Investigation of the impact of stool collection methods on the metabolomics analysis/profiles of infant fecal samples
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".