Body mass index and blood volume influence plasma biomarkers and positron emission tomography classification in preclinical Alzheimer's disease
Bibliographic record
Abstract
Abstract INTRODUCTION Blood‐based biomarkers (BBMs) are promising tools for Alzheimer's disease (AD) diagnosis, but their accuracy may be affected by body mass index (BMI) and blood volume (BV) through dilution. We investigated how BMI and BV influence BBM concentrations and PET prediction. METHODS Data from 241 cognitively unimpaired participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were examined to evaluate the influence of BMI/BV on BBMs (Aβ 42/40 , p‐Tau 181 , p‐Tau 217 , glial fibrillary acidic protein [GFAP], neurofilament light chain [NfL]) and BBM‐based PET predictions. RESULTS Elevated BMI/BV associated with lower BBM concentrations, especially for p‐Tau 217 and NfL, independent of brain amyloid burden. BMI‐stratified thresholds improved amyloid PET prediction, with higher BBM thresholds and area under the curve (AUC) values seen in normal weight compared to overweight or obese participants. Drastic BMI/BV declines due to weight loss increased BBM variability and systematic PET misclassification. DISCUSSION Adjusting for BMI/BV in BBM‐based diagnostics appears to improve accuracy and reliable detection of AD pathology, especially in preclinical stages. Highlights Body mass index (BMI) and blood volume (BV) significantly influenced plasma BBM concentrations in cognitively unimpaired (CU) individuals. Blood‐based biomarkers (BBMs) associated more strongly with BV than with BMI. Dilution effects were independent of brain amyloid burden. BMI‐stratified BBM thresholds improved amyloid positron emission tomography (PET) classification accuracy. Declines in BMI/BV resulted in PET prediction bias and systematic errors.
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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 | 0.004 |
| 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 | 0.001 | 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".