P3‐019: An Alzheimer's genetic risk composite, but not ApoE, intensifies diabetes‐related neurocognitive slowing in nondemented older adults
Bibliographic record
Abstract
Risk factors (and their synergistic interactions) associated with Alzheimer's disease (AD) may predict normal or preclinical deficits and decline. Although ApoE (rs429358, rs7412) is the gene most consistently linked with AD risk, genome-wide association studies have identified others, including CLU (rs11136000), CR1 (rs6656401), and PICALM (rs541458). Type 2 diabetes (T2D) is a risk factor for AD and for increased cognitive deficits in nondemented older adults. We examined if the effect of diabetes on neurocognitive speed performance (level) and longitudinal change was intensified by (a) genetic risk from each of the four variants independently or (b) an AD Genetic Risk Composite (AGRC) representing combined risk from all four variants. This longitudinal design included non-demented older adults (n=591, baseline M age=69, age range 53–91, 68% women, 8% with T2D) followed over 9 years. Saliva was processed with standard procedures from Oragene-DNA Genotek. Genotyping was carried out using a PCR-RFLP strategy. The AGRC was created by summing allelic risk across the four specified genotypes: 0=no risk, 1=moderate risk, 2=full risk and then grouped into low and high risk using median split (Mdn=3.0). Statistical analyses included latent growth modeling testing independent and interactive effects on level (centering age=75) and change using a confirmed neurocognitive speed latent variable consisting of choice reaction time, sentence verification, and lexical decision measures. First, adults with T2D exhibited slower speed performance at age 75 than adults without T2D (b=.494, p=.007). Second, none of the genetic risk variants showed independent effects on speed performance or change. Third, interaction analyses (e.g., T2D x ApoE) showed no magnification of speed decrements. Fourth, intensification interaction analyses (T2D x AGRC) showed that adults in the high risk AGRC group with T2D exhibited significantly greater 9-year decline in speed (b=.048, p=.004). Finally, education and pulse pressure where significant covariates but did not alter the observed effects. Independently, as expected, diabetes was associated with cognitive slowing in nondemented aging. Only the AD Genetic Risk Composite, not ApoE or other variants, intensified the effects of T2D on neurocognitive speed in the form of exacerbated slowing over 9 years.
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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 | 0.000 | 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) | 0.002 | 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".