Diabetes, insulin resistance, and metabolic syndrome in women at high risk for breast cancer.
Bibliographic record
Abstract
e12522 Background: Despite the growing body of research on diabetes (Db), insulin resistance (IR) and metabolic syndrome (MetS) in association with breast cancer, the prevalence of these metabolic conditions has yet to be explored in the high risk population. Delineating the numerous factors that contribute to these women’s risk is key in the management of this vulnerable population, especially when outlining potentially modifiable risk factors. The overall objective of this prospective study was to quantify the prevalence of Db, IR and MetS in women at high risk for breast cancer. Methods: Participants consisted of 100 Caucasian women above the age of 35 with an estimated 5yr risk of ≥1.7%. This criteria was met by (a) BRCA mutation carriers, (b) history of LCIS, (c) history of ADH, (d) history of mantle radiation, or (e) calculated 5yr risk of ≥1.7% using the Gail model. A comprehensive metabolic profile was obtained for each participant based on a questionnaire, fasting blood sample and biophysical measurements. The diagnostic criteria used for Db were those established by the Candian Practice Guidelines. The threshold for IR was a HOMA-IR value of 2.29. The MetS diagnostic criteria were those set by the IDF where 3 of 5 criteria from ↑waist circumference, ↑triglycerides, ↓HDL, HTN and hyperglycemia must be met. Results: The frequency (%) of Db (n=97), IR (n=96) and MetS (n=88) was 5(5), 17(18) and 29(33) respectively. Among the components of MetS, ↑waist circumference had the highest prevalence 60(68.2), followed by HTN 33(37.5), hyperglycemia 27(30.7), ↑triglycerides 23(26.1) and ↓HDL 22(29.7). There was a significant correlation observed between the Gail score and HDL (0.37, p<0.01), as well as systolic Bp (0.28, p<0.01). Conclusions: The prevalence of IR and MetS in this sample of women at high risk for breast cancer is considerably higher than the prevalence of these metabolic conditions in a similar population of average risk women, based on population data. This supports the significance and feasibility of an experimental study comparing the prevalence of IR and MetS in women at high risk and average risk for breast cancer. The prevalence of Db in this sample was comparable to the prevalence described in the general population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".