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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 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.

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.

Diet plays a crucial role in health, with low-carbohydrate diets often proposed to exert metabolic benefits. We aim to investigate metabolomic adaptations in 164 adults with overweight or obesity who were randomly assigned to high- ( n  = 54), moderate- ( n  = 53), or low-carbohydrate ( n  = 57) diets during a 20-week weight-loss maintenance phase of the Framingham State Food Study [(FS)2], a controlled, parallel feeding trial (ClinicalTrials.gov: NCT02068885). We measure fasting plasma metabolites by liquid chromatography-tandem mass spectrometry using samples from 147 participants who completed the study ( n  = 45, 48, and 54 in the high-, moderate-, and low-carbohydrate diet groups, respectively). Significant associations (False Discovery Rate<0.05) are identified between carbohydrate-to-fat ratio (CFR) and diet-induced changes in 148 of 479 metabolites at 20 weeks, with nearly all showing consistent trends at 10 and 20 weeks. Phosphatidylcholines plasmanyls/plasmalogens, phosphatidylethanolamines plasmanyls/plasmalogens, and sphingomyelins generally decrease with higher CFR, whereas lysophosphatidylcholines, lysophosphatidylethanolamines, and triglycerides generally increase. Our findings are largely reproducible in an independent feeding trial involving diets with similar CFR (Popular Diets Study, ClinicalTrials.gov: NCT00315354). Eleven triglyceride species (≤3 double bonds), linked to type 2 diabetes risk, increase with higher CFR. Our findings demonstrate metabolomic changes caused by varying CFR dietary patterns, offering potential insights into mechanisms that could guide targeted dietary intervention strategies. Increasing dietary carbohydrate-to-fat ratio in a randomized controlled feeding study altered circulating small molecules (metabolites), including ones associated with diabetes risk, underscoring important metabolic effects of dietary composition.

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.

Rationale We previously validated five clinically distinct asthma metabo-endotypes (mechanistically derived asthma subgroups). We hypothesize that metabo-endotype membership may be partially driven by differences in serum microRNA profiles and their influence on metabolite levels. Objectives To determine whether serum miRNA levels can help understand the underlying drivers of metabolic dysregulation across metabo-endotypes. Method We compared expression levels of serum microRNAs across 1121 children grouped into five asthma metabo-endotypes using ANCOVA. A LASSO model was leveraged to determine the most important miRNAs for discriminating metabo-endotype membership. Finally, multiple linear regression models and two-sample t-tests were employed to determine whether serum microRNA ~ plasma metabolite relationships differed between individuals within different metabo-endotypes. Measurements and main results Of 317 serum miRNAs, 132 (41.6%) demonstrated significantly different expression across metabo-endotypes (FDR < 0.05), with miR-143-3p showing the greatest variation (FDR p  = 5.7 × 10 − 19 ). Most differences were driven by metabo-endotypes 2 and 3, the most and least severe. Enrichment analysis of microRNAs’ predicted target genes revealed critical asthma pathways, including Th17 and Th1/Th2 cell differentiation. A model based on 17 miRNAs was able to discriminate membership of metabo-endotype 2 versus 3 (AUC:81%, CI: 73%-88%). There was some evidence that relationships between specific miRNAs and metabolites differed between individuals in metabo-endotypes 2 and 3, which may suggest differential posttranscriptional regulation of pathways including eicosanoid and arginine metabolism. Conclusions The results provide some evidence to suggest differential miRNA regulated gene expression between biologically and clinically distinct asthma metabo-endotypes, with a potentially important role for miR-143-3p. Understanding these relationships may uncover novel therapeutic targets and guide more personalized treatment strategies.

In this review, we discuss the growing literature demonstrating robust and pervasive associations between the microbiome and metabolome. We focus on the gut microbiome, which harbors the taxonomically most diverse and the largest collection of microorganisms in the human body. Methods for integrative analysis of these “omics” are under active investigation and we discuss the advances and challenges in the combined use of metabolomics and microbiome data. Findings from large consortia, including the Human Microbiome Project and Metagenomics of the Human Intestinal Tract (MetaHIT) and others demonstrate the impact of microbiome-metabolome interactions on human health. Mechanisms whereby the microbes residing in the human body interact with metabolites to impact disease risk are beginning to be elucidated, and discoveries in this area will likely be harnessed to develop preventive and treatment strategies for complex diseases.

While genome-wide association studies have identified genes involved in differential treatment responses to inhaled corticosteroids (ICS) in asthma, few studies have evaluated the potential effects of age in this context. A significant proportion of asthmatics experience exacerbations (hospitalizations and emergency department visits) during ICS treatment. We evaluated the interaction of genetic variation and age on ICS response (measured by the occurrence of exacerbations) through a genome-wide interaction study (GWIS) of 1,321 adult and child asthmatic patients of European ancestry. We identified 107 genome-wide suggestive (P<10-05) age-by-genotype interactions, two of which also met genome-wide significance (P<5x10-08) (rs34631960 [OR 2.3±1.6-3.3] in thrombospondin type 1 domain-containing protein 4 (THSD4) and rs2328386 [OR 0.5±0.3-0.7] in human immunodeficiency virus type I enhancer binding protein 2 (HIVEP2)) by joint analysis of GWIS results from discovery and replication populations. In addition to THSD4 and HIVEP2, age-by-genotype interactions also prioritized genes previously identified as asthma candidate genes, including DPP10, HDAC9, TBXAS1, FBXL7, and GSDMB/ORMDL3, as pharmacogenomic loci as well. This study is the first to link these genes to a pharmacogenetic trait for asthma.

The lack of biomarkers to identify individuals at risk of asthma exacerbations remains a significant limitation to improving patient outcomes. To address this need, we analyze data from three asthma cohorts, combining up to 25 years of electronic medical records with sequential metabolomics studies, to develop and replicate a predictive model for asthma exacerbation risk. We identify asthma-associated biochemical pathways via global circulatory metabolomics and then apply targeted mass spectrometry methods to quantify selected steroids, sphingolipids, and microbial-derived metabolites. The sphingolipid-to-steroid ratios robustly associate with 5-year exacerbation risk (discovery p value = 1.63×10⁻ 26 -0.029; replication p value = 1.89×10⁻ 36 -0.033). Based upon these findings, we derive and replicate a simple 5-year predictive model of asthma exacerbations using 21 sphingolipid-to-steroid ratios that outperforms current clinical measures (discovery AUC = 0.90; replication AUC = 0.89). These findings underscore the value of metabolomic profiling to develop a practical, cost-effective clinical assay for asthma exacerbation risk that may improve patient care. Asthma exacerbations remain hard to predict with routine tests. Here, the authors show that simple blood sphingolipid-to-steroid ratios predict five-year exacerbation risk and can underpin a practical, low-cost assay that outperforms standard clinical measures.

Metabolomic Epidemiology is a growing area of research within the metabolomics research community. In response to this, we describe the establishment of the Metabolomics Society Metabolomic Epidemiology Task Group. The overall mission of this group is to promote the growth and understanding of metabolomic epidemiology as an independent research discipline and to drive collaborative efforts that can shape the field. In this article we define metabolomic epidemiology and identify the key challenges that need to be addressed in order to advance population-based scientific discovery in metabolomics.