Metabolomic differences in lung function metrics: evidence from two cohorts

Kelly, Rachel S.; Stewart, Isobel D.; Bayne, Haley; Kachroo, Priyadarshini; Spiro, Avron; Vokonas, Pantel; Sparrow, David; Weiss, Scott T.; Knihtilä, Hanna; Litonjua, Augusto A.; Wareham, Nicholas J.; Langenberg, Claudia; Lasky‐Su, Jessica

doi:10.1136/thoraxjnl-2020-216639

Cited by 4 publications

(5 citation statements)

References 57 publications

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“…For instance, our metSS was developed in a population enriched with COPD, which could limit the generalizability and the deconvolution of those metabolomic pathways related to COPD progression versus those that regulate lung development and variability in the general population. This is supported by the overall low, but not negligible, overlap of metabolites associated with FEV 1 between our analysis and those involving general population cohorts that included a significant proportion of individuals who never smoked and/or who had normal FEV 1 [ 5 , 6 ]. Beyond disease progression versus lung-development-related metabolites, differences in methodological approaches (adaLASSO vs. multivariable regression models) between analyses might also explain these results and warrant further investigation.…”

Section: Discussionsupporting

confidence: 68%

“…KEGG analysis also identified aminoacyl-tRNA biosynthesis and glycine, serine, and threonine pathway overrepresentation in the metSS, which suggests disturbances in general amino acid metabolism. Aminoacyl-tRNAs are vital for protein synthesis and have been associated with oxidative stress [ 5 , 6 , 26 ]. Similarly, multiple studies have found enrichment of the glycine, serine, and threonine metabolism pathways, which have been associated with COPD exacerbation severity [ 4 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…Yu et al also showed associations between FEV 1 and four metabolic pathways, including aminoacyl-tRNA biosynthesis; phenylalanine metabolism; nitrogen metabolism; and alanine, aspartate, and glutamate metabolism [ 5 ]. Kelly et al found 156 metabolites associated with FEV 1 in a general population study of 10,460 participants with a validation cohort of 437 participants using blood and plasma samples [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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A Metabolomic Severity Score for Airflow Obstruction and Emphysema

et al. 2022

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Chronic obstructive pulmonary disease (COPD) is a disease with marked metabolic disturbance. Previous studies have shown the association between single metabolites and lung function for COPD, but whether a combination of metabolites could predict phenotype is unknown. We developed metabolomic severity scores using plasma metabolomics from the Metabolon platform from two US cohorts of ever-smokers: the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) (n = 648; training/testing cohort; 72% non-Hispanic, white; average age 63 years) and the COPDGene Study (n = 1120; validation cohort; 92% non-Hispanic, white; average age 67 years). Separate adaptive LASSO (adaLASSO) models were used to model forced expiratory volume at one second (FEV1) and MESA-adjusted lung density using 762 metabolites common between studies. Metabolite coefficients selected by the adaLASSO procedure were used to create a metabolomic severity score (metSS) for each outcome. A total of 132 metabolites were selected to create a metSS for FEV1. The metSS-only models explained 64.8% and 31.7% of the variability in FEV1 in the training and validation cohorts, respectively. For MESA-adjusted lung density, 129 metabolites were selected, and metSS-only models explained 59.0% of the variability in the training cohort and 17.4% in the validation cohort. Regression models including both clinical covariates and the metSS explained more variability than either the clinical covariate or metSS-only models (53.4% vs. 46.4% and 31.6%) in the validation dataset. The metabolomic pathways for arginine biosynthesis; aminoacyl-tRNA biosynthesis; and glycine, serine, and threonine pathway were enriched by adaLASSO metabolites for FEV1. This is the first demonstration of a respiratory metabolomic severity score, which shows how a metSS can add explanation of variance to clinical predictors of FEV1 and MESA-adjusted lung density. The advantage of a comprehensive metSS is that it explains more disease than individual metabolites and can account for substantial collinearity among classes of metabolites. Future studies should be performed to determine whether metSSs are similar in younger, and more racially and ethnically diverse populations as well as whether a metabolomic severity score can predict disease development in individuals who do not yet have COPD.

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Section: Discussionsupporting

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Metabolomic Severity Score for Airflow Obstruction and Emphysema

et al. 2022

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“…The depression and anxiety subscales comprised six questions in total, and the phobic anxiety subscale comprised five questions. The scores were categorized as “abnormal” on a given subscale if the score was one standard deviation (SD) above the sample mean, as previously defined [ 24 , 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

“… Note: Individuals are categorized as having an abnormal score on a given BSI subscale if their score is one standard deviation above the mean, as previously described in [ 24 , 26 , 27 ]. …”

Section: Figurementioning

confidence: 99%

Metabolomics and Self-Reported Depression, Anxiety, and Phobic Symptoms in the VA Normative Aging Study

et al. 2023

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Traditional approaches to understanding metabolomics in mental illness have focused on investigating a single disorder or comparisons between diagnoses, but a growing body of evidence suggests substantial mechanistic overlap in mental disorders that could be reflected by the metabolome. In this study, we investigated associations between global plasma metabolites and abnormal scores on the depression, anxiety, and phobic anxiety subscales of the Brief Symptom Inventory (BSI) among 405 older males who participated in the Normative Aging Study (NAS). Our analysis revealed overlapping and distinct metabolites associated with each mental health dimension subscale and four metabolites belonging to xenobiotic, carbohydrate, and amino acid classes that were consistently associated across all three symptom dimension subscales. Furthermore, three of these four metabolites demonstrated a higher degree of alteration in men who reported poor scores in all three dimensions compared to men with poor scores in only one, suggesting the potential for shared underlying biology but a differing degree of perturbation when depression and anxiety symptoms co-occur. Our findings implicate pathways of interest relevant to the overlap of mental health conditions in aging veterans and could represent clinically translatable targets underlying poor mental health in this high-risk population.

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A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

Halama,

Zaghlool,

Thareja

et al. 2024

Nat Commun

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In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays “The Molecular Human”. We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to “The Molecular Human” ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

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Metabolomic differences in lung function metrics: evidence from two cohorts

Cited by 4 publications

References 57 publications

A Metabolomic Severity Score for Airflow Obstruction and Emphysema

A Metabolomic Severity Score for Airflow Obstruction and Emphysema

Metabolomics and Self-Reported Depression, Anxiety, and Phobic Symptoms in the VA Normative Aging Study

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

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