A faecal microbiota signature with high specificity for pancreatic cancer

Cancer prevention; Intestinal microbiology; Pancreatic tumoursPrevenció del càncer; Microbiologia intestinal; Tumors pancreàticsPrevención de cáncer; Microbiología intestinal; Tumores pancreáticosBackground Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression. Objective To explore the faecal and salivary microbiota as potential diagnostic biomarkers. Methods We applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case–control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case–control study (n=76), in the validation phase. Results Faecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19–9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation. Conclusion Taken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.We acknowledge funding from EMBL, CNIO, World Cancer Research (#15–0391), the European Research Council (ERC-AdG-669830 MicrobioS), the BMBF-funded Heidelberg CenterCentre for Human Bioinformatics (HD-HuB) within the German Network for Bioinformatics Infrastructure (de.NBI #031A537B), Fondo de Investigaciones Sanitarias (FIS), Instituto de Salud Carlos III-FEDER, Spain (grant numbers PI15/01573, PI18/01347, FIS PI17/02303); Red Temática de Investigación Cooperativa en Cáncer, Spain (grant numbers RD12/0036/0034, RD12/0036/0050, RD12/0036/0073); III beca Carmen Delgado/Miguel Pérez-Mateo de AESPANC-ACANPAN; EU-6FP Integrated Project (#018771-MOLDIAG-PACA); EU-FP7-HEALTH (#259737-CANCERALIA). Funders had no involvement in the study design, patient enrolment, analysis, manuscript writing or reviewing

Elevated Serum Triglyceride Levels in Acute Pancreatitis: A Parameter to be Measured and Considered Early

Triglicéridos séricos; Pancreatitis agudaTriglicèrids sèrics; Pancreatitis agudaAcute pancreatitis; Serum triglycerideBackground The value of serum triglycerides (TGs) related to complications and the severity of acute pancreatitis (AP) has not been clearly defined. Our study aimed to analyze the association of elevated levels of TG with complications and the severity of AP. Methods The demographic and clinical data of patients with AP were prospectively analyzed. TG levels were measured in the first 24 h of admission. Patients were divided into two groups: one with TG values of<200 mg/dL and another with TG≥200 mg/dL. Data on the outcomes of AP were collected. Results From January 2016 to December 2019, 247 cases were included: 200 with TG<200 mg/dL and 47 with TG≥200 mg/dL. Triglyceride levels≥200 mg/dL were associated with respiratory failure (21.3 vs. 10%, p=0.033), renal failure (23.4 vs. 12%, p=0.044), cardiovascular failure (19.1 vs. 7.5%, p=0.025), organ failure (34 vs. 18.5%, p=0.02), persistent organ failure (27.7 vs. 9.5%, p=0.001), multiple organ failure (19.1 vs. 8%, p=0.031), moderately severe and severe AP (68.1 vs. 40.5%, p=0.001), pancreatic necrosis (63.8 vs. 34%, p<0.001), and admission to the intensive care unit (27.7 vs. 9.5%, p=0.003). In the multivariable analysis, a TG level of≥200 mg/dL was independently associated with respiratory, renal, and cardiovascular failure, organ failure, persistent organ failure, multiple organ failure, pancreatic necrosis, severe pancreatitis, and admission to the intensive care unit (p<0.05). Conclusions In our cohort, TG≥200 mg/dL was related to local and systemic complications. Early determinations of TG levels in AP could help identify patients at risk of complications.Open Access Funding provided by Universitat Autonoma de Barcelona

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

Implementing stakeholder engagement to explore alternative models of consent: An example from the PREP-IT trials

Introduction: Cluster randomized crossover trials are often faced with a dilemma when selecting an optimal model of consent, as the traditional model of obtaining informed consent from participant's before initiating any trial related activities may not be suitable. We describe our experience of engaging patient advisors to identify an optimal model of consent for the PREP-IT trials. This paper also examines surrogate measures of success for the selected model of consent. Methods: The PREP-IT program consists of two multi-center cluster randomized crossover trials that engaged patient advisors to determine an optimal model of consent. Patient advisors and stakeholders met regularly and reached consensus on decisions related to the trial design including the model for consent. Patient advisors provided valuable insight on how key decisions on trial design and conduct would be received by participants and the impact these decisions will have. Results: Patient advisors, together with stakeholders, reviewed the pros and cons and the requirements for the traditional model of consent, deferred consent, and waiver of consent. Collectively, they agreed upon a deferred consent model, in which patients may be approached for consent after their fracture surgery and prior to data collection. The consent rate in PREP-IT is 80.7%, and 0.67% of participants have withdrawn consent for participation. Discussion: Involvement of patient advisors in the development of an optimal model of consent has been successful. Engagement of patient advisors is recommended for other large trials where the traditional model of consent may not be optimal