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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 |
| 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".