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
Title: 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
AbstractBackground
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.
Jaywant, Abhishek; Gunning, Faith M.; Oberlin, Lauren E.; Santillana, Mauricio; Ognyanova, Katherine; Druckman, James N.; Baum, Matthew A.; Lazer, David; Perlis, Roy H.(
, JAMA Network Open)
Importance
The frequent occurrence of cognitive symptoms in post–COVID-19 condition has been described, but the nature of these symptoms and their demographic and functional factors are not well characterized in generalizable populations.
Objective
To investigate the prevalence of self-reported cognitive symptoms in post–COVID-19 condition, in comparison with individuals with prior acute SARS-CoV-2 infection who did not develop post–COVID-19 condition, and their association with other individual features, including depressive symptoms and functional status.
Design, Setting, and Participants
Two waves of a 50-state nonprobability population-based internet survey conducted between December 22, 2022, and May 5, 2023. Participants included survey respondents aged 18 years and older.
Exposure
Post–COVID-19 condition, defined as self-report of symptoms attributed to COVID-19 beyond 2 months after the initial month of illness.
Main Outcomes and Measures
Seven items from the Neuro-QoL cognition battery assessing the frequency of cognitive symptoms in the past week and patient Health Questionnaire-9.
Results
The 14 767 individuals reporting test-confirmed COVID-19 illness at least 2 months before the survey had a mean (SD) age of 44.6 (16.3) years; 568 (3.8%) were Asian, 1484 (10.0%) were Black, 1408 (9.5%) were Hispanic, and 10 811 (73.2%) were White. A total of 10 037 respondents (68.0%) were women and 4730 (32.0%) were men. Of the 1683 individuals reporting post–COVID-19 condition, 955 (56.7%) reported at least 1 cognitive symptom experienced daily, compared with 3552 of 13 084 (27.1%) of those who did not report post–COVID-19 condition. More daily cognitive symptoms were associated with a greater likelihood of reporting at least moderate interference with functioning (unadjusted odds ratio [OR], 1.31 [95% CI, 1.25-1.36]; adjusted [AOR], 1.30 [95% CI, 1.25-1.36]), lesser likelihood of full-time employment (unadjusted OR, 0.95 [95% CI, 0.91-0.99]; AOR, 0.92 [95% CI, 0.88-0.96]) and greater severity of depressive symptoms (unadjusted coefficient, 1.40 [95% CI, 1.29-1.51]; adjusted coefficient 1.27 [95% CI, 1.17-1.38). After including depressive symptoms in regression models, associations were also found between cognitive symptoms and at least moderate interference with everyday functioning (AOR, 1.27 [95% CI, 1.21-1.33]) and between cognitive symptoms and lower odds of full-time employment (AOR, 0.92 [95% CI, 0.88-0.97]).
Conclusions and Relevance
The findings of this survey study of US adults suggest that cognitive symptoms are common among individuals with post–COVID-19 condition and associated with greater self-reported functional impairment, lesser likelihood of full-time employment, and greater depressive symptom severity. Screening for and addressing cognitive symptoms is an important component of the public health response to post–COVID-19 condition.
How human respiratory physiology and the transport phenomena associated with inhaled airflow in the upper airway proceed to impact transmission of SARS-CoV-2, leading to the initial infection, stays an open question. An answer can help determine the susceptibility of an individual on exposure to a COVID-2019 carrier and can also provide a preliminary projection of the still-unknown infectious dose for the disease. Computational fluid mechanics enabled tracking of respiratory transport in medical imaging-based anatomic domains shows that the regional deposition of virus-laden inhaled droplets at the initial nasopharyngeal infection site peaks for the droplet size range of approximately 2.5–19$$\upmu $$. Through integrating the numerical findings on inhaled transmission with sputum assessment data from hospitalized COVID-19 patients and earlier measurements of ejecta size distribution generated during regular speech, this study further reveals that the number of virions that may go on to establish the SARS-CoV-2 infection in a subject could merely be in the order of hundreds.
Villanustre, Flavio; Chala, Arjuna; Dev, Roger; Xu, Lili; LexisNexis, Jesse Shaw; Furht, Borko; Khoshgoftaar, Taghi(
, Journal of Big Data)
Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.
Esper, Frank P.; Adhikari, Thamali M.; Tu, Zheng Jin; Cheng, Yu-Wei; El-Haddad, Kim; Farkas, Daniel H.; Bosler, David; Rhoads, Daniel; Procop, Gary W.; Ko, Jennifer S.; et al(
, The Journal of Infectious Diseases)
AbstractBackground
Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited.
Method
Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes.
Results
In total 2779 patients identified with either Alpha (n = 1153), Gamma (n = 122), Delta (n = 808), or Omicron variants (n = 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (χ2 = 12.8, P < .001; χ2 = 21.6, P < .002; χ2 = 9.6, P = .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9%] and 513/696 [73.7%], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2.
