Increased Risk of Severe Hypoglycemic Events Before and After Cardiovascular Outcomes in TECOS Suggests an At-Risk Type 2 Diabetes Frail Patient Phenotype
Bibliographic record
Abstract
OBJECTIVE Severe hypoglycemic events (SHEs) in type 2 diabetes are associated with subsequent cardiovascular (CV) event risk. We examined whether CV events were associated with subsequent SHE risk. RESEARCH DESIGN AND METHODS Time-dependent associations between SHEs and a composite CV end point (fatal/nonfatal myocardial infarction or stroke, hospitalization for unstable angina, hospitalization for heart failure [hHF]) were examined post hoc in 14,671 TECOS (Trial Evaluating Cardiovascular Outcomes With Sitagliptin) participants with type 2 diabetes and CV disease followed for a median of 3.0 years. RESULTS SHEs were uncommon and unassociated with sitagliptin therapy (N = 160 [2.2%], 0.78/100 patient-years vs. N = 143 [1.9%], 0.70/100 patient-years for placebo; hazard ratio [HR] 1.12 [95% CI 0.89, 1.40], P = 0.33). Patients with (versus without) SHEs were older with longer diabetes duration, lower body weight, and lower estimated glomerular filtration rate; were more frequently women, nonwhite, and insulin treated; and more often had microalbuminuria or macroalbuminuria. Analyses adjusted for clinical factors showed SHEs were associated with increased risk of the primary composite CV end point (1.55 [1.06, 2.28], P = 0.025), all-cause death (1.83 [1.22, 2.75], P = 0.004), and CV death (1.72 [1.02, 2.87], P = 0.040). Conversely, nonfatal myocardial infarction (3.02 [1.83, 4.96], P < 0.001), nonfatal stroke (2.77 [1.36, 5.63], P = 0.005), and hHF (3.68 [2.13, 6.36], P < 0.001) were associated with increased risk of SHEs. Fully adjusted models showed no association between SHEs and subsequent CV or hHF events, but the association between CV events and subsequent SHEs remained robust. CONCLUSIONS These findings, showing greater risk of SHEs after CV events and greater risk of CV events after SHEs, suggest a common at-risk type 2 diabetes frail patient phenotype.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".