Kidney and Heart Failure Events are Bidirectionally Associated in Patients with Type 2 Diabetes and Cardiovascular Disease
Bibliographic record
Abstract
AIMS: This study aimed to evaluate the bidirectional relationship between kidney and cardiovascular (CV) events in trial participants with type 2 diabetes and CV disease. METHODS AND RESULTS: Post hoc analyses of EMPA-REG OUTCOME using Cox regression models were performed to assess the association of baseline factors with risk of a kidney event and bidirectional associations of incident kidney events and CV events. Among placebo-treated participants, baseline factors significantly associated with greater kidney event risk included lower baseline estimated glomerular filtration rate, albuminuria, higher uric acid, low-density lipoprotein cholesterol levels, and prior heart failure (HF). Coronary artery disease was not associated with increased risk. In placebo-treated participants, occurrence of an incident non-fatal kidney event increased the subsequent risk of hospitalization for HF (HHF) but not 3-point major adverse CV events (non-fatal stroke, non-fatal myocardial infarction, and CV death). Vice versa, HHF (but not myocardial infarction/stroke) increased the risk of subsequent kidney events. These associations were generally also seen in empagliflozin-treated participants and in the overall population. Interestingly, the risk of kidney events following HHF was not significantly increased in the relatively small number of placebo-treated participants already diagnosed with HF at baseline. CONCLUSIONS: These findings demonstrate a bidirectional inter-relationship between HHF and kidney events. Further exploration of this relationship and strategies to optimize the use of therapies to reduce both kidney and HF outcomes is warranted.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".