Exome-Wide Association Study on Alanine Aminotransferase Identifies Sequence Variants in the GPAM and APOE Associated With Fatty Liver Disease

Background & Aims: Fatty liver disease (FLD) is a growing epidemic that is expected to be the leading cause of end-stage liver disease within the next decade. Both environmental and genetic factors contribute to the susceptibility of FLD. Several genetic variants contributing to FLD have been identified in exome-wide association studies. However, there is still a missing hereditability indicating that other genetic variants are yet to be discovered. Methods: To find genes involved in FLD, we first examined the association of missense and nonsense variants with alanine aminotransferase at an exome-wide level in 425,671 participants from the UK Biobank. We then validated genetic variants with liver fat content in 8930 participants in whom liver fat measurement was available, and replicated 2 genetic variants in 3 independent cohorts comprising 2621 individuals with available liver biopsy. Results: We identified 190 genetic variants independently associated with alanine aminotransferase after correcting for multiple testing with Bonferroni method. The majority of these variants were not previously associated with this trait. Among those associated, there was a striking enrichment of genetic variants influencing lipid metabolism. We identified the variants rs2792751 in GPAM/GPAT1, the gene encoding glycerol-3-phosphate acyltransferase, mitochondrial, and rs429358 in APOE, the gene encoding apolipoprotein E, as robustly associated with liver fat content and liver disease after adjusting for multiple testing. Both genes affect lipid metabolism in the liver. Conclusions: We identified 2 novel genetic variants in GPAM and APOE that are robustly associated with steatosis and liver damage. These findings may help to better elucidate the genetic susceptibility to FLD onset and progression

The secreted triose phosphate isomerase of Brugia malayi is required to sustain microfilaria production in vivo

Human lymphatic filariasis is a major tropical disease transmitted through mosquito vectors which take up microfilarial larvae from the blood of infected subjects. Microfilariae are produced by long-lived adult parasites, which also release a suite of excretory-secretory products that have recently been subject to in-depth proteomic analysis. Surprisingly, the most abundant secreted protein of adult Brugia malayi is triose phosphate isomerase (TPI), a glycolytic enzyme usually associated with the cytosol. We now show that while TPI is a prominent target of the antibody response to infection, there is little antibody-mediated inhibition of catalytic activity by polyclonal sera. We generated a panel of twenty-three anti-TPI monoclonal antibodies and found only two were able to block TPI enzymatic activity. Immunisation of jirds with B. malayi TPI, or mice with the homologous protein from the rodent filaria Litomosoides sigmodontis, failed to induce neutralising antibodies or protective immunity. In contrast, passive transfer of neutralising monoclonal antibody to mice prior to implantation with adult B. malayi resulted in 60–70% reductions in microfilarial levels in vivo and both oocyte and microfilarial production by individual adult females. The loss of fecundity was accompanied by reduced IFNγ expression by CD4+ T cells and a higher proportion of macrophages at the site of infection. Thus, enzymatically active TPI plays an important role in the transmission cycle of B. malayi filarial parasites and is identified as a potential target for immunological and pharmacological intervention against filarial infections

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

Predicting and elucidating the etiology of fatty liver disease : A machine learning modeling and validation study in the IMI DIRECT cohorts

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n= 795) or at high risk of developing the disease (n= 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (= 5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86;p = 5%) rather than a continuous one. Conclusions In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see:) and made it available to the community.Peer reviewe

Development of a partitioned coupling method based on multi-scale data exchanges between porous and CFD solvers for a nuclear core

International audienceThe objective is to perform multi-scale simulations of flows across a fuel rod bundle with coupling methods between a sub-channel code that takes advantage of the very general features and a CFD code representing the realistic three-dimensional features. Then, a supervisor code ICoCo is used to perform the coupling work based on a partitioned implicit domain decomposition technique. Two domains exchange thermal–hydraulics variables at the coupling interface with specific treatments (prolongation and restriction). When the flow is perpendicular to the coupling interface, non-intersecting domain decomposition approach is applied; otherwise, local domains should be partially overlapping. A verification procedure with increasing complexity is described, and related results employed CFD resolved simulations as references. The MANIVEL benchmark experiment has been chosen as the demonstrative case to validate developed methods by comparing velocity distributions and pressure variations

A Predictive Model of Weight Loss After Roux-en-Y Gastric Bypass up to 5 Years After Surgery: a Useful Tool to Select and Manage Candidates to Bariatric Surgery.

Different factors, such as age, gender, preoperative weight but also the patient's motivation, are known to impact outcomes after Roux-en-Y gastric bypass (RYGBP). Weight loss prediction is helpful to define realistic expectations and maintain motivation during follow-up, but also to select good candidates for surgery and limit failures. Therefore, developing a realistic predictive tool appears interesting. A Swiss cohort (n = 444), who underwent RYGBP, was used, with multiple linear regression models, to predict weight loss up to 60 months after surgery considering age, height, gender and weight at baseline. We then applied our model on two French cohorts and compared predicted weight to the one finally reached. Accuracy of our model was controlled using root mean square error (RMSE). Mean weight loss was 43.6 ± 13.0 and 40.8 ± 15.4 kg at 12 and 60 months respectively. The model was reliable to predict weight loss (0.37 < R <sup>2</sup>  < 0.48) and RMSE between 5.0 and 12.2 kg. High preoperative weight and young age were positively correlated to weight loss, as well as male gender. Correlations between predicted weight and real weight were highly significant in both validation cohorts (R ≥ 0.7 and P < 0.01) and RMSE increased throughout follow-up between 6.2 and 15.4 kg. Our statistical model to predict weight loss outcomes after RYGBP seems accurate. It could be a valuable tool to define realistic weight loss expectations and to improve patient selection and outcomes during follow-up. Further research is needed to demonstrate the interest of this model in improving patients' motivation and results and limit the failures

Cancer incidence and mortality in France over the period 1980-2005.

BACKGROUND: The objective of this study was to provide updated estimates of national trends in cancer incidence and mortality for France for 1980-2005. METHODS: Twenty-five cancer sites were analysed. Incidence data over the 1975-2003 period were collected from 17 registries working at the department level, covering 16% of the French population. Mortality data for 1975-2004 were provided by the Inserm. National incidence estimates were based on the use of mortality as a correlate of incidence, mortality being available at both department and national levels. Observed incidence and mortality data were modelled using an age-cohort approach, including an interaction term. Short-term predictions from that model gave estimates of new cancer cases and cancer deaths in 2005 for France. RESULTS: The number of new cancer cases in 2005 was approximately 320,000. This corresponds to an 89% increase since 1980. Demographic changes were responsible for almost half of that increase. The remainder was largely explained by increases in prostate cancer incidence in men and breast cancer incidence in women. The relative increase in the world age-standardised incidence rate was 39%. The number of deaths from cancer increased from 130,000 to 146,000. This 13% increase was much lower than anticipated on the basis of demographic changes (37%). The relative decrease in the age-standardised mortality rate was 22%. This decrease was steeper over the 2000-2005 period in both men and women. Alcohol-related cancer incidence and mortality continued to decrease in men. The increasing trend of lung cancer incidence and mortality among women continued; this cancer was the second cause of cancer death among women. Breast cancer incidence increased regularly, whereas mortality has decreased slowly since the end of the 1990s. CONCLUSION: This study confirmed the divergence of cancer incidence and mortality trends in France over the 1980-2005 period. This divergence can be explained by the combined effects of a decrease in the incidence of the most aggressive cancers and an increase in the incidence of less aggressive cancers, partly due to changes in medical practices leading to earlier diagnoses