Citations

Objective Glucocorticoids (GCs) remain the primary treatment for polymyalgia rheumatica (PMR) but are associated with substantial long‐term toxicity. Although sex‐based differences in GC‐related adverse effects have been suggested, they have not been well studied in PMR. Methods We conducted a retrospective cohort study of patients with PMR treated between 2017 and 2024 within a multisite health system. Patients ≥50 years of age with at least two PMR International Classification of Diseases, Tenth Revision, codes and documented GC dosing for ≥75% of days during the first six months of treatment were included. Sex‐based differences in GC toxicity outcomes including incident or worsening hypertension, type 2 diabetes mellitus, and incidence of fragility fractures were evaluated using multivariable regression models adjusted for age, body mass index, treatment site, and baseline type 2 diabetes mellitus. Results A total of 1,899 patients met inclusion criteria (915 female patients and 984 male patients; mean age 72.5 years). At one year, the cumulative GC exposure and composite toxicity was not statistically different between sexes. At three years, cumulative GC exposure was higher in female patients (4,419.5 mg prednisone equivalents compared to 4,106.2 mg in male patients, P = 0.042) as well as daily GC dose ( P = 0.037). Female patients had a higher incidence of fractures at three years (8.5% vs 3.2%, P ≤ 0.001) and incidence of giant cell arteritis (GCA; 7.6% vs 3.4%, P < 0.001). Conclusion The median cumulative GC exposure in patients with PMR remains high, with higher exposure in female patients. Female patients were more likely to experience fracture and the development of GCA within the same time frame. image

Abstract Patients with rare genetic disorders often wait years for a correct diagnosis, highlighting the urgent need for early and efficient triage for those at risk. Electronic health records (EHRs) are rich in useful information, yet the extent to which this data is sufficient to generate prediagnostic signals is uncertain. We trained machine learning models on billing codes and phenotypes extracted from clinical notes and applied these across eight conditions in a longitudinal cohort of roughly 3 million patient records from the Mayo Clinic, seeking early identification of conditions. The number of patients identified early varied by condition, ranging from 10% to 89% at 99% specificity. Median lead times exceeded one year prior to the first diagnostic billing code for most conditions, including Fabry disease (1,408 days), hereditary angioedema (2,755 days), neurofibromatosis type 1 (1,067 days), and hereditary hemorrhagic telangiectasia (2,320 days). We show that ML models trained on EHR data can prioritize patients for clinician review and confirmatory genetic testing and that the integration of diverse phenotypic data sources provides superior predictive value.

Background. Rare diseases collectively affect an estimated 300 million people worldwide, yet patients wait an average of 4-8 years for a correct diagnosis after visiting multiple physicians, accumulating unexplained findings, and suffering preventable disease progression before a unifying diagnosis is reached. Electronic health records (EHRs) capture this pre-diagnostic trajectory in rich detail, but conventional methods typically collapse years of longitudinal phenotypic signals into a single static snapshot; our approach instead models this temporal structure directly. Methods. We conducted a retrospective cohort study using longitudinal EHR data from approximately 3 million patients at Mayo Clinic Platform_Accelerate. Cases were patients with confirmed diagnoses of 10 rare diseases; controls (~5,200 per disease) had no record of any rare disease. HPO phenotype terms were extracted from clinical notes and laboratory results using a medspaCy NLP pipeline; ICD codes were grouped at three-character level. We developed and trained two temporal models, GRU-Attn and Conformer, that encode patient histories as quarterly time-binned sequences spanning up to 30 years, and compared these against three static baselines (CatBoost, XGBoost, logistic regression). Models were evaluated systematically at prediction horizons h = 1-10 years before diagnosis. Results. In the full held-out cohort benchmark, temporal models outperformed static baselines across the large majority of diseases and prediction horizons, with the advantage widening substantially as the horizon lengthened. At 1-2 years prior to diagnosis, mean AUROC was 0.928 (GRU-Attn) versus 0.871 (CatBoost); at 6-10 years prior to diagnosis, 0.726 versus 0.693, with static models degrading to near-chance for some diseases. Combining HPO and ICD features yielded the strongest performance, with a mean gain of +0.063 AUROC over the best single source at 1-2 years prior to diagnosis. Separately, in a manually curated cohort of 143 confirmed-undiagnosed cases with extended EHR histories, GRU-Attn identified 138 cases before first disease mention and all 143 cases before formal diagnosis at the prespecified 99th-percentile specificity threshold, with median lead times of 1.1-8.3 years and 1.9-21.7 years, respectively. Conclusions. By learning from the temporal trajectory of phenotypic signals rather than their static aggregate, temporal EHR models improved retrospective early detection performance across several rare diseases, most powerfully for conditions where pre-diagnostic findings accrue gradually and heterogeneously over years. These results show that longitudinal EHR trajectories carry rare-disease signal beyond static phenotypic burden and support prospective evaluation of temporal EHR modeling for rare-disease risk stratification.

