Omar Mohamed Makram

PURPOSE: Patients with prostate cancer (PC) face elevated cardiovascular (CV) risk. Conventional CV risk scores are often poorly calibrated for PC and require laboratory tests that are not routinely available in PC clinics, limiting oncology use. METHODS: The development cohort (DC) included men with PC (age ≥18 years) diagnosed in 2010-2014. Validation cohort 1 (V1) included men age 45 years and older within 1 year of PC diagnosis or near initiation of androgen deprivation therapy. Validation cohort 2 (V2) included men with PC diagnosed in 2006-2019. Outcomes were cardiovascular disease (CVD), atherosclerotic CVD (ASCVD), and heart failure (HF). Predictors were selected with XGBoost and LASSO, modeled with Fine-Gray and penalized Cox regression, and scaled to additive point scores (low/intermediate/high risk). Performance was assessed using time-dependent AUC (TDAUC), which quantifies the model's ability to correctly rank patients by risk at each time point, and compared with conventional scores. RESULTS: The DC included 1,815 patients, and V1 and V2 included 4,022 and 1,729 patients, respectively (median follow-up 10.3 years). At 10 years, cumulative incidence was 15.0% for composite CVD, 9.4% for ASCVD, and 10.1% for HF. The composite CVD score (age, race, smoking history, high-risk Gleason score, and household members) achieved a 10-year TDAUC of 0.71 (95% CI, 0.64 to 0.78) in the DC and 0.59 (95% CI, 0.52 to 0.65) in V2, with a 2-year TDAUC of 0.66 (95% CI, 0.56 to 0.75) in V1. In the DC, ASCVD and HF scores achieved 10-year TDAUCs of 0.66 (95% CI, 0.58 to 0.75) and 0.70 (95% CI, 0.62 to 0.79), respectively, and retained discrimination in validation. CONCLUSION: To our knowledge, these are the first PC-specific CV risk scores derived from oncology-available variables, supporting pragmatic CV risk stratification in PC clinics and motivating further validation and implementation studies.

Aims: Malignancy-related pericardial effusion (MRPE) is a serious complication of advanced cancer, potentially leading to life-threatening cardiac tamponade. While both pericardiocentesis and surgical pericardial window are used for management, data on comparative efficacy and safety remain heterogeneous. This study aims to compare the efficacy and safety of pericardiocentesis vs. surgical pericardial window in the management of the first occurrence of MRPE. Methods and results: We systematically searched PubMed, Cochrane, and Google Scholar up until May 2025. Articles directly comparing pericardiocentesis to surgical pericardial window, which include patients with MRPE, have first occurrence procedure outcome data, and published in English, were included. Our outcomes consisted of procedure failure (composite of inability to drain, inability to relieve symptoms, or recurrence of effusion), bleeding, infection, pneumothorax, supraventricular tachyarrhythmias (SVT), and death. A total of six articles met our inclusion criteria, representing a total of 1369 patients, 1086 of whom underwent pericardiocentesis and 253 surgical pericardial windows. Procedure failure was significantly higher in the pericardiocentesis group when compared to the surgical approach [odds ratio (OR): 2.99, 95% CI 1.10–8.14, P = 0.03], while more deaths were reported in the surgical pericardial window group (OR: 0.68, 95%CI 0.46–0.99, P = 0.05). There was no difference between the two groups for bleeding (P = 0.051), infection (P = 0.11), pneumothorax (P = 0.56), or SVT (P = 0.31). Conclusion: Both pericardiocentesis and surgical pericardial window showed comparable rates of infection, bleeding, SVT, and pneumothorax in patients with MRPE. However, due to the nature of the procedures, a lower failure rate was seen in surgical pericardial window with a higher death rate.

