Explore the vast range of titles available.

Impact statement Through the analysis of data from children aged 6 months to 8 years enrolled in the Vitamin D Antenatal Asthma Reduction Trial (VDAART), significant simultaneous associations were identified between variants in the fragile histidine triad (FHIT) gene, children's body mass index, microbiome features related to obesity, and key lipids and amino acids. These patterns represent evidence of the genotype influence in shaping the host microbiome in developing stages and new potential biomarkers for childhood obesity, insulin resistance, and type 2 diabetes.

Background Long-term exposures to air pollution has been reported to be associated with inflammation and oxidative stress. However, the underlying metabolic mechanisms remain poorly understood. Objectives We aimed to determine the changes in the blood metabolome and thus the metabolic pathways associated with long-term exposure to outdoor air pollution and ambient temperature. Methods We quantified metabolites using mass-spectrometry based global untargeted metabolomic profiling of plasma samples among men from the Normative Aging Study (NAS). We estimated the association between long-term exposure to PM 2.5 , NO 2 , O 3 , and temperature (annual average of central site monitors) with metabolites and their associated metabolic pathways. We used multivariable linear mixed-effect regression models (LMEM) while simultaneously adjusting for the four exposures and potential confounding and correcting for multiple testing. As a reduction method for the intercorrelated metabolites (outcome), we further used an independent component analysis (ICA) and conducted LMEM with the same exposures. Results Men ( N  = 456) provided 648 blood samples between 2000 and 2016 in which 1158 metabolites were quantified. On average, men were 75.0 years and had an average body mass index of 27.7 kg/m 2 . Almost all men (97%) were not current smokers. The adjusted analysis showed statistically significant associations with several metabolites (58 metabolites with PM 2.5 , 15 metabolites with NO 2 , and 6 metabolites with temperature) while no metabolites were associated with O 3 . One out of five ICA factors (factor 2) was significantly associated with PM 2.5 . We identified eight perturbed metabolic pathways with long-term exposure to PM 2.5 and temperature: glycerophospholipid, sphingolipid, glutathione, beta-alanine, propanoate, and purine metabolism, biosynthesis of unsaturated fatty acids, and taurine and hypotaurine metabolism. These pathways are related to inflammation, oxidative stress, immunity, and nucleic acid damage and repair. Conclusions Using a global untargeted metabolomic approach, we identified several significant metabolites and metabolic pathways associated with long-term exposure to PM 2.5 , NO 2 and temperature. This study is the largest metabolomics study of long-term air pollution, to date, the first study to report a metabolomic signature of long-term temperature exposure, and the first to use ICA in the analysis of both.

The serpin family A member 1 (SERPINA1) Z allele is present in approximately one in 25 individuals of European ancestry. Z allele homozygosity (Pi*ZZ) is the most common cause of alpha 1‐antitrypsin deficiency and is a proven risk factor for cirrhosis. We examined whether heterozygous Z allele (Pi*Z) carriers in United Kingdom (UK) Biobank, a population‐based cohort, are at increased risk of liver disease. We replicated findings in Massachusetts General Brigham Biobank, a hospital‐based cohort. We also examined variants associated with liver disease and assessed for gene–gene and gene–environment interactions. In UK Biobank, we identified 1,493 cases of cirrhosis, 12,603 Z allele heterozygotes, and 129 Z allele homozygotes among 312,671 unrelated white British participants. Heterozygous carriage of the Z allele was associated with cirrhosis compared to noncarriage (odds ratio [OR], 1.53; P = 1.1×10−04); homozygosity of the Z allele also increased the risk of cirrhosis (OR, 11.8; P = 1.8 × 10−09). The OR for cirrhosis of the Z allele was comparable to that of well‐established genetic variants, including patatin‐like phospholipase domain containing 3 (PNPLA3) I148M (OR, 1.48; P = 1.1 × 10−22) and transmembrane 6 superfamily member 2 (TM6SF2) E167K (OR, 1.34; P = 2.6 × 10−06). In heterozygotes compared to noncarriers, the Z allele was associated with higher alanine aminotransferase (ALT; P = = 4.6 × 10−46), aspartate aminotransferase (AST; P = 2.2 × 10−27), alkaline phosphatase (P = 3.3 × 10−43), gamma‐glutamyltransferase (P = 1.2 × 10−05), and total bilirubin (P = 6.4 × 10−06); Z allele homozygotes had even greater elevations in liver biochemistries. Body mass index (BMI) amplified the association of the Z allele for ALT (P interaction = 0.021) and AST (P interaction = 0.0040), suggesting a gene–environment interaction. Finally, we demonstrated genetic interactions between variants in PNPLA3, TM6SF2, and hydroxysteroid 17‐beta dehydrogenase 13 (HSD17B13); there was no evidence of epistasis between the Z allele and these variants. Conclusion: SERPINA1 Z allele heterozygosity is an important risk factor for liver disease; this risk is amplified by increasing BMI.

