Body Composition During Androgen Deprivation Therapy in Prostate Cancer

Corresponding Author

Prostate cancer is the most prevalent cancer among men, with 288,300 new cases estimated in the United States alone in 2023.¹ With advances in treatments and the indolent nature of prostate cancer, men with prostate cancer are more likely to die of nonprostate causes, including cardiovascular disease, than prostate cancer itself.² Androgen deprivation therapy (ADT) is a fundamental treatment in men with advanced-stage prostate cancer; however, ADT is also associated with an increased risk of cardiovascular events and mortality.³ Therefore, it is critical to investigate potential modifiable risk factors for cardiovascular disease in men treated with ADT for prostate cancer.

Skeletal muscle and adipose tissue function as endocrine organs, which can produce and secrete myokines/adipokines that exert their effects in autocrine, paracrine, or endocrine functions.⁴ Body composition is a biomarker of overall health that provides useful information for predicting the outcomes of men with prostate cancer. In a pooled analysis of data from 2,066 men from NRG/RTOG 9406 and NRG/RTOG 0126, McDonald et al² reported that a lower pretreatment computed tomography (CT)-measured psoas muscle area was independently associated with increased all-cause mortality risk. Additionally, a meta-analysis of “real-world” studies revealed that men presenting with lower levels of muscle mass had 50% greater all-cause mortality risk than those with higher levels of muscle mass.⁵ Total adiposity and visceral adipose tissue were not associated with all-cause mortality, whereas higher subcutaneous adipose tissue levels were associated with higher survival. Although these studies revealed the prognostic significance of body composition in men with prostate cancer, several notable scientific gaps remained. First, these studies did not report an association among body composition, cardiovascular events, and mortality, which is highly relevant to men with prostate cancer. Second, most studies have only analyzed body composition using CT scans. Although CT-based body composition is a widely validated quantitative measurement, physical activity and muscle strength are functional measurements that may also be predictive of cardiovascular outcomes. Third, most previous studies only evaluated body composition at a single time point (ie, pretreatment); longitudinal measurements of body composition can provide a more comprehensive view of how body composition changes impact outcomes.⁶

In this issue of JACC: CardioOncology, Leong et al⁷ address several of these gaps with their well-conducted analysis of RADICAL-PC (Role of Androgen Deprivation Therapy in Cardiovascular Disease – A Longitudinal Prostate Cancer). In this large, international prospective cohort study, the authors examined the trajectory of physical measurements, handgrip strength, and gait speed at baseline and at 12 months among 3,967 men with prostate cancer (mean age: 68.5 years; use of ADT: 1,731 men). Patients were newly diagnosed in the last year or beginning ADT for the first time. At 12 months, among men not receiving ADT and men receiving ADT, weight increased by 0.0% and 1.6%, waist circumference increased by 0.6% and 2.2%, handgrip strength decreased by 17.8% and 27.4% (54% larger decrease in the ADT group), and the strength-weight ratio decreased by 15.6% and 26.8% (72% larger decrease in the ADT group), respectively. Furthermore, waist circumference above the highest quartile (110 cm) and handgrip strength below the lowest quartile (29.5 kg) were independently associated with an increased risk of adverse cardiovascular events (waist circumference, HR: 1.40; 95% CI: 1.03-1.90; P = 0.029; handgrip strength, HR: 1.59; 95% CI: 1.14-2.22; P = 0.006).

