Genetics of Latin American Diversity Project: Insights into population genetics and association studies in admixed groups in the Americas.

Latin Americans are underrepresented in genetic studies, increasing disparities in personalized genomic medicine. Despite available genetic data from thousands of Latin Americans, accessing and navigating the bureaucratic hurdles for consent or access remains challenging. To address this, we introduce the Genetics of Latin American Diversity (GLAD) Project, compiling genome-wide information from 53,738 Latin Americans across 39 studies representing 46 geographical regions. Through GLAD, we identified heterogeneous ancestry composition and recent gene flow across the Americas. Additionally, we developed GLAD-match, a simulated annealing-based algorithm, to match the genetic background of external samples to our database, sharing summary statistics (i.e., allele and haplotype frequencies) without transferring individual-level genotypes. Finally, we demonstrate the potential of GLAD as a critical resource for evaluating statistical genetic software in the presence of admixture. By providing this resource, we promote genomic research in Latin Americans and contribute to the promises of personalized medicine to more people.

The potential mediating role of the gut microbiome and metabolites in the association between PFAS and kidney function in young adults: A proof-of-concept study.

BACKGROUND: Chronic kidney disease (CKD) affects over 10 % of the global population and can lead to kidney failure and death. Exposure to per- and polyfluoroalkyl substances (PFAS) is associated with increased risk of CKD, yet studies examining the mechanisms linking PFAS and kidney function are lacking. In this exploratory study, we examined longitudinal associations of PFAS exposure with kidney function, and tested if associations were mediated by altered gut bacterial taxa or plasma metabolites using a multi-omics mediation analysis. METHODS: Seventy-eight young adults from the Children's Health Study were included in this longitudinal cohort study. At baseline, seven plasma PFAS and untargeted plasma metabolomics were measured using liquid chromatography/mass-spectrometry. Baseline gut bacterial abundance was characterized using 16S rRNA sequencing and examined at the genus level. At follow-up, serum creatinine and cystatin-C concentrations were quantified to estimate glomerular filtration rate (eGFR). High-dimensional multi-omics analyses were conducted to assess the association between baseline PFAS exposure with follow-up eGFR, mediated by gut microbiome and circulating metabolite levels. RESULTS: PFAS burden score, a variable developed to estimate exposure to chemical mixtures, was associated with kidney function. Each standard deviation increase in baseline PFAS burden score was associated with a 2.4 % lower eGFR at follow-up (95 % CI:[0.1 %,4.8 %]). Following high-dimensional mediation analyses with the microbiome and circulating metabolites, a joint component (characterized by reduced Lachnospiraceae and 17b-estradiol and increased succinate, retinoate and dodecanoic acid) and a metabolite component (characterized by increased hypotaurine and decreased D-pinitol and ureidopropionate) mediated 38 % and 50 % of the effect between PFAS burden score and eGFR, respectively. CONCLUSION: Our proof-of-concept analysis provides the first evidence that reduced short-chain fatty acid-producing bacteria and anti-inflammatory metabolites may link PFAS exposure with impaired kidney function. This study raises the possibility of future targeted interventions that can alter gut microbiome or circulating metabolite profiles to prevent PFAS induced kidney damage.

COVID-19 Is a Coronary Artery Disease Risk Equivalent and Exhibits a Genetic Interaction With ABO Blood Type.

BACKGROUND: COVID-19 is associated with acute risk of major adverse cardiac events (MACE), including myocardial infarction, stroke, and mortality (all-cause). However, the duration and underlying determinants of heightened risk of cardiovascular disease and MACE post-COVID-19 are not known. METHODS: Data from the UK Biobank was used to identify COVID-19 cases (n=10 005) who were positive for polymerase chain reaction (PCR+)-based tests for SARS-CoV-2 infection (n=8062) or received hospital-based International Classification of Diseases version-10 (ICD-10) codes for COVID-19 (n=1943) between February 1, 2020 and December 31, 2020. Population controls (n=217 730) and propensity score-matched controls (n=38 860) were also drawn from the UK Biobank during the same period. Proportional hazard models were used to evaluate COVID-19 for association with long-term (>1000 days) risk of MACE and as a coronary artery disease risk equivalent. Additional analyses examined whether COVID-19 interacted with genetic determinants to affect the risk of MACE and its components. RESULTS: The risk of MACE was elevated in COVID-19 cases at all levels of severity (HR, 2.09 [95% CI, 1.94-2.25]; P<0.0005) and to a greater extent in cases hospitalized for COVID-19 (HR, 3.85 [95% CI, 3.51-4.24]; P<0.0005). Hospitalization for COVID-19 represented a coronary artery disease risk equivalent since incident MACE risk among cases without history of cardiovascular disease was even higher than that observed in patients with cardiovascular disease without COVID-19 (HR, 1.21 [95% CI, 1.08-1.37]; P<0.005). A significant genetic interaction was observed between the ABO locus and hospitalization for COVID-19 (Pinteraction=0.01), with risk of thrombotic events being increased in subjects with non-O blood types (HR, 1.65 [95% CI, 1.29-2.09]; P=4.8×10-5) to a greater extent than subjects with blood type O (HR, 0.96 [95% CI, 0.66-1.39]; P=0.82). CONCLUSIONS: Hospitalization for COVID-19 represents a coronary artery disease risk equivalent, with post-acute myocardial infarction and stroke risk particularly heightened in non-O blood types. These results may have important clinical implications and represent, to our knowledge, one of the first examples of a gene-pathogen exposure interaction for thrombotic events.

Exposure to per- and polyfluoroalkyl substances and high-throughput proteomics in Hispanic youth.

BACKGROUND: Strong epidemiological evidence shows positive associations between exposure to per- and polyfluoroalkyl substances (PFAS) and adverse cardiometabolic outcomes (e.g., diabetes, hypertension, and dyslipidemia). However, the underlying cardiometabolic-relevant biological activities of PFAS in humans remain largely unclear. AIM: We evaluated the associations of PFAS exposure with high-throughput proteomics in Hispanic youth. MATERIAL AND METHODS: We included 312 overweight/obese adolescents from the Study of Latino Adolescents at Risk (SOLAR) between 2001 and 2012, along with 137 young adults from the Metabolic and Asthma Incidence Research (Meta-AIR) between 2014 and 2018. Plasma PFAS (i.e., PFOS, PFOA, PFHxS, PFHpS, PFNA) were quantified using liquid-chromatography high-resolution mass spectrometry. Plasma proteins (n = 334) were measured utilizing the proximity extension assay using an Olink Explore Cardiometabolic Panel I. We conducted linear regression with covariate adjustment to identify PFAS-associated proteins. Ingenuity Pathway Analysis, protein-protein interaction network analysis, and protein annotation were used to investigate alterations in biological functions and protein clusters. RESULTS: Results after adjusting for multiple comparisons showed 13 significant PFAS-associated proteins in SOLAR and six in Meta-AIR, sharing similar functions in inflammation, immunity, and oxidative stress. In SOLAR, PFNA demonstrated significant positive associations with the largest number of proteins, including ACP5, CLEC1A, HMOX1, LRP11, MCAM, SPARCL1, and SSC5D. After considering the mixture effect of PFAS, only SSC5D remained significant. In Meta-AIR, PFAS mixtures showed positive associations with GDF15 and IL6. Exploratory analysis showed similar findings. Specifically, pathway analysis in SOLAR showed PFOA- and PFNA-associated activation of immune-related pathways, and PFNA-associated activation of inflammatory response. In Meta-AIR, PFHxS-associated activation of dendric cell maturation was found. Moreover, PFAS was associated with common protein clusters of immunoregulatory interactions and JAK-STAT signaling in both cohorts. CONCLUSION: PFAS was associated with broad alterations of the proteomic profiles linked to pro-inflammation and immunoregulation. The biological functions of these proteins provide insight into potential molecular mechanisms of PFAS toxicity.

Cardiometabolic profiles and proteomics associated with obesity phenotypes in a longitudinal cohort of young adults.

To assess cardiometabolic profiles and proteomics to identify biomarkers associated with the metabolically healthy and unhealthy obesity. Young adults (N = 156) enrolled were classified as not having obesity, metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO) based on NCEP ATP-III criteria. Plasma proteomics at study entry were measured using Olink Cardiometabolic Explore panel. Linear regression was used to assess associations between proteomics and obesity groups as well as cardiometabolic traits of glucose, insulin, and lipid profiles at baseline and follow-up visits. Enriched biological pathways were further identified based on the significant proteomic features. Among the baseline 95 (61%) and 61 (39%) participants classified as not having obesity and having obesity (8 MHO and 53 MUHO), respectively. Eighty of the participants were followed-up with an average 4.6 years. Forty-one proteins were associated with obesity (FDR < 0.05), 29 of which had strong associations with insulin-related traits and lipid profiles (FDR < 0.05). Inflammation, immunomodulation, extracellular matrix remodeling and endoplasmic reticulum lumen functions were enriched by 40 proteins. In this study population, obesity and MHO were associated with insulin resistance and dysregulated lipid profiles. The underlying mechanism included elevated inflammation and deteriorated extracellular matrix remodeling function.

Associations of dietary intake and longitudinal measures of per- and polyfluoroalkyl substances (PFAS) in predominantly Hispanic young Adults: A multicohort study.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are pollutants linked to adverse health effects. Diet is an important source of PFAS exposure, yet it is unknown how diet impacts longitudinal PFAS levels. OBJECTIVE: To determine if dietary intake and food sources were associated with changes in blood PFAS concentrations among Hispanic young adults at risk of metabolic diseases. METHODS: Predominantly Hispanic young adults from the Children's Health Study who underwent two visits (CHS; n = 123) and young adults from NHANES 2013-2018 who underwent one visit (n = 604) were included. Dietary data at baseline was collected using two 24-hour dietary recalls to measure individual foods and where foods were prepared/consumed (home/restaurant/fast-food). PFAS were measured in blood at both visits in CHS and cross-sectionally in NHANES. In CHS, multiple linear regression assessed associations of baseline diet with longitudinal PFAS; in NHANES, linear regression was used. RESULTS: In CHS, all PFAS except PFDA decreased across visits (all p < 0.05). In CHS, A 1-serving higher tea intake was associated with 24.8 %, 16.17 %, and 12.6 % higher PFHxS, PFHpS, and PFNA at follow-up, respectively (all p < 0.05). A 1-serving higher pork intake was associated with 13.4 % higher PFOA at follow-up (p < 0.05). Associations were similar in NHANES, including unsweetened tea, hot dogs, and processed meats. For food sources, in CHS each 200-gram increase in home-prepared food was associated with 0.90 % and 1.6 % lower PFOS at baseline and follow-up, respectively, and in NHANES was associated with 0.9 % lower PFDA (all p < 0.05). CONCLUSION: Results suggest that beverage consumption habits and food preparation are associated with differences in PFAS levels in young adults. This highlights the importance of diet in determining PFAS exposure and the necessity of public monitoring of foods and beverages for PFAS contamination.

Proteomic and Metabolomic Signatures of Diet Quality in Young Adults.

The assessment of "omics" signatures may contribute to personalized medicine and precision nutrition. However, the existing literature is still limited in the homogeneity of participants' characteristics and in limited assessments of integrated omics layers. Our objective was to use post-prandial metabolomics and fasting proteomics to identify biological pathways and functions associated with diet quality in a population of primarily Hispanic young adults. We conducted protein and metabolite-wide association studies and functional pathway analyses to assess the relationships between a priori diet indices, Healthy Eating Index-2015 (HEI) and Dietary Approaches to Stop Hypertension (DASH) diets, and proteins (n = 346) and untargeted metabolites (n = 23,173), using data from the MetaAIR study (n = 154, 61% Hispanic). Analyses were performed for each diet quality index separately, adjusting for demographics and BMI. Five proteins (ACY1, ADH4, AGXT, GSTA1, F7) and six metabolites (undecylenic acid, betaine, hyodeoxycholic acid, stearidonic acid, iprovalicarb, pyracarbolid) were associated with both diets (p < 0.05), though none were significant after adjustment for multiple comparisons. Overlapping proteins are involved in lipid and amino acid metabolism and in hemostasis, while overlapping metabolites include amino acid derivatives, bile acids, fatty acids, and pesticides. Enriched biological pathways were involved in macronutrient metabolism, immune function, and oxidative stress. These findings in young Hispanic adults contribute to efforts to develop precision nutrition and medicine for diverse populations.

Postprandial Metabolite Profiles and Risk of Prediabetes in Young People: A Longitudinal Multicohort Study.

