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Key Points Serum glycerol-3-phosphate (G3P) levels were significantly associated with higher fibroblast growth factor 23 levels in ESKD. Pathway and mediation analyses identified the pentose phosphate pathway as the top mediator of the relationship between serum G3P and fibroblast growth factor 23. The role of G3P in ESKD provides new insights into potential therapeutic targets for CKD-mineral and bone disorder. Background Fibroblast growth factor 23 (FGF23) is a key regulator of mineral metabolism that is independently associated with mortality in patients with ESKD. Glycerol-3-phosphate (G3P), a byproduct of glycolysis that can be derived from injured kidneys, stimulates FGF23 production. We aimed to determine if serum G3P is associated with FGF23 levels in patients with ESKD and identify potential molecular pathways that mediate their relationship. Methods We conducted a cross-sectional study of 99 nondiabetic patients with ESKD on hemodialysis. We used linear regression to examine the association between G3P terciles and log-transformed C-terminal FGF23 levels, adjusting for demographics, coronary artery disease, serum calcium, phosphorus, and parathyroid hormone. Mann–Whitney U tests compared 247 serum metabolite levels between the first and third FGF23 terciles; significant metabolites ( P < 0.01) were selected for pathway enrichment analyses. Top pathway scores were used in mediation analyses. Results The median age of participants was 54 years (interquartile range [IQR], 44–63), 38% were women, 71% self-identified as Black, and 27% had coronary artery disease. The median FGF23 level was 777 (interquartile range, 222–1310) RU/ml. In adjusted analyses, compared with participants with the lowest G3P tercile, those with the highest G3P tercile had a 95% higher FGF23 level (95% Confidence Interval, 6% to 260%; P = 0.04). Of the 27 metabolites significantly associated with FGF23 levels, pathway enrichment analysis identified the pentose phosphate pathway as the top hit (impact score=0.33, false discovery rate-adjusted P value = 0.01). The pentose phosphate pathway mediated the relationship between G3P and FGF23, resulting in a 62% change in the β coefficient. Conclusions In nondiabetic patients with ESKD on hemodialysis, serum G3P positively correlated with serum C-terminal FGF23, and this relationship was mediated by the pentose phosphate pathway. Exploring the pentose phosphate pathway could yield critical mechanistic insights into the regulation of FGF23, enhancing our understanding of its broader biologic functions.

Importance The effect of cornea donor diabetes on endothelial cell density (ECD) and morphometry after Descemet membrane endothelial keratoplasty (DMEK) is not known. Objective To determine whether endothelial cell loss (ECL) and morphometric changes 1 year after successful DMEK are related to cornea donor’s diabetes status. Design, Setting, and Participants This was a multicenter, double-masked, randomized clinical trial conducted from February 2022 to July 2024 at 28 US clinical sites (46 surgeons) and 13 eye banks. Included in the trial were the eyes of recipients (some of whom received tissue from donors without diabetes and others who received tissue from donors with diabetes) who underwent successful DMEK, primarily for Fuchs endothelial corneal dystrophy, and had at least 1 analyzable postoperative endothelial image. Intervention DMEK performed with a cornea from a donor without or with diabetes, assigned using a minimization procedure to achieve an approximate 2:1 distribution. Main Outcomes and Measures ECD, ECL, coefficient of variation in cell area (CV), and percentage of hexagonal cells (HEX) at 1 year from eye bank and postoperative specular central endothelial images. Results A total 1274 eyes of 982 recipients (mean [SD] age, 70 [8] years; 569 female [57.9%]; 816 [64.1%] with tissue from donors without diabetes and 458 [35.9%] with tissue from donors with diabetes) were included in the study. Preoperatively, mean (SD) central ECD in tissue from donors without diabetes and with diabetes were 2676 (290) cells/mm 2 and 2671 (286) cells/mm 2 , respectively. At 1 year, mean (SD) ECL was 28.3% (16.1%) and 28.0% (17.0%), in the donor groups without and with diabetes, respectively (adjusted mean difference = −0.4%; 95% CI, −2.3% to 1.4%), resulting in a mean (SD) 1-year ECD of 1927 (498) cells/mm 2 and 1920 (496) cells/mm 2 , respectively (adjusted mean difference = 10 cells/mm 2 ; 95% CI, −36 to 56; P = .95). No difference in ECD at 1 year associated with diabetes severity was noted ( P = .97). Mean (SD) CV did not differ at 1 year between the 2 groups of eyes (31.5% [4.1%] vs 31.4% [4.1%]; adjusted mean difference = −0.4%; 95% CI, −0.9% to 0.1%; P = .51), and mean (SD) HEX did not differ at 1 year between the 2 groups of eyes (57.7% [5.8%] vs 57.2% [5.8%]; adjusted mean difference = 0.1%, 95% CI, −0.8% to 0.9%; P = .33). Conclusions and Relevance This randomized clinical trial found that ECL and morphometry 1 year after DMEK were not affected by cornea donor diabetes status. With comparable 1-year graft success with tissue from donors with and without diabetes demonstrated in this trial, these findings support the use of corneas from donors with diabetes for endothelial keratoplasty procedures. Trial Registration ClinicalTrials.gov Identifier: NCT05134480

