The burden of severe asthma in sub-Saharan Africa: Findings from the African Severe Asthma Project

Background

Severe asthma is associated with high morbidity, mortality, and health care utilization, but its burden in Africa is unknown.

Objective

We sought to determine the burden (prevalence, mortality, and activity and work impairment) of severe asthma in 3 countries in East Africa: Uganda, Kenya, and Ethiopia.

Methods

Using the American Thoracic Society/European Respiratory Society case definition of severe asthma, we analyzed for the prevalence of severe asthma (requiring Global Initiative for Asthma [GINA] steps 4-5 asthma medications for the previous year to achieve control) and severe refractory asthma (remains uncontrolled despite treatment with GINA steps 4-5 asthma medications) in a cohort of 1086 asthma patients who had been in care for 12 months and had received all GINA-recommended medications. Asthma control was assessed by the asthma control questionnaire (ACQ).

Results

Overall, the prevalence of severe asthma and severe refractory asthma was 25.6% (95% confidence interval [CI], 23.1-28.3) and 4.6% (95% CI, 3.5-6.0), respectively. Patients with severe asthma were (nonsevere vs severe vs severe refractory) older (39, 42, 45 years, P = .011), had high skin prick test reactivity (67.1%, 76.0%, 76.0%, P = .004), had lower forced expiratory volume in 1 second percentage (81%, 61%, 55.5%, P < .001), had lower quality of life score (129, 127 vs 121, P < .001), and had higher activity impairment (10%, 30%, 50%, P < .001). Factors independently associated with severe asthma were hypertension comorbidity; adjusted odds ratio 2.21 (1.10-4.47), P = .027, high bronchial hyperresponsiveness questionnaire score; adjusted odds ratio 2.16 (1.01-4.61), P = .047 and higher ACQ score at baseline 2.80 (1.55-5.08), P = .001.

Conclusion

The prevalence of severe asthma in Africa is high and is associated with high morbidity and poor quality of life.

Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.

Aryl hydrocarbon receptor and IL-13 signaling crosstalk in human keratinocytes and atopic dermatitis

Introduction

Atopic dermatitis (AD) is an allergic skin disease mediated by skin barrier impairment and IL-13-driven immune response. Activation of the aryl hydrocarbon receptor (AHR) has shown promise in early clinical trials for AD; however, the mechanism by which AHR partially ameliorates AD is not well known.

Methods

Gene expression data from human biopsies were analyzed, and compared to gene expression from RNA-sequencing in our in-vitro HaCaT cell model system. Western blot, ELISA qRT-PCR were used to further explore the relationship between AHR and IL-13 signaling in HaCaT cells.

Results

The AHR target gene CYP1A1 was decreased in lesional skin compared with healthy control skin (p = 4.30 × 10-9). Single-cell RNA sequencing (scRNAseq) demonstrated increased AHR expression (p < 1.0 × 10-4) and decreased CYP1A1 expression in lesional AD keratinocytes compared with healthy control keratinocytes (p < 0.001). Activation of AHR by AHR agonists in HaCaT cells reversed IL-13-dependent gene expression of several key genes in AD pathogenesis, most notably the eosinophil chemoattractant CCL26 (eotaxin-3). Differentially expressed genes in keratinocytes of patients with AD substantially overlapped with genes regulated by AHR agonists from HaCaT cells by RNAseq, but in reverse direction. Mechanistically, there was evidence for direct transcriptional effects of AHR; AHR binding motifs were identified in the differentially expressed genes from lesional AD keratinocytes compared to control keratinocytes, and AHR activation did not modify IL-13-dependent signal transducer and activator of transcription 6 (STAT6) translocation to the nucleus.

Discussion

Together, these data suggest that the AHR pathway is dysregulated in AD and that AHR modulates IL-13 downstream signaling in keratinocytes through genome-wide, transcriptional regulatory effects.

Integrative analysis identifies gene signatures mediating the effect of DNA methylation on asthma severity and lung function

DNA methylation (DNAm) changes play a key role in regulating gene expression in asthma. To investigate the role of epigenetics and transcriptomics change in asthma, we used publicly available DNAm (asthmatics, n = 96 and controls, n = 46) and gene expression (asthmatics, n = 79 and controls, n = 39) data derived from bronchial epithelial cells (BECs). We performed differential methylation/expression and weighted co-methylation/co-expression network analyses to identify co-methylated and co-expressed modules associated with asthma severity and lung function. For subjects with both DNAm and gene expression data (asthmatics, n = 79 and controls, n = 39), machine-learning technique was used to prioritize CpGs and differentially expressed genes (DEGs) for asthma risk prediction, and mediation analysis was used to uncover DEGs that mediate the effect of DNAm on asthma severity and lung function in BECs. Finally, we validated CpGs and their associated DEGs and the asthma risk prediction model in airway epithelial cells (AECs) dataset. The asthma risk prediction model based on 18 CpGs and 28 DEGs showed high accuracy in both the discovery BEC dataset with area under the receiver operating characteristic curve (AUC) = 0.99 and the validation AEC dataset (AUC = 0.82). Genes in the three co-methylated and six co-expressed modules were enriched in multiple pathways including WNT/beta-catenin signaling and notch signaling. Moreover, we identified 35 CpGs correlated with DEGs in BECs, of which 17 CpGs including cg01975495 (SERPINE1), cg10528482 (SLC9A3), cg25477769 (HNF1A) and cg26639146 (CD9), cg17945560 (TINAGL1) and cg10290200 (FLNC) were replicated in AECs. These DEGs mediate the association between DNAm and asthma severity and lung function. Overall, our study investigated the role of DNAm and gene expression change in asthma and provided an insight into the mechanisms underlying the effects of DNA methylation on asthma, asthma severity and lung function.

Racial differences in length of stay and readmission for asthma in the all of us research program

BACKGROUND

This study addresses the limited research on racial disparities in asthma hospitalization outcomes, specifically length of stay (LOS) and readmission, across the U.S.

METHODS

We analyzed in-patient and emergency department visits from the All of Us Research Program, identifying various risk factors (demographic, comorbid, temporal, and place-based) associated with asthma LOS and 30-day readmission using Bayesian mixed-effects models.