OBJECTIVE: Prediabetes in young people is an emerging epidemic that disproportionately impacts Hispanic populations. We aimed to develop a metabolite-based prediction model for prediabetes in young people with overweight/obesity at risk for type 2 diabetes. RESEARCH DESIGN AND METHODS: In independent, prospective cohorts of Hispanic youth (discovery; n = 143 without baseline prediabetes) and predominately Hispanic young adults (validation; n = 56 without baseline prediabetes), we assessed prediabetes via 2-h oral glucose tolerance tests. Baseline metabolite levels were measured in plasma from a 2-h postglucose challenge. In the discovery cohort, least absolute shrinkage and selection operator regression with a stability selection procedure was used to identify robust predictive metabolites for prediabetes. Predictive performance was evaluated in the discovery and validation cohorts using logistic regression. RESULTS: Two metabolites (allylphenol sulfate and caprylic acid) were found to predict prediabetes beyond known risk factors, including sex, BMI, age, ethnicity, fasting/2-h glucose, total cholesterol, and triglycerides. In the discovery cohort, the area under the receiver operator characteristic curve (AUC) of the model with metabolites and known risk factors was 0.80 (95% CI 0.72-0.87), which was higher than the risk factor-only model (AUC 0.63 [0.53-0.73]; P = 0.001). When the predictive models developed in the discovery cohort were applied to the replication cohort, the model with metabolites and risk factors predicted prediabetes more accurately (AUC 0.70 [95% CI 40.55-0.86]) than the same model without metabolites (AUC 0.62 [0.46-0.79]). CONCLUSIONS: Metabolite profiles may help improve prediabetes prediction compared with traditional risk factors. Findings suggest that medium-chain fatty acids and phytochemicals are early indicators of prediabetes in high-risk youth.

Exposure to perfluoroalkyl substances and longitudinal changes in bone mineral density in adolescents and young adults: A multi-cohort study.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) may impair bone development in adolescence, which impacts life-long bone health. No previous studies have examined prospective associations of individual PFAS and their mixture with bone mineral density (BMD) changes in Hispanic young persons, a population at high risk of osteoporosis in adulthood. OBJECTIVES: To examine associations of individual PFAS and PFAS mixtures with longitudinal changes in BMD in an adolescent Hispanic cohort and examine generalizability of findings in a mixed-ethnicity young adult cohort (58.4% Hispanic). METHODS: Overweight/obese adolescents from the Study of Latino Adolescents at Risk of Type 2 Diabetes (SOLAR; n = 304; mean follow-up = 1.4 years) and young adults from the Southern California Children's Health Study (CHS; n = 137; mean follow-up = 4.1 years) were included in this study. Plasma PFAS were measured at baseline and dual x-ray absorptiometry scans were performed at baseline and follow-up to measure BMD. We estimated longitudinal associations between BMD and five PFAS via separate covariate-adjusted linear mixed effects models, and between BMD and the PFAS mixture via quantile g-computation. RESULTS: In SOLAR adolescents, baseline plasma perfluorooctanesulfonic acid (PFOS) was associated with longitudinal changes in BMD. Each doubling of PFOS was associated with an average -0.003 g/cm2 difference in change in trunk BMD per year over follow-up (95% CI: -0.005, -0.0002). Associations with PFOS persisted in CHS young adults, where each doubling of plasma PFOS was associated with an average -0.032 g/cm2 difference in total BMD at baseline (95% CI -0.062, -0.003), though longitudinal associations were non-significant. We did not find associations of other PFAS with BMD; associations of the PFAS mixture with BMD outcomes were primarily negative though non-significant. DISCUSSION: PFOS exposure was associated with lower BMD in adolescence and young adulthood, important periods for bone development, which may have implications on future bone health and risk of osteoporosis in adulthood.

Critical windows of greenness exposure during preconception and gestational periods in association with birthweight outcomes.

Few studies have examined the association between greenness exposure and birth outcomes. This study aims to identify critical exposure time windows during preconception and pregnancy for the association between greenness exposure and birth weight. A cohort of 13 890 pregnant women and newborns in Shanghai, China from 2016-2019 were included in the study. We assessed greenness exposure using Normalized Difference Vegetation Index (NDVI) during the preconception and gestational periods, and evaluated the association with term birthweight, birthweight z-score, small-for-gestational age, and large-for-gestational age using linear and logistic regressions adjusting for key maternal and newborn covariates. Ambient temperature, relative humidity, ambient levels of fine particles (PM2.5) and nitrogen dioxide (NO2) assessed during the same period were adjusted for as sensitivity analyses. Furthermore, we explored the potential different effects by urbanicity and park accessibility through stratified analysis. We found that higher greenness exposure at the second trimester of pregnancy and averaged exposure during the entire pregnancy were associated with higher birthweight and birthweight Z-score. Specifically, a 0.1 unit increase in second trimester averaged NDVI value was associated with an increase in birthweight of 10.2 g (95% CI: 1.8-18.5 g) and in birthweight Z-score of 0.024 (0.003-0.045). A 0.1 unit increase in an averaged NDVI during the entire pregnancy was associated with 10.1 g (95% CI: 1.0-19.2 g) increase in birthweight and 0.025 (0.001-0.048) increase in birthweight Z-score. Moreover, the associations were larger in effect size among urban residents than suburban residents and among residents without park accessibility within 500 m compared to those with park accessibility within 500 m. Our findings suggest that increased greenness exposure, particularly during the second trimester, may be beneficial to birth weight in a metropolitan area.

