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Abstract Purpose Validate the semiquantitative (SQ)-MRI score for chronic pancreatitis (CP) diagnosis in an independent, multicenter cohort. Methods We analyzed MRIs performed at baseline in 337 participants with acute (AP) or recurrent AP (RAP) and 67 with non-calcific chronic pancreatitis (NCCP) enrolled in the PROCEED study from 6/2017 to 3/2023. MRIs were performed at multiple clinical centers using site-specific clinical protocols and a variety of MRI vendors to evaluate the SQ-MRI score in a real-world setting. The SQ-MRI score was calculated using the T1 signal intensity ratio of the pancreas to the spleen (T1 score), arteriovenous enhancement ratio (AVR), and pancreatic body diameter (PBD). We adjusted for confounding by participant characteristics via propensity score weighting. Results Median AVR was higher in AP/RAP (1.18, IQR: 0.97–1.44) than in the NCCP group (1.03, IQR: 0.78–1.22, p = 0.007). Pancreatic body diameters were larger in AP/RAP than in the NCCP group − 7–14 mm (10% vs 25%), 14–20 mm (40% vs 54%), and > 20 mm (50% vs 21%), respectively ( p < 0.01). Median T1 score was higher in AP/RAP (1.18, IQR: 0.96–1.40) than in the NCCP group (1.04, IQR: 0.86–1.32, p = 0.10). Median SQ-MRI score in the AP/RAP group was significantly lower, 1.91 (IQR: 0.97, 2.95), than in the NCCP group, 2.74 (IQR: 2.04–4.03) ( p < 0.001). The propensity score-adjusted AUC for SQ-MRI to distinguish AP/RAP from NCCP was 0.64, which was higher than for individual parameters. Conclusion We have validated the SQ-MRI score in a multicenter cohort using an independent, real-world MRI dataset.

The adult chronic pancreatitis (CP) working group, formed at the inception of the Consortium for the Study of Chronic Pancreatitis, Diabetes, and Pancreatic Cancer (CPDPC), has developed a robust framework and infrastructure to conduct clinical, translational, and mechanistic studies of CP. At its core is PRO spective Evaluation of C hronic Pancreatitis for E pid E miologic and Translational Stu D ies (PROCEED), the first longitudinal cohort study of CP in US adults, launched in 2017. PROCEED has developed a well-annotated clinical data set of over 2000 deeply-phenotyped participants, a biorepository and an imaging repository. Investigators have published promising data on blood-based and imaging-based biomarkers of CP diagnosis and pain, which are awaiting validation. Clinical observations include classification of patients into mechanism-based pain phenotypes using responses to PROMIS questionnaires, prevalence and predictors of opioid use, prevalence of psychological comorbidity, and osteopathy in CP patients. Pilot clinical trials have evaluated the effect of oral indomethacin on pancreatic fluid prostaglandin E2 levels and Internet-based cognitive behavioral therapy (CBT) for chronic pain. Results of pilot trials have led to an ongoing definitive randomized clinical trial for CBT. The rich environment and resources have facilitated mentorship and academic development of many early-stage investigators. Investigators outside of the consortium have opportunities for collaboration. In the next 5 years, the working group plans to further strengthen the research platform, complete primary and key secondary analyses of PROCEED and ancillary studies, continue efforts to develop biomarkers for diagnosis and prognosis of CP, and complete ongoing clinical trials to address unanswered questions in the field.