RESULTS

Of 17,233 patients (48.0% White, 30.7% Black, 19.7% Hispanic/Latino, 1.3% Asian, and 0.3% Middle Eastern and North African) with 82,188 asthma visits, Black participants had 20% shorter LOS and 12% higher odds of readmission, compared to White participants in multivariate analyses. Public-insured patients had 14% longer LOS and 39% higher readmission odds than commercially insured patients. Weekend admissions resulted in a 12% shorter LOS but 10% higher readmission odds. Asthmatics with chronic diseases had a longer LOS (range: 6-39%) and higher readmission odds (range: 9-32%) except for those with allergic rhinitis, who had a 23% shorter LOS.

CONCLUSIONS

A comprehensive understanding of the factors influencing asthma hospitalization, in conjunction with diverse datasets and clinical-community partnerships, can help physicians and policymakers to systematically address racial disparities, healthcare utilization and equitable outcomes in asthma care.

Gender-based violence in the context of armed conflict in Northern Ethiopia

BACKGROUND

Gender-based violence (GBV) particularly against women is unfortunately common during armed conflicts. No rigorous and comprehensive empirical work has documented the extent of GBV and its consequences that took place during the two years of devastating armed conflict in Northern Ethiopia. This study aims to assess GBV and its consequences in war-torn areas of northern Ethiopia.

METHODS

We used a qualitative method augmented by quantitative method to enroll research participants. We conducted in-depth interviews to characterize the lived experiences of GBV survivors. All interviews were conducted confidentially. The data were collected to the point of data saturation. All interviews were transcribed verbatim into local language, translated into English, and analyzed using a thematic analysis approach. We also used reports from healthcare facilities and conducted a descriptive analysis of the demographic characteristics of study participants.

RESULTS

One thousand one hundred seventy-seven persons reported GBV to healthcare providers. The qualitative study identified several forms of violence (sexual, physical, and psychological). Gang rape against women including minors as young as 14 years old girls was reported. Additionally, the perpetrators sexually violated women who were pregnant, and elderly women as old as 65 years, who took refuge in religious institutions. The perpetrators committed direct assaults on the body with items (e.g., burning the body with cigarette fire) or weapons, holding women and girls as captives, and deprivation of sleep and food. GBV survivors reported stigma, prejudice, suicide attempts, nightmares, and hopelessness. GBV survivors dealt with the traumatic stress by outmigration (leaving their residences), seeking care at healthcare facilities, self-isolation, being silent, dropping out of school, and seeking counseling.

CONCLUSION

GBV survivors were subjected to multiple and compounding types of violence, with a wide range of adverse health consequences for survivors and their families. GBV survivors require multifaceted interventions including psychological, health, and economic support to rehabilitate them to lead a productive life.

Ancestry Specific Polygenic Risk Score, Dietary Patterns, Physical Activity, and Cardiovascular Disease

Background

It is unknown whether the impact of high diet-quality and physical activity (PA) depends on the level of polygenic risk score (PRS) in different ancestries.

Objective

Determine the associations and interactions between high-risk PRSs, dietary patterns, and high PA with atherosclerotic cardiovascular disease (ASCVD) in European Americans (EAs) and African Americans (AAs). Another aim determined the molecular pathways of PRS-mapped genes and their relationships with dietary intake.

Methods

Cross-sectional analyses utilized de-identified data from 1987-2010 from 7-National Heart, Lung, and Blood Institute Candidate Gene Association Resource studies from the Database of Genotypes and Phenotypes studies for EAs (n=6,575) and AAs (n=1,606).

Results

The high-risk PRS increased ASCVD risk by 59% (Risk Ratio=1.59;95% Confidence Interval:1.16-2.17) in the highest tertile for AAs and by 15% (RR=1.15;1.13-1.30) and 18% (RR=1.18;1.04-1.35) in the second and highest tertiles compared to the lowest tertile in EAs. Within the highest PRS tertiles, high PA-diet combinations (Dietary Approaches to Stop High Blood Pressure (DASH), or Mediterranean, or Southern) reduced ASCVD risks by 9% (RR=0.91;0.85-0.96) to 15% (RR=0.85;0.80-0.90) in EAs; and by 13% (RR=0.87;0.78-0.97) and 18% (RR=0.82;0.72-0.95) for the DASH and Mediterranean diets, respectively in AAs. Top molecular pathways included fructose metabolism and catabolism linked to obesity, insulin resistance, and type 2 diabetes in both ancestries. Additional molecular pathways for AAs were Vitamin D linked to depression and aging acceleration; and death signaling associated with cancer.

Conclusions

Effects of high diet-quality and high PA can counterbalance the influences of genetically high-risk PRSs on ASCVD risk, especially in AAs.

Atopic dermatitis and ocular allergy: common mechanisms and uncommon questions

PURPOSE OF REVIEW

Atopic dermatitis (AD) and ocular allergy aka allergic eye disease (AED) are two common conditions that often coexist in patients. However, molecular connections between these two conditions are incompletely understood. While common etiologic components including Th2 immune signaling have been suggested for AD and AED, the mechanism how current Th2-targetd therapies (dupilumab, tralokinumab) for AD can augment conjunctivitis is not well understood.

RECENT FINDINGS

Differentially regulated genes and pathways relevant for AD disease manifestation are known. In contrast, similar information is not yet available for AED, which could be largely addressed by emerging noninvasive ocular sampling techniques. Emerging evidence indicated a reduction in goblet cell number and mucin production in a subpopulation of AD patients with AD leading to adverse ocular outcomes, while other potential mechanisms could also be involved. Involvement of particular barrier function protein(s) in AED needs further investigation.

SUMMARY

Modern cytokine-targeted therapies for AD showed elevated risk for developing conjunctivitis. Recently developed noninvasive sampling techniques should be leveraged to identify AD endotypes associated with AED and with dupilumab-associated ocular outcomes.

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods

Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.

Airway transcriptome networks identify susceptibility to frequent asthma exacerbations in children

BACKGROUND

Frequent asthma exacerbators, defined as those experiencing more than 1 hospitalization in a year for an asthma exacerbation, represent an important subgroup of individuals with asthma. However, this group remains poorly defined and understudied in children.

OBJECTIVE

Our aim was to determine the molecular mechanisms underlying asthma pathogenesis and exacerbation frequency.

METHODS

We performed RNA sequencing of upper airway cells from both frequent and nonfrequent exacerbators enrolled in the Ohio Pediatric Asthma Repository.

RESULTS

Through molecular network analysis, we found that nonfrequent exacerbators display an increase in modules enriched for immune system processes, including type 2 inflammation and response to infection. In contrast, frequent exacerbators showed expression of modules enriched for nervous system processes, such as synaptic formation and axonal outgrowth.

