The association of glycaemic risk factors and diabetes duration with risk of heart failure in people with type 2 diabetes: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: To conduct a systematic review in order to better understand the association of glycaemic risk factors and diabetes duration with risk of heart failure (HF) in individuals with type 2 diabetes (T2D). METHODS: We identified longitudinal studies investigating the association of glycaemic factors (glycated haemoglobin [HbA1c], HbA1c variability, and hypoglycaemia) and diabetes duration with HF in individuals with T2D. Hazard ratios and odds ratios were extracted and meta-analysed using a random-effects model where appropriate. Risk of bias assessment was carried out using a modified Newcastle-Ottawa Scale. Egger's test along with the trim-and-fill method were used to assess and account for publication bias. RESULTS: Forty studies representing 4 102 589 people met the inclusion criteria. The risk of developing HF significantly increased by 15% for each percentage point increase in HbA1c, by 2% for each additional year of diabetes duration, and by 43% for having a history of severe hypoglycaemia. Additionally, variability in HbA1c levels was associated with a 20%-26% increased risk of HF for each unit increase in the metrics of variability (HbA1c standard deviation, coefficient of variation, and average successive variability). All included studies scored high in the risk of bias assessment. Egger's test suggested publication bias, with trim-and-fill analyses revealing a significant 14% increased risk of HF per percentage point increase in HbA1c. CONCLUSIONS: Glycaemic risk factors and diabetes duration significantly contribute to the heightened risk of HF among individuals with T2D. A reduction in risk of HF is anticipated with better management of glycaemic risk factors.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".