Background:Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. Researchers have begun to explore whether Paxlovid treatment in the acute phase of COVID-19 could help prevent the onset of PASC. Methods and Findings:We used electronic health records from the National Clinical Cohort Collaborative (N3C) to define a cohort of 410,026 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation framework to estimate the effect of Paxlovid treatment on PASC incidence. The treatment group was defined as outpatients prescribed Paxlovid within five days of COVID-19 index, and the control group was defined as all patients meeting eligibility criteria not in the treatment group. The follow-up period was 180 days. We estimated overall PASC incidence using a computable phenotype. We also measured incident cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment had a small effect on overall PASC incidence (relative risk [RR] 0.94; 95% CI [0.90, 0.99]; p=0.011). It had a slightly stronger protective effect against cognitive (RR 0.86; 95% CI [0.77, 0.95]; p<0.001) and fatigue (RR 0.92; 95% CI [0.86, 0.97]; p=0.002) symptoms. Conclusions:In this study, Paxlovid had a weaker preventative effect on PASC than in prior observational studies, suggesting that Paxlovid is unlikely to become a definitive solution for preventing PASC. Differing effects by symptom cluster suggest that the etiology of cognitive and fatigue symptoms may be more closely related to viral load than that of respiratory symptoms. Future research should explore potential heterogeneous treatment effects across PASC subphenotypes.

Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.

Objectives To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset.Materials and Methods Employing the National Clinical Cohort Collaborative Tenant Pilot (N3C Clinical) dataset, our approach integrates machine learning algorithms-logistic regression and eXtreme Gradient Boosting (XGBoost)-with a diagnostic hierarchical model for nuanced classification of dementia subtypes based on comorbidities and gender. The methodology is enhanced by multi-site EHR data, implementing a hybrid sampling strategy combining 65% Synthetic Minority Over-sampling Technique (SMOTE), 35% Random Under-Sampling (RUS), and Tomek Links for class imbalance. The hierarchical model further refines the analysis, allowing for layered understanding of disease patterns.Results The study identified significant comorbidity patterns associated with diagnosis of Alzheimer's, Vascular, and Lewy Body dementia subtypes. The classification models achieved accuracies up to 69% for Alzheimer's/Vascular dementia and highlighted challenges in distinguishing Dementia with Lewy Bodies. The hierarchical model elucidates the complexity of diagnosing Dementia with Lewy Bodies and reveals the potential impact of regional clinical practices on dementia classification.Conclusion Our methodology underscores the importance of leveraging multi-site datasets and tailored sampling techniques for dementia research. This framework holds promise for extending to other disease subtypes, offering a pathway to more nuanced and generalizable insights into dementia and its complex interplay with comorbid conditions.Discussion This study underscores the critical role of multi-site data analyzes in understanding the relationship between comorbidities and disease subtypes. By utilizing diverse healthcare data, we emphasize the need to consider site-specific differences in clinical practices and patient demographics. Despite challenges like class imbalance and variability in EHR data, our findings highlight the essential contribution of multi-site data to developing accurate and generalizable models for disease classification. This study aims to enhance our understanding and classification of dementia subtypes using data from multiple healthcare sites. Dementia includes forms like Alzheimer's, Vascular, and Lewy Body dementia, each with unique health conditions. Researchers analyzed data from 9 US sites using a multi-stage approach with machine learning techniques, specifically logistic regression and eXtreme Gradient Boosting (XGBoost).The methodology involved 3 steps. First, the dataset was refined to focus on well-represented dementia subtypes. Next, advanced techniques balanced the data for fair representation. Finally, machine learning models classified the dementia types based on comorbidities and gender differences, achieving up to 70% accuracy for Alzheimer's and Vascular dementia, but finding Lewy Body dementia more challenging. A hierarchical model was used to address site-specific variations, revealing disparities among sites and improving generalization across populations.This study highlights the complexity of diagnosing dementia subtypes and the limitations of single-site studies, which often suffer from biases. By leveraging data from multiple sites, the research underscores the importance of multi-site dataset analysis for better generalization. This approach enhances understanding of dementia and provides a framework applicable to other diseases.

Objective: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population. Study Design: Retrospective. Setting: Clinical data in the National COVID Cohort Collaborative database (N3C). Methods: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period. Results: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29–2.50; P < 0.001), alpha (OR, 3.63; CI, 3.48–3.78; P < 0.001), delta (OR, 3.03; CI, 2.94–3.12; P < 0.001), omicron 21K variant (OR, 2.97; CI, 2.90–3.04; P < 0.001), and omicron 23A variant (OR, 8.80; CI, 8.35–9.27; P < 0.001). Conclusions: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings.

