P4‐187: Midlife adiposity predicts earlier onset of Alzheimer's dementia, neuropathology, and presymptomatic cerebral amyloid accumulation
Bibliographic record
Abstract
Understanding how midlife risk factors influence age-at-onset (AAO) of Alzheimer's disease (AD) may provide clues to delay disease expression. Although midlife adiposity predicts increased incidence of AD, it is unclear whether it affects AAO and severity of Alzheimer's neuropathology. Using a prospective population-based cohort, the Baltimore Longitudinal Study of Aging (BLSA), we sought to examine the relationships between midlife body mass index (BMI) and: (1) AAO of AD; (2) severity of Alzheimer's neuropathology; and (3) fibrillar brain amyloid deposition during aging. We analyzed data from 1,394 cognitively normal individuals at baseline (8643 visits; average follow up interval 13.9 years), 142 of whom developed incident AD. In two sub-samples of BLSA, 191 participants underwent autopsy and neuropathological assessment, and 75 non-demented individuals underwent brain amyloid imaging by C-PiB PET. Midlife adiposity was derived from BMI data at 50 years of age. Each unit increase in midlife BMI predicted earlier onset of AD by 6.7 months (P=0.013). Higher midlife BMI was associated with greater Braak neurofibrillary tangle but not CERAD neuritic plaque scores at autopsy overall. Higher midlife BMI was associated with greater fibrillar amyloid measured by global mean cortical distribution volume ratio and within the precuneus. Midlife overweight predicts earlier onset of AD and increased burden of Alzheimer's neuropathology. Accordingly, a healthy BMI in midlife may delay the onset of AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".