Risk Differences Between Prediabetes And Diabetes According To Breast Cancer Molecular Subtypes
Bibliographic record
Abstract
Hyperglycemia and hyperinsulinemia may play a role in breast carcinogenesis and prediabetes and diabetes have been associated with increased breast cancer (BC) risk. However, whether BC molecular subtypes may modify these associations is less clear. We therefore investigated these associations in all cases and by BC molecular subtypes among women living in Southern Italy. Cases were 557 patients with non-metastatic incident BC and controls were 592 outpatients enrolled during the same period as cases and in the same hospital for skin-related non-malignant conditions. Adjusted multivariate logistic regression models were built to assess the risks of developing BC in the presence of prediabetes or diabetes. The analyses were repeated by strata of BC molecular subtypes: Luminal A, Luminal B, HER2+, and Triple Negative (TN). Prediabetes and diabetes were significantly associated with higher BC incidence after controlling for known risk factors (OR = 1.94, 95% CI 1.32-2.87 and OR = 2.46, 95% CI 1.38-4.37, respectively). Similar results were seen in Luminal A and B while in the TN subtype only prediabetes was associated with BC (OR = 2.43, 95% CI 1.11-5.32). Among HER2+ patients, only diabetes was significantly associated with BC risk (OR = 3.04, 95% CI 1.24-7.47). Furthermore, when postmenopausal HER2+ was split into hormone receptor positive versus negative, the association with diabetes remained significant only in the former (OR = 5.13, 95% CI 1.53-17.22). These results suggest that prediabetes and diabetes are strongly associated with BC incidence and that these metabolic conditions may be more relevant in the presence of breast cancer molecular subtypes with positive hormone receptors. J. Cell. Physiol. 232: 1144-1150, 2017. © 2016 Wiley Periodicals, Inc.
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 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) | 0.000 | 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) | 0.002 | 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".