The haemoglobin glycation index as predictor of diabetes-related complications in the AleCardio trial
Bibliographic record
Abstract
The haemoglobin glycation index (HGI) quantifies the interindividual variation in the propensity for glycation and is a predictor of diabetes complications and adverse effects of intensive glucose lowering. We investigated the relevance of HGI as independent predictor of complications by using data of the AleCardio trial. The AleCardio trial randomized 7226 type 2 diabetes patients with an acute coronary syndrome to aleglitazar or placebo. From 6458 patients with baseline glycated haemoglobin (HbA1c) and fasting plasma glucose (FPG), a linear regression equation, HbA1c (%) = 5.45 + 0.0158 * FPG (mg/dl), was used to calculate predicted HbA1c and derive HGI (= observed – predicted HbA1c). With multivariate Cox regression we examined the association with major adverse cardiac events, cardiovascular mortality, total mortality and hypoglycaemia, irrespective of treatment allocation, using HGI subgroups (low, intermediate and high) and HGI as continuous variable. Patients with high HGI were younger, more often non-Caucasian, had a longer duration of diabetes, showed more retinopathy and used insulin more often. Hypoglycaemia occurred less often in the low HGI subgroup, but this difference disappeared after adjustment for duration of diabetes, insulin and sulphonylurea use. Low HGI patients were at lower risk for cardiovascular mortality (hazard ratio 0.64; 95% confidence interval 0.44–0.93, p = 0.020) and total mortality (hazard ratio 0.69; 95% confidence interval 0.50–0.95, p = 0.025), as compared with high HGI patients. Every percentage increase in HGI was associated with a 16% increase in the risk for cardiovascular mortality (p = 0.005). The association between HGI and mortality disappeared with additional adjustment for HbA1c. HGI predicts mortality in diabetes patients with acute coronary syndromes, but no better than HbA1c.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".