Wearable sensor-based detection of influenza in presymptomatic and asymptomatic individuals.

BACKGROUND: The COVID-19 pandemic highlighted the need for early detection of viral infections in symptomatic and asymptomatic individuals to allow for timely clinical management and public health interventions. METHODS: Twenty healthy adults were challenged with an influenza A (H3N2) virus and prospectively monitored from 7 days before through 10 days after inoculation, using wearable electrocardiogram and physical activity sensors (Clinical Trial: NCT04204493; https://clinicaltrials.gov/ct2/show/NCT04204993). This framework allowed for responses to be accurately referenced to the infection event. For each participant, we trained a semi-supervised multivariable anomaly detection model on data acquired before inoculation and used it to classify the post-inoculation dataset. RESULTS: Inoculation with this challenge virus was well-tolerated with an infection rate of 85%. With the model classification threshold set so that no alarms were recorded in the 170 healthy days recorded, the algorithm correctly identified 16 of 17 (94%) positive presymptomatic and asymptomatic individuals, on average 58 hours post inoculation and 23 hrs before the symptom onset. CONCLUSION: The data processing and modeling methodology show promise for the early detection of respiratory illness. The detection algorithm is compatible with data collected from smartwatches using optical techniques but needs to be validated in large heterogeneous cohorts in normal living conditions.

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease

Summary Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection. Graphical abstract Highlights • We present a computational framework for alternative splicing (AS) diagnostic markers• Our AS biomarkers outperform gene-expression biomarkers in COVID-19 detection• Microfluidic PCR diagnostic assay of AS biomarkers achieves greater than 98% accuracy• We interpret the biological importance of identified AS biomarkers Motivation Host-based response assays (HRAs) can often diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) in HRAs remains unexplored, as existing HRAs are restricted to gene expression signatures. We report a computational framework for the identification, optimization, and evaluation of blood AS-based diagnostic assay development for infectious disease. Using SARS-CoV-2 infection as a case study, we demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis when compared against six reported transcriptome signatures and when implemented as a microfluidic PCR diagnostic assay. Host-based response assays can diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) remains unexplored. Zhang et al. present a computational framework for AS diagnostic biomarkers. Using SARS-CoV-2 as a case study, they demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis.

COVID-19 Diagnosis and SARS-CoV-2 Strain Identification by a Rapid, Multiplexed, Point-of-Care Antibody Microarray.

Antigen tests to detect SARS-CoV-2 have emerged as a promising rapid diagnostic method for COVID-19, but they are unable to differentiate between variants of concern (VOCs). Here, we report a rapid point-of-care test (POC-T), termed CoVariant-SPOT, that uses a set of antibodies that are either tolerant or intolerant to spike protein mutations to identify the likely SARS-CoV-2 strain concurrent with COVID-19 diagnosis using antibodies targeting the nucleocapsid protein. All reagents are incorporated into a portable, multiplexed, and sensitive diagnostic platform built upon a nonfouling polymer brush. To validate CoVariant-SPOT, we tested recombinant SARS-CoV-2 proteins, inactivated viruses, and nasopharyngeal swab samples from COVID-19 positive and negative individuals and showed that CoVariant-SPOT can readily distinguish between two VOCs: Delta and Omicron. We believe that CoVariant-SPOT can serve as a valuable adjunct to next-generation sequencing to rapidly identify variants using a scalable and deployable POC-T, thereby enhancing community surveillance efforts worldwide and informing treatment selection.

COVID-19 Infection Risk Among Previously Uninfected Adults: Development of a Prognostic Model.

