Citations

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.

538 Background: The phase 3 PALLAS trial compared 2 years of the CDK4/6 inhibitor palbociclib with endocrine therapy of provider choice, versus endocrine therapy alone, as adjuvant treatment for patients (pts) with Stage II-III hormone receptor-positive HER2-negative (HR+/HER2-) breast cancer. The SET2,3 index adjusts the measurement of SETER/PR index of endocrine receptor-related transcriptional activity with a baseline prognostic index (BPI) derived from tumor size (T), number of involved lymph nodes (N) and gene expression levels of ESR1, PGR, ERBB2 and AURKA (RNA4 subtype score). High SET2,3 (index ≥ 2.10) represents high endocrine sensitivity with favorable prognosis. Clinical outcome results from PALLAS were previously published, and the trial now has 5-year median follow-up. Methods: Participants in PALLAS provided a tumor tissue block prior to randomization. RNA was extracted from samples with sufficient tumor tissue and cellularity (>25mm2 with ≥20% cancer nuclei). SET2,3 index was measured from 300ng total RNA using the QuantiGene Plex platform (ThermoFisher, Waltham, MA) following the manufacturer’s protocol and using pre-defined quality control (QC) parameters and cut point. Results from primary tumor samples prior to systemic therapy were analyzed. Invasive disease-free survival (iDFS) was summarized with Kaplan-Meier plots using log-rank test. Cox models were adjusted for treatment arm assignment, with a treatment arm by SET index value interaction term to test for prediction of palbociclib benefit. The pre-defined level of significance was a two-sided 0.05. Results: There were 4075 pts from the total PALLAS population (N=5796) who provided a primary tumor sample that met pre-analytical requirements to perform the SET assay, from which 3388 (83%) results passed quality control, including 3093 from a sample obtained prior to receiving systemic therapy (1559 in palbociclib arm and 1534 in the control arm). SET2,3 index could not be calculated for 12 pts due to missing tumor or nodal information. There was no evidence of an interaction between treatment arm and SET indices (continuous) for iDFS: SET2,3 index interaction p=0.12 and SETER/PR index interaction p=0.92. Estimated 5-year iDFS was superior for those with high SET2,3: iDFS was 91.4% (95%CI 89.9–92.8) in 1719 (55.8%) pts with high SET2,3, versus 78.5% (95%CI 76.0–80.8) in 1362 (44.2%) pts with low SET2,3 (HR 0.38, 95%CI 0.31–0.47, p<0.0001). The SETER/PR index was also significantly associated with iDFS (HR 0.70 per 0.5 units, 95%CI 0.65–0.77, p<0.0001). Conclusions: There was no evidence that the SET2,3 or SETER/PR index of endocrine-related transcriptional activity predicts benefit from palbociclib treatment. SET2,3 index was strongly prognostic for pts with Stage II-III breast cancer, with excellent 5-year IDFS for pts classified as SET2,3 high. Clinical trial information: NCT02513394 .

DNA replication is a vulnerable cellular process, and its deregulation leads to genomic instability. Here, we demonstrate that chromobox protein homolog 3 (CBX3) binds replication protein A 32-kDa subunit (RPA2) and regulates RPA2 retention at stalled replication forks. CBX3 is recruited to stalled replication forks by RPA2 and inhibits ring finger and WD repeat domain 3 (RFWD3)–facilitated replication restart. Phosphorylation of CBX3 at serine-95 by casein kinase 2 (CK2) kinase augments cadherin 1 (CDH1)–mediated CBX3 degradation and RPA2 dynamics at stalled replication forks, which permits replication fork restart. Increased expression of CBX3 due to gene amplification or CK2 inhibitor treatment sensitizes prostate cancer cells to poly(ADP-ribose) polymerase (PARP) inhibitors while inducing replication stress and DNA damage. Our work reveals CBX3 as a key regulator of RPA2 function and DNA replication, suggesting that CBX3 could serve as an indicator for targeted therapy of cancer using PARP inhibitors.

