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Abstract Rationale Dysanapsis refers to a mismatch between airway tree caliber and lung size arising early in life. Dysanapsis assessed by computed tomography (CT) is evident by early adulthood and associated with chronic obstructive pulmonary disease (COPD) risk later in life. Objectives By examining the genetic factors associated with CT-assessed dysanapsis, we aimed to elucidate its molecular underpinnings and physiological significance across the lifespan. Methods We performed a genome-wide association study of CT-assessed dysanapsis in 11,951 adults, including individuals from two population-based and two COPD-enriched studies. We applied colocalization analysis to integrate genome-wide association study and gene expression data from whole blood and lung. Genetic variants associated with dysanapsis were combined into a genetic risk score that was applied to examine association with lung function in children from a population-based birth cohort (n = 1,278) and adults from the UKBiobank (n = 369,157). Measurements and Main Results CT-assessed dysanapsis was associated with genetic variants from 21 independent signals in 19 gene regions, implicating HHIP (hedgehog interacting protein), DSP, and NPNT as potential molecular targets based on colocalization of their expression. A higher dysanapsis genetic risk score was associated with obstructive spirometry among 5-year-old children and among adults in the fifth, sixth, and seventh decades of life. Conclusions CT-assessed dysanapsis is associated with variation in genes previously implicated in lung development, and dysanapsis genetic risk is associated with obstructive lung function from early life through older adulthood. Dysanapsis may represent an endophenotype link between the genetic variations associated with lung function and COPD.

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.

Abstract Vitamin D plays a critical role in many physiological functions, including calcium metabolism and musculoskeletal health. This commentary aims to explore the intricate relationships among skin complexion, race, and 25-hydroxyvitamin D (25[OH]D) levels, focusing on challenges the Endocrine Society encountered during clinical practice guideline development. Given that increased melanin content reduces 25(OH)D production in the skin in response to UV light, the guideline development panel addressed the potential role for 25(OH)D screening in individuals with dark skin complexion. The panel discovered that no randomized clinical trials have directly assessed vitamin D related patient-important outcomes based on participants' skin pigmentation, although race and ethnicity often served as presumed proxies for skin pigmentation in the literature. In their deliberations, guideline panel members and selected Endocrine Society leaders underscored the critical need to distinguish between skin pigmentation as a biological variable and race and ethnicity as socially determined constructs. This differentiation is vital to maximize scientific rigor and, thus, the validity of resulting recommendations. Lessons learned from the guideline development process emphasize the necessity of clarity when incorporating race and ethnicity into clinical guidelines. Such clarity is an essential step toward improving health outcomes and ensuring equitable healthcare practices.

Racial, ethnic, and socioeconomic disparities exist in the prevalence and natural history of chronic liver disease, access to care, and clinical outcomes. Solutions to improve health equity range widely, from digital health tools to policy changes. The current review outlines the disparities along the chronic liver disease health care continuum from screening and diagnosis to the management of cirrhosis and considerations of pre-liver and post-liver transplantation. Using a health equity research and implementation science framework, we offer pragmatic strategies to address barriers to implementing high-quality equitable care for patients with chronic liver disease.

