Association of Obesity With Breast Cancer Outcome in Relation to Cancer Subtypes: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Obesity at breast cancer (BC) diagnosis has been associated with poor outcome, although the magnitude of effect in different BC subtypes is uncertain. We report on the association of obesity or overweight at diagnosis of nonmetastatic BC with disease-free (DFS) and overall survival (OS) in the following defined subtypes: hormone receptor positive/HER2 negative (HR+HER2-), HER2 positive (HER2+), and triple negative (TNBC). METHODS: We searched MEDLINE, EMBASE, and COCHRANE databases up to January 1, 2019. Study eligibility was performed independently by 2 authors. Studies reporting hazard ratios (HRs) of OS and/or DFS for obesity or overweight in BC subtypes were included. The pooled hazard ratio was computed and weighted using generic inverse variance and random effects models. RESULTS: Twenty-seven studies were included. Obese compared with nonobese women had worse DFS in all subtypes: the hazard ratios were 1.26 (95% confidence interval [CI] = 1.13 to 1.41, P < .001) for HR+HER2- BC, 1.16 (95% CI = 1.06 to 1.26, P < .001) for HER2+ BC, and 1.17 (95% CI = 1.06 to 1.29, P = .001) for TNBC. OS was also worse in obese vs nonobese women (HR+HER2- BC HR = 1.39, 95% CI = 1.20 to 1.62, P < .001; HER2+ BC HR = 1.18, 95% CI = 1.05 to 1.33, P = .006; and TNBC HR = 1.32, 95% CI = 1.13 to 1.53, P < .001). As opposed to obesity, overweight was not associated with either DFS or OS in HER2+ BC (HR = 1.02, 95% CI = 0.81 to 1.28, P = .85; and HR = 0.96, 95% CI = 0.76 to 1.21, P = .99, respectively) or TNBC (HR = 1.04, 95% CI = 0.93 to 1.18, P = .49; and HR = 1.08, 95% CI = 0.81 to 1.44, P = .17), respectively. In HR+HER2- BC, being overweight was associated with worse OS (HR = 1.14, 95% CI = 1.07 to 1.22, P < .001). CONCLUSIONS: Obesity was associated with modestly worse DFS and OS in all BC subtypes.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.046 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".