CONCLUSION

These data suggest that the upper airway of frequent exacerbators undergoes peripheral nervous system remodeling, representing a novel mechanism underlying pediatric asthma exacerbation.

Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations

Polygenic risk scores (PRS) have improved in predictive performance supporting their use in clinical practice. Reduced predictive performance of PRS in diverse populations can exacerbate existing health disparities. The NHGRI-funded eMERGE Network is returning a PRS-based genome-informed risk assessment to 25,000 diverse adults and children. We assessed PRS performance, medical actionability, and potential clinical utility for 23 conditions. Standardized metrics were considered in the selection process with additional consideration given to strength of evidence in African and Hispanic populations. Ten conditions were selected with a range of high-risk thresholds: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. We developed a pipeline for clinical PRS implementation, used genetic ancestry to calibrate PRS mean and variance, created a framework for regulatory compliance, and developed a PRS clinical report. eMERGE's experience informs the infrastructure needed to implement PRS-based implementation in diverse clinical settings.

Analysis of ancestry-specific polygenic risk score and diet composition in type 2 diabetes

BACKGROUND

Carbohydrate and protein dietary proportions have been debated as to whether higher or lower levels are optimal for diabetes metabolic control.

OBJECTIVE

The objective of this study was to investigate the associations, interactions, and mediational relationships between a polygenic risk score (PRS), carbohydrate and protein intake, and physical activity level on type 2 diabetes (T2DM) by genetic ancestry, in European Americans and African Americans. A secondary objective examined the biological pathways associated with the PRS-linked genes and their relationships to dietary intake.

METHODS

We performed a cross-sectional study in 9,393 participants: 83.3% European Americans and 16.7% African Americans from 7-NHLBI Care studies obtained from the database of Genotypes and Phenotypes. The main outcome was T2DM. Carbohydrate and protein intake derived from food frequency questionnaires were calculated as percent calories. Data were analyzed using multivariable generalized estimation equation models to derive odds ratios (OR) and 95% confidence intervals (CI). Ancestry-specific PRSs were constructed using joint-effects Summary Best Linear Unbiased Estimation in the train dataset and replicated in the test dataset. Mediation analysis was performed using VanderWeele's method.

RESULTS

The PRS in the highest tertile was associated with higher risk of T2DM in European Americans (OR = 1.25;CI = 1.03-1.51) and African Americans (OR = 1.54;1.14-2.09). High carbohydrate and low protein intake had lower risks of T2DM when combined with the PRS after adjusting for covariates. In African Americans, high physical activity combined with the high PRS and high protein diet was associated with a 28% lower incidence of T2DM when compared to low physical activity. In mediational models in African Americans, the PRS-T2DM association was mediated by protein intake in the highest tertile by 55%. The top PRS tertile had the highest magnitude of risks with metabolic factors that were significantly associated with T2DM, especially in European Americans. We found metabolic pathways associated with the PRS-linked genes that were related to insulin/IGF and ketogenesis/ketolysis that can be activated by moderate physical activity and intermittent fasting for better T2DM control.

CONCLUSIONS

Clinicians may want to consider diets with a higher portion of carbohydrates than protein, especially when the burden of high-risk alleles is great in patients with T2DM. In addition, clinicians and other medical professionals may want to emphasize the addition of physical activity as part of treatment regimen especially for African Americans. Given the metabolic pathways we identified, moderate physical activity and intermittent fasting should be explored. Researchers may want to consider longitudinal or randomized clinical trials to determine the predictive ability of different dietary patterns to inhibit T2DM in the presence of obesity and an elevated PRS.

Multi-omics profiling approach in food allergy

The prevalence of food allergy (FA) among children is increasing, affecting nearly 8% of children, and FA is the most common cause of anaphylaxis and anaphylaxis-related emergency department visits in children. Importantly, FA is a complex, multi-system, multifactorial disease mediated by food-specific immunoglobulin E (IgE) and type 2 immune responses and involving environmental and genetic factors and gene-environment interactions. Early exposure to external and internal environmental factors largely influences the development of immune responses to allergens. Genetic factors and gene-environment interactions have established roles in the FA pathophysiology. To improve diagnosis and identification of FA therapeutic targets, high-throughput omics approaches have emerged and been applied over the past decades to screen for potential FA biomarkers, such as genes, transcripts, proteins, and metabolites. In this article, we provide an overview of the current status of FA omics studies, namely genomic, transcriptomic, epigenomic, proteomic, exposomic, and metabolomic. The current development of multi-omics integration of FA studies is also briefly discussed. As individual omics technologies only provide limited information on the multi-system biological processes of FA, integration of population-based multi-omics data and clinical data may lead to robust biomarker discovery that could translate into advances in disease management and clinical care and ultimately lead to precision medicine approaches.

Genome-wide admixture and association analysis identifies African ancestry-specific risk loci of eosinophilic esophagitis in African Americans

BACKGROUND

Eosinophilic esophagitis (EoE), a chronic allergic inflammatory disease, is linked to multiple genetic risk factors, but studies have focused on populations of European ancestry. Few studies have assessed Black or African American (AA) populations for loci involved in EoE susceptibility.

OBJECTIVE

We performed admixture mapping (AM) and genome-wide association study (GWAS) of EoE using participants from AA populations.

METHODS

We conducted AM and GWAS of EoE using 137 EoE cases and 1465 healthy controls from the AA population. Samples were genotyped using molecular evolutionary genetics analysis (MEGA). Genotype imputation was carried out with the Consortium on Asthma Among African-Ancestry Populations in the Americas (CAAPA) reference panel using the Michigan Imputation Server. Global and local ancestry inference was carried out, followed by fine mapping and RNA sequencing. After quality control filtering, over 6,000,000 variants were tested by logistic regression adjusted for sex, age, and global ancestry.

RESULTS

The global African ancestry proportion was found to be significantly lower among cases than controls (0.751 vs 0.786, P = .012). Case-only AM identified 3 significant loci (9p13.3, 12q24.22-23, and 15q11.2) associated with EoE, of which 12q24.22-23 and 9p13.3 were further replicated in the case-control analysis, with associations observed with African ancestry. Fine mapping and multiomic functional annotations prioritized the variants rs11068264 (FBXW8) and rs7307331 (VSIG10) at 12q24.23 and rs2297879 (ARHGEF39) at 9p13.3. GWAS identified 1 genome-wide significant locus at chromosome 1p22.3 (rs17131726, DDAH1) and 10 other suggestive loci. Most GWAS variants were low-frequency African ancestry-specific variants. RNA sequencing revealed that esophageal DDAH1 and VSIG10 were downregulated and ARHGEF39 upregulated among EoE cases.

