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Abstract Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly personalized and plays a key role in the metabolite responses to foods and nutrients. Accurately predicting metabolite responses to dietary interventions based on individuals’ gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a method McMLP (Metabolite response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.

Abstract Background Bronchodilator response (BDR) is a measure of improvement in airway smooth muscle tone, inhibition of liquid accumulation and mucus section into the lumen in response to short-acting beta-2 agonists that varies among asthmatic patients. MicroRNAs (miRNAs) are well-known post-translational regulators. Identifying miRNAs associated with BDR could lead to a better understanding of the underlying complex pathophysiology. Objective The purpose of this study is to identify circulating miRNAs associated with bronchodilator response in asthma and decipher possible mechanism of bronchodilator response variation. Methods We used available small RNA sequencing on blood serum from 1,134 asthmatic children aged 6 to 14 years who participated in the Genetics of Asthma in Costa Rica Study (GACRS). We filtered the participants into the highest and lowest bronchodilator response (BDR) quartiles and used DeSeq2 to identify miRNAs with differential expression (DE) in high (N = 277) vs. low (N = 278) BDR group. Replication was carried out in the Leukotriene modifier Or Corticosteroids or Corticosteroid-Salmeterol trial (LOCCS), an adult asthma cohort. The putative target genes of DE miRNAs were identified, and pathway enrichment analysis was performed. Results We identified 10 down-regulated miRNAs having odds ratios (OR) between 0.37 and 0.76 for a doubling of miRNA counts and one up-regulated miRNA (OR = 2.26) between high and low BDR group. These were assessed for replication in the LOCCS cohort, where two miRNAs (miR-200b-3p and miR-1246) were associated. Further, functional annotation of 11 DE miRNAs were performed as well as of two replicated miRs. Target genes of these miRs were enriched in regulation of cholesterol biosynthesis by SREBPs, ESR-mediated signaling, G1/S transition, RHO GTPase cycle, and signaling by TGFB family pathways. Conclusion MiRNAs miR-1246 and miR-200b-3p are associated with both childhood and adult asthma BDR. Our findings add to the growing body of evidence that miRNAs play a significant role in the difference of asthma treatment response among patients as it points to genomic regulatory machinery underlying difference in bronchodilator response among patients. Trial registration LOCCS cohort [ClinicalTrials.gov number NCT00156819, Registration date 20050912], GACRS cohort [ClinicalTrials.gov number NCT00021840].

Abstract The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N = 200,003), TOPMed (N = 87,652) and All of Us (N = 45,445). We performed rare (

Abstract Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach—Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Additionally, we find that METRIC can still correct the random errors well even without including gut microbial compositions. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

Abstract Cerebral vasospasm (CVS) is an important contributor to delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage (aSAH), leading to high morbidity and long-term disability. While several microRNAs (miRNAs) have been implicated in vasospasm, the underlying mechanisms for CVS remain poorly understood. Our study aims to identify miRNAs that may contribute to the development of CVS. Whole-blood samples were obtained during or outside of vasospasm from aSAH patients whose maximal vasospasm was moderate or severe. MiRNAs were isolated from serial whole-blood samples, and miRNA sequencing was performed. Differentially expressed miRNAs were identified and the expression levels in patients’ samples were verified using real-time qPCR. The biological functions of identified miRNA were evaluated in human brain endothelial cells (HBECs). MiRNA profiling revealed significant upregulation of miR-148b-3p in patients during CVS. We demonstrated that miR-148b-3p directly targeted and decreased the expression of ROCK1, affecting cell proliferation, migration, and invasion of HBECs through the ROCK-LIMK-Cofilin pathway. We propose that the upregulation of miRNA-148b-3p plays a role in the development of CVS by regulating actin cytoskeletal dynamics in HBECs, which is crucial for vascular function. Our study highlights miR-148b-3p as a potential diagnostic marker as well as therapeutic target for CVS following aSAH.

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.

Abstract 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.

Abstract Genome-wide association studies (GWAS) have become well-powered to detect loci associated with telomere length. However, no prior work has validated genes nominated by GWAS to examine their role in telomere length regulation. We conducted a multi-ancestry meta-analysis of 211,369 individuals and identified five novel association signals. Enrichment analyses of chromatin state and cell-type heritability suggested that blood/immune cells are the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6 or POP5 and demonstrated that both lengthened telomeres. CRISPR/Cas9 deletion of the predicted causal regions in K562 blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5. Our results demonstrate the utility of telomere length GWAS in the identification of telomere length regulation mechanisms and validate KBTBD6 and POP5 as genes affecting telomere length regulation.

