Definition Of Phenotype Research Articles

Detection of genetic variability in dairy cattle infectivity for bovine tuberculosis.

This study investigated the genetics of bovine tuberculosis (bTB) infectivity in Holstein-Friesian dairy cows using British national data. The analyses included cows with recorded sires from herds affected by bTB outbreaks between 2000 and 2022. Animals were considered bTB-positive if they reacted positively to the skin test and/or had positive post-mortem findings. We introduced the "index case approach," based on the assumption that once the initial positively tested animals (index cases) are detected in a herd, subsequent infections (secondary cases) in the early stages of the breakdown are likely to be attributed to these animals. Genetic analysis of the number of secondary cases (NSC) associated with a given index case was used to establish evidence of genetic variability in bTB infectivity of cattle, and derive Estimated Breeding Values (EBVs) for infectivity for the sires of the index cases. Data were analyzed by employing Markov Chain Monte Carlo techniques to fit Generalized Linear Mixed Models with either Poisson, Zero-Inflated Poisson (ZIP), Hurdle Poisson, or Geometric distributions. All 4 models demonstrated presence of genetic variance in cattle infectivity, with the strongest evidence provided by the ZIP and Hurdle Poisson models. The Hurdle Poisson model offered the most accurate and least biased predictions. Sire infectivity EBVs from the Poisson, ZIP, and Geometric models showed strong concordance, with pairwise correlations of 0.90 or higher. In contrast, correlations between EBVs from the Hurdle Poisson model and the other models ranged from 0.36 to 0.39. The association of the sire infectivity EBVs with the average observed NSC per sire and the proportion of infectious index case daughters per sire was generally moderate with correlations between 44 and 47% and 65-69%, respectively. Agreement among models for identifying the genetically most infectious sires was also reasonable, with 151 out of 285 sires appearing in the top 10% across models, and 122 (42.8%) also aligning with the top 10% based on observed average NSC. Results provide novel evidence for exploitable genetic variance in bTB infectivity allowing the derivation of meaningful EBVs. Based on the estimated posterior mean genetic variances obtained, reduction in infectivity by one genetic standard deviation would result in a 32% - 44% decrease in the expected NSC per index case. Further research is warranted to refine the phenotypic definition of infectivity and assess correlation with other dairy traits.

WebGWAS: A web server for instant GWAS on arbitrary phenotypes.

Complex disease genetics is a key area of research for reducing disease and improving human health. Genome-wide association studies (GWAS) help in this research by identifying regions of the genome that contribute to complex disease risk. However, GWAS are computationally intensive and require access to individual-level genetic and health information, which presents concerns about privacy and imposes costs on researchers seeking to study complex diseases. Publicly released pan-biobank GWAS summary statistics provide immediate access to results for a subset of phenotypes, but they do not inform about all phenotypes or hand-crafted phenotype definitions, which are often more relevant to study. Here, we present WebGWAS, a new tool that allows researchers to obtain GWAS summary statistics for a phenotype of interest without needing access to individual-level genetic and phenotypic data. Our public web app can be used to study custom phenotype definitions, including inclusion and exclusion criteria, and to produce approximate GWAS summary statistics for that phenotype. WebGWAS computes approximate GWAS summary statistics very quickly ( < 10 seconds), and it does not store private health information. We also show how the statistical approximation underlying WebGWAS can be used to accelerate the computation of multi-phenotype GWAS among correlated phenotypes. Our tool provides a faster approach to GWAS for researchers interested in complex disease, providing approximate summary statistics in short order, without the need to collect, process, and produce GWAS results. Overall, this method advances complex disease research by facilitating more accessible and cost-effective genetic studies using large observational data.

Expanding the genetic landscape of endometriosis: Integrative -omics analyses uncover key pathways from a multi-ancestry study of over 900,000 women.

