Abstract B019: Lifestyle and reproductive factors and early-onset invasive epithelial ovarian cancer risk
Bibliographic record
Abstract
Abstract Background and Objective Epithelial ovarian cancer is typically diagnosed later in life, at a median age of 63 years. Early-onset ovarian cancer (<50 years of age) comprises roughly a fifth of all new diagnoses. Few risk factors are established for ovarian cancer, and there may be differences according to age at onset. Given this, we aimed to describe and explore whether associations with multiple lifestyle and reproductive risk factors differed between early and late-onset invasive ovarian cancer. Methods This study utilized data from a population-based case-control study conducted in Montreal, Canada from 2011 to 2016, including 364 incident cases of invasive epithelial ovarian cancer and 908 population controls frequency-matched to cases by age. Information on various lifestyle and reproductive factors were collected during in-person interviews. Unconditional logistic regression was used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) between each factor and risk. To assess potential differences according to age at onset (early vs. late), models included an interaction term between the factor of interest and a binary age at diagnosis variable (<50 vs. 50+ years). The specific lifestyle and reproductive factors examined in this analysis included alcohol consumption, caffeine intake, vitamin D, residential air pollution, childhood body shape, adult body mass index, shift work, sleep, regular analgesic use, smoking, age at menarche, parity, age at first/last birth, oral contraceptive duration, and lifetime number of ovulatory cycles. Results Overall, 19.0% of all cases were early-onset (n=69). The distribution by histotype varied greatly, with high-grade serous comprising 43.5% of early-onset cancers vs. 72.2% of later-onset. The second most frequent histotype among early-onset cases was mucinous (14.5%). In addition, stage at diagnosis differed with 40.6% and 20.3% of early- and later-onset cases, respectively, diagnosed at stage 1 and 45.0% and 65.1% at stage 3/4. Among the lifestyle factors examined in relation to risk, lifetime intake of alcohol and caffeine as well as cumulative smoking exposure tended to be associated with an increased risk of early-onset ovarian cancer but showed a null association with later-onset cancer. Conversely, lifetime vitamin D exposure was associated with a reduced risk only for later-onset disease. Confidence intervals were wide for all results, and with other lifestyle factors, differences by age at onset were difficult to discern. For reproductive factors, observed associations were similar for early- and later-onset ovarian cancer, except for lifetime number of ovulatory cycles for which a positive association was stronger for early-onset cancers. Conclusion We observed some suggestion that certain lifestyle and reproductive factors may differently influence the risk of invasive epithelial ovarian cancer depending on age at onset. These differences may reflect differences in histotype distributions and pathogenesis. Further research is needed to better understand differences. Citation Format: Anita Koushik, Marie-Hélène Mayrand, Ari N. Meguerditchian, Claudia Waddingham. Lifestyle and reproductive factors and early-onset invasive epithelial ovarian cancer risk [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B019.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".