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Abstract Background The phase III RxPONDER trial has affected treatment for node-positive (1-3), hormone receptor-positive, HER2-negative breast cancer with a 21-gene recurrence score (RS) less than 26. We investigated how these findings apply to different racial and ethnic groups within the trial. Methods The trial randomly assigned women to endocrine therapy (ET) or to chemotherapy plus ET. The primary clinical outcome was invasive disease-free survival (IDFS), with distant relapse-free survival (DRFS) as a secondary outcome. Multivariable Cox models were used to evaluate the association between race/ethnicity and survival outcomes, adjusting for clinicopathological characteristics, RS, and treatment. Results A total of 4048 women with self-reported race/ethnicity were included: Hispanic (15.1%), non-Hispanic Black (NHB) (6.1%), Native American/Pacific Islander (0.8%), Asian (8.0%), and non-Hispanic White (NHW) (70%). No differences in RS distribution, tumor size, or number of positive nodes were observed by race/ethnicity. Relative to NHWs, IDFS was worse for NHB participants (5-year IDFS 91.6% vs 87.1%, HR = 1.37; 95% CI = 1.03 to 1.81) and better for Asians (91.6% vs 93.9%, HR = 0.64; 95% CI = 0.46 to 0.91). Relative to NHW, DRFS was worse for NHB participants (5-year DRFS 95.8% vs 91.0%, HR = 1.65; 95% CI = 1.17 to 2.32) and better for Asians (95.8% vs 96.7%, HR = 0.59; 95% CI = 0.37 to 0.95). Adjusting for clinical characteristics, particularly body mass index, diminished the effect of race on outcomes. Chemotherapy treatment efficacy did not differ by race/ethnicity. Conclusions NHB women had worse clinical outcomes compared with NHWs in the RxPONDER trial despite similar RS and comparable treatment. Our study emphasizes the persistent racial disparities in breast cancer outcomes while highlighting complex interactions among contributing factors. Trial Registration ClinicalTrials.gov: NCT01272037.

Background: Heart failure (HF) is a heterogeneous syndrome with high mortality. Previous work characterizing HF subgroups primarily relied on clinical data to cluster patients, an approach constrained by the boundaries of documented clinical features. Clustering based on biomarker data, such as proteomics, may provide insights into underlying molecular processes. However, few studies have used this approach, and all used targeted cardiovascular proteomics panels and a restricted HF ejection fraction group. Hypothesis: We used unsupervised clustering analysis to characterize molecular phenogroups in a population-based HF cohort using a large-scale array of circulating plasma proteins. We hypothesized that distinct proteomic-defined phenogroups would exhibit differences in clinical characteristics, mortality, and protein expression. Methods: We measured 7151 SOMAmers (Slow off-rate modified aptamers) targeting human proteins via the SomaLogic 7K SomaScan assay in 1351 HF patients. After feature selection, 831 SOMAmers were used in k-means clustering of patients via R package “ConsensusClusterPlus.” Phenogroups were compared for clinical characteristics, all-cause mortality, and protein expression. Results: Three phenogroups were identified, exhibiting different clinical characteristics ( Table 1 ) and survival probabilities (5-year survival probability in phenogroup 1: 65% [61%, 68%], phenogroup 2: 45% [40%, 51%], and phenogroup 3: 26% [22%, 30%]). In covariate-adjusted Cox models, membership in phenogroups 2 and 3 was significantly associated with increased rate of mortality compared to phenogroup 1. Phenogroups did not differ by ejection fraction or New York Heart Association class. Pathway overrepresentation analysis of differentially expressed SOMAmers identified pathways associated with inflammatory response and immune cell trafficking. Conclusions: Unsupervised clustering analysis using large-scale proteomics data identified three HF phenogroups with different proteomic signatures, clinical characteristics, and outcomes. Future work is necessary to elucidate how molecular differences contribute to heterogeneity in HF to improve risk stratification beyond current recommended clinical guidelines.