We report the findings of a genome-wide association study (GWAS) meta-analysis of endometriosis consisting of a large portion (31%) of non-European samples across 14 biobanks worldwide as part of the Global Biobank Meta-Analysis Initiative (GBMI). We identified 45 significant loci using a wide phenotype definition, seven of which are previously unreported and detected first genome-wide significant locus ( POLR2M ) among only African-ancestry. Our narrow phenotypes and surgically confirmed case definitions for endometriosis analyses replicated the known loci near CDC42 , SKAP1 , and GREB1 . Through this large ancestry stratified analyses, we document heritability estimates in range of 10-12% for all ancestral groups. Thirty-eight loci had at least one variant in the credible set after fine-mapping. An imputed transcriptome-wide association study (TWAS) identified 11 associated genes (two previously unreported), while the proteome-wide association study (PWAS) suggests significant association of R-spondin 3 (RSPO3) with wide endometriosis, which plays a crucial role in modulating the Wnt signaling pathway. Our diverse, comprehensive GWAS, coupled with integrative -omics analysis, identifies critical roles of immunopathogenesis, Wnt signaling, and balance between proliferation, differentiation, and migration of endometrial cells as hallmarks for endometriosis. These interconnected pathways and risk factors underscore a complex, multi-faceted etiology of endometriosis, suggesting multiple targets for precise and effective therapeutic interventions.

Towards automated phenotype definition extraction using large language models

Electronic phenotyping involves a detailed analysis of both structured and unstructured data, employing rule-based methods, machine learning, natural language processing, and hybrid approaches. Currently, the development of accurate phenotype definitions demands extensive literature reviews and clinical experts, rendering the process time-consuming and inherently unscalable. Large language models offer a promising avenue for automating phenotype definition extraction but come with significant drawbacks, including reliability issues, the tendency to generate non-factual data (“hallucinations”), misleading results, and potential harm. To address these challenges, our study embarked on two key objectives: (1) defining a standard evaluation set to ensure large language models outputs are both useful and reliable and (2) evaluating various prompting approaches to extract phenotype definitions from large language models, assessing them with our established evaluation task. Our findings reveal promising results that still require human evaluation and validation for this task. However, enhanced phenotype extraction is possible, reducing the amount of time spent in literature review and evaluation.

No association between the novel international classification of bicuspid aortic valve anatomy and performed surgery of the valve or aorta

Abstract Introduction In 2021, international experts introduced a new classification of the diverse anatomy of bicuspid aortic valves (BAV). This classification identifies three BAV types: Fusion, 2-sinus, and Partial-fusion BAV, which may be further subdivided into specific phenotypes according to orientation of the commissure and the presence of a raphe. However, it remains uncertain whether this new BAV classification can identify subgroups of patients with an unfavorable long-term prognosis. Purpose To assess whether the BAV anatomy according to the new classification is associated with the risk of surgery of the aortic valve or aorta. Methods A multicenter case-control study of all current patients with BAV from the outpatient clinics of three major cardiology departments in Denmark in the years 2018, 2019 and 2022. We excluded patients with more than minor concomitant heart defects, uncertain BAV anatomy, and genetic syndromes. Due to the novelty of the concept at the time of study, we excluded 15 patients with Partial-Fusion BAV. Because echocardiography examinations performed prior to January 1 2006 were unavailable, we excluded patients diagnosed prior to this date to ensure comparability between cases and controls. Cases were all patients with BAV, who had had surgery of the aortic valve and/or surgery of the ascending aorta at the end of their accrual year. All other patients with BAV were controls. We assessed BAV anatomy by review of routinely performed echocardiography examinations and compared patients according to BAV type: Fusion or 2-sinus BAV. Secondarily, we compared patients with specific phenotypes: Right-Left Fusion, Right-Non-Coronary Fusion, Left-Non-Coronary Fusion, Latero-Lateral 2-sinus, and Antero-Posterior 2-sinus BAV. Results We included 931 patients (237 cases, 694 controls). Table 1 displays clinical characteristics in BAV phenotype subgroups. In the primary analysis, we found no significant difference in the odds of surgery between the Fusion versus 2-sinus BAV: unadjusted OR 0.65, 95% CI [0.41-1.03]. This did not change after adjusting for age and sex in a logistic regression model: OR 0.72, 95% CI [0.45-1.15]. Results were similar when comparing phenotypes; no difference in odds of surgery between phenotypes in both unadjusted and age- and sex-adjusted analyses (see Fig. 1). In exploratory analyses, we compared patients according to the orientation of the BAV commissure (latero-lateral versus antero-posterior) and a more complex definition of the fusion phenotype that incorporates the visibility of a raphe. Findings were similar; no significant difference in the odds between subgroups. In all models, increasing age and male sex were associated with higher odds of surgery. Conclusion In this study, we did not find any association between BAV anatomy as defined by the new international consensus classification, and performed surgery of the valve or aorta.

CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.

Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.

Scale at the interface of spatial and social ecology.

Animals simultaneously navigate spatial and social environments, and their decision-making with respect to those environments constitutes their spatial (e.g. habitat selection) and social (e.g. conspecific associations) phenotypes. The spatial-social interface is a recently introduced conceptual framework linking these components of spatial and social ecology. The spatial-social interface is inherently scale-dependent, yet it has not been integrated with the rich body of literature on ecological scale. Here, we develop a conceptual connection between the spatial-social interface and ecological scale. We propose three key innovations that incrementally build upon each other. First, the use-availability framework that underpins a large body of literature in behavioural ecology can be used in analogy to the phenotype-environment nomenclature and is transferable across the spatial and social realms. Second, both spatial and social phenotypes are hierarchical, with nested components that are linked via constraints-from the top down-or emergent properties-from the bottom up. Finally, in both the spatial and social realms, the definitions of environment and phenotype depend on the focal scale of inquiry. These conceptual innovations cast our understanding of the relationships between social and spatial dimensions of animal ecology in a new light, allowing a more holistic understanding and clearer hypothesis development for animal behaviour. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Assessing the Effect of Electronic Health Record Data Quality on Identifying Patients With Type 2 Diabetes: Cross-Sectional Study.

Increasing and substantial reliance on electronic health records (EHRs) and data types (ie, diagnosis, medication, and laboratory data) demands assessment of their data quality as a fundamental approach, especially since there is a need to identify appropriate denominator populations with chronic conditions, such as type 2 diabetes (T2D), using commonly available computable phenotype definitions (ie, phenotypes). To bridge this gap, our study aims to assess how issues of EHR data quality and variations and robustness (or lack thereof) in phenotypes may have potential impacts in identifying denominator populations. Approximately 208,000 patients with T2D were included in our study, which used retrospective EHR data from the Johns Hopkins Medical Institution (JHMI) during 2017-2019. Our assessment included 4 published phenotypes and 1 definition from a panel of experts at Hopkins. We conducted descriptive analyses of demographics (ie, age, sex, race, and ethnicity), use of health care (inpatient and emergency room visits), and the average Charlson Comorbidity Index score of each phenotype. We then used different methods to induce or simulate data quality issues of completeness, accuracy, and timeliness separately across each phenotype. For induced data incompleteness, our model randomly dropped diagnosis, medication, and laboratory codes independently at increments of 10%; for induced data inaccuracy, our model randomly replaced a diagnosis or medication code with another code of the same data type and induced 2% incremental change from -100% to +10% in laboratory result values; and lastly, for timeliness, data were modeled for induced incremental shift of date records by 30 days to 365 days. Less than a quarter (n=47,326, 23%) of the population overlapped across all phenotypes using EHRs. The population identified by each phenotype varied across all combinations of data types. Induced incompleteness identified fewer patients with each increment; for example, at 100% diagnostic incompleteness, the Chronic Conditions Data Warehouse phenotype identified zero patients, as its phenotypic characteristics included only diagnosis codes. Induced inaccuracy and timeliness similarly demonstrated variations in performance of each phenotype, therefore resulting in fewer patients being identified with each incremental change. We used EHR data with diagnosis, medication, and laboratory data types from a large tertiary hospital system to understand T2D phenotypic differences and performance. We used induced data quality methods to learn how data quality issues may impact identification of the denominator populations upon which clinical (eg, clinical research and trials, population health evaluations) and financial or operational decisions are made. The novel results from our study may inform future approaches to shaping a common T2D computable phenotype definition that can be applied to clinical informatics, managing chronic conditions, and additional industry-wide efforts in health care.

