Citations

Objective Lewy body disease (LBD) is a complex neurodegenerative disorder characterized by the accumulation of misfolded α‐synuclein in the brain. Neuroinflammation has long been implicated in LBD pathogenesis, and recent genetic studies in Parkinson's disease (a clinical manifestation of LBD) have shown consistent association with the human leukocyte antigen (HLA) gene complex. Here, we assessed whether variation in HLA alleles influences neuropathological burden in a neuropathologically‐defined series of LBD cases. Methods We conducted a comprehensive analysis of HLA allelic variants in a cohort of 539 LBD cases of European descent from the Mayo Clinic brain bank. High‐resolution whole‐genome sequencing was used, and the HLA alleles of each sample were called using the HLA*LA tool and assessed for association with neuropathological outcomes. Results Our analysis identified 1 significant ( P < 3.43 × 10 −5 ) and 4 suggestive ( P < 0.001) associations between certain HLA alleles and specific neuropathological outcomes in LBD, suggesting a potential role for HLA‐mediated immune mechanisms in disease progression and subtype differentiation. Specifically, HLA‐DPB1*06:01 was significantly associated with lower Lewy body counts in the parahippocampal gyrus ( P = 3.30 × 10 −5 ), with weaker and suggestive associations observed in the middle frontal ( P = 1.80 × 10 −4 ) and inferior parietal gyrus ( P = 6.33 × 10 −4 ). Additionally, although only suggestive, HLA‐DRB1*11:01 correlated with a lower Thal amyloid phase ( P = 1.56 x 10 −4 ), and HLA‐B*15:01 correlated with an increased risk of diffuse LBD ( P = 7.58 x 10 −4 ). Interpretation This study provides a detailed evaluation of the relationship between HLA alleles and LBD pathology, highlighting the importance of immune‐related genetic factors in the etiology of LBD. ANN NEUROL 2026;99:492–501

Objective To provide evidence‐based and expert guidance for the treatment and management of non‐renal systemic lupus erythematosus (SLE); treatment and management of lupus nephritis are addressed in a separate guideline. Methods Clinical questions for treatment and management of SLE were developed in the PICO format (population, intervention, comparator, and outcome). Systematic literature reviews were developed for each PICO question, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was used to assess evidence quality and formulate recommendations. The Voting Panel achieved a consensus of ≥70% agreement on the direction (for or against) and strength (strong or conditional) of each recommendation. Results We present recommendations and ungraded, consensus‐based good practice statements for the treatment and management of SLE that are applicable to pediatric and adult patients. Recommendations emphasize uniform treatment with hydroxychloroquine, limiting duration of glucocorticoid use, and early introduction of conventional and/or biologic immunosuppressive therapies to achieve and maintain control of SLE inflammation (remission or a low level of disease activity), reduce SLE‐related morbidity and mortality, and minimize medication‐related toxicities. Conclusion This guideline presents direction regarding treatment and management of SLE and provides a foundation for well‐informed, shared clinician–patient decision‐making. These recommendations should not be used to limit or deny access to therapies, as treatment decisions may vary due to the unique clinical situation and personal preferences of each person with SLE.

Objective To provide evidence‐based and expert guidance for the treatment and management of non‐renal systemic lupus erythematosus (SLE); treatment and management of lupus nephritis are addressed in a separate guideline. Methods Clinical questions for treatment and management of SLE were developed in the PICO format (population, intervention, comparator, and outcome). Systematic literature reviews were developed for each PICO question, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was used to assess evidence quality and formulate recommendations. The Voting Panel achieved a consensus of ≥70% agreement on the direction (for or against) and strength (strong or conditional) of each recommendation. Results We present recommendations and ungraded, consensus‐based good practice statements for the treatment and management of SLE that are applicable to pediatric and adult patients. Recommendations emphasize uniform treatment with hydroxychloroquine, limiting duration of glucocorticoid use, and early introduction of conventional and/or biologic immunosuppressive therapies to achieve and maintain control of SLE inflammation (remission or a low level of disease activity), reduce SLE‐related morbidity and mortality, and minimize medication‐related toxicities. Conclusion This guideline presents direction regarding treatment and management of SLE and provides a foundation for well‐informed, shared clinician–patient decision‐making. These recommendations should not be used to limit or deny access to therapies, as treatment decisions may vary due to the unique clinical situation and personal preferences of each person with SLE.

