Background: Adjuvant endocrine therapy improves the prognosis of early breast cancer with hormone receptor positivity. However, there is no systematic report on the effect of endocrine therapy (especially ovarian function suppression, OFS) on serum lipids in premenopausal women. This retrospective cohort study aimed to determine whether various endocrine treatments had different effects on blood lipids in young premenopausal breast cancer patients.
Methods: This study enrolled 160 premenopausal patients with stage I-III breast cancer in eastern China. The initial diagnostic information was retrieved from patient's medical records, including age at the time of diagnosis, tumor characteristics, anticancer treatment, weight and height, and past medical history. They have no history of cardiovascular disease. The changes of blood lipids in the types of endocrine therapy were compared at the 3rd, 6th, 12th, and 24th months after the start of endocrine therapy. Generalized Linear Mixed Model (GLMM) was used in analyses
Results: Our data revealed that LDL-C of patients with TAM group in the 6th, 12th, and 24th months was significantly lower than that in the 3rd month, while HDL-C in the 6th, 12th, and 24th months was significantly higher than that in the 3rd month, indicating that blood lipid levels generally improved with time. While in TAM plus OFS group, HDL-C in the 24th month was significantly higher than that in the 3rd month, TC in the 24th month was significantly higher than that in the 6th month. The lipid profiles of OFS plus AI group did not show significant differences at any time point but were significantly higher than those of the other two groups.
Conclusions: TAM group tended to have lower serum lipid levels. With longer follow-up, no statistically significant difference in values at various time points was observed between TAM and TAM plus OFS groups. Compared with the other two groups, OFS plus AI group presented an increasing trend toward LDL-C and TC. The risk of dyslipidemia requires further investigation using a large sample size.