FRELSA: A dataset for frailty in elderly people originated from ELSA and evaluated through machine learning models

BackgroundFrailty is an age-related syndrome characterized by loss of strength and exhaustion and associated with multi-morbidity. Early detection and prediction of the appearance of frailty could help older people age better and prevent them from needing invasive and expensive treatments. Machine learning techniques show promising results in creating a medical support tool for such a task. MethodsThis study aims to create a dataset for machine learning-based frailty studies, using Fried's Frailty Phenotype definition. Starting from a longitudinal study on aging in the UK population, we defined a frailty label for each subject. We evaluated the definition by training seven different models for detecting frailty with data that were contemporary to the ones used for the definition. We then integrated more data from two years before to obtain prediction models with a 24-month horizon. Features selection was performed using the MultiSURF algorithm, which ranks all features in order of relevance to the detection or prediction task. ResultsWe present a new frailty dataset of 5303 subjects and more than 6500 available features. It is publicly available, provided one has access to the original English Longitudinal Study of Ageing dataset. The dataset is balanced after grouping frailty with pre-frailty, and it is suitable for multiclass or binary classification and prediction problems. The seven tested architectures performed similarly, forming a solid baseline that can be improved with future work. Linear regression achieved the best F-score and AUROC in detection and prediction tasks. ConclusionsCreating new frailty-annotated datasets of this size is necessary to develop and improve the frailty prediction techniques. We have shown that our dataset can be used to study and test machine learning models to detect and predict frailty. Future work should improve models' architecture and performance, consider explainability, and possibly enrich the dataset with older waves.

US public health surveillance, reimagined.

This study presents two novel concepts for standardizing electronic health records (EHR)-based public health surveillance through utilization of existing informatics methods and data platforms. Drawing from the collective experience in applied epidemiology, health services research and health informatics, the author presents a vision for an alternative path to public health surveillance by repurposing existing tools and resources, such as (1) computable phenotypes which have already been created and validated for a variety of chronic diseases of interest to public health and (2) large data platforms/collaboratives, such as All of Us Research Program and National COVID Cohort Collaborative. Opportunities and challenges are discussed regarding EHR-based chronic disease surveillance, as well as the concept of phenotype definitions and large data platforms reuse for public health needs. Reusing of computable phenotypes for EHR-based public health surveillance would require secure data platforms and nationally representative data. Standardization metrics for reuse of previously developed and validated computable phenotypes are also necessary and are currently being developed by the author. This study presents a reimagined Learning Health System framework by incorporating Public Health and two novel concept sets of solutions into the healthcare ecosystem. Alternative approaches to limited resources and current infrastructure of the US Public Health System, especially as applied to disease surveillance, are needed and may be possible when repurposing the resources and methodologies across the Learning Health System.

A scalable and transparent data pipeline for AI-enabled health data ecosystems.

Transparency and traceability are essential for establishing trustworthy artificial intelligence (AI). The lack of transparency in the data preparation process is a significant obstacle in developing reliable AI systems which can lead to issues related to reproducibility, debugging AI models, bias and fairness, and compliance and regulation. We introduce a formal data preparation pipeline specification to improve upon the manual and error-prone data extraction processes used in AI and data analytics applications, with a focus on traceability. We propose a declarative language to define the extraction of AI-ready datasets from health data adhering to a common data model, particularly those conforming to HL7 Fast Healthcare Interoperability Resources (FHIR). We utilize the FHIR profiling to develop a common data model tailored to an AI use case to enable the explicit declaration of the needed information such as phenotype and AI feature definitions. In our pipeline model, we convert complex, high-dimensional electronic health records data represented with irregular time series sampling to a flat structure by defining a target population, feature groups and final datasets. Our design considers the requirements of various AI use cases from different projects which lead to implementation of many feature types exhibiting intricate temporal relations. We implement a scalable and high-performant feature repository to execute the data preparation pipeline definitions. This software not only ensures reliable, fault-tolerant distributed processing to produce AI-ready datasets and their metadata including many statistics alongside, but also serve as a pluggable component of a decision support application based on a trained AI model during online prediction to automatically prepare feature values of individual entities. We deployed and tested the proposed methodology and the implementation in three different research projects. We present the developed FHIR profiles as a common data model, feature group definitions and feature definitions within a data preparation pipeline while training an AI model for "predicting complications after cardiac surgeries". Through the implementation across various pilot use cases, it has been demonstrated that our framework possesses the necessary breadth and flexibility to define a diverse array of features, each tailored to specific temporal and contextual criteria.