Abstract Background There is limited data on immune checkpoint inhibitor (ICI) use in patients with non-small cell lung cancer (NSCLC) and known pericardial effusions. Methods This international multi-center observational retrospective study included patients with advanced NSCLC with or without pericardial effusions receiving ICIs. Immune-related adverse events (irAEs) were graded per CTCAE v5.0. Overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan-Meier method. Cox proportional hazards multivariable models assessed the association between survival outcomes and clinicopathologic variables, and an inverse probability of treatment weighting (IPTW) model compared outcomes between the two groups. Results A total of 1248 patients with advanced NSCLC treated with ICIs across 13 institutions were included. Of these, 85 patients (6.8%) had pre-existing pericardial effusions. Compared to controls, patients with pericardial effusions were younger (median age 63 vs 67 years, P = .007), diagnosed more frequently with PD-L1 positive tumors (96% vs 76%, P &lt; .001), and had a higher median number of baseline metastatic sites (3 vs 1, P &lt; .001). IrAEs were similar between both groups (42% vs 38%), and no cardiac irAEs occurred in patients with pericardial effusions. PFS (median 5.6 vs 4.6 months, P = .3) and OS (median 10.2 vs 14.7 months, P = .50) were not statistically different between patients with pericardial effusions and the controls, respectively. In both multivariable and IPTW models, pericardial effusions were not associated with PFS or OS. Conclusions Among patients with NSCLC and pre-existing pericardial effusions, ICIs demonstrate meaningful clinical benefit similar to those without effusions, with no new cardiac or severe toxicities.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Abstract Background Adipose tissue influences blood pressure regulation through depot-specific endocrine, inflammatory, and exosome-dependent mechanisms. While obesity is a well-established risk factor for hypertension, emerging evidence suggests that distinct fat depots may contribute to vascular dysfunction independent of total adiposity. Methods: This review synthesizes post-2020 literature on 6 anatomically defined depots: visceral, perivascular, epicardial, bone marrow, brown, and beige adipose tissue, with an emphasis on their mechanistic roles, imaging biomarkers, and translational relevance. Results Alterations in adipose depot function can impair nitric oxide signaling, promote oxidative stress, and modulate sympathetic tone, even in the absence of obesity. Exosomal microRNAs from dysfunctional adipose tissue further disrupt vascular homeostasis. Although clinical tools for assessing depot dysfunction are limited, advances in imaging and molecular profiling may enable targeted therapeutic strategies. Conclusions Reframing adipose tissue as a modular regulator of blood pressure, rather than a passive consequence of obesity, may unlock new venues for precision management of hypertension.

