Citations

Introduction: Patients with variant transthyretin (TTR) amyloid cardiomyopathy (ATTRv-CM) experience early onset disease, rapid progression, and poor outcomes due to amyloid aggregated from destabilized tetrameric TTR. Acoramidis, a highly selective, oral TTR stabilizer, achieves ≥90% TTR stabilization and is approved in the USA, EU, Japan, and UK for adults with wild-type (ATTRwt-CM) or ATTRv-CM. In the phase 3 ATTRibute-CM study and its open-label extension (OLE), acoramidis reduced both the risk of all-cause mortality (ACM) or first cardiovascular-related hospitalization (CVH) through Month 30 and risk of ACM through Month 42 by 36%. Research Question: Is acoramidis efficacy in ATTRv-CM consistent with that observed in the overall ATTRibute-CM population? Methods: In ATTRibute-CM, participants with ATTR-CM were randomized 2:1 to acoramidis HCl 800 mg or PBO, twice daily (BID) for 30 months. After Month 30, all participants enrolled in the OLE received acoramidis HCl 800 mg BID. Genotype (ATTRwt-CM or ATTRv-CM) was captured at randomization; efficacy was analyzed separately in ATTRv-CM. Time-to-event analyses used a stratified Cox model with terms for treatment, baseline 6MWT, genotype, genotype×treatment interaction, stratified by randomization NT-proBNP and eGFR levels. ACM was analyzed at Month 42 (ATTRibute-CM 30 months + OLE 12 months). Results: The ATTRv-CM group comprised 59 patients (39 acoramidis, 20 PBO). Baseline characteristics were mostly similar in both groups ( Table ). The three most common variants reported were p.V142I (n=35), p.I88L (n=7), and p.T80A (n=5). Compared with PBO, acoramidis reduced the risk of ACM/first CVH through Month 30 by 59% (acoramidis, 46.2%; PBO, 75.0%; HR 0.41; 95% CI 0.21–0.81; P =0.011; Fig 1 ) and ACM through Month 42 by 59% (continuous acoramidis, 30.8%; PBO to acoramidis, 60.0%; HR 0.41; 95% CI 0.19–0.93; P =0.032; Fig 2 ) , with Kaplan–Meier curves separating at Months 5 and 10, respectively. Conclusions: In ATTRibute-CM, acoramidis use was associated with clinical benefit in the ATTRv-CM group as demonstrated by a 59% risk reduction in both ACM/first CVH at Month 30, and ACM at Month 42 compared to PBO. These previously unreported findings support the hypothesis that near complete TTR stabilization observed experimentally with acoramidis across multiple TTR mutations translates into improvement in long-term outcomes. Further validation by individual TTR variant is warranted.

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.

Introduction: Patients with transthyretin amyloid cardiomyopathy (ATTR-CM) typically have low serum transthyretin (sTTR) levels, worsening heart failure, and increased mortality. A recent study has shown the association of sTTR levels ≥20mg/dL with reduced risk of all-cause mortality and cardiovascular mortality (CVM). It has also been shown that regardless of baseline sTTR level, greater increases in sTTR levels are associated with greater improvements in cardiovascular outcomes. Acoramidis, a near-complete (≥90%) TTR stabilizer, demonstrated a rapid and sustained increase from baseline in sTTR by Day 28 and reduced risk of cardiovascular outcomes in patients with ATTR-CM. Hypothesis: Acoramidis-mediated early increase in sTTR levels ≥20 mg/dL (lower limit of normal) at Day 28 reduces risks of CVM and cardiovascular-related hospitalizations (CVH) in patients with ATTR-CM. Methods: Analyses were conducted in the ATTRibute-CM modified intention-to-treat (mITT) population (acoramidis: 409; placebo: 202). The sTTR concentrations (normal range is 20-40 mg/dL) were assessed using an immunoturbidimetric method (Abbott ARCHITECT system) in a central laboratory. The proportion of participants with sTTR levels below normal at baseline and Day 28 were determined. The relationship between sTTR levels <20 or ≥20 mg/dL, at Day 28 across the pooled acoramidis and placebo treatment groups, and subsequent risk of CVM and of CVH over 30 months were analyzed using a stratified log-rank test. Results: At baseline about 25% of study participants had sTTR levels below normal in both treatment groups. At Day 28, less than 2% of participants had sTTR levels <20 mg/dL in the acoramidis arm compared with 26% in the placebo arm ( Table 1 ). When acoramidis and placebo treatment groups were pooled based on sTTR levels <20 or ≥20 mg/dL at Day 28, participants with sTTR ≥20 mg/dL were associated with a lower risk of the composite endpoint of CVM or first CVH at Month 30 in comparison with <20 mg/dL ( p <0.0001) ( Figure 1A ). Comparable findings were observed for the individual components (CVM, first CVH) ( Figure 1B and C ). Conclusion: Across treatment groups, sTTR levels above normal range (≥20 mg/dL) at Day 28 were associated with a lower risk of cardiovascular outcomes at Month 30, when compared with sTTR levels below normal range (<20 mg/dL). Thus, these observations demonstrate that higher sTTR levels over time may have the potential for clinical benefits in both CVM and CVH.