Defining Suicidal Thought and Behavior Phenotypes for Genetic Studies.

Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation. A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394). Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported. Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.

Uptake of Revised CLSI Breakpoints and Potential Impact among Hospitals Reporting to The NHSN AR Option, 2022

Background: Antimicrobial susceptibility testing (AST) is critical for detecting antimicrobial resistance (AR) and guiding antimicrobial treatment. The Clinical and Laboratory Standards Institute (CLSI) regularly publishes and revises breakpoints to guide the interpretation of AST results. In 2010–2019, CLSI has lowered many breakpoints for Enterobacterales and Pseudomonas aeruginosa. Timely implementation of updated breakpoints can vary across hospital laboratories, leading to shifts in the interpretation of AST results. This issue is a potential threat to the estimation of national prevalence estimates for AR and limits the comparability of AR data across hospitals. Hospitals submit AST data with clinical laboratory interpretations to the AR Option of CDC’s National Healthcare Safety Network (NHSN). NHSN tracks whether a hospital adopted the revised CLSI breakpoints for six organism-antimicrobial combinations involving AR phenotypes commonly associated with healthcare associated infections through hospital self-reporting status into a structured survey — 2022 NHSN Annual Hospital Survey (Table). For this analysis, we describe the uptake of revised CLSI breakpoints and compare cumulative antibiograms among hospitals that used various breakpoints. Methods: We included hospitals that completed the 2022 NHSN annual survey and submitted data to the NHSN AR Option for at least 9 months in 2022 by November 1, 2023. The percentage of hospitals that implemented CLSI breakpoints, published during 2010–2019, were determined for combinations of antibiotic class, organism, and CLSI revision year (Table). We calculated percent resistance (%R) as the number of isolates meeting AR phenotype definitions divided by the total number of Isolates tested for the following phenotypes: carbapenem-resistant Enterobacterales (CRE) which included E. coli, Klebsiella, and Enterobacter species; extended-spectrum cephalosporin-resistant Enterobacterales (ESC); carbapenem-non-susceptible P. aeruginosa; fluoroquinolone-resistant P. aeruginosa; and fluoroquinolone-resistant Enterobacterales. Results: Among the 741 hospitals included, 75%–93% implemented any of the six revised CLSI breakpoints (Table). The %R was higher among isolates from hospitals that adopted revised breakpoints compared to those that did not (p &lt; 0 .0001). The largest difference was observed for carbapenem-non-susceptible P. aeruginosa (14.91% vs 10.11%). Conclusions: The uptake of revised CLSI breakpoints varied across hospitals, organism-antimicrobial combinations, and CLSI versions. This analysis indicates that the prevalence of AR for corresponding phenotypes could be underestimated if data from hospitals using higher, outdated breakpoints are included. It is important for NHSN to continue tracking breakpoints used in individual hospitals and encourage hospitals to report complete data on the original AST results, such as MIC, to optimize the accuracy of national AR surveillance.

Structure-based network analysis predicts pathogenic variants in human proteins associated with inherited retinal disease.

Advances in gene sequencing technologies have accelerated the identification of genetic variants, but better tools are needed to understand which are causal of disease. This would be particularly useful in fields where gene therapy is a potential therapeutic modality for a disease-causing variant such as inherited retinal disease (IRD). Here, we apply structure-based network analysis (SBNA), which has been successfully utilized to identify variant-constrained amino acid residues in viral proteins, to identify residues that may cause IRD if subject to missense mutation. SBNA is based entirely on structural first principles and is not fit to specific outcome data, which makes it distinct from other contemporary missense prediction tools. In 4 well-studied human disease-associated proteins (BRCA1, HRAS, PTEN, and ERK2) with high-quality structural data, we find that SBNA scores correlate strongly with deep mutagenesis data. When applied to 47 IRD genes with available high-quality crystal structure data, SBNA scores reliably identified disease-causing variants according to phenotype definitions from the ClinVar database. Finally, we applied this approach to 63 patients at Massachusetts Eye and Ear (MEE) with IRD but for whom no genetic cause had been identified. Untrained models built using SBNA scores and BLOSUM62 scores for IRD-associated genes successfully predicted the pathogenicity of novel variants (AUC = 0.851), allowing us to identify likely causative disease variants in 40 IRD patients. Model performance was further augmented by incorporating orthogonal data from EVE scores (AUC = 0.927), which are based on evolutionary multiple sequence alignments. In conclusion, SBNA can used to successfully identify variants as causal of disease in human proteins and may help predict variants causative of IRD in an unbiased fashion.