Introduction: Chronic lymphocytic leukemia and small lymphocytic lymphoma (CLL/SLL) are the most prevalent types of leukemia in the United States. Given the emergence of novel CLL/SLL therapies, it is important to understand real-world treatment characteristics and unmet needs. The objectives of this retrospective cohort study were to examine patient characteristics, treatment patterns, and associated outcomes for patients with CLL/SLL, overall, and by CLL and SLL, respectively. Methods: A retrospective study was conducted using Symphony Integrated Dataverse (IDV ®), a comprehensive, longitudinal, open-claims database, and integrated electronic medical record data. Patients aged ≥18 years with ≥1 diagnosis for CLL or SLL and who initiated a first-line or second-line treatment between January 1, 2020, and December 31, 2022 (index period) were included in the study. Index date was defined as the date of treatment initiation. Patients were required to be continuously enrolled for 365 days prior to the index date and ≥90 days following the index date. Patient data were included until patients were lost to follow-up or to the end of the study period (March 31, 2023). Treatment patterns were examined with the following treatment regimens and stratified by line of therapy: chemotherapy (including bendamustine-based and other chemotherapy), anti-CD20-based, Bruton tyrosine kinase inhibitors (BTKiibrutinib- and acalabrutinib-based), and venetoclax-based therapy. Zanubrutinib use was not captured in the BTKi treatment category as its CLL/SLL indication approval date (January 19, 2023) was outside the index period. Discontinuation rate was defined as the percentage of patients who discontinued their current treatment regimen within 90 days. Healthcare resource utilization was measured by number of outpatient visits, inpatient visits, and other medical/hospital services per patient per month (PPPM) during the treatment regimen. Results:There were 17,748 and 5,667 patients with CLL/SLL who initiated a first-line or second-line treatment regimen during the index period, respectively. The most frequently used treatment regimens in first line were BTKi (49.9%), followed by anti-CD20 (26.1%), venetoclax (15.4%), and chemotherapy (8.6%). BTKi continued to be the predominant treatment (49.6%) in second line, followed by venetoclax (30.9%), chemotherapy (10.4%), and anti-CD20 (9.2%). First-line patients treated with BTKi regimens were older and Medicare-insured when compared with patients receiving venetoclax-based regimens (65+ years: 77.1% vs 66.9%, P-value <0.001; Medicare: 61.7% vs 53.0%, P-value <0.001). Patients receiving BTKi had significantly lower discontinuation rates compared with other treatment regimens during both first and second lines of therapies (Figure 1). Mean PPPM outpatient visits, inpatient visits, and other medical/hospital services were less frequent with BTKi therapies vs other treatment regimens across both lines of therapy (Table 1). Of the 17,748 patients initiating first line of therapy, 13.3% had SLL while the remaining 86.7% had CLL. The proportion of patients with SLL vs CLL decreased in second line and second line or later (8.0% vs 92.0% and 6.8% vs 93.2%, respectively). Treatment discontinuation rate in SLL patients was higher than that in CLL patients in first line (32.41% and 25.08%, respectively); however, discontinuation rates became comparable with later lines of therapy. Across all lines of therapy, healthcare resource utilization was significantly higher in SLL patients than CLL patients. Median PPPM outpatient visits were 1.02 for SLL and 0.67 for CLL. Median PPPM for other medical/hospital services was 0.7 for SLL and 0.23 for CLL. Conclusions: BTKi therapy, the primary treatment regimen across first- and second-line therapies, has significantly lower discontinuation rate and healthcare resource utilization compared with other treatment regimens. Further studies are needed to evaluate real-world clinical outcomes of CLL/SLL regimens to support evidence-based treatment decisions.

