Genetic risk for Alzheimer disease in children: Evidence from early‐life IQ and brain white‐matter microstructure
Bibliographic record
Abstract
It remains unclear whether the genetic risk for late-onset Alzheimer disease (AD) is linked to premorbid individual differences in general cognitive ability and brain structure. The objective of the present study was to determine whether the genetic risk of late-onset AD is related to premorbid individual differences in intelligence quotient (IQ) and characteristics of the cerebral white-matter in children. The study sample included children of the Generation R Study from Rotterdam, The Netherlands. IQ was measured using a well-validated Dutch nonverbal IQ test (n = 1908) at ages 5 to 9 years. White-matter microstructure was assessed by measuring fractional anisotropy (FA) of white-matter tracts using diffusion tensor imaging (DTI) (n = 919) at ages 9 to 12 years. Genetic risk was quantified using three biologically defined genetic risk scores (GRSs) hypothesized to be related to the pathophysiology of late-onset AD: immune response, cholesterol/lipid metabolism and endocytosis. Higher genetic risk for late-onset AD that included genes associated with immune responsivity had a negative influence on cognition and cerebral white-matter microstructure. For each unit increase in the immune response GRS, IQ decreased by 0.259 SD (95% CI [-0.500, -0.017]). For each unit increase in the immune response GRS, global FA decreased by 0.373 SD (95% CI [-0.721, -0.026]). Neither cholesterol/lipid metabolism nor endocytosis GRSs were associated with IQ or cerebral white-matter microstructure. Our findings suggest that elevated genetic risk for late-onset AD may in part be manifest during childhood neurodevelopment through alterations in immune responsivity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".