Summary: Background: There are important inter-relationships between miRNAs and metabolites: alterations in miRNA expression can be induced by various metabolic stimuli, and miRNAs play a regulatory role in numerous cellular processes, impacting metabolism. While both specific miRNAs and metabolites have been identified for their role in childhood asthma, there has been no global assessment of the combined effect of miRNAs and the metabolome in childhood asthma. Methods: We performed miRNAome-metabolome-wide association studies (‘miR-metabo-WAS’) in two childhood cohorts of asthma to evaluate the contemporaneous and persistent miRNA-metabolite associations: 1) Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (N = 1121); 2) the Childhood Asthma Management Program (CAMP) (NBaseline = 312 and NEnd of trial = 454). We conducted a meta-analysis of the two cohorts to identify common contemporaneous associations between CAMP and GACRS (false-discovery rate (FDR) = 0.05). We assessed persistent miRNA-metabolome associations using baseline miRNAs and metabolomic profiling in CAMP at the end of the trial. The relation between miRNAs, metabolites and clinical phenotypes, including airway hyper-responsiveness (AHR), peripheral blood eosinophilia, and airflow obstruction, were then assessed via. Mediation analysis with 1000 bootstraps at an FDR significance level of 0.05. Findings: The meta-analysis yielded a total of 369 significant contemporaneous associations, involving 133 miRNAs and 60 metabolites. We identified 13 central hub metabolites (taurine, 12,13-diHOME, sebacate, 9-cis-retinoic acid, azelate, asparagine, C5:1 carnitine, cortisol, 3-methyladipate, inosine, NMMA, glycine, and Pyroglutamic acid) and four hub miRNAs (hsa-miR-186-5p, hsa-miR-143-3p, hsa-miR-192-5p, and hsa-miR-223-3p). Nine of these associations, between eight miRNAs and eight metabolites, were persistent in CAMP from baseline to the end of trial. Finally, five central hub metabolites (9-cis-retinoic acid, taurine, sebacate, azelate, and 12,13-diHOME) were identified as primary mediators in over 100 significant indirect miRNA-metabolite associations, with a collective influence on peripheral blood eosinophilia, AHR, and airflow obstruction. Interpretation: The robust association between miRNAs and metabolites, along with the substantial indirect impact of miRNAs via 5 hub metabolites on multiple clinical asthma metrics, suggests important integrated effects of miRNAs and metabolites on asthma. These findings imply that the indirect regulation of metabolism and cellular functions by miRNA influences Th2 inflammation, AHR, and airflow obstruction in childhood asthma. Funding: Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

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.

Abstract 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.

Abstract 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.

Abstract 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 $$L_2$$ L 2 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.

Abstract Introduction Biomarkers are needed to inform the choice of biologic therapy in patients with asthma given the increasing number of biologics. We aimed to identify proteins associated with response to omalizumab and mepolizumab. Methods Aptamer-based proteomic profiling (SomaScan) was used to assess 1437 proteins from 51 patients with moderate to severe asthma who received omalizumab (n = 29) or mepolizumab (n = 22). Response was defined as the change in asthma-related exacerbations in the 12 months following therapy initiation. All models were adjusted for age, sex, and pre-treatment exacerbation rate. Additionally, body mass index was included in the omalizumab model and eosinophil count in the mepolizumab model. We evaluated the association between molecular signatures and response using negative binomial regression correcting for the false discovery rate (FDR) and gene set enrichment analyses (GSEA) to identify associated pathways. Results Over two-thirds of patients were female. The average age for omalizumab patients was 42 years and 57 years for mepolizumab. At baseline, the average exacerbation rate was 1.5/year for omalizumab and 2.4/year for mepolizumab. Lower levels of LOXL2 (unadjusted p: 1.93 × 10E−05, FDR-corrected: 0.028) and myostatin (unadjusted: 3.87 × 10E−05, FDR-corrected: 0.028) were associated with better response to mepolizumab. Higher levels of CD9 antigen (unadjusted: 5.30 × 10E−07, FDR-corrected: 0.0006) and MUC1 (unadjusted: 1.15 × 10E−06, FDR-corrected: 0.0006) were associated with better response to omalizumab, and LTB4R (unadjusted: 1.12 × 10E−06, FDR-corrected: 0.0006) with worse response. Protein–protein interaction network modeling showed an enrichment of the TNF- and NF-kB signaling pathways for patients treated with mepolizumab and multiple pathways involving MAPK, including the FcER1 pathway, for patients treated with omalizumab. Conclusions This study provides novel fundamental data on proteins associated with response to mepolizumab or omalizumab in severe asthma and warrants further validation as potential biomarkers for therapy selection.

Abstract Atopic dermatitis (AD) is a common inflammatory skin condition and prior genome-wide association studies (GWAS) have identified 71 associated loci. In the current study we conducted the largest AD GWAS to date (discovery N = 1,086,394, replication N = 3,604,027), combining previously reported cohorts with additional available data. We identified 81 loci (29 novel) in the European-only analysis (which all replicated in a separate European analysis) and 10 additional loci in the multi-ancestry analysis (3 novel). Eight variants from the multi-ancestry analysis replicated in at least one of the populations tested (European, Latino or African), while two may be specific to individuals of Japanese ancestry. AD loci showed enrichment for DNAse I hypersensitivity and eQTL associations in blood. At each locus we prioritised candidate genes by integrating multi-omic data. The implicated genes are predominantly in immune pathways of relevance to atopic inflammation and some offer drug repurposing opportunities.

