Brain atrophy rates in first degree relatives at risk for Alzheimer's
Bibliographic record
Abstract
A positive family history (FH) raises the risk for late-onset Alzheimer's disease though, other than the known risk conferred by apolipoprotein ε4 (ApoE4), much of the genetic variance remains unexplained. We examined the effect of family history on longitudinal regional brain atrophy rates in 184 subjects (42% FH+, mean age 79.9) with mild cognitive impairment (MCI) enrolled in a national biomarker study. An automated image analysis method was applied to T1-weighted MR images to measure atrophy rates for 20 cortical and subcortical regions. Mixed-effects linear regression models incorporating repeated-measures to control for within-subject variation over multiple time points tested the effect of FH over a follow-up of up to 48 months. Most of the 20 regions showed significant atrophy over time. Adjusting for age and gender, subjects with a positive FH had greater atrophy of the amygdala (p < 0.01), entorhinal cortex (p < 0.01), hippocampus (p < 0.053) and cortical gray matter (p < 0.009). However, when E4 genotype was added as a covariate, none of the FH effects remained significant. Analyses by ApoE genotype showed that the effect of FH on amygdala atrophy rates was numerically greater in ε3 homozygotes than in E4 carriers, but this difference was not significant. FH+ subjects had numerically greater 4-year cognitive decline and conversion rates than FH- subjects but the difference was not statistically significant after adjusting for ApoE and other variables. We conclude that a positive family history of AD may influence cortical and temporal lobe atrophy in subjects with mild cognitive impairment, but it does not have a significant additional effect beyond the known effect of the E4 genotype.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".