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ObjectiveThe objective was to determine the mortality rate as well as the causes and predictors of death in antiphospholipid antibody (aPL)–positive patients with and without antiphospholipid syndrome (APS) classification.MethodsThe inclusion criterion for the multicenter international Antiphospholipid Syndrome Alliance for Clinical Trials and International Networking (APS ACTION) registry is positive aPLs according to the Revised Sapporo Classification Criteria tested within one year before enrollment. Patients are observed every 12 ± 3 months with clinical data and blood collection. For this prospective analysis, we first analyzed the causes of death for patients reported as “deceased.” Secondly, we analyzed risk factors for death using the adjusted Cox proportional hazards model and calculated survival probability using the Kaplan‐Meier model based on different age groups.ResultsOf 967 patients, 43 (5%) were deceased after a median follow‐up of 5.3 years. Based on the univariate analysis, deceased patients, compared to living patients, were more likely to be older and have a history of arterial thrombosis, catastrophic APS, concomitant systemic autoimmune diseases (SAIDs), and baseline cardiovascular disease (CVD) risk factors. Based on the Cox proportional hazards model adjusted for age and for each of the strongest predictors of death, arterial thrombosis (hazard ratio [HR] 2.94, 95% confidence interval [CI] 1.50–5.76), concomitant SAIDs (HR 2.97, 95% 1.56–5.63), and baseline any CVD risk factor (HR 2.43, 95% CI 1.05–5.71) were significantly associated with mortality.ConclusionIn our cohort of persistently aPL‐positive patients, the mortality rate was 5% after a median follow‐up of five years and was highest for patients ≥60 years old at registry entry. History of arterial thrombosis, concomitant SAIDs, and baseline any CVD risk factor independently predicted future death.

AbstractAutoimmune pancreatitis (AIP) is identified as a severe chronic immune‐related disorder in pancreas, including two subtypes. In this study, pancreatic lesions in patients diagnosed as either type 1 AIP or type 2 AIP are examined, and these patients’ peripheral blood at single‐cell level. Furthermore, flow cytometry, immunofluorescence, and functional assays are performed to verify the identified cell subtypes. In type 1 AIP, there is a notable increase in the amount of B cells and plasma cells, and IgG4+ plasma cells are key pathogenic cells of AIP. The differentiation path of naïve‐stage B cells into IgG4+ produced plasma cells is observed, and an increased amount of T helper cells and T follicular helper (Tfh) cells. This study also reveals that HIF‐1α, an activated transcriptional factor, can directly bind to promoter site of NAMPT, promoting higher levels of visfatin production in HIF1A+ classical monocytes. Pancreatic stellate cells can be activated by extracellular visfatin and promote the development of fibrotic response in pancreatic lesions across both AIP subtypes. The current findings shed light on the exploration of dynamic alterations in peripheral blood cells and cell subgroups in pancreatic lesions of AIP, while elucidating a pathogenic cell subset and potential fibrosis mechanism of AIP.