CONCLUSIONS

GWAS and AM for EoE in AA revealed that African ancestry-specific genetic susceptibility loci exist at 1p22.3, 9p13.3, and 12q24.23, providing evidence of ancestry-specific inheritance of EoE. More independent genetic studies of different ancestries for EoE are needed.

Racial Fairness in Precision Medicine: Pediatric Asthma Prediction Algorithms

PURPOSE

Quantify and examine the racial fairness of two widely used childhood asthma predictive precision medicine algorithms: the asthma predictive index (API) and the pediatric asthma risk score (PARS).

DESIGN

Apply the API and PARS and evaluate model performance overall and when stratified by race.

SETTING

Cincinnati, OH, USA.

SUBJECTS

A prospective birth cohort of 590 children with clinically measured asthma diagnosis by age seven.

MEASURES

Model diagnostic criteria included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

ANALYSIS

Significant differences in model performance between Black and white children were considered to be present if the P-value associated with a t-test based on 100 bootstrap replications was less than .05.

RESULTS

Compared to predictions for white children, predictions for Black children using the PARS had a higher sensitivity (.88 vs .57), lower specificity (.55 vs .83), higher PPV (.42 vs .33), but a similar NPV (.93 vs .93). Within the API and compared to predictions for white children, predictions for Black children had a higher sensitivity (.63 vs .53), similar specificity (.81 vs .80), higher PPV (.54 vs .28), and lower NPV (.86 vs .92).

CONCLUSIONS

Overall, racial disparities in model diagnostic criteria were greatest for sensitivity and specificity in the PARS, but racial disparities existed in three of the four criteria for both the PARS and the API.

Urinary volatile organic compound metabolites and reduced lung function in U.S. adults

BACKGROUND

Volatile organic compounds (VOCs) are associated with adverse respiratory outcomes at high occupational exposures. However, whether exposure levels found in the general population have similar effects is unknown.

METHODS

We analyzed data on 1342 adult participants in the 2011-2012 National Health and Nutrition Examination Survey aged ≥18 years old who had urinary VOC metabolites and spirometry measurements available. Linear regression models adjusting for covariates were fitted to estimate the associations of VOC exposures levels and spirometry outcomes, while accounting for survey design and sampling weights to generate nationally representative estimates.

RESULTS

The urinary metabolites for xylene, acrylamide, acrolein, 1,3-butadiene, cyanide, toluene, 1-bromopropane, acrylonitrile, propylene oxide, styrene, ethylbenzene, and crotonaldehyde in our analysis were all detected in >75% of participants. Forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC) ratio % was lower with urinary metabolites of acrylamide (β: -2.65, 95% CI: -4.32, -0.98), acrylonitrile (β: -1.02, 95% CI: -2.01, -0.03), and styrene (β: -3.13, 95% CI: -5.35, -0.90). FEV1% predicted was lower with the urinary metabolites of acrolein (β: -7.77, 95% CI: -13.29, -2.25), acrylonitrile (β: -2.05, 95% CI: -3.77, -0.34), propylene oxide (β: -2.90, 95% CI: -5.50, -0.32), and styrene (β: -4.41, 95% CI: -6.97, -1.85).

CONCLUSIONS

This is the first study of a representative sample of the U.S. adult population to reveal associations of acrylonitrile, propylene oxide, and styrene urinary metabolites with reduced lung function at non-occupational exposures. Results also support previous evidence of acrylamide and acrolein's association with adverse respiratory outcomes.

Understanding racial disparities in childhood asthma using individual- and neighborhood-level risk factors

BACKGROUND

Racial disparities in childhood asthma outcomes result from a complex interplay of individual- and neighborhood-level factors.

OBJECTIVES

We sought to examine racial disparities in asthma-related emergency department (ED) visits between African American (AA) and European American (EA) children.

METHODS

This is a retrospective study of patients younger than 18 years who visited the ED at Cincinnati Children's for asthma from 2009 to 2018. The outcome was number of ED visits during a year. We assessed 11 social, economic, and environmental variables. Mediation and mixed-effects analyses were used to assess relationships between race, mediators, and number of ED visits.

RESULTS

A total of 31,114 children (46.1% AA, 53.9% EA) had 186,779 asthma-related ED visits. AA children had more visits per year than EA children (2.23 vs 2.15; P < .001). Medicaid insurance was associated with a 7% increase in rate of ED visits compared with commercial insurance (1.07; 95% CI, 1.03-1.1). Neighborhood socioeconomic deprivation was associated with an increased rate of ED visits in AA but not in EA children. Area-level particulate matter with diameter less than 2.5 μm, pollen, and outdoor mold were associated with an increased rate of ED visits for both AA and EA children (all P < .001). Associations between race and number of ED visits were mediated by insurance, area-level deprivation, particulate matter with diameter less than 2.5 μm, and outdoor mold (all P < .001), altogether accounting for 55% of the effect of race on ED visits. Race was not associated with number of ED visits (P = .796) after accounting for mediators.

CONCLUSIONS

Racial disparities in asthma-related ED visits are mediated by social, economic, and environmental factors, which may be amenable to interventions aimed at improving outcomes and eliminating inequities.

Multiancestral polygenic risk score for pediatric asthma

BACKGROUND

Asthma is the most common chronic condition in children and the third leading cause of hospitalization in pediatrics. The genome-wide association study catalog reports 140 studies with genome-wide significance. A polygenic risk score (PRS) with predictive value across ancestries has not been evaluated for this important trait.

OBJECTIVES

This study aimed to train and validate a PRS relying on genetic determinants for asthma to provide predictions for disease occurrence in pediatric cohorts of diverse ancestries.

METHODS

This study applied a Bayesian regression framework method using the Trans-National Asthma Genetic Consortium genome-wide association study summary statistics to derive a multiancestral PRS score, used one Electronic Medical Records and Genomics (eMERGE) cohort as a training set, used a second independent eMERGE cohort to validate the score, and used the UK Biobank data to replicate the findings. A phenome-wide association study was performed using the PRS to identify shared genetic etiology with other phenotypes.