Association between acquiring SARS-CoV-2 during pregnancy and post-acute sequelae of SARS-CoV-2 infection: RECOVER electronic health record cohort analysis

Little is known about post-acute sequelae of SARS-CoV-2 infection (PASC) after acquiring SARS-CoV-2 infection during pregnancy. We aimed to evaluate the association between acquiring SARS-CoV-2 during pregnancy compared with acquiring SARS-CoV-2 outside of pregnancy and the development of PASC. This retrospective cohort study from the Researching COVID to Enhance Recovery (RECOVER) Initiative Patient-Centred Clinical Research Network (PCORnet) used electronic health record (EHR) data from 19 U.S. health systems. Females aged 18-49 years with lab-confirmed SARS-CoV-2 infection from March 2020 through June 2022 were included. Validated algorithms were used to identify pregnancies with a delivery at >20 weeks' gestation. The primary outcome was PASC, as previously defined by computable phenotype in the adult non-pregnant PCORnet EHR dataset, identified 30-180 days post-SARS-CoV-2 infection. Secondary outcomes were the 24 component diagnoses contributing to the PASC phenotype definition. Univariable comparisons were made for baseline characteristics between individuals with SARS-CoV-2 infection acquired during pregnancy compared with outside of pregnancy. Using inverse probability of treatment weighting to adjust for baseline differences, the association between SARS-CoV-2 infection acquired during pregnancy and the selected outcomes was modelled. The incident risk is reported as the adjusted hazard ratio (aHR) with 95% confidence intervals. In total, 83,915 females with SARS-CoV-2 infection acquired outside of pregnancy and 5397 females withSARS-CoV-2 infection acquired during pregnancy were included in analysis. Non-pregnant females with SARS-CoV-2 infection were more likely to be older and have comorbid health conditions. SARS-CoV-2 infection acquired in pregnancy as compared with acquired outside of pregnancy was associated with a lower incidence of PASC (25.5% vs 33.9%; aHR 0.85, 95% CI 0.80-0.91). SARS-CoV-2 infection acquired in pregnant females was associated with increased risk for some PASC component diagnoses including abnormal heartbeat (aHR 1.67, 95% CI 1.43-1.94), abdominal pain (aHR 1.34, 95% CI 1.16-1.55), and thromboembolism (aHR 1.88, 95% CI 1.17-3.04), but decreased risk for other diagnoses including malaise (aHR 0.35, 95% CI 0.27-0.47), pharyngitis (aHR 0.36, 95% CI 0.26-0.48) and cognitive problems (aHR 0.39, 95% CI 0.27-0.56). SARS-CoV-2 infection acquired during pregnancy was associated with lower risk of development of PASC at 30-180 days after incident SARS-CoV-2 infection in this nationally representative sample. These findings may be used to counsel pregnant and pregnant capable individuals, and direct future prospective study. National Institutes of Health (NIH) Other Transaction Agreement (OTA) OT2HL16184.

Pursuing Clinical Predictors and Biomarkers for Progression in ILD: Analysis of the Pulmonary Fibrosis Foundation (PFF) Registry

IntroductionPulmonary fibrosis is a characteristic of various interstitial lung diseases (ILDs) with differing etiologies. Clinical trials in progressive pulmonary fibrosis (PPF) enroll patients based on previously described clinical criteria for past progression, which include a clinical practice guideline for PPF classification and inclusion criteria from the INBUILD trial. In this study, we compared the ability of past FVC (forced vital capacity) progression and baseline biomarker levels to predict future progression in a cohort of patients from the PFF Patient Registry.MethodsBiomarkers previously associated with pathobiology and/or progression in pulmonary fibrosis were selected to reflect cellular senescence (telomere length), pulmonary epithelium (SP-D, RAGE), myeloid activation (CXCL13, YKL40, CCL18, OPN) and fibroblast activation (POSTN, COMP, PROC3).ResultsPFF or INBUILD-like clinical criteria was used to separate patients into past progressor and non-past progressor groups, and neither clinical criterion appeared to enrich for patients with greater future lung function decline. All baseline biomarkers measured were differentially expressed in patient groups compared to healthy controls. Baseline levels of SP-D and POSTN showed the highest correlations with FVC slope over one year, though correlations were low.ConclusionsOur findings provide further evidence that prior decline in lung function may not predict future disease progression for ILD patients, and elevate the need for molecular definitions of a progressive phenotype. Across ILD subtypes, certain shared pathobiologies may be present based on the molecular profile of certain biomarker groups observed. In particular, SP-D may be a common marker of pulmonary injury and future lung function decline across ILDs.

THE INVESTIGATION OF TRANSFERABILITY OF DEFINITIONS OF CARDIOMETABOLIC AND VASCULAR AGING PHENOTYPES ACROSS MULTIPLE COHORTS

Objective: According to the European guidelines for the prevention of cardiovascular diseases (CVD) (2021), Russia was identified as a country with a very high CV risk. We aimed to investigate the applicability of the standardized definitions of vascular aging phenotypes (VP) in Russian population and align them with the existing reference datasets. Design and method: In 2012-2013 1,600 residents of St. Petersburg (age 21-67) were randomly examined as a part of the multicenter epidemiological study. Anthropometry, blood pressure measurement, blood biochemistry analysis were taken. Subjects completed questionnaires describing their lifestyle, current health condition and medication usage. 618 patients (42% men) from initial cohort with a mean age of 48 [37;55] years were randomly subsampled for carotid-femoral pulse wave velocity assessment (Sphygmocor). The VP were defined using the ‘Reference Values for Arterial Stiffness’ Collaboration database which included 16,867 measurements from eight European countries populations15 to 97 years. According to the criteria, patients with overt CVD, diabetes mellitus and the use of antihypertensive, lipid-lowering therapy were excluded from the analysis of VP. Results: The Russian cohort had significantly higher prevalence of CVD (11,4% compared to 6,9% in the Nreference population, p=5.65x10-5), intake of antihypertensives (28,1% compared to 14,3% in the reference, p&lt;1x10-16) and lipid-lowering therapy (6,1% compared to 4,1%, p=0.025, Figure 1A). Additionally, our cohort had significantly higher prevalence of optimal BP (40,2% vs 28%, p=4.6x10-7) and lower prevalence of first-degree (p=0.048) and second degree (p=0.00084) hypertension (Figure 1B). Comparison of the prevalence of VP between the European and Russian cohorts showed a significant decrease of the early vascular aging (EVA) phenotype with age in the Russian population (p=0.011). The prevalence of the supernormal vascular aging phenotype (SUPERNOVA) in the group of 60-69 years old was higher in the Russian population (p=0.0035) Conclusions: A higher prevalence of CVD was demonstrated in the study cohort despite lower hypertension burden and higher CV drugs intake compared to the European cohort. Multidirectional trajectories of EVA and SUPERNOVA VP with age were detected in Russian population, potentially indicating the presence of the clearing selection pressure at the older age (Agreement No 075-15-2022-301).

