLUSION: Our informatics approach leveraging Semantic Web technologies provides an effective way to build a CIMI-compliant metadata repository that would facilitate the detailed clinical modeling to support use cases beyond TCGA in clinical cancer study domains.

Adverse Drug Events-based Tumor Stratification for Ovarian Cancer Patients Receiving Platinum Therapy.

The underlying molecular mechanisms of adverse drug events (ADEs) associated with cancer therapy drugs may overlap with their antineoplastic mechanisms. In a previous study, we developed an ADE-based tumor stratification framework (known as ADEStrata) with a case study of breast cancer patients receiving aromatase inhibitors, and demonstrated that the prediction of per-patient ADE propensity simultaneously identifies high-risk patients experiencing poor outcomes. In this study, we aim to evaluate the ADEStrata framework with a different tumor type and chemotherapy class - ovarian cancer treated with platinum chemotherapeutic drugs. We identified a cohort of ovarian cancer patients receiving cisplatin (a standard platinum therapy) from The Cancer Genome Atlas (TCGA) (n=156). We demonstrated that somatic variant prioritization guided by known ADEs associated with cisplatin could be used to stratify patients treated with cisplatin and uncover tumor subtypes with different clinical outcomes.

Drug Normalization for Cancer Therapeutic and Druggable Genome Target Discovery.

Heterogeneous drug data representation among different druggable genome knowledge resources and datasets delays effective cancer therapeutic target discovery within the broad scientific community. The objective of the present paper is to describe the challenges and lessons learned from our efforts in developing and evaluating a standards-based drug normalization framework targeting cancer druggable genome datasets. Our findings suggested that mechanisms need to be established to deal with spelling errors and irregularities in normalizing clinical drug data in The Cancer Genome Atlas (TCGA), whereas the annotations from NCI Thesaurus (NCIt) and PubChem are two layers of normalization that potentially bridge between the clinical phenotypes and the druggable genome knowledge for effective cancer therapeutic target discovery.

A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.

Textual eligibility criteria in clinical trial protocols contain important information about potential clinically relevant pharmacogenomic events. Manual curation for harvesting this evidence is intractable as it is error prone and time consuming. In this paper, we develop and evaluate a Semantic Web-based system that captures and manages mutation evidences and related contextual information from cancer clinical trials. The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager. We evaluated the performance of the annotator in terms of precision and recall. We demonstrated the usefulness of the system by conducting case studies in retrieving relevant clinical trials using a collection of mutations identified from TCGA Leukemia patients and Atlas of Genetics and Cytogenetics in Oncology and Haematology. In conclusion, our system using Semantic Web technologies provides an effective framework for extraction, annotation, standardization and management of genetic mutations in cancer clinical trials.

Granular Quality Reporting for Cervical Cytology Testing.

Quality reporting for cervical cancer prevention is focused on patients with normal cervical cytology, and excludes patients with cytological abnormalities that may be at higher risk. The major obstacles for granular reporting are the complexity of surveillance guidelines and free-text data. We performed automated chart review to compare the cytology testing rates for patients with 'atypical squamous cells of undetermined significance' (ASCUS) cytology, with the rates for patients with normal cytology. We modeled the surveillance guidelines, and extracted information from free-text cytology reports, to perform this study on 28101 female patients. Our results show that patients with ASCUS cytology had significantly higher adherence rates (94.9%) than those for patients with normal cytology (90.4%). Overall our study indicates that the quality of care varies significantly between the high and average risk patients. Our study demonstrates the use of health information technology for higher granularity of reporting for cervical cytology testing.

Cancer based pharmacogenomics network supported with scientific evidences: from the view of drug repurposing.

BACKGROUND: Pharmacogenomics (PGx) as an emerging field, is poised to change the way we practice medicine and deliver health care by customizing drug therapies on the basis of each patient's genetic makeup. A large volume of PGx data including information among drugs, genes, and single nucleotide polymorphisms (SNPs) has been accumulated. Normalized and integrated PGx information could facilitate revelation of hidden relationships among drug treatments, genomic variations, and phenotype traits to better support drug discovery and next generation of treatment. METHODS: In this study, we generated a normalized and scientific evidence supported cancer based PGx network (CPN) by integrating cancer related PGx information from multiple well-known PGx resources including the Pharmacogenomics Knowledge Base (PharmGKB), the FDA PGx Biomarkers in Drug Labeling, and the Catalog of Published Genome-Wide Association Studies (GWAS). We successfully demonstrated the capability of the CPN for drug repurposing by conducting two case studies. CONCLUSIONS: The CPN established in this study offers comprehensive cancer based PGx information to support cancer orientated research, especially for drug repurposing.

Quality Assurance of Cancer Study Common Data Elements Using A Post-Coordination Approach.

Domain-specific common data elements (CDEs) are emerging as an effective approach to standards-based clinical research data storage and retrieval. A limiting factor, however, is the lack of robust automated quality assurance (QA) tools for the CDEs in clinical study domains. The objectives of the present study are to prototype and evaluate a QA tool for the study of cancer CDEs using a post-coordination approach. The study starts by integrating the NCI caDSR CDEs and The Cancer Genome Atlas (TCGA) data dictionaries in a single Resource Description Framework (RDF) data store. We designed a compositional expression pattern based on the Data Element Concept model structure informed by ISO/IEC 11179, and developed a transformation tool that converts the pattern-based compositional expressions into the Web Ontology Language (OWL) syntax. Invoking reasoning and explanation services, we tested the system utilizing the CDEs extracted from two TCGA clinical cancer study domains. The system could automatically identify duplicate CDEs, and detect CDE modeling errors. In conclusion, compositional expressions not only enable reuse of existing ontology codes to define new domain concepts, but also provide an automated mechanism for QA of terminological annotations for CDEs.

