Equilibre glycémique post-opératoire après Gastric Bypass chez 152 patients souffrant de diabète de type 2 (facteurs de risque de non-rémission précoce du diabète)

Contexte : Le Gastric Bypass (GBP) améliore l équilibre glycémique dès les premiers jours postopératoires avant toute perte de poids. Malgré l uniformité de la procédure, le bénéfice métabolique varie d un patient à l autre amenant dans 20 % des cas à la persistance d un diabète parfois sévère. Aucune recommandation sur la prise en charge du diabète après GBP n est consensuelle. But de l étude. Décrire l évolution post-opératoire précoce de la glycémie chez les patients diabétique de type 2 et rechercher les facteurs prédictifs de non-rémission à 1 mois. permettant d identifier les patients devant bénéficier d un suivi médical renforcé. Méthodes : Nous avons inclus, dans cette étude rétrospective, 152 patients diabétiques consécutifs opérés d un GBP au CHRU de Lille et au CH de Valenciennes, de janvier 2009 à décembre 2011. Les caractéristiques cliniques et biologiques pré-opératoires et post-opératoires des patients ont été extraites des dossiers cliniques et de la base de données Filemaker de la Lille Cohorte Study (recueil prospectif des données). La rémission précoce était définie, 1 mois après l intervention, par l absence de traitement antidiabétique associée à une glycémie inférieure à 7 mmol/L. L analyse statistique a été réalisée à l aide du logiciel NCSS 2007. Afin de prédire l absence de rémission du diabète, nous avons effectué une régression logistique pas à pas s appuyant sur le calcul de l aire sous la courbe ROC des variables pertinentes en analyse univariée. Résultats : Le taux de complications post-opératoires sévères (Clavien >= 3) était de 10,5 %. Aucun patient n est décédé au cours de l étude. Un mois après le GBP, la perte de poids est de 9,5 +- 3,1 %. La glycémie à jeun a diminué de 1,6 +- 2,6 mmol/L associée à une réduction des traitements par antidiabétiques oraux et par insuline. Néanmoins, 61 % des patients sont restés diabétiques selon les critères de l American Diabetes Association. Seul 1 patient sous insuline sur 33 a présenté une rémission précoce complète alors que 86% des patients diabétiques traités par régime seul avaient normalisé leur glycémie. La réalisation de courbes ROC a montré que l ancienneté du diabète (AUC = 0,77 ; p = 8,3 mmol/L ou un score pronostic >= 3 quel que soit leur traitement antidiabétique bénéficient d une surveillance spécialisée rapprochée au cours du 1er mois post opératoire.LILLE2-BU Santé-Recherche (593502101) / SudocSudocFranceF

Quantifying the climate impacts of albedo changes due to biofuel production: a comparison with biogeochemical effects

Lifecycle analysis is a tool widely used to evaluate the climate impact of greenhouse gas emissions attributable to the production and use of biofuels. In this paper we employ an augmented lifecycle framework that includes climate impacts from changes in surface albedo due to land use change. We consider eleven land-use change scenarios for the cultivation of biomass for middle distillate fuel production, and compare our results to previous estimates of lifecycle greenhouse gas emissions for the same set of land-use change scenarios in terms of CO2e per unit of fuel energy. We find that two of the land-use change scenarios considered demonstrate a warming effect due to changes in surface albedo, compared to conventional fuel, the largest of which is for replacement of desert land with salicornia cultivation. This corresponds to 222 gCO2e/MJ, equivalent to 3890% and 247% of the lifecycle GHG emissions of fuels derived from salicornia and crude oil, respectively. Nine of the land-use change scenarios considered demonstrate a cooling effect, the largest of which is for the replacement of tropical rainforests with soybean cultivation. This corresponds to − 161 gCO2e/MJ, or − 28% and − 178% of the lifecycle greenhouse gas emissions of fuels derived from soybean and crude oil, respectively. These results indicate that changes in surface albedo have the potential to dominate the climate impact of biofuels, and we conclude that accounting for changes in surface albedo is necessary for a complete assessment of the aggregate climate impacts of biofuel production and use.Federal Aviation AdministrationUnited States. Air Force Research LaboratoryUnited States. Defense Logistics Agency (DLA Energy, Project 47 of the Partnership for Air Transportation Noise and Emissions Reduction (PARTNER)

Peroxisomal β-oxidation acts as a sensor for intracellular fatty acids and regulates lipolysis

To liberate fatty acids (FAs) from intracellular stores, lipolysis is regulated by the activity of the lipases adipose triglyceride lipase (ATGL), hormone-sensitive lipase and monoacylglycerol lipase. Excessive FA release as a result of uncontrolled lipolysis results in lipotoxicity, which can in turn promote the progression of metabolic disorders. However, whether cells can directly sense FAs to maintain cellular lipid homeostasis is unknown. Here we report a sensing mechanism for cellular FAs based on peroxisomal degradation of FAs and coupled with reactive oxygen species (ROS) production, which in turn regulates FA release by modulating lipolysis. Changes in ROS levels are sensed by PEX2, which modulates ATGL levels through post-translational ubiquitination. We demonstrate the importance of this pathway for non-alcoholic fatty liver disease progression using genetic and pharmacological approaches to alter ROS levels in vivo, which can be utilized to increase hepatic ATGL levels and ameliorate hepatic steatosis. The discovery of this peroxisomal β-oxidation-mediated feedback mechanism, which is conserved in multiple organs, couples the functions of peroxisomes and lipid droplets and might serve as a new way to manipulate lipolysis to treat metabolic disorders

Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study

Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. Methods In this multinational retrospective observational study we enrolled adult participants (aged $\ge$18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) were female, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was 4$\bullet$7 kg/m${}^2$ (4$\bullet$4-5$\bullet$0), and the mean difference between predicted and observed BMI was-0$\bullet$3 kg/m${}^2$ (SD 4$\bullet$7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. InterpretationWe developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.Comment: The Lancet Digital Health, 202