Application of artificial intelligence in quantifying lung deposition dose of black carbon in people with exposure to ambient combustion particles.

BACKGROUND: Understanding lung deposition dose of black carbon is critical to fully reconcile epidemiological evidence of combustion particles induced health effects and inform the development of air quality metrics concerning black carbon. Macrophage carbon load (MaCL) is a novel cytology method that quantifies lung deposition dose of black carbon, however it has limited feasibility in large-scale epidemiological study due to the labor-intensive manual counting. OBJECTIVE: To assess the association between MaCL and episodic elevation of combustion particles; to develop artificial intelligence based counting algorithm for MaCL assay. METHODS: Sputum slides were collected during episodic elevation of ambient PM2.5 (n = 49, daily PM2.5 > 10 µg/m3 for over 2 weeks due to wildfire smoke intrusion in summer and local wood burning in winter) and low PM2.5 period (n = 39, 30-day average PM2.5 < 4 µg/m3) from the Lovelace Smokers cohort. RESULTS: Over 98% individual carbon particles in macrophages had diameter <1 µm. MaCL levels scored manually were highly responsive to episodic elevation of ambient PM2.5 and also correlated with lung injury biomarker, plasma CC16. The association with CC16 became more robust when the assessment focused on macrophages with higher carbon load. A Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP) was developed based on the Mask Region-based Convolutional Neural Network. MacLEAP algorithm yielded excellent correlations with manual counting for number and area of the particles. The algorithm produced associations with ambient PM2.5 and plasma CC16 that were nearly identical in magnitude to those obtained through manual counting. IMPACT STATEMENT: Understanding lung black carbon deposition is crucial for comprehending health effects of combustion particles. We developed "Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP)", the first artificial intelligence algorithm for quantifying airway macrophage black carbon. Our study bolstered the algorithm with more training images and its first use in air pollution epidemiology. We revealed macrophage carbon load as a sensitive biomarker for heightened ambient combustion particles due to wildfires and residential wood burning.

Characteristics associated with attitudes and behaviors towards mask wearing during the COVID-19 pandemic: The Trojan Pandemic Response Initiative.

BACKGROUND: Attitudes and behaviors towards mask wearing may influence the ability to reduce transmission of COVID-19 and other diseases. METHODS: University students, staff, and faculty (N = 9653) responded to an email invitation to complete electronic surveys (November 2021 and April 2022). Surveys included 19 items measuring attitudes and behaviors towards mask wearing from the Understanding America Study. Linear mixed models including variables for sex, age group, division, race and ethnicity, political affiliation, and history of COVID-19, were used to estimate the mean difference of the mean score for attitudes and behavior between Time 1 (November 2021) and Time 2 (April 2022). RESULTS: Participants were mostly female (62.1%), students (70.6%), White (39.5%) and Asian (34.7%). More than half identified their political affiliation as Democrat (65.5%). Characteristic variable-by-time interactions for difference in mean mask attitude scores difference were significant at Time 1 (T1) and Time 2 (T2) between Black and White participants (B = 0.18 (0.05), 95% CI: 0.07, 0.28, p = 0.001), Asian and White participants (B = 0.07 (0.02), 95% CI: 0.03-0.12, p = 0.001), participants with self-reported history of COVID-19 and no history of COVID-19 (B= -0.13 (0.02), 95% CI: -0.07, -0.18, p < 0.0001), females and males (B = 0.07 (0.02), 95% CI: 0.03, 0.11, p = 0.001), Republicans and Democrats (B= -0.18 (0.04), 95%CI: -0.26, -0.10, p < 0.0001) and Independents and Democrats (B= -0.10 (0.03), 95%CI: -0.15, -0.05, p < 0.0001). Mean difference in mean scores for mask behaviors at Time and Time 2 were significant between participants with COVID-19 and participants who did not have COVID-19 (B= -0.12 (0.04), 95% CI: -0.19, -0.04, p = 0.004), students compared to faculty and staff (B=-0.22 (0.05), -0.32, -0.12, p < 0.0001), between Republicans and Democrats (B-= -0.16 (0.07), 95% CI: -0.28, -0.03, p = 0.020, and between Independents and Democrats (B=-0.08 (0.04), 95% CI: -0.16, -0.002, p = 0.04). CONCLUSION: Race and ethnicity, political affiliation, and division may affect attitudes and behaviors in mask wearing. Further investigation into how characteristics influence public health measures such as mask wearing is needed to contain the spread of the COVID-19 virus, other infectious diseases, and future pandemics.