Acute pancreatitis (AP) is among the most common hospital gastrointestinal diagnoses; understanding the mechanisms underlying the severity of AP is critical for development of new treatment options for this disease. Here, we evaluate the biological function of phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) in AP pathogenesis in 2 independent genetically engineered mouse models of AP. PFKFB3 was elevated in AP and severe AP (SAP), and KO of Pfkfb3 abrogated the severity of alcoholic SAP (FAEE-SAP). Using a combination of genetic, pharmacological, and molecular studies, we defined the interaction of PFKFB3 with inositol 1,4,5-trisphosphate receptor (IP3R) as a key event mediating this phenomenon. Further analysis demonstrated that the interaction between PFKFB3 and IP3R promotes FAEE-SAP severity by altering intracellular calcium homeostasis in acinar cells. Together, our results support a PFKFB3-driven mechanism controlling AP pathobiology and define this enzyme as a therapeutic target to ameliorate the severity of this condition.

Objective: To determine out-of-pocket (OOP) costs 2 years after sleeve gastrectomy (SG) or initiating Ozempic for patients with type 2 diabetes (T2D) and obesity. Background: Individuals with obesity and T2D have a variety of treatment options. The risks and benefits of these treatment options are becoming more well-documented; however, the real-world patient costs of these options are not known. Methods: Adults with body mass index of 35 kg/m2 or higher and T2D who had an SG or used Ozempic were identified in the employer-based retrospective claims database Merative (previously Truven IBM Marketscan) from 2017 to 2021. SG cohort was defined as having an SG (without filling a prescription for Ozempic), and the Ozempic cohort was defined as continuously filling a prescription for Ozempic for at least 2 years (and not having any bariatric surgery). Individuals in each cohort were 1:1 propensity matched on demographics, obesity-related comorbidities, and baseline OOP costs. in the year before treatment. OOP costs were compared in the 2 years after treatment using paired t tests. Results: A total of 302 SGs were matched to 302 Ozempic patients (mean age: 50, mean baseline body mass index: 40, 41% males). OOP health care costs were similar for the SG ($2267) and Ozempic ($2131) cohorts 1 year after the index date (difference = $136, P = 0.19). OOP health care costs were significantly lower in the SG cohort ($1155 vs $2,084, P < 0.01) 2 years after the index date. Conclusions: Within 2 years of starting treatment, OOP health care costs were significantly lower among individuals who had a SG versus those treated with Ozempic.

