Relationship between genetic risk for Alzheimer's, cognition and neuropsychiatric symptoms: A case study of DNA sampling and analysis through digital platform cohort studies
Bibliographic record
Abstract
Abstract Background With the evolution of technologies to extract DNA and undertake analysis from saliva samples, it is now feasible to collect postal samples for genetic analysis as part of cohort studies run on digital platforms. Ethics and engagement will be discussed and Mild Behavioral Impairment (MBI) is presented as an example of where this approach has delivered a substantial and cost‐effective study. MBI, a late‐life neuropsychiatric syndrome, is associated with faster progression to dementia and Aβ deposition even in healthy adults. MBI screening may enrich samples with individuals at risk for dementia, with benefits to clinical studies and possibly trials. Method 25,000 participants from the PROTECT digital platform were invited to provide a saliva sample by post. Ethics approval was underpinned by an on‐line consent process, genotypes were not disclosed to participants. Polygenic scores (PRS) for Alzheimer’s disease (AD) were calculated and split by tertile (representing low, medium and high AD genetic risk). Data were analysed as a whole sample then stratified by the presence of MBI. Result 87% of PROTECT participants agreed to provide genetic samples by post. 10,000 of these have been genotyped. 75% of the sample were women and the mean age was 62 (range: 50‐100). At the time of data freeze, genetic, MBI and cognitive data were available for 3,126. AD genetic risk was associated with a lower cognitive score (F(2,3119)=3.93, p=0.02; mean difference between low and high genetic risk: ‐15, p=0.02, Cohen’s d=0.13). In stratified analysis, genetic risk for AD was associated with worse cognition but only in the MBI group (MBI: F(2,1746)=4.95, p=0.007; no MBI: F(2,1366)=0.72, p=0.49). There was a significant difference between the high and low genetic risk groups (mean difference: ‐0.22, p=0.005); the effect size was stronger than in the whole sample analysis (Cohen’s d increase from 0.13 to 0.19). Conclusion High degrees of engagement can be achieved to obtain DNA samples from participants in online cohort studies. With respect to MBI, these findings demonstrate that neuropsychiatric symptoms may modify the relationship between genetic risk for AD and cognitive impairment. MBI screening may represent a useful sample enrichment strategy for clinical studies and trials.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".