ForCEPSS—A framework for cardiac electrophysiology simulations standardization

Background and Objective:Simulation of cardiac electrophysiology (CEP) is an important research tool that is increasingly being adopted in industrial and clinical applications. Typical workflows for CEP simulation consist of a sequence of processing stages starting with building an anatomical model and then calibrating its electrophysiological properties to match observable data. While the calibration stages are common and generalizable, most CEP studies re-implement these steps in complex and highly variable workflows. This lack of standardization renders the execution of computational CEP studies in an efficient, robust, and reproducible manner a significant challenge. Here, we propose ForCEPSS as an efficient and robust, yet flexible, software framework for standardizing CEP simulation studies. Methods and Results:Key processing stages of CEP simulation studies are identified and implemented in a standardized workflow that builds on openCARP11https://opencarp.org/. Plank et al. (2021) and the Python-based carputils22https://git.opencarp.org/openCARP/carputils. framework. Stages include (i) the definition and initialization of action potential phenotypes, (ii) the tissue scale calibration of conduction properties, (iii) the functional initialization to approximate a limit cycle corresponding to the dynamic reference state according to an experimental protocol, and, (iv) the execution of the CEP study where the electrophysiological response to a perturbation of the limit cycle is probed. As an exemplar application, we employ ForCEPSS to prepare a CEP study according to the Virtual Arrhythmia Risk Prediction protocol used for investigating the arrhythmogenic risk of developing infarct-related ventricular tachycardia (VT) in ischemic cardiomyopathy patients. We demonstrate that ForCEPSS enables a fully automated execution of all stages of this complex protocol. Conclusion:ForCEPSS offers a novel comprehensive, standardized, and automated CEP simulation workflow. The high degree of automation accelerates the execution of CEP simulation studies, reduces errors, improves robustness, and makes CEP studies reproducible. Verification of simulation studies within the CEP modeling community is thus possible. As such, ForCEPSS makes an important contribution towards increasing transparency, standardization, and reproducibility of in silico CEP experiments.

Local Allergic Rhinitis

Local allergic rhinitis (LAR) is defined by a clinical history suggestive of allergic rhinitis (AR), negativity of systemic IgE measurement and positive response to nasal allergen challenge (NAC). The term local respiratory allergy includes LAR, local allergic asthma (positive response in bronchial allergen challenge) and dual allergic rhinitis defined by the coexistence of AR and LAR. LAR worsens in severity and presence of comorbidities over time, and it is an independent entity from AR. Prevalence is higher in Mediterranean countries. LAR onset occurs during childhood in 36% of cases. Physiopathological features of LAR are: increased nasal eosinophilic inflammation, tryptase and eosinophil cationic protein, and presence of nasal specific IgE in secretions of 20-40% of subjects. A recent study demonstrated increase in sequential class switch recombination to IgE markers in mucosa of LAR with accumulation of IgE+ CD38+ plasmablasts. Moreover, there is increased expression in B cells of mucosal homing receptors CXCR3+ and CXCR4 in peripheral blood, with accumulation of Th9 and Th2 cells. NAC is the gold standard in the diagnosis of LAR. The measurement of specific IgE in nasal secretions basophil activation test or are still not suitable for diagnosis. There is ample evidence of the usefulness of allergen immunotherapy in the treatment in LAR after 4 DBPCRT in 152 patients. In conclusion, knowledge about LAR is continuously increasing, with detailed definition of physiopathological mechanisms and new phenotypes. More awareness of the disease should be promoted among different specialists, and NAC must be considered an essential diagnostic tool in any age group, including children.

Creating a next-generation phenotype library: the health data research UK Phenotype Library.

To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms. We undertook a structured approach to identifying requirements for a phenotype algorithm platform by engaging with key stakeholders. User experience analysis was used to inform the design, which we implemented as a web application featuring a novel metadata standard for defining phenotyping algorithms, access via Application Programming Interface (API), support for computable data flows, and version control. The application has creation and editing functionality, enabling researchers to submit phenotypes directly. We created and launched the Phenotype Library in October 2021. The platform currently hosts 1049 phenotype definitions defined against 40 health data sources and >200K terms across 16 medical ontologies. We present several case studies demonstrating its utility for supporting and enabling research: the library hosts curated phenotype collections for the BREATHE respiratory health research hub and the Adolescent Mental Health Data Platform, and it is supporting the development of an informatics tool to generate clinical evidence for clinical guideline development groups. This platform makes an impact by being open to all health data users and accepting all appropriate content, as well as implementing key features that have not been widely available, including managing structured metadata, access via an API, and support for computable phenotypes. We have created the first openly available, programmatically accessible resource enabling the global health research community to store and manage phenotyping algorithms. Removing barriers to describing, sharing, and computing phenotypes will help unleash the potential benefit of health data for patients and the public.