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

Loss-of-function mutations in SIM1 contribute to obesity and Prader-Willi-like features

Sim1 haploinsufficiency in mice induces hyperphagic obesity and developmental abnormalities of the brain. In humans, abnormalities in chromosome 6q16, a region that includes SIM1, were reported in obese children with a Prader-Willi–like syndrome; however, SIM1 involvement in obesity has never been conclusively demonstrated. Here, SIM1 was sequenced in 44 children with Prader-Willi–like syndrome features, 198 children with severe early-onset obesity, 568 morbidly obese adults, and 383 controls. We identified 4 rare variants (p.I128T, p.Q152E, p.R581G, and p.T714A) in 4 children with Prader-Willi–like syndrome features (including severe obesity) and 4 other rare variants (p.T46R, p.E62K, p.H323Y, and p.D740H) in 7 morbidly obese adults. By assessing the carriers’ relatives, we found a significant contribution of SIM1 rare variants to intra-family risk for obesity. We then assessed functional effects of the 8 substitutions on SIM1 transcriptional activities in stable cell lines using luciferase gene reporter assays. Three mutations showed strong loss-of-function effects (p.T46R, p.H323Y, and p.T714A) and were associated with high intra-family risk for obesity, while the variants with mild or no effects on SIM1 activity were not associated with obesity within families. Our genetic and functional studies demonstrate a firm link between SIM1 loss of function and severe obesity associated with, or independent of, Prader-Willi–like features.Amélie Bonnefond, Anne Raimondo, Fanny Stutzmann, Maya Ghoussaini, Shwetha Ramachandrappa, David C. Bersten, Emmanuelle Durand, Vincent Vatin, Beverley Balkau, Olivier Lantieri, Violeta Raverdy, François Pattou, Wim Van Hul, Luc Van Gaal, Daniel J. Peet, Jacques Weill, Jennifer L. Miller, Fritz Horber, Anthony P. Goldstone, Daniel J. Driscoll, John B. Bruning, David Meyre, Murray L. Whitelaw and Philippe Frogue

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

A Federated Database for Obesity Research:An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.</p

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. [Abstract copyright: © 2023. The Author(s).

Stability and compatibility of vancomycin for administration by continuous infusion

BACKGROUND: Vancomycin is increasingly used by continuous infusion, but few specific data are available about stability under practical conditions of preparation and use, and compatibility with other intravenous drugs commonly used in the routine hospital setting. METHODS: Vancomycin stability [defined as recovery ≥ 93% of the original content (validated HPLC assay)] was examined throughout the whole process of centralized preparation, storage and use in the ward by infusion for up to 48 h, with allowances for deviations from recommended practice [exposure to high temperature; use of concentrated solutions (up to 83 g/L)]. Compatibility was assessed by mimicking co-administration in a single line via Y-shaped connectors with contact of 1 h at 25°C, followed by visual inspection (professional viewer), detection of particulate matter (particle analyser) and HPLC assay of vancomycin. RESULTS: Vancomycin was stable during the whole process and also during 72 h exposure of concentrated solutions at temperatures up to 37°C. Major incompatibilities were seen with β-lactams (temocillin, piperacillin/tazobactam, ceftazidime, imipenem, cefepime and flucloxacillin) and moxifloxacin, but not with ciprofloxacin, aminoglycosides and macrolides. Propofol, valproic acid, phenytoin, theophylline, methylprednisolone and furosemide were also incompatible, whereas ketamine, sufentanil, midazolam, morphine, piritramide, nicardipine, urapidil, dopamine, dobutamine and adrenaline were compatible. No effect or incompatibility with N-acetyl-cysteine or amino acid solutions was detected. CONCLUSIONS: Centralized preparation of vancomycin and its use by continuous infusion in wards is safe concerning stability, but careful attention must be paid to incompatibilities. Several drugs (including all β-lactams) require distinct intravenous lines or appropriate procedures to avoid undue contact

