No abstract

PLO is therefore associated with significant morbidity, a high prevalence of recognized risk factors for osteoporosis and a risk of recurrence in subsequent pregnancies. Bisphosphonate therapy administered soon after presentation substantially increases spinal bone density in patients with PLO.

Presence of fat in the pancreas increases the risk of metabolic co-morbidities. Detection and quantification of pancreatic fat is not a routine clinical practice, at least in part because of need to use expensive imaging techniques. We aimed to systematically review common markers of pancreatic fat in blood and to investigate differences in these markers associated with fatty pancreas. The search was conducted in 3 databases (EMBASE, Scopus, and MEDLINE). Studies in humans were eligible for inclusion if they reported on biological markers and percentage of pancreatic fat or fatty pancreas prevalence. Data were pooled for correlation and effect size meta-analysis. A total of 17 studies including 11 967 individuals were eligible for meta-analysis. Markers of lipid metabolism, including circulating triglycerides (r = 0.38 [95% confidence interval (CI) 0.31, 0.46]) and high-density lipoprotein cholesterol (r = -0.33 [95% CI -0.35, -0.31]), and markers of glucose metabolism, including glycated haemoglobin (r = 0.39 [95% CI 0.30, 0.48], insulin (r = 0.38 [95% CI 0.33, 0.43]), and homeostasis model assessment-insulin resistance (r = 0.37 [95% CI 0.30, 0.44], yielded the best correlations with percentage of pancreatic fat. Further, effect size analysis showed large and medium effects for the above markers of lipid and glucose metabolism. Circulating levels of triglycerides and glycated haemoglobin appear to be the best currently available markers of pancreatic fat. The approach of non-invasive and accurate detection of pancreatic fat by blood analysis should be further explored in the future, by investigating other potential biological markers of pancreatic fat.

Current knowledge of biomarkers of intra-pancreatic fat deposition (IFD) is limited. We aimed to analyse comprehensively body composition and insulin traits as biomarkers of IFD in healthy normoglycaemic individuals as well as in individuals with new-onset prediabetes or diabetes after acute pancreatitis (NODAP). A total of 29 healthy individuals and 34 individuals with NODAP took part in this cross-sectional study. The studied biomarkers belonged to the following domains: body composition (anthropometric and MRI-derived variables); indices of insulin secretion; indices of insulin sensitivity; incretins and related peptides; and pancreatitis-related factors. All MRI-derived variables (including IFD) were measured using ImageJ software. Univariate and step-wise regression analyses were conducted to determine variables that best explained variance in IFD. Visceral fat volume and oxyntomodulin were the best biomarkers of IFD in normoglycaemic healthy individuals, contributing to 64% variance. The Raynaud index was the best biomarker of IFD in individuals with NODAP, contributing to 20% variance. Longitudinal studies are warranted to investigate the cause and effect relationship between oxyntomodulin and IFD in healthy individuals, as well as insulin sensitivity and IFD in individuals with NODAP.

Background New-onset diabetes is the most common sequela of acute pancreatitis (AP). Yet, prospective changes in glycaemia over time have never been investigated comprehensively in this study population. The primary aim was to determine the cumulative incidence of new-onset prediabetes and new-onset diabetes after AP over 24 months of follow-up in a prospective cohort study. The secondary aim was to identify trajectories of glycaemia during follow-up and their predictors at the time of hospitalisation. Methods Patients with a prospective diagnosis of AP and no diabetes based on the American Diabetes Association criteria were followed up every 6 months up to 24 months after hospital discharge. Incidence of new-onset prediabetes/diabetes over each follow-up period was calculated. Group-based trajectory modelling was used to identify common changes in glycaemia. Multinomial regression analyses were conducted to investigate the associations between a wide array of routinely available demographic, anthropometric, laboratory, imaging, and clinical factors and membership in the trajectory groups. Results A total of 152 patients without diabetes were followed up. The cumulative incidence of new-onset prediabetes and diabetes was 20% at 6 months after hospitalisation and 43% over 24 months of follow-up (p trend \ 0.001). Three discrete trajectories of glycaemia were identified: normal-stable glycaemia (32%), moderatestable glycaemia (60%), and high-increasing glycaemia (8%). Waist circumference was a significant predictor of moderate-stable glycaemia. None of the studied predictors were significantly associated with high-increasing glycaemia.Conclusions This first prospective cohort study of changes in glycaemia (determined at structured time points in unselected AP patients) showed that at least one out of five patients develops new-onset prediabetes or diabetes at 6 months of follow-up and more than four out of ten-in the first 2 years. Changes in glycaemia after AP follow three discrete trajectories. This may inform prevention or early detection of critical changes in blood glucose metabolism following an attack of AP and, hence, reduce the burden of new-onset diabetes after acute pancreatitis.