Abstract Accurate species identification and abundance estimation are critical for the interpretation of whole metagenome sequencing (WMS) data. Yet, existing metagenomic profilers suffer from false-positive identifications, which can account for more than 90% of total identified species. Here, by leveraging species-specific Type IIB restriction endonuclease digestion sites as reference instead of universal markers or whole microbial genomes, we present a metagenomic profiler, MAP2B (MetAgenomic Profiler based on type IIB restriction sites), to resolve those issues. We first illustrate the pitfalls of using relative abundance as the only feature in determining false positives. We then propose a feature set to distinguish false positives from true positives, and using simulated metagenomes from CAMI2, we establish a false-positive recognition model. By benchmarking the performance in metagenomic profiling using a simulation dataset with varying sequencing depth and species richness, we illustrate the superior performance of MAP2B over existing metagenomic profilers in species identification. We further test the performance of MAP2B using real WMS data from an ATCC mock community, confirming its superior precision against sequencing depth. Finally, by leveraging WMS data from an IBD cohort, we demonstrate the taxonomic features generated by MAP2B can better discriminate IBD and predict metabolomic profiles.

Vitamin D possesses immunomodulatory functions and vitamin D deficiency has been associated with the rise in chronic inflammatory diseases, including asthma (Litonjua and Weiss, 2007). Vitamin D supplementation studies do not provide insight into the molecular genetic mechanisms of vitamin D-mediated immunoregulation. Here, we provide evidence for vitamin D regulation of two human chromosomal loci, Chr17q12-21.1 and Chr17q21.2, reliably associated with autoimmune and chronic inflammatory diseases. We demonstrate increased vitamin D receptor (Vdr) expression in mouse lung CD4+ Th2 cells, differential expression of Chr17q12-21.1 and Chr17q21.2 genes in Th2 cells based on vitamin D status and identify the IL-2/Stat5 pathway as a target of vitamin D signaling. Vitamin D deficiency caused severe lung inflammation after allergen challenge in mice that was prevented by long-term prenatal vitamin D supplementation. Mechanistically, vitamin D induced the expression of the Ikzf3-encoded protein Aiolos to suppress IL-2 signaling and ameliorate cytokine production in Th2 cells. These translational findings demonstrate mechanisms for the immune protective effect of vitamin D in allergic lung inflammation with a strong molecular genetic link to the regulation of both Chr17q12-21.1 and Chr17q21.2 genes and suggest further functional studies and interventional strategies for long-term prevention of asthma and other autoimmune disorders.

Summary: Background: Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants. Methods: We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n = 660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n = 1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value. Findings: The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value = 0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value = 2.6 × 10−5), SLC8A1 (P-value = 3.9 × 10−5); and TENM4 (P-value = 4.9 × 10−5). Interpretation: This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health. Funding: Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

Abstract Studying human dietary intake may help us identify effective measures to treat or prevent many chronic diseases whose natural histories are influenced by nutritional factors. Here, by examining five cohorts with dietary intake data collected on different time scales, we show that the food intake profile varies substantially across individuals and over time, while the nutritional intake profile appears fairly stable. We refer to this phenomenon as ‘nutritional redundancy’ and attribute it to the nested structure of the food-nutrient network. This network enables us to quantify the level of nutritional redundancy for each diet assessment of any individual. Interestingly, this nutritional redundancy measure does not strongly correlate with any classical healthy diet scores, but its performance in predicting healthy aging shows comparable strength. Moreover, after adjusting for age, we find that a high nutritional redundancy is associated with lower risks of cardiovascular disease and type 2 diabetes.

Abstract Background Although currently most widely used in mechanical ventilation and cardiopulmonary resuscitation, features of the carbon dioxide (CO2) waveform produced through capnometry have been shown to correlate with V/Q mismatch, dead space volume, type of breathing pattern, and small airway obstruction. This study applied feature engineering and machine learning techniques to capnography data collected by the N-Tidal™ device across four clinical studies to build a classifier that could distinguish CO2 recordings (capnograms) of patients with COPD from those without COPD. Methods Capnography data from four longitudinal observational studies (CBRS, GBRS, CBRS2 and ABRS) was analysed from 295 patients, generating a total of 88,186 capnograms. CO2 sensor data was processed using TidalSense’s regulated cloud platform, performing real-time geometric analysis on CO2 waveforms to generate 82 physiologic features per capnogram. These features were used to train machine learning classifiers to discriminate COPD from ‘non-COPD’ (a group that included healthy participants and those with other cardiorespiratory conditions); model performance was validated on independent test sets. Results The best machine learning model (XGBoost) performance provided a class-balanced AUROC of 0.985 ± 0.013, positive predictive value (PPV) of 0.914 ± 0.039 and sensitivity of 0.915 ± 0.066 for a diagnosis of COPD. The waveform features that are most important for driving classification are related to the alpha angle and expiratory plateau regions. These features correlated with spirometry readings, supporting their proposed properties as markers of COPD. Conclusion The N-Tidal™ device can be used to accurately diagnose COPD in near-real-time, lending support to future use in a clinical setting. Trial registration: Please see NCT03615365, NCT02814253, NCT04504838 and NCT03356288.

