Freedman, Neal D.

Importance The prevalence of cardiovascular disease differs substantially across neighborhoods. However, few studies have examined the contribution of neighborhood-level structural racism to inequities in cardiovascular health. Objective To investigate the association between neighborhood-level manifestations of structural racism and the prevalence of cardiovascular clinical and behavioral risk factors and cardiovascular diseases. Design, Setting, and Participants This cross-sectional study consisted of 71 915 US census tracts. The Structural Racism Effect Index (SREI) was appended to the 2020 Centers for Disease Control and Prevention’s PLACES dataset. Race and ethnicity were self-reported and obtained from the 2019 American Community Survey 5-year estimates. Data analysis was conducted from June 2024 to October 2024. Exposure The SREI is a composite score of census tract measures within 9 domains affected by structural racism (built environment, criminal justice, education, employment, housing, income and poverty, social cohesion, transportation, and wealth). The SREI scores were categorized into distribution-based quintiles, where higher quintiles represented greater neighborhood structural racism. Main Outcomes and Measures Neighborhood-level prevalence of cardiovascular clinical (high blood pressure, high cholesterol, obesity, and diabetes) and behavioral (current cigarette smoking and no leisure-time physical activity) risk factors and cardiovascular disease (coronary heart disease and stroke). Multilevel-linear mixed models, adjusting for confounders, were used to estimate adjusted prevalence rate ratios (aPRR). Results Among 71 915 census tracts, neighborhoods in the highest quintiles of structural racism were more likely than neighborhoods in the lowest quintile to be in the southern region (quintile 1, 19.9% vs quintile 5, 56.8%). The proportion of the population that was Black was largest in neighborhoods in the highest quintile of structural racism (quintile 1, 2.2% vs quintile 5, 23.0%). After adjustment, compared with the lowest quintile of structural racism, the highest quintile had significantly higher prevalence rates of cardiovascular clinical risk factors, including high blood pressure (aPRR, 1.37; 95% CI, 1.37-1.38), obesity (aPRR, 1.40; 95% CI, 1.39-1.40), and diabetes (aPRR, 1.80; 95% CI, 1.79-1.81). Similarly, for cardiovascular behavioral risk factors, the highest quintile of structural racism had significantly higher prevalence rates of current cigarette smoking (aPRR, 1.84; 95% CI, 1.83-1.85) and no leisure-time physical activity (aPRR, 1.88; 95% CI, 1.87-1.89). Additionally, for cardiovascular diseases, the highest quintile of structural racism had higher prevalence rates of coronary heart disease (aPRR, 1.80; 95% CI, 1.79-1.82) and stroke (aPRR, 1.99; 95% CI, 1.98-2.00). Conclusions and Relevance In this cross-sectional study, structural racism was associated with inequities in neighborhood cardiovascular health, highlighting opportunities for place-based prevention efforts.

ImportanceThere are consistent data demonstrating that socioeconomic disadvantage is associated with risk of premature mortality, but research on the relationship between neighborhood socioeconomic factors and premature mortality is limited. Most studies evaluating the association between neighborhood socioeconomic status (SES) and mortality have used a single assessment of SES during middle to older adulthood, thereby not considering the contribution of early life neighborhood SES.ObjectiveTo investigate the association of life course neighborhood SES and premature mortality.Design, Setting, and ParticipantsThis cohort study included Black and White participants of the multicenter Atherosclerosis Risk in Communities Study, a multicenter study conducted in 4 US communities: Washington County, Maryland; Forsyth County, North Carolina; Jackson, Mississippi; and the northwestern suburbs of Minneapolis, Minnesota. Participants were followed up for a mean (SD) of 18.8 (5.7) years (1996-2020). Statistical analysis was performed from March 2023 through May 2024.ExposureParticipants’ residential addresses during childhood, young adulthood, and middle adulthood were linked with US Census–based socioeconomic indicators to create summary neighborhood SES scores for each of these life epochs. Neighborhood SES scores were categorized into distribution-based tertiles.Main Outcomes and MeasuresPremature death was defined as all-cause mortality occurring before age 75 years. Multivariable-adjusted Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs.ResultsAmong 12 610 study participants, the mean (SD) age at baseline was 62.6 (5.6) years; 3181 (25.2%) were Black and 9429 (74.8%) were White; and 7222 (57.3%) were women. The lowest, compared with the highest tertile, of neighborhood SES score in middle adulthood was associated with higher risk of premature mortality (HR, 1.28; 95% CI, 1.07-1.54). Similar associations were observed for neighborhood SES in young adulthood among women (HR, 1.25; 95% CI, 1.00-1.56) and neighborhood SES in childhood among White participants (HR, 1.25; 95% CI, 1.01-1.56). Participants whose neighborhood SES remained low from young to middle adulthood had an increased premature mortality risk compared with those whose neighborhood SES remained high (HR, 1.25; 95% CI, 1.05-1.49).Conclusions and RelevanceIn this study, low neighborhood SES was associated with premature mortality. The risk of premature mortality was greatest among individuals experiencing persistently low neighborhood SES from young to middle adulthood. Place-based interventions that target neighborhood social determinants of health should be designed from a life course perspective that accounts for early-life socioeconomic inequality.