Introduction: PH increases RV afterload, often leading to RV systolic and diastolic failure. The metabolic mechanisms underlying RV dysfunction remain poorly understood and are largely unexplored across non-PAH forms of PH. Aims: We aimed to identify which metabolites and metabolic pathways associated with imaging measures of RV systolic function. Methods: We analyzed data from the multi-center PVDOMICS cohort, which includes echo, CMR, invasive hemodynamics, clinical phenotyping, and untargeted plasma metabolomics (via Metabolon). RV systolic function metrics selected as the outcome measures for this study were fractional area change (echo), global longitudinal strain (echo), and RVEF (CMR). Associations between non-xenobiotic, named metabolites and RV function measures were assessed using linear regression adjusted for age, sex, BMI, and PH group, with significance defined by FDR <0.05. Metabolite set enrichment analysis (MSEA) was performed to identify significantly enriched pathways (FDR <0.20). To complement MSEA, we conducted mean-aggregation analysis: we averaged metabolite concentrations within each pathway and tested associations with RV function using the same covariate adjustments (FDR <0.05). Results: We identified 979 participants with plasma metabolomics and at least one available outcome measure (317 Group 1 PH; 106 Group 2 PH; 140 Group 3 PH; 51 Group 4 PH; 26 Group 5 PH; 81 Healthy Controls; 258 Disease Comparators). Linear regression identified 170 metabolites from the 647 named metabolites significantly associated with all 3 RV metrics (Top 10 by average ranked p-value displayed in Image 1 ). MSEA revealed seven enriched pathways across all RV metrics: Androgenic Steroids, Pregnenolone Steroids, Progestin Steroids, Fatty Acid Metabolism (Acyl Choline), Histidine metabolism, Vitamin A metabolism, Gamma-glutamyl Amino Acid ( Image 2) . Mean aggregation analysis showed that higher concentrations of Androgenic/Pregnenolone Steroids and Vitamin A metabolites strongly associated with improved RV function, while higher levels of Fatty Acid Metabolism (Acyl Carnitine) and Acetylated Peptides associated with worse function ( Image 3 ). Conclusions: Using data from the multi-institutional PVDOMICS cohort, we provide a blueprint of metabolites and metabolic pathways associated with RV systolic function across the spectrum of PH. Steroid hormones as well as Vitamin A and fatty acid metabolites emerged as central to RV systolic function.

Background: Critical congenital heart disease (CCHD) screening algorithms based on pulse oximetry have up to 27% false positive rate at high altitudes (>2500 m) despite altitude-specific cutoff changes. We examined the added value of an adult-based AI model applied to phonocardiography (AI-PCG) in the digital stethoscope EKO Core 500 in these settings. Research Question: What is the diagnostic performance of AI-PCG model compared to pulse oximetry in detecting neonatal CCHD across different altitudes in Latin America? Methods: This observational, prospective, case-control study included newborns born at different altitudes from 0 to 4380 meters in Peru, Mexico, Colombia and Bolivia. All underwent preductal and postductal oximetry at least 18 hours after birth, electrocardiography and PCG with the digital stethoscope. All CCHD cases were confirmed by echocardiography. Non-CCHD cases were defined by echocardiography or clinically if they were alive for 1 month and had 1) negative pulse oximetry, 2) no hospitalization due to cardiac or pulmonary causes and 3) no cyanosis or pneumonia. Those with abnormal pulse oximetry, genetic syndromes or murmur detection underwent echocardiography. Diagnostic performance metrics [area under the receiving operating characteristic curve (AUROC), sensitivity, specificity and false positive rate] were evaluated per modality. Results: A total of 1152 newborns were enrolled, 725 at <2500 m [13 (1.8%) with CCHD] and 427 at >2500 m [6 (1.4%) with CCHD]. Out of 19 CCHD cases, 17 had positive pulse oximetry and 9 had a positive AI-PCG model. One newborn with severe coarctation of the aorta and atrial septal defect had negative pulse oximetry and AI-PCG model. Pulse oximetry showed superior AUROC (0.932 vs 0.664), sensitivity (89.5% vs 47.4%), specificity (97% vs 85.5%) and lower false positive rate (3% vs 14.5%) compared to the AI-PCG model across altitudes. Diagnostic accuracy varied with altitude (Figure): at <2500 m, pulse oximetry showed higher AUROC (0.999 vs 0.612) and lower false positive rate (0.1% vs 16%) than AI-PCG model, but at >2500 m, pulse oximetry had similar AUROC (0.794 vs 0.774) and lower false positive rate (7.8% vs 11.9%) than the AI-PCG model. Conclusion: Pulse oximetry has superior diagnostic performance than an adult-based AI-PCG model when screening for neonatal CCHD at <2500 m, but similar performance is found >2500 m.

