Researching COVID to enhance recovery (RECOVER) pediatric study protocol: Rationale, objectives and design.

IMPORTANCE: The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or "Long COVID") in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults. OBSERVATIONS: We describe the protocol for the Pediatric Observational Cohort Study of the NIH's REsearching COVID to Enhance Recovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of four cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study (n = 10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n = 6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n = 6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n = 600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science. CONCLUSIONS AND RELEVANCE: RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions. CLINICAL TRIALS.GOV IDENTIFIER: Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT05172011.

Long COVID incidence in adults and children between 2020 and 2023: a real-world data study from the RECOVER Initiative.

Estimates of post-acute sequelae of SARS-CoV-2 infection (PASC) incidence, also known as Long COVID, have varied across studies and changed over time. We estimated PASC incidence among adult and pediatric populations in three nationwide research networks of electronic health records (EHR) participating in the RECOVER Initiative using different classification algorithms (computable phenotypes). Overall, 7% of children and 8.5%-26.4% of adults developed PASC, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 4% in children and ranged from 4-7% among adults, representing a lower-bound incidence estimation based on two control groups - contemporary COVID-19 negative and historical patients (2019). Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. Our findings indicate that preventing and mitigating Long COVID remains a public health priority. Examining temporal patterns and risk factors of PASC incidence informs our understanding of etiology and can improve prevention and management.

Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network.

The differential performance of polygenic risk scores (PRSs) by group is one of the major ethical barriers to their clinical use. It is also one of the main practical challenges for any implementation effort. The social repercussions of how people are grouped in PRS research must be considered in communications with research participants, including return of results. Here, we outline the decisions faced and choices made by a large multi-site clinical implementation study returning PRSs to diverse participants in handling this issue of differential performance. Our approach to managing the complexities associated with the differential performance of PRSs serves as a case study that can help future implementers of PRSs to plot an anticipatory course in response to this issue.

Risk factors affecting polygenic score performance across diverse cohorts.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N=491,111) and African (N=21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best and worst performing quintiles for certain covariates. 28 covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account non-linear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge GWAS effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Polygenic Risk for Type 2 Diabetes in African Americans.

African Americans (AAs) have been underrepresented in polygenic risk score (PRS) studies. Here, we integrated genome-wide data from multiple observational studies on type 2 diabetes (T2D), encompassing a total of 101,987 AAs, to train and optimize an AA-focused T2D PRS (PRSAA), using a Bayesian polygenic modeling method. We further tested the score in three independent studies with a total of 7,275 AAs and compared the PRSAA with other published scores. Results show that a 1-SD increase in the PRSAA was associated with 40-60% increase in the odds of T2D (odds ratio [OR] 1.60, 95% CI 1.37-1.88; OR 1.40, 95% CI 1.16-1.70; and OR 1.45, 95% CI 1.30-1.62) across three testing cohorts. These models captured 1.0-2.6% of the variance (R2) in T2D on the liability scale. The positive predictive values for three calculated score thresholds (the top 2%, 5%, and 10%) ranged from 14 to 35%. The PRSAA, in general, performed similarly to existing T2D PRS. The need remains for larger data sets to continue to evaluate the utility of within-ancestry scores in the AA population.

Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations.

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Decoding Genetics, Ancestry, and Geospatial Context for Precision Health.

Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, environmental context, and disease manifestations within clinical practice. In this study, we highlight the impact of ancestral diversity in the MGBB by employing population genetics, geospatial assessment, and association analyses of rare and common genetic variants. The population structures captured by the genetics mirror the sequential immigration to the Greater Boston area throughout American history, highlighting communities tied to shared genetic and environmental factors. Our investigation underscores the potency of unbiased, large-scale analyses in a healthcare-affiliated biobank, elucidating the dynamic interplay across genetics, immigration, structural geospatial factors, and health outcomes in one of the earliest American sites of European colonization.

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records.

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection.

Clostridioides difficile (C. diff.) infection (CDI) is a leading cause of hospital acquired diarrhea in North America and Europe and a major cause of morbidity and mortality. Known risk factors do not fully explain CDI susceptibility, and genetic susceptibility is suggested by the fact that some patients with colons that are colonized with C. diff. do not develop any infection while others develop severe or recurrent infections. To identify common genetic variants associated with CDI, we performed a genome-wide association analysis in 19,861 participants (1349 cases; 18,512 controls) from the Electronic Medical Records and Genomics (eMERGE) Network. Using logistic regression, we found strong evidence for genetic variation in the DRB locus of the MHC (HLA) II region that predisposes individuals to CDI (P > 1.0 × 10-14; OR 1.56). Altered transcriptional regulation in the HLA region may play a role in conferring susceptibility to this opportunistic enteric pathogen.

