Allen, Norrina B.

ImportanceIdentifying factors associated with resilience during the COVID-19 pandemic can inform targeted interventions and resource allocation for groups disproportionately affected by systemic inequities.ObjectiveTo examine factors associated with self-reported resilience during the COVID-19 pandemic in racially and ethnically diverse, community-dwelling US adults.Design, Setting, and ParticipantsThis cross-sectional study was conducted as part of the Collaborative Cohort of Cohorts for COVID-19 Research (C4R) study, which assessed the associations of the pandemic with self-reported resilience of participants from 14 established US prospective cohorts since January 2021. This report includes participants who responded to the self-reported resilience question on C4R questionnaires. Data was initially analyzed from October 2023 to May 2024, with updated analyses performed from August 2024 to April 2025.ExposureRace and ethnicity, behavior factors, health conditions, and social determinants of health measurements accessed before and during the COVID-19 pandemic through cohort visits and C4R questionnaires.Main Outcomes and MeasuresSelf-reported resilience was collected via 1 question (from the Brief Resilience Scale) in C4R questionnaires, “I tend to bounce back quickly after hard times.” Participants who answered agree or strongly agree were classified as resilient, and those who reported neutral, disagree, or strongly disagree were classified as not resilient. Modified Poisson regression was performed to estimate prevalence ratios (PRs) and access multivariable-adjusted associations with resilience.ResultsOf 31 045 participants (18 672 [60%] women; 10 746 [34.6%] aged <65 years), 1185 (3.8%) identified as American Indian, 6728 (21.7%) as Black, 293 (0.9%) as East Asian, 6311 (20.3%) as Hispanic, 565 (1.8%) as South Asian, and 15 961 (51.3%) as White; a total of 23 103 participants (74.4%) self-identified as resilient. Compared with White participants, Black and Hispanic participants had higher prevalence of self-reported resilience (adjusted PR [aPR], 1.04; 95% CI, 1.02-1.06; aPR, 1.08; 95% CI, 1.06-1.11; respectively) and American Indian and East Asian participants had lower prevalence (aPR, 0.90; 95% CI, 0.86-0.94; aPR, 0.76; 95% CI, 0.68-0.84; respectively). Higher education, being married or living as married, higher income, and overweight were also associated with higher prevalence of resilience. Being female, having diabetes, and being unemployed were associated with lower prevalence of self-reported resilience. Compared with participants with public insurance only, participants with private insurance had higher prevalence of resilience (aPR, 1.07; 95% CI, 1.03-1.10). COVID-19 vaccination and infection statuses were not significantly associated with resilience. Modification analyses showed important racial and ethnic differences in how factors such as hypertension, marital status, and insurance status were associated with resilience.Conclusions and RelevanceIn this cross-sectional study of 31 045 adults, self-reported resilience varied by race, ethnicity, and sociodemographic factors. These findings highlight the complex interplay of individual and social factors in shaping the perception of resilience.

Importance SARS-CoV-2 infection has been linked to neurotoxic effects and cognitive deficits. Objective To determine whether decreases in cognitive function were accelerated after SARS-CoV-2 infection compared with individuals not infected. Design, Setting, and Participants Multicenter, prospective cohort study from 2016 to 2022 among 3525 participants alive on March 1, 2020, and enrolled in The Atherosclerosis Risk in Communities (ARIC) study and the Collaborative Cohort of Cohorts for COVID-19 Research study who completed a prepandemic cognitive assessment and a pandemic-era assessment of SARS-CoV-2 infection. Final analyses performed in November 2024. Exposure SARS-CoV-2 infection determined via self-report of a positive SARS-CoV-2 test or health care professional diagnosis of COVID-19, a positive SARS-CoV-2 antinucleocapsid antibody response, or presence of an administrative code for COVID-19 on medical records. Main outcomes and measures A neuropsychological battery assessed multiple cognitive domains, and a cocalibrated confirmatory factor analysis generated factor scores for global cognitive function. The primary outcome was the rate of excess change in cognitive function. Results The 3525 eligible participants had a mean (SD) age of 80.8 (4.7) years, 2085 (59.1%) were female, 752 (21.4%) were Black, and 2773 (78.6%) were White. SARS-CoV-2 infection was detected among 307 participants (8.7%), 103 of whom (33.6%) were hospitalized. Among uninfected participants, the mean annualized change in cognitive function was −0.09 (95% CI, −0.13 to −0.04). Compared with this rate, change was faster (β = −0.06; 95% CI, −0.09 to −0.02) among participants hospitalized for infection, but not different from participants who were infected but not hospitalized (β = 0.00; 95% CI, −0.02 to 0.03). The association among participants hospitalized for infection was evident in the cognitive domains of memory and executive function, but not language. Conclusions and relevance This cohort study of older participants found accelerated decreases in cognition among individuals hospitalized for SARS-CoV-2 infection, but not nonhospitalized infection, in comparison with individuals not yet infected.