Background: Few models exist that incorporate measures from an array of individual characteristics to predict the risk of COVID-19 infection in the general population. The aim was to develop a prognostic model for COVID-19 using readily obtainable clinical variables. Methods: Over 74 weeks surveys were periodically administered to a cohort of 1381 participants previously uninfected with COVID-19 (June 2020 to December 2021). Candidate predictors of incident infection during follow-up included demographics, living situation, financial status, physical activity, health conditions, flu vaccination history, COVID-19 vaccine intention, work/employment status, and use of COVID-19 mitigation behaviors. The final logistic regression model was created using a penalized regression method known as the least absolute shrinkage and selection operator. Model performance was assessed by discrimination and calibration. Internal validation was performed via bootstrapping, and results were adjusted for overoptimism. Results: Of the 1381 participants, 154 (11.2%) had an incident COVID-19 infection during the follow-up period. The final model included six variables: health insurance, race, household size, and the frequency of practicing three mitigation behavior (working at home, avoiding high-risk situations, and using facemasks). The c-statistic of the final model was 0.631 (0.617 after bootstrapped optimism-correction). A calibration plot suggested that with this sample the model shows modest concordance with incident infection at the lowest risk. Conclusion: This prognostic model can help identify which community-dwelling older adults are at the highest risk for incident COVID-19 infection and may inform medical provider counseling of their patients about the risk of incident COVID-19 infection.

Mitigation behavior prior to COVID-19 vaccination availability is associated with COVID-19 infection and time to vaccination.

BACKGROUND: Mitigation behaviors reduce the incidence of COVID-19 infection. Determining characteristics of groups defined by mitigation behaviors compliance may be useful to inform targeted public health policies and interventions. This study aimed to identify groups of individuals according to self-reported compliance with COVID-19 mitigation behaviors, define compliance class characteristics, and explore associations between compliance classes and important study and public health outcomes. METHODS AND FINDINGS: We studied 1,410 participants in the Cabarrus County COVID-19 Prevalence and Immunity longitudinal cohort study (June 2020 to December 2021) who were asked 10 questions regarding compliance with recommended COVID-19 mitigation behaviors. By Latent Class Analysis, 1,381 participants were categorized into 3 classes (most [49.4%], moderately [45.0%], and least [5.6%] compliant). Compared with the most compliant class, the least and moderately compliant classes were younger (mean = 61.9 v. 59.0 v. 53.8 years), had fewer medical conditions per individual (1.37 v. 1.08 v. 0.77), and differed in Hispanic ethnicity (6.2% v. 2.8% v. 9.1%) and COVID-19 vaccine intention (65.8% v. 59.8% v. 35.1%). Compared to the most compliant class, the least compliant class had fewer women (54.6% v. 76.3%), fewer insured individuals (92.2% v. 97.4%), and more withdrew from study participation early (28.6% v. 16.0%). Relative to the most compliant class, the least compliant class had a higher likelihood of COVID-19 infection (OR = 2.08 [95% CI 1.13, 3.85]), lower rate of COVID-19 vaccination (72.6% v. 95.1%), and longer time to 50% COVID-19 vaccination following eligibility (8-9 vs 16 days). CONCLUSIONS: Classes defined by mitigation behaviors compliance had distinct characteristics, including age, sex, medical history, and ethnicity, and were associated with important study and public health outcomes. Targeted public health policies and interventions according to the compliance group characteristics may be of value in current and future pandemic responses to increase compliance.

Hydroxychloroquine for pre-exposure prophylaxis of COVID-19 in health care workers: a randomized, multicenter, placebo-controlled trial Healthcare Worker Exposure Response and Outcomes of Hydroxychloroquine (HERO-HCQ).

OBJECTIVES: To determine whether hydroxychloroquine (HCQ) is safe and effective at preventing COVID-19 infections among health care workers (HCWs). METHODS: In a 1: 1 randomized, placebo-controlled, double-blind, parallel-group, superiority trial at 34 US clinical centers, 1360 HCWs at risk for COVID-19 infection were enrolled between April and November 2020. Participants were randomized to HCQ or matched placebo. The HCQ dosing included a loading dose of HCQ 600 mg twice on day 1, followed by 400 mg daily for 29 days. The primary outcome was a composite of confirmed or suspected COVID-19 clinical infection by day 30, defined as new-onset fever, cough, or dyspnea and either a positive SARS-CoV-2 polymerase chain reaction test (confirmed) or a lack of confirmatory testing due to local restrictions (suspected). RESULTS: Study enrollment closed before full accrual due to recruitment challenges. The primary end point occurred in 41 (6.0%) participants receiving HCQ and 53 (7.8%) participants receiving placebo. No difference in the proportion of participants experiencing clinical infection (estimated difference of -1.8%, 95% confidence interval -4.6-0.9%, P = 0.20) was identified nor any significant safety issues. CONCLUSION: Oral HCQ taken as prescribed appeared safe among HCWs. No significant clinical benefits were observed. The study was not powered to detect a small but potentially important reduction in infection. TRIAL REGISTRATION: NCT04334148.

