Evaluating the relationship between glycemic control and bone fragility within the UK Biobank: observational and one-sample Mendelian randomization analyses
Bibliographic record
Abstract
Abstract We aimed to: (1) examine the relationship between glycemic control, BMD estimated from heel ultrasound (eBMD) and fracture risk in individuals with type 1 (T1D) and type 2 diabetes (T2D) and (2) perform a one-sample Mendelian randomization (MR) study to explore potential causal associations between glycemic control, eBMD, and fractures. This study comprised 452 131 individuals from the UK Biobank with glycated hemoglobin A1C (HbA1c) and eBMD levels. At baseline, 4078 participants were diagnosed with T1D and 23 682 with T2D. HbA1c was used to classify patients into “adequately-” (ACD; n = 17 078; HbA1c < 7.0%/53 mmol/mol) and “inadequately-” (ICD; n = 10 682; HbA1c ≥ 7.0%/53 mmol/mol) controlled diabetes. In individuals with T1D, a 1% unit (11 mmol/mol) increase in HbA1c levels was associated with a 12% increase in fracture risk (HR: 1.12, 95% CI [1.05-1.19]). Fracture risk was highest in individuals with T1D and ICD (HR 2.84, 95%CI [2.53, 3.19]), followed by those with ACD (HR 2.26, 95%CI [1.91, 2.69]), as compared to subjects without diabetes. Evidence for a non-linear association between HbA1c and fracture risk was observed (F-test ANOVA p-value = 0.002) in individuals with T2D, with risk being increased at both low and high levels of HbA1c. Fracture risk between the T2D ACD and ICD groups was not significantly different (HR: 0.97, 95%CI [0.91-1.16]), despite increased BMD. In MR analyses genetically predicted higher HbA1c levels were not significantly associated with fracture risk (causal risk ratio: 1.04, 95%CI [0.95-1.14]). We did observe evidence of a non-linear causal association with eBMD (quadratic test p-value = 0.0002), indicating U-shaped relationship between HbA1c and eBMD. We obtained evidence that lower HbA1c levels will reduce fracture risk in patients with T1D. In individuals with T2D, lowering HbA1c levels can mitigate the risk of fractures up to a threshold, beyond which the risk may begin to rise again.
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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 | 0.038 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".