Abstract Respiratory infections are a leading cause of morbidity and mortality in early life, and recurrent infections increase the risk of developing chronic diseases. The maternal environment during pregnancy can impact offspring health, but the factors leading to increased infection proneness have not been well characterized during this period. Steroids have been implicated in respiratory health outcomes and may similarly influence infection susceptibility. Our objective was to describe relationships between maternal steroid levels and offspring infection proneness. Using adjusted Poisson regression models, we evaluated associations between sixteen androgenic and corticosteroid metabolites during pregnancy and offspring respiratory infection incidence across two pre-birth cohorts (N = 774 in VDAART and N = 729 in COPSAC). Steroid metabolites were measured in plasma samples from pregnant mothers across all trimesters of pregnancy by ultrahigh-performance-liquid-chromatography/mass-spectrometry. We conducted further inquiry into associations of steroids with related respiratory outcomes: asthma and lung function spirometry. Higher plasma corticosteroid levels in the third trimester of pregnancy were associated with lower incidence of offspring respiratory infections (P = 4.45 × 10–7 to 0.002) and improved lung function metrics (P = 0.020–0.036). Elevated maternal androgens were generally associated with increased offspring respiratory infections and worse lung function, with some associations demonstrating nominal significance at P

Summary: Background: Preeclampsia has been associated with maternal epigenetic changes, in particular DNA methylation changes in the placenta. It has been suggested that preeclampsia could also cause DNA methylation changes in the neonate. We examined DNA methylation in relation to gene expression in the cord blood of offspring born to mothers with preeclampsia. Methods: This study included 128 mother-child pairs who participated in the Vitamin D Antenatal Asthma Reduction Trial (VDAART), where assessment of preeclampsia served as secondary outcome. We performed an epigenome-wide association study of preeclampsia and cord blood DNA methylation (Illumina 450 K chip). We then examined gene expression of the same subjects for validation and replicated the gene signatures in independent DNA methylation datasets. Lastly, we applied functional enrichment and network analyses to identify biological pathways that could potentially be involved in preeclampsia. Findings: In the cord blood samples (n = 128), 263 CpGs were differentially methylated (FDR

Summary: The early life microbiome plays an important role in developmental and long-term health outcomes. However, it is unknown whether adverse pregnancy complications affect the offspring’s gut microbiome postnatally and in early years. In a longitudinal cohort with a five-year follow-up of mother-child pairs affected by preeclampsia (PE) or spontaneous preterm birth (sPTB), we evaluated offspring gut alpha and beta diversity as well as taxa abundances considering factors like breastfeeding and mode of delivery. Our study highlights a trend where microbiome diversity exhibits comparable development across adverse and normal pregnancies. However, specific taxa at genus level emerge with distinctive abundances, showing enrichment and/or depletion over time in relation to PE or sPTB. These findings underscore the potential for certain adverse pregnancy complications to induce alterations in the offspring’s microbiome over the course of early life. The implications of these findings on the immediate and long-term health of offspring should be investigated in future studies.

ABSTRACTBackground: Recent studies have identified thousands of associations between DNA methylation CpGs and complex diseases/traits, emphasizing the critical role of epigenetics in understanding disease aetiology and identifying biomarkers. However, association analyses based on methylation array data are susceptible to batch/slide effects, which can lead to inflated false positive rates or reduced statistical powerResults: We use multiple DNA methylation datasets based on the popular Illumina Infinium MethylationEPIC BeadChip array to describe consistent patterns and the joint distribution of slide effects across CpGs, confirming and extending previous results. The susceptible CpGs overlap with the Illumina Infinium HumanMethylation450 BeadChip array content.Conclusions: Our findings reveal systematic patterns in slide effects. The observations provide further insights into the characteristics of these effects and can improve existing adjustment approaches.

Abstract Background MicroRNAs (miRNAs) have been implicated in the pathobiology of preeclampsia, a common hypertensive disorder of pregnancy. In a nested matched case‐control cohort within the Vitamin D Antenatal Asthma Reduction Trial (VDAART), we previously identified peripheral blood mRNA signatures related to preeclampsia and vitamin D status (≤30 ng/mL) during gestation from 10 to 18 weeks, using differential expression analysis. Methods Using quantitative PCR arrays, we conducted profiling of circulating miRNAs at 10–18 weeks of gestation in the same VDAART cohort to identify differentially expressed (DE) miRNAs associated with preeclampsia and vitamin D status. For the validation of the expression of circulating miRNA signatures in the placenta, the HTR‐8/SVneo trophoblast cell line was used. Targets of circulating miRNA signatures in the preeclampsia mRNA signatures were identified by consensus ranking of miRNA‐target prediction scores from four sources. The connected component of target signatures was identified by mapping to the protein‐protein interaction (PPI) network and hub targets were determined. As experimental validation, we examined the gene and protein expression of IGF1R, one of the key hub genes, as a target of the DE miRNA, miR‐182‐5p, in response to a miR‐182‐5p mimic in HTR‐8/SVneo cells. Results Pregnant women with preeclampsia had 16 circulating DE miRNAs relative to normal pregnancy controls that were also DE under vitamin D insufficiency (9/16 = 56% upregulated, FDR

Abstract Background Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. Objective We aimed to investigate the computational methods in disease status prediction using multi-omics data. Method We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. Results Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. Conclusions Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.