Identifying critical windows of air pollution exposure during preconception and gestational period on birthweight: a prospective cohort study.

BACKGROUND: Few studies have assessed air pollution exposure association with birthweight during both preconception and gestational periods. METHODS: Leveraging a preconception cohort consisting of 14220 pregnant women and newborn children in Shanghai, China during 2016-2018, we aim to assess associations of NO2 and PM2.5 exposure, derived from high-resolution spatial-temporal models, during preconception and gestational periods with outcomes including term birthweight, birthweight Z-score, small-for-gestational age (SGA) and large-for-gestational age (LGA). Linear and logistic regressions were used to estimate 3-month preconception and trimester-averaged air pollution exposure associations; and distributed lag models (DLM) were used to identify critical exposure windows at the weekly resolution from preconception to delivery. Two-pollutant models and children's sex-specific associations were explored. RESULTS: After controlling for covariates, one standard deviation (SD) (11.5 µg/m3, equivalent to 6.1 ppb) increase in NO2 exposure during the second and the third trimester was associated with 13% (95% confidence interval: 2 - 26%) and 14% (95% CI: 1 - 29%) increase in SGA, respectively; and one SD (9.6 µg/m3) increase in PM2.5 exposure during the third trimester was associated with 15% (95% CI: 1 - 31%) increase in SGA. No association have been found for outcomes of birthweight, birthweight Z-score and LGA. DLM found that gestational weeks 22-32 were a critical window, when NO2 exposure had strongest associations with SGA. The associations of air pollution exposure tended to be stronger in female newborns than in male newborns. However, no significant associations of air pollution exposure during preconception period on birthweight outcomes were found. CONCLUSION: Consistent with previous studies, we found that air pollution exposure during mid-to-late pregnancy was associated with adverse birthweight outcomes.

ACDC: a general approach for detecting phenotype or exposure associated co-expression.

Background: Existing module-based differential co-expression methods identify differences in gene-gene relationships across phenotype or exposure structures by testing for consistent changes in transcription abundance. Current methods only allow for assessment of co-expression variation across a singular, binary or categorical exposure or phenotype, limiting the information that can be obtained from these analyses. Methods: Here, we propose a novel approach for detection of differential co-expression that simultaneously accommodates multiple phenotypes or exposures with binary, ordinal, or continuous data types. Results: We report an application to two cohorts of asthmatic patients with varying levels of asthma control to identify associations between gene co-expression and asthma control test scores. Results suggest that both expression levels and covariances of ADORA3, ALOX15, and IDO1 are associated with asthma control. Conclusion: ACDC is a flexible extension to existing methodology that can detect differential co-expression across varying external variables.

The genetic determinants of recurrent somatic mutations in 43,693 blood genomes.

Nononcogenic somatic mutations are thought to be uncommon and inconsequential. To test this, we analyzed 43,693 National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine blood whole genomes from 37 cohorts and identified 7131 non-missense somatic mutations that are recurrently mutated in at least 50 individuals. These recurrent non-missense somatic mutations (RNMSMs) are not clearly explained by other clonal phenomena such as clonal hematopoiesis. RNMSM prevalence increased with age, with an average 50-year-old having 27 RNMSMs. Inherited germline variation associated with RNMSM acquisition. These variants were found in genes involved in adaptive immune function, proinflammatory cytokine production, and lymphoid lineage commitment. In addition, the presence of eight specific RNMSMs associated with blood cell traits at effect sizes comparable to Mendelian genetic mutations. Overall, we found that somatic mutations in blood are an unexpectedly common phenomenon with ancestry-specific determinants and human health consequences.

Longitudinal hierarchical Bayesian models of covariate effects on airway and alveolar nitric oxide.

