Extracellular vesicles (EVs) are small membrane-enclosed particles released by cells under various conditions specific to cells’ biological states. Hence, mass-spectrometry (MS) based proteome analysis of EVs in plasma has gained much attention as a method to discover novel protein biomarkers. MS analysis of EVs in plasma is challenging and EV isolation is usually necessary. Therefore, we compared differences in abundance, subtypes, and contamination for EVs isolated by high-speed centrifugation, size exclusion chromatography (SEC), and peptide-affinity precipitation (PAP/ME kit) for subsequent MS-based proteome analysis. Successful EV isolation was evaluated by nanoparticle-tracking analysis, immunoblotting, and transmission electron microscopy, while EV abundance, EV subtypes, and contamination was evaluated by label-free tandem MS. High-speed centrifugation and SEC isolates showed high EV abundance at the expense of contamination by non-EV proteins and lipoproteins, respectively. These two methods also resulted in EVs of a similar type, however, with smaller EVs in SEC isolates. PAP isolates had a relatively low EV abundance and high contamination. We consider high-speed centrifugation and SEC suitable as EV isolation for MS biomarker studies, where the choice between the two should depend on the scientific questions and whether the focus is on larger or smaller EVs or a combination of both.

Intra-pancreatic fat deposition (IPFD) is suspected to be associated with various medical conditions. This study aimed to assess pancreatic fat content in lean and obese individuals, characterize obese individuals with and without IPFD, and explore the underlying mechanisms. Sixty-two obese individuals without diabetes and 35 lean controls underwent magnetic resonance imaging (MRI) using proton density fat fraction (PDFF) maps to evaluate pancreatic and hepatic fat content, and visceral adipose tissue (VAT) content. Pancreatic fibrosis was explored by T1 relaxation time and MR elastography (MRE) measurements. Associations between pancreatic fat, measures of obesity and metabolic syndrome were examined using uni- and multivariate regression analyses. Pancreatic PDFF was higher in obese than in lean controls (median 8.0%, interquartile range (6.1;13.3) % vs 2.6(1.7;3.9)%, p < 0.001). Obese individuals with IPFD (PDFF ≥6.2%) had higher waist circumference (114.0 ± 12.5 cm vs 105.2 ± 8.7 cm, p = 0.007) and VAT (224.9(142.1; 316.1) cm² vs 168.2(103.4; 195.3) cm², p < 0.001) than those without. In univariate analysis, pancreatic PDFF in obese individuals correlated with BMI (r = 0.27, p = 0.03), waist circumference (r = 0.44, p < 0.001), VAT (r = 0.37, p = 0.004), hepatic PDFF (r = 0.25, p = 0.046) and diastolic blood pressure (r = 0.32, p = 0.01). However, in multivariate analysis, only VAT was associated to pancreatic fat content. MRI measures of pancreatic fibrosis indicated no evident fibrosis in relation to increased pancreatic fat content. Pancreatic fat content was increased in obese individuals compared with lean controls and predominantly correlated with the amount of visceral adipose tissue. Pancreatic fat content was not clearly linked to measures of pancreatic fibrosis.

A toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, ). The package is an extension package of the 'admiral' package focusing on the metabolism therapeutic area.