Cardiac autonomic neuropathy and risk of cardiovascular disease and mortality in type 1 and type 2 diabetes: a meta-analysis
Bibliographic record
Abstract
We aimed to determine the prognostic association between cardiac autonomic neuropathy (CAN) and cardiovascular disease events (CVE) and mortality in type 1 and type 2 diabetes through a systematic review and meta-analysis. This systematic review and meta-analysis was registered with PROSPERO (CRD42020216305) and was conducted with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological criteria. CAN was defined on the basis of 1 (early/possible CAN) or ≥2 (definite CAN) positive autonomic function tests as per the Toronto Consensus guidelines. Studies included those with prospective CVE or mortality data. Methodological variables/risk of bias were assessed using ROBINS-I (Risk Of Bias In Non-randomized Studies - of Interventions) and RoB-2 (Risk-Of-Bias tool for randomized trials) appraisal tools. Electronic database searches yielded 18 467 articles; 84 articles were screened full-text, 26 articles fulfilled the inclusion criteria for quantitative synthesis. Sixteen studies from patients with (n=2875) and without (n=11 722) CAN demonstrated a pooled relative risk (RR) of 3.16 (95%CI 2.42 to 4.13; p<0.0001) of future CVE in favour of CAN. Nineteen studies provided all-cause mortality data from patients with (n=3679) and without (n=12 420) CAN, with a pooled RR of 3.17 (95%CI 2.11 to 4.78; p<0.0001) in favour of CAN. The risk of both future CVE and mortality was higher in type 1 compared with type 2 diabetes and with a definite CAN (vs possible CAN) diagnosis. Three studies were considered to have risk of serious bias. This study confirms the significant association between CAN and CVE and all-cause mortality. The implementation of population-based CAN screening will identify a subgroup with disproportionately higher cardiovascular and mortality risk that will allow for earlier targeted intervention.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.065 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".