Differential effects of whisky brands on human gut microbiome and fecal metabolome
Bibliographic record
Abstract
The gut bacteria have significant impact on human physiology and are influenced by dietary habit [1]. Apart from normal diet, alcoholic beverages have also been shown to influence gut microbial makeup. The wine polyphenols have been linked to increase the beneficial bacteria in the gut after 4 weeks of consumption [2]. Consumption of alcoholic beverages for longer period (>10 years) has also been correlated to detrimental gut bacterial dysbiosis [3]. The contrasting effects of alcoholic beverages in these two studies necessitate further research. Globally, 45.7% of alcoholic drinkers are spirit drinkers with India having the highest (71%) [4]. In India whisky is preferred by most of the drinkers and 1400 million liters of whisky was consumed in India in the year 2012 [5]. Till date, no study has been reported to understand the effect of long-term consumption of different types of whisky on gut bacterial profile (GBP). In this purview apilot study of gut bacterial and metabolite profile was performed between the whisky drinker (n=18) and non-drinker (n=8) along with rice beer drinkers (n=3). PCR-denaturing gradient gel electrophoresis (PCR-DGGE) coupled with next generation sequencing (NGS) analysis on illumina miseq platform revealed decrease in gut bacterial diversity in the drinkers compared to the non-drinkers. The whisky types have differential effects on the GBP. The GBP of whisky type 1 drinkers had higher abundance of Clostridiaceae and Enterobacteriaceae (fold change log 2: 3.33 & 3.1537, respectively; p< 0.002) in comparison to the non-drinker group, while the type 2 whisky drinkers had increased abundance of Lactococcus and Streptococcus (fold change log 2: 9.1827 & 4.2986; p< 0.002) compared to the non-drinker group. The butyric acid producing genera, Ruminococcaceae was found to be decreased in both the whisky drinking cohorts (fold change log 2: -1.5449 & -2.7327, respectively; p<0.002). Short-chain fatty acids (SCFA), mainly butyric acid, acetic acid and propanoic acid were found to be decreased in both the whisky drinker groups in comparison to the non-drinkers (p< 0.05). The differential effects of whisky types with equal alcohol content indicate that constituents of whisky other than the alcohol also influence the gut bacterial composition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".