Association of Gut Microbiota with Atherogenic Dyslipidemia, and Its Impact on Serum Lipid Levels after Bariatric Surgery
Bibliographic record
Abstract
Gut microbiota has been suggested to modulate circulating lipids. However, the relationship between the gut microbiota and atherogenic dyslipidemia (AD), defined as the presence of both low HDL-C and hypertriglyceridemia, is not fully understood. Moreover, because obesity is among the main causes of secondary AD, it is important to analyze the effect of gut microbiota composition on lipid profiles after a weight loss intervention. We compared the microbial diversity and taxonomic composition in patients with AD (n = 41) and controls (n = 38) and sought correlations of genera abundance with serum lipid levels in 20 patients after weight loss induced by Roux-en-Y gastric bypass (RYGB) surgery. Gut microbiota composition was profiled using next-generation sequencing of 16S rRNA. Gut microbiota diversity was significantly lower in atherogenic dyslipidemia. Moreover, relative abundance of two genera with LDA score >3.5 (Megasphaera and LPS-producing Escherichia-Shigella), was significantly higher in AD subjects, while the abundance of four short chain fatty acids (SCFA) producing-genera (Christensenellaceae R-7, Ruminococcaceae UCG-014; Akkermansia and [Eubacterium] eligens group) was significantly higher in controls. Notably, [Eubacterium] eligens group abundance was also significantly associated with higher HDL-C levels in RYGB patients one year after surgery. Although dietary polyunsaturated fatty acid/saturated fatty acid (PUFA/SFA) ratio and PUFA intake were higher in controls than in AD subjects, of the four genera differentiated in cases and controls, only Akkermansia abundance showed a positive and significant correlation with PUFA/SFA ratio. Our results suggest that SCFA-producing bacteria promote a healthy lipid homeostasis, while the presence of LPS-producing bacteria such Escherichia-Shigella may contribute to the development of atherogenic dyslipidemia.
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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 | 0.000 |
| 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".