RESULTS

The multiancestral asthma PRS was associated with asthma in the 2 pediatric validation datasets. Overall, the multiancestral asthma PRS has an area under the curve (AUC) of 0.70 (95% CI, 0.69-0.72) in the pediatric validation 1 and AUC of 0.66 (0.65-0.66) in the pediatric validation 2 datasets. We found significant discrimination across pediatric subcohorts of European (AUC, 95% CI, 0.60 and 0.66), African (AUC, 95% CI, 0.61 and 0.66), admixed American (AUC, 0.64 and 0.70), Southeast Asian (AUC, 0.65), and East Asian (AUC, 0.73) ancestry. Pediatric participants with the top 5% PRS had 2.80 to 5.82 increased odds of asthma compared to the bottom 5% across the training, validation 1, and validation 2 cohorts when adjusted for ancestry. Phenome-wide association study analysis confirmed the strong association of the identified PRS with asthma (odds ratio, 2.71, PFDR = 3.71 × 10-65) and related phenotypes.

CONCLUSIONS

A multiancestral PRS for asthma based on Bayesian posterior genomic effect sizes identifies increased odds of pediatric asthma.

Comorbidities in childhood-onset and adult-onset asthma

BACKGROUND

Age of asthma onset has emerged as an important determinant of asthma phenotypes; however, the comorbidities that predominate in either childhood- or adult-onset asthma are not known.

OBJECTIVE

To identify comorbidities associated with adult-onset asthma vs childhood-onset asthma and with age of asthma diagnosis.

METHODS

We analyzed data on 27,437 adult participants in the National Health and Nutrition Examination Surveys conducted from 2001 to 2018. Logistic regression adjusted for covariates was used to identify comorbidities associated with the asthma phenotypes and age of asthma diagnosis.

RESULTS

Approximately 12.6% of participants were ever diagnosed with asthma; the prevalence of childhood-onset (before 18 years old) and adult-onset (≥ 18 years old) current asthma was 2.7% and 5.5%, respectively. After adjustment for covariates including age, adult-onset asthma was associated with higher odds of obesity (odds ratio [OR], 1.46; 95% confidence interval [CI], 1.09-1.96), hypercholesterolemia (OR, 1.67; 95% CI, 1.08-2.56), borderline high serum triglycerides (OR, 1.78; 95% CI, 1.17-2.71), and osteoarthritis (OR, 1.52; 95% CI, 1.04-2.20) than was childhood-onset asthma. Older age of asthma diagnosis (per 5-year increase) was also associated with higher odds of diabetes (OR, 1.04; 95% CI, 1.00-1.07) and hypertension (OR, 1.05; 95% CI, 1.02-1.07), whereas younger age of asthma diagnosis was associated with higher odds of chronic obstructive pulmonary disease (OR, 1.12; 95% CI, 1.04-1.19).

CONCLUSION

Age- and covariates-adjusted prevalence of obesity, dyslipidemia, arthritis, diabetes, and hypertension is higher in adult-onset asthma than in childhood-onset asthma, and with older age of asthma diagnosis. Conversely, the prevalence of chronic obstructive pulmonary disease increases with younger age of asthma diagnosis.

Cardiometabolic genomics and pharmacogenomics investigations in Filipino Americans: Steps towards precision health and reducing health disparities

Background

Filipino Americans (FAs) are the third-largest Asian American subgroup in the United States (US). Some studies showed that FAs experience more cardiometabolic diseases (CMDs) than other Asian subgroups and non-Hispanic Whites. The increased prevalence of CMD observed in FAs could be due to genetics and social/dietary lifestyles. While FAs are ascribed as an Asian group, they have higher burdens of CMD, and adverse social determinants of health compared to other Asian subgroups. Therefore, studies to elucidate how FAs might develop CMD and respond to medications used to manage CMD are warranted. The ultimate goals of this study are to identify potential mechanisms for reducing CMD burden in FAs and to optimize therapeutic drug selection. Collectively, these investigations could reduce the cardiovascular health disparities among FAs.

Rationale and design

This is a cross-sectional epidemiological design to enroll 300 self-identified Filipino age 18 yrs. or older without a history of cancer and/or organ transplant from Virginia, Washington DC, and Maryland. Once consented, a health questionnaire and disease checklist are administered to participants, and anthropometric data and other vital signs are collected. When accessible, we collect blood samples to measure basic blood biochemistry, lipids, kidney, and liver functions. We also extract DNA from the blood or saliva for genetic and pharmacogenetic analyses. CMD prevalence in FAs will be compared to the US population. Finally, we will conduct multivariate analyses to ascertain the role of genetic and non-genetic factors in developing CMD in FAs. Virginia Commonwealth University IRB approved all study materials (Protocol HM20018500).

Summary

This is the first community-based study to involve FAs in genomics research. The study is actively recruiting participants. Participant enrollment is ongoing. At the time of this publication, the study has enrolled 97 participants. This ongoing study is expected to inform future research to reduce cardiovascular health disparities among FAs.

A roadmap for multi-omics data integration using deep learning

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

Skin depletion of Kif3a resembles the pediatric atopic dermatitis transcriptome profile

Skin deficiency of kinesin family member 3A causes disrupted skin barrier function and promotes development of atopic dermatitis (AD). It is not known how well Kif3aK14∆/∆ mice approximate the human AD transcriptome. To determine the skin transcriptomic profile of Kif3aK14∆/∆ mice and compare it with other murine AD models and human AD, we performed RNA-seq of full-thickness skin and epidermis from 3- and 8-week-old Kif3aK14∆/∆ mice and compared the differentially expressed genes (DEGs) with transcriptomic datasets from mite-induced NC/Nga, flaky tail (Tmem79ma/ma Flgft/ft), and filaggrin-mutant (Flgft/ft) mice, as well as human AD transcriptome datasets including meta-analysis derived atopic dermatitis [MADAD] and the pediatric atopic dermatitis [PAD]. We then interrogated the Kif3aK14∆/∆ skin DEGs using the LINCS-L1000 database to identify potential novel drug targets for AD treatment. We identified 471 and 901 DEGs at 3 and 8 weeks of age, respectively, in the absence of Kif3a. Kif3aK14∆/∆ mice had 3.5-4.5 times more DEGs that overlapped with human AD DEGs compared to the flaky tail and Flgft/ft mice. Further, 55%, 85% and 75% of 8-week Kif3aK14∆/∆ DEGs overlapped with the MADAD and PAD non-lesional and lesional gene lists, respectively. Kif3aK14∆/∆ mice spontaneously develop a human AD-like gene signature, which better represents pediatric non-lesional skin compared to other mouse models including flaky tail, Flgft/ft and NC/Nga. Thus, Kif3aK14∆/∆ mice may model pediatric skin that is a precursor to the development of lesions and inflammation, and hence may be a useful model to study AD pathogenesis.