Association of GLP-1 Receptor Agonists with Chronic Obstructive Pulmonary Disease Exacerbations among Patients with Type 2 Diabetes.

Rationale: Patients with chronic obstructive pulmonary disease (COPD) and type 2 diabetes (T2D) have worse clinical outcomes compared with patients without metabolic dysregulation. GLP-1 (glucagon-like peptide 1) receptor agonists (GLP-1RAs) reduce asthma exacerbation risk and improve FVC in patients with COPD. Objectives: To determine whether GLP-1RA use is associated with reduced COPD exacerbation rates, and severe and moderate exacerbation risk, compared with other T2D therapies. Methods: A retrospective, observational, electronic health records-based study was conducted using an active comparator, new-user design of 1,642 patients with COPD in a U.S. health system from 2012 to 2022. The COPD cohort was identified using a previously validated machine learning algorithm that includes a natural language processing tool. Exposures were defined as prescriptions for GLP-1RAs (reference group), DPP-4 (dipeptidyl peptidase 4) inhibitors (DPP-4is), SGLT2 (sodium-glucose cotransporter 2) inhibitors, or sulfonylureas. Measurements and Main Results: Unadjusted COPD exacerbation counts were lower in GLP-1RA users. Adjusted exacerbation rates were significantly higher in DPP-4i (incidence rate ratio, 1.48 [95% confidence interval, 1.08-2.04]; P = 0.02) and sulfonylurea (incidence rate ratio, 2.09 [95% confidence interval, 1.62-2.69]; P < 0.0001) users compared with GLP-1RA users. GLP-1RA use was also associated with significantly reduced risk of severe exacerbations compared with DPP-4i and sulfonylurea use, and of moderate exacerbations compared with sulfonylurea use. After adjustment for clinical covariates, moderate exacerbation risk was also lower in GLP-1RA users compared with DPP-4i users. No statistically significant difference in exacerbation outcomes was seen between GLP-1RA and SGLT2 inhibitor users. Conclusions: Prospective studies of COPD exacerbations in patients with comorbid T2D are warranted. Additional research may elucidate the mechanisms underlying these observed associations with T2D medications.

Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection.

Importance: SARS-CoV-2 infection is associated with persistent, relapsing, or new symptoms or other health effects occurring after acute infection, termed postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID. Characterizing PASC requires analysis of prospectively and uniformly collected data from diverse uninfected and infected individuals. Objective: To develop a definition of PASC using self-reported symptoms and describe PASC frequencies across cohorts, vaccination status, and number of infections. Design, Setting, and Participants: Prospective observational cohort study of adults with and without SARS-CoV-2 infection at 85 enrolling sites (hospitals, health centers, community organizations) located in 33 states plus Washington, DC, and Puerto Rico. Participants who were enrolled in the RECOVER adult cohort before April 10, 2023, completed a symptom survey 6 months or more after acute symptom onset or test date. Selection included population-based, volunteer, and convenience sampling. Exposure: SARS-CoV-2 infection. Main Outcomes and Measures: PASC and 44 participant-reported symptoms (with severity thresholds). Results: A total of 9764 participants (89% SARS-CoV-2 infected; 71% female; 16% Hispanic/Latino; 15% non-Hispanic Black; median age, 47 years [IQR, 35-60]) met selection criteria. Adjusted odds ratios were 1.5 or greater (infected vs uninfected participants) for 37 symptoms. Symptoms contributing to PASC score included postexertional malaise, fatigue, brain fog, dizziness, gastrointestinal symptoms, palpitations, changes in sexual desire or capacity, loss of or change in smell or taste, thirst, chronic cough, chest pain, and abnormal movements. Among 2231 participants first infected on or after December 1, 2021, and enrolled within 30 days of infection, 224 (10% [95% CI, 8.8%-11%]) were PASC positive at 6 months. Conclusions and Relevance: A definition of PASC was developed based on symptoms in a prospective cohort study. As a first step to providing a framework for other investigations, iterative refinement that further incorporates other clinical features is needed to support actionable definitions of PASC.

Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations.

Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.

Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration.

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.

Impact of selection bias on polygenic risk score estimates in healthcare settings.

BACKGROUND: Hospital-based biobanks are being increasingly considered as a resource for translating polygenic risk scores (PRS) into clinical practice. However, since these biobanks originate from patient populations, there is a possibility of bias in polygenic risk estimation due to overrepresentation of patients with higher frequency of healthcare interactions. METHODS: PRS for schizophrenia, bipolar disorder, and depression were calculated using summary statistics from the largest available genomic studies for a sample of 24 153 European ancestry participants in the Mass General Brigham (MGB) Biobank. To correct for selection bias, we fitted logistic regression models with inverse probability (IP) weights, which were estimated using 1839 sociodemographic, clinical, and healthcare utilization features extracted from electronic health records of 1 546 440 non-Hispanic White patients eligible to participate in the Biobank study at their first visit to the MGB-affiliated hospitals. RESULTS: Case prevalence of bipolar disorder among participants in the top decile of bipolar disorder PRS was 10.0% (95% CI 8.8-11.2%) in the unweighted analysis but only 6.2% (5.0-7.5%) when selection bias was accounted for using IP weights. Similarly, case prevalence of depression among those in the top decile of depression PRS was reduced from 33.5% (31.7-35.4%) to 28.9% (25.8-31.9%) after IP weighting. CONCLUSIONS: Non-random selection of participants into volunteer biobanks may induce clinically relevant selection bias that could impact implementation of PRS in research and clinical settings. As efforts to integrate PRS in medical practice expand, recognition and mitigation of these biases should be considered and may need to be optimized in a context-specific manner.

Researching COVID to enhance recovery (RECOVER) pediatric study protocol: Rationale, objectives and design.

Importance: The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or "Long COVID") in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults. Observations: We describe the protocol for the Pediatric Observational Cohort Study of the NIH's RE searching COV ID to E nhance R ecovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of five cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study ( n =10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n=6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n=6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n=600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science. Conclusions and Relevance: RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions. Clinical Trialsgov Identifier: Clinical Trial Registration: http://www.clinicaltrials.gov . Unique identifier: NCT05172011.

Alcohol Consumption and Risk of Total Hip Replacement Due to Hip Osteoarthritis in Women.

OBJECTIVE: This study was undertaken to examine the relationship between alcohol consumption and hip osteoarthritis in women. Alcohol has been associated with both adverse and beneficial health effects generally; however, the relationship between alcohol consumption and hip osteoarthritis has been minimally studied. METHODS: Among women in the Nurses' Health Study cohort in the US, alcohol consumption was assessed every 4 years, starting in 1980. Intake was computed as cumulative averages and simple updates with latency periods of 0-4 through 20-24 years. We followed 83,383 women without diagnosed osteoarthritis in 1988 to June 2012. We identified 1,796 cases of total hip replacement due to hip osteoarthritis defined by self-report of osteoarthritis with hip replacement. RESULTS: Alcohol consumption was positively associated with hip osteoarthritis risk. Compared with nondrinkers, multivariable hazard ratios (HRs) and 95% confidence intervals (95% CIs) were HR 1.04 (95% CI 0.90, 1.19) for drinkers of >0 to <5 grams/day, HR 1.12 (95% CI 0.94, 1.33) for 5 to <10 grams/day, HR 1.31 (95% CI 1.10, 1.56) for 10 to <20 grams/day, and HR 1.34 (95% CI 1.09, 1.64) for ≥20 grams/day (P for trend < 0.0001). This association held in latency analyses of up to 16-20 years, and for alcohol consumption between 35-40 years of age. Independent of other alcoholic beverages, the multivariable HRs (per 10 grams of alcohol) were similar for individual types of alcohol intake (wine, liquor, and beer; P = 0.57 for heterogeneity among alcohol types). CONCLUSION: Higher alcohol consumption was associated with greater incidence of total hip replacement due to hip osteoarthritis in a dose-dependent manner in women.

Diagnostic TR-FRET assays for detection of antibodies in patient samples.

Serological assays are important diagnostic tools for surveying exposure to the pathogen, monitoring immune response post vaccination, and managing spread of the infectious agent among the population. Current serological laboratory assays are often limited because they require the use of specialized laboratory technology and/or work with a limited number of sample types. Here, we evaluate an alternative by developing time-resolved Förster resonance energy transfer (TR-FRET) homogeneous assays that exhibited exceptional versatility, scalability, and sensitivity and outperformed or matched currently used strategies in terms of sensitivity, specificity, and precision. We validated the performance of the assays measuring total immunoglobulin G (IgG) levels; antibodies against severe acute respiratory syndrome coronavirus (SARS-CoV) or Middle Eastern respiratory syndrome (MERS)-CoV spike (S) protein; and SARS-CoV-2 S and nucleocapsid (N) proteins and applied it to several large sample sets and real-world applications. We further established a TR-FRET-based ACE2-S competition assay to assess the neutralization propensity of the antibodies. Overall, these TR-FRET-based serological assays can be rapidly extended to other antigens and are compatible with commonly used plate readers.