Background: Obesity is a risk factor for both cardiovascular disease (CVD) and cancer. The traditional body mass index (BMI)-based obesity definition (BMI ≥30 kg/m²) has limitations across adulthood and ancestry. The Global Commission on Clinical Obesity in 2025 proposed a new definition incorporating central adiposity measures. The magnitude of increased disease risk among the newly classified obese persons remains undetermined. We compared the associations between obesity definitions and incident CVD and obesity-related cancer in a community-based cohort. Methods: We analyzed 14,834 Atherosclerosis Risk in Communities (ARIC) study participants followed for a median of 25 years. Participants were classified as obese using the traditional definition (BMI ≥30 kg/m²) and the new definition (BMI ≥25 kg/m² plus ≥1 central adiposity measure, or two central adiposity measures as follows: waist circumference ≥102 cm Male/ ≥88 cm Female; waist-to-hip ratio >0.90 Male/ >0.85 Female; waist-to-height ratio >0.5). We identified participants classified as obese only by the new definition. Cox models adjusted for demographics, socioeconomic, and clinical risk factors estimated the adjusted hazard ratios (aHR). A subgroup analyses by age, sex, and race, and time-dependent sensitivity analyses were conducted. Results: 54% and 27% were classified as obese, respectively, according to the new-only (median BMI 26 kg/m ² ) versus the traditional (median BMI 33 kg/m ² ) definition; 19% were non-obese by both. 7% of the “obese by new-only definition” group (i.e. BMI 25-2 and central adiposity) reverted to non-obese status. Compared to non-obese, both new-only (aHR 1.25, 95% CI 1.15-1.35) and traditionally-defined (aHR 1.65, 95% CI 1.51-1.81) obesity were associated with higher CVD risk. The same pattern was noted in coronary heart disease and heart failure. For obesity-related cancers, the traditional definition (aHR 1.36, 95% CI 1.16-1.59), but not the new-only definition (aHR 1.09, 95% CI 0.95-1.26), conferred significantly higher risk. The traditional definition was also associated with higher risk of total and colorectal cancers. Time-dependent analysis and subgrouping by sex, race, and age at time of diagnosis yielded similar results. Conclusion: Both obesity definitions identified increased CVD risk, with significant trends across groups. Only traditionally-defined obesity consistently showed increased risk for total and obesity-related cancers. The newer, more sensitive, definition identified a large cohort with intermediate CVD risk, not previously captured by BMI alone, highlighting a distinct risk profile, suggesting potential for targeted interventions. These findings warrant careful consideration of potentially inflated risk in patients defined as obese by the new criteria. Funding: NHLBI, NCI, NPCR, AHA

Background: Socioeconomic position (SEP) is a fundamental social determinant of health (SDoH) contributing to observed disparities in cancer and cardiovascular (CV) care among individuals with prostate cancer (PC). Understanding the influence of SEP on CV health is essential for addressing outcome inequities within this population. Methods: We conducted a retrospective study utilizing Surveillance, Epidemiology, and End Results (SEER)-Medicare to evaluate CV outcomes in patients ≥65 years with PC from 2009 to 2017. We studied the impact of SEP on cardiovascular events (CVEs), cardiovascular mortality (CVm), PC-specific mortality (PCsm), and all-cause mortality. CVE, CVm, and PCsm were analyzed using competing risk models and all-cause mortality with Cox proportional hazards models. A subgroup analysis was performed in Non-Hispanic Black and Non-Hispanic White individuals. Results: We included 141 242 patients of whom 76 844 were in high SEP areas (45.6%) and 64 398 in low SEP ones (54.4%). Both groups had a mean age of 72 years; patients in low SEP areas were 67.5% Non-Hispanic White, 34.4% having diabetes, 73.3% hypertension, and 67.7% dyslipidemia versus the high SEP group with 85.6% Non-Hispanic White, 30.4% diabetes, 69.9% hypertension, and 70.7% dyslipidemia. Patients in low SEP areas had higher risks of CVm (subdistribution hazard ratio [sHR] = 1.18, 95% CI = 1.12 to 1.25), PCsm (sHR = 1.12, 95% CI = 1.06 to 1.17), CVE (sHR = 1.07, 95% CI = 1.04 to 1.09), and all-cause mortality (HR 1.16, 95% CI = 1.13 to 1.20). Accentuated results were shown in the Non-Hispanic Black subgroup. Conclusion: Adverse SEP plays a significant role in shaping outcomes among older patients with PC, contributing to elevated risks of adverse CV and survival outcomes.

Background: Polycystic ovary syndrome (PCOS) is the most common endocrine disorder among women of reproductive age and the most common cause of infertility due to a lack of ovulation. This pilot study investigated the efficacy and safety of a three-month herbal tea intervention combining marjoram and cinnamon for managing PCOS symptoms. Methods: A randomized, double-blind, placebo-controlled trial was conducted with women diagnosed with PCOS ( n = 60). A limited sample size was used for this pilot study. Participants were randomly assigned to receive either a twice-daily herbal tea ( n = 30) or a placebo tea ( n = 30) for 3 months. Monthly monitoring assessed compliance and adverse effects. Results: Only 36 participants completed the treatment in the herbal intervention and placebo groups. No major side effects were reported in either group. In the placebo group, a significant decrease in anti-Müllerian hormone levels ( P = 0.023, 95%-CI: 0.27 to 2.99), and significant increases in prolactin ( P = 0.032, 95%-CI: −11.20 to −0.59) and high-density lipoprotein ( P = 0.042, 95%-CI: −7.10 to −0.15) levels were observed. In the herbal intervention group, a significant increase in follicle-stimulating hormone levels ( P = 0.032, 95%-CI: −0.95 to −0.05) and a significant decrease in AMH levels ( P = 0.003, 95%-CI: 0.58 to 2.56) were found. Conclusion: This pilot trial suggests that a combination of marjoram and cinnamon herbal tea may positively influence hormonal profiles in women with PCOS, warranting further investigation with larger sample sizes and longer durations to confirm these findings and explore the clinical significance of the observed changes.

