Preschool impulse oscillometry predicts active asthma and impaired lung function at school age.

BACKGROUND: Asthmatic symptoms often start during early childhood. Impulse oscillometry (IOS) is feasible in preschool children who may be unable to reliably perform spirometry measurements. OBJECTIVE: We sought to evaluate the use of IOS in a multicenter, multiethnic high-risk asthma cohort titled the Vitamin D Antenatal Asthma Reduction Trial. METHODS: The trial recruited pregnant women whose children were followed from birth to age 8 years. Lung function was assessed with IOS at ages 4, 5, and 6 years and spirometry at ages 5, 6, 7, and 8 years. Asthma status, respiratory symptoms, and medication use were assessed with repeated questionnaires from birth to age 8 years. RESULTS: In total, 220 children were included in this secondary analysis. Recent respiratory symptoms and short-acting ß2-agonist use were associated with increased respiratory resistance at 5 Hz at age 4 years (ß = 2.6; 95% CI, 1.0 to 4.4; P = .002 and ß = 3.4; 95% CI, 0.7 to 6.2; P = .015, respectively). Increased respiratory resistance at 5 Hz at age 4 years was also associated with decreased lung function from ages 5 to 8 years (ß = -0.3; 95% CI, -0.5 to -0.1; P < .001 for FEV1 at 8 years) and active asthma at age 8 years (ß = 2.0; 95% CI, 0.2 to 3.8; P = .029). CONCLUSIONS: Increased respiratory resistance in preschool IOS is associated with frequent respiratory symptoms as well as school-age asthma and lung function impairment. Our findings suggest that IOS may serve as a potential objective measure for early identification of children who are at high risk of respiratory morbidity.

Urinary eicosanoid levels in early life and risk of atopic disease in childhood.

BACKGROUND: Eicosanoids are lipid mediators including thromboxanes (TXs), prostaglandins (PGs), and leukotrienes with a pathophysiological role in established atopic disease. However, their role in the inception of disease is unclear. This study aimed to investigate the association between urinary eicosanoids in early life and development of atopic disease. METHODS: This study quantified the levels of 21 eicosanoids in urine from children from the COPSAC2010 (Copenhagen Prospective Studies on Asthma in Childhood 2010) (age 1 year, n = 450) and VDAART (Vitamin D Antenatal Asthma Reduction Trial) (age 3 years, n = 575) mother-child cohorts and analyzed the associations with development of wheeze/asthma, atopic dermatitis, and biomarkers of type-2 inflammation, applying false discovery rate of 5% (FDR5%) multiple testing correction. RESULTS: In both cohorts, analyses adjusted for environmental determinants showed that higher TXA2 eicosanoids in early life were associated with increased risk of developing atopic dermatitis (P < FDR5%) and type-2 inflammation (P < .05). In VDAART, lower PGE2 and PGI2 eicosanoids and higher isoprostanes were also associated with increased risk of atopic dermatitis (P < FDR5%). For wheeze/asthma, analyses in COPSAC2010 showed that lower isoprostanes and PGF2 eicosanoids and higher PGD2 eicosanoids at age 1 year associated with an increased risk at age 1-10 years (P < .05), whereas analyses in VDAART showed that lower PGE2 and higher TXA2 eicosanoids at age 3 years associated with an increased risk at 6 years (P < FDR5%). CONCLUSIONS: This study suggests that early life perturbations in the eicosanoid metabolism are present before the onset of atopic disease in childhood, which provides pathophysiological insight in the inception of atopic diseases.

Fast computation of the eigensystem of genomic similarity matrices.

