Abstract 3602: The association between longitudinal lipid trajectories and all-cause mortality in breast cancer survivors: A population-based study
Bibliographic record
Abstract
Abstract Background: Given breast cancer is the second leading cause of cancer-related death in women, it is important to identify breast cancer patients at high risk of mortality. Lipid levels before and after diagnosis may be key indicators for overall health and prognostic outcomes for breast cancer patients. This study aimed to identify distinct lipid trajectories in those diagnosed with breast cancer and examined their association with all-cause mortality. Methods: This population-based retrospective cohort study included females diagnosed with incident breast cancer who survived at least five years after diagnosis (i.e., breast cancer survivors). Total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (mmol/L) were used to develop trajectory patterns for each lipid through group-based trajectory modelling. Cox proportional hazards models estimated hazard ratios (HR) and 95% confidence intervals (CI) of all-cause mortality by lipid trajectories. Results: The study included 32, 537 female breast cancer survivors. Approximately 30% of the study population lipid levels within recommended ranges before and after a breast cancer diagnosis (moderate-stable trajectory). Nearly 10% of the population had very low lipids before and after a breast cancer diagnosis (very low-stable trajectory); this pattern was associated with an increased risk of all-cause mortality for total cholesterol (HR 1.38, 95%CI 1.16, 1.66), LDL (HR 1.24, 95%CI 1.04, 1.49), and HDL (HR 1.20, 95%CI 1.02, 1.40), compared to the moderate-stable trajectory. Approximately 5-6% had very high total cholesterol or LDL that rapidly declined after a breast cancer diagnosis (very high-declining trajectory); this pattern was associated with an increased risk of all-cause mortality compared to a moderate-stable trajectory (HR 1.59, 95%CI 1.23, 2.05; HR 1.40, 95%CI 1.09, 1.81, respectively). The association between lipid trajectories and all-cause mortality did not significantly differ by lipid-lowering agent use, suggesting pharmacologic management did not impact the findings. However, those not on a lipid-lowering agent had a mortality risk of higher magnitude than those on pharmacologic intervention. Conclusions: Breast cancer patients with very low lipids before and after diagnosis, and patients with very high lipids that rapidly decline to low levels after diagnosis were identified as subgroups at high risk of mortality. These findings highlight the importance of considering longitudinal, repeat lipid measures to improve long-term outcomes in this population. Citation Format: Shana Jean Kim, Vanessa De Rubeis, Brendan T. Smith, Husam Abdel-Qadir, Jennifer D. Brooks. The association between longitudinal lipid trajectories and all-cause mortality in breast cancer survivors: A population-based study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3602.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".