Background: Gastrointestinal (GI) disorders like chronic constipation are more prevalent with aging. Aging GI tract becomes stiffer and exhibits delayed GI transit. Smooth muscle cells (SMCs) are involved in controlling GI transit and show reduced contractility with age. Piezo1 is a mechanically activated ion channel that responds to membrane tension by opening its pore to allow flow of ions such as calcium (Ca2+) and regulates various cellular processes through Ca2+-calcineurin signaling pathway. We wondered GI stiffness changes might control SMC contractile to non-contractile phenotype through Piezo1. Objective: To determine how substrate stiffness regulates GI SMC contractile phenotype through Piezo1. Hypothesis: SMC Piezo1 senses GI tissue stiffness changes and alters SMC phenotype. Methods: Pathway analysis on human colon single cell sequencing data was performed using Gene Set Enrichment Analysis (GSEA) pre-ranked module with publicly available gene-sets from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Immortalized human jejunal SMCs (HuSMCs) and freshly dissociated SMCs from control (CTRL, Myh11-creERT2) and SMC specific Piezo1 knockout [Myh11-creERT2xPiezo1 Flox (Piezo1-KO)] mouse colons (primary SMCs) were cultured on soft (1kPa) and stiff (50kPa) substrates to model age-dependent shifts in intestinal stiffness for 48 hours either in the presence/absence of Piezo1 siRNA, Piezo1 agonist (Yoda1, 1μM), and calcineurin blocker (Cyclosporin A, CsA, 1μM) and used for Ca2+ imaging, RT-qPCR and immunostaining. Results: GSEA showed Piezo1+ve SMCs were significantly enriched in pathways involved in the regulation of Ca2+ signaling compared to Piezo1−ve SMCs (p<0.05). HuSMCs and primary SMCs showed increased Piezo1 expression (HuSMCs:1.1±0.2-fold, n=8, p<0.05; primary SMCs:2.1±0.1-fold, n=3, p<0.05) and cytoplasmic Ca2+ (3.6-fold, n=10, p<0.05) in response to Yoda1 on stiff substrates compared to soft. HuSMCs on stiff substrate showed decreased expression of contractile SMC markers smoothelin ( SMTN:0.7±0.2-fold, n=7, p<0.05) and transgelin ( TAGLN:0.6±0.1-fold, n=7, p<0.05) which was rescued by Piezo1 siRNA treatment ( SMTN:1.2±0.1-fold, n=2-7, p<0.05; TAGLN:1.15±0.02-fold, n=2-7, p<0.05) (Piezo1 is necessary). Stimulation by Yoda1 of HuSMCs on the soft substrate resulted in decreased expression of SMTN (0.7±0.3-fold, n=2-7, p<0.05) and TAGLN (0.7±0.1-fold, n=2-7, p<0.05) (Piezo1 is suffcient). CTRL primary SMCs on stiff substrate showed decreased expression of Smtn (0.4±0.1-fold, n=3, p<0.05) and Tagln (0.3±0.2-fold, n=3, p<0.05) compared to SMCs on soft substrate which was reversed when SMCs from Piezo1-KO mice were cultured on these substrates ( Smtn:1.8±0.5-fold, n=3, p<0.05; Tagln:2.4±0.07-fold, n=3, p<0.05). Treatment of CsA in HuSMCs cultured on stiff substrate also rescued from loss of SMTN (1.3±0.1-fold, n=3, p<0.05) and TAGLN (1.7±0.3-fold, n=3, p<0.05) expression and prevented nuclear factor of activated T cells (NFAT) nuclear translocation. Summary and Conclusion: GI SMC Piezo1 senses stiffness changes and regulates SMC phenotype through Ca2+-calcineurin signaling pathway. Targeting Piezo1 might be protective from age induced reduced GI SMC contractility. NIH DK052766, DK123549. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Abstract Introduction Geriatric emergency department (ED) guidelines emphasize timely identification of delirium. This article updates previous diagnostic accuracy systematic reviews of history, physical examination, laboratory testing, and ED screening instruments for the diagnosis of delirium as well as test–treatment thresholds for ED delirium screening. Methods We conducted a systematic review to quantify the diagnostic accuracy of approaches to identify delirium. Studies were included if they described adults aged 60 or older evaluated in the ED setting with an index test for delirium compared with an acceptable criterion standard for delirium. Data were extracted and studies were reviewed for risk of bias. When appropriate, we conducted a meta‐analysis and estimated delirium screening thresholds. Results Full‐text review was performed on 55 studies and 27 were included in the current analysis. No studies were identified exploring the accuracy of findings on history or laboratory analysis. While two studies reported clinicians accurately rule in delirium, clinician gestalt is inadequate to rule out delirium. We report meta‐analysis on three studies that quantified the accuracy of the 4 A's Test (4AT) to rule in (pooled positive likelihood ratio [LR+] 7.5, 95% confidence interval [CI] 2.7–20.7) and rule out (pooled negative likelihood ratio [LR−] 0.18, 95% CI 0.09–0.34) delirium. We also conducted meta‐analysis of two studies that quantified the accuracy of the Abbreviated Mental Test‐4 (AMT‐4) and found that the pooled LR+ (4.3, 95% CI 2.4–7.8) was lower than that observed for the 4AT, but the pooled LR− (0.22, 95% CI 0.05–1) was similar. Based on one study the Confusion Assessment Method for the Intensive Care Unit (CAM‐ICU) is the superior instrument to rule in delirium. The calculated test threshold is 2% and the treatment threshold is 11%. Conclusions The quantitative accuracy of history and physical examination to identify ED delirium is virtually unexplored. The 4AT has the largest quantity of ED‐based research. Other screening instruments may more accurately rule in or rule out delirium. If the goal is to rule in delirium then the CAM‐ICU or brief CAM or modified CAM for the ED are superior instruments, although the accuracy of these screening tools are based on single‐center studies. To rule out delirium, the Delirium Triage Screen is superior based on one single‐center study.