Comparison of cerebrospinal fluid biomarkers relevant to neurodegenerative diseases in healthy cynomolgus and rhesus macaque monkeys
Bibliographic record
Abstract
Structured Abstract INTRODUCTION Non-human primates are important translational models of neurodegenerative disease. We characterized how species, sex, age, and site of sampling affected concentrations of key biomarkers of neurodegeneration. METHODS Amyloid-beta (Aβ40, Aβ42), tau (tTau, pTau), and neurofilament light (NFL) in CSF were measured in 82 laboratory-housed naïve cynomolgus and rhesus macaques of both sexes. RESULTS Aβ40, Aβ42, and NFL were significantly higher in rhesus compared with cynomolgus macaques. tTau and NFL were higher in males. pTau was not affected by species or sex. Site of acquisition only affected NFL, with NFL being higher in CSF acquired from lumbar compared with cisterna magna puncture. DISCUSSION Normative values for key neurodegeneration biomarkers were established for laboratory housed cynomolgus and rhesus macaque monkeys. Differences were observed as a function of species, sex and site of CSF acquisition that should be considered when employing primate models. Research In Context Systematic review: We reviewed reports characterizing CSF biomarkers of neurodegenerative diseases in non-human primates – an increasingly important model of disease - revealing that studies with laboratory housed macaque monkeys were of small sample size, with a paucity of data about how biomarkers varied as a function of species, sex, age, and site of acquisition. Interpretation: To address this gap, we collected CSF from 82 naïve laboratory housed male and female macaques of two species and measured Aβ40, Aβ42, tTau, pTau, and NFL. In addition to providing normative statistics for concentrations of these biomarkers, we revealed various species and sex differences. Future directions: Establishing normative values of biomarkers is an important step to the efficient development of cynomolgus and rhesus macaques as models of neurodegenerative disorders such as Alzheimer’s disease. Reference values reduce the need for large control groups by which to compare with disease model animals.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".