Evaluating Prostate-Specific Antigen Screening for Young African American Men With Cancer.

BACKGROUND: Despite higher risks associated with prostate cancer, young African American men are poorly represented in prostate-specific antigen (PSA) trials, which limits proper evidence-based guidance. We evaluated the impact of PSA screening, alongside primary care provider utilization, on prostate cancer outcomes for these patients. METHODS: We identified African American men aged 40-55 years, diagnosed with prostate cancer between 2004 and 2017 within the Veterans Health Administration. Inverse probability of treatment-weighted propensity scores were used in multivariable models to assess PSA screening on PSA levels higher than 20, Gleason score of 8 or higher, and metastatic disease at diagnosis. Lead-time adjusted Fine-Gray regression evaluated PSA screening on prostate cancer-specific mortality (PCSM), with noncancer death as competing events. All statistical tests were 2-sided. RESULTS: The cohort included 4726 patients. Mean age was 51.8 years, with 84-month median follow-up. There were 1057 (22.4%) with no PSA screening prior to diagnosis. Compared with no screening, PSA screening was associated with statistically significantly reduced odds of PSA levels higher than 20 (odds ratio [OR] = 0.56, 95% confidence interval [CI] = 0.49 to 0.63; P < .001), Gleason score of 8 or higher (OR = 0.78, 95% CI = 0.69 to 0.88; P < .001), and metastatic disease at diagnosis (OR = 0.50, 95% CI = 0.39 to 0.64; P < .001), and decreased PCSM (subdistribution hazard ratio = 0.52, 95% CI = 0.36 to 0.76; P < .001). Primary care provider visits displayed similar effects. CONCLUSIONS: Among young African American men diagnosed with prostate cancer, PSA screening was associated with statistically significantly lower risk of PSA levels higher than 20, Gleason score of 8 or higher, and metastatic disease at diagnosis and statistically significantly reduced risk of PCSM. However, the retrospective design limits precise estimation of screening effects. Prospective studies are needed to validate these findings.

Uptake of KRAS Testing and Anti-EGFR Antibody Use for Colorectal Cancer in the VA.

Advances in precision oncology, including RAS testing to predict response to epidermal growth factor receptor monoclonal antibodies (EGFR mAbs) in colorectal cancer (CRC), can extend patients' lives. We evaluated uptake and clinical use of KRAS molecular testing, guideline recommended since 2010, in the Veterans Affairs Healthcare System (VA). MATERIALS AND METHODS: We conducted a retrospective cohort study of patients with stage IV CRC diagnosed in the VA 2006-2015. We gathered clinical, demographic, molecular, and treatment data from the VA Corporate Data Warehouse and 29 commercial laboratories. We performed multivariable analyses of associations between patient characteristics, KRAS testing, and EGFR mAb treatment. RESULTS: Among 5,943 patients diagnosed with stage IV CRC, only 1,053 (17.7%) had KRAS testing. Testing rates increased from 2.3% in 2006 to 28.4% in 2013. In multivariable regression, older patients (odds ratio, 0.17; 95% CI, 0.09 to 0.32 for ≥ age 85 v < 45 years) and those treated in the Northeast and South regions were less likely, and those treated at high-volume CRC centers were more likely to have KRAS testing (odds ratio, 2.32; 95% CI, 1.48 to 3.63). Rates of potentially guideline discordant care were high: 64.3% (321/499) of KRAS wild-type (WT) went untreated with EGFR mAb and 8.8% (401/4,570) with no KRAS testing received EGFR mAb. Among KRAS-WT patients, survival was better for patients who received EGFR mAb treatment (29.6 v 18.8 months; P < .001). CONCLUSION: We found underuse of KRAS testing in advanced CRC, especially among older patients and those treated at lower-volume CRC centers. We found high rates of potentially guideline discordant underuse of EGFR mAb in patients with KRAS-WT tumors. Efforts to understand barriers to precision oncology are needed to maximize patient benefit.

Association Between Genetic Variation in Blood Pressure and Increased Lifetime Risk of Peripheral Artery Disease.

[Figure: see text].

Evaluation and Prediction of Early Alzheimer's Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping.

BACKGROUND: Because Alzheimer's Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, novel machine learning methods have been applied to solve limitations. OBJECTIVE: The objective of this study was to investigate the approach of classification and prediction methods using the Machine Learning (ML)-based Optimized Combination-Feature (OCF) set on Gray Matter Volume (GMV) and Quantitative Susceptibility Mapping (QSM) in the subjects of Cognitive Normal (CN) elderly, Amnestic Mild Cognitive Impairment (aMCI), and mild and moderate AD. MATERIALS AND METHODS: 57 subjects were included: 19 CN, 19 aMCI, and 19 AD with GMV and QSM. Regions-of-Interest (ROIs) were defined at the well-known regions for rich iron contents and amyloid accumulation areas in the AD brain. To differentiate the three subject groups, the Support Vector Machine (SVM) with the three different kernels and with the OCF set was conducted with GMV and QSM values. To predict the aMCI stage, regression-based ML models were performed with the OCF set. The result of prediction was compared with the accuracy of clinical data. RESULTS: In the group classification between CN and aMCI, the highest accuracy was shown using the combination of GMVs (hippocampus and entorhinal cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.94). In the group classification between aMCI and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.93). In the group classification between CN and AD, the highest accuracy was shown using the combination of GMVs (amygdala, entorhinal cortex, and posterior cingulate cortex) and QSMs (hippocampus and pulvinar) data using the 2nd SVM classifier (AUC = 0.99). To predict aMCI from CN, the exponential Gaussian process regression model with the OCF set using GMV and QSM data was shown the most similar result (RMSE = 0.371) to clinical data (RMSE = 0.319). CONCLUSION: The proposed OCF based ML approach with GMV and QSM was shown the effective performance of the subject group classification and prediction for aMCI stage. Therefore, it can be used as personalized analysis or diagnostic aid program for diagnosis.

Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis.

We investigated type 2 diabetes (T2D) genetic susceptibility via multi-ancestry meta-analysis of 228,499 cases and 1,178,783 controls in the Million Veteran Program (MVP), DIAMANTE, Biobank Japan and other studies. We report 568 associations, including 286 autosomal, 7 X-chromosomal and 25 identified in ancestry-specific analyses that were previously unreported. Transcriptome-wide association analysis detected 3,568 T2D associations with genetically predicted gene expression in 687 novel genes; of these, 54 are known to interact with FDA-approved drugs. A polygenic risk score (PRS) was strongly associated with increased risk of T2D-related retinopathy and modestly associated with chronic kidney disease (CKD), peripheral artery disease (PAD) and neuropathy. We investigated the genetic etiology of T2D-related vascular outcomes in the MVP and observed statistical SNP-T2D interactions at 13 variants, including coronary heart disease (CHD), CKD, PAD and neuropathy. These findings may help to identify potential therapeutic targets for T2D and genomic pathways that link T2D to vascular outcomes.