Abstract Background Although the COVID-19 pandemic has persisted for over 3 years, reinfections with SARS-CoV-2 are not well understood. We aim to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. Methods We use an electronic health record study cohort of over 3 million patients from the National COVID Cohort Collaborative as part of the NIH Researching COVID to Enhance Recovery Initiative. We calculate summary statistics, effect sizes, and Kaplan–Meier curves to better understand COVID-19 reinfections. Results Here we validate previous findings of reinfection incidence (6.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present findings that the proportion of Long COVID diagnoses is higher following initial infection than reinfection for infections in the same epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between initial infection and reinfection (chi-squared value: 25,697, p-value: <0.0001) with a medium effect size (Cramer’s V: 0.20, DoF = 3). Individuals who experienced severe initial and first reinfection were older in age and at a higher mortality risk than those who had mild initial infection and reinfection. Conclusions In a large patient cohort, we find that the severity of reinfection appears to be associated with the severity of initial infection and that Long COVID diagnoses appear to occur more often following initial infection than reinfection in the same epoch. Future research may build on these findings to better understand COVID-19 reinfections.

Since the outbreak of the COVID-19 pandemic in 2020, numerous studies have focused on the long-term effects of COVID infection. On 1 October 2021, the Centers for Disease Control (CDC) implemented a new code in the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for reporting ‘Post COVID-19 condition, unspecified (U09.9)’. This change indicated that the CDC recognized Long COVID as a real illness with associated chronic conditions. The National COVID Cohort Collaborative (N3C) provides researchers with abundant electronic health record (EHR) data by harmonizing EHR data across more than 80 different clinical organizations in the United States. This paper describes the creation of a COVID-positive N3C cohort balanced by the presence or absence of Long COVID (U09.9) and evaluates whether or not documented Long COVID (U09.9) is associated with decreased survival length.

Since the COVID-19 pandemic in 2020, there are numerous studies and researches on the long term effect of COVID-19 on both patient level and social level with disparities noted in infection rates and outcomes. However, differences in the COVID related healthcare decisions after patients present for care (e.g. hospitalization after emergency department visit) has not been studied much at a national level. The National COVID Cohort Collaborative (N3C) provides researchers with abundant data collected from different clinical sites, making it suitable for Bayesian hierarchical modeling while analyzing the disparity in hospitalization after emergency department visit, where prior information or belief could be easily included in the modeling process by adjusting the prior distribution of parameters. In this analysis, we select demographic information (age, sex, race and ethnicity) and the Charlson Comorbidity Index (CCI) as features and study the relationship between these features and whether a patient would be hospitalized after having a COVID related visit to an emergency department (ED).

Long COVID, or complications arising from COVID-19 weeks after infection, has become a central concern for public health experts. The United States National Institutes of Health founded the RECOVER initiative to better understand long COVID. We used electronic health records available through the National COVID Cohort Collaborative to characterize the association between SARS-CoV-2 vaccination and long COVID diagnosis. Among patients with a COVID-19 infection between August 1, 2021 and January 31, 2022, we defined two cohorts using distinct definitions of long COVID-a clinical diagnosis (n=47,404) or a previously described computational phenotype (n=198,514)-to compare unvaccinated individuals to those with a complete vaccine series prior to infection. Evidence of long COVID was monitored through June or July of 2022, depending on patients' data availability. We found that vaccination was consistently associated with lower odds and rates of long COVID clinical diagnosis and high-confidence computationally derived diagnosis after adjusting for sex, demographics, and medical history. The extent to which COVID-19 vaccination protects against long COVID is not well understood. Here, the authors use electronic health record data from the United States and find that, for people who received their vaccination prior to infection, vaccination was associated with lower incidence of long COVID.

STUDY OBJECTIVES:Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC). METHODS:We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities. RESULTS:Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis. CONCLUSIONS:Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae.

Although the COVID-19 pandemic has persisted for over 2 years, reinfections with SARS-CoV-2 are not well understood. We use the electronic health record (EHR)-based study cohort from the National COVID Cohort Collaborative (N3C) as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. We validate previous findings of reinfection incidence (5.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present novel findings that Long COVID diagnoses occur closer to the index date for infection or reinfection in the Omicron BA epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between first infection and reinfection (chi-squared value: 9446.2, p-value: 0) with a medium effect size (Cramer's V: 0.18, DoF = 4).

National COVID Cohort Collaborative (N3C) enclave provides health researchers with a rich dataset from 76 contributing clinical sites. However, the harmonized data lacks certain details available in sites’ local electronic health records (EHRs), such as the principal diagnosis code for reported emergency department (ED) and inpatient (IP) visits. This means a principal diagnosis of COVID-19 can only be inferred by applying a time relationship between the visit dates and the record of infection and diagnosis. The purpose of this study is to perform a single-site sensitivity analysis modeled after an N3C study examining potential race-ethnicity based bias in hospitalization decisions during COVID-19 related ED visits. The analytic pipeline was first run in N3C, then reproduced locally with N3C data fields from a single-site, and finally run a third time using the additional principal diagnosis data. We find the effects of patient comorbidities and race-ethnicity groups on direct IP admittance to be consistent among the three cohorts with varying levels of statistical significance due to different sample sizes.