TPS6126 Background: Oral cavity cancers (OCC) are a rare subtype of head and neck cancer (HNC) with poor outcomes and limited second-line treatment options in the recurrent or metastatic (R/M) setting. Treatment-related morbidity and repeated local recurrences in OCC result in significant impairment of function and quality of life. Rural OCC patients comprise a sizable portion (50%) of our practice and are usually of lower socioeconomic status with lack of access to specialist care. They also have multiple barriers to treatment access and ancillary services, translating to poorer clinical outcomes. There is a critical need for effective and accessible therapies for R/M OCC in rural patients. Triple oral metronomic chemotherapy (TOMC) with Methotrexate, Erlotinib and Celecoxib (MEC) suppresses angiogenesis and activates antitumor microenvironment in HNC. Studies using TOMC in R/M OCC outside the US show reasonable efficacy (6-month OS 52.9%), better safety and lower cost compared to standard second line options. We hypothesize that a decentralized clinical trial with TOMC in R/M OCC is feasible, safe and effective in addition to being economical and accessible for rural patients. Methods: MC240701 is a single institution, open label, decentralized, single arm pilot study of MEC in patients with R/M OCC. Eligible pts must be ≥18 years old, have pathologically confirmed R/M OCC not amenable to curative-intent therapy, ECOG 0-2 and adequate organ function. At least 1 measurable lesion by RECIST 1.1 OR non-measurable disease (evident mucosal lesions, CNS/bone disease, lesions not meeting RECIST criteria) is permitted. Patients must have received standard first line immunotherapy with or without chemotherapy OR should be unable/unwilling to receive first line treatment. Key exclusion criteria include patients unable to take pills by mouth, serious medical co-morbidity or autoimmune disease, immunocompromised patients and other active malignancy. Primary endpoint is to demonstrate feasibility of decentralized clinical trials, secondary outcomes include overall response rate, progression free and overall survival and toxicity. Additional goals are to evaluate patient impact of decentralized treatment through surveys and interviews After initial screening and evaluation, patients are remotely consented and will receive study drugs by mail. All patients will receive the same treatment of Methotrexate 9mg/m 2 PO weekly, Erlotinib 150 mg PO daily, and Celecoxib 200 mg PO BID, each cycle will be 28 days. Treatment will continue until disease progression or treatment tolerance, study duration is for 2 years. All subsequent clinical and laboratory monitoring and response assessments (every 3 cycles) will be remotely performed or have an option for local testing. Enrollment began July 2025 and 4 out of planned 25 patients have been accrued. FDA IND:174540. Clinical trial information: NCT06997068 .

6123 Background: Pemetrexed (PTX) is safe and tolerable with responses reported in pts with R/M SGC. Given enhanced responses with PTX and pembrolizumab (PMB) for lung cancer, we hypothesized that PTX and PMB will have activity for SGC. Herein we present the efficacy results in pts with adenoid cystic carcinoma (ACC). Methods: MC200708 is a single arm phase II study of PTX and PMB in pts with R/M SGC (NCT04895735) with 2 cohorts: ACC (cohort A) and non-ACC (cohort B). Key eligibility criteria: ≥18 years, ECOG 0-1, measurable disease. Prior ICI and/or PTX was allowed. Key exclusion criteria: serious comorbidities, autoimmune disease and brain metastases. Simon’s 2-stage design was used for each cohort. Primary endpoint was overall response rate (ORR) for Cohort A but was amended to clinical benefit rate (CBR=SD+PR+CR) due to prolonged SD in several pts. Secondary endpoints: progression free survival (PFS), overall survival (OS), and toxicity. Exploratory analyses in Cohort A1 (post-amendment pts) included PSMA PET imaging and circulating PSMA extravesicles (EVs) as a biomarker. All pts received PTX 500 mg/m2 IV + PMB 200 mg IV q3 weeks until progression or treatment intolerance. Imaging was q3 cycles. Results: 20 pts (11 Cohort A + 9 Cohort A1) were enrolled from August 2021-Feb 2025. All 20 patients were eligible and received ≥ 1 cycle of treatment. Median age was 60.5 yrs, 50% male, performance status 0 (80%) or 1 (20%). 11 pts had no prior therapies (55.0%). The remaining pts had 1 (35%) or 2 (10%) prior lines of therapy. 75% of pts had no prior ICI. In Cohort A, the ORR was 0% (0 of 11). Cohort A1 met criteria for success based on CBR with 1 PR (12.5%) and 5 SD (62.5%) in the first 8 pts. For the whole cohort of 20 patients, the ORR was 5% (95% CI: 0.1-24.9) and the CBR rate was 75% (95% CI: 51-91) with 1 PR and 14 pts with SD. The duration of response for the 1 PR patient was 12.4 months. The median duration of response for the 15 pts with SD was 7.5 (2.0-29.7) months. 3 pts (15%) had PD and 2 pts (10%) went off prior to response assessment. Median cycles of treatment was 4 (2-30). In the 17 pts who discontinued, treatment was stopped in 9 (53%) for PD, 4 (23.5%) due to adverse events (AE), and 4 (23.5%) due to physician/patient preference. With a median follow-up of 14.5 months (1.5-39.2), the 1-year PFS rate is 52.1% (32.6 – 83.2%) and the OS rate is 82.1% (95% CI: 65.6 – 100%). The overall grade 3+ AE rate regardless of attribution was 80% (16/20). The most common grade 3 AEs were lymphocyte count decrease (40%), hypertension (20%), fatigue (10%), and pneumonitis (10%). 2 pts had a grade 4 AE (lymphocyte count decrease and thrombotic thrombocytopenic purpura). There were no treatment-related deaths. Analysis of correlative studies for Cohort A1 with PSMA imaging and EV biomarker ongoing. Conclusions: PTX and PMB has modest activity in pts with R/M ACC with CBR of 75% and prolonged benefit for some patients. Clinical trial information: NCT04895735 .