The promise of integrating Electronic Medical Records (EMR) and genetic data for precision medicine has largely fallen short due to its omission of environmental context over time. Post-genomic data can bridge this gap by capturing the real-time dynamic relationship between underlying genetics and the environment. This perspective highlights the pivotal role of integrating EMR and post-genomics for personalized health, reflecting on lessons from past efforts, and outlining a roadmap of challenges and opportunities that must be addressed to realize the potential of precision medicine. The authors outline a framework uniting electronic medical records with post-genomic data to capture real-time physiological changes via periodic molecular snapshots, enabling a shift from reactive treatments to proactive, inclusive care.

Background Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition. Methods Here we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes. Results Our meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67–0.72), which increased to 0.83 (0.80–0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity. Conclusions This work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.

Short‐acting β agonists (e.g., albuterol) are the most commonly used medications for asthma, a disease that affects over 300 million people in the world. Metabolomic profiling of asthmatics taking β agonists presents a new and promising resource for identifying the molecular determinants of asthma control. The objective is to identify novel genetic and biochemical predictors of asthma control using an integrative “omics” approach. We generated lipidomic data by liquid chromatography tandem mass spectrometry (LC‐MS), ­ using plasma samples from 20 individuals with asthma. The outcome of interest was a binary indicator of asthma control defined by the use of albuterol inhalers in the preceding week. We integrated metabolomic data with genome‐wide genotype, gene expression, and methylation data of this cohort to identify genomic and molecular indicators of asthma control. A Conditional Gaussian Bayesian Network (CGBN) was generated using the strongest predictors from each of these analyses. Integrative and metabolic pathway over‐representation analyses (ORA) identified enrichment of known biological pathways within the strongest molecular determinants. Of the 64 metabolites measured, 32 had known identities. The CGBN model based on four SNPs (rs9522789, rs7147228, rs2701423, rs759582) and two metabolites—monoHETE_0863 and sphingosine‐1‐phosphate (S1P) could predict asthma control with an AUC of 95%. Integrative ORA identified 17 significantly enriched pathways related to cellular immune response, interferon signaling, and cytokine‐related signaling, for which arachidonic acid, PGE2 and S1P, in addition to six genes (CHN1, PRKCE, GNA12, OASL, OAS1, and IFIT3) appeared to drive the pathway results. Of these predictors, S1P, GNA12, and PRKCE were enriched in the results from integrative and metabolic ORAs. Through an integrative analysis of metabolomic, genomic, and methylation data from a small cohort of asthmatics, we implicate altered metabolic pathways, related to sphingolipid metabolism, in asthma control. These results provide insight into the pathophysiology of asthma control. Through an integrative analysis of metabolomic, genomic, and methylation data from a small cohort of asthmatics, we implicate altered metabolic pathways, related to sphingolipid metabolism, in asthma control. These results provide insight into the pathophysiology of asthma control.