Importance If the success of Descemet membrane endothelial keratoplasty (DMEK) is not affected by whether the donor has diabetes, then the donor pool should expand. Objective To determine whether the 1-year DMEK success rate is affected by the presence of diabetes in the donor. Design, Setting, and Participants This was a multicenter, double-masked, randomized clinical trial conducted from February 2022 to July 2025 at 28 clinical sites (46 surgeons), with donor corneas provided by 13 eye banks in the US. Included in the study were individuals undergoing low to moderate risk DMEK (95% for Fuchs endothelial corneal dystrophy). Study data were analyzed from April to September 2025. Intervention DMEK performed with a cornea from a donor without or with diabetes, assigned using a minimization procedure (comparable with randomization) to achieve an approximate 2:1 distribution, respectively. Main Outcomes and Measures Graft success at 1 year Results A total of 1097 individuals (1421 study eyes; median [IQR] age, 71 [66-76] years; 631 female [57.5%]) were included in the study. The 1-year cumulative probability of graft success was 96.3% (95% CI, 95.0%-97.5%) among 912 study eyes (64.2%) receiving tissue from donors without diabetes and 97.1% (95% CI, 95.5%-98.4%) among 509 study eyes (35.8%) receiving tissue from donors with diabetes (difference between groups = 0.7 percentage points; 95% CI, −1.2 to 2.6; P = .63). The 1-year cumulative probability of graft success was 96.5% (95% CI, 93.6%-98.9%) in the mild donor diabetes severity subgroup (n = 173) and 97.3% (95% CI, 95.4%-98.8%) in the moderate to severe donor diabetes severity subgroup (n = 336), using a diabetes severity rating scale based on medical history. The rates of primary donor failure, early failure related to surgical complications, and subsequent failure were as follows: 2.5% (23 of 912), 0.7% (6 of 912), and 0.3% (3 of 912), respectively, in recipients of tissue from a donor without diabetes, and 2.6% (13 of 509), 0.4% (2 of 509), and 0%, respectively, among recipients of tissue from a donor with diabetes. There were no failures due to graft rejection. Conclusions and Relevance The 1-year success rate in eyes undergoing DMEK with successfully prepared tissue was very high regardless of donor diabetes status. These results, supported by the separately reported finding that endothelial cell loss and cornea morphometry after 1 year were not affected by donor diabetes status, provide strong support for having no restrictions on the use of tissue from donors with diabetes for DMEK. Trial Registration ClinicalTrials.gov Identifier: NCT05134480

Abstract Our inability to obtain nerve samples from the vast majority of neuropathic pain patients impedes our ability to understand the disease, creates challenges in understanding mechanisms in specific patient populations, and limits our ability to make treatment decisions based on quantifiable molecular data. Fields like oncology have overcome these problems to take advantage of the insight that sequencing offers for understanding mechanisms of disease and have leveraged these molecular insights to dramatically change the treatment landscape in the past decade. Here we tested the hypothesis that skin biopsies could be used to gain insight into neuronal transcriptomic changes in patients with paclitaxel-induced peripheral neuropathy (PIPN). Our analysis reveals that hundreds of differentially expressed genes (DEGs) found through bulk RNA sequencing in these skin biopsies are likely contributed by dorsal root ganglion (DRG) neuronal axons and/or terminals. Up-regulated genes were representative of broad class of nociceptors whereas down-regulated genes were associated with putative injured DRG neurons expressing the PDIA2 gene. DEGs that could be confidently associated with specific subsets of skin cells were mostly expressed by keratinocytes supporting a growing literature tying keratinocyte-neuron communication abnormalities to pain in PIPN. We validated these findings by analyzing additional previously published datasets and through prospectively conducted spatial transcriptomics experiments on human skin biopsies. Our findings warrant further assessment of skin biopsies in additional neuropathic pain populations to gain insight into DRG neuron changes that have previously been thought to be inaccessible in routine clinical or scientific assessments in most patients.