Leong et al⁷ provide the first evidence to confirm the relationship between muscle strength, adiposity, and adverse cardiovascular events in men with prostate cancer. It should be noted that 2 large prospective studies have revealed associations between lower handgrip strength and cardiovascular mortality in the general population. In the PURE (Prospective Urban Rural Epidemiology) study,⁸ handgrip strength (per 5 kg reduction) was inversely associated with cardiovascular mortality (HR: 1.17, 95% CI: 1.11-1.25; P < 0.0001). In the UK Biobank study,⁹ lower handgrip strength (per 5 kg) was associated with higher cardiovascular mortality risk in men (HR: 1.22; 95% CI: 1.18-1.26; P < 0.001) but not with prostate cancer mortality risk (HR: 1.05; 95% CI: 0.96-1.15; P = 0.29). Notably, Leong et al⁷ used the lowest and highest quartile as the cutoff value to stratify handgrip strength and waist circumference as categoric variables, respectively, to assess their association with cardiovascular outcomes. However, in their Supplemental Figure 3, they also revealed a nonlinear and bimodal relationship between handgrip strength and waist circumference as continuous variables with cardiovascular outcomes. Although we are confident in the robustness of the associations between higher waist circumference and lower handgrip strength with higher risk of adverse cardiovascular events based on the Kaplan-Meier curves in their Figure 2, the use of quartiles as cutpoints may still need caution in interpretation. The possible explanation behind this observation is that the relationship between body composition and cardiovascular outcome is potentially nonlinear and involves complex interactions. Thus, Cox proportional hazards regression as a linear model may not optimally account for nonlinear relationships and interaction effects and may not offer optimal predictive capability. As an alternative to the use of spline curves to model the relationship in the current study, machine learning approaches could be used to handle nonlinear relationships and have been shown to perform favorably in predicting clinical outcomes. With the advancement of explainable artificial intelligence, the SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-Agnostic Explanations) method can help interpret machine learning models by visualizing their inner workings.¹⁰ Future study with machine learning and explainable artificial intelligence may further elucidate the relationships between body composition and cardiovascular outcomes in prostate cancer.

Moreover, although Leong et al⁷ extended the existing evidence by focusing on men with prostate cancer and providing data on the impact of handgrip strength and adiposity on the risk of adverse cardiovascular events, this study was limited by the lack of analysis of the association between changes in handgrip strength and adiposity over time and cardiovascular outcomes. Furthermore, given the short follow-up period of the current study, a longer follow-up period would be valuable to characterize the relationships between changes in these outcomes. The study also lacked an analysis of muscle quantity at baseline and 12 months, which may be a modifiable factor. However, the large prospective design, inclusion of men receiving ADT, and improved adjustment for a greater number of potential confounding factors than in previous studies reflect the methodological strengths of this study. As a result, this study determines that handgrip strength and adiposity are potential modifiable factors for mitigating cardiovascular risk in men receiving ADT for prostate cancer. Future studies need to evaluate whether interventions targeting these factors can help prevent adverse outcomes.

Multimodal interventions including exercise, nutrition, and other anti-inflammatory interventions are potential strategies for improving body composition. Exercise can ameliorate the adverse effects of ADT on body composition, and patients should partake in resistance/aerobic/impact exercise, prolonged exercise duration, and exercise programs when initiating ADT.¹¹ Exercise can increase lean mass and reduce body fat mass, waist circumference, and systolic and diastolic blood pressure in a meta-analysis of studies of men with prostate cancer undergoing ADT.¹² The combination of exercise and nutrition interventions appeared to be effective in improving waist circumference and systolic and diastolic blood pressure, whereas an exercise-only intervention may be more effective in increasing lean mass and reducing body fat mass. However, exercise-only or combined exercise and nutrition interventions did not significantly improve triglycerides, high-density lipoprotein cholesterol level, or the fasting blood glucose level. Regular monitoring of blood pressure, lipid panels, and fasting glucose levels is essential and should ideally be performed in collaboration with cardio-oncologists.

Systemic chronic low-grade inflammation may establish a detrimental vicious circle that results in the maintenance of adipose tissue and skeletal muscle inflammation and supports a sarcopenic obesity status.¹³ Anti-inflammatory interventions with physical activity and nutrition may mitigate the detrimental effects of the deleterious cycle between adipose tissue and muscle inflammation. However, the role of anti-inflammatory interventions in improving body composition is unclear.

Additionally, luteinizing hormone-releasing hormone agonists, androgen synthesis inhibitors (eg, abiraterone), and androgen receptor antagonists (eg, enzalutamide) have disparate effects on body composition.¹⁴ These findings suggest that ADT regimens should be considered when determining the optimal strategies to improve body composition. Future studies are needed to investigate the optimal strategies for improving body composition during ADT and whether the benefits of body composition improvement can translate to a lower risk of adverse cardiovascular events.

In summary, this study highlights the need for transdisciplinary teams that include cardio-oncologists, oncologists, exercise physiologists, and nutritionists to define optimal strategies to prevent muscle loss and fat gain during ADT and potentially improve treatment outcomes for men with prostate cancer.

Funding Support and Author Disclosures

This work was supported by the National Science and Technology Council Taiwan (grant number: Contract No. NSTC 113-2314-B-195-011-MY3) and MacKay Memorial Hospital (grant number: MMH-113-53, MMH-113-109). Both authors have reported that they have no relationships relevant to the contents of this paper to disclose.