The computation of a similarity measure for genomic data is a standard tool in computational genetics. The principal components of such matrices are routinely used to correct for biases due to confounding by population stratification, for instance in linear regressions. However, the calculation of both a similarity matrix and its singular value decomposition (SVD) are computationally intensive. The contribution of this article is threefold. First, we demonstrate that the calculation of three matrices (called the covariance matrix, the weighted Jaccard matrix, and the genomic relationship matrix) can be reformulated in a unified way which allows for the application of a randomized SVD algorithm, which is faster than the traditional computation. The fast SVD algorithm we present is adapted from an existing randomized SVD algorithm and ensures that all computations are carried out in sparse matrix algebra. The algorithm only assumes that row-wise and column-wise subtraction and multiplication of a vector with a sparse matrix is available, an operation that is efficiently implemented in common sparse matrix packages. An exception is the so-called Jaccard matrix, which does not have a structure applicable for the fast SVD algorithm. Second, an approximate Jaccard matrix is introduced to which the fast SVD computation is applicable. Third, we establish guaranteed theoretical bounds on the accuracy (in [Formula: see text] norm and angle) between the principal components of the Jaccard matrix and the ones of our proposed approximation, thus putting the proposed Jaccard approximation on a solid mathematical foundation, and derive the theoretical runtime of our algorithm. We illustrate that the approximation error is low in practice and empirically verify the theoretical runtime scalings on both simulated data and data of the 1000 Genome Project.

Gasdermin B, an asthma-susceptibility gene, promotes MAVS-TBK1 signalling and airway inflammation.

RATIONALE: Respiratory virus-induced inflammation is the leading cause of asthma exacerbation, frequently accompanied by induction of interferon-stimulated genes (ISGs). How asthma-susceptibility genes modulate cellular response upon viral infection by fine-tuning ISG induction and subsequent airway inflammation in genetically susceptible asthma patients remains largely unknown. OBJECTIVES: To decipher the functions of gasdermin B (encoded by GSDMB) in respiratory virus-induced lung inflammation. METHODS: In two independent cohorts, we analysed expression correlation between GSDMB and ISG s. In human bronchial epithelial cell line or primary bronchial epithelial cells, we generated GSDMB-overexpressing and GSDMB-deficient cells. A series of quantitative PCR, ELISA and co-immunoprecipitation assays were performed to determine the function and mechanism of GSDMB for ISG induction. We also generated a novel transgenic mouse line with inducible expression of human unique GSDMB gene in airway epithelial cells and infected the mice with respiratory syncytial virus to determine the role of GSDMB in respiratory syncytial virus-induced lung inflammation in vivo. RESULTS: GSDMB is one of the most significant asthma-susceptibility genes at 17q21 and acts as a novel RNA sensor, promoting mitochondrial antiviral-signalling protein (MAVS)-TANK binding kinase 1 (TBK1) signalling and subsequent inflammation. In airway epithelium, GSDMB is induced by respiratory viral infections. Expression of GSDMB and ISGs significantly correlated in respiratory epithelium from two independent asthma cohorts. Notably, inducible expression of human GSDMB in mouse airway epithelium led to enhanced ISGs induction and increased airway inflammation with mucus hypersecretion upon respiratory syncytial virus infection. CONCLUSIONS: GSDMB promotes ISGs expression and airway inflammation upon respiratory virus infection, thereby conferring asthma risk in risk allele carriers.

Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium.

INTRODUCTION: Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility. OBJECTIVE: The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework. METHODS: We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure. RESULTS: Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction. CONCLUSIONS: Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Circulating miRNAs associate with historical childhood asthma hospitalization in different serum vitamin D groups.

