Variation in Serum PCSK9 (Proprotein Convertase Subtilisin/Kexin Type 9), Cardiovascular Disease Risk, and an Investigation of Potential Unanticipated Effects of PCSK9 Inhibition
Bibliographic record
Abstract
PCSK9 inhibitors have strong effects on lowering low-density lipoprotein cholesterol (LDL-C) and subsequent risk of cardiovascular disease. While a number of trials have evaluated the safety of such inhibitors in long-term clinical trials, a broad investigation of potential outcomes over the life-time, leveraging genetic variation in PCSK9 levels, has not been conducted. We aimed to investigate genetic variants associated with serum PCSK9 and the effect of LDL-C lowering variants on a range of potential outcomes. To achive this, we used data from the Nord-Trøndelag Health Study (HUNT)(n=69,424), a large population-based health study of the inhabitants of Nord-Trøndelag county, Norway. Firstly, we preformed a genome-wide association study of serum PCSK9 measured in 3697adults. Secondly we created a genetic risk score for PCSK9 as well as utilized existing scores for PCSK9 and HMGCR, and regressed these on a borad range of outcomes in the total sample. Thirdly, we perfomed two-sample medelian randomization on 50 outcomes using publically available datasets. Finally, we assessed the genetic correlation between PCSK9 and 229 diseases or traits using LD score regression. Three independent variants were GWAS significant (rs11591147, rs499883 and rs192265866). Using both existing genetic risk scores and scores defined from this discovery GWAS; we confirmed a strong association between serum PCSK9 and lower LDL-C and reducing risk of cardiovascular disease. We did not observe any consistently strong potentially adverse or beneficial associations. We observed some strong but not statistically significant genetic correlations between serum PCSK9 and fasting insulin, urate, uric acid and caudate volume. The lack of association between the genetic risk scores for PCSK9 and HMGCR is reassuring for the use of these treatments. However, further studies are warranted to confirm our findings, follow-up on the functional influences of the variants identified and extend our investigations to other outcomes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".