Does hormone-modulating therapy for breast cancer treatment affect risk for Alzheimer disease and related dementias?

In a recent study published in JAMA Network Open, researchers investigated the association between hormone-modulating therapy (HMT) for breast cancer treatment and the risk of developing Alzheimer's disease and related dementias (ADRD) (a group of cognitive disorders causing memory loss and impaired thinking) in women aged 65 years or older.

Study: Alzheimer Disease and Related Dementia Following Hormone-Modulating Therapy in Patients With Breast Cancer. Image Credit: LightField Studios/Shutterstock.comStudy: Alzheimer Disease and Related Dementia Following Hormone-Modulating Therapy in Patients With Breast Cancer. Image Credit: LightField Studios/Shutterstock.com

Background 

Breast cancer is the most commonly diagnosed cancer among women in the United States (US), with 83% of invasive cases occurring in women aged 50 and above. Although breast cancer incidence rates have risen by 0.5% annually since 2000, declining mortality rates have led to over 2.5 million survivors aged 65 and older.

This increase raises concerns about treatment-related complications, particularly the risk of ADRD. While HMT has improved survival rates, its impact on cognitive function and ADRD risk remains unclear.

Further research is needed to clarify the inconsistent findings on HMT's impact on ADRD risk among breast cancer survivors.

About the study 

The study cohort, drawn from the Surveillance, Epidemiology, and End Results (SEER)-Medicare-linked database, included sociocultural, demographic, and clinical information for individuals diagnosed with breast cancer. The database links SEER cancer registry data with Medicare claims, allowing comprehensive assessment over a patient's coverage period. 

Women aged 65 and older with newly diagnosed breast cancer from 2007 to 2009 were included, excluding those with preexisting ADRD or prior HMT use.

HMT exposure was defined as initiating at least one HMT medication within three years of diagnosis, identified by National Drug Code (NDC) and Healthcare Common Procedure Coding System (HCPCS) codes for selective estrogen receptor modulators (SERMs), aromatase inhibitors (AIs), and selective estrogen receptor degraders (SERDs).

The outcome was time to ADRD, identified using the International Classification of Diseases, Ninth Revision (ICD-9) and ICD-10 codes.

Baseline characteristics were summarized and evaluated using t-tests or χ2 tests. Patients were assigned to the HMT group based on their status as HMT initiators, with a propensity score weighting approach used to balance covariates between groups. Death was treated as a competing risk, and immortal time bias was addressed by attributing it to the control group.

Two models were used to evaluate the association between HMT and ADRD risk, considering age and race interactions.

Subgroup analyses examined disparities across racial groups and HMT types. Statistical analyses were performed using SAS and R, with significance set at a two-sided P value of .05. 

Study results 

Of 184,979 newly diagnosed breast cancer patients (2007-2009), 18,808 women met inclusion criteria. Among them, 12,356 (65.7%) received HMT within three years post-diagnosis, while 6,452 (34.3%) did not.

The most common age group was 75 to 79 years. Most women were White: HMT cohort had 809 Black (6.6%), 10,904 White (88.3%), and 643 other (5.2%); non-HMT cohort had 457 Black (7.1%), 5,622 White (87.1%), and 373 other (5.7%).

Mean age at diagnosis was 75 years (HMT) and 76 years (non-HMT). HMT initiation: 76.1% AIs, 23.6% selective estrogen receptor modulators, 0.3% selective estrogen receptor degraders. The mean HMT duration was 24 months.

Propensity score weighting addressed potential confounding effects and enhanced comparability between the non-HMT and HMT groups.

Before weighting, imbalances were observed among sociocultural, demographic, and clinical variables. After weighting, equilibrium in baseline characteristics was achieved, and all subsequent analyses used this weighting method.

Among 18,808 women, 2,926 (23.7%) HMT users and 1,802 (27.9%) non-HMT users developed ADRD by the end of the follow-up period.

A total of 5,038 women (26.8%) died during the follow-up period (HMT: 3,262 [26.4%]; non-HMT: 1,776 [27.5%]). HMT use was associated with a statistically relative reduction in ADRD risk (Hazard ratio (HR), 0.93; 95% CI, 0.88-0.98; P = .005).

Specifically, the HRs for initiating AI and SERM were significant (AI: HR, 0.93; 95% CI, 0.88-0.99; P = .02; SERM: HR, 0.89; 95% CI, 0.81-0.96; P = .005), while SERD was not (HR, 0.37; 95% CI, 0.13-1.05; P = .06).

Subgroup analyses showed age-modified associations between HMT and ADRD risk. The greatest reduced risk was in the 65-69 age group (HR, 0.48), diminishing with age and becoming positive in those 80 and older (HR, 1.40).

Racial differences were evident, with Black women experiencing greater reductions in ADRD risk (HR, 0.78) compared to White women (HR, 0.94).

For Black women aged 65-74, HMT significantly reduced ADRD risk (HR, 0.76), with AIs showing a slightly greater effect. White women aged 65-74 also benefited (HR, 0.89), especially with SERMs. HMT did not significantly change ADRD risk for other races in either age group.

Conclusions 

To summarize, HMT is crucial for treating hormone-positive breast cancer but raises concerns about cognitive impairment.

Research shows mixed results regarding HMT's association with ADRD risk. Using a large cohort, the study found a 7% relative risk reduction in ADRD among HMT users, confirmed by various analytic methods.

Age and race significantly influence ADRD risk, with younger Black women showing the greatest protective benefits, which diminish but remain significant with age.

Younger White women also benefit, but this effect becomes insignificant after age 75. The type of HMT also affects ADRD risk, highlighting the need for personalized treatment plans.

Journal reference:
Vijay Kumar Malesu

Written by

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.    

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