Abstract Introduction: Surveillance biopsies after kidney transplantation provide an opportunity to assess early renal disease processes, including diabetic nephropathy, before clinical manifestation. This study aimed to identify proteomic alterations in renal allografts at 5 years post-transplant (5yrPT) and their associations with histologic features in diabetic/obese (DM/OB) versus non-diabetic/non-obese (nDM/nOB) recipients. Methods: We studied protocol biopsies from 17 DM/OB and 14 nDM/nOB kidney transplant recipients at Mayo Clinic (2000–2012). Laser capture microdissection followed by mass spectrometry was used for proteomic profiling. Histologic assessment included Banff and Tervaert scoring and morphometric analysis. Statistical comparisons were performed using ANOVA with Tukey post-hoc testing. Results: DM/OB recipients demonstrated greater histologic and metabolic injury at 5yrPT. Proteomic analysis identified 101 glomerular and 86 non-glomerular proteins differentially expressed between groups. Significant alterations were observed in proteins associated with mitochondrial dysfunction, including VDAC1 (fold change [FC] 2.6, p = 0.03), NDUFB9 (FC 0.15, p = 0.009), and NDUFA9 (FC 1.72, p = 0.01). Extracellular matrix remodeling proteins COL6A1, COL6A2, and COL6A3 were upregulated (FC 1.6–1.8, p < 0.05). Immune and inflammatory markers C9 (FC 1.6, p < 0.03) and TGFBI (FC 1.7, p < 0.001), as well as oxidative stress–related CLU, were increased. GGACT showed the greatest decrease (FC 0.30, p < 0.001), implicating altered energy metabolism. Conclusion: Distinct proteomic signatures at 5yrPT in DM/OB recipients highlight early mitochondrial, metabolic, immune, and extracellular matrix pathways involved in diabetic kidney disease. These changes precede clinical disease and identify candidate biomarkers and therapeutic targets to improve graft longevity.

Background: Pancreatitis significantly alters the microbial composition of the oral and intestinal compartments, causing dysbiosis that may contribute to disease mechanisms and potentially serve as a basis for diagnosis or treatment. Objective: To determine whether the oral or gut microbial signature can classify chronic pancreatitis (CP). Methods: Stool samples (n=707) were collected from participants in the Prospective Evaluation of Chronic Pancreatitis for Epidemiologic and Translational Studies (PROCEED). Samples were distributed among 200 healthy (HC), 310 CP, 49 acute pancreatitis (AP), and 148 recurrent acute pancreatitis (RAP). In addition, saliva samples were collected for a subset of participants (n=156). Whole genome sequencing was performed to assess microbiome composition. Machine learning algorithms were utilized to identify a signature with microbial features predictive of CP. Results: Gut alpha diversity was significantly decreased in AP, RAP, and CP compared with HC, with CP exhibiting the lowest diversity. In contrast, oral microbial diversity showed no significant variation across groups. Beta diversity analysis revealed distinct gut microbiome compositions between HC and pancreatitis subtypes, with CP showing the most pronounced differences. Random forest models using gut microbial species demonstrated robust predictive performance for CP using a minimum of 10 species (Area under the curve—AUC: 0.834; accuracy: 0.774). Despite similarities in gut microbiome composition across pancreatitis subtypes, a unique gut microbial signature for CP was identified highlighting the microbiome’s potential in CP diagnosis. Conclusion: Our study reveals a gut microbial signature predictive of CP using machine learning models in a large US multi-institutional cohort.

ABSTRACT Predicting delayed graft function (DGF) relies on donor, recipient, and perioperative factors. Despite the growing recognition that DGF duration strongly influences patient outcomes, no models currently address or predict its impact—highlighting an important gap in current research and clinical practice. This study aimed to develop an ensemble‐based machine learning model using perioperative data to predict DGF occurrence and duration. The gradient‐boosted decision trees model was trained and validated on 2725 patients, with k‐fold cross‐validation in an external cohort of 284 patients. Model performance was evaluated based on accuracy, ROC‐AUC, and other metrics using R and Python libraries. The binary DGF prediction model achieved a ROC‐AUC of 0.77, while the DGF duration classification model had an accuracy of 79.2%. DGF duration AUC values by time interval were 0.76 for 0–1 weeks, 0.81 for >1–2 weeks, 0.80 for >2–3 weeks, and 0.87 for >3 weeks; the overall macro‐averaged AUC was 0.81. The mean Brier score for multi‐class predictions was 0.14. External validation showed a 78% accuracy for DGF duration prediction. Acute kidney injury (AKI) and donor donation after circulatory death (DCD) status were key DGF predictors. The use of gradient‐boosted decision trees (GBDT) improves the prediction of both the likelihood and duration of DGF, addressing a current gap in kidney transplant patient care. By facilitating personalized transplant care, this model supports more effective perioperative planning and timely interventions, which may contribute to better patient outcomes.

