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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 Rationale Increased risk of coronavirus disease (COVID-19) hospitalization and death has been reported among patients with clinical lung disease. Objectives To test the association of objective measures of prepandemic lung function and structure with COVID-19 outcomes in U.S. adults. Methods Prepandemic obstruction (FEV1/FVC < 0.70) and restriction (FEV1/FVC ⩾ 0.7, FVC < 80%) were defined based on the most recent spirometry exam conducted in 11 prospective U.S. general population–based cohorts. Severe obstruction was classified by FEV1 < 50%. Percentage emphysema, percentage high-attenuation areas, and interstitial lung abnormalities were defined on computed tomography in a subset. Incident COVID-19 was ascertained via questionnaires, serosurvey, and medical records from 2020 to 2023 and classified as severe (hospitalized or fatal) or nonsevere. Cause-specific hazard models were adjusted for sociodemographics, anthropometry, smoking, comorbidities, and COVID-19 vaccination status. Measurements and Main Results Among 29,323 participants (mean age, 67 yr), there were 748 severe incident COVID-19 cases over median follow-up of 17.3 months from March 1, 2020. Greater hazards of severe COVID-19 were associated with severe obstruction (vs. normal; adjusted hazard ratio [aHR], 2.11; 95% confidence interval [CI], 1.02–1.27), restriction (vs. normal; aHR, 1.40; 95% CI, 1.12–1.76), and percentage emphysema (highest vs. lowest quartile; aHR, 1.64; 95% CI, 1.03–2.61), but not greater high-attenuation areas or interstitial lung abnormalities. COVID-19 vaccination provided greater absolute risk reduction in these groups. Results were similar in participants without smoking, obesity, or clinical cardiopulmonary disease. Conclusions Prepandemic severe spirometric obstruction, spirometric restriction, and greater percentage emphysema lung on computed tomography were associated with risk of severe COVID-19. These findings support enhanced COVID-19 risk mitigation for individuals with impaired lung health and warrant further mechanistic studies on interactions of lung function, structure, and vulnerability to acute respiratory illnesses.

AbstractIntermittent hypoxia has been used to enhance oxygen delivery in athletes and patients; however, it is unclear whether acute exposure is sufficient to elicit lasting physiologic adaptation(s). The purpose of this study was to evaluate physiologic response(s) to hypobaric‐hypoxic (HH) exercise. Nine participants (4 M/5F; 37.9 ± 12.7 yrs.; 174.3 ± 9.4 cm; 75.3 ± 15.9 kg; 24.4 ± 3.4 kg/m2) were exposed to progressively higher simulated altitudes and completed two HH submaximal exercise sessions (~30 min ea., ≥72 h apart) on a cycle ergometer at the first altitude that posed a significant challenge to them. Altitude was dependent on individual response as determined from heart rate (HR), peripheral oxygenation (SpO2), and the ratio of HR response to SpO2 (HR/SpO2). Statistical analyses included paired samples t‐test (p ≤ 0.05). No significant change in SpO2 (HH‐1: 85 ± 4% vs. HH‐2: 85 ± 4%, p = 0.684) was observed between sessions. However, there were significant decreases in: HR (HH‐1: 150 ± 18 bpm vs. HH‐2: 133 ± 27 bpm, p = 0.001) of 18 bpm (11%); HR/SpO2 (HH‐1: 1.76 ± 0.22 vs. HH‐2: 1.57 ± 0.33, p = 0.012); and RPE (HH‐1: 15 ± 2 vs. HH‐2: 11 ± 4, p = 0.017). While workload significantly increased (HH‐1: 89 ± 36 W vs. HH‐2: 105 ± 36 W, p = 0.024). Some participants had a threshold/challenging altitude, but from a single bout there is evidence of improved tolerance that can last over a week. Further investigation is required to replicate and understand possible mechanisms.

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.

AbstractBackgroundIn this pilot study, the diagnostic agreement for sleep biomarker‐based neurodegenerative disorder (NDD) risk probabilities was evaluated in patients with Alzheimer’s disease dementia (AD), Lewy body disease (LBD), mild cognitive impairment (MCI), and controls (CG) with a Mini‐Mental State Exam scores ≥28.MethodsSleep biomarkers recorded with the Sleep Profiler (Advanced Brain Monitoring, Inc.) were used as inputs to a 4‐class machine learning algorithm trained to assign NDD risk probabilities to AD=27, LBD=19, isolated REM sleep behavior disorder=15, and CG=58. Input variables included age, time‐REM, non‐REM hypertonia, autonomic‐activation index, spindle‐duration, atypical‐N3, time‐supine, sleep‐efficiency, relative‐theta, and theta/alpha. Distributions of NDD risk were then assigned to AD=37, LBD=19, MCI=71, and CG=106 records.Records with CG output probabilities ≥70% were labeled Probably‐Normal, and from 45‐70% Likely‐Normal with indications of a NDD added when the LBD, AD or prodromal synucleinopathy (pSYN) risks exceeded approximately 25%. Similar thresholds were used to assign Likely‐ or Probably‐ LBD, AD, pSYN, or Mixed based on the group probabilities. Likely‐Normal with indications consistent with the diagnosis trended toward agreement.ResultsThe proportions of each group with classifications consistent with the diagnosis or trended toward agreement were CG=86%, MCI=75%, LBD=79%, and AD=87%.In the CG group, NDD risk assignments were Probably‐Normal=68% and Likely‐Normal with NDD indications=18%, Likely‐NDD=10%, and Probable‐NDD=4%.The MCI group was assigned Probable‐NDD=38% (AD=26%, Mixed=10%, LBD/pSYN=2%), Likely‐NDD=17% (AD=7%, Mixed=6%, LBD=3%, pSYN=1%), Likely‐Normal with indications=20% (AD=9%, pSYN=7%, Mixed=4%) and Probably‐Normal=25%The LBD group’s assigned risks consistent with the diagnoses included Probable‐NDD=74% and Likely‐Normal with pSYN indications=6% but included inconsistencies from Probable‐AD=5%, Likely‐Normal with AD indications=5%, and Probably‐Normal=11%.AD group’s consistent risk classifications included Probable‐NDD=60%, Likely‐NDD=11%, Likely‐Normal with AD/Mixed indications=13% and countered by inconsistencies from Probable‐LBD/pSYN=8%, Likely‐Normal with pSYN indications=3%, and Probably‐Normal=5%.ConclusionsConsistencies between the diagnoses and assigned NDD risks agreements exceeded 75% in all four groups. Studies are underway to confirm the test‐retest reliability and/or evaluate NDD risk probabilities in other patient populations.Support: NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, R34AG056639.

