Hypertension Shifts Gut Microbiota and Tryptophan Metabolism in Women
Bibliographic record
Abstract
ABSTRACT Background Hypertension affects over 1.28 billion adults worldwide, including a significant number of women. Although the gut microbiome is implicated in the onset and progression of hypertension, few studies have examined the relationship in middle-aged women. Methods Within an established cohort, we investigated the relationship between gut microbiota and its metabolites in normotensive vs. hypertensive middle-aged women (n=108) matched for age (56.6±0.91 years) and body mass index (24.3±0.24 kg/m²). Fecal microbiota analysis was performed using 16S rRNA sequencing and serum metabolites were analyzed using LC-MS/QTOF. Associations between the microbiota, metabolomic alterations and systemic inflammatory cytokines were statistically examined to uncover their interrelationships and potential role in disease progression. Results Women with hypertension had gut dysbiosis with an increased Firmicutes/Bacteroidetes ratio and higher abundances of inflammatory taxa including Anaerostipes and Collinsella . Untargeted serum metabolomics demonstrated that hypertensive participants had elevated levels of tryptophan, the pro-inflammatory metabolite kynurenine and lower levels of health-promoting indoles produced by the action of gut microbiota on tryptophan (p<0.05). These findings were confirmed in microbiota analysis showing a reduced abundance of indole-producing species ( Alistipes shahii , Bacteroides faecichinchillae, Bacteroides stercoris )(p<0.05) suggesting a lower microbial activity of tryptophan-indole metabolism. Furthermore, hypertension increased inflammatory markers including an elevated IL12/IL10 ratio, interferon-γ and tumor necrosis factor-α. The IL-12/IL-10 ratio demonstrated a positive correlation with kynurenine levels, emphasizing the involvement of cytokines and gut microbiota in driving systemic inflammation in hypertension. Conclusion Imbalances in microbiota-regulated tryptophan metabolism contribute to systemic inflammation in hypertensive, middle-aged women, presenting a potentially modifiable target for intervention. GRAPHICAL ABSTRACT
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 | 0.000 |
| 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) | 0.002 | 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".