Conclusions
Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.
Martin, Melanie A.; Keith, Monica; Pace, Ryan M.; Williams, Janet E.; Ley, Sylvia H.; Barbosa-Leiker, Celestina; Caffé, Beatrice; Smith, Caroline B.; Kunkle, Amanda; Lackey, Kimberly A.; et al(
, Frontiers in Immunology)
Infants exposed to caregivers infected with SARS-CoV-2 may have heightened infection risks relative to older children due to their more intensive care and feeding needs. However, there has been limited research on COVID-19 outcomes in exposed infants beyond the neonatal period. Between June 2020 – March 2021, we conducted interviews and collected capillary dried blood spots from 46 SARS-CoV-2 infected mothers and their infants (aged 1-36 months) for up to two months following maternal infection onset (COVID+ group, 87% breastfeeding). Comparative data were also collected from 26 breastfeeding mothers with no known SARS-CoV-2 infection or exposures (breastfeeding control group), and 11 mothers who tested SARS-CoV-2 negative after experiencing symptoms or close contact exposure (COVID- group, 73% breastfeeding). Dried blood spots were assayed for anti-SARS-CoV-2 S-RBD IgG and IgA positivity and anti-SARS-CoV-2 S1 + S2 IgG concentrations. Within the COVID+ group, the mean probability of seropositivity among infant samples was lower than that of corresponding maternal samples (0.54 and 0.87, respectively, for IgG; 0.33 and 0.85, respectively, for IgA), with likelihood of infant infection positively associated with the number of maternal symptoms and other household infections reported. COVID+ mothers reported a lower incidence of COVID-19 symptoms among their infants as compared to themselves and other household adults, and infants had similar PCR positivity rates as other household children. No samples returned by COVID- mothers or their infants tested antibody positive. Among the breastfeeding control group, 44% of mothers but none of their infants tested antibody positive in at least one sample. Results support previous research demonstrating minimal risks to infants following maternal COVID-19 infection, including for breastfeeding infants.
Hurst, Jillian H., McCumber, Alexander W., Aquino, Jhoanna N., Rodriguez, Javier, Heston, Sarah M., Lugo, Debra J., Rotta, Alexandre T., Turner, Nicholas A., Pfeiffer, Trevor S., Gurley, Thaddeus C., Moody, M. Anthony, Denny, Thomas N., Rawls, John F., Clark, James S., Woods, Christopher W., and Kelly, Matthew S. 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. Clinical Infectious Diseases 75.1 Web. doi:10.1093/cid/ciac184.
Hurst, Jillian H., McCumber, Alexander W., Aquino, Jhoanna N., Rodriguez, Javier, Heston, Sarah M., Lugo, Debra J., Rotta, Alexandre T., Turner, Nicholas A., Pfeiffer, Trevor S., Gurley, Thaddeus C., Moody, M. Anthony, Denny, Thomas N., Rawls, John F., Clark, James S., Woods, Christopher W., & Kelly, Matthew S. 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. Clinical Infectious Diseases, 75 (1). https://doi.org/10.1093/cid/ciac184
Hurst, Jillian H., McCumber, Alexander W., Aquino, Jhoanna N., Rodriguez, Javier, Heston, Sarah M., Lugo, Debra J., Rotta, Alexandre T., Turner, Nicholas A., Pfeiffer, Trevor S., Gurley, Thaddeus C., Moody, M. Anthony, Denny, Thomas N., Rawls, John F., Clark, James S., Woods, Christopher W., and Kelly, Matthew S.
"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". Clinical Infectious Diseases 75 (1). Country unknown/Code not available: Oxford University Press. https://doi.org/10.1093/cid/ciac184.https://par.nsf.gov/biblio/10370045.
@article{osti_10370045,
place = {Country unknown/Code not available},
title = {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},
url = {https://par.nsf.gov/biblio/10370045},
DOI = {10.1093/cid/ciac184},
abstractNote = {Abstract BackgroundChildren 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. MethodsWe 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. ResultsNasopharyngeal 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. ConclusionsWe 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.},
journal = {Clinical Infectious Diseases},
volume = {75},
number = {1},
publisher = {Oxford University Press},
author = {Hurst, Jillian H. and McCumber, Alexander W. and Aquino, Jhoanna N. and Rodriguez, Javier and Heston, Sarah M. and Lugo, Debra J. and Rotta, Alexandre T. and Turner, Nicholas A. and Pfeiffer, Trevor S. and Gurley, Thaddeus C. and Moody, M. Anthony and Denny, Thomas N. and Rawls, John F. and Clark, James S. and Woods, Christopher W. and Kelly, Matthew S.},
}
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