SummaryGlobal obesity rates have risen dramatically, now exceeding deaths from starvation. Metabolic and bariatric surgery (MBS), initially for severe obesity (BMI ≥35 kg/m2), is performed globally over 500 000 times annually, offering significant metabolic benefits beyond weight loss. However, varying eligibility criteria globally impact patient care and healthcare resources. Updated in 2022, ASMBS and IFSO guidelines aim to standardise MBS indications, reflecting current understanding and emphasising comprehensive preoperative assessments. Yet, clinical variability persists, necessitating consensus‐based recommendations. This modified Delphi study engaged 45 global experts to establish consensus on perioperative management in MBS. Experts selected from bariatric societies possessed expertise in MBS and participated in a two‐round Delphi protocol. Consensus was achieved on 90 of 169 statements (53.3%), encompassing multidisciplinary team composition, patient selection criteria, preoperative testing, and referral pathways. The agreement highlighted the critical role of comprehensive preoperative assessments and the integration of healthcare professionals in MBS. These findings offer essential insights to standardise perioperative practices and advocate for evidence‐based guidelines in MBS globally. The study underscores the need for unified protocols to optimise outcomes and guide future research in MBS.

Abstract Biomarkers that aid in early detection of neurodegeneration are needed to enable early symptomatic treatment and enable identification of people who may benefit from neuroprotective interventions. Increasing evidence suggests that sleep biomarkers may be useful, given the bi-directional relationship between sleep and neurodegeneration and the prominence of sleep disturbances and altered sleep architectural characteristics in several neurodegenerative disorders. This study aimed to demonstrate that sleep can accurately characterize specific neurodegenerative disorders (NDD). A four-class machine-learning algorithm was trained using age and nine sleep biomarkers from patients with clinically-diagnosed manifest and prodromal NDDs, including Alzheimer’s disease dementia (AD = 27), Lewy body dementia (LBD = 18), and isolated REM sleep behavior disorder (iRBD = 15), as well as a control group (CG = 58). The algorithm was validated in a total of 381 recordings, which included the training data set plus an additional AD = 10, iRBD = 18, Parkinson disease without dementia (PD = 29), mild cognitive impairment (MCI = 78) and CG = 128. Test-retest consistency was then assessed in LBD = 10, AD = 9, and CG = 46. The agreement between the NDD profiles and their respective clinical diagnoses exceeded 75% for the AD, LBD, and CG, and improved when NDD participants classified Likely Normal with NDD indications consistent with their clinical diagnosis were considered. Profiles for iRBD, PD and MCI participants were consistent with the heterogeneity of disease severities, with the majority of overt disagreements explained by normal sleep characterization in 27% of iRBD, 21% of PD, and 26% of MCI participants. For test-retest assignments, the same or similar NDD profiles were obtained for 88% of LBD, 86% in AD, and 98% of CG participants. The potential utility for NDD subtyping based on sleep biomarkers demonstrates promise and requires further prospective development and validation in larger NDD cohorts.

Abstract Rationale Idiopathic pulmonary fibrosis (IPF) is a complex and heterogeneous disease. Given this, we reasoned that differences in genetic profiles may be associated with unique clinical and radiologic features. Computational image analysis, sometimes referred to as radiomics, provides objective, quantitative assessments of radiologic features in subjects with pulmonary fibrosis. Objectives To determine if the genetic risk profile of patients with IPF identifies unique computational imaging phenotypes. Methods Participants with IPF were included in this study if they had genotype data and computed tomography (CT) scans of the chest available for computational image analysis. The extent of lung fibrosis and the likelihood of a usual interstitial pneumonia (UIP) pattern were scored automatically using two separate, previously validated deep learning techniques for CT analysis. UIP pattern was also classified visually by radiologists according to established criteria. Results Among 329 participants with IPF, MUC5B and ZKSCAN1 were independently associated with the deep learning–based UIP score. None of the common variants were associated with fibrosis extent by computational imaging. We did not find an association between MUC5B or ZKSCAN1 and visually assessed UIP pattern. Conclusions Select genetic variants are associated with computer-based classification of UIP on CT in this IPF cohort. Analysis of radiologic features using deep learning may enhance our ability to identify important genotype–phenotype associations in fibrotic lung diseases.