Association Between Glycated Haemoglobin and the Risk of Congestive Heart Failure in Diabetes Mellitus: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Clinical trials to date have not provided definitive evidence regarding the effects of glucose lowering on the incidence of congestive heart failure (CHF). We synthesized available prospective epidemiological data on the association between glycaemia measured by haemoglobin A(1c) (HbA(1c)) and incident CHF in individuals with diabetes. METHODS AND RESULTS: We searched electronic databases and reference lists of selected articles for relevant prospective epidemiological studies. We abstracted data from relevant studies using standardized forms and obtained additional data from investigators when required. We pooled study-specific relative risk estimates using random-effects model meta-analysis. Of the 1044 citations identified, we included 10 studies comprising 178 929 participants with diabetes and 14 176 incident CHF cases. Five studies included only patients with type 2 diabetes, four studies had predominantly patients with type 2 diabetes, and one study included only patients with type 1 diabetes. All studies except one showed an increased risk of CHF with higher HbA(1c). The overall adjusted risk ratio (RR) for CHF was 1.15 [95% confidence interval (CI) 1.10-1.21] for each percentage point higher HbA(1c). There was substantial heterogeneity across the 10 studies (I(2): 83%; 95% CI 69-91%; P < 0.001) not explained by available study-level characteristics such as study design or average HbA(1c) level. In seven studies reporting RRs with more than one degree of adjustment, the association was minimally altered after adjustment for several cardiovascular risk factors. CONCLUSIONS: In observational studies of individuals with diabetes, a higher HbA(1c) level was associated with a significantly increased incidence of CHF.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".