Abstract Rationale Mismatch of airway to lung size has been implicated in chronic obstructive pulmonary disease (COPD) pathophysiology, whether arising from disproportioned growth or from acquired pathologies over the life course. Lower airway-to-lung ratio (ALR), assessed reproducibly via computed tomography (CT) as the mean airway lumen diameters (trachea-to-subsegments) divided by the cubed root of lung volume, is associated with COPD risk and mortality (all-cause, respiratory, cardiovascular, lung cancer). We sought to characterize the airway biology associated with lower ALR by profiling bronchial gene expression across current and former smokers. Methods We leveraged clinical, radiologic, and bronchial epithelial RNA sequencing data from the SPIROMICS (SubPopulations and Intermediate Outcomes Measures in COPD Study) Phase 1 Bronchoscopy Sub-study (n = 161). To analyze transcriptional differential expression (DE) between participants based on CT-assessed ALR, we utilized DESeq2 and then identified impacted pathways by Ingenuity Pathway Analysis (IPA). Generating cell-type transcriptional signatures from lung and blood single cell sequencing atlases, we also assessed association of ALR with differences in airway cellular composition. Results Across all ever-smokers, after adjustment for age, sex, height, COPD status, smoking status, and pack-years, n = 1,324 genes from airway epithelial samples exhibited DE at a false discovery rate (FDR) &lt;0.2. Per IPA, enriched pathways associated with lower ALR included neutrophilic degranulation and macrophage activity. In cell type analysis, lower ALR was associated with higher signatures of macrophages, neutrophils, dendritic cells, and B cells, and lower ciliated and club cell signatures. When we repeated DE analysis using the same covariate adjustments plus an interaction term for ALR and smoking, n = 713 DE genes were identified at FDR &lt;0.2; the association of DE with lower ALR was overwhelmingly in current rather than former smokers (3699 vs. 2 genes). By pathway and cell type analyses, the association between lower ALR and heightened inflammation (greater macrophage and neutrophil signaling) and epithelial dysregulation (downregulated ciliated and club cell function) was primarily restricted to current smokers. Conclusions In adult ever-smokers, lower ALR is associated with transcriptomic evidence of heightened neutrophil- and macrophage-driven airway inflammation and reduced ciliated and club cell expression, associations that were most evident among current smokers. Whether these cross-sectional patterns, which suggest impaired mucociliary defense and a diminished capacity to clear inhaled particulates, are the result of pre-existing reduced ALR, its cause, or both, will require further investigation. This abstract is funded by: SPIROMICS was supported by contracts from the NIH/NHLBI (HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHSN268200900020C, 75N92024D00012), grants from the NIH/NHLBI (U01HL137880, U24HL141762, R01HL182622, R01HL144718, and R01HL093081), and supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from Amgen; AstraZeneca/MedImmune; Bayer; Bellerophon Therapeutics; Boehringer-Ingelheim Pharmaceuticals, Inc.; Bristol Myers Squibb; Chiesi Farmaceutici S.p.A.; Forest Research Institute, Inc.; Genentech; GlaxoSmithKline; Grifols Therapeutics, Inc.; Ikaria, Inc.; MGC Diagnostics; Novartis Pharmaceuticals Corporation; Nycomed GmbH; Polarean; ProterixBio; Regeneron Pharmaceuticals, Inc.; Sanofi; Sunovion; Takeda Pharmaceutical Company; Theravance Biopharma; Verona; and Mylan/Viatris.