Background. Acute cellular rejection (ACR) remains a major cause of morbidity after heart transplantation despite advances in immunosuppression. Whole genome transcriptomic profiling offers a systems-based, unbiased approach to elucidate the molecular mechanisms underlying ACR. However, noninvasive, longitudinal biomarker assessments capable of capturing the temporal dynamics of rejection biology remain scarce. Methods. RNA sequencing of peripheral blood from heart transplant recipients before, during, and after ACR was compared with nonrejection controls. Pathway analysis was conducted using differentially expressed genes (DEGs), and a machine learning approach was applied to assess gene-based prediction of ACR. Results. A total of 235 rejection-specific significant DEGs and 863 postrejection DEGs (false discovery rate < 0.05) were identified. During ACR, DEGs were enriched for T-cell activation/differentiation, apoptosis, and B-cell receptor signaling pathways. By combining the 2 sets of DEGs, a panel of 71 common genes was identified that reflected the significant, longitudinal transcriptomic dynamics of ACR. In an elastic net machine learning–based classifier, DYNLL1 and SERF2 were identified as ACR predictive genes, and achieved a cross-validated area under the receiver operating characteristic curve of 0.63. Conclusions. Peripheral blood transcriptomics identify dynamic temporal responses in ACR including T- and B-cell pathways with potential ACR predictive genes that warrant further investigation.

Objectives: Smoking is a key risk factor for pancreatitis, contributing to pathogenesis and disease progression. Data regarding second-hand smoking, or passive smoking (PS) exposure, are lacking. We therefore aimed to assess the impact of PS exposure across the pancreatitis spectrum. Methods: We analyzed baseline data from the PROCEED study, a multicenter study in the United States including patients with acute (AP), recurrent acute (RAP), and chronic (CP) pancreatitis. Participants detailed their individual smoking history and PS exposure, which was compared between AP, RAP, and CP subgroups. Participant factors and clinical characteristics were compared by level of individual and PS exposure. Results: Among 1369 participants (190 AP, 498 RAP, 681 CP), 346 (25.3%) were current and 409 (29.9%) were former smokers. The CP subgroup had the highest proportion of current smokers (36.7% CP vs 15.1% RAP, 11.1% AP, P < 0.001). PS exposure was also significantly higher in the CP subgroup (68.6% CP vs 58.2% RAP, 55.3% AP, P < 0.001). A minority (16.3%) with CP reported no smoking exposure (by self or passive) while nearly half smoked ≥20 pack-years. The mean PS duration was significantly higher in CP participants (21.8 y) compared with those with AP (17.8 y) or RAP (18.9 y) ( P < 0.0166). Conclusions: In this multicenter study, we affirmed an association with smoking use and intensity with CP-related complications. For the first time, we report a high prevalence of prior exposure to passive smoking in all pancreatitis subtypes, which requires further study to understand the impact on disease outcomes.

Objectives: Participant retention is essential in prospective studies to minimize bias and maintain validity. This study aimed to estimate retention rates and identify factors influencing attrition in a multicenter cohort (PROCEED) of US adults with acute/recurrent acute pancreatitis (AP/RAP) and chronic pancreatitis (CP). Methods: Participants from the PROCEED study with AP/RAP or CP eligible for follow-up within the designated visit window were included. Retention was defined as completing at least one follow-up visit during the observation period. Retention was analyzed by follow-up mode (in-person and/or medical record review) and disease type (AP/RAP and CP). Retention factors were assessed using univariate analyses (Wilcoxon rank-sum for continuous variables, χ 2 /Fisher's exact for categorical) and multivariable logistic regression. Missing data were adjusted using multiple imputations in statistical modeling. Results: Among 1279 participants (AP/RAP: n=632, median age 47, 48.9% female; CP: n=647, median age 54, 46.5% female), we observed high cumulative and annualized retention rates, with improved retention when incorporating medical record reviews. In multivariable regression analyses, older age was statistically significantly associated with in-person retention in the AP/RAP group ( P =0.0001), while a higher self-reported physical health (assessed with PROMIS Global Health Physical Health T-score) was a key predictor of retention in CP ( P =0.034). Conclusions: Integrating medical records review substantially enhanced retention rates, enabling robust analyses of pancreatitis outcomes and disease progression. Older age and better self-reported physical health were key predictors of retention, highlighting the need for targeted strategies to enhance engagement among younger participants and those with poorer health in long-term studies.