Summarizing Study Characteristics and Diagnostic Performance of Commercially Available Tests for Respiratory Syncytial Virus: A Scoping Literature Review in the COVID-19 Era.

BACKGROUND: Nonpharmaceutical interventions to prevent the spread of coronavirus disease 2019 also decreased the spread of respiratory syncytial virus (RSV) and influenza. Viral diagnostic testing in patients with respiratory tract infections (RTI) is a necessary tool for patient management; therefore, sensitive and specific tests are required. This scoping literature review aimed to summarize the study characteristics of commercially available sample-to-answer RSV tests. CONTENT: PubMed and Embase were queried for studies reporting on the diagnostic performance of tests for RSV in patients with RTI (published January 2005-January 2021). Information on study design, patient and setting characteristics, and published diagnostic performance of RSV tests were extracted from 77 studies that met predefined inclusion criteria. A literature gap was identified for studies of RSV tests conducted in adult-only populations (5.3% of total subrecords) and in outpatient (7.5%) or household (0.8%) settings. Overall, RSV tests with analytical time >30 min had higher published sensitivity (62.5%-100%) vs RSV tests with analytical time ≤30 min (25.7%-100%); this sensitivity range could be partially attributed to the different modalities (antigen vs molecular) used. Molecular-based rapid RSV tests had higher published sensitivity (66.7%-100%) and specificity (94.3%-100%) than antigen-based RSV tests (sensitivity: 25.7%-100%; specificity:80.3%-100%). SUMMARY: This scoping review reveals a paucity of literature on studies of RSV tests in specific populations and settings, highlighting the need for further assessments. Considering the implications of these results in the current pandemic landscape, the authors preliminarily suggest adopting molecular-based RSV tests for first-line use in these settings.

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease.

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.

Pre-exposure cognitive performance variability is associated with severity of respiratory infection.

Using data from a longitudinal viral challenge study, we find that the post-exposure viral shedding and symptom severity are associated with a novel measure of pre-exposure cognitive performance variability (CPV), defined before viral exposure occurs. Each individual's CPV score is computed from data collected from a repeated NeuroCognitive Performance Test (NCPT) over a 3 day pre-exposure period. Of the 18 NCPT measures reported by the tests, 6 contribute materially to the CPV score, prospectively differentiating the high from the low shedders. Among these 6 are the 4 clinical measures digSym-time, digSym-correct, trail-time, and reaction-time, commonly used for assessing cognitive executive functioning. CPV is found to be correlated with stress and also with several genes previously reported to be associated with cognitive development and dysfunction. A perturbation study over the number and timing of NCPT sessions indicates that as few as 5 sessions is sufficient to maintain high association between the CPV score and viral shedding, as long as the timing of these sessions is balanced over the three pre-exposure days. Our results suggest that variations in cognitive function are closely related to immunity and susceptibility to severe infection. Further studying these relationships may help us better understand the links between neurocognitive and neuroimmune systems which is timely in this COVID-19 pandemic era.

The US Department of Veterans Affairs Science and Health Initiative to Combat Infectious and Emerging Life-Threatening Diseases (VA SHIELD): A Biorepository Addressing National Health Threats.

