Effect of Obesity, Dyslipidemia, and Diabetes on Trastuzumab-related Cardiotoxicity in Breast Cancer
Bibliographic record
Abstract
Background: Clinical trials have demonstrated an increased risk of cardiotoxicity in patients with breast cancer (BCA) receiving trastuzumab-based therapy. Diabetes, dyslipidemia, and obesity are known risk factors for cardiovascular disease. Studies have yielded conflicting results about whether those factors increase the risk of cardiotoxicity in patients with BCA receiving trastuzumab. Methods: In this retrospective cohort study, data were collected for 243 patients with BCA positive for HER2 (the human epidermal growth factor receptor 2) who were receiving trastuzumab and who were referred to The Ottawa Hospital Cardio-oncology Referral Clinic between 2008 and 2013. The data collected included patient demographics, reason for referral, cardiac function, chemotherapy regimen (including anthracycline use), and 3 comorbidities (diabetes, dyslipidemia, obesity). Rates of symptomatic cancer treatment–related cardiac dysfunction (SCTCD) and asymptomatic decline in left ventricular ejection fraction (adLVEF) were calculated for patients with and without the comorbidities of interest. Results: Of the 243 identified patients, 104 had either diabetes, dyslipidemia, or obesity. In that population, the most likely reason for referral to the cardio-oncology clinic was adLVEF. Conclusions: The combination of 2 or 3 comorbidities significantly increased the incidence of sctcd in our population, reaching a rate of 67% for patients with obesity and dyslipidemia [relative risk (RR): 2.2; p = 0.04], 69% for patients with obesity and diabetes (RR: 2.3; p = 0.02), and 72% for patients with all 3 risk factors (RR: 2.4; p = 0.08). The combination of 2 or 3 comorbidities significantly increases the incidence of symptomatic cancer treatment–related cardiotoxicity. Patients with BCA experiencing cancer treatment–related cardiotoxicity who have a history of diabetes, dyslipidemia, and obesity might require more proactive strategies for prevention, detection, and treatment of cardiotoxicity while receiving trastuzumab-based treatment.
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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 | 0.005 |
| 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 | 0.000 |
| 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".