A Roadmap for Building Data Science Capacity for Health Discovery and Innovation in Africa

Technological advances now make it possible to generate diverse, complex and varying sizes of data in a wide range of applications from business to engineering to medicine. In the health sciences, in particular, data are being produced at an unprecedented rate across the full spectrum of scientific inquiry spanning basic biology, clinical medicine, public health and health care systems. Leveraging these data can accelerate scientific advances, health discovery and innovations. However, data are just the raw material required to generate new knowledge, not knowledge on its own, as a pile of bricks would not be mistaken for a building. In order to solve complex scientific problems, appropriate methods, tools and technologies must be integrated with domain knowledge expertise to generate and analyze big data. This integrated interdisciplinary approach is what has become to be widely known as data science. Although the discipline of data science has been rapidly evolving over the past couple of decades in resource-rich countries, the situation is bleak in resource-limited settings such as most countries in Africa primarily due to lack of well-trained data scientists. In this paper, we highlight a roadmap for building capacity in health data science in Africa to help spur health discovery and innovation, and propose a sustainable potential solution consisting of three key activities: a graduate-level training, faculty development, and stakeholder engagement. We also outline potential challenges and mitigating strategies.

Achieving Precision Medicine in Allergic Disease: Progress and Challenges

Allergic diseases (atopic dermatitis, food allergy, eosinophilic esophagitis, asthma and allergic rhinitis), perhaps more than many other traditionally grouped disorders, share several overlapping inflammatory pathways and risk factors, though we are still beginning to understand how the relevant patient and environmental factors uniquely shape each disease. Precision medicine is the concept of applying multiple levels of patient-specific data to tailor diagnoses and available treatments to the individual; ideally, a patient receives the right intervention at the right time, in order to maximize effectiveness but minimize morbidity, mortality and cost. While precision medicine in allergy is in its infancy, the recent success of biologics, development of tools focused on large data set integration and improved sampling methods are encouraging and demonstrates the utility of refining our understanding of allergic endotypes to improve therapies. Some of the biggest challenges to achieving precision medicine in allergy are characterizing allergic endotypes, understanding allergic multimorbidity relationships, contextualizing the impact of environmental exposures (the “exposome”) and ancestry/genetic risks, achieving actionable multi-omics integration, and using this information to develop adequately powered patient cohorts and refined clinical trials. In this paper, we highlight several recently developed tools and methods showing promise to realize the aspirational potential of precision medicine in allergic disease. We also outline current challenges, including exposome sampling and building the “knowledge network” with multi-omics integration.

Air pollution and the pandemic: Long-term PM2.5 exposure and disease severity in COVID-19 patients

Background and objective

Ecological studies have suggested an association between exposure to particulate matter ≤2.5 μm (PM_2.5) and coronavirus disease 2019 (COVID‐19) severity. However, these findings are yet to be validated in individual‐level studies. We aimed to determine the association of long‐term PM_2.5 exposure with hospitalization among individual patients infected with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2).

Methods

We estimated the 10‐year (2009–2018) PM_2.5 exposure at the residential zip code of COVID‐19 patients diagnosed at the University of Cincinnati healthcare system between 13 March 2020 and 30 September 2020. Logistic regression was used to determine the odds ratio (OR) and 95% CI for COVID‐19 hospitalizations associated with PM_2.5, adjusting for socioeconomic characteristics and comorbidities.

Results

Among the 14,783 COVID‐19 patients included in our study, 13.6% were hospitalized; the geometric mean (SD) PM_2.5 was 10.48 (1.12) μg/m³. In adjusted analysis, 1 μg/m³ increase in 10‐year annual average PM_2.5 was associated with 18% higher hospitalization (OR: 1.18, 95% CI: 1.11–1.26). Likewise, 1 μg/m³ increase in PM_2.5 estimated for the year 2018 was associated with 14% higher hospitalization (OR: 1.14, 95% CI: 1.08–1.21).

Conclusion

Long‐term PM_2.5 exposure is associated with increased hospitalization in COVID‐19. Therefore, more stringent COVID‐19 prevention measures may be needed in areas with higher PM_2.5 exposure to reduce the disease morbidity and healthcare burden.

Genetic ancestry differences in pediatric asthma readmission are mediated by socioenvironmental factors

BACKGROUND

Social and financial hardships, combined with disease managment and environmental factors explain approximately 80% of the observed disparity in asthma-related readmissions between Black and White children.

OBJECTIVE

We sought to determine whether asthma-related readmissions differed by degree of African ancestry and the extent to which such an association would also be explained by socioenvironmental risk factors.

METHODS

This study used data from a prospective cohort study of 695 Black and White children aged 1 to 16 years with an asthma-related admission. The primary outcome was a similar readmission within 12 months. Each subject's African ancestry was determined by single nucleotide polymorphisms on a continuous scale ranging from 0 to 1 (0 = no African ancestry; 1 = 100% African ancestry). We also assessed 37 social, environmental, and clinical variables that we clustered into 6 domains (for example, hardship, disease management). Survival and mediation analyses were conducted.

RESULTS

A total of 134 children (19.3%) were readmitted within 12 months. Higher African ancestry was associated with asthma readmission (odds ratio 1.11, 95% confidence interval 1.05-1.18 for every 10% increase in African ancestry) with adjustment for age and gender. The association between African ancestry and readmission was mediated by hardship (sβ = 3.42, P < .001) and disease management (sβ = 0.046, P = .001), accounting for >50% of African ancestry's effect on readmission. African ancestry was no longer significantly associated with readmission (sβ = 0.035, P = .388) after accounting for these mediators.

CONCLUSIONS

African ancestry was strongly associated with readmission, and the association was mediated by family hardship and disease management. These results are consistent with the notion that asthma-related racial disparities are driven by factors like structural racism and social adversity.

Ancestry specific associations of a genetic risk score, dietary patterns and metabolic syndrome: a longitudinal ARIC study

Background

Associations have been observed among genetic variants, dietary patterns, and metabolic syndrome (MetS). A gap in knowledge is whether a genetic risk score (GRS) and dietary patterns interact to increase MetS risk among African Americans. We investigated whether MetS risk was influenced by interaction between a GRS and dietary patterns among Whites and African Americans. A secondary aim examined if molecular genetic clusterings differed by racial ancestry.