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (Infertility after Cancer Predictor) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817–0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666–0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives—patients incorrectly classified as likely to regain menses—could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources.

Abstract Disclosure: A. Bancos: None. Y. Hleibiehova: None. R. Gregg garcia: None. N. Genere: None. S. Achenbach: None. E. Atkinson: None. S. Mahrokhian: None. A. Vaidya: None. I. Bancos: None. Background: Bilateral adrenalectomy (BLA) is a seldomly performed treatment for hypercortisolism, pheochromocytoma, or metastatic adrenal disease that leads to permanent primary adrenal insufficiency (PAI). PAI due to BLA is seldomly studied independently of PAI due to autoimmune adrenalitis (AA), despite differences in onset of disease, and neither group has large-scale data focusing on factors that impact quality of life (QOL) post-PAI. Furthermore, no study to date has compared patients’ burden of disease or number of adrenal crises in patients with PAI due to BLA versus patients with PAI due to AA. Methods: We conducted a dual-center, cross-sectional study of adults with PAI due to AA or BLA, 2022-2025. All participants completed a questionnaire on symptoms, management, adrenal crises, and an adrenal insufficiency-specific QoL survey (AddiQoL). Outcomes included AddiQoL scores and self-reported adrenal crises. Pearson correlations were used to explore relationships with AddiQoL scores. Logistic regression models were used to examine associations with having an adrenal crisis in the past year. Results: Of the 343 participants, 203 had AA (median age of 54 years, IQR 42-67, 72% women) and 140 had BLA (median age of 57 years, IQR 44-67, 76% women). Median duration of PAI was similar for AA and BLA cohorts (13 vs 10 years, P=0.205). Patients with AA and BLA were treated with hydrocortisone (84% vs 78%), prednisone (19% vs 21%), or dexamethasone (1% vs 1%), with a similar median daily hydrocortisone equivalent dose (21 mg vs. 20 mg, P=0.259). No group differences in availability of glucocorticoid injections (85% vs 83%) and medical alert bracelet (81 vs 83%) were reported. AddiQoL scores were similar between patients with AA and BLA (median 84 vs 83, P=0.984). AddiQoL score was better in older patients (R=0.16, P=0.003), men (R=0.17, P=0.002), daily hydrocortisone ≤ 20 mg/day (R=0.11, P=0.04), and those with supportive health insurance (R=0.20, P&lt;0.001). Patients with AA reported a higher number of adrenal crises since diagnosis (median of 0.1/person-years, IQR 0-0.4 vs 0/person-years, IQR 0-0.3, P=0.046), as well as in the last year (25% vs 13%, P=0.022), compared to patients with BLA. In a multivariable model of age, sex, glucocorticoid dose, availability of glucocorticoid injection/medical bracelet, type of PAI, and number of symptoms, patients with AA (OR 2.1, 95%CI 1.0-4.4) and those with more symptoms (OR 1.2, 95% CI 1.1-1.3) were more likely to have an adrenal crisis within the last year. Conclusion: We found no differences in the QoL scores, burden of disease, symptoms, or management of patients with AA and BLA. However, patients with AA reported more adrenal crises both overall and within the last year of assessment. Presentation: Sunday, July 13, 2025