Macrophages are important regulators of obesity-associated inflammation and PPARα and -γ agonism in macrophages has anti-inflammatory effects. In this study, we tested the efficacy with which liposomal delivery could target the PPARα/γ dual agonist tesaglitazar to macrophages while reducing drug action in common sites of drug toxicity: the liver and kidney, and whether tesaglitazar had anti-inflammatory effects in an in vivo model of obesity-associated dysmetabolism. Methods: Male leptin-deficient (ob/ob) mice were administered tesaglitazar or vehicle for one week in a standard oral formulation or encapsulated in liposomes. Following the end of treatment, circulating metabolic parameters were measured and pro-inflammatory adipose tissue macrophage populations were quantified by flow cytometry. Cellular uptake of liposomes in tissues was assessed using immunofluorescence and a broad panel of cell subset markers by flow cytometry. Finally, PPARα/γ gene target expression levels in the liver, kidney, and sorted macrophages were quantified to determine levels of drug targeting to and drug action in these tissues and cells. Results: Administration of a standard oral formulation of tesaglitazar effectively treated symptoms of obesity-associated dysmetabolism and reduced the number of pro-inflammatory adipose tissue macrophages. Macrophages are the major cell type that took up liposomes with many other immune and stromal cell types taking up liposomes to a lesser extent. Liposome delivery of tesaglitazar did not have effects on inflammatory macrophages nor did it improve metabolic parameters to the extent of a standard oral formulation. Liposomal delivery did, however, attenuate effects on liver weight and liver and kidney expression of PPARα and -γ gene targets compared to oral delivery. Conclusions: These findings reveal for the first time that tesaglitazar has anti-inflammatory effects on adipose tissue macrophage populations in vivo. These data also suggest that while nanoparticle delivery reduced off-target effects, yet the lack of tesaglitazar actions in non-targeted cells such (as hepatocytes and adipocytes) and the uptake of drug-loaded liposomes in many other cell types, albeit to a lesser extent, may have impacted overall therapeutic efficacy. This fulsome analysis of cellular uptake of tesaglitazar-loaded liposomes provides important lessons for future studies of liposome drug delivery.

Rationale: PPARα/γ agonist tesaglitazar effectively improves insulin sensitivity and dyslipidemia in diabetic mice and human subjects, but also causes unwanted side effects in some patients. An alternative method to deliver tesaglitazar that reduces side effects while remaining therapeutically effective is currently unavailable, but is clinically valuable. We hypothesized that liposome delivery of tesaglitazar would attenuate macrophage-mediated inflammation and improve diabetic symptoms while reducing PPAR agonism in the kidneys and liver. Methods & Results: Ob/ob mice were treated with tesaglitazar packaged in DiD-labelled liposomes or as free drug. After one week of treatment, flow cytometry analysis revealed macrophages are the major cell type taking up liposomes but other cell types take up liposomes to a lesser extent. At earlier time points, macrophages were not the major DiD+ cell type, but they took up the most liposomes on a per cell basis as quantified by DiD mean fluorescence intensity (MFI). Non-liposomal oral delivery of tesagalitazar led to reductions in CD11c+CD206-CD301- inflammatory macrophages and robust improvements in metabolism (circulating glucose, insulin, and triglycerides), while liposomal delivery did not. Liposomal delivery did, however, attenuate effects on liver weight and liver and kidney PPAR gene target expression compared to non-liposomal oral delivery. Conclusions: While nanoparticle delivery is a promising approach to reduce off-target effects, therapeutic efficacy of tesaglitazar may require paracrine effects that depend on non-liposome-targeted cells such as adipocytes. Furthermore, unexpected liposome uptake in many cell types emphasizes the value of thorough biodistribution studies prior to employing such techniques for therapies.

Atherosclerosis is the primary pathological process of CVD and is strongly controlled by heritable factors; however, the mechanisms and pathways through which many of these genetic components regulate plaque development are poorly understood. Recently, a SNP in the coding region of the gene inhibitor of differentiation 3 ( ID3 ) at rs11574 was identified in multiple studies to be associated with CVD. Mutation of rs11574 from the major allele (G) to the minor allele (A) changes the 105 th amino acid of ID3 from an alanine to a threonine and is associated with an attenuated ability of ID3 to antagonize bHLH transcription factors and to prevent them from binding to DNA and activating transcription. The current study utilized co-immunoprecipitation to demonstrate that the minor allele of rs11574 specifically impairs the ability of ID3 to bind to and sequester E12 and no other member of the bHLH family. ChIP studies further demonstrated that the impaired regulatory ability of the ID3 minor allele variant increases E12 occupancy at promoter regions and enhances transcription of E12 target genes including smooth muscle alpha actin ( Acta2 ) and p21 in murine vascular smooth muscle cells (VSMCs). To study the role of this SNP in human cells in intact chromatin under endogenous regulation, we genome edited human 293T cells using CRISPR/Cas9 to produce the allelic variants of rs11574 in the ID3 gene (GG, AG, and AA). Edited cells showed significant changes in cellular proliferation, gene expression, and promoter occupancy depending upon genotype at rs11574. Cells containing the minor allele of rs11574 displayed significantly increased p21 expression and proliferated more slowly than cells containing the major allele of rs11574; whereas, cells heterozygote at rs11574 displayed an intermediate phenotype. These data implicate rs11574 in the regulation of cellular proliferation and mature VSMC marker expression, key phenotypes regulated in the processes of lesion development and formation.