HCC recurrence following liver transplantation (LT) is highly morbid and occurs despite strict patient selection criteria. Individualized prediction of post-LT HCC recurrence risk remains an important need. Clinico-radiologic and pathologic data of 4981 patients with HCC undergoing LT from the US Multicenter HCC Transplant Consortium (UMHTC) were analyzed to develop a REcurrent Liver cAncer Prediction ScorE (RELAPSE). Multivariable Fine and Gray competing risk analysis and machine learning algorithms (Random Survival Forest and Classification and Regression Tree models) identified variables to model HCC recurrence. RELAPSE was externally validated in 1160 HCC LT recipients from the European Hepatocellular Cancer Liver Transplant study group. Of 4981 UMHTC patients with HCC undergoing LT, 71.9% were within Milan criteria, 16.1% were initially beyond Milan criteria with 9.4% downstaged before LT, and 12.0% had incidental HCC on explant pathology. Overall and recurrence-free survival at 1, 3, and 5 years was 89.7%, 78.6%, and 69.8% and 86.8%, 74.9%, and 66.7%, respectively, with a 5-year incidence of HCC recurrence of 12.5% (median 16 months) and non-HCC mortality of 20.8%. A multivariable model identified maximum alpha-fetoprotein (HR = 1.35 per-log SD, 95% CI,1.22–1.50, p < 0.001), neutrophil-lymphocyte ratio (HR = 1.16 per-log SD, 95% CI,1.04–1.28, p < 0.006), pathologic maximum tumor diameter (HR = 1.53 per-log SD, 95% CI, 1.35–1.73, p < 0.001), microvascular (HR = 2.37, 95%–CI, 1.87–2.99, p < 0.001) and macrovascular (HR = 3.38, 95% CI, 2.41–4.75, p < 0.001) invasion, and tumor differentiation (moderate HR = 1.75, 95% CI, 1.29–2.37, p < 0.001; poor HR = 2.62, 95% CI, 1.54–3.32, p < 0.001) as independent variables predicting post-LT HCC recurrence (C-statistic = 0.78). Machine learning algorithms incorporating additional covariates improved prediction of recurrence (Random Survival Forest C-statistic = 0.81). Despite significant differences in European Hepatocellular Cancer Liver Transplant recipient radiologic, treatment, and pathologic characteristics, external validation of RELAPSE demonstrated consistent 2- and 5-year recurrence risk discrimination (AUCs 0.77 and 0.75, respectively). We developed and externally validated a RELAPSE score that accurately discriminates post-LT HCC recurrence risk and may allow for individualized post-LT surveillance, immunosuppression modification, and selection of high-risk patients for adjuvant therapies.

Objectives: Administrative criteria are often used to define COVID-19 outcomes despite challenges in differentiating hospitalization “for” vs “with” COVID-19 and unknown reliability for identifying COVID-related cardiopulmonary complications. We implemented a protocol to adjudicate COVID-related events across 14 studies participating in C4R. Methods: C4R cohorts ascertained COVID-related hospitalizations and deaths via questionnaires or ongoing surveillance. Medical records were reviewed by C4R physicians using a standardized protocol to assign COVID-related diagnoses as definite or probable. C4R diagnoses were compared against ICD codes assigned to the events. This report includes data from events centrally adjudicated by C4R as of September 2022. Results: Among 144 events ascertained across 7 cohorts as potentially COVID-related, SARS-CoV-2 infection was confirmed in 107/117 (91%) non-fatal hospitalizations and 26/27 (96%) deaths. Of confirmed infections, COVID-19 illness was diagnosed as the cause of 101/107 (94%) hospitalizations and 24/26 (92%) deaths ( Table ). Of non-fatal hospitalizations with infection, 72% were diagnosed with severe or critical COVID-19, 78% with COVID-associated pneumonia, and 29% with renal failure; other complications were less common. C4R review did not validate diagnoses indicated by ICD codes for 1 COVID-19 infection, 5 pneumonias, 1 stroke, and 7 renal failure cases. C4R review did identify diagnoses that were not ICD-coded for 13 infections, 31 pneumonias, 6 myocardial infarctions, 5 venous thromboses, and 21 renal failure cases. Conclusions: Protocolized medical records review by C4R confirmed COVID-19 as the cause of hospitalization or death in 87% of events ascertained as potentially COVID-related and 94% of those in which SARS-CoV-2 infection was confirmed. Both false-positive and false-negative misclassification by ICD criteria was observed. Protocolized review may be useful to identify and validate COVID-related events for epidemiologic research.