Introduction and Hypothesis: New bleeding in cerebral cavernous malformations (CCM) heralds increased risk of future hemorrhage for several years, yet conventional imaging only detects new bleeding that occurred in the prior weeks. A biomarker of hemorrhage could help identifying high risk lesions. An increase of mean lesional quantitative susceptibility mapping (QSM) ≥6% on MRI has been adjudicated as reflecting new bleeding in CCM during longitudinal follow-up. However, mean lesional QSM from a single acquisition could not diagnose or prognosticate a bleed. We hypothesize that machine learning (ML) may identify diagnostic and prognostic features of bleeding within QSM maps at a single point in time. Material and Methods: Two hundred and sixty-five QSM maps of CCM lesions were acquired in 120 patients enrolled in National Institute of Health (NIH) multisite trial readiness project (NCT03652181). Each map was classified (Yes/No) in association with symptomatic hemorrhage (SH) and/or biomarker event with QSM increase ≥6% in the prior (diagnostic association) and subsequent (prognostic association) year. Twenty-eight features were extracted including 14 texture, 5 first-order statistical, as well as 3 size, shape, and morphological. Five-fold cross-validation was conducted on a support-vector machine (SVM) with linear stepwise kernel for both diagnostic and prognostic associations. Performance of individual features and composite classifiers was evaluated using student t-test ( p <0.05) with Bonferroni-correction and receiver operating characteristic (ROC) analysis, area under the curve (AUC). Results: Lesions with SH in the prior year had lower average values for sum variance (AUC=0.79, p<0.001), variance (AUC=0.79, p<0.001), and correlation (AUC=0.71, p=0.004) as compared to lesions without SH. The SVM classification method tasked with distinguishing lesions with mean QSM increase ≥6% in the prior year included recurrent selection of sum average, mean, sphericity and margin sharpness, yielding an AUC of 0.61 (p=0.02). In the prognostic cohort, no individual feature nor any SVM classification model was able to distinguish cases with a subsequent clinical bleed or biomarker event. Conclusion: This proof-of-concept suggests that ML may assist in deriving features on QSM maps acquired at a single timepoint, which reflect prior hemorrhagic activity in CCM. Further investigation is planned in larger cohorts and in conjunction with clinical trials of bleeding in CCM.

Importance Normothermic machine perfusion (NMP) has been shown to reduce peritransplant complications. Despite increasing NMP use in liver transplant (LT), there is a scarcity of real-world clinical experience data. Objective To compare LT outcomes between donation after brain death (DBD) and donation after circulatory death (DCD) allografts preserved with NMP or static cold storage (SCS). Design, Setting, and Participants This single-center, retrospective observational cohort study included all consecutive adult LTs performed between January 2019 and December 2023 at the Mayo Clinic in Arizona. Data analysis was performed between February 2024 and June 2024. Outcomes of DBD-SCS, DBD-NMP, DCD-SCS, and DCD-NMP transplants were compared. Exposure DBD and DCD livers preserved on NMP or SCS. Main Outcomes and Measures The primary outcomes were early allograft dysfunction (EAD), intraoperative transfusion, and post-LT hospital resource use, including length of stay (LOS) and readmissions. Secondary outcomes included acute kidney injury (AKI) and 1-year graft and patient survival. Results A total of 1086 LTs were included in the following 4 groups: DBD-SCS (n = 480), DBD-NMP (n = 63), DCD-SCS (n = 264), and DCD-NMP (n = 279). Among LT recipients, median (IQR) age was 60.0 years (52.0-66.0); 399 LT recipients (36.7%) were female. DCD-NMP had the lowest EAD rate (17.5%), followed by DCD-SCS (50.0%), DBD-NMP (36.8%), and DBD-SCS (27.3%) ( P &amp;lt; .001). DCD-NMP had the lowest intraoperative transfusion requirement compared to all other groups. Hospital and intensive care unit (ICU) LOS were shortest in DCD-NMP (median [IQR] hospital LOS, 5.0 days [4.0-7.0]; P = .01; median [IQR] ICU LOS, 1.5 days [1.2-3.1]; P = .01). One-year cumulative readmission probability was 86% lower for DCD-NMP vs DCD-SCS (95% CI, 0.09-0.22; P &amp;lt; .001) and 53% lower for DBD-NMP vs DBD-SCS (95% CI, 0.26-0.87; P &amp;lt; .001). AKI events were lower in DCD-NMP (31.1%) vs DCD-SCS (47.4%) ( P = .001). Compared to SCS, the NMP group had a 78% overall reduction in graft failure (hazard ratio [HR], 0.22; 95% CI, 0.10-0.49; P &amp;lt; .001). For those receiving DCD allografts, the risk reduction was even more pronounced, with an 87% decrease in graft failure (HR, 0.13; 95% CI, 0.05-0.33; P &amp;lt; .001). NMP was significantly protective from patient mortality vs SCS (HR, 0.31; 95% CI, 0.12-0.80; P = .02). Conclusions and Relevance In this observational high-volume cohort study, NMP significantly improved LT clinical outcomes and reduced hospital resource use, especially in DCD allografts. NMP may enhance access to LT by addressing the challenges historically linked with DCD liver use.