The underlying mechanisms of how obesity increases asthma prevalence and severity are not well understood. Here, the authors show that antagonizing cholecystokinin and its receptor, CCKAR, in the lung attenuates obesity-associated airway hyperresponsiveness in mice.

Abstract As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.

Summary: Background: Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases. Methods: We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables. Findings: We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P

Abstract Background Polygenic risk scores (PRS), which offer information about genomic risk for common diseases, have been proposed for clinical implementation. The ways in which PRS information may influence a patient’s health trajectory depend on how both the patient and their primary care provider (PCP) interpret and act on PRS information. We aimed to probe patient and PCP responses to PRS clinical reporting choices Methods Qualitative semi-structured interviews of both patients (N=25) and PCPs (N=21) exploring responses to mock PRS clinical reports of two different designs: binary and continuous representations of PRS. Results Many patients did not understand the numbers representing risk, with high numeracy patients being the exception. However, all the patients still understood a key takeaway that they should ask their PCP about actions to lower their disease risk. PCPs described a diverse range of heuristics they would use to interpret and act on PRS information. Three separate use cases for PRS emerged: to aid in gray-area clinical decision-making, to encourage patients to do what PCPs think patients should be doing anyway (such as exercising regularly), and to identify previously unrecognized high-risk patients. PCPs indicated that receiving “below average risk” information could be both beneficial and potentially harmful, depending on the use case. For “increased risk” patients, PCPs were favorable towards integrating PRS information into their practice, though some would only act in the presence of evidence-based guidelines. PCPs describe the report as more than a way to convey information, viewing it as something to structure the whole interaction with the patient. Both patients and PCPs preferred the continuous over the binary representation of PRS (23/25 and 17/21, respectively). We offer recommendations for the developers of PRS to consider for PRS clinical report design in the light of these patient and PCP viewpoints. Conclusions PCPs saw PRS information as a natural extension of their current practice. The most pressing gap for PRS implementation is evidence for clinical utility. Careful clinical report design can help ensure that benefits are realized and harms are minimized.

Here, using whole-metagenome shotgun sequencing data from patients with COVID-19 and controls, the authors reconstruct 11,584 microbial metagenome-assembled genomes (MAGs) including 5,403 non-redundant MAGs, revealing microbiota and metabolic pathways associations with SARS-CoV-2 infection at strain-level resolution.

Abstract Variability in response to short-acting β2-agonists (e.g., albuterol) among patients with asthma from diverse racial/ethnic groups may contribute to asthma disparities. We sought to identify genetic variants associated with bronchodilator response (BDR) to identify potential mechanisms of drug response and risk factors for worse asthma outcomes. Genome-wide association studies of bronchodilator response (BDR) were performed using TOPMed Whole Genome Sequencing data of the Asthma Translational Genomic Collaboration (ATGC), which corresponded to 1136 Puerto Rican, 656 Mexican and 4337 African American patients with asthma. With the population-specific GWAS results, a trans-ethnic meta-analysis was performed to identify BDR-associated variants shared across the three populations. Replication analysis was carried out in three pediatric asthma cohorts, including CAMP (Childhood Asthma Management Program; n = 560), GACRS (Genetics of Asthma in Costa Rica Study; n = 967) and HPR (Hartford-Puerto Rico; n = 417). A genome-wide significant locus (rs35661809; P = 3.61 × 10–8) in LINC02220, a non-coding RNA gene, was identified in Puerto Ricans. While this region was devoid of protein-coding genes, capture Hi-C data showed a distal interaction with the promoter of the DNAH5 gene in lung tissue. In replication analysis, the GACRS cohort yielded a nominal association (1-tailed P