Background/Objectives: Menopause, marked by permanent cessation of menstruation, is a universal transition associated with vasomotor, genitourinary, psychological, and metabolic changes. These conditions significantly affect health-related quality of life (HRQoL) and increase the risk of chronic diseases. Despite their impact, timely diagnosis and individualized management are often limited by delayed care, fragmented health systems, and cultural barriers. Methods: This review summarizes current applications of artificial intelligence (AI) in postmenopausal health, focusing on risk prediction, early detection, and personalized treatment. Evidence was compiled from studies using biomarkers, imaging, wearable sensors, electronic health records, natural language processing, and digital health platforms. Results: AI enhances disease prediction and diagnosis, including improved accuracy in breast cancer and osteoporosis screening through imaging analysis, and cardiovascular risk stratification via machine learning models. Wearable devices and natural language processing enable real-time monitoring of underreported symptoms such as hot flushes and mood disorders. Digital technologies further support individualized interventions, including lifestyle modification and optimized medication regimens. By improving access to telemedicine and reducing bias, AI also has the potential to narrow healthcare disparities. Conclusions: AI can transform postmenopausal care from reactive to proactive, offering personalized strategies that improve outcomes and quality of life. However, challenges remain, including algorithmic bias, data privacy, and clinical implementation. Ethical frameworks and interdisciplinary collaboration among clinicians, data scientists, and policymakers are essential for safe and equitable adoption.

Abstract The SNF2 family chromatin remodeler HELLS has emerged as an important regulator of cell proliferation, genome stability, and several cancer pathways. Significant upregulation of HELLS has been reported in 33 human cancer types. While HELLS has been implicated in DNA damage response, its function in DNA repair is poorly understood. Here, we report a new regulatory link between HELLS and single-strand break (SSB) repair in cellular responses to DNA alkylation damage. We found that loss of HELLS impairs SSB repair and selectively sensitizes cells to DNA alkylating agents and PARP inhibitors (PARPi). Our data reveal non-epistatic interactions between HELLS and PARP1 and suggest that HELLS functionally compensates for PARP1 deficiency in promoting cell survival in response to DNA alkylation damage. Furthermore, we found that HELLS is co-expressed with PARP1 in cancer cells, and its loss is synthetic lethal with homologous recombination deficiency (HRD). This work unveils new functions of HELLS in modulating SSB repair and responses to clinically relevant DNA alkylation damage, thus offering new insights into the potential therapeutic value of targeting HELLS in cancer.

Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model.

426 Background: Testing for DPYD gene variations prior to administration of fluoropyrimidines ([FP] e.g. 5-FU or capecitabine) can determine if patients are at risk of significant toxicity and may benefit from dose reduction or elimination of chemotherapy. Barriers to testing include lack of recommendations in US guidelines for routine DPYD testing, absence of insurance coverage, and limitations in provider support. We designed an electronic health record (EHR)-based intervention to support shared decision-making (SDM) between providers and patients regarding DPYD testing. Methods: We built an EHR alert identifying all patients initiating FP without DPYD testing. The alert fired to remind providers upon signing FP. Clinical decision support (CDS) was included in the alert to help with DPYD testing selection along with an educational note template containing the incidence of DPYD deficiency, rationale for testing, test costs, and turnaround time. Interactions with the EHR were recorded to determine why providers declined ordering pharmacogenomic testing. Testing rates and results for patients initiating FP were collected from the EHR. Finally, providers were surveyed to determine the impact of the alert on conversations with patients as well as the utility of the embedded CDS and educational note template. Results: Within the first 8 months after alert activation, 34 patients completed DPYD testing of 444 starting FP (testing rate of 7.7%) vs the baseline testing rate in the 3 months prior to alert go-live of 6.8% (p = 0.86 via chi-squared analysis, two-tailed). Of the 34 patients, 5 were intermediate DPYD metabolizers. Reasons for alert dismissal per provider selection were: “Patient already tolerated prior fluoropyrimidines” (15.7%), “Patient declines” (12.9%), “Will order separately” (5.7%), “Not discussed as not appropriate” (5.3%), and “Not discussed, deferred” (60.4%). Thirty-nine providers (n = 77 eligible) responded to the survey; 22 prescribed FP in their practice and had encountered the alert. 21 (95.5%) of these reported the alert was “Very Useful” or “Somewhat Useful” and 18 (81.8%) used the DPYD testing options available in the alert. Sixteen (72.7%) discussed DPYD testing more frequently with patients as a result of the alert. Barriers to DPYD testing were “Concerned about the impact of lowering FP dosing” and “Lack of knowledge about DPYD testing”, while “Cost of DPYD testing” was scored lowest. Conclusions: Utilizing an EHR alert with embedded CDS had a positive impact on provider awareness and led to more discussions regarding DPYD pharmacogenomic testing with patients in line with current FDA recommendations. Limitations in provider knowledge regarding DPYD testing and concerns about lowering chemotherapy doses posed barriers to ordering testing. Further interventions including additional education are required.

Abstract Background Obstructive sleep apnea (OSA) is a risk factor for hypertension and some evidence suggests this risk may not be mitigated with positive airway pressure. Low-intensity statin therapy can modestly reduce blood pressure (BP). In this exploratory analysis, we examined if six months of high-intensity statin therapy could lower resting or ambulatory BP in patients with OSA. Methods 39 patients with OSA (13F, age = 49 ± 9yrs, body mass index = 32.9 ± 4.2kg/m2, apnea-hypopnea index = 22.2 ± 12.4 events/hour) were randomized to high-intensity atorvastatin or placebo. BP was assessed using seated, “office” measurements and in 20-minute intervals over 24 consecutive hours at three timepoints (baseline, three- and six-months post-randomization). Participants maintained a diary denoting sleep and wake times for analysis. Changes (Δ) in BP from baseline were assessed and adjusted for adherence to participants’ intervention (atorvastatin or placebo) as well as to their treatment for OSA (yes or no). Results There were no between-group differences in baseline BP variables (P = .09-.96). At three months, the changes in resting (P = .74-.87), 24 hour mean (P = .54-.96), daytime (P = .70-.96), nor nighttime (P = .74-.96) BP did not differ between groups. Similarly, the changes in resting (P = .23-.92), 24 hour mean (P = .51-.82), daytime (P = .17-.78), and nighttime (P = .51-1.00) BP did not differ between groups following six months of their respective intervention. Conclusions Our data suggest that high-intensity atorvastatin does not lower resting or ambulatory BP in patients with OSA relative to a placebo. Thus, it does not appear that atorvastatin is a viable adjunct intervention for BP reduction in patients with OSA. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03308578.

Abstract Although follicular lymphoma (FL) typically follows an indolent course, patients with FL who experience early events, such as transformation or progression, have increased risk of death related to lymphoma. The FL24Cx is an algorithm based on a 45-target gene expression profiling (GEP) assay, which was developed and trained using 265 formalin-fixed, paraffin-embedded tissue samples on a reliable platform to predict, at the time of diagnosis, whether a patient will experience an event within 24 months. The modeling also confirmed and relied upon previously reported synergy between immune response (IR) gene expression signatures IR1 and IR2. Once locked, the 5-factor logistic regression FL24Cx model was independently validated in a retrospectively assessed cohort of 232 patients from 2 immunochemotherapy-treated arms of SWOG Cancer Research Network S0016 phase 3 clinical trial, in which it assigned 169 patients to the low-risk group with 29 events before 24 months (17.2%) and 63 patients to the high-risk group with 24 events before 24 months (38.1%). The relative risk of an event within 24 months after registration among patients who were classified into the high-risk group relative to patients who were classified into the low-risk group was 2.2 (95% confidence interval, 1.41 to 3.51). An up-front GEP biomarker, such as the FL24Cx, rigorously validated in a clinical laboratory and with a clinically relevant turnaround time, could identify and steer enrollment of patients at high risk for early events in clinical trials, thus enabling timely interpretation of such trials and increasing the pace of innovation.

This study evaluated the efficacy of the multiple sclerosis disease-modifying therapies, intramuscular interferon beta-1a (Avonex) and subcutaneous peginterferon beta-1a (Plegridy), using data from the United States Network of Pediatric Multiple Sclerosis Centers. In this retrospective analysis, 154 patients with multiple sclerosis were included who were treated with Avonex (n = 130), Plegridy (n = 23), or both treatments (n = 1) before the age of 18 years. After 3 months’ sustained use acclimation (“wash-in”), the probability of being relapse-free during the first year was 68.3% for Avonex-treated patients and 69.9% for Plegridy-treated patients; annualized relapse rates were 0.50 and 0.59, respectively. Both disease-modifying therapies demonstrated efficacy similar to that reported in adult populations. Despite the lack of formal approval for pediatric multiple sclerosis, these outcomes indicate that patients may benefit from treatment with Avonex or Plegridy. Understanding efficacy of specific disease-modifying therapies in pediatric multiple sclerosis is essential to making informed treatment decisions.