Polygenic Risk Score for Alzheimer’s Disease: Implications for Memory Performance and Hippocampal Volumes in Early Life
Bibliographic record
Abstract
OBJECTIVE: Alzheimer's disease is a heritable neurodegenerative disorder in which early-life precursors may manifest in cognition and brain structure. The authors evaluate this possibility by examining, in youths, associations among polygenic risk score for Alzheimer's disease, cognitive abilities, and hippocampal volume. METHOD: Participants were children 6-14 years of age in two Brazilian cities, constituting the discovery (N=364) and replication samples (N=352). As an additional replication, data from a Canadian sample (N=1,029), with distinct tasks, MRI protocol, and genetic risk, were included. Cognitive tests quantified memory and executive function. Reading and writing abilities were assessed by standardized tests. Hippocampal volumes were derived from the Multiple Automatically Generated Templates (MAGeT) multi-atlas segmentation brain algorithm. Genetic risk for Alzheimer's disease was quantified using summary statistics from the International Genomics of Alzheimer's Project. RESULTS: Analyses showed that for the Brazilian discovery sample, each one-unit increase in z-score for Alzheimer's polygenic risk score significantly predicted a 0.185 decrement in z-score for immediate recall and a 0.282 decrement for delayed recall. Findings were similar for the Brazilian replication sample (immediate and delayed recall, β=-0.259 and β=-0.232, both significant). Quantile regressions showed lower hippocampal volumes bilaterally for individuals with high polygenic risk scores. Associations fell short of significance for the Canadian sample. CONCLUSIONS: Genetic risk for Alzheimer's disease may affect early-life cognition and hippocampal volumes, as shown in two independent samples. These data support previous evidence that some forms of late-life dementia may represent developmental conditions with roots in childhood. This result may vary depending on a sample's genetic risk and may be specific to some types of memory tasks.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".