Abstract Background Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. Methods We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. Results The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5–4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. Conclusions By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Abstract Background As genomic sequencing moves closer to clinical implementation, there has been an increasing acceptance of returning incidental findings to research participants and patients for mutations in highly penetrant, medically actionable genes. A curated list of genes has been recommended by the American College of Medical Genetics and Genomics (ACMG) for return of incidental findings. However, the pleiotropic effects of these genes are not fully known. Such effects could complicate genetic counseling when returning incidental findings. In particular, there has been no systematic evaluation of psychiatric manifestations associated with rare variation in these genes. Results Here, we leveraged a targeted sequence panel and real-world electronic health records from the eMERGE network to assess the burden of rare variation in the ACMG-56 genes and two psychiatric-associated genes (CACNA1C and TCF4) across common mental health conditions in 15,181 individuals of European descent. As a positive control, we showed that this approach replicated the established association between rare mutations in LDLR and hypercholesterolemia with no visible inflation from population stratification. However, we did not identify any genes significantly enriched with rare deleterious variants that confer risk for common psychiatric disorders after correction for multiple testing. Suggestive associations were observed between depression and rare coding variation in PTEN (P = 1.5 × 10–4), LDLR (P = 3.6 × 10–4), and CACNA1S (P = 5.8 × 10–4). We also observed nominal associations between rare variants in KCNQ1 and substance use disorders (P = 2.4 × 10–4), and APOB and tobacco use disorder (P = 1.1 × 10–3). Conclusions Our results do not support an association between psychiatric disorders and incidental findings in medically actionable gene mutations, but power was limited with the available sample sizes. Given the phenotypic and genetic complexity of psychiatric phenotypes, future work will require a much larger sequencing dataset to determine whether incidental findings in these genes have implications for risk of psychopathology.

Abstract Background Chronic obstructive pulmonary disease (COPD) is a leading cause of death in adults that may have origins in early lung development. It is a complex disease, influenced by multiple factors including genetic variants and environmental factors. Maternal smoking during pregnancy may influence the risk for diseases during adulthood, potentially through epigenetic modifications including methylation. Methods In this work, we explore the fetal origins of COPD by utilizing lung DNA methylation marks associated with in utero smoke (IUS) exposure, and evaluate the network relationships between methylomic and transcriptomic signatures associated with adult lung tissue from former smokers with and without COPD. To identify potential pathobiological mechanisms that may link fetal lung, smoke exposure and adult lung disease, we study the interactions (physical and functional) of identified genes using protein–protein interaction networks. Results We build IUS-exposure and COPD modules, which identify connected subnetworks linking fetal lung smoke exposure to adult COPD. Studying the relationships and connectivity among the different modules for fetal smoke exposure and adult COPD, we identify enriched pathways, including the AGE-RAGE and focal adhesion pathways. Conclusions The modules identified in our analysis add new and potentially important insights to understanding the early life molecular perturbations related to the pathogenesis of COPD. We identify AGE-RAGE and focal adhesion as two biologically plausible pathways that may reveal lung developmental contributions to COPD. We were not only able to identify meaningful modules but were also able to study interconnections between smoke exposure and lung disease, augmenting our knowledge about the fetal origins of COPD.

Summary: Background: It has been widely recognized that a critical time window for neurodevelopment occurs in early life and the host's gut microbiome plays an important role in neurodevelopment. Following recent demonstrations that the maternal prenatal gut microbiome influences offspring brain development in murine models, we aim to explore whether the critical time window for the association between the gut microbiome and neurodevelopment is prenatal or postnatal for human. Methods: Here we leverage a large-scale human study and compare the associations between the gut microbiota and metabolites from mothers during pregnancy and their children with the children's neurodevelopment. Specifically, using multinomial regression integrated in Songbird, we assessed the discriminating power of the maternal prenatal and child gut microbiome for children's neurodevelopment at early life as measured by the Ages & Stages Questionnaires (ASQ). Findings: We show that the maternal prenatal gut microbiome is more relevant than the children's gut microbiome to the children's neurodevelopment in the first year of life (maximum Q2 = 0.212 and 0.096 separately using the taxa at the class level). Moreover, we found that Fusobacteriia is more associated with high fine motor skills in ASQ in the maternal prenatal gut microbiota but become more associated with low fine motor skills in the infant gut microbiota (rank = 0.084 and −0.047 separately), suggesting the roles of the same taxa with respect to neurodevelopment can be opposite at the two stages of fetal neurodevelopment. Interpretation: These findings shed light, especially in terms of timing, on potential therapeutic interventions to prevent neurodevelopmental disorders. Funding: This work was supported by the National Institutes of Health (grant numbers: R01AI141529, R01HD093761, RF1AG067744, UH3OD023268, U19AI095219, U01HL089856, R01HL141826, K08HL148178, K01HL146980), and the Charles A. King Trust Postdoctoral Fellowship.