608 Background: Tinengotinib, a spectrum-selective multi-kinase inhibitor with unique binding properties to FGFR , potently inhibited FGFR2 fusion/rearrangement and acquired resistant/gatekeeper mutations in pre-clinical models and exhibited antitumor activity in cholangiocarcinoma (CCA) patients (pts) in phase 1/2 trials. Here we present the overall survival (OS) data and biomarker correlative analysis from a phase 2 clinical trial of tinengotinib in CCA. Methods: Eligible pts with advanced/metastatic CCA who had received ≥ 1 prior systemic chemotherapy with ECOG PS 0-1 were treated with tinengotinib 10 mg QD across four Cohorts: A1: FGFR2 fusion(s) with primary progression on previous FGFR inhibitor ( FGFR i); A2: FGFR2 fusion(s) with progression after prior response to FGFR i; B: FGFR alteration(s) without FGFR2 fusion(s); C: FGFR wild-type. Efficacy was evaluated per RECIST v1.1 and safety was assessed by CTCAE v5.0. Genomic alterations in circulating tumor DNA (ctDNA) were assessed by Foundation Medicine NGS panel at baseline, C3D1 and EOT. Planned correlative analyses of response (PR+SD>6 months) and PFS in pt subgroups based on ctDNA mutational status were performed. Results: 55 eligible pts were enrolled (18 in A1, 11 in A2, 13 in B, 13 in C) with median age 61.0 [range 24-81] years, ECOG PS 0 in 50.9% pts, 56.4% female, and 60.0% with ≥ 3 lines of prior therapy. Among 42 pts with FGFR alterations, 78.4% had ≥ 1 prior FGFR i, and 97.6% had prior chemotherapy. Median follow up time was 8.6 months (0.4-30.5). The median OS (months) was 17.1 (95%CI 7.5-19.5) in A1; not reached (95%CI 9.6, -) in A2; 18.0 (95%CI 9.6, -) in A1+A2; not reached (95%CI 8.0, -) in B; 6.5 (95%CI 4.8-16.4) in C. The OS rate in A, B and C was 100%, 91.7% and 75% at month 3; 83.7%, 83.3% and 66.7% at month 6, and 65.8%, 55.6% and 23.8% at month 12. Partial response was observed in 26% (10/39) of evaluable FGFR-altered pts (A1+A2+B). The safety profile was consistent with previous reports. The most common any-grade treatment-related AEs were hypertension (63.6%) and diarrhea (47.3%). Baseline ctDNA results were available in 46 pts, 35 in cohorts A+B and 11 in C. A positive correlation was shown in MED12 mutation vs response (P=0.03476). BCOR and ARID1A mutation were found to be negatively associated with PFS (P=0.01366 and P=0.0437). Acquired/secondary mutations at baseline were observed in cohort A, including V564F/I (3/24 and 5/24) and N549K/H (5/24 and 5/24); favorable anti-tumor activity was noted despite these resistant mutations, consistent with preclinical data. Conclusions: Tinengotinib has shown promising anti-tumor efficacy in CCA pts with FGFR fusion after prior FGFR i and in those with primary FGFR mutations. An ongoing randomized phase III study will investigate tinengotinib vs. chemotherapy in FGFR inhibitor refractory CCA (NCT05948475). Clinical trial information: NCT04919642 .