Limits of a Glucose-Insulin Model to Investigate Intestinal Absorption in Type 2 Diabetes

International audienceAbnormal regulation of glucose absorption in the small intestine is an important cause of Type 2 Diabetes (T2D). Even if this hypothesis is clinically well-known, it has not been fundamentally validated yet, mainly due to a lack of reliable metabolic knowledge on the glucose regulation. The main objective of this paper is to test this hypothesis on a highly referenced model composed of ordinary differential equations. This model is tested on an original dataset featuring the observations of obese diabetic patients. It shows its limits to predict our post-prandial glycemia and insulinemia time series especially with regard to the crucial complexity of gastro-intestinal regulation

Weight loss independent association of TCF7 L2 gene polymorphism with fasting blood glucose after Roux-en-Y gastric bypass in type 2 diabetic patients

Background: Roux-en-Y gastric bypass (RYGB) surgery improves glucose control in most but not all patients with type 2 diabetes mellitus (T2 DM). Transcription factor 7-like 2 (TCF7 L2) gene variation (rs790314O, C: wild-type allele, T: risk-allele) is the strongest contributor to T2 DM risk. Until now, there are no studies investigating gene interactions with changes of glycemia in obese patients with T2 DM after RYGB. The objective of this study was to assess the effect of TCF7 L2 genotype on RYGB-induced changes in glucose homeostasis in 99 obese patients with T2 DM at 1- year follow-up.Methods: Body mass index (BMI) and fasting blood glucose (FBG) were measured before and 1, 3, 6, and 12 months after RYGB. Genotyping was performed with TaqMan technology. The effect of the interaction between TCF7 L2 genotype and postoperative time on BMI and FBG changes was analyzed with a linear mixed model.Results: Preoperatively, there was no difference in BMI, FBG, and other diabetes associated traits between homozygous (CC) (n = 49) and heterozygous (CT) or homozygous (TT) T risk-allele carriers (n 50). One year after RYGB, 48 out of 99 patients had glycosylated hemoglobin (HbA1 c) lower than 6.5% in absence of any antidiabetic medication. BMI decreased similarly in both groups (P = .769, genotype-time interaction), however, the decrease in FBG over time was lower in T risk-allele carriers (P = .0 16, genotype-time interaction). At 1 year, FBG was 6.42 2.98 mmol/ L in CT/TT versus 5.36 0.98 mmol/L in CC (P = .022, t test).Conclusion: TCF7 L2 gene variation affected the decrease of FBG after RYGB in obese patients with T2 DM, independently of weight loss. (Surg Obes Relat Dis 2014:10:679 683.)

Modeling Intestinal Glucose Absorption from D-xylose Data

International audienceType 2 Diabetes (T2D) is one of the main epidemics of this century. One of the hypothesis of medical research is that an important cause of T2D may be the abnormal regulation of intestinal glucose absorption (IGA). Early detection of IGA disorders, and, more generally, precision medicine, may help to prevent the risk of T2D. This could be achieved by predictive models of glucose dynamics in blood following an oral ingestion. Even though many such models have been proposed, they either do not cope with IGA at all, or their calibration requires the use of complex and invasive tracer protocols that make them clinically unusable on a daily basis. To overcome this issue, D-xylose may be used as an IGA marker. Indeed, it is a glucose analogue with similar intestinal absorption mechanisms but, contrary to glucose, its dynamics in blood only results from gastric emptying, intestinal absorption and elimination by the kidney. In this paper, we investigate, for the first time, a model-based assessment of IGA based on D-xylose dynamics in blood after oral absorption. We show that a multi-compartment model of instestinal absorption can fit very well D-xylose data obtained from different experimental conditions and be a good qualitative estimate of IGA. And addition, because gastric emptying is a possible confounding factor with intestinal absorption, we explore the relative contribution of both mechanisms to the rate of D-xylose (and thus glucose) appearance in blood