Abstract Psychological distress, moral injury, and burnout are prevalent among intensive care unit (ICU) professionals, impacting individual well-being, team dynamics, and patient safety. Structured peer support programmes, delivered by trained colleagues rather than mental health professionals, could be implemented as low-threshold, scalable interventions to promote psychological safety and resilience. This review outlines five foundational pillars for implementing peer support in the ICU, including formal programme design, training, inclusivity, confidentiality, and continuous evaluation. It also addresses practical strategies to overcome cultural and institutional barriers, such as stigma, time constraints, and hierarchical norms. Drawing on current evidence and recent unpublished survey data from North American critical care cardiologists, the article provides a practical framework for integrating peer support into ICU culture. Peer support represents a complementary approach to formal mental health services and offers tangible benefits for clinician well-being, retention, and quality of care.

Immune checkpoint inhibitors (ICIs) are an important class of cancer treatment. Endocrine immune-related adverse events (E-irAEs) account for a significant proportion of irAEs in ICI-treated patients. Diagnosing E-irAEs accurately and timely can be challenging in the absence of clear categorization and standardization across oncology, endocrinology and other specialties. While existing guidelines provide some broad directions to diagnosis and management, they lack clarity on irAE-specific symptom evaluation and work-up, as well as assessment of severity. These limitations can then impact triage, time-sensitive management and care escalation to specialists such as oncoendocrinologists. The objective of this study was to develop consensus-based statements on disease definitions for E-irAEs. A core working group of endocrinologists with expertise in the oncoendocrinology field drafted a survey with statements outlining the general approach to E-irAEs, disease definition and management approach of six specific diagnoses: ir-thyroiditis thyrotoxic phase, ir-thyroiditis hypothyroidism, ir-Graves’ disease, ir-hypophysitis, ir-adrenalitis and ir-type 1 diabetes mellitus. Severity grading tables were drafted for three disease categories—“thyroid”, “pituitary and adrenal”, and “diabetes mellitus”. A two-round modified Delphi process, using the RAND/University of California Los Angeles (RAND/UCLA) Appropriateness Method, was employed. In this process, a 25-member voting panel consisting of endocrinologists, oncologists and other specialists and healthcare providers with experience in management of ICI-treated patients and irAEs was recruited. The panel rated anonymously on usability, accuracy, appropriateness or agreement of 41 items on a 9-point scale in Survey 1, after which a meeting was convened. Statements were modified based on voting results from Survey 1 and Meeting 1, and the process was repeated in Survey 2 and Meeting 2. At the end of this process, consensus was achieved for all statements. Our study findings address the gap in standardized nomenclature, clinical, laboratory and radiological evaluations, and management principles of E-irAEs. With consensus achieved from a panel of experts from a variety of disciplines, we anticipate that the statements can form the basis for standardization of the diagnostic process and improvement of patient care.

Abstract Aims The Society for Cardiovascular Angiography and Interventions (SCAI) Classification provides risk stratification of patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS). This sub-study of the ECLS-SHOCK trial investigates the prognostic impact of SCAI stages in AMI-CS and the influence of SCAI stages on the effect of extracorporeal life support (ECLS) therapy in AMI-CS patients. Methods Patients with AMI-CS enrolled in the multicentre, randomized ECLS-SHOCK trial were included. The outcomes, treatment effect and safety of ECLS were stratified according to SCAI stage at admission using a post-hoc classification. Results From a total of 417 patients enrolled in the ECLS-SHOCK trial between June 2019 and November 2022, 51.6% (n = 215), 13.4% (n = 56) and 35.0% (n = 146) presented in SCAI Stages C, D and E, respectively. SCAI stages were associated with the risk of 30 day all-cause mortality (C vs. D vs. E: 32.6% vs. 67.9% vs. 64.4%, P &lt; 0.001), with rates of renal replacement therapy at 30 days (C vs. D vs. E: 7.0% vs. 19.6% vs. 13.7%, P = 0.03) and with poor neurological outcomes (C vs. D vs. E: 17.2% vs. 44.4% vs. 36.5%, P &lt; 0.001). No interaction was observed between SCAI stage and the treatment effect of ELCS on 30 day all-cause mortality (ELCS vs. control SCAI C: 32.7% vs. 32.4%; SCAI D: 68.4% vs. 66.7%; SCAI E: 59.7% vs. 68.4%, P for interaction = 0.65). Conclusions In AMI-CS patients included in the ECLS-SHOCK trial, SCAI stages at admission were predictive for mortality and for the incidence of safety events. The efficacy of ECLS treatment was not affected by SCAI stage.