Background: This study evaluated the impact of diabetes mellitus (DM) and its treatments on cardiovascular outcomes in prostate cancer (PC) patients aged 66 years and older, with or without androgen deprivation therapy (ADT) exposure. Methods: Using the SEER-Medicare database (2009–2017), two cohorts were created: Cohort 1 included all PC patients enrolled in Medicare Parts A and B; Cohort 2 was a subset of Cohort 1 receiving ADT and enrolled in Medicare Part D. Exposures were DM and DM medications. Outcomes included cardiovascular events (CVEs), cardiovascular mortality (CVm), PC-specific mortality (PCsm), and all-cause mortality, analyzed using multivariable Fine-Gray and Cox models. Results: Cohort 1 included 150,647 PC patients (32% with DM, median age 72). DM was associated with higher risk of CVE (subdistribution hazard ratio [sHR] 1.20, 95% CI 1.17–1.22), CVm (sHR 1.35, 1.28–1.43), and all-cause mortality (adjusted HR [aHR] 1.22, 1.19–1.26) (all p < 0.001). Non-Hispanic Blacks (NHBs) and patients from lower socioeconomic (SES) and education areas experienced comparable or worse outcomes. In Cohort 2 (n = 14,938), DM patients on non-metformin therapies had higher all-cause mortality (aHR 1.33, 1.11–1.25; p = 0.002) than those on metformin, particularly in NHB and low education groups. Sensitivity analyses with follow-up limited to two years showed consistent results as overall. Conclusions: Diabetic PC patients, especially NHB, lower SES and lower education subgroups, were associated with worse cardiovascular and all-cause mortality outcomes. Metformin may be associated with better outcomes in these populations, warranting further research on the disparities in PC and diabetes, and cardioprotective effects of DM medications across different subpopulations.

Background/Objectives Recent advances in prostate cancer management have improved overall survival and quality of life for those afflicted. However, these patients continue to suffer from a high burden of cardiovascular disease through a variety of mechanisms, including both the cancer itself as well as prostate cancer-specific treatments. Subjects/Methods Cardiovascular disease is now the leading cause of death in this population, and current data suggests that many patients remain sub-optimally controlled from a risk factor standpoint. There is a lack of consensus and standardized guidelines on cardiovascular risk stratification and specific management strategies in the urology, medical oncology, radiation oncology, and primary care settings. Results/Conclusions We review available literature and provide a cardiovascular risk stratification schema for prostate cancer patients that clinicians in each of these settings can utilize. We also highlight a shared decision-making model, consistent risk restratification, and social determinants of health as care priorities to optimize cardiovascular health for this at-risk population.

Background: Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them with conventional scores in predicting 10-year CVD risk for patients with breast cancer (BC), colorectal cancer (CRC), or lung cancer (LC). Methods: We analyzed adults diagnosed with BC, CRC, or LC between 2005 and 2012. An machine learning (ML) Extreme Gradient Boosting algorithm ranked 40-50 covariates for predicting CVD for each cancer type using SHapley Additive exPlanations values. The top 10 ML-predictors were used to create predictive equations using logistic regression and compared with American College of Cardiology (ACC)/American Heart Association (AHA) Pooled Cohort Equations (PCE), Predicting Risk of cardiovascular disease EVENTs (PREVENT), and Systematic COronary Risk Evaluation-2 (SCORE2) using the area under the curve (AUC). Results: We included 10 339 patients: 55.5% had BC, 15.6% had CRC, and 29.7% had LC. The actual 10-year CVD rates were: BC 21%, CRC 10%, and LC 28%. The predictors derived from the ML algorithm included cancer-specific and socioeconomic factors. The cancer-specific predictive scores achieved AUCs of 0.84, 0.76, and 0.83 for BC, CRC, and LC, respectively, and outperformed PCE, PREVENT, and SCORE2, increasing the absolute AUC values by up to 0.31 points (with AUC ranging from 0 to 1). Similar results were found when excluding patients with cardiac history or advanced cancer from the analysis. Conclusions: Cancer-specific CVD predictive scores outperform conventional scores and emphasize the importance of integrating cancer-related covariates for precise prediction.