Abstract RATIONALE: Susceptibility to severe COVID-19 and long COVID may be influenced by pre-existing lung structure. ECG-gated, non-contrast cardiac CT scans, originally acquired for assessing coronary artery calcium, are available in multiple longitudinal cohort studies and have been previously validated against lung CT scans for densitometry; however, protocol variation across cohorts may hinder direct comparison. To address this, we developed a quality control (QC) rubric to grade cardiac CT scans for standardized quantitative lung measures across cohorts. METHODS: C4R assessed SARS-CoV-2 infection and outcomes in 14 NHLBI cohorts, of which 10 (six community-based and four longitudinal case-control studies) included CT scans (12,459 participants with lung and 13,752 with cardiac CTs). Logic rules were applied to categorize scans (Table): Grade “A-B” scans had 2-3.5mm slice thickness, at least 20 axial slices, no lung cropping in axial view and lung and airway volumes greater than the cohort-specific 1st percentile. “C-D” scans had one lung obscured by cropping around the heart, so quantitative measures were restricted to the lung in complete view. “F-H” scans were deemed unusable, suffering from low slice count (<20), severe image cropping, low lung or airway volumes (< 1st%ile). Visual inspection was performed on a sample from each category to ensure accuracy. Percent of lung voxels in low attenuation areas < -950 HU (LAA950), high attenuation areas (-600 to -250 HU, HAA) and 15th percentile HU value (perc15) were measured. Within-participant variability was quantified using the intraclass correlation coefficient (ICC). RESULTS: We performed QC on cardiac CT scans from an initial sample of 6,275 participants from three C4R cohorts (ARIC, CARDIA, MASALA), imaged with GE or Siemens scanners. Of these, 5,125 (81.7%) were graded “A-B”, 496 (7.9%) were “C-D” and 654 (10.4%) were “F-H”. The percentage of “F-H” scans varied widely across cohorts [<1% to 26%] due to image cropping. Among the 5,621 cardiac CT scans graded A-D, the mean±SD for LAA950, HAA and perc15 respectively were 1.1±2.1%, 7.2±4.7% and -887±33 HU. Repeated imaging was available for 107 participants (time interval 5.1±0.7y). For paired scans in grades A-B (n=101/107), the within-participant correlation for LAA950, HAA and perc15 were 0.91, 0.38 and 0.74 respectively. CONCLUSION: Initial application of QC to cardiac CT scans identified scans with high reproducibility, with the aim to provide valid quantitative lung metrics on over 20,000 highly characterized participants in C4R.

