Kringle IV Type 2, Not Low Lipoprotein(a), as a Cause of Diabetes: A Novel Genetic Approach Using SNPs Associated Selectively with Lipoprotein(a) Concentrations or with Kringle IV Type 2 Repeats
Bibliographic record
Abstract
BACKGROUND: Low plasma lipoprotein(a) concentrations are associated with type 2 diabetes. Whether this is due to low lipoprotein(a) concentrations per se or to a large number of kringle IV type 2 (KIV-2) repeats remains unclear. We therefore aimed to identify genetic variants associated selectively with lipoprotein(a) concentrations or with the number of KIV-2 repeats, to investigate which of these traits confer risk of diabetes. METHODS: gene, and examined the association of these SNPs with plasma concentrations of lipoprotein(a) and with KIV-2 number of repeats. SNPs that were selectively associated with lipoprotein(a) concentrations but not with KIV-2 number of repeats, or vice versa, were included in a Mendelian randomization study. RESULTS: We identified 3 SNPs (rs12209517, rs12194138, and rs641990) that were associated selectively with lipoprotein(a) concentrations and 3 SNPs (rs1084651, rs9458009, and rs9365166) that were associated selectively with KIV-2 number of repeats. For SNPs selectively associated with lipoprotein(a) concentrations, an allele score of 4-6 vs 0-2 had an odds ratio for type 2 diabetes of 1.03 (95% CI, 0.86-1.23). In contrast, for SNPs selectively associated with KIV-2 number of repeats, an allele score of 4-6 vs 0-2 had an odds ratio for type 2 diabetes of 1.42 (95% CI, 1.17-1.69). CONCLUSIONS: Using a novel genetic approach, our results indicate that it is a high number of KIV-2 repeats that are associated causally with increased risk of type 2 diabetes, and not low lipoprotein(a) concentrations per se. This is a reassuring finding for lipoprotein(a)-lowering therapies that do not increase the KIV-2 number of repeats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".