Biomarkers such as exhaled nitric oxide (FeNO), a marker of airway inflammation, have applications in the study of chronic respiratory disease where longitudinal studies of within-participant changes in the biomarker are particularly relevant. A cutting-edge approach to assessing FeNO, called multiple flow FeNO, repeatedly assesses FeNO across a range of expiratory flow rates at a single visit and combines these data with a deterministic model of lower respiratory tract NO to estimate parameters quantifying airway wall and alveolar NO sources. Previous methodological work for multiple flow FeNO has focused on methods for data from a single participant or from cross-sectional studies. Performance of existing ad hoc two-stage methods for longitudinal multiple flow FeNO in cohort or panel studies has not been evaluated. In this paper, we present a novel longitudinal extension to a unified hierarchical Bayesian (L_U_HB) model relating longitudinally assessed multiple flow FeNO to covariates. In several simulation study scenarios, we compare the L_U_HB method to other unified and two-stage frequentist methods. In general, L_U_HB produced unbiased estimates, had good power, and its performance was not sensitive to the magnitude of the association with a covariate and correlations between NO parameters. In an application relating height to longitudinal multiple flow FeNO in schoolchildren without asthma, unified analysis methods estimated positive, statistically significant associations of height with airway and alveolar NO concentrations and negative associations with airway wall diffusivity while estimates from two-stage methods were smaller in magnitude and sometimes non-significant.

Metabolic pathways altered by air pollutant exposure in association with lipid profiles in young adults.

Mounting evidence suggests that air pollution influences lipid metabolism and dyslipidemia. However, the metabolic mechanisms linking air pollutant exposure and altered lipid metabolism is not established. In year 2014-2018, we conducted a cross-sectional study on 136 young adults in southern California, and assessed lipid profiles (triglycerides, total cholesterol, high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, very-low-density lipoprotein (VLDL)-cholesterol), and untargeted serum metabolomics using liquid chromatography-high-resolution mass spectrometry, and one-month and one-year averaged exposures to NO2, O3, PM2.5 and PM10 air pollutants at residential addresses. A metabolome-wide association analysis was conducted to identify metabolomic features associated with each air pollutant. Mummichog pathway enrichment analysis was used to assess altered metabolic pathways. Principal component analysis (PCA) was further conducted to summarize 35 metabolites with confirmed chemical identity. Lastly, linear regression models were used to analyze the associations of metabolomic PC scores with each air pollutant exposure and lipid profile outcome. In total, 9309 metabolomic features were extracted, with 3275 features significantly associated with exposure to one-month or one-year averaged NO2, O3, PM2.5 and PM10 (p < 0.05). Metabolic pathways associated with air pollutants included fatty acid, steroid hormone biosynthesis, tryptophan, and tyrosine metabolism. PCA of 35 metabolites identified three main PCs which together explained 44.4% of the variance, representing free fatty acids and oxidative byproducts, amino acids and organic acids. Linear regression indicated that the free fatty acids and oxidative byproducts-related PC score was associated with air pollutant exposure and outcomes of total cholesterol and LDL-cholesterol (p < 0.05). This study suggests that exposure to NO2, O3, PM2.5 and PM10 contributes to increased level of circulating free fatty acids, likely through increased adipose lipolysis, stress hormone and response to oxidative stress pathways. These alterations were associated with dysregulation of lipid profiles and potentially could contribute to dyslipidemia and other cardiometabolic disorders.

Surveillance of Severe Acute Respiratory Syndrome Coronavirus 2 and Variants Using Digital Droplet Polymerase Chain Reaction at a Large University and Healthcare System in California.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with different infectivity, transmission potential, and morbidity change the characteristics of local epidemics and affect vaccine effectiveness. As part of the University of Southern California COVID-19 Pandemic Research Center's efforts to understand, control, and inform local community on coronavirus disease 2019 (COVID-19), we implemented a SARS-CoV-2 surveillance program among students, employees, and USC Keck Medical Center patients. We present the epidemiology and distribution of SARS-CoV-2 and its variants among the population. Methods: We used digital droplet reverse-transcriptase polymerase chain reaction (PCR) to analyze in real-time remnant SARS-CoV-2 PCR-positive saliva specimens stored at the USC Keck Medicine laboratory between September 2020 and April 2022. Samples were tested for the original strain (A20) and 9 SARS-CoV-2 variants: α(B.1.1.7, Q.1-Q.8), ß(B.1.351, B.1.351.2, B.1.351.3), γ(P.1, P.1.1, P.1.2), δ(B.1.617.2), δ+(or δ417N), ε(B.1.427 and B.1.429), η(B.1.525), λ(C.37) and ο(B.1.1.529, ΒΑ.1, BA.2). We reviewed deidentified health information from positive cases including demographics, history of COVID-19 (eg, symptoms, hospitalizations, and repeat infections), and COVID-19 vaccination status. Results: We reviewed 1169 cases and determined the variant type of 482 specimens: 77 specimens were original strain, 119 "Delta", 165 "Omicron". The original strain was detected during the third and fourth quarters of 2020. The Delta variant appeared during the second quarter of 2021, whereas Omicron appeared in the fourth quarter of 2021. Conclusions: Prospectively tracking SARS-CoV-2 variants in a university population and a hospital system, utilizing a low-cost, high-throughput PCR assay, was feasible. Local variant monitoring remains important to inform prevention and control efforts among university and clinical settings.