BACKGROUND: Vitamin D may help to alleviate asthma exacerbation because of its anti-inflammation effect, but the evidence is inconsistent in childhood asthma. MiRNAs are important mediators in asthma pathogenesis and also excellent non-invasive biomarkers. We hypothesized that circulating miRNAs are associated with asthma exacerbation and modified by vitamin D levels. METHODS: We sequenced baseline serum miRNAs from 461 participants in the Childhood Asthma Management Program (CAMP). Logistic regression was used to associate miRNA expression with asthma exacerbation through interaction analysis first and then stratified by vitamin D insufficient and sufficient groups. Microarray from lymphoblastoid B-cells (LCLs) treated by vitamin D or sham of 43 subjects in CAMP were used for validation in vitro. The function of miRNAs was associated with gene modules by weighted gene co-expression network analysis (WGCNA). RESULTS: We identified eleven miRNAs associated with asthma exacerbation with vitamin D effect modification. Of which, five were significant in vitamin D insufficient group and nine were significant in vitamin D sufficient group. Six miRNAs, including hsa-miR-143-3p, hsa-miR-192-5p, hsa-miR-151a-5p, hsa-miR-24-3p, hsa-miR-22-3p and hsa-miR-451a were significantly associated with gene modules of immune-related functions, implying miRNAs may mediate vitamin D effect on asthma exacerbation through immune pathways. In addition, hsa-miR-143-3p and hsa-miR-451a are potential predictors of childhood asthma exacerbation at different vitamin D levels. CONCLUSIONS: miRNAs are potential mediators of asthma exacerbation and their effects are directly impacted by vitamin D levels.

Lung function trajectories in a cohort of patients with moderate-to-severe asthma on mepolizumab, omalizumab, or dupilumab.

BACKGROUND: Lung function is an independent predictor of mortality. We evaluated the lung function trajectories of a cohort of patients with asthma receiving biologic therapy. METHODS: We identified 229 monoclonal antibody-naïve adult patients with moderate-to-severe asthma who initiated omalizumab, mepolizumab, or dupilumab between 2010 and 2022 in a large healthcare system in Boston, MA. Generalized additive mixed models were used to estimate the lung function trajectories during the 156 weeks following biologic initiation. Response was defined as an improvement in FEV1 or a decrease of ≤0.5% per year. The Kaplan-Meier estimator was used to assess time to no additional improvement in FEV1 in responders. All models were adjusted for age, sex, body mass index, smoking status, baseline exacerbation rate, and baseline blood eosinophil count. RESULTS: Eighty-eight patients initiated mepolizumab, 76 omalizumab, and 65 dupilumab. Baseline eosinophil count was highest in the mepolizumab group (405 cells/mcL) and lowest for omalizumab (250 cells/mcL). Both FEV1 and FVC improved in the mepolizumab group (FEV1 + 20 mL/year; FVC +43 mL/year). For omalizumab, there was an initial improvement in the first year followed by decline with an overall FEV1 loss of -44 mL/year and FVC -32 mL/year. For dupilumab, both FEV1 (+61 mL/year) and FVC (+74 mL/year) improved over time. Fifty percent of the mepolizumab group, 58% omalizumab, and 72% of dupilumab were responders. The median time to no additional FEV1 improvement in responders was 24 weeks for omalizumab, 48 weeks for mepolizumab, and 57 weeks for dupilumab. CONCLUSION: In this clinical cohort, mepolizumab, omalizumab, and dupilumab had beneficial effects on FEV1 and FVC with distinct post-initiation trajectories.

Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning.

INTRODUCTION: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study's aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense's N-TidalTM capnometer. METHOD: For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1-4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity. RESULTS: The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1. CONCLUSION: The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring.

Vitamin D beyond blood: Tissue distribution of vitamin D metabolites after supplementation.