Abstract Chronic obstructive pulmonary disease (COPD) and pulmonary emphysema were the third-leading cause of death globally in 2019. Besides alpha-1 antitrypsin deficiency, emphysema lacks disease-modifying medications due to its heterogeneity and there remains a need to further investigate emphysema subtypes to advance its diagnosis and treatment. Recent unsupervised machine learning of the texture and anatomical location of emphysema on computed tomography (CT) images of the lung defined six quantitative CT emphysema subtypes uniquely associated with specific gene variants, environmental exposures, symptoms, physiology and clinical events. We aim to identify associations of plasma proteins with each CT emphysema subtype cross-sectionally in a multi-ethnic general population sample. MESA is a prospective cohort study that recruited 6,814 participants from six communities who self-identified as White, Black, Hispanic, or Asian race/ethnicity, ages 45-84 years old and free of clinical cardiovascular disease in 2000-02. The MESA Lung Study performed CT scans among 3,137 participants in 2010-12, which were labeled with previously described (PMID: 37268414) CT emphysema subtypes. Proteomics was assessed using SomaScan version 4.1 assay (7,596 plasma proteins). Linear regression models were adjusted for age, height, weight, sex, race, educational attainment, smoking status, pack years, urine cotinine level, body-mass index (BMI), estimated glomerular filtration rate (eGFR), CT scanner manufacturer and other CT emphysema subtypes. Results were adjusted to reflect a False Discovery Rate (FDR) p-value of less than 5% using the Benjamini-Hochberg method. Among 2,343 participants with both CT and proteomic data (mean age 69.5 years, 52.3% women, 23.7% with COPD), we identified statistically significant associations of proteins with the vanishing lung subtype (42 proteins; top hit Natural cytotoxicity triggering receptor 3 ligand 1, q = 2.9[asterisk]10-9), restrictive combined pulmonary fibrosis/emphysema (CPFE) subtype (11 proteins; top hit Integrin alpha-2/b1, q=1.0[asterisk]10-5), the combined bronchitis-apical emphysema subtype (9 proteins; top hit Desmoglein-2, q=8.8[asterisk]10-13), and the diffuse emphysema subtype (4 proteins; top hit Pulmonary surfactant-associated protein D, q=5.7[asterisk]10-3). No significant proteins were associated with the obstructive CPFE or senile subtypes after FDR correction. Most proteins were uniquely associated with one CT emphysema subtype, while a few proteins were associated with more than one subtype. This study identified specific proteins for different CT emphysema subtypes, many of which have been related less specifically to emphysema, including pulmonary surfactant production, anti-inflammatory pathways, and mitochondrial deregulation, amongst others. Validation of these proteomic associations can provide insight into further pathway assessment, clinical biomarker discovery, and targeted prevention of emphysema in the general population.

Background: Despite the availability of effective vaccines and a recent decrease in annual deaths, coronavirus disease-2019 (COVID-19) remains a leading cause of death. Serological studies of anti-nucleocapsid (N) antibody response to infection provide insights into host immunobiology of adaptive immune response, which holds promise for identifying high-risk individuals for adverse acute and chronic COVID-19 outcomes. Hypothesis: Among participants previously infected with SARS-CoV-2, traditional vascular disease risk factors are associated with higher anti-N antibodies while vaccination is associated with lower anti-N antibodies. Methods: Among previously infected participants, anti-N antibodies were measured from dried blood spots collected between February 2021-February 2023 among 1,419 Collaborative Cohort of Cohorts for COVID-19 Research (C4R) participants with prior SARS-CoV-2 infection. We measured anti-N IgG antibodies reported as median fluorescence intensity (MFI) units; reactivity (i.e., seropositivity) was defined based on MFI above a validated threshold. Vascular disease risk factors were assessed via pre-pandemic in-person examination, questionnaires, and medical record review. Multivariable generalized linear models regressed anti-N reactivity (modified Poisson) or LN transformed MFI (linear). Results: Among 1,419 participants with prior infection, mean age (standard deviation) was 65.8(12.1) years, 61% were women, and 42.8% were self-reported from a race/ethnicity minority group. Participant reactivity to nucleocapsid peaked at 69% by 4 months post-infection and waned to only 44% ≥12 months after infection. After multivariable adjustment, higher anti-N antibody response was associated with older age, Hispanic (vs. White) or American Indian (vs. White) race/ethnicity, lower income and education, former smoking, and higher anti-spike antibody levels. Asian race (vs. White) and vaccination ( even after infection ) were associated with lower nucleocapsid reactivity; common cardiometabolic co-morbidities were not associated with anti-N reactivity or levels (Figures 1 and 2). Conclusions: Among participants previously infected with SARS-CoV-2, select sociodemographic and behavioral risk factors were associated with higher anti-N antibody levels while vaccination was associated with lower anti-N antibody levels. The observation that vaccination, even after infection, is related to lower anti-N antibody levels merits further investigation.

