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Record W2974767039 · doi:10.1152/ajpendo.00091.2019

Exercise plasma metabolomics and xenometabolomics in obese, sedentary, insulin-resistant women: impact of a fitness and weight loss intervention

2019· article· en· W2974767039 on OpenAlexaff
Dmitry Grapov, Oliver Fiehn, Caitlin Campbell, Carol J. Chandler, Dustin J. Burnett, Elaine C. Souza, Gretchen A. Casazza, Nancy L. Keim, John W. Newman, Gary R. Hunter, José R. Fernández, W. Timothy Garvey, Charles L. Hoppel, Mary‐Ellen Harper, Sean H. Adams

Bibliographic record

VenueAmerican Journal of Physiology-Endocrinology and Metabolism · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Ottawa
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of Health
KeywordsMetaboliteInternal medicineEndocrinologyMetabolomeAerobic exerciseMetabolomicsInsulinInsulin resistanceWeight lossChemistryObesityMedicineBiologyBioinformatics

Abstract

fetched live from OpenAlex

Insulin resistance has wide-ranging effects on metabolism, but there are knowledge gaps regarding the tissue origins of systemic metabolite patterns and how patterns are altered by fitness and metabolic health. To address these questions, plasma metabolite patterns were determined every 5 min during exercise (30 min, ∼45% of V̇o 2peak , ∼63 W) and recovery in overnight-fasted sedentary, obese, insulin-resistant women under controlled conditions of diet and physical activity. We hypothesized that improved fitness and insulin sensitivity following a ∼14-wk training and weight loss intervention would lead to fixed workload plasma metabolomics signatures reflective of metabolic health and muscle metabolism. Pattern analysis over the first 15 min of exercise, regardless of pre- versus postintervention status, highlighted anticipated increases in fatty acid tissue uptake and oxidation (e.g., reduced long-chain fatty acids), diminution of nonoxidative fates of glucose [e.g., lowered sorbitol-pathway metabolites and glycerol-3-galactoside (possible glycerolipid synthesis metabolite)], and enhanced tissue amino acid use (e.g., drops in amino acids; modest increase in urea). A novel observation was that exercise significantly increased several xenometabolites (“non-self” molecules, from microbes or foods), including benzoic acid-salicylic acid-salicylaldehyde, hexadecanol-octadecanol-dodecanol, and chlorogenic acid. In addition, many nonannotated metabolites changed with exercise. Although exercise itself strongly impacted the global metabolome, there were surprisingly few intervention-associated differences despite marked improvements in insulin sensitivity, fitness, and adiposity. These results and previously reported plasma acylcarnitine profiles support the principle that most metabolic changes during submaximal aerobic exercise are closely tethered to absolute ATP turnover rate (workload), regardless of fitness or metabolic health status.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.234
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations39
Published2019
Admission routes1
Has abstractyes

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