Genetic ancestry and racial differences in prostate tumours

The complex interplay of modifiable risk factors affecting prostate cancer disparities in African American men

Prostate cancer is the second most commonly diagnosed non-skin malignancy and the second leading cause of cancer death among men in the USA. However, the mortality rate of African American men aged 40-60 years is almost 2.5-fold greater than that of European American men. Despite screening and diagnostic and therapeutic advances, disparities in prostate cancer incidence and outcomes remain prevalent. The reasons that lead to this disparity in outcomes are complex and multifactorial. Established non-modifiable risk factors such as age and genetic predisposition contribute to this disparity; however, evidence suggests that modifiable risk factors (including social determinants of health, diet, steroid hormones, environment and lack of diversity in enrolment in clinical trials) are prominent contributing factors to the racial disparities observed. Disparities involved in the diagnosis, treatment and survival of African American men with prostate cancer have also been correlated with low socioeconomic status, education and lack of access to health care. The effects and complex interactions of prostate cancer modifiable risk factors are important considerations for mitigating the incidence and outcomes of this disease in African American men.

Understanding and Addressing Prostate Cancer Disparities in Diagnosis, Treatment, and Outcomes Among Black Men

Despite advances in screening, diagnosis, and treatment for prostate cancer (PCa), Black men tend to be diagnosed at younger ages, have higher mortality rates, and are at increased risk of recurrence or metastasis compared to their White counterparts. PCa disparities among Black men are caused by a complex interaction of social, behavioral, and biological factors across the public policy, community, organizational, interpersonal, and individual levels. Key contributing factors include mistrust in the health care system, poor communication between patients and providers, low awareness of screening guidelines, and high medical costs. These disparities are further exacerbated by the low representation of Black men in clinical trials, which limits access to high-quality cancer care and generalizability for PCa treatments. In this narrative review of the existing literature, we examined the epidemiology and identified contributing factors, and propose multi-level strategies to address and mitigate disparities among Black men with PCa.

A microbial causal mediation analytic tool for health disparity and applications in body mass index

BACKGROUND

Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework can be directly used to analyze microbiome as a mediator between health disparity and clinical outcome, due to the non-manipulable nature of the exposure and the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality.

METHODS

Considering the modifiable and quantitative features of the microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g., ethnicity or region) to the outcome through the microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups and innovatively and successfully extends the existing microbial mediation methods, which are originally proposed under potential outcome or counterfactual outcome study design, to address health disparities.

RESULTS

Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating the microbiome's contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between ethnicities or regions. 20.63%, 33.09%, and 25.71% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 18, and 16 species are identified to play the mediating role respectively.

CONCLUSIONS

The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles. Video Abstract.

A microbial causal mediation analytic tool for health disparity and applications in body mass index

Background: Emerging evidence suggests the potential mediating role of microbiome in health disparities. However, no analytic framework is available to analyze microbiome as a mediator between health disparity and clinical outcome, due to the unique structure of microbiome data, including high dimensionality, sparsity, and compositionality. Methods: Considering the modifiable and quantitative features of microbiome, we propose a microbial causal mediation model framework, SparseMCMM_HD, to uncover the mediating role of microbiome in health disparities, by depicting a plausible path from a non-manipulable exposure (e.g. race or region) to a continuous outcome through microbiome. The proposed SparseMCMM_HD rigorously defines and quantifies the manipulable disparity measure that would be eliminated by equalizing microbiome profiles between comparison and reference groups. Moreover, two tests checking the impact of microbiome on health disparity are proposed. Results: Through three body mass index (BMI) studies selected from the curatedMetagenomicData 3.4.2 package and the American gut project: China vs. USA, China vs. UK, and Asian or Pacific Islander (API) vs. Caucasian, we exhibit the utility of the proposed SparseMCMM_HD framework for investigating microbiome’s contributions in health disparities. Specifically, BMI exhibits disparities and microbial community diversities are significantly distinctive between the reference and comparison groups in all three applications. By employing SparseMCMM_HD, we illustrate that microbiome plays a crucial role in explaining the disparities in BMI between races or regions. 11.99%, 12.90%, and 7.4% of the overall disparity in BMI in China-USA, China-UK, and API-Caucasian comparisons, respectively, would be eliminated if the between-group microbiome profiles were equalized; and 15, 21, and 12 species are identified to play the mediating role respectively. Conclusions: The proposed SparseMCMM_HD is an effective and validated tool to elucidate the mediating role of microbiome in health disparity. Three BMI applications shed light on the utility of microbiome in reducing BMI disparity by manipulating microbial profiles.