Cardiovascular Autonomic Neuropathy and Risk of Kidney Function Decline in Type 1 and Type 2 Diabetes: Findings From the PERL and ACCORD Cohorts
Bibliographic record
Abstract
Results of previous studies have suggested that cardiovascular autonomic neuropathy (CAN) may predict rapid kidney function decline among people with diabetes. We analyzed the association between baseline CAN and subsequent glomerular filtration rate (GFR) decline among individuals with type 1 diabetes (T1D) from the Preventing Early Renal Loss in Diabetes (PERL) study (N = 469) and with type 2 diabetes (T2D) from Action to Control Cardiovascular Risk in Diabetes (ACCORD) (N = 7,973). Baseline CAN was ascertained with electrocardiogram-derived heart rate variability indices. Its association with GFR slopes, rapid kidney function decline (GFR loss of ≥5 mL/min/1.73 m2/year), and ≥40% GFR loss was evaluated by linear mixed-effects, logistic, and Cox regression, respectively. Participants with CAN experienced more rapid GFR decline, by an excess 1.15 mL/min/1.73 m2/year (95% CI -1.93 to -0.37; P = 4.0 × 10-3) in PERL and 0.34 mL/min/1.73 m2/year (95% CI -0.49 to -0.19; P = 6.3 × 10-6) in ACCORD. This translated to 2.11 (95% CI 1.23-3.63; P = 6.9 × 10-3) and 1.39 (95% CI 1.20-1.61; P = 1.1 × 10-5) odds ratios of rapid kidney function decline in PERL and ACCORD, respectively. Baseline CAN was also associated with a greater risk of ≥40% GFR loss events during follow-up (hazard ratio 2.60 [95% CI 1.15-5.45], P = 0.02, in PERL and hazard ratio 1.54 [95% CI 1.28-1.84], P = 3.8 × 10-6, in ACCORD). These associations remained significant after adjustment for potential confounders, including baseline GFR and albuminuria. Our findings indicate that CAN is a strong, independent predictor of rapid kidney function decline in both T1D and T2D. Further studies of the link between these two complications may help with development of new therapies to prevent kidney function decline in patients with diabetes.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".