Cambios funcionales en el páncreas exocrino y en la morfología pancreática en pacientes con diabetes tipo 1

Con el presente estudio queremos analizar la prevalencia de cambios morfológicos, sugestivos de pancreatitis crónica, en el páncreas de diabéticos tipo 1. Se determinará además la prevalencia de insuficiencia pancreática exocrina. Posteriormente se correlacionarán ambos hallazgos: alteración morfológica y función exocrina. Se analizará su relación con factores de la diabetes como años de evolución, control glucémico y complicaciones crónicas. Se investigará por ello además el papel de la autoinmunidad en los cambios funcionales y morfológicos analizando la prevalencia de anticuerpos frente a antígenos del páncreas endocrino y el diagnóstico previo de otras enfermedades autoinmunes. Se analizarán también, por último, las posibles repercusiones clínicas y nutricionales de la alteración morfológica y funcional exocrina pancreática.Programa de Doctorado en Biotecnología (RD 99/2011)Bioteknologiako Doktoretza Programa (ED 99/2011

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: a machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accuracy.This research was funded by: Instituto de Salud Carlos III (ISCIII) co-financed by Fondo Europeo de Desarrollo Regional (FEDER) Una manera de hacer Europa, grant numbers: PI16/01126 (M.A.A.), PI19/00819 (M.J.M. and J.J.G.M.), PI15/01132, PI18/01075 and Miguel Servet Program CON14/00129 (J.M.B.); Fundación Científica de la Asociación Española Contra el Cáncer (AECC Scientific Foundation), grant name: Rare Cancers 2017 (J.M.U., M.L.M., J.M.B., M.J.M., R.I.R.M., M.G.F.-B., C.B., M.A.A.); Gobierno de Navarra Salud, grant number 58/17 (J.M.U., M.A.A.); La Caixa Foundation, grant name: HEPACARE (C.B., M.A.A.); AMMF The Cholangiocarcinoma Charity, UK, grant number: 2018/117 (F.J.C. and M.A.A.); PSC Partners US, PSC Supports UK, grant number 06119JB (J.M.B.); Horizon 2020 (H2020) ESCALON project, grant number H2020-SC1-BHC-2018–2020 (J.M.B.); BIOEF (Basque Foundation for Innovation and Health Research: EiTB Maratoia, grant numbers BIO15/CA/016/BD (J.M.B.) and BIO15/CA/011 (M.A.A.). Department of Health of the Basque Country, grant number 2017111010 (J.M.B.). La Caixa Foundation, grant number: LCF/PR/HP17/52190004 (M.L.M.), Mineco-Feder, grant number SAF2017-87301-R (M.L.M.), Fundación BBVA grant name: Ayudas a Equipos de Investigación Científica Umbrella 2018 (M.L.M.). MCIU, grant number: Severo Ochoa Excellence Accreditation SEV-2016-0644 (M.L.M.). Part of the equipment used in this work was co-funded by the Generalitat Valenciana and European Regional Development Fund (FEDER) funds (PO FEDER of Comunitat Valenciana 2014–2020). Gobierno de Navarra fellowship to L.C. (Leticia Colyn); AECC post-doctoral fellowship to M.A.; Ramón y Cajal Program contracts RYC-2014-15242 and RYC2018-024475-1 to F.J.C. and M.G.F.-B., respectively. The generous support from: Fundación Eugenio Rodríguez Pascual, Fundación Echébano, Fundación Mario Losantos, Fundación M Torres and Mr. Eduardo Avila are acknowledged. The CNB-CSIC Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0001 (F.J.C.). Comunidad de Madrid Grant B2017/BMD-3817 (F.J.C.).Peer reviewe

Low Serum Levels of Prealbumin, Retinol Binding Protein, and Retinol Are Frequent in Adult Type 1 Diabetic Patients

Aim. To determine the serum prealbumin (PA), retinol binding protein (RBP), and retinol levels in adult patients with type 1 diabetes (T1D) and to analyze some factors related to those levels. Methods. A total of 93 patients (47 women) were studied. Age, gender, BMI, duration of diabetes, chronic complications, HbA1c, lipid profile, creatinine, albumin, PA, RBP, and retinol were recorded. High and low parameter groups were compared by Mann–Whitney U and χ2 tests. Correlation between parameters was analyzed by Spearman’s test. Odds of low levels were analyzed by univariate logistic regression and included in the multivariate analysis when significant. Results. 49.5%, 48.4%, and 30.1% of patients displayed serum PA, RBP, and retinol levels below normal values, respectively. A high correlation (Rho > 0.8) between PA, RBP, and retinol serum levels was found. Patients presenting low levels of any of them were predominantly women, normal-weighted, and with lower levels of triglycerides and serum creatinine. No differences in age, macrovascular complications, duration of diabetes, or HbA1c values were observed when comparing low and normal parameter groups. Conclusion. Low serum levels of PA, RBP, and retinol are frequent in T1D adult patients. This alteration is influenced by female sex and serum creatinine and triglyceride levels