Abstract Quantifying the contributions of possible environmental sources (“sources”) to a specific microbial community (“sink”) is a classical problem in microbiology known as microbial source tracking (MST). Solving the MST problem will not only help us understand how microbial communities were formed, but also have far‐reaching applications in pollution control, public health, and forensics. MST methods generally fall into two categories: target‐based methods (focusing on the detection of source‐specific indicator species or chemicals); and community‐based methods (using community structure to measure similarity between sink samples and potential source environments). As next‐generation sequencing becomes a standard community‐assessment method in microbiology, numerous community‐based computational methods, referred to as MST solvers hereafter have been developed and applied to various real datasets to demonstrate their utility across different contexts. Yet, those MST solvers do not consider microbial interactions and priority effects in microbial communities. Here, we revisit the performance of several representative MST solvers. We show compelling evidence that solving the MST problem using existing MST solvers is impractical when ecological dynamics plays a role in community assembly. In particular, we clearly demonstrate that the presence of either microbial interactions or priority effects will render the MST problem mathematically unsolvable for MST solvers. We further analyze data from fecal microbiota transplantation studies, finding that the state‐of‐the‐art MST solvers fail to identify donors for most of the recipients. Finally, we perform community coalescence experiments to demonstrate that the state‐of‐the‐art MST solvers fail to identify the sources for most of the sinks. Our findings suggest that ecological dynamics imposes fundamental challenges in MST. Interpretation of results of existing MST solvers should be done cautiously.

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.

Abstract Electronic health records (EHR) provide an unprecedented opportunity to conduct large, cost-efficient, population-based studies. However, the studies of heterogeneous diseases, such as chronic obstructive pulmonary disease (COPD), often require labor-intensive clinical review and testing, limiting widespread use of these important resources. To develop a generalizable and efficient method for accurate identification of large COPD cohorts in EHRs, a COPD datamart was developed from 3420 participants meeting inclusion criteria in the Mass General Brigham Biobank. Training and test sets were selected and labeled with gold-standard COPD classifications obtained from chart review by pulmonologists. Multiple classes of algorithms were built utilizing both structured (e.g. ICD codes) and unstructured (e.g. medical notes) data via elastic net regression. Models explicitly including and excluding spirometry features were compared. External validation of the final algorithm was conducted in an independent biobank with a different EHR system. The final COPD classification model demonstrated excellent positive predictive value (PPV; 91.7%), sensitivity (71.7%), and specificity (94.4%). This algorithm performed well not only within the MGBB, but also demonstrated similar or improved classification performance in an independent biobank (PPV 93.5%, sensitivity 61.4%, specificity 90%). Ancillary comparisons showed that the classification model built including a binary feature for FEV1/FVC produced substantially higher sensitivity than those excluding. This study fills a gap in COPD research involving population-based EHRs, providing an important resource for the rapid, automated classification of COPD cases that is both cost-efficient and requires minimal information from unstructured medical records.

Vitamin D supplementation and its impact on immunoregulation are widely investigated. We aimed to assess the prevention and treatment efficiency of vitamin D supplementation in the context of coronavirus disease 2019 (COVID-19) and any disease-related complications. For this systematic review and meta-analysis, we searched databases (PubMed, Embase, Scopus, Web of Science, The Cochrane Library, medRxiv, Cochrane COVID-19 Study Register, and ClinicalTrial.gov) for studies published between 1 November 2019 and 17 September 2021. We considered randomized trials (RCTs) as potentially eligible when patients were tested for SARS-CoV-2 infection and received vitamin D supplementation versus a placebo or standard-of-care control. A random-effects model was implemented to obtain pooled odds ratios for the effect of vitamin D supplementation on the main outcome of mortality as well as clinical outcomes. We identified a total of 5,733 articles, of which eight RCTs (657 patients) met the eligibility criteria. Although no statistically significant effects were reached, the use of vitamin D supplementation showed a trend for reduced mortality [odds ratio (OR) 0.74, 95% confidence interval (CI) 0.32–1.71, p = 0.48] compared with the control group, with even stronger effects, when vitamin D was administered repeatedly (OR 0.33, 95% CI 0.1–1.14). The mean difference for the length of hospitalization was −0.28 (95% CI −0.60 to 0.04), and the ORs were 0.41 (95% CI 0.15–1.12) and 0.52 (95% CI 0.27–1.02) for ICU admission and mechanical ventilation, respectively. In conclusion, vitamin D supplementation did not improve the clinical outcomes in COVID-19 patients, but trends of beneficial effects were observed. Further investigations are required, especially studies focusing on the daily administration of vitamin D.

Cigarette smoking impacts DNA methylation, but the investigation of sex-specific features of lung tissue DNA methylation in smokers has been limited. Women appear more susceptible to cigarette smoke, and often develop more severe lung disease at an earlier age with less smoke exposure. We aimed to analyse whether there are sex differences in DNA methylation in lung tissue and whether these DNA methylation marks interact with smoking. We collected lung tissue samples from former smokers who underwent lung tissue resection. One hundred thirty samples from white subjects were included for this analysis. Regression models for sex as a predictor of methylation were adjusted for age, presence of COPD, smoking variables and technical batch variables revealed 710 associated sites. 294 sites demonstrated robust sex-specific methylation associations in foetal lung tissue. Pathway analysis identified 6 nominally significant pathways including the mitophagy pathway. Three CpG sites demonstrated a suggested interaction between sex and pack-years of smoking: GPR132, ANKRD44 and C19orf60. All of them were nominally significant in both male- and female-specific models, and the effect estimates were in opposite directions for male and female; GPR132 demonstrated significant association between DNA methylation and gene expression in lung tissue (P

