Impact of Cardio-Renal-Metabolic Comorbidities on Cardiovascular Outcomes and Mortality in Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: We evaluated the incremental contribution of chronic kidney disease (CKD) to the risk of major adverse cardiovascular (CV) events (MACE), heart failure (HF), and all-cause mortality (ACM) in type 2 diabetes mellitus (T2DM) patients and its importance relative to the presence of other cardio-renal-metabolic (CaReMe) comorbidities. METHODS: Patients (≥40 years) were identified at the time of T2DM diagnosis from US (Humedica/Optum) and UK (Clinical Practice Research Datalink) databases. Patients were monitored post-diagnosis for modified MACE (myocardial infarction, stroke, ACM), HF, and ACM. Adjusted hazard ratios were obtained using Cox proportional-hazards regression to evaluate the relative risk of modified MACE, HF, and ACM due to CKD. Patients were stratified by the presence or absence of atherosclerotic CV disease (ASCVD) and age. RESULTS: Between 2011 and 2015, of 227,224 patients identified with incident T2DM, 40,063 (17.64%) had CKD. Regardless of prior ASCVD, CKD was associated with higher risk of modified MACE, HF, and ACM; this excess hazard was more pronounced in older patients with prior ASCVD. In time-to-event analyses in the overall cohort, patients with T2DM + CKD or T2DM + CKD + hypertension + hyperlipidemia had increased risks for modified MACE, HF, and ACM versus patients with T2DM and no CaReMe comorbidities. Patients with CKD had higher risks for and shorter times to modified MACE, HF, and ACM than those without CKD. CONCLUSION: In T2DM patients, CKD presence was associated with higher risk of modified MACE, HF, and ACM. This may have risk-stratification implications for T2DM patients based on background CKD and highlights the potential importance of novel renoprotective strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".