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Importance The increasing use of glucagon-like peptide-1 receptor agonists (GLP-1RA) demands a better understanding of their association with thyroid cancer. Objective To estimate the risk of incident thyroid cancer among adults with type 2 diabetes being treated with GLP-1RA vs other common glucose-lowering medications. Design, Setting, and Participants This was a prespecified secondary analysis of a target trial emulation of a comparative effectiveness study using claims data for enrollees in commercial, Medicare Advantage, and Medicare fee-for-service plans across the US. Eligible participants were adults with type 2 diabetes at moderate risk for cardiovascular disease and without history of thyroid cancer who had newly filled prescriptions for GLP-1RA, sodium-glucose cotransporter 2 inhibitor (SGLT2i), dipeptidyl peptidase-4 inhibitor (DPP4i), or sulfonylurea from January 1, 2014, to December 31, 2021. Data were analyzed February 1 to October 31, 2024. Main Outcomes and Measures Overall and piecewise (&amp;lt;1, 1-2, and ≥2 years since treatment initiation) hazard ratios (HRs) for thyroid cancer with use of GLP-1RA vs the other 3 drug classes were estimated using inverse propensity score weighted Cox proportional hazards models. Modified intention-to-treat (mITT) (primary) and as-treated (sensitivity) analyses were performed. Results Of 351 913 patients (mean [SD] age, 65.3 [8.5] years; 173 391 [49.3%] females and 178 522 [50.7%] males), 41 112 started treatment with GLP-1RA; 76 093, with DPP4i; 43 499, with SGLT2i; and 191 209, with sulfonylurea therapy. The numbers of patients diagnosed with thyroid cancer were 69 (0.17%) in the GLP-1RA group, 172 (0.23%) in the DPP4i group, 72 (0.17%) in the SGLT2i group, and 381 (0.20%) in the sulfonylurea group. In the mITT analysis, GLP-1RA initiation was not significantly associated with increased overall risk for thyroid cancer compared to the other 3 diabetes drugs (HR, 1.24; 95% CI, 0.88-1.76). However, the risk for thyroid cancer was significantly higher within the first year after GLP-1RA initiation (HR, 1.85; 95% CI, 1.11-3.08) and was amplified in the overall as-treated analysis that censored patients when therapy was discontinued or another medication was added (HR, 2.07; 95% CI, 1.10-3.95). Conclusions and Relevance This secondary analysis of a target trial emulation of a comparative effectiveness study found that despite the low absolute risk of thyroid cancer among patients receiving GLP-1RA therapy, there was an increased risk of new thyroid cancer diagnoses within the first year of GLP-1RA initiation compared to 3 other diabetes drugs. This finding may have been due to enhanced early detection; therefore, further research is necessary to understand the underlying causes of this association.

AbstractBackgroundLate Onset Alzheimer’s Disease (LOAD) is the most common neurodegenerative disorder. Carriers of an ɛ4 allele of the apolipoprotein E gene (APOE) have significantly increased risk of developing LOAD. LOAD is also strongly sex biased. Besides the well‐established role of sex hormones, the impact of genetic sex combined with APOE genotypes has not been studied. Sex chromosome dosage (SCD) is the main genetic difference between females (XX) and males (XY). Despite dosage compensation by X chromosome inactivation (XCI), some X‐linked genes remain expressed from the inactive X (Xi) and are female biased in expression. Many of these genes are related to neurological and immune functions and could contribute to neuroinflammation in AD.MethodTo understand the genetic interactions between autosomal risk (APOE) and sex‐bias in AD, we are generating isogenic sets of hiPSCs with various sex chromosome complements and APOE genotypes. We have derived XXY/XY, XY/X, and XX/X isogenic pairs by reprogramming cells from naturally mosaic individuals or by selective removal of the Xi in vitro. These lines uniquely position us to differentiate the effects of X chromosome dosage and/or presence of a Y chromosome, while minimizing genetic variability and environmental or hormonal confounders. Currently, we are also constructing APOE ɛ3 and ɛ4 alleles in our paired hiPSC lines with differing sex chromosome content using gene editing.ResultStarting from an XXY/XY isogenic pair, we have generated an hiPSC series with six different genotypes of APOE ɛ3/ɛ3, ɛ3/ɛ4 or ɛ4/ɛ4 alleles with either the XXY or XY genotype. Preliminary results in differentiated neural precursor cells show differential expression of sex‐linked genes in relation to SCD. We also observed genome‐wide changes in gene expression and DNA methylation. Subsets of autosomal sex‐biased genes were differentially affected either by X chromosome dosage, presence of a Y, or both.ConclusionThe new hiPSC lines are being differentiated to AD‐relevant cell types, including neurons, microglia, and cortical organoids, for transcriptomic and functional analyses to elucidate how APOE alleles and genetic sex interact to modulate risk in LOAD pathology.