metabolic profiling of Parkinson's disease and mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Early diagnosis of Parkinson's disease and mild cognitive impairment is important to enable prompt treatment and improve patient welfare, yet no standard diagnostic test is available. Metabolomics is a powerful tool used to elucidate disease mechanisms and identify potential biomarkers. OBJECTIVES: The objective of this study was to use metabolic profiling to understand the pathoetiology of Parkinson's disease and to identify potential disease biomarkers. METHODS: This study compared the serological metabolomic profiles of early-stage Parkinson's patients (diagnosed < 12 months) to asymptomatic matched controls using an established array based detection system (DiscoveryHD4™, Metabolon, UK), correlating metabolite levels to clinical measurements of cognitive impairment. RESULTS: A total of 1434 serological metabolites were assessed in early-stage Parkinson's disease cases (n = 41) and asymptomatic matched controls (n = 40). Post-quality control, statistical analysis identified n = 20 metabolites, predominantly metabolites of the fatty acid oxidation pathway, associated with Parkinson's disease and mild cognitive impairment. Receiver operator curve assessment confirmed that the nine fatty acid oxidation metabolites had good predictive accuracy (area under curve = 0.857) for early-stage Parkinson's disease and mild cognitive impairment (area under curve = 0.759). CONCLUSIONS: Our study indicates that fatty acid oxidation may be an important component in the pathophysiology of Parkinson's disease and may have potential as a diagnostic biomarker for disease onset and mild cognitive impairment. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.000 |
| 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".