Autoantibody profiling to identify biomarkers of key pathogenic pathways in mucinous ovarian cancer

Mucinous epithelial ovarian cancers are clinically and morphologically distinct from the other histopathologic subtypes of ovarian cancer. Unlike other ovarian subtypes, epidemiologic studies have indicated that tobacco exposure is a significant risk factor for developing mucinous ovarian cancer. Detection of autoantibody reactivity is useful in biomarker discovery and for explaining the role of important pathophysiologic pathways in disease. In order to study if there are specific antibody biomarkers in the plasma samples of mucinous ovarian cancer patients, we have initiated a screen by employing a “reverse capture antibody microarray” platform that uses native host antigens derived from mucinous ovarian tissues as “baits” for the capture of differentially labeled patient and control autoantibodies. 35 autoantibodies that were significantly elevated in the cancer plasma samples compared with healthy controls, and six autoantibodies that segregated smoking and nonsmoking patients were identified. Functional annotation of the antibody targets has identified nine target antigens involved in integrin and Wnt signaling pathways. Immunohistochemistry of archived ovarian specimens showed significant overexpression of eight of the nine target antigens in mucinous ovarian tumor tissues, suggesting that plasma autoantibodies from mucinous ovarian cancer patients might have heightened reactivities with epitopes presented by these overexpressed antigens. Autoantibody profiling may have an unexpected utility in uncovering key signaling pathways that are dysregulated in the system of interest

Meta-analysis of genome-wide DNA methylation and integrative omics of age in human skeletal muscle

International audienceBackground: Knowledge of age-related DNA methylation changes in skeletal muscle is limited, yet this tissue is severely affected by ageing in humans.Methods: We conducted a large-scale epigenome-wide association study meta-analysis of age in human skeletal muscle from 10 studies (total n = 908 muscle methylomes from men and women aged 18-89 years old). We explored the genomic context of age-related DNA methylation changes in chromatin states, CpG islands, and transcription factor binding sites and performed gene set enrichment analysis. We then integrated the DNA methylation data with known transcriptomic and proteomic age-related changes in skeletal muscle. Finally, we updated our recently developed muscle epigenetic clock (https://bioconductor.org/packages/release/bioc/html/MEAT.html).Results: We identified 6710 differentially methylated regions at a stringent false discovery rate <0.005, spanning 6367 unique genes, many of which related to skeletal muscle structure and development. We found a strong increase in DNA methylation at Polycomb target genes and bivalent chromatin domains and a concomitant decrease in DNA methylation at enhancers. Most differentially methylated genes were not altered at the mRNA or protein level, but they were nonetheless strongly enriched for genes showing age-related differential mRNA and protein expression. After adding a substantial number of samples from five datasets (+371), the updated version of the muscle clock (MEAT 2.0, total n = 1053 samples) performed similarly to the original version of the muscle clock (median of 4.4 vs. 4.6 years in age prediction error), suggesting that the original version of the muscle clock was very accurate.Conclusions: We provide here the most comprehensive picture of DNA methylation ageing in human skeletal muscle and reveal widespread alterations of genes involved in skeletal muscle structure, development, and differentiation. We have made our results available as an open-access, user-friendly, web-based tool called MetaMeth (https://sarah-voisin.shinyapps.io/MetaMeth/)

Orbital decay in an accreting and eclipsing 13.7 minute orbital period binary with a luminous donor

We report the discovery of ZTF J0127+5258, a compact mass-transferring binary with an orbital period of 13.7 minutes. The system contains a white dwarf accretor, which likely originated as a post-common envelope carbon-oxygen (CO) white dwarf, and a warm donor ($T_{\rm eff,\,donor}= 16,400\pm1000\,\rm K$). The donor probably formed during a common envelope phase between the CO white dwarf and an evolving giant which left behind a helium star or helium white dwarf in a close orbit with the CO white dwarf. We measure gravitational wave-driven orbital inspiral with $\sim 35\sigma$ significance, which yields a joint constraint on the component masses and mass transfer rate. While the accretion disk in the system is dominated by ionized helium emission, the donor exhibits a mixture of hydrogen and helium absorption lines. Phase-resolved spectroscopy yields a donor radial-velocity semi-amplitude of $771\pm27\,\rm km\, s^{-1}$, and high-speed photometry reveals that the system is eclipsing. We detect a {\it Chandra} X-ray counterpart with $L_{X}\sim 3\times 10^{31}\,\rm erg\,s^{-1}$. Depending on the mass-transfer rate, the system will likely evolve into either a stably mass-transferring helium CV, merge to become an R Crb star, or explode as a Type Ia supernova in the next million years. We predict that the Laser Space Interferometer Antenna (LISA) will detect the source with a signal-to-noise ratio of $24\pm6$ after 4 years of observations. The system is the first \emph{LISA}-loud mass-transferring binary with an intrinsically luminous donor, a class of sources that provide the opportunity to leverage the synergy between optical and infrared time domain surveys, X-ray facilities, and gravitational-wave observatories to probe general relativity, accretion physics, and binary evolution.Comment: 13 pages, 7 figures, 2 tables, submitted to ApJ

Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue

Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue

Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Background & Aims: Excess liver iron content is common and is linked to hepatic and extrahepatic disease risk. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals in UK Biobank with MRI quantified liver iron, and validated our findings in an independent cohort (n=1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 29 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 anthropometric traits and diseases. Results: We identified three independent genetic variants (rs1800562 (C282Y) and rs1799945 (H63D) in HFE and rs855791 (V736A) in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p<5x10-8). The two HFE variants account for ~85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases