A blood cholesterol polygenic score in two independent AD cohorts
Bibliographic record
Abstract
Abstract Background While blood total cholesterol (TC) levels is a recognized factor contributing to Alzheimer’s disease (Alz. Ass., 2017), its role is debated (Wood et al., 2014). E.g. several studies have shown that midlife hypercholesterolemia is associated with increased risk of developing AD (Reiman et al., 2010; Solomon et al., 2009; Toro et al., 2014), but others have shown no association (Li et al., 2005; Tan et al., 2003). High TC has also been associated with increased amyloid load in the hippocampus (Pappolla et al., 2003) and hypometabolism in brain regions affected by AD (Reiman et al., 2010). Studies with statins have shown similar discrepancies with retrospective studies indicating a protective effect but have had no consistent positive effects in randomized controlled trials (Shobab et al., 2005). Thus, in an attempt to address these discrepancies, we constructed a polygenic score (TC‐pgs) capturing some of the variance in peripheral TC levels and evaluated it for associations with AD risk and biomarkers. Method A weighted TC‐pgs was constructed using summary data from Willer et al., (2013; http://lipidgenetics.org/ ) in two AD cohorts; PREVENT‐AD ( https://preventad.loris.ca/ ) and ROSMAP ( https://www.radc.rush.edu/home.htm ). The TC‐pgs was optimized for correlation with TC levels in the PREVENT‐AD cohort, and then evaluated for correlations with AD risk and CSF biomarkers using both cohorts. Results We found that by stratifying for statin use and sex we more than doubled the amount of variance explained by the score (from ∼7.5% to ∼17.5%) specifically in statin free females. Furthermore, the TC‐pgs improved the prediction of hypercholesterolemia (AUC 0.805 vs 0.646, p = 0.0016) in the same group. The TC‐pgs were evaluated for associations with CSF Aβ42, p‐tau and tau in PREVENT‐AD and for clinically and pathologically defined AD in ROSMAP. We did not find any significant associations, but for a trend towards a positive association between TC‐pgs and CSF p‐tau levels (p = 0.09). Conclusion Our TC‐pgs did improve prediction of hypercholesterolemia but failed to correlate with AD or AD biomarkers. Thus, the findings do not support that an increased cumulative genetic risk of hypercholesterolemia influence the risk of AD.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".