Gut Microbiome Signatures Are Predictive of Cognitive Impairment in Hypertension Patients—A Cohort Study
Bibliographic record
Abstract
Growing evidence has demonstrated that hypertension was associated with dysbiosis of intestinal flora. Since intestinal microbes could critically regulate neurofunction via the intestinal–brain axis, the study aimed to reveal the role and prediction value of intestinal flora alteration in hypertension-associated cognitive impairment. A cohort of 97 participants included 63 hypertension patients and 34 healthy controls. The structure of intestinal flora was analyzed by V3–V4 16S rRNA amplicon sequencing. The cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale, and 31 patients were considered to have cognitive impairment (MoCA < 26). Patients with cognitive impairment had considerable alterations in intestinal flora structure, composition, and function compared with normal-cognitive patients. In particular, the abundance of LPS-containing taxa (Proteobacteria, Gammaproteobacteria, Enterobacterales, Enterobacteriaceae, and Escherichia–Shigella) and SCFA-producing taxon (Prevotella) significantly changed in cognition-impaired patients. Tax4Fun predication results showed downregulation of glycan biosynthesis and metabolism in hypertension patients with cognitive impairment. Additionally, the pathway was demonstrated to be significantly correlated with LPS-containing taxa (Proteobacteria, Gammaproteobacteria, Enterobacterales, Enterobacteriaceae, and Escherichia–Shigella) and SCFA-producing taxon Prevotella. Furthermore, the taxa-based multiple joint prediction model (9×) was demonstrated to have excellent diagnostic potential for cognitive impairment of hypertension patients (AUC = 0.944). The current study revealed the involvement of intestinal microbiota dysbiosis in cognition-impaired hypertension patients and provided an objective predictive index for this cognition disorder.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".