Faculty Bibliography

BACKGROUND:For over a decade the Advanced Prostate Cancer Consensus Conference (APCCC) covers a variety of topics that greatly impact daily practice. In 2025, a dedicated event was organised to discuss key questions in clinical management of patients with prostate cancer (PC) related to diagnostic tools (APCCC Diagnostics). Here we present the voting results of the APCCC Diagnostics questions. OBJECTIVE; DESIGN, SETTING, AND PARTECIPANTS: APCCC Diagnostics 2025 is a pilot project. The scientific committee for APCCC Diagnostics 2025 developed 88 multiple-choice consensus questions on six different topics. Prior to the conference, the panel members (''panellists'') voted on these questions via a web-based survey. Consensus was defined as ≥75% agreement, with strong consensus defined as ≥90% agreement. OUTCOMES MEASUREMENTS AND STATISTICAL ANALYSIS/METHODS:Consensus was only reached on 17 of 88 questions (19%), of which six (7%) received a strong consensus. Specifically, consensus was reached for two of 17 questions (14%) in "how to diagnose PC"; seven of 16 (44%) in "how to stage PC"; three of 14 (21%) in "Biochemical Recurrence Scenario"; two of 11 (18%) in "metastatic disease: what to do?"; zero of 18 (0%) in "monitoring metastatic PC"; and three of 12 (25%) in "radioligand therapy and imaging." CONCLUSIONS:The voting results and their discussion may assist physicians in navigating controversial areas of clinical management related to diagnosis, staging, and restaging in the different clinical settings for PC, particularly where high-level evidence is scarce or conflicting. The findings can also help funders and policymakers in prioritising areas for future research.

PURPOSE/OBJECTIVE:Mobile health cardiac rehabilitation may improve access to care among older adults with ischemic heart disease, but engagement remains poorly understood. We analyzed weekly engagement data from the RESILIENT (Rehabilitation Using Mobile Health for Older Adults with Ischemic Heart Disease in the Home Setting) trial, a large, randomized trial of mobile health cardiac rehabilitation in older adults conducted in the United States. METHODS:Data from 298 intervention participants were analyzed. Weekly engagement was scored from 0 to 11 based on exercise entry (7 points), communication with exercise therapist (2 points), video viewing (1 point), and blood pressure measurement (1 point). Latent class analysis identified digital engagement phenotypes. Participant characteristics were compared, and multivariable logistic regression identified factors associated with phenotype membership. RESULTS:Median age was 71.0 years, 28% were women, 23% were non-White, and 62% were enrolled after elective percutaneous coronary intervention. Latent class analysis identified 3 phenotypes: persistently low (n = 81), intermediate declining (n = 93), and persistently high (n = 124). Participants with persistently low engagement were more likely to be non-White (48% vs 12% vs 15%, P < .001), Medicaid enrolled (22% vs 8% vs 7%, P = .001), have less than high school education (16% vs 4% vs 3%, P < .001), have frailty phenotype (28% vs 10% vs 7%, P < .001), and have a greater mean number of comorbidities (3.1 vs 3.0 vs 2.6; P = .012). After adjustment, non-White race and frailty remained independently associated with low engagement. Improvement in 6-minute walk test distance varied: 20.8 m (low), 29.7 m (intermediate), and 54.5 m (high) (P = .003). CONCLUSIONS:Three distinct digital engagement phenotypes emerged. Persistently low engagement was more common among non-White and frail participants, underscoring ongoing disparities despite efforts to overcome the digital divide.

BACKGROUND:The SOUND randomized controlled trial demonstrated that omitting sentinel lymph node biopsy (SLNB) is noninferior to performing SLNB in early-stage breast cancer. Lymphovascular invasion (LVI), however, is an adverse pathologic feature associated with axillary nodal metastasis. In this study, we evaluated the association of LVI with occult axillary nodal metastasis in early-stage, SOUND-eligible breast cancer. PATIENTS AND METHODS/METHODS:We retrospectively reviewed patients with cT1N0 breast cancer from 2011 to 2025 who underwent upfront breast-conserving surgery and lymph node surgery who met SOUND trial eligibility criteria. Clinicopathologic variables were collected, and Pearson's chi-square test, unpaired t-test, and logistic regression were used to analyze the cohort. RESULTS:We identified 364 cT1N0 breast tumors, of which 63 (17.3%) were LVI-present. The overall nodal upstaging rate (pN+) was 11.3%. However, pN+ among LVI-present patients was 33.3% compared with 6.6% among LVI-absent patients (OR 7.03; 95%CI 3.51, 14.05; p < 0.001). Only 1 case (0.3%) had extensive nodal disease (pN2) in which LVI was present. On multivariate analysis for predictors of LVI-present, premenopausal (OR 4.23, 95%CI 2.31, 7.76; p < 0.001) was significantly associated with LVI-present, and well differentiated was significantly associated with LVI-absent (OR 0.060, 95%CI 0.010, 0.47; p = 0.0071). On multivariate analysis for predictors of pN+, only LVI-present (OR 6.47, 95%CI 3.14, 13.34 p < 0.001) was significantly associated with pN+. CONCLUSIONS:Pathologic-confirmed LVI was present in one-third of patients with SOUND-eligible breast cancer with axillary nodal metastasis. LVI-present was independently predictive of axillary nodal metastasis and may have implications for decision-making regarding SLNB-omission.

BACKGROUND:There are limited studies on telehealth use patterns among patients with prostate cancer. OBJECTIVE:We assessed the patterns of delivery of care for prostate cancer management in the Veterans Health Administration (VHA). DESIGN/METHODS:A retrospective observational cohort study from January 2016 to February 2023. PARTICIPANTS/METHODS:Data were from the VHA's Corporate Data Warehouse (CDW). Veterans with a new diagnosis of prostate cancer were included in the study. Those who died within 1 year of diagnosis, had missing staging information, or had no prostate-specific antigen (PSA), biopsy, or treatment recorded within 2 years of initial diagnosis were excluded. MAIN MEASURES/METHODS:Veterans were categorized into watchful waiting, active surveillance, and active treatment management groups based on subsequent care received and categorized into National Comprehensive Cancer Network (NCCN) risk categories. We categorized outpatient urology or oncology visits as telephone-based, video-based, or in-person using administrative stop codes. We used logistic regression models to evaluate the characteristics associated with at least one video/virtual visit. KEY RESULTS/RESULTS:In total, 60,381 Veterans were included in the study (20.3% low risk, 49.8% intermediate risk, and 29.8% high risk). Even during the COVID-19 pandemic, less than 6% and 9% of Veterans had at least one urology or oncology video visit, respectively, in the first year after diagnosis across all management groups. In the regression model, Veterans aged 60 and older were less likely to have video visits for both urology and oncology. In contrast, living in urban areas, being diagnosed during the COVID-19 pandemic, and being in the intermediate NCCN risk group were associated with higher odds of having at least one video visit in both specialties. CONCLUSIONS:Despite improvements in telehealth use among Veterans with prostate cancer, telehealth utilization, particularly video visits, remains low, warranting attention from leadership and policymakers.

The rapid expansion of plasma proteomic data and protein quantitative trait loci (pQTLs) provides an opportunity to identify genes that confer disease risk through their effect on plasma protein abundance. We conducted an Alzheimer's disease (AD) proteome-wide association study (PWAS) integrating publicly available plasma cis-pQTL data (1348 European American and 1385 African American genetically determined protein models) with AD dementia GWAS summary statistics. Whereas the African American PWAS identified one candidate [apolipoprotein E (APOE)] and multiple suggestive genes, the European American PWAS identified 18 genes with putative causal relationships with AD through cis regulation of plasma protein abundance. Thirteen of these candidate genes were additionally supported by colocalization and complementary causal-inference analyses such as summary data-based Mendelian randomization. Four of these proteins were not previously detected in AD GWAS [complement decay-accelerating factor (CD55), leukocyte immunoglobulin-like receptor B1 (LILRB1), scavenger receptor class A member 5 (SCARA5), and signal regulatory protein alpha (SIRPA)]. A subset of candidate gene-associated proteins was associated with 8- and 20-year dementia risk, markers of AD pathology, and a CSF proteomic signature enriched for immune and metabolic processes. Putative causal proteins were enriched for adaptive (lymphocyte-mediated) immunity and, compared with GWAS candidates, showed less enrichment for synaptic and amyloid regulatory processes. LILRB1 and SIRPA, two immunoregulatory proteins not previously implicated in AD GWAS, showed the strongest mechanistic link to AD in the European American PWAS. These results shed additional light on AD etiology and enable the prioritization of potential AD therapeutic targets in peripheral circulation.

Growing evidence suggests that exposure to air pollution during early life is associated with autism spectrum disorder (ASD). However, results have been inconsistent for autism-like traits and in low exposure settings. Here, we assessed sensitive windows of exposure to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) and joint effects of exposure to sources and constituents of PM_2.5 on autism-like traits among 798 children in the Upstate KIDS cohort. Residential air pollution exposures were assessed with machine learning and land use regression models. Autism-like traits at age 10 were assessed via the ASD subscale on the Child Behavior Checklist. Treed distributed lag mixture model was used to identify critical windows of exposure to PM_2.5, NO₂, and O₃ and partial linear single index model was applied to estimate contributions of sources and constituents of PM_2.5 on autism-like traits. The median PM_2.5 exposures were 8.36 and 8.00 μg/m³ for pregnancy and the first year of life. We did not observe critical windows of exposure to PM_2.5, NO₂, or O₃ for autism-like traits. Exposures to elemental carbon (EC) PM_2.5 during gestation and to EC and traffic PM_2.5 during the first year of life were associated with higher odds of autism-like traits (prenatal EC adjusted odds ratio (aOR): 1.72, 95% CI: 1.04-2.83; first year EC aOR: 1.72, 95% CI: 1.21-2.46; traffic aOR: 1.31, 95% CI: 1.01-1.71). Our results suggest that exposure to motor vehicle traffic sources of PM_2.5 may drive previously reported associations between total PM_2.5 and autism-like traits.

Given intensifying political polarization and sweeping legal actions targeting marginalized populations across the U.S., there are increasing calls to examine how these policy changes are shaping population health and health inequities. To meet these calls, researchers have developed and applied various approaches to support rigorous, theoretically-informed quantitative research on policies and health. To date, much of the literature has examined single policies; however, there is growing interest in examining alternative ways of conceptualizing policy exposures, namely as policy clusters or policy climates, to better capture how policies are enacted (and experienced) in "real-world" contexts. To advance this work, greater clarity is needed regarding how different approaches to policy conceptualization, measurement, and analysis align with distinct research questions and goals, ranging from identifying specific, manipulable policy levers to informing ways of extending the "policy space" beyond already existing laws to support broader social change. In this essay, we help fill this gap by outlining key issues related to policy exposures, including conceptualization, methods for measure development, and analytic approaches for understanding the relationship between policies and health. We end by discussing future directions for population health research on single policies, policy clusters, and policy climates, with an eye towards advancing health equity.