Background Cell stress promotes degradation of mitochondria which release danger-associated molecular patterns that are catabolized to N -formylmethionine. We hypothesized that in critically ill adults, the response to N -formylmethionine is associated with increases in metabolomic shift-related metabolites and increases in 28-day mortality. Methods We performed metabolomics analyses on plasma from the 428-subject Correction of Vitamin D Deficiency in Critically Ill Patients trial (VITdAL-ICU) cohort and the 90-subject Brigham and Women’s Hospital Registry of Critical Illness (RoCI) cohort. In the VITdAL-ICU cohort, we analyzed 983 metabolites at Intensive Care Unit (ICU) admission, day 3, and 7. In the RoCI cohort, we analyzed 411 metabolites at ICU admission. The association between N -formylmethionine and mortality was determined by adjusted logistic regression. The relationship between individual metabolites and N -formylmethionine abundance was assessed with false discovery rate correction via linear regression, linear mixed-effects, and Gaussian graphical models. Results Patients with the top quartile of N -formylmethionine abundance at ICU admission had a significantly higher adjusted odds of 28-day mortality in the VITdAL-ICU (OR, 2.4; 95%CI 1.5–4.0; P  = 0.001) and RoCI cohorts (OR, 5.1; 95%CI 1.4–18.7; P  = 0.015). Adjusted linear regression shows that with increases in N -formylmethionine abundance at ICU admission, 55 metabolites have significant differences common to both the VITdAL-ICU and RoCI cohorts. With increased N -formylmethionine abundance, both cohorts had elevations in individual short-chain acylcarnitine, branched chain amino acid, kynurenine pathway, and pentose phosphate pathway metabolites. Conclusions The results indicate that circulating N -formylmethionine promotes a metabolic shift with heightened mortality that involves incomplete mitochondrial fatty acid oxidation, increased branched chain amino acid metabolism, and activation of the pentose phosphate pathway. Graphic Abstract

Abstract Rationale Severe α1-antitrypsin deficiency (typically PiZZ homozygosity) is associated with a significantly increased risk of airflow obstruction and emphysema but the risk of chronic obstructive pulmonary disease (COPD) in PiMZ heterozygotes remains uncertain. Objectives This was a family-based study to determine the risk of COPD in PiMZ individuals. Methods We compared 99 PiMM and 89 PiMZ nonindex subjects recruited from 51 index probands who were confirmed PiMZ heterozygotes and also had a diagnosis of COPD Global Initiative for Chronic Obstructive Lung Disease stage II–IV. The primary outcome measures of interest were quantitative variables of pre- and post-bronchodilator FEV1/FVC ratio, FEV1 (liters), FEV1 (% predicted), forced expiratory flow midexpiratory phase (FEF25–75; liters per second), FEF25–75 (% predicted), and a categorical outcome of COPD. Measurements and Main Results PiMZ heterozygotes compared with PiMM individuals had a reduced median (interquartile range) post-bronchodilator FEV1 (% predicted) (92.0 [75.6–105.4] vs. 98.6 [85.5–109.7]; P = 0.04), FEV1/FVC ratio (0.75 [0.66–0.79] vs. 0.78 [0.73–0.83]; P = 0.004), and FEF25–75 (% predicted) (63.84 [38.45–84.35] vs. 72.8 [55.5–97.7]; P = 0.0013) compared with PiMM individuals. This effect was abrogated in never-smoking and accentuated in ever-smoking PiMZ individuals. PiMZ heterozygosity was associated with an adjusted odds ratio for COPD of 5.18 (95% confidence interval, 1.27–21.15; P = 0.02) and this was higher (odds ratio, 10.65; 95% confidence interval, 2.17–52.29; P = 0.004) in ever-smoking individuals. Conclusions These results indicate that PiMZ heterozygotes have significantly more airflow obstruction and COPD than PiMM individuals and cigarette smoke exposure exerts a significant modifier effect.

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide, with a complex pathophysiology and phenotypic diversity. Here, we apply Similarity Network Fusion (SNF) to cluster AMD patients into putative metabolomics-derived endotypes. Using a discovery cohort of 163 AMD patients from Boston, US, and a validation cohort of 214 patients from Coimbra, Portugal, we identified four distinct metabolomics-derived endotypes with varying retinal structural and functional characteristics, confirmed across both cohorts. Patients clustered into Endotype 1 exhibited a milder form of AMD and were characterized by low levels of amino acids in specific metabolic pathways. Meanwhile, patients clustered into both Endotype 3 and 4 were associated with more severe AMD and exhibited low levels of fatty acid metabolites and elevated levels of sphingomyelins and fatty acid metabolites, respectively. These preliminary findings indicate that metabolomics-derived endotyping may offer a refined strategy for categorizing AMD patients based on their specific pathophysiological underpinnings, rather than relying solely on traditional observational clinical indicators.