Rationale: The impact of the COVID-19 pandemic on tobacco use patterns remains incompletely understood. We aimed to examine changes in cigarette smoking patterns over the first 2 years of the COVID-19 pandemic (2020-22) in a large, multiethnic, US community-based sample. Methods: The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) ascertained the impact of the COVID-19 pandemic on participants from 14 longitudinal NIH-funded cohorts via 2 waves of standardized questionnaires conducted 2020-22. The C4R questionnaire collected data on cigarette use patterns for the current and immediate pre-pandemic periods (January-March 2020). These data were used to define pandemic-era smoking initiation, smoking cessation, and changes in smoking intensity. Multivariable logistic regression models were adjusted for centrally harmonized pre-pandemic data on sociodemographic characteristics, remote smoking history, and co-morbidities and pandemic-era psychological health, insomnia, and SARS-CoV-2 infection history ascertained through C4R questionnaires. Multiple imputation by chained equations was used to account for missing covariate data. Results: Of 17179 participants completing C4R questions on pandemic-era smoking behaviors, 1899 (11%) participants reported current smoking in the immediate pre-pandemic period (mean age 70 years, 59% women, 31% African American, 2% Hispanic, 1% Asian, 22% American Indian/Alaskan Native [AIAN]). Of these 181 (10%) self-reported quitting during the pandemic. Among 1718 (90%) who continued smoking, smoking intensity increased in 17% and decreased in 26%. Among 15093 participants who were not smoking prior to March 2020, 135 (0.9%) initiated smoking during the pandemic period, including 120 (89%) with a remote history of smoking and 15 (11%) with no history of smoking. Pandemic-era smoking cessation was associated with older age(80+ years vs <65 years [adjusted odds ratio; 95%CI] 2.1; 1.1-4.0), obesity(vs normal weight 1.6; 1.02-2.6) and history of SARS-CoV-2 infection (2.1; 1.3-3.1). Pandemic-era smoking initiation was associated with concurrent use of cannabis (3.1; 1.9-5.2), e-cigarette (29.6; 14.8-59.2), and/or alcohol (1.8; 1.1-2.9). Compared to non-Hispanic white participants, AIAN participants had lower odds of quitting (0.1; 0.03-0.6) and African American participants had higher odds of increased smoking intensity (2.0; 1.3-3.1). Depressive symptoms were associated with smoking initiation (2.4; 1.3-4.3) and increased smoking intensity (2.6; 1.5-4.5). Conclusion: Greater odds of smoking initiation and/or increased smoking intensity were observed in minoritized groups and participants reporting depressive symptoms. Majority of those who initiated smoking during pandemic-era were relapsers. Smoking cessation programs remain important to improving public health and reducing health disparities.

Background: Underrepresented persons are often not included in biomedical research. It is unknown if the general Asian American population is being represented in All of Us . The purpose of this study was to compare the Asian demographic data in the All of Us cohort with the Asian nationally representative data from the American Community Survey. Method: Demographic characteristics and health literacy of Asians in All of Us were examined. Findings were qualitatively compared with the Asian data in the 2019 American Community Survey 1-year estimate. Results: Compared with the national composition of Asians, less All of Us participants were born outside the United States (64% vs 79%), were younger, and had higher levels of education (76% vs 52%). Over 60% of All of Us participants reported high levels of health literacy. Conclusion: This study had implications for the development of strategies that ensure diverse populations are represented in biomedical research.

Abstract Objective To describe and demonstrate use of pediatric data collected by the All of Us Research Program. Materials and Methods All of Us participant physical measurements and electronic health record (EHR) data were analyzed including investigation of trends in childhood obesity and correlation with adult body mass index (BMI). Results We identified 19 729 participants with legacy pediatric EHR data including diagnoses, prescriptions, visits, procedures, and measurements gathered since 1980. We found an increase in pediatric obesity diagnosis over time that correlates with BMI measurements recorded in participants’ adult EHRs and those physical measurements taken at enrollment in the research program. Discussion We highlight the availability of retrospective pediatric EHR data for nearly 20 000 All of Us participants. These data are relevant to current issues such as the rise in pediatric obesity. Conclusion All of Us contains a rich resource of retrospective pediatric EHR data to accelerate pediatric research studies.