ABSTRACT Newly diagnosed patients with non‐Hodgkin lymphoma (NHL) often have a history of other diseases, and these comorbidities can impact patient treatment and management options, as well as overall survival (OS). We developed the Lymphoma Epidemiology of Outcomes (LEO) comorbidity index (LCI) as a sum of 10 comorbidities adapted from the Self‐Report‐Generated Charlson Comorbidity Index (SRG‐CCI) for use in the LEO cohort, a national prospective study of newly diagnosed NHL. Of the 5502 participants with self‐reported comorbidity data, 3107 (56.4%) were male and the mean age at diagnosis was 60.9 years (range, 18–99 years). The LCI ranged from 0 to 6, with 48.6% having 0, 30.2% having 1, 21.2% having 2 or more comorbidities. With a median follow‐up of 62.4 months among surviving participants, 2099 patients had an event and 1219 died. Continuous LCI similarly predicted both 1‐year mortality (c‐statistic = 0.654) and OS (c‐statistic = 0.655), while it showed a weaker but still statistically significant predictive ability for lymphoma‐specific (c‐statistics = 0.617) and event‐free (c‐statistic = 0.574) survival. Participants with 1 (HR = 1.21, 95% CI 1.05–1.39) and 2+ (HR = 1.80, 95% CI 1.56–2.08) comorbidities had inferior OS compared to those with no comorbidities after adjustment for age and sex (c‐statistic = 0.654), and performance strengthened after adjustment for the International Prognostic Index (c‐statistic = 0.672). LCI predicted OS most strongly in marginal zone (c‐statistics = 0.748) and weakest in T‐cell (c‐statistic = 0.579) lymphoma. The cumulative incidence of death due to lymphoma, lymphoma treatment, and other causes all increased with increasing comorbidities, with the greatest increase observed for death due to other causes. The LCI performs comparable to other published comorbidity indices, supporting its use in the LEO cohort to better model real‐world outcomes and more generally providing an approach to implementing comorbidity indices in cancer survivorship cohorts.

592 Background: While not all fevers occurring during hematopoietic stem cell transplantation (HCT) are dangerous, episodes of febrile neutropenia tend to be managed aggressively given the significant mortality and morbidity associated with infectious complications of transplantation. This can potentially lead to unnecessary diagnostic testing, antibiotic treatment, and prolonged hospital stays for non-infectious fever etiologies such as engraftment. Our group has previously demonstrated that high-frequency body temperature monitoring (HFTM) using a wearable device detects fevers earlier in transplant recipients (Flora et al, Cancer Cell, 2021). We hypothesized that applying supervised machine learning to HFTM data collected from inpatient allogeneic HCT patients would allow for accurately distinguishing between non-infectious and infectious etiologies of fever and could potentially guide safe de-escalation of antibiotics. Methods: We analyzed 74 independent fever events detected in HCT patients (n=42) outfitted with an FDA-cleared, wireless sensor measuring axillary temperatures every 2 minutes for the duration of their respective hospitalizations. After data filtering, independent fever events were then retrospectively grouped by fever etiology using physician-determined annotation as previously described (Flora et al., 2021). A split ratio of 80:20 was used to create training and test sets from the parent data set, consisting of 59 and 15 fever events for each split, respectively. Training sets were used to create a classification algorithm identifying bacterial infections requiring antibiotics using the machine learning model XGBoost. The performance of this model was then evaluated using test sets of the remaining fevers (n=15). Results: Averaging over 1000 test sets of 15 independent fevers comprising 5 bacterial infection-related fevers and 10 non-infectious fevers, our model correctly identified 82% (mean recall) of cases requiring antibiotics with a mean precision of 0.41. The model also correctly identified 42% of non-infectious fevers (mean true negative rate, 0.42) that would otherwise have been treated with unnecessary antibiotics. The mean AUCPR was 0.58, with the best performing individual model achieving an AUCPR of 0.97. Conclusions: The preliminary results of this study demonstrate that our classification model shows promise for differentiating fevers requiring antibiotics due to bacterial infections from other non-infectious fever etiologies. Prospective studies further developing supervised machine learning models for subcategorization of febrile neutropenia could pave the way for safely de-escalating antibiotics in HCT and other immunocompromised patients undergoing cancer treatment monitored using HFTM.