Vitamin D3's role in mineral homeostasis through its endocrine function, associated with the main circulating metabolite 25-hydroxyvitamin D3, is well characterized. However, the increasing recognition of vitamin D3's paracrine and autocrine functions-such as cell growth, immune function, and hormone regulation-necessitates examining vitamin D3 levels across different tissues post-supplementation. Hence, this review explores the biodistribution of vitamin D3 in blood and key tissues following oral supplementation in humans and animal models, highlighting the biologically active metabolite, 1,25-dihydroxyvitamin D3, and the primary clearance metabolite, 24,25-dihydroxyvitamin D3. While our findings indicate significant progress in understanding how circulating metabolite levels respond to supplementation, comprehensive insight into their tissue concentrations remains limited. The gap is particularly significant during pregnancy, a period of drastically increased vitamin D3 needs and metabolic alterations, where data remains sparse. Within the examined dosage ranges, both human and animal studies indicate that vitamin D3 and its metabolites are retained in tissues selectively. Notably, vitamin D3 concentrations in tissues show greater variability in response to administered doses. In contrast, its metabolites maintain a more consistent concentration range, albeit different among tissues, reflecting their tighter regulatory mechanisms following supplementation. These observations suggest that serum 25-hydroxyvitamin D3 levels may not adequately reflect vitamin D3 and its metabolite concentrations in different tissues. Therefore, future research should aim to generate robust human data on the tissue distribution of vitamin D3 and its principal metabolites post-supplementation. Relating this data to clinically appropriate exposure metrics will enhance our understanding of vitamin D3's cellular effects and guide refinement of clinical trial methodologies.

Untargeted metabolomic analysis reveals different metabolites associated with response to mepolizumab and omalizumab in asthma.

Background: There is limited evidence on biomarkers associated with response to the monoclonal antibodies currently approved for asthma treatment. We sought to identify circulatory metabolites associated with response to treatment with mepolizumab or omalizumab. Methods: We conducted global metabolomic profiling of pre-treatment plasma samples from 100 patients with moderate-to-severe asthma who initiated mepolizumab (n=31) or omalizumab (n=69). The primary outcome was the change in exacerbations within 12 months of therapy. Negative binomial models were used to assess the association between each metabolite and exacerbations, adjusting for age, sex, body mass index, baseline exacerbations and inhaled corticosteroid use. Chemical similarity enrichment analysis (ChemRICH) was conducted to identify chemical subclasses associated with treatment response. Results: The mean age of the mepolizumab group was 58.7 years with on average 2.9 exacerbations over the year prior to initiation of biologic therapy. The mean age in the omalizumab group was 48.8 years with 1.5 exacerbations in the preceding year. Patients with higher levels of two tocopherol metabolites were associated with more exacerbations on mepolizumab (δ-carboxyethyl hydroxychroman (CEHC) (p=2.65E-05, false discovery rate (FDR=0.01) and δ-CEHC glucuronide (p=2.47E-06, FDR=0.003)). Higher levels of six androgenic steroids, three carnitine metabolites and two bile acid metabolites were associated with decreased exacerbations in the omalizumab group. In enrichment analyses, xanthine metabolites (cluster FDR=0.0006) and tocopherol metabolites (cluster FDR=0.02) were associated with worse mepolizumab response, while androgenic steroids (cluster FDR=1.9E-18), pregnenolone steroids (cluster p=3.2E-07, FDR=1.4E-05) and secondary bile acid metabolites (cluster p=0.0003, FDR=0.006) were the top subclasses associated with better omalizumab response. Conclusion: This study identifies distinct metabolites associated with response to mepolizumab and omalizumab, with androgenic steroids associated with response to both mepolizumab and omalizumab.

The metabolic role of vitamin D in children's neurodevelopment: a network study.

Neurodevelopmental disorders are rapidly increasing in prevalence and have been linked to various environmental risk factors. Mounting evidence suggests a potential role of vitamin D in child neurodevelopment, though the causal mechanisms remain largely unknown. Here, we investigate how vitamin D deficiency affects children's communication development, particularly in relation to Autism Spectrum Disorder (ASD). We do so by developing an integrative network approach that combines metabolomic profiles, clinical traits, and neurodevelopmental data from a pediatric cohort. Our results show that low levels of vitamin D are associated with changes in the metabolic networks of tryptophan, linoleic, and fatty acid metabolism. These changes correlate with distinct ASD-related phenotypes, including delayed communication skills and respiratory dysfunctions. Additionally, our analysis suggests the kynurenine and serotonin sub-pathways may mediate the effect of vitamin D on early life communication development. Altogether, our findings provide metabolome-wide insights into the potential of vitamin D as a therapeutic option for ASD and other communication disorders.

Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma.

BACKGROUND: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma. METHODS: The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest. RESULTS: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67-0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70-0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others. CONCLUSIONS: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.

The Asthma Risk Gene, GSDMB, Promotes Mitochondrial DNA-induced ISGs Expression.

Released mitochondrial DNA (mtDNA) in cells activates cGAS-STING pathway, which induces expression of interferon-stimulated genes (ISGs) and thereby promotes inflammation, as frequently seen in asthmatic airways. However, whether the genetic determinant, Gasdermin B (GSDMB), the most replicated asthma risk gene, regulates this pathway remains unknown. We set out to determine whether and how GSDMB regulates mtDNA-activated cGAS-STING pathway and subsequent ISGs induction in human airway epithelial cells. Using qPCR, ELISA, native polyacrylamide gel electrophoresis, co-immunoprecipitation and immunofluorescence assays, we evaluated the regulation of GSDMB on cGAS-STING pathway in both BEAS-2B cells and primary normal human bronchial epithelial cells (nHBEs). mtDNA was extracted in plasma samples from human asthmatics and the correlation between mtDNA levels and eosinophil counts was analyzed. GSDMB is significantly associated with RANTES expression in asthmatic nasal epithelial brushing samples from the Genes-environments and Admixture in Latino Americans (GALA) II study. Over-expression of GSDMB promotes DNA-induced IFN and ISGs expression in bronchial epithelial BEAS-2B cells and nHBEs. Conversely, knockout of GSDMB led to weakened induction of interferon (IFNs) and ISGs in BEAS-2B cells. Mechanistically, GSDMB interacts with the C-terminus of STING, promoting the translocation of STING to Golgi, leading to the phosphorylation of IRF3 and induction of IFNs and ISGs. mtDNA copy number in serum from asthmatics was significantly correlated with blood eosinophil counts especially in male subjects. GSDMB promotes the activation of mtDNA and poly (dA:dT)-induced activation of cGAS-STING pathway in airway epithelial cells, leading to enhanced induction of ISGs.

Network Analysis Reveals Protein Modules Associated with Childhood Respiratory Diseases.

Background: The first year of life is a period of rapid immune development that can impact health trajectories and the risk of developing respiratory-related diseases, such as asthma, recurrent infections, and eczema. However, the biology underlying subsequent disease development remains unknown. Methods: Using weighted gene correlation network analysis (WGCNA), we derived modules of highly correlated immune-related proteins in plasma samples from children at age 1 year (N=294) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART). We applied regression analyses to assess relationships between protein modules and development of childhood respiratory diseases up to age 6 years. We then characterized genomic, environmental, and metabolomic factors associated with modules. Results: WGCNA identified four protein modules at age 1 year associated with incidence of childhood asthma and/or recurrent wheeze (Padj range: 0.02-0.03), respiratory infections (Padj range: 6.3×10-9-2.9×10-6), and eczema (Padj=0.01) by age 6 years; three modules were associated with at least one environmental exposure (Padj range: 2.8×10-10-0.03) and disrupted metabolomic pathway(s) (Padj range: 2.8×10-6-0.04). No genome-wide SNPs were identified as significant genetic risk factors for any protein module. Relationships between protein modules with clinical, environmental, and 'omic factors were temporally sensitive and could not be recapitulated in protein profiles at age 6 years. Conclusion: These findings suggested protein profiles as early as age 1 year predicted development of respiratory-related diseases through age 6 and were associated with changes in pathways related to amino acid and energy metabolism. These may inform new strategies to identify vulnerable individuals based on immune protein profiling.

Joint clinical and molecular subtyping of COPD with variational autoencoders.

Chronic Obstructive Pulmonary Disease (COPD) is a complex, heterogeneous disease. Traditional subtyping methods generally focus on either the clinical manifestations or the molecular endotypes of the disease, resulting in classifications that do not fully capture the disease's complexity. Here, we bridge this gap by introducing a subtyping pipeline that integrates clinical and gene expression data with variational autoencoders. We apply this methodology to the COPDGene study, a large study of current and former smoking individuals with and without COPD. Our approach generates a set of vector embeddings, called Personalized Integrated Profiles (PIPs), that recapitulate the joint clinical and molecular state of the subjects in the study. Prediction experiments show that the PIPs have a predictive accuracy comparable to or better than other embedding approaches. Using trajectory learning approaches, we analyze the main trajectories of variation in the PIP space and identify five well-separated subtypes with distinct clinical phenotypes, expression signatures, and disease outcomes. Notably, these subtypes are more robust to data resampling compared to those identified using traditional clustering approaches. Overall, our findings provide new avenues to establish fine-grained associations between the clinical characteristics, molecular processes, and disease outcomes of COPD.

Pregnancy complications and birth outcomes following low-level exposure to per- and polyfluoroalkyl substances in the vitamin D antenatal asthma reduction trial.

Per- and polyfluoroalkyl substances (PFAS) are a group of synthetic, highly fluorinated aliphatic compounds, commonly utilised in a wide variety of consumer products with diverse applications. Since the genesis of these compounds, a growing body of evidence has demonstrated adverse health effects associated with PFAS exposure. In a racially diverse cohort of 459 pregnant mothers, demographically weighted towards minority representation (black 44.4%, white 38.4%, other 17.2%), across three major populous cities of the US, PFAS profiling was performed. Nine distinct PFAS species were quantified using mass spectrometry in plasma samples collected during the third trimester. Multivariable logistic and linear regression analyses were conducted to interrogate the associations of PFAS with gestational and birth outcomes: gestational diabetes, preeclampsia, gestational age at delivery, low birth weight, birth weight-, birth length- and head circumference-for-gestational-age. Detectable levels for eight out of nine profiled PFAS species were found in the plasma of pregnant mothers with a median range of 0.1-2.70 ng ml-1. Using a mixtures approach, we observe that increased quantile-based g-computation (Qg-comp) "total" PFAS levels were associated with increased newborn birth-weight-for-gestational-age (ß 1.28; 95% CI 1.07-1.52; FDR p 0.006). In study centre-stratified analyses, we observed a similar trend in Boston pregnant mothers, with Qg-comp total PFAS associated with higher newborn birth-weight-for-gestational-age (ß 1.39; 95% CI 1.01-1.92, FDR p 0.05). We additionally found elevated PFUA concentrations were associated with longer gestational terms in San Diego pregnant mothers (ß 0.60; 95% CI 0.18-1.02, FDR p 0.05). In this multi-city study, we detected lower levels of PFAS than in many previous US environmental studies, concordant with current US trends indicating environmental PFAS levels are falling, and we note geographical variation in the associations between PFAS levels and birth outcomes.

Urine metabolomics signature reveals novel determinants of adrenal suppression in children taking inhaled corticosteroids to control asthma symptoms.

BACKGROUND: Asthma is routinely treated with inhaled corticosteroids (ICS). Asthma patients on ICS are at increased risk of adrenal suppression, a potentially serious effect of long-term glucocorticoid exposure; however, this relationship is poorly understood. Therefore, this study aims to identify metabolite biomarkers related to adrenal suppression in asthma patients taking ICS. METHODS: A total of 571 urine metabolites from 200 children with asthma on ICS in the Pharmacogenetics of Adrenal Suppression with Inhaled Steroids (PASS) cohort were profiled. Samples were grouped by peak plasma cortisol measurement as adrenal sufficient (>350 nmol/L) or insufficient (≤350 nmol/L) (outcome). Regression and discriminant-based statistical models combined with network analyses were utilized to assess relationships between metabolites and the outcome. Finally, prioritized metabolites were validated using data from an ancillary study of the Childhood Asthma Management (CAMP) cohort with similar characteristics to PASS. RESULTS: Ninety metabolites were significantly associated with adrenal suppression, of which 57 also could discriminate adrenal status. While 26 metabolites (primarily steroids) were present at lower levels in the adrenal insufficient patients, 14 were significantly elevated in this group; the top metabolite, mannitol/sorbitol, was previously associated with asthma exacerbations. Network analyses identified unique clusters of metabolites related to steroids, fatty acid oxidation, and nucleoside metabolism, respectively. Four metabolites including urocanic acid, acetylcarnitine, uracil, and sorbitol were validated in CAMP cohort for adrenal suppression. CONCLUSIONS: Urinary metabolites differ among asthma patients on ICS, by adrenal status. While steroid metabolites were reduced in patients with poor adrenal function, our findings also implicate previously unreported metabolites involved in amino acid, lipid, and nucleoside metabolism.

Consistent Multi-Omic Relationships Uncover Molecular Basis of Pediatric Asthma IgE Regulation.

Serum total immunoglobulin E levels (total IgE) capture the state of the immune system in relation to allergic sensitization. High levels are associated with airway obstruction and poor clinical outcomes in pediatric asthma. Inconsistent patient response to anti-IgE therapies motivates discovery of molecular mechanisms underlying serum IgE level differences in children with asthma. To uncover these mechanisms using complementary metabolomic and transcriptomic data, abundance levels of 529 named metabolites and expression levels of 22,772 genes were measured among children with asthma in the Childhood Asthma Management Program (CAMP, N=564) and the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS, N=309) via the TOPMed initiative. Gene-metabolite associations dependent on IgE were identified within each cohort using multivariate linear models and were interpreted in a biochemical context using network topology, pathway and chemical enrichment, and representation within reactions. A total of 1,617 total IgE-dependent gene-metabolite associations from GACRS and 29,885 from CAMP met significance cutoffs. Of these, glycine and guanidinoacetic acid (GAA) were associated with the most genes in both cohorts, and the associations represented reactions central to glycine, serine, and threonine metabolism and arginine and proline metabolism. Pathway and chemical enrichment analysis further highlighted additional related pathways of interest. The results of this study suggest that GAA may modulate total IgE levels in two independent pediatric asthma cohorts with different characteristics, supporting the use of L-Arginine as a potential therapeutic for asthma exacerbation. Other potentially new targetable pathways are also uncovered.

Family history as the strongest predictor of aortic and peripheral aneurysms in patients with intracranial aneurysms.

OBJECTIVE: Intracranial aneurysms (IA) and aortic aneurysms (AA) are both abnormal dilations of arteries with familial predisposition and have been proposed to share co-prevalence and pathophysiology. Associations of IA and non-aortic peripheral aneurysms are less well-studied. The goal of the study was to understand the patterns of aortic and peripheral (extracranial) aneurysms in patients with IA, and risk factors associated with the development of these aneurysms. METHODS: 4701 patients were included in our retrospective analysis of all patients with intracranial aneurysms at our institution over the past 26 years. Patient demographics, comorbidities, and aneurysmal locations were analyzed. Univariate and multivariate analyses were performed to study associations with and without extracranial aneurysms. RESULTS: A total of 3.4% of patients (161 of 4701) with IA had at least one extracranial aneurysm. 2.8% had thoracic or abdominal aortic aneurysms. Age, male sex, hypertension, coronary artery disease, history of ischemic cerebral infarction, connective tissues disease, and family history of extracranial aneurysms in a 1st degree relative were associated with the presence of extracranial aneurysms and a higher number of extracranial aneurysms. In addition, family history of extracranial aneurysms in a second degree relative is associated with the presence of extracranial aneurysms and atrial fibrillation is associated with a higher number of extracranial aneurysms. CONCLUSION: Significant comorbidities are associated with extracranial aneurysms in patients with IA. Family history of extracranial aneurysms has the strongest association and suggests that IA patients with a family history of extracranial aneurysms may benefit from screening.