The association between sclerostin and incident type 2 diabetes risk: a cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine whether sclerostin is associated with fasting glucose, insulin levels, insulin resistance or increased risk of incident type 2 diabetes. BACKGROUND: Type 2 diabetic patients have a higher risk of fractures. Recent studies suggest sclerostin, a regulator of osteoblast activity, is associated with diabetes. MATERIALS AND METHODS: Sclerostin levels were obtained from 1778 individuals with no history of type 2 diabetes participating in the population-based Canadian Multicentre Osteoporosis Study (CaMos) cohort. Participants were followed until diagnosis of type 2 diabetes, death or end of the study period (31 December 2013). The relationship of sclerostin with fasting glucose, insulin levels and homoeostatic model assessment-insulin resistance (HOMA-IR) was studied in linear regression models. Cox proportional hazards models were used to determine the association of sclerostin levels and the risk of incident type 2 diabetes during a mean 7·5 years of follow-up. RESULTS: Fasting glucose, fasting insulin levels and HOMA-IR were weakly correlated with sclerostin levels (Spearman's correlation coefficient: 0·11, P < 0·05; -0·09, P < 0·05; and -0·07, P = 0·02, respectively). Multiple linear regression analyses confirmed a significant association between sclerostin and fasting insulin and HOMA-IR but no significant association with fasting glucose levels. Sclerostin levels were not found to be significantly associated with the risk of incident type 2 diabetes (HR: 1·30; 95% CI: 0·37-4·57). CONCLUSIONS: We observed an association between sclerostin levels with fasting insulin levels and HOMA-IR, but there was no clear association with type 2 diabetes risk. Further studies are needed to understand the role of sclerostin in type 2 diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".