Importance Office-based phototherapy is cost-effective for psoriasis but difficult to access. Home-based phototherapy is patient preferred but has limited clinical data, particularly in patients with darker skin. Objective To compare the effectiveness of home- vs office-based narrowband UV-B phototherapy for psoriasis. Design, Setting, and Participants The Light Treatment Effectiveness study was an investigator-initiated, pragmatic, open-label, parallel-group, multicenter, noninferiority randomized clinical trial embedded in routine care at 42 academic and private clinical dermatology practices in the US. Enrollment occurred from March 1, 2019, to December 4, 2023, with follow-up through June 2024. Participants were 12 years and older with plaque or guttate psoriasis who were candidates for home- and office-based phototherapy. Interventions Participants were randomized to receive a home narrowband UV-B machine with guided mode dosimetry or routine care with office-based narrowband UV-B for 12 weeks, followed by an additional 12-week observation period. Main Outcomes and Measures The coprimary effectiveness outcomes were Physician Global Assessment (PGA) dichotomized as clear/almost clear skin (score of ≤1) at the end of the intervention period and Dermatology Life Quality Index (DLQI) score of 5 or lower (no to small effect on quality of life) at week 12. Results Of 783 patients enrolled (mean [SD] age, 48.0 [15.5] years; 376 [48.0%] male), 393 received home-based phototherapy and 390 received office-based phototherapy, with 350 (44.7%) having skin phototype (SPT) I/II, 350 (44.7%) having SPT III/IV, and 83 (10.6%) having SPT V/VI. A total of 93 patients (11.9%) were receiving systemic treatment. At baseline, mean (SD) PGA was 2.7 (0.8) and DLQI was 12.2 (7.2). At week 12, 129 patients (32.8%) receiving home-based phototherapy and 100 patients (25.6%) receiving office-based phototherapy achieved clear/almost clear skin, and 206 (52.4%) and 131 (33.6%) achieved DLQI of 5 or lower, respectively. Home-based phototherapy was noninferior to office-based phototherapy for PGA and DLQI in the overall population and across all SPTs. Home-based phototherapy, compared to office-based phototherapy, was associated with better treatment adherence (202 patients [51.4%] vs 62 patients [15.9%]; P < .001), lower burden of indirect costs to patients, and more episodes of persistent erythema (466 of 7957 treatments [5.9%] vs 46 of 3934 treatments [1.2%]; P < .001). Both treatments were well tolerated with no discontinuations due to adverse events. Conclusions and Relevance In this randomized clinical trial, home-based phototherapy was as effective as office-based phototherapy for plaque or guttate psoriasis in everyday clinical practice and had less burden to patients. Trial Registration ClinicalTrials.gov Identifier: NCT03726489

Abstract Objectives Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. Materials and Methods A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Results Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). Discussion These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Conclusions Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

BACKGROUND: Senescence, a mechanism of cellular aging, which is characterized by irreversible proliferation arrest and a proinflammatory secretory phenotype, has been documented in women with preeclampsia. As cellular senescence can persist and progress, we postulated that it is associated with accelerated aging phenotype and accumulation of comorbidities in women with a history of preeclampsia. METHODS: We included a cohort of women with a history of preeclampsia (n=40) age- and parity-matched to a group of referent women with normotensive pregnancies (n=40). Women with prior major cardiovascular events, neurological, or autoimmune conditions were excluded. We collected urine and blood samples to study markers of aging, data on multimorbidity at the time of enrollment, and prospectively followed them for events over the course of 6 years, on average. RESULTS: Women with a history of preeclampsia exhibited unfavorable aging profiles compared with referent women, including decreased urinary α-Klotho ( P =0.018); increased leptin ( P =0.016) and leptin/adiponectin ratio ( P =0.027), and increased extracellular vesicles positive for tissue factor ( P =0.025). Women with a history of preeclampsia likewise had a higher rate of comorbidities at the time of enrollment ( P =0.003) and had a 4× higher risk of developing major cardiovascular events compared with referent women ( P =0.003). CONCLUSIONS: Our data suggest that a history of preeclampsia is associated with accelerated aging as indicated by senescence marker differences and the accumulation of multimorbidity later in life. Targeting cellular senescence may offer novel, mechanism-based approaches for the diagnosis and treatment of adverse health outcomes in women with a history of preeclampsia.

Background Clinical risk scores are used to identify those at high risk of atherosclerotic cardiovascular disease (ASCVD). Despite preventative efforts, residual risk remains for many individuals. Very low‐density lipoprotein cholesterol (VLDL‐C) and lipid discordance could be contributors to the residual risk of ASCVD. Methods and Results Cardiovascular disease–free residents, aged ≥40 years, living in Olmsted County, Minnesota, were identified through the Rochester Epidemiology Project. Low‐density lipoprotein cholesterol (LDL‐C) and VLDL‐C were estimated from clinically ordered lipid panels using the Sampson equation. Participants were categorized into concordant and discordant lipid pairings based on clinical cut points. Rates of incident ASCVD, including percutaneous coronary intervention, coronary artery bypass grafting, stroke, or myocardial infarction, were calculated during follow‐up. The association of LDL‐C and VLDL‐C with ASCVD was assessed using Cox proportional hazards regression. Interaction between LDL‐C and VLDL‐C was assessed. The study population (n=39 098) was primarily White race (94%) and female sex (57%), with a mean age of 54 years. VLDL‐C (per 10‐mg/dL increase) was significantly associated with an increased risk of incident ASCVD (hazard ratio, 1.07 [95% CI, 1.05–1.09]; P <0.001]) after adjustment for traditional risk factors. The interaction between LDL‐C and VLDL‐C was not statistically significant ( P =0.11). Discordant individuals with high VLDL‐C and low LDL‐C experienced the highest rate of incident ASCVD events, 16.9 per 1000 person‐years, during follow‐up. Conclusions VLDL‐C and lipid discordance are associated with a greater risk of ASCVD and can be estimated from clinically ordered lipid panels to improve ASCVD risk assessment.

Background Frailty is common in heart failure (HF) and is associated with death but not routinely captured clinically. Frailty is linked with inflammation and malnutrition, which can be assessed by a novel plasma multimarker score: the metabolic vulnerability index (MVX). We sought to evaluate the associations between frailty and MVX and their prognostic impact. Methods and Results In an HF community cohort (2003–2012), we measured frailty as a proportion of deficits present out of 32 physical limitations and comorbidities, MVX by nuclear magnetic resonance spectroscopy, and collected extensive longitudinal clinical data. Patients were categorized by frailty score (≤0.14, >0.14 and ≤0.27, >0.27) and MVX score (≤50, >50 and ≤60, >60 and ≤70, >70). Cox models estimated associations of frailty and MVX with death, adjusted for Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) score and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide). Uno's C‐statistic measured the incremental value of MVX beyond frailty and clinical factors. Weibull's accelerated failure time regression assessed whether MVX mediated the association between frailty and death. We studied 985 patients (median age, 77; 48% women). Frailty and MVX were weakly correlated (Spearman's ρ=0.21). The highest frailty group experienced an increased rate of death, independent of MVX, MAGGIC score, and NT‐proBNP (hazard ratio, 3.3 [95% CI, 2.5–4.2]). Frailty improved Uno's c‐statistic beyond MAGGIC score and NT‐proBNP (0.69–0.73). MVX only mediated 3.3% and 4.5% of the association between high and medium frailty groups and death, respectively. Conclusions In this HF cohort, frailty and MVX are weakly correlated. Both independently contribute to stratifying the risk of death, suggesting that they capture distinct domains of vulnerability in HF.

BACKGROUND: Heart failure is heterogeneous syndrome with persistently high mortality. Nuclear magnetic resonance spectroscopy enables high-throughput metabolomics, suitable for precision phenotyping. We aimed to use targeted metabolomics to derive a metabolic risk score (MRS) that improved mortality risk stratification in heart failure. METHODS: Nuclear magnetic resonance was used to measure 21 metabolites (lipoprotein subspecies, branched-chain amino acids, alanine, GlycA (glycoprotein acetylation), ketone bodies, glucose, and citrate) in plasma collected from a heart failure community cohort. The MRS was derived using least absolute shrinkage and selection operator penalized Cox regression and temporal validation. The association between the MRS and mortality and whether risk stratification was improved over the Meta-Analysis Global Group in Chronic Heart Failure clinical risk score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels were assessed. RESULTS: The study included 1382 patients (median age, 78 years, 52% men, 43% reduced ejection fraction) with a 5-year survival rate of 48% (95% CI, 46%–51%). The MRS included 9 metabolites measured. In the validation data set, a 1 standard deviation increase in the MRS was associated with a large increased rate of death (hazard ratio, 2.2 [95% CI, 1.9–2.5]) that remained after adjustment for Meta-Analysis Global Group in Chronic Heart Failure score and NT-proBNP (hazard ratio, 1.6 [95% CI, 1.3–1.9]). These associations did not differ by ejection fraction. The integrated discrimination and net reclassification indices, and Uno’s C statistic, indicated that the addition of the MRS improved discrimination over Meta-Analysis Global Group in Chronic Heart Failure and NT-proBNP. CONCLUSIONS: This MRS developed in a heart failure community cohort was associated with a large excess risk of death and improved risk stratification beyond an established risk score and clinical markers.

Background Although VCAM‐1 (vascular cell adhesion molecule‐1) and ICAM‐1 (intercellular adhesion molecule‐1) have been associated with incident heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF), the associations of VCAM‐1 and ICAM‐1 with sensitive measures of cardiac structure/function are unclear. The objective of this study is to evaluate associations between VCAM‐1, ICAM‐1, and measures of cardiac structure and function as potential pathways through which cellular adhesion molecules promote HFpEF and AF risk. Methods and Results In MESA (Multi‐Ethnic Study of Atherosclerosis), we evaluated the associations of circulating VCAM‐1 and ICAM‐1 at examination 2 (2002–2004) with measures of cardiac structure/function on cardiac magnetic resonance imaging at examination 5 (2010–2011) after multivariable adjustment. Mediation analysis of left atrial (LA) strain on the association between VCAM‐1 or ICAM‐1 and AF or HFpEF was also performed. Overall, 2304 individuals (63±10 years; 47% men) with VCAM‐1 or ICAM‐1, cardiac magnetic resonance imaging, and covariate data were included in analysis. Higher VCAM‐1 and ICAM‐1 were associated with lower LA peak longitudinal strain and worse global circumferential left ventricular strain but were not associated with left ventricular myocardial scar or interstitial fibrosis. Lower LA peak longitudinal strain mediated 8% (95% CI, 2–30) of the relationship between VCAM‐1 and HFpEF and 9% (95% CI, 2–21) of the relationship between VCAM‐1 and AF. Conclusions Higher VCAM‐1 and ICAM‐1 were associated with lower LA function and left ventricular systolic function but were not associated with myocardial scar or interstitial fibrosis. VCAM‐1 and ICAM‐1 may promote HFpEF and AF risk through impaired LA reservoir function.