Tryptophan Metabolism and Neurodegeneration: Longitudinal Associations of Kynurenine Pathway Metabolites with Cognitive Performance and Plasma Alzheimer’s Disease and Related Dementias Biomarkers in the Duke Physical Performance Across the LifeSpan Study
Bibliographic record
Abstract
BACKGROUND: The kynurenine pathway (KP) comprises a family of tryptophan-derived metabolites that some studies have reported are associated with poorer cognitive performance and an increased risk of Alzheimer's disease and related dementias (ADRD). OBJECTIVE: The objective of this study was to determine the associations of plasma KP metabolites (kynurenine [KYN], kynurenic acid [KA], and tryptophan [TRP]) with a panel of plasma ADRD biomarkers (Aβ42/ β40 ratio, pTau-181, glial fibrillary acidic protein [GFAP], and neurofilament light [NfL]) and cognitive performance in a subset of older adults drawn from the Duke Physical Performance Across the LifeSpan (PALS) study. METHODS: The Montreal Cognitive Assessment (MoCA) was used to assess cognitive performance. We used multivariate multiple regression to evaluate associations of the KYN/TRP and KA/KYN ratios with MoCA score and plasma ADRD biomarkers at baseline and over two years (n = 301; Age = 74.8±8.7). RESULTS: Over two years, an increasing KYN/TRP ratio was associated with increasing plasma concentrations of plasma p-Tau181 (β= 6.151; 95% CI [0.29, 12.01]; p = 0.040), GFAP (β= 11.12; 95% CI [1.73, 20.51]; p = 0.020), and NfL (β= 11.13; 95% CI [2.745, 19.52]; p = 0.009), but not MoCA score or the Aβ42/Aβ40 ratio. There were no significant associations of KA/KYN with MoCA score or plasma ADRD biomarkers. CONCLUSION: Our findings provide evidence that greater concentrations of KP metabolites are associated longitudinally over two years with greater biomarker evidence of neurofibrillary tau pathology (pTau-181), neuroinflammation (GFAP), and neurodegeneration (NfL), suggesting that dysregulated KP metabolism may play a role in ADRD pathogenesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".