Abstract Disclosure: J. Herndon: None. Y. Hleibiehova: None. A. Bancos: None. S. Achenbach: None. E. Atkinson: None. D. Donegan: Camarus, Recordati. I. Bancos: None. Introduction: Studies show that quality of life (QOL) among patients with adrenal insufficiency (AI) (primary (PAI) or secondary (SAI)) improves following adequate glucocorticoid replacement, however decreased QOL persists. SAI due to hypophysitis or infrequently PAI due to adrenalitis are recognized complications of immune-checkpoint-inhibitors (ICI). It is unknown if patients with ICI induced AI (ICI-AI) would report similar findings in QOL. Aim: To assess the impact of ICI-AI on specific determinants of health-related QOL (HRQOL) and compare management and outcomes to SAI due to other etiologies (oSAI). Methods: A cross-sectional survey study of adult patients who completed at least 1 AddiQOL survey with a diagnosis of ICI- AI or oSAI evaluated between 2015 and 2024 were included. Patients with opioid- or glucocorticoid-induced-AI were excluded. A study survey obtained information regarding diagnosis and treatment. Results: 112 patients with ICI-AI were included (SAI n=109, PAI n=3). Among those with ICI-AI, 59% were female, median age at diagnosis was 59.7 ±12.8 years and melanoma was the most common indication for ICI use (51%). The median time to ICI-AI diagnosis was 5.5 (range 0.3-80.8) months and 46% used combination ICI therapy. When compared to those with oSAI (n=139), patients with central ICI-AI were older at diagnosis (59 ±12.7 vs 46 ±18.1 years, p &lt;0.01), more were female (59 vs 43%, p=0.015) and most were diagnosed in the last year (88% vs 42%, p&lt;0.01). Those with ICI-AI received higher hydrocortisone (HC) equivalent daily doses than those with oSAI (25.1± 18.3 vs 22.1 ±16.9 mg/day, p&lt;0.01), however there was no difference in the number of adrenal crises (AC) reported within the last year (14 vs 16, p=0.78). Patients with ICI-AI felt that emergency personnel understood their diagnosis and treated them promptly (75% vs 0% p=0.012). Logistic regression analysis of the entire group (ICI-AI + oSAI) did not identify an association between the occurrence of AC in the last year and HC dose &gt;20 mg/day or type of glucocorticoid. Furthermore, there was no difference in number of AC per person year assessed between the ICI-AI and oSAI (0.23 ±0.5 vs 0.21 ±0.5, p= 0.4). The mean total AddiQOL score was higher in those with ICI-AI (87.2 ±13.9 vs 81.8 ±15.8, p=0.02). When adjusted for age and sex, total AddiQOL score was inversely related to number of symptoms present (r: -0.49, p&lt;0.01; r: -0.58, p&lt;0.01), occurrence of AC in the last year (r: -0.27, p=0.01; r: -0.33, p&lt;0.01) and positively related to current employment (r:0.33 p&lt;0.01 0.2,p=0.02) in ICI-AI and oSAI respectively. Conclusion: This is the first study to compare management and HRQOL in patients with ICI-AI compared to oSAI and although there were no significant differences in total number of AC events between the 2 groups, differences exist in terms of AI presentation, management and reported QOL. Presentation: Sunday, July 13, 2025

Patient-derived organoids (PDOs) have emerged as powerful tools in personalized medicine applicable to both non-malignant conditions and to cancer, where they are increasingly used for personalized drug screening and precision treatment strategies in part due to their ability to replicate tumor heterogeneity. They also serve as study model systems to understand disease mechanisms, pathways, and the impact of ex vivo exposures. We present a detailed step-by-step protocol for generating organoids from normal crypts, polyps, and tumors, including methods for tissue processing, crypt isolation, culture establishment, and the transition from basolateral to apical-out polarity for co-culture and exposure-based studies. The protocol also includes immunofluorescence staining procedures for cellular characterization and quality control measures. Our standardized approach successfully generates organoids from diverse colorectal tissues with high efficiency and reproducibility. This comprehensive guide addresses common technical challenges and provides troubleshooting strategies to improve success rates across different sample types. We believe that this resource will enhance reproducibility in organoid research and expand their utility in translational applications, particularly for personalized medicine approaches in colorectal cancer.