Background: Asthmatic patients’ responses to inhaled corticosteroids (ICS) are variable and difficult to quantify. We have previously defined a Cross-sectional Asthma STEroid Response (CASTER) measure of ICS response. MicroRNAs (miRNAs) have shown strong effects on asthma and inflammatory processes. Objective: The purpose of this study was to identify key associations between circulating miRNAs and ICS response in childhood asthma. Methods: Small RNA sequencing in peripheral blood serum from 580 children with asthma on ICS treatment from The Genetics of Asthma in Costa Rica Study (GACRS) was used to identify miRNAs associated with ICS response using generalized linear models. Replication was conducted in children on ICS from the Childhood Asthma Management Program (CAMP) cohort. The association between replicated miRNAs and the transcriptome of lymphoblastoid cell lines in response to a glucocorticoid was assessed. Results: The association study on the GACRS cohort identified 36 miRNAs associated with ICS response at 10% false discovery rate (FDR), three of which (miR-28-5p, miR-339-3p, and miR-432-5p) were in the same direction of effect and significant in the CAMP replication cohort. In addition, in vitro steroid response lymphoblastoid gene expression analysis revealed 22 dexamethasone responsive genes were significantly associated with three replicated miRNAs. Furthermore, Weighted Gene Co-expression Network Analysis (WGCNA) revealed a significant association between miR-339-3p and two modules (black and magenta) of genes associated with immune response and inflammation pathways. Conclusion: This study highlighted significant association between circulating miRNAs miR-28-5p, miR-339-3p, and miR-432-5p and ICS response. miR-339-3p may be involved in immune dysregulation, which leads to a poor response to ICS treatment.

Abstract We present a cohort of patients with anterior communicating artery (ACoA) aneurysms to investigate morphological characteristics and clinical factors associated with rupture of the aneurysms. 505 patients with ACoA aneurysms were identified at the Brigham and Women’s Hospital and Massachusetts General Hospital between 1990 and 2016, with available CT angiography (CTA). Three-dimensional (3D) reconstructions were performed to evaluate aneurysmal morphologic features, including location, projection, irregularity, the presence of daughter dome, height, height/width ratio, and relationships between surrounding vessels. Patient risk factors assessed included patient age, sex, tobacco use, alcohol use, and family history of aneurysms and aneurysmal subarachnoid hemorrhage. Logistic regression was used to build a predictive ACoA score for rupture. Morphologic features associated with ruptured ACoA aneurysms were the presence of a daughter dome (OR 21.4, 95% CI 10.6–43.1), smaller neck diameter (OR 0.55, 95% CI 0.42–0.71), larger aspect ratio (OR 3.57, 95% CI 2.05–6.24), larger flow angle (OR 1.03, 95% CI 1.02–1.05), and smaller ipsilateral A2-ACoA angle (OR 0.98, 95% CI 0.97–1.00). Tobacco use was predominantly associated with morphological factors intrinsic to the aneurysm that were associated with rupture while younger age was also associated with morphologic features extrinsic to the aneurysm that were associated with rupture. The ACoA score had good predictive capacity for rupture with AUC = 0.92 using the 0.632 bootstrap cross-validation for correction of overfitting bias. Ruptured ACoA aneurysms were associated with morphological features that are simple to assess using a simple scoring system. Tobacco use and younger age were predominantly associated with intrinsic and extrinsic morphological features characteristic of rupture, respectively.

Abstract Morphological factors of intracranial aneurysms and the surrounding vasculature could affect aneurysm rupture risk in a location specific manner. Our goal was to identify image-based morphological parameters that correlated with ruptured basilar tip aneurysms. Three-dimensional morphological parameters obtained from CT-angiography (CTA) or digital subtraction angiography (DSA) from 200 patients with basilar tip aneurysms diagnosed at the Brigham and Women’s Hospital and Massachusetts General Hospital between 1990 and 2016 were evaluated. We examined aneurysm wall irregularity, the presence of daughter domes, hypoplastic, aplastic or fetal PCoAs, vertebral dominance, maximum height, perpendicular height, width, neck diameter, aspect and size ratio, height/width ratio, and diameters and angles of surrounding parent and daughter vessels. Univariable and multivariable statistical analyses were performed to determine statistical significance. In multivariable analysis, presence of a daughter dome, aspect ratio, and larger flow angle were significantly associated with rupture status. We also introduced two new variables, diameter size ratio and parent-daughter angle ratio, which were both significantly inversely associated with ruptured basilar tip aneurysms. Notably, multivariable analyses also showed that larger diameter size ratio was associated with higher Hunt-Hess score while smaller flow angle was associated with higher Fisher grade. These easily measurable parameters, including a new parameter that is unlikely to be affected by the formation of the aneurysm, could aid in screening strategies in high-risk patients with basilar tip aneurysms. One should note, however, that the changes in parameters related to aneurysm morphology may be secondary to aneurysm rupture rather than causal.