Methods

We used longitudinal data over 4-visits (1987–1998) that included 10,681 participants aged 45–64y at baseline from the Atherosclerosis Risk in Communities study (8451 Whites and 2230 African Americans). We constructed a simple-count GRS as the linear weighted sum of high-risk alleles (0, 1, 2) from cardiovascular disease polymorphisms from the genome-wide association studies catalog associated with MetS risk. Three dietary patterns were determined by factor analysis of food frequency questionnaire data: Western, healthy, and high-fat dairy. MetS was defined according to the 2016 National Cholesterol Education Program Adult Treatment Panel III criteria but used 2017 American Heart Association/American College of Cardiology criteria for elevated blood pressure. Analyses included generalized linear model risk ratios (RR), 95% confidence intervals (CI), and Bonferroni correction for multiple testing.

Results

The Western dietary pattern was associated with higher risk for MetS across increasing GRS tertiles among Whites (p < 0.017). The high-fat dairy pattern was protective against MetS, but its impact was most effective in the lowest GRS tertile in Whites (RR = 0.62; CI: 0.52–0.74) and African Americans (RR = 0.67; CI: 0.49–0.91). Among each racial group within GRS tertiles, the Western dietary pattern was associated with development and cycling of MetS status between visits, and the high-fat dairy pattern with being free from MetS (p < 0.017). The healthy dietary pattern was associated with higher risk of MetS among African Americans which may be explained by higher sucrose intake (p < 0.0001). Fewer genes, but more metabolic pathways for obesity, body fat distribution, and lipid and carbohydrate metabolism were identified in African Americans than Whites. Some polymorphisms were linked to the Western and high-fat dairy patterns.

Conclusion

The influence of dietary patterns on MetS risk appears to differ by genetic predisposition and racial ancestry.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-00961-8.

Identification of Vulnerable Populations and Areas at Higher Risk of COVID-19-Related Mortality during the Early Stage of the Epidemic in the United States

We characterized vulnerable populations located in areas at higher risk of COVID-19-related mortality and low critical healthcare capacity during the early stage of the epidemic in the United States. We analyze data obtained from a Johns Hopkins University COVID-19 database to assess the county-level spatial variation of COVID-19-related mortality risk during the early stage of the epidemic in relation to health determinants and health infrastructure. Overall, we identified highly populated and polluted areas, regional air hub areas, race minorities (non-white population), and Hispanic or Latino population with an increased risk of COVID-19-related death during the first phase of the epidemic. The 10 highest COVID-19 mortality risk areas in highly populated counties had on average a lower proportion of white population (48.0%) and higher proportions of black population (18.7%) and other races (33.3%) compared to the national averages of 83.0%, 9.1%, and 7.9%, respectively. The Hispanic and Latino population proportion was higher in these 10 counties (29.3%, compared to the national average of 9.3%). Counties with major air hubs had a 31% increase in mortality risk compared to counties with no airport connectivity. Sixty-eight percent of the counties with high COVID-19-related mortality risk also had lower critical care capacity than the national average. The disparity in health and environmental risk factors might have exacerbated the COVID-19-related mortality risk in vulnerable groups during the early stage of the epidemic.

Genetics of Food Allergy

The risk factors for food allergy (FA) include both genetic variants and environmental factors. Advances using both candidate-gene association studies and genome-wide approaches have led to the identification of FA-associated genes involved in immune responses and skin barrier functions. Epigenetic changes have also been associated with the risk of FA. In this chapter, we outline current understanding of the genetics, epigenetics and the interplay with environmental risk factors associated with FA. Future studies of gene-environment interactions, gene-gene interactions, and multi-omics integration may help shed light on the mechanisms of FA, and lead to improved diagnostic and treatment strategies.

Long-term exposure to fine particulate matter and hospitalization in COVID-19 patients

BACKGROUND

Ecological evidence suggests that exposure to air pollution affects coronavirus disease 2019 (COVID-19) outcomes. However, no individual-level study has confirmed the association to date.

METHODS

We identified COVID-19 patients diagnosed at the University of Cincinnati hospitals and clinics and estimated particulate matter ≤2.5 μm (PM_2.5) exposure over a 10-year period (2008-2017) at their residential zip codes. We used logistic regression to evaluate the association between PM_2.5 exposure and hospitalizations for COVID-19, adjusting for socioeconomic characteristics and comorbidities.

RESULTS

Among the 1128 patients included in our study, the mean (standard deviation) PM_2.5 was 11.34 (0.70) μg/m³ for the 10-year average exposure and 13.83 (1.03) μg/m³ for the 10-year maximal exposures. The association between long-term PM_2.5 exposure and hospitalization for COVID-19 was contingent upon having pre-existing asthma or chronic obstructive pulmonary (COPD) (P_interaction = 0.030 for average PM_2.5 and P_interaction = 0.001 for maximal PM_2.5). In COVID-19 patients with asthma or COPD, the odds of hospitalization were 62% higher with 1 μg/m³ increment in 10-year average PM_2.5 (odds ratio [OR]: 1.62, 95% confidence interval [CI]: 1.00-2.64) and 65% higher with 1 μg/m³ increase in 10-year maximal PM_2.5 levels (OR: 1.65, 95% CI: 1.16-2.35). However, among COVID-19 patients without asthma or COPD, PM_2.5 exposure was not associated with higher hospitalizations (OR: 0.84, 95% CI: 0.65-1.09 for average PM_2.5 and OR: 0.78, 95% CI: 0.65-0.95 for maximal PM_2.5).

CONCLUSIONS

Long-term exposure to PM_2.5 is associated with higher odds of hospitalization in COVID-19 patients with pre-existing asthma or COPD.

The social, economic, political, and genetic value of race and ethnicity in 2020

Disparities across racial and ethnic groups are present for a range of health outcomes. In this opinion piece, we consider the origin of racial and ethnic groupings, a history that highlights the sociopolitical nature of these terms. Indeed, the terms race and ethnicity exist purely as social constructs and must not be used interchangeably with genetic ancestry. There is no scientific evidence that the groups we traditionally call "races/ethnicities" have distinct, unifying biological or genetic basis. Such a focus runs the risk of compounding equity gaps and perpetuating erroneous conclusions. That said, we suggest that the terms race and ethnicity continue to have purpose as lenses through which to quantify and then close racial and ethnic disparities. Understanding the root cause of such health disparities-namely, longstanding racism and ethnocentrism-could promote interventions and policies poised to equitably improve population health.

