Omics signature of new-onset mild cognitive impairment and dementia in a population- based study
Bibliographic record
Abstract
Plasma proteomics and metabolomics snapshots reveal a molecular signature in circulation delineating pathophysiology of major and minor neurocognitive disorder. To identify new cues to disease aetiology and diagnostic approach, we applied plasma proteomics and metabolomics profiling platforms to samples collected in a population-based study of the Singapore Longitudinal Ageing Studies Wave 2 (SLAS-2). In this longitudinal study, blood samples were analysed with standard clinical chemistry, plasma proteomics (Sengenics) and metabolomics (Nightingale) panels. Participants were followed up for the development of mild cognitive impairment (MCI) and dementia for 3-5 years. Of the total 1,892 molecules in all assay types, 463 demonstrated significant associations with baseline prevalent MCI and dementia. We trained an automatic linear modelling of predictors for follow-up new-onset MCI and dementia. The best model consists of 10 variables including ZSCAN18, PRKD3, SPANXN4, DDX43, saturated fatty acids, PPP3CA, NFATC4, IL-8, PAK6, and PDGFB. In terms of molecular function, these molecular markers are involved in immunological dysfunction and inflammatory reaction, protein coding, lipids, DNA-binding transcription factor activity, and nervous system development. In conclusion, our current research has identified an omics signature linked to new-onset mild cognitive disorder and dementia, which we hope can help enhance the accuracy of their diagnosis using circulating blood samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".