Automated recommendation for cervical cancer screening and surveillance.

Because of the complexity of cervical cancer prevention guidelines, clinicians often fail to follow best-practice recommendations. Moreover, existing clinical decision support (CDS) systems generally recommend a cervical cytology every three years for all female patients, which is inappropriate for patients with abnormal findings that require surveillance at shorter intervals. To address this problem, we developed a decision tree-based CDS system that integrates national guidelines to provide comprehensive guidance to clinicians. Validation was performed in several iterations by comparing recommendations generated by the system with those of clinicians for 333 patients. The CDS system extracted relevant patient information from the electronic health record and applied the guideline model with an overall accuracy of 87%. Providers without CDS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings. Overall, our work demonstrates the feasibility and potential utility of automated recommendation system for cervical cancer screening and surveillance.

The ATXN2-SH2B3 locus is associated with peripheral arterial disease: an electronic medical record-based genome-wide association study.

OBJECTIVES: In contrast to coronary heart disease (CHD), genetic variants that influence susceptibility to peripheral arterial disease (PAD) remain largely unknown. BACKGROUND: We performed a two-stage genomic association study leveraging an electronic medical record (EMR) linked-biorepository to identify genetic variants that mediate susceptibility to PAD. METHODS: PAD was defined as a resting/post-exercise ankle-brachial index (ABI) ≤0.9 or ≥1.4 and/or history of lower extremity revascularization. Controls were patients without history of PAD. In Stage I we performed a genome-wide association analysis adjusting for age and sex, of 537, 872 SNPs in 1641 PAD cases (66 ± 11 years, 64% men) and 1604 control subjects (61 ± 7 year, 60% men) of European ancestry. In Stage II we genotyped the top 48 SNPs that were associated with PAD in Stage I, in a replication cohort of 740 PAD cases (70 ± 11 year, 63% men) and 1051 controls (70 ± 12 year, 61% men). RESULTS: The SNP rs653178 in the ATXN2-SH2B3 locus was significantly associated with PAD in the discovery cohort (OR = 1.23; P = 5.59 × 10(-5)), in the replication cohort (OR = 1.22; 8.9 × 10(-4)) and in the combined cohort (OR = 1.22; P = 6.46 × 10(-7)). In the combined cohort this SNP remained associated with PAD after additional adjustment for cardiovascular risk factors including smoking (OR = 1.22; P = 2.15 × 10(-6)) and after excluding patients with ABI > 1.4 (OR = 1.24; P = 3.98 × 10(-7)). The SNP is in near-complete linkage disequilibrium (LD) (r (2) = 0.99) with a missense SNP (rs3184504) in SH2B3, a gene encoding an adapter protein that plays a key role in immune and inflammatory response pathways and vascular homeostasis. The SNP has pleiotropic effects and has been previously associated with multiple phenotypes including myocardial infarction. CONCLUSIONS: Our findings suggest that the ATXN2-SH2B3 locus influences susceptibility to PAD.

Invited commentary: Observational research in the age of the electronic health record.

Historically, clinical epidemiologic research has been constrained by the costs and time associated with manually identifying cases and abstracting clinical data. In this issue, Carrell et al. (Am J Epidemiol. 2014;179(6);749-758) report on their impressive success using natural language processing techniques to correctly identify cases of cancer recurrence among women with previous breast cancer. They report a 10-fold decrease in the need for chart abstraction, though with an 8% loss in case detection. This commentary outlines some recent history associated with the development of "high-throughput clinical phenotyping" of electronic health records and speculates on the impact such computational capabilities may have for observational research and patient consent.

A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects.

Platelets are enucleated cell fragments derived from megakaryocytes that play key roles in hemostasis and in the pathogenesis of atherothrombosis and cancer. Platelet traits are highly heritable and identification of genetic variants associated with platelet traits and assessing their pleiotropic effects may help to understand the role of underlying biological pathways. We conducted an electronic medical record (EMR)-based study to identify common variants that influence inter-individual variation in the number of circulating platelets (PLT) and mean platelet volume (MPV), by performing a genome-wide association study (GWAS). We characterized genetic variants associated with MPV and PLT using functional, pathway and disease enrichment analyses; we assessed pleiotropic effects of such variants by performing a phenome-wide association study (PheWAS) with a wide range of EMR-derived phenotypes. A total of 13,582 participants in the electronic MEdical Records and GEnomic network had data for PLT and 6,291 participants had data for MPV. We identified five chromosomal regions associated with PLT and eight associated with MPV at genome-wide significance (P < 5E-8). In addition, we replicated 20 SNPs [out of 56 SNPs (α: 0.05/56 = 9E-4)] influencing PLT and 22 SNPs [out of 29 SNPs (α: 0.05/29 = 2E-3)] influencing MPV in a published meta-analysis of GWAS of PLT and MPV. While our GWAS did not find any new associations, our functional analyses revealed that genes in these regions influence thrombopoiesis and encode kinases, membrane proteins, proteins involved in cellular trafficking, transcription factors, proteasome complex subunits, proteins of signal transduction pathways, proteins involved in megakaryocyte development, and platelet production and hemostasis. PheWAS using a single-SNP Bonferroni correction for 1,368 diagnoses (0.05/1368 = 3.6E-5) revealed that several variants in these genes have pleiotropic associations with myocardial infarction, autoimmune, and hematologic disorders. We conclude that multiple genetic loci influence interindividual variation in platelet traits and also have significant pleiotropic effects; the related genes are in multiple functional pathways including those relevant to thrombopoiesis.