Abstract Despite the availability of effective vaccines and a recent decrease in annual deaths, COVID-19 remains a leading cause of death. Serological studies provide insights into host immunobiology of adaptive immune response to infection, which holds promise for identifying high-risk individuals for adverse COVID-19 outcomes. We investigated correlates of anti-nucleocapsid antibody responses following SARS-CoV-2 infection in a US population-based meta-cohort of adults participating in longstanding National Institutes of Health–funded cohort studies. Anti-nucleocapsid antibodies were measured from dried blood spots collected between February 2021 and February 2023. Among 1419 Collaborative Cohort of Cohorts for COVID-19 Research participants with prior SARS-CoV-2 infection, the mean age (standard deviation) was 65.8 (12.1), 61% were women, and 42.8% self-reported membership in a race/ethnicity minority group. The proportion of participants reactive to nucleocapsid peaked at 69% by 4 months after infection and waned to only 44% ≥12 months after infection. Higher anti-nucleocapsid antibody response was associated with older age, Hispanic or American Indian Alaskan Native (vs White) race/ethnicity, lower income, lower education, former smoking, and higher anti-spike antibody levels. Asian race (vs White) and vaccination (even after infection) were associated with lower nucleocapsid reactivity. Neither vaccine manufacturer nor common cardiometabolic comorbidities were not associated with anti-nucleocapsid response. These findings inform the underlying immunobiology of adaptive immune response to infection, as well as the potential utility of anti-nucleocapsid antibody response for clinical practice and COVID-19 serosurveillance.

ImportancePersistent symptoms and disability following SARS-CoV-2 infection, known as post–COVID-19 condition or “long COVID,” are frequently reported and pose a substantial personal and societal burden.ObjectiveTo determine time to recovery following SARS-CoV-2 infection and identify factors associated with recovery by 90 days.Design, Setting, and ParticipantsFor this prospective cohort study, standardized ascertainment of SARS-CoV-2 infection was conducted starting in April 1, 2020, across 14 ongoing National Institutes of Health–funded cohorts that have enrolled and followed participants since 1971. This report includes data collected through February 28, 2023, on adults aged 18 years or older with self-reported SARS-CoV-2 infection.ExposurePreinfection health conditions and lifestyle factors assessed before and during the pandemic via prepandemic examinations and pandemic-era questionnaires.Main Outcomes and MeasuresProbability of nonrecovery by 90 days and restricted mean recovery times were estimated using Kaplan-Meier curves, and Cox proportional hazards regression was performed to assess multivariable-adjusted associations with recovery by 90 days.ResultsOf 4708 participants with self-reported SARS-CoV-2 infection (mean [SD] age, 61.3 [13.8] years; 2952 women [62.7%]), an estimated 22.5% (95% CI, 21.2%-23.7%) did not recover by 90 days post infection. Median (IQR) time to recovery was 20 (8-75) days. By 90 days post infection, there were significant differences in restricted mean recovery time according to sociodemographic, clinical, and lifestyle characteristics, particularly by acute infection severity (outpatient vs critical hospitalization, 32.9 days [95% CI, 31.9-33.9 days] vs 57.6 days [95% CI, 51.9-63.3 days]; log-rank P < .001). Recovery by 90 days post infection was associated with vaccination prior to infection (hazard ratio [HR], 1.30; 95% CI, 1.11-1.51) and infection during the sixth (Omicron variant) vs first wave (HR, 1.25; 95% CI, 1.06-1.49). These associations were mediated by reduced severity of acute infection (33.4% and 17.6%, respectively). Recovery was unfavorably associated with female sex (HR, 0.85; 95% CI, 0.79-0.92) and prepandemic clinical cardiovascular disease (HR, 0.84; 95% CI, 0.71-0.99). No significant multivariable-adjusted associations were observed for age, educational attainment, smoking history, obesity, diabetes, chronic kidney disease, asthma, chronic obstructive pulmonary disease, or elevated depressive symptoms. Results were similar for reinfections.Conclusions and RelevanceIn this cohort study, more than 1 in 5 adults did not recover within 3 months of SARS-CoV-2 infection. Recovery within 3 months was less likely in women and those with preexisting cardiovascular disease and more likely in those with COVID-19 vaccination or infection during the Omicron variant wave.