Plasma biomarkers combined with UPDRS and MoCA for differential assessments of amnestic cognitive impairment (aMCI), Parkinson’s disease (PD+PDD) and dementia with Lewy body (DLB): A pilot study
Bibliographic record
Abstract
Abstract Background There would be tremendous value for having biomarker panels as screening tools for the assessment of AD and PD pathologies. Technologies that increase the sensitivity and accuracy of the measurement of plasma Aβ42, total Tau (t‐Tau), phosphorylated Tau (P‐tau),) have emerged and there is interest in testing plasm alpha synuclein (α‐syn). The specific aim of the present study was to assess the accuracy of plasma Aβ42, total Tau (t‐Tau), and α‐syn combined with clinical measures in identifying mild cognitive impaired (MCI), PD/PDD, and DLB cases. Methods The total sample size was 77 subjects including 37 NC, 16 MCI [NIA‐AA Criteria, Alberts 2011], 12PD/PDD [Hughes criteria] and 12 DLB [McKeith criteria]. NC subjects reported no demonstrable cognitive complaints, were intact functionally and cognitively.. The variables included age, education, MOCA, UPDRS, and H‐Y stage. Plasma biomarkers were assayed by ImmunoMagnetic Reduction (IMR) technology (MagQu, Inc). Results MOCA scores were lower in the aMCI and DLB groups. The UPDRS and H‐Y stage scores were significantly higher in the PD and DLB groups. The sensitivities using biomarkers (abeta42xT‐Tau) or MoCA individually to discriminate aMCI from NC are 0.625 to 0.92, respectively. The combination of biomarker and MoCA shows the 0.929 for the sensitivity. As to the specificity, individual biomarker or MoCA shows 0.778, whereas the combination of biomarker and MoCA shows 0.833. .When clinical measures were combined with biomarker values (UPDRS w/alpha‐syn; DLB status could be differentiated from PD/PDD with a sensitivity 0.955, Specificity 0 980, The combination of p‐tau181 with UPDRS had a sensitivity of 0.833 and specificity of 0.833 which was better than the sensitivity and specificity of the individual measures. Conclusions Investigations on the discrimination of DLB vs. PDD are rare. In this pilot study, we show that IMR assays of plasma α‐Syn, P‐tau 181, and Aβ42 x t‐tau in combination with clinical measures (UPDRS and MOCA), could differentiate PD/PDD from DLB, as well as NC from aMCI. Thus, our data suggest clinical utility of these novel biomarker modalities and panels. Differentiating DLB from PDD could be consequential in clinical characterization. Future studies will aim to replicate our findings on larger group sizes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".