Abstract Rationale Eosinophilic airway inflammation in COPD is associated with more frequent exacerbations,lower lung function,and corticosteroid responsiveness.DNA methylation captures information about genetic factors and environmental exposures, but is under-studied in eosinophilic COPD.We hypothesized that sputum cell methylation patterns reflect genomic regulation of inflammatory endotypes in COPD by DNA sequence variation. Methods We analyzed DNA previously extracted from induced sputum cells for a prior study(PMID38261629) for which sufficient quantity DNA remained for methylation arrays.For this analysis,we focused on n = 132 SPIROMICS participants with ≥20 pack-years smoking history.Samples were stratified into tertiles based on sputum eosinophil proportions,previously determined from sputum cell differential analysis by the centralized sputum analysis core for SPIROMICS(Univ of North Carolina).We assessed proportion of DNA methylation (β values) spanning 936,866 CpG sites using the Illumina Infinium Methylation EPICv2.0 BeadChip Array,which we transformed into M values(meffil R package). To identify differentially methylated regions (DMRs)(FDR&lt;0.05 in minimum of 2 CpG sites per gene region),we contrasted CpG β values between the high-eosinophil and low-eosinophil tertiles(N = 44 each),using the DMRcate package.We tested for enrichment of genes overlapping with significant DMRs using Enrichr (GWAS Catalog 2025 gene set library).To identify genetic variants associated with DNA methylation levels,we performed Methylation Quantitative Trait Loci (meQTL) analysis using TensorQTL.For each CpG site,we tested associations with SNPs within a ± 500 kb window (cis-meQTL).The linear regression model included age,sex,BMI,smoking status,smoking pack-years,study site,inhaled corticosteroid use, the first two genetic principal components, cell-type proportions (macrophages/monocytes, neutrophils, squamous epithelial cells, lymphocytes), and eosinophil tertile . Significant meQTLs were identified at an FDR&lt;0.05 using Benjamini-Hochberg correction. Results A total of 532 eosinophil-associated DMRs (FDR &lt; 0.05) were identified between the high- and low-eosinophil groups,encompassing 446 unique genes.The top enriched pathways related to counts of eosinophils (adjusted p = 0. 00002464), lymphocytes (adjusted p = 0.0001860), and the sum of eosinophils-plus-basophils (adjusted p = 0.0001860).We identified 18,432 significant SNP-CpG/meQTL pairs (FDR &lt; 0.05),suggesting extensive genetic influence on airway DNA methylation patterns.Among the n = 521 eosinophil-associated CpGs that overlapped with meQTLs,rs55687040 (TENT2,a gene involved in mRNA and miRNA processing) is a GWAS locus for FEV1/FVC ratio (PMID: 26634245), suggesting possible genetic control of airway eosinophilic methylation signatures. Conclusions Our findings disclose distinct DNA methylation signatures associated with sputum eosinophil proportion in COPD.A subset of these CpGs are regulated by genetic variants,suggesting an integrated genetic-epigenetic mechanism underlying airway eosinophilic inflammation with the potential to influence COPD risk and severity. This abstract is funded by: SPIROMICS was supported by contracts from the NIH/NHLBI (HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHSN268200900020C), grants from the NIH/NHLBI (U01 HL137880, U24 HL141762, R01 HL182622, and R01 HL144718), and supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from Amgen; AstraZeneca/MedImmune; Bayer; Bellerophon Therapeutics; Boehringer-Ingelheim Pharmaceuticals, Inc.; Chiesi Farmaceutici S.p.A.; Forest Research Institute, Inc.; Genentech; GlaxoSmithKline; Grifols Therapeutics, Inc.; Ikaria, Inc.; MGC Diagnostics; Novartis Pharmaceuticals Corporation; Nycomed GmbH; Polarean; ProterixBio; Regeneron Pharmaceuticals, Inc.; Sanofi; Sunovion; Takeda Pharmaceutical Company; and Theravance Biopharma and Mylan/Viatris.