MétaCan
Menu
← Back to cohort
Record W3200395054 · doi:10.1101/2021.09.21.461251

Investigation of the impact of stool collection methods on the metabolomics analysis/profiles of infant fecal samples

2021· preprint· en· W3200395054 on OpenAlexafffund
Chiara-Maria Homann, Sara Dizzell, Sandi M. Azab, Eileen K. Hutton, Katherine M. Morrison, Jennifer C. Stearns

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsPopulation Health Research InstituteMcMaster University
FundersMcMaster University
KeywordsFecesMetabolomicsMetaboliteMetabolomeCoefficient of variationGut microbiomeBiologyChromatographyMicrobiomeFood scienceChemistryMedicineInternal medicineMicrobiologyBioinformatics

Abstract

fetched live from OpenAlex

Abstract Metabolomic studies are important to understand microbial metabolism and interaction between the host and the gut microbiome. Although there have been efforts to standardize sample processing in metabolomic studies, infant samples are mostly disregarded. In birth cohort studies, the use of diaper liners is prevalent and its impact on fecal metabolic profile remains untested. In this study, we compared metabolite profiles of fecal samples collected as solid stool and those collected from stool saturated liner. One infant’s stool sample was collected in triplicate for solid stool and stool saturated liner. Comprehensive metabolomics analysis of the fecal samples was performed using NMR, UPLC and DI-MS. The total number, identities and concentrations of the metabolites were determined and compared between stool sample collection methods (stool vs. liner). The number and identity of metabolites did not differ between collection methods for NMR and DI-MS when excluding metabolites with a coefficient of variation (CV) > 40%. NMR analysis demonstrated lowest bias between collection methods, and lowest technical precision between triplicates of the same method followed by DI-MS then UPLC. Concentrations of many metabolites from stool and stool saturated liner differed significantly as revealed by Bland-Altman plots and t-tests. Overall, a mean bias of 10.2% in the Bland-Altman analysis was acceptable for some metabolites confirming mutual agreement but not for others with a wide range of bias (−97-117%). Consequently, stool and stool-saturated liner could be used interchangeably only for some select metabolite classes e.g. amino acids. Differences between the metabolomic profiles of solid stool samples and stool saturated liner samples for some important molecules e.g., ethanol, fumarate, short chain fatty acids and bile acids, indicate the need for standardization in stool collection method for metabolomic studies performed in infants.

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)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.277
Teacher spread0.252 · 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.

Study designBench or experimental
DomainMethods
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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicMetabolomics and Mass Spectrometry Studies→French-language works237,207→