Serum trimethylamine N-oxide and its precursors are associated with the occurrence of mild cognition impairment as well as changes in neurocognitive status
Bibliographic record
Abstract
Background This study aims to examine the association between gut microbe-dependent trimethylamine N-oxide (TMAO) and its precursors (choline, betaine, and carnitine) levels and mild cognition impairment (MCI), alongside changes in the Chinese version of the Montreal Cognitive Assessment-Basic (ΔMoCA-BC) score in rural adults. Methods Drawing data from a large-scale epidemiological study conducted in rural areas of Fuxin County, Liaoning Province, China. 1,535 participants free from brain-related ailments were initially surveyed. MCI was assessed through the MoCA-BC score. Logistic regression models and restricted cubic spline were used to investigate the association between TMAO and its precursors levels and MCI. Additionally, the association between TMAO and its precursors levels and ΔMoCA-BC was analyzed using a generalized linear model in the longitudinal study. Results The average age of the study participants was 58.6 ± 9.4 years and the prevalence rate of MCI was 34.5%. With the second quartile as the reference in the logistic regression model, the OR for risk of MCI in the highest quartile for TMAO, betaine, and carnitine was 1.685 (95% CI: 1.232–2.303, p = 0.001), 2.367 (95% CI: 1.722–3.255, p < 0.001), and 2.239 (95% CI: 1.742–3.295, p < 0.001), respectively. The OR of choline for the highest versus lowest quartile was 2.711 (95% CI: 2.012–3.817, p < 0.001) for the risk of MCI. We find a J-shaped association between betaine ( P non-linear = 0.001) and carnitine ( P non-linear = 0.003) levels and MCI. Furthermore, TMAO and its precursors levels were associated with ΔMoCA-BC in the third and fourth quartiles group (All p < 0.05). Conclusion The findings suggest the existence of an optimal concentration range for serum levels of TMAO, betaine, and carnitine that mitigates MCI risk, paving the way for enhanced dietary interventions aimed at preventing and treating MCI.
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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 | 0.001 | 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".