Background: The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has demonstrated the need to share data and biospecimens broadly to optimize clinical outcomes for US military Veterans. Methods: In response, the Veterans Health Administration established VA SHIELD (Science and Health Initiative to Combat Infectious and Emerging Life-threatening Diseases), a comprehensive biorepository of specimens and clinical data from affected Veterans to advance research and public health surveillance and to improve diagnostic and therapeutic capabilities. Results: VA SHIELD now comprises 12 sites collecting de-identified biospecimens from US Veterans affected by SARS-CoV-2. In addition, 2 biorepository sites, a data processing center, and a coordinating center have been established under the direction of the Veterans Affairs Office of Research and Development. Phase 1 of VA SHIELD comprises 34 157 samples. Of these, 83.8% had positive tests for SARS-CoV-2, with the remainder serving as contemporaneous controls. The samples include nasopharyngeal swabs (57.9%), plasma (27.9%), and sera (12.5%). The associated clinical and demographic information available permits the evaluation of biological data in the context of patient demographics, clinical experience and management, vaccinations, and comorbidities. Conclusions: VA SHIELD is representative of US national diversity with a significant potential to impact national healthcare. VA SHIELD will support future projects designed to better understand SARS-CoV-2 and other emergent healthcare crises. To the extent possible, VA SHIELD will facilitate the discovery of diagnostics and therapeutics intended to diminish COVID-19 morbidity and mortality and to reduce the impact of new emerging threats to the health of US Veterans and populations worldwide.

Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature.

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Systematic comparison of published host gene expression signatures for bacterial/viral discrimination.

BACKGROUND: Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. METHODS: This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. RESULTS: Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69-0.97 for viral classification. Signature size varied (1-398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months-1 year and 2-11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. CONCLUSIONS: In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature's size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.

The Cabarrus County COVID-19 Prevalence and Immunity (C3PI) Study: design, methods, and baseline characteristics.

OBJECTIVES: Coronavirus Disease 2019 (COVID-19) is a viral illness with public health importance. The Cabarrus County COVID-19 Prevalence and Immunity (C3PI) Study is a prospective, longitudinal cohort study designed to contribute valuable information on community prevalence of active COVID-19 infection and SARS-CoV-2 antibodies as the pandemic and responses to it have and continue to evolve. We present the rationale, study design, and baseline characteristics of the C3PI Study. METHODS: We recruited 1,426 participants between June 2020 and August 2020 from the Measurement to Understand the Reclassification of Disease of Cabarrus/Kannapolis (MURDOCK) Study Community Registry and Biorepository, a previously established, community-based, longitudinal cohort. Participants completed a baseline survey and follow-up surveys every two weeks. A nested weighted, random sub-cohort (n=300) was recruited to measure the incidence and prevalence of active COVID-19 infection and SARS-CoV-2 IgG antibodies. RESULTS: The sub-cohort was younger (56 vs 61 years), had more men (39.0% vs 30.9%), and a higher proportion of Hispanic (11.0% vs 5.1%) and Black participants (17.0% vs 8.2%) compared with the overall cohort. They had similar anthropometrics and medical histories, but a greater proportion of the sub-cohort had a higher educational degree (36.1% vs 31.3%) and reported a pre-pandemic annual household income of >$90,000 (57.1% vs 47.9%). CONCLUSION: This study is part of a multisite consortium that will provide critical data on the epidemiology of COVID-19 and community perspectives about the pandemic, behaviors and mitigation strategies, and individual and community burden in North Carolina.

A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19.

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.

Comparison of a Blood Self-Collection System with Routine Phlebotomy for SARS-CoV-2 Antibody Testing.

The Coronavirus Disease 2019 (COVID-19) pandemic forced researchers to reconsider in-person assessments due to transmission risk. We conducted a pilot study to evaluate the feasibility of using the Tasso-SST (Tasso, Inc, Seattle, Washington) device for blood self-collection for use in SARS-CoV-2 antibody testing in an ongoing COVID-19 prevalence and immunity research study. 100 participants were recruited between January and March 2021 from a previously identified sub-cohort of the Cabarrus County COVID-19 Prevalence and Immunity (C3PI) Study who were under-going bimonthly COVID-19 antibody testing. Participants were given a Tasso-SST kit and asked to self-collect blood during a scheduled visit where trained laboratory personnel performed routine phlebotomy. All participants completed an after-visit survey about their experience. Overall, 70.0% of participants were able to collect an adequate sample for testing using the device. Among those with an adequate sample, there was a high concordance in results between the Tasso-SST and phlebotomy blood collection methods (Cohen's kappa coefficient = 0.88, Interclass correlation coefficient 0.98 [0.97, 0.99], p < 0.0001). The device received a high-level (90.0%) of acceptance among all participants. Overall, the Tasso-SST could prove to be a valuable tool for seroprevalence testing. However, future studies in larger, diverse populations over longer periods may provide a better understanding of device usability and acceptance among older participants and those with comorbidities in various use scenarios.

