Association of the gut microbiota with clinical variables in obese and lean Emirati subjects
Bibliographic record
Abstract
Background Growing evidence supports the role of gut microbiota in obesity, yet exact associations remain largely unknown. Specifically, very little is known about this association in the Emirati population. Methods We explored differences in gut microbiota composition, particularly the Firmicutes/Bacteroidetes (F/B) ratio, between 43 obese and 31 lean adult Emirate counterparts, and its association with obesity markers, by using V3-V4 regions of 16 S ribosomal RNA gene sequencing data. Furthermore, we collected anthropometric and biochemical data. Results The two major phyla in obese and lean groups were Firmicutes and Bacteroidetes. We observed a significantly lower alpha diversity (Shannon index) in obese subjects and a significant difference in beta diversity and phylum and genus levels between the two groups. The obese group had higher abundances of Verrucomicrobia and Saccharibacteira and lower abundances of Lentisphaerae. Acidaminococcus and Lachnospira were more abundant in obese subjects and positively correlated with adiposity markers. No correlations were found between the gut microbiota and biochemical variables, such as fasting blood sugar, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides. Conclusion We reveal significant differences in the gut microbiota between obese and lean adult Emiratis and an association between certain microbial genera of the gut microbiota and obesity. A better understanding of the interactions between gut microbes, diet, lifestyle, and health is warranted.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".