Incidence rates of childhood asthma with recurrent exacerbations in the US Environmental influences on Child Health Outcomes (ECHO) program.

BACKGROUND: Descriptive epidemiological data on incidence rates (IRs) of asthma with recurrent exacerbations (ARE) are sparse. OBJECTIVES: This study hypothesized that IRs for ARE would vary by time, geography, age, and race and ethnicity, irrespective of parental asthma history. METHODS: The investigators leveraged data from 17,246 children born after 1990 enrolled in 59 US with 1 Puerto Rican cohort in the Environmental Influences on Child Health Outcomes (ECHO) consortium to estimate IRs for ARE. RESULTS: The overall crude IR for ARE was 6.07 per 1000 person-years (95% CI: 5.63-6.51) and was highest for children aged 2-4 years, for Hispanic Black and non-Hispanic Black children, and for those with a parental history of asthma. ARE IRs were higher for 2- to 4-year-olds in each race and ethnicity category and for both sexes. Multivariable analysis confirmed higher adjusted ARE IRs (aIRRs) for children born 2000-2009 compared with those born 1990-1999 and 2010-2017, 2-4 versus 10-19 years old (aIRR = 15.36; 95% CI: 12.09-19.52), and for males versus females (aIRR = 1.34; 95% CI 1.16-1.55). Black children (non-Hispanic and Hispanic) had higher rates than non-Hispanic White children (aIRR = 2.51; 95% CI 2.10-2.99; and aIRR = 2.04; 95% CI: 1.22-3.39, respectively). Children born in the Midwest, Northeast and South had higher rates than those born in the West (P < .01 for each comparison). Children with a parental history of asthma had rates nearly 3 times higher than those without such history (aIRR = 2.90; 95% CI: 2.43-3.46). CONCLUSIONS: Factors associated with time, geography, age, race and ethnicity, sex, and parental history appear to influence the inception of ARE among children and adolescents.

Trust in science and scientists among university students, staff, and faculty of a large, diverse university in Los Angeles during the COVID-19 pandemic, the Trojan Pandemic Response Initiative.

BACKGROUND: Mistrust in science and scientists may adversely influence the rate of COVID-19 vaccination and undermine public health initiatives to reduce virus transmission. METHODS: Students, staff and faculty responded to an email invitation to complete an electronic survey. Surveys included 21-items from the Trust in Science and Scientists Inventory questionnaire. Responses were coded so higher scores indicated a higher trust in science and scientists, A linear regression model including sex, age group, division, race and ethnicity, political affiliation, and history of COVID-19, was used to determine variables significantly associated with trust in science and scientists scores at the p < 0.05 level. RESULTS: Participants were mostly female (62.1%), Asian (34.7%) and White (39.5%) and students (70.6%). More than half identified their political affiliation as Democrat (65%). In the final regression model, all races and ethnicities had significantly lower mean trust in science and scientists scores than White participants [Black ([Formula: see text]= -0.42, 95% CI: -0.55, -0.43, p < 0.001); Asian ([Formula: see text]= -0.20, 95% CI: -0.24, -0.17, p < 0.001); Latinx ([Formula: see text]= -0.22, 95% CI: -0.27, -0.18, p < 0.001); Other ([Formula: see text]= -0.19, 95% CI: -0.26, -0.11, p < 0.001)]. Compared to those identifying as Democrat, all other political affiliations had significantly lower mean scores. [Republican ([Formula: see text] =-0.49, 95% CI: -0.55, -0.43, p < 0.0001); Independent ([Formula: see text] =-0.29, 95% CI: -0.33, -0.25, p < 0.0001); something else ([Formula: see text] =-0.19, 95% CI: -0.25, -0.12, p < 0.0001)]. Having had COVID-19 ([Formula: see text]= -0.10, 95% CI: -0.15, -0.06, p < 0.001) had significantly lower scores compared to those who did not have COVID-19. CONCLUSION: Despite the setting of a major research University, trust in science is highly variable. This study identifies characteristics that could be used to target and curate educational campaigns and university policies to address the COVID19 and future pandemics.