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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".