Complement C3 Associates With Incidence of Diabetes, but No Evidence of a Causal Relationship
Bibliographic record
Abstract
Purpose: This study explored whether complement factor 3 (C3) in plasma is associated with incidence of diabetes in a population-based cohort. We also identified genetic variants related to C3 and explored whether C3 and diabetes share common genetic determinants. Methods: C3 was analyzed in plasma from 4368 nondiabetic subjects, 46 to 68 years old, from the Malmö Diet and Cancer Study. Incidence of diabetes was studied in relationship to C3 levels during 17.7± 4.4 years of follow-up. Genotypes associated with C3 were identified in a genome-wide association study. Diabetes Genetics Replication and Meta-Analysis and the European Genetic Database were used for in silico look-up. Results: In all, 538 (12.3%) subjects developed diabetes during 18 years of follow-up. High C3 was significantly associated with incidence of diabetes after risk factor adjustments (hazard ratio comparing 4th vs 1st quartile, 1.54 (95% confidence interval, 1.13 to 2.09; P = 0.005). C3 was associated with polymorphisms at the complement factor H locus (P < 10-8). However, no relationship with diabetes was observed for this locus. Another eight loci were associated with C3 with P < 10-5. One of them, the glucose kinase regulatory protein (GCKR) locus, has been previously associated with diabetes. The relationship between C3 levels and the GCKR locus was replicated in the European Genetic Database cohort. Conclusions: Plasma concentration of C3 is a risk marker for incidence of diabetes. The results suggest that this association could, in part, be explained by pleiotropic effects related to the GCKR gene.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".