Clinical proteomic analysis across the Alzheimer’s disease continuum
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD), defined by amyloid‐β (Aβ) and neurofibrillary tau tangles, can be viewed as a continuum, starting with an asymptomatic “pre‐clinical” phase that progresses to a symptomatic clinical stage characterized as mild cognitive impairment (MCI), and finally, AD dementia. Identifying biomarkers reflective of the different disease stages is important to detect individuals at risk of developing AD, to monitor disease progression and the effect of treatments, as well as to determine new therapeutic targets. Method We present data from a cross‐sectional tandem mass tag (TMT) proteomic study of cerebrospinal fluid (CSF) samples from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort (young adults (n = 22), cognitively unimpaired (n = 54), cognitively impaired (n = 26), MCI (n = 19), AD (n = 19), non‐AD (n = 16), frontotemporal dementia (n = 9); total n = 214); a highly profiled cohort across the AD continuum with clinical and neuropsychological assessments, MRI, and Aβ and tau positron emission tomography (PET). To enhance detection of brain‐derived proteins, we evaluated the use of a TMT booster channel consisting of brain protein extract. Planned statistical analyses include linear regression modelling to compare biomarker distributions across groups, covariating for sex and age where appropriate as well as correlation analyses of biomarker levels with Aβ and tau PET measurements. Result Our preliminary data analysis suggests that a set of proteins differed with high significance between amyloid PET negative and positive individuals (Fig. 1), including proteins that showed changes already at the pre‐clinical stage of AD. We will explore the correlation of the identified biomarker candidates with pathological process and disease progression, as well as with Aβ‐ and tau‐PET measurements. Also, protein changes associated with aging and other neurodegenerative disorders will be evaluated. Finally, the use of a TMT booster based on brain protein extract to enhance identification of CSF biomarkers will be assessed. Conclusion This study describes the most extensive unbiased proteomic profiling in CSF across the AD continuum, identifying proteins that may serve as novel therapeutic targets and fluid biomarkers for the disease. Additionally, the use of a TMT booster to enhance the detection of brain‐derived proteins was evaluated.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".