Deciphering the interrelation of gut microbiota and BMI in atherosclerosis: a metagenomic approach
Bibliographic record
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is a global health concern, leading to higher rates of morbidity and mortality. Gut microbial dysbiosis significantly contributes to obesity related ASCVD. However, the interrelation of gut microbiome in driving obesity or overweight mediated ASCVD has not been sufficiently investigated. To unravel this complex interplay, we have compared the gut microbial shotgun metagenome data of ASCVD subjects across normal BMI (Body Mass Index) and overweight/obese (OW/OB) BMI categories. We identified a distinct gut microbial composition and function in normal and OW/OB ASCVD subjects. Using gut microbial abundance, a machine learning model was built to predict ASCVD in the normal and OW/OB samples. The gut microbiome-based signature for ASCVD discrimination was achieved with an AUC of 0.87 and 0.83 for distinguishing control and ASCVD in normal and OW/OB BMI groups, respectively. In addition, we have also identified that Pseudoflavonifractor capillosus could act as a prognostic organism in identifying OW/OB associated ASCVD. Therefore, an appropriate diet could modify the ASCVD contributing gut microbiome, hence minimizing the risk of ASCVD in OW/OB individuals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".