Associations of gut microbiota, dietary intake, and serum short-chain fatty acids with fecal short-chain fatty acids in healthy Japanese adults
In a sample of 12 volunteers, correlations between gut microbiota, diet, and fecal SCFAs were identified, but the sample size prevents any generalizable inference — effect directions are exploratory and inconclusive.
| Population | 12 healthy Japanese adults (10 male, 2 female), university students and staff, Sapporo, Japan, September 2017 |
|---|---|
| Exposure | Gut microbiota characterization (composition and functional profile), dietary intake (BDHQ), and fecal and serum SCFA measurement by NMR (600 MHz) |
| Comparator | Not applicable — cross-sectional observational study without a separate control group |
| Outcome | Correlation between fecal SCFAs and gut microbiota composition; Correlation between fecal SCFAs and gut microbiota functional profile; Correlation between fecal SCFAs and dietary intake; Correlation between fecal SCFAs and serum SCFAs |
Summary of findings
| Outcome | Effect | 95% CI | Certainty | Clinical relevance | Notes |
|---|---|---|---|---|---|
| Correlation between fecal SCFAs and gut microbiota composition | r not reported with 95% CI; n=12 | — | Very low | — | 1 studies |
| Correlation between fecal SCFAs and gut microbiota functional profile | r not reported with 95% CI; n=12 | — | Very low | — | 1 studies |
| Correlation between fecal SCFAs and dietary intake | r not reported with 95% CI; n=12 | — | Very low | — | 1 studies |
| Correlation between fecal SCFAs and serum SCFAs | r not reported with 95% CI; n=12 | — | Very low | — | 1 studies |
Context
SCFAs produced by colonic microbial fermentation are biomarkers of interest for gut health. Prior studies did not simultaneously integrate gut microbiota functional profiles and dietary intake. This study attempts to address that gap in a healthy Japanese population.
What the study showed
The study identified correlations between specific bacterial taxa and fecal SCFAs, and between fecal and serum SCFAs, in a sample of n=12. No 95% CI or standardized effect sizes were reported for the main correlations. The absence of precision data with CIs makes it impossible to assess clinical relevance. Dietary correlations were exploratory and unadjusted for confounders.
How it was done
Cross-sectional observational study with 12 participants. Microbiota analyzed by fecal DNA extraction with guanidine kit; SCFAs by NMR spectroscopy at 600 MHz; diet by self-administered BDHQ questionnaire covering the week prior to collection. Fecal samples collected within 0–7 days after blood draw.
Effect magnitude
No effect sizes with 95% CI were reported in the available text. Correlations are presented without precision statistics, making magnitude assessment impossible.
Risk of bias
Sample of n=12 is critically insufficient for any multivariate correlation analysis with adequate statistical power (no power calculation reported). Sex imbalance (10M:2F) and university convenience sample limit representativeness. BDHQ was applied for the prior week, deviating from the validated protocol (prior month), introducing measurement bias. No risk-of-bias tool applied (study does not use RoB 2 or ROBINS-I). Fecal collection window of 0–7 days after blood introduces temporal variability.
What this study does NOT prove
This study does not prove causality between diet, microbiota, and SCFAs. It is not generalizable to non-Japanese, non-university, diseased, or Western-diet populations.
In clinical practice
This study provides no basis for changing clinical practice. Fecal SCFAs remain investigational biomarkers without established diagnostic or prognostic value in this context. Clinicians should not use these findings to recommend specific dietary interventions.
Limitations
Sample of n=12 is critically insufficient for any multivariate correlation analysis with adequate statistical power (no power calculation reported). Sex imbalance (10M:2F) and university convenience sample limit representativeness. BDHQ was applied for the prior week, deviating from the validated protocol (prior month), introducing measurement bias. No risk-of-bias tool applied (study does not use RoB 2 or ROBINS-I). Fecal collection window of 0–7 days after blood introduces temporal variability.
What is still missing
Studies with samples ≥100 participants, balanced sex and age representation, adjusted for confounders, and with longitudinal measurements are needed to establish direction and magnitude of associations.
Technical appendix
Version history
- 1.0 · 2026-06-24 — Auto-generated under Evidence Standard v1.0