INTRODUCTION: Chronic pancreatitis (CP) is a disease associated with chronic inflammation, fibrosis, and pain. There is a lack of tools available that facilitate early diagnosis, when intervention could prevent irreversible damage. Pilot data suggested prostaglandin E2 (PGE2) as a candidate biomarker for early CP. PGE2 activates signaling pathways that promote inflammation, pain, and fibrosis. METHODS: We assessed PGE2, metabolites, and downstream targets in pancreatic fluid collected endoscopically 0–10 (n = 110) and 10–20 (n = 111) minutes after intravenous secretin administration. PGE2 and metabolites were measured in plasma (n = 75) and urine (n = 71) from the same subjects. Subjects were enrolled in the PROCEED study and classified symptomatic controls, acute/recurrent acute pancreatitis (AP/RAP), or CP. RESULTS: A significant main effect was detected in 10–20 minutes pancreas fluid ( P = 0.027) and plasma ( P = 0.046); post hoc testing showed PGE2 was lower in the AP/RAP group compared with symptomatic controls. There was also trend toward lower PGE2 in urine ( P = 0.062). To elucidate the active downstream pathways, calcitonin gene-related peptide, substance P, and matrix metalloproteinases (MMPs) 1, 2, 3, 7, 9, and 13 were measured in pancreas fluid. A significant difference between the 3 groups was detected for both MMP7 and MMP9. MMP7 was elevated in individuals with CP vs AP/RAP ( P = 0.012) for samples collected early but both time points for MMP9 ( P = 0.027, P = 0.002). DISCUSSION: While PGE2 is detectable in pancreas fluid, these data suggest that it may not be sensitive enough to distinguish between AP/RAP and CP. However, MMPs may distinguish between stages of pancreatitis and require further testing as potential diagnostic biomarkers.

Pancreatogenic or type 3c diabetes, which occurs as a consequence of pancreatitis, may have several mechanisms. Mechanisms overlapping with those of type 2 diabetes have been explored, including insulin resistance and insulin deficiency. However, autoimmune mechanisms in response to unmasking β-cell antigens, similar to those of type 1 diabetes (T1D), have not been thoroughly examined. We performed cross-sectional T1D autoantibody profiling in a large prospective cohort (N = 927) of adults with acute, recurrent acute, or chronic pancreatitis. We examined correlations of prevalence of autoantibodies against GAD (GAD antibody), IA-2 (IA-2 antibody), zinc transporter 8 (ZnT8; ZnT8 antibody), and insulin (insulin autoantibody [IAA]) with clinical and demographic features, including presence of diabetes, diabetes treatment, and family history of diabetes. Diabetes was present in 11 (12%) of 94 participants with acute pancreatitis, 69 (27%) of 273 with recurrent acute pancreatitis, and 235 (43%) of 560 with chronic pancreatitis. Among these groups, islet autoantibodies were respectively found in five (5.3%) of 94, 48 (17.6%) of 273, and 127 (22.6%) of 560. The proportions of individuals with diabetes who were autoantibody positive were 45%, 70%, and 54%, respectively (most commonly IAA, which may reflect exogenous insulin use). However, across the entire cohort, 27 participants (2.9%) had two, two (0.2%) had three, and one (0.1%) had four T1D-associated autoantibodies, respectively, suggesting a potential pathogenic link that should be further explored. ARTICLE HIGHLIGHTS Type 1 diabetes–associated autoimmune mechanisms have not been thoroughly examined in pancreatitis-associated diabetes. We assessed the prevalence of four islet autoantibodies in serum from a large prospective cohort of patients with acute, recurrent acute, or chronic pancreatitis. Diabetes was present in 11 (12%) of 94 participants with acute pancreatitis, 69 (27%) of 273 with recurrent acute pancreatitis, and 235 (43%) of 560 with chronic pancreatitis. Excluding those with only insulin autoantibody positivity, which is confounded by insulin treatment, islet autoantibodies were identified in 51 (5.5%) of 927 participants, and 30 (3.2%) of 297 were positive for two or more autoantibodies. Our findings suggest pancreatitis may elicit islet autoimmunity in a subset of patients, necessitating prospective longitudinal follow-up.