Identification of Vulnerable Populations and Areas at Higher Risk of COVID-19 Related Mortality in the U.S

Background

The role of health-related disparities including sociodemographic, environmental, and critical care capacity in the COVID-19 pandemic are poorly understood. In the present study, we characterized vulnerable populations located in areas at higher risk of COVID-19 related mortality and low critical healthcare capacity in the U.S.

Methods

Using Bayesian multilevel analysis and small area disease risk mapping, we assessed the spatial variation of COVID-19 related mortality risk for the U.S. in relation with healthcare disparities including race, ethnicity, poverty, air quality, and critical healthcare capacity.

Results

Overall, highly populated, regional air hub areas, and minorities had an increased risk of COVID-19 related mortality. We found that with an increase of only 1 ug/m3 in long term PM2.5 exposure, the COVID-19 mortality rate increased by 13%. Counties with major air hubs had 18% increase in COVID-19 related death compared to counties with no airport connectivity. Sixty-eight percent of the counties with high COVID-19 related mortality risk were also counties with lower critical care capacity than national average. These counties were primary located at the North- and South-Eastern regions of the country.

Conclusion

The existing disparity in health and environmental risk factors that exacerbate the COVID-19 related mortality, along with the regional healthcare capacity, determine the vulnerability of populations to COVID-19 related mortality. The results from this study can be used to guide the development of strategies for the identification and targeting preventive strategies in vulnerable populations with a higher proportion of minority groups living in areas with poor air quality and low healthcare capacity.

Correction to: Comprehensive functional annotation of susceptibility variants associated with asthma

In the original article published, the "p" value in the Fig. 5 legend is incorrectly presented as *p < 0.50. The correct p value is *p < 0.050.

MI-MAAP: marker informativeness for multi-ancestry admixed populations

BACKGROUND

Admixed populations arise when two or more previously isolated populations interbreed. A powerful approach to addressing the genetic complexity in admixed populations is to infer ancestry. Ancestry inference including the proportion of an individual's genome coming from each population and its ancestral origin along the chromosome of an admixed population requires the use of ancestry informative markers (AIMs) from reference ancestral populations. AIMs exhibit substantial differences in allele frequency between ancestral populations. Given the huge amount of human genetic variation data available from diverse populations, a computationally feasible and cost-effective approach is becoming increasingly important to extract or filter AIMs with the maximum information content for ancestry inference, admixture mapping, forensic applications, and detecting genomic regions that have been under recent selection.

RESULTS

To address this gap, we present MI-MAAP, an easy-to-use web-based bioinformatics tool designed to prioritize informative markers for multi-ancestry admixed populations by utilizing feature selection methods and multiple genomics resources including 1000 Genomes Project and Human Genome Diversity Project. Specifically, this tool implements a novel allele frequency-based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genotype-based methods such as Principal Component Analysis (PCA), Support Vector Machine (SVM), and Random Forest (RF). We demonstrated that MI-MAAP is a useful tool in prioritizing informative markers and accurately classifying ancestral populations. LEI is an efficient feature selection strategy to retrieve ancestry informative variants with different allele frequency/selection pressure among (or between) ancestries without requiring computationally expensive individual-level genotype data.

CONCLUSIONS

MI-MAAP has a user-friendly interface which provides researchers an easy and fast way to filter and identify AIMs. MI-MAAP can be accessed at https://research.cchmc.org/mershalab/MI-MAAP/login/.

Joint variable selection and network modeling for detecting eQTLs

In this study, we conduct a comparison of three most recent statistical methods for joint variable selection and covariance estimation with application of detecting expression quantitative trait loci (eQTL) and gene network estimation, and introduce a new hierarchical Bayesian method to be included in the comparison. Unlike the traditional univariate regression approach in eQTL, all four methods correlate phenotypes and genotypes by multivariate regression models that incorporate the dependence information among phenotypes, and use Bayesian multiplicity adjustment to avoid multiple testing burdens raised by traditional multiple testing correction methods. We presented the performance of three methods (MSSL - Multivariate Spike and Slab Lasso, SSUR - Sparse Seemingly Unrelated Bayesian Regression, and OBFBF - Objective Bayes Fractional Bayes Factor), along with the proposed, JDAG (Joint estimation via a Gaussian Directed Acyclic Graph model) method through simulation experiments, and publicly available HapMap real data, taking asthma as an example. Compared with existing methods, JDAG identified networks with higher sensitivity and specificity under row-wise sparse settings. JDAG requires less execution in small-to-moderate dimensions, but is not currently applicable to high dimensional data. The eQTL analysis in asthma data showed a number of known gene regulations such as STARD3, IKZF3 and PGAP3, all reported in asthma studies. The code of the proposed method is freely available at GitHub (https://github.com/xuan-cao/Joint-estimation-for-eQTL).

The effect of age, sex, race/ethnicity, health insurance, and food specific serum immunoglobulin E on outcomes of oral food challenges

Background

Although oral food challenge (OFC) is an important clinical procedure for diagnosing food allergy, there is a paucity of literature on the outcome of the procedure and specifically the patients on whom the procedure is performed from the aspects of their age, sex, race/ethnicity, health insurance status, and serum specific IgE to the food tested.

Objective

We aimed to review results of OFC and determine the impact of patient age, sex, race/ethnicity, insurance status, private or public, and food specific serum IgE on the outcome of OFC.

Methods

A retrospective chart review was performed of patients undergoing OFCs at a children's hospital outpatient allergy clinic over a two-year period. The outcome of OFC was allergic or non-allergic based on determination and documentation by the treating physician. A logistic regression model was built to determine the association between the OFC outcomes, age, and symptoms at the time of OFC. A Chi-square analysis was performed to check for any significant relationship between the OFC outcome and age when stratified by insurance status.

Results

Five hundred and eight children underwent 641 OFCs. Twenty nine percent of OFCs had an allergic outcome with the most commonly challenged foods being peanuts, eggs, and milk. Patient age and gender, when stratified by insurance status, did not have a significant effect on OFC outcomes. Serum IgE to peanuts and egg was significantly different between allergic OFC and non-allergic outcome. Vomiting and urticaria/angioedema correlated with an allergic OFC outcome.

Conclusion

OFCs confirm the food allergy diagnosis in about one-third of patients tested, and they should continue to be used when possible for an accurate diagnosis. Age, sex, and insurance status do not have a significant association with the outcome of OFC and cannot be added as predictive factors.