Body mass index in relation to prostate-specific antigen-related parameters

Can weight-adjusted waist circumference index become a single anthropometric predictor of prostate-specific antigen concentration? A National Health and Nutrition Examination Survey analysis (2003-2010)

Recent studies have introduced the weight-adjusted waist circumference index (WWI) as a viable obesity indicator that may better reflect centripetal obesity and its associated risks. In examining the connection between WWI and prostate-specific antigen (PSA), this study leveraged data from the National Health and Nutrition Examination Survey 2003-2010, including 5732 participants. Our initial analysis indicated a significant positive association between WWI and PSA levels. However, subsequent models that adjusted for covariates such as age, race, and a range of metabolic and cardiovascular health-related factors revealed that the strength and significance of this relationship were attenuated. Model 1 showed a highly significant correlation (p < 0.0001). Yet, in Model 2, which accounted for age and race, the association softened (p = 0.0520). Moreover, when a full spectrum of health covariates was included in Model 3, the association was no longer significant (p = 0.9775). These findings suggest that while an unadjusted correlation exists, its potential use as a diagnostic predictor is limited without considering the broader health context. Therefore, it is crucial to review such data with multiple considerations in mind, and extensive attention should be paid to the evaluation of covariates.

Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study

BACKGROUND

Deep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort.

METHODS

An automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012 and 2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α = 0.05/1222).

FINDINGS

17,646 adults (mean age, 56 years ± 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95% CI], 0.59 [0.55-0.64]; P < 0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P < 0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P < 0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P < 0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P < 0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P < 0.0001).

INTERPRETATION

PheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes.

FUNDING

National Institutes of Health, Stanford AIMI-HAI pilot grant, Stanford Precision Health and Integrated Diagnostics, Stanford Cardiovascular Institute, Stanford Center for Digital Health, and Stanford Knight-Hennessy Scholars.

Obesity and prostate cancer: A narrative review

Overweight and obese men with prostate cancer are at an increased risk of disease recurrence, exacerbated treatment-related adverse effects, development of obesity-related comorbidities, earlier progression and development of metastatic disease, and higher all-cause and prostate cancer-specific mortality. The physiological mechanisms associating obesity with poor prostate cancer outcomes remain largely unknown; however, an increased inflammatory environment and metabolic irregularities associated with excess fat mass are commonly postulated. Although research is limited, fat loss strategies using exercise and nutrition programmes may slow down prostate cancer progression and improve a patient's prognosis. This review is an overview of: 1) the association between obesity and poor prostate cancer prognosis; 2) potential physiological mechanisms linking obesity and prostate cancer progression; 3) the effect of obesity on treatments for prostate cancer; and 4) the potential for weight loss strategies to improve outcomes in patients with prostate cancer.