Differential chromatin accessibility in peripheral blood mononuclear cells underlies COVID-19 disease severity prior to seroconversion.

SARS-CoV-2 infection triggers profound and variable immune responses in human hosts. Chromatin remodeling has been observed in individuals severely ill or convalescing with COVID-19, but chromatin remodeling early in disease prior to anti-spike protein IgG seroconversion has not been defined. We performed the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and RNA-seq on peripheral blood mononuclear cells (PBMCs) from outpatients with mild or moderate symptom severity at different stages of clinical illness. Early in the disease course prior to IgG seroconversion, modifications in chromatin accessibility associated with mild or moderate symptoms were already robust and included severity-associated changes in accessibility of genes in interleukin signaling, regulation of cell differentiation and cell morphology. Furthermore, single-cell analyses revealed evolution of the chromatin accessibility landscape and transcription factor motif accessibility for individual PBMC cell types over time. The most extensive remodeling occurred in CD14+ monocytes, where sub-populations with distinct chromatin accessibility profiles were observed prior to seroconversion. Mild symptom severity was marked by upregulation of classical antiviral pathways, including those regulating IRF1 and IRF7, whereas in moderate disease, these classical antiviral signals diminished, suggesting dysregulated and less effective responses. Together, these observations offer novel insight into the epigenome of early mild SARS-CoV-2 infection and suggest that detection of chromatin remodeling in early disease may offer promise for a new class of diagnostic tools for COVID-19.

Age-Related Changes in the Nasopharyngeal Microbiome Are Associated With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection and Symptoms Among Children, Adolescents, and Young Adults.

BACKGROUND: Children are less susceptible to SARS-CoV-2 infection and typically have milder illness courses than adults, but the factors underlying these age-associated differences are not well understood. The upper respiratory microbiome undergoes substantial shifts during childhood and is increasingly recognized to influence host defense against respiratory pathogens. Thus, we sought to identify upper respiratory microbiome features associated with SARS-CoV-2 infection susceptibility and illness severity. METHODS: We collected clinical data and nasopharyngeal swabs from 285 children, adolescents, and young adults (<21 years) with documented SARS-CoV-2 exposure. We used 16S ribosomal RNA gene sequencing to characterize the nasopharyngeal microbiome and evaluated for age-adjusted associations between microbiome characteristics and SARS-CoV-2 infection status and respiratory symptoms. RESULTS: Nasopharyngeal microbiome composition varied with age (PERMANOVA, P < .001; R2 = 0.06) and between SARS-CoV-2-infected individuals with and without respiratory symptoms (PERMANOVA, P  = .002; R2 = 0.009). SARS-CoV-2-infected participants with Corynebacterium/Dolosigranulum-dominant microbiome profiles were less likely to have respiratory symptoms than infected participants with other nasopharyngeal microbiome profiles (OR: .38; 95% CI: .18-.81). Using generalized joint attributed modeling, we identified 9 bacterial taxa associated with SARS-CoV-2 infection and 6 taxa differentially abundant among SARS-CoV-2-infected participants with respiratory symptoms; the magnitude of these associations was strongly influenced by age. CONCLUSIONS: We identified interactive relationships between age and specific nasopharyngeal microbiome features that are associated with SARS-CoV-2 infection susceptibility and symptoms in children, adolescents, and young adults. Our data suggest that the upper respiratory microbiome may be a mechanism by which age influences SARS-CoV-2 susceptibility and illness severity.