Association between self-reported multimorbidity and longitudinal brain Aβ deposition in Alzheimer’s disease
Bibliographic record
Abstract
Multimorbidity is common in older adults. However, whether multimorbidity accelerates brain beta-amyloid (Aβ) deposition, the molecular driver of Alzheimer’s disease (AD), in humans remains largely unknown. In this study, we selected 435 brain Aβ-positive participants with available longitudinal Aβ-PET data (mean duration 3.9 years) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Twenty-two self-reported chronic disorders were considered as a measure of the severity of multimorbidity. After adjustment for age, sex, education level, APOE-ε4 status and baseline cognitive state, individuals with a high or medium multimorbidity burden had faster rates of brain Aβ accumulation than individuals with a low multimorbidity burden. Moreover, both the central nervous system and peripheral system multimorbidity burdens were associated with longitudinal brain Aβ deposition. These results indicate that peripheral organ and tissue dysfunctions may contribute to AD pathogenesis, which may help researchers better understand AD pathogenesis and tailor interventions for AD from a systemic view. Whether multimorbidity accelerates brain Aβ deposition in humans remains largely unknown. Here, the authors demonstrate that higher self-reported multimorbidity burden predicts increased brain Aβ accumulation rates in the ADNI cohort.
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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 | 0.003 |
| 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 | 0.001 |
| 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".