AbstractBackgroundRecent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk‐prediction model (called SPLC‐RAT) was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. The predictive performance of SPLC‐RAT was evaluated in a hospital‐based cohort of lung cancer survivors.MethodsThe authors analyzed data from 8448 ever‐smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997–2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC‐RAT and further explored the potential of improving SPLC detection through risk model‐based surveillance using SPLC‐RAT versus existing clinical surveillance guidelines.ResultsOf 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person‐years. The application of SPLC‐RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10‐year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC‐RAT development cohort), the observed SPLC incidence was significantly elevated in the high‐risk versus low‐risk subgroup (13.1% vs. 1.1%, p < 1 × 10–6). The risk‐based surveillance through SPLC‐RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow‐ups needed to detect one SPLC (162 vs. 202).ConclusionIn a large, hospital‐based cohort, the authors validated the predictive performance of SPLC‐RAT in identifying high‐risk survivors of SPLC and showed its potential to improve SPLC detection through risk‐based surveillance.Plain Language Summary Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC). However, no evidence‐based guidelines for SPLC surveillance are available for lung cancer survivors. Recently, an SPLC risk‐prediction model was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. Using a large, real‐world cohort of lung cancer survivors, we showed the high predictive accuracy and risk‐stratification ability of the SPLC risk‐prediction model. Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model‐based surveillance strategies compared to the existing consensus‐based clinical guidelines, including the National Comprehensive Cancer Network.

Abstract Background: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. Methods: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. Results: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88–1.22; MR-Egger OR, 0.89; 95% CI, 0.65–1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90–1.27; MR-Egger OR, 0.93; 95% CI, 0.67–1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. Conclusions: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. Impact: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.

Introduction. Acquired severe aplastic anemia (SAA) is a life-threatening disorder characterized by severe progressive pancytopenia and hypocellular bone marrow. The etiology of acquired SAA is not understood but believed to be related to abnormal immune responses to environmental exposures. We conducted a genome-wide association study (GWAS) to identify common germline variants associated with SAA. Methods. We identified 895 patients with SAA who underwent related or unrelated hematopoietic cell transplant (HCT) with clinical data and pre-HCT blood samples available in the Center for International Blood and Marrow Transplant Research (CIBMTR) database and biorepository. Pre-HCT DNA was extracted from blood of patients with SAA and genome-wide genotyping was conducted using the Illumina OmniExpress array. We excluded 93 inherited bone marrow failure cases. The SAA cases were grouped cases into discovery and validation sets based on time of batch sample receipt. Analyses were limited to patients of European ancestry based on principal component analyses to minimize the potential effect of population stratification. Controls were genomically matched and selected from previously scanned cancer-free subjects at the Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, NCI. The final analysis included 534 acquired SAA cases (359 in the discovery set and 175 in the validation set), and 2,455 controls (1,396 in the discovery set, and 1,059 in the validation set). Results. Patients with SAA in this study received HCT between 1989-2015 at a median age of 21 years, 56% were male, and the median time between SAA diagnosis and HCT was 11 months. Strong genome-wide association signals were identified across the human leukocyte antigen (HLA) genes encoded at the major histocompatibility complex (MHC) on chromosome 6p21 (Figure 1). The top SNP was located in the P4 binding pocket of the HLA class II gene HLA-DPB1(rs1042151A>G, p.Met105Val, pooled-odds ratio [OR]=1.75, 95% confidence interval [CI]=1.50-2.03, p=1.94x10-13). The expression of HLA-DP in CD19+ cells from 175 healthy donors was significantly different by rs1042151 A>G genotype (p=2.04x10-6) (Figure 2). A second SNP near HLA-B, rs28367832G>A, also reached genome-wide significance (pooled-OR=1.49, 95% CI=1.22-1.78, p=7.27x10-9). Copy-number variant analysis and next generation sequencing also identified somatic, clonal copy-neutral loss-of-heterozygosity affecting class I HLA genes in 8.6% of the SAA cases and none of the controls. Conclusion. This SAA GWAS identified strong association signals between common germline genetic variants in HLA class I and II genes and SAA. The main SNP is associated with changes in HLA-DP expression suggesting a key role for this locus in SAA etiology. This study adds further evidence to the connection between SAA and immune dysregulation. Disclosures Cerhan: Janssen: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; Celgene: Research Funding. Lee:Incyte: Research Funding; Syndax: Research Funding; Amgen: Research Funding; Novartis: Research Funding; Takeda: Research Funding; Kadmon: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; AstraZeneca: Research Funding.