Early prediction of the impact of public health policies on obesity and lifetime risk of type 2 diabetes: A modelling approach.

OBJECTIVE: Help public health decision-making requires a better understanding of the dynamics of obesity and type 2 diabetes and an assessement of different strategies to decrease their burdens. METHODS: Based on 97,848 individual data, collected in the French Health, Health Care and Insurance Survey over 1998-2014, a Markov model was developed to describe the progression of being overweight to obesity, and the onset of type 2 diabetes. This model traces and predicts 2022-2027 burdens of obesity and type 2 diabetes, and lifetime risk of diabetes, according to different scenarios aiming at minimum to stabilize obesity at 5 years. RESULTS: Estimated risks of type 2 diabetes increase from 0.09% (normal weight) to 1.56% (obesity II-III). Compared to the before 1995 period, progression risks are estimated to have nearly doubled for obesity and tripled for type 2 diabetes. Consequently, over 2022-2027, the prevalence of obesity and type 2 diabetes will continue to increase from 17.3% to 18.2% and from 7.3% to 8.1%, respectively. Scenarios statibilizing obesity would require a 22%-decrease in the probability of move up (scenario 1) or a 33%-increase in the probability of move down (scenario 2) one BMI class. However, this stabilization will not affect the increase of diabetes prevalence whereas lifetime risk of diabetes would decrease (30.9% to 27.0%). Combining both scenarios would decrease obesity by 9.9%. Only the prevalence of obesity III shows early change able to predict the outcome of a strategy: for example, 6.7%-decrease at one year, 13.3%-decrease at two years with scenario 1 stabilizing obesity at 5 years. CONCLUSIONS: Prevalences of obesity and type 2 diabetes will still increase over the next 5 years. Stabilizing obesity may decrease lifetime risks of type 2 diabetes without affecting its short-term prevalence. Our study highlights that, to early assess the effectiveness of their program, public health policy makers should rely on the change in prevalence of obesity III.

Genetic Evidence of Causal Relation Between Intestinal Glucose Absorption and Early Postprandial Glucose Response: A Mendelian Randomization Study.

The postprandial glucose response is an independent risk factor for type 2 diabetes. Observationally, early glucose response after an oral glucose challenge has been linked to intestinal glucose absorption, largely influenced by the expression of sodium-glucose cotransporter 1 (SGLT1). This study uses Mendelian randomization (MR) to estimate the causal effect of intestinal SGLT1 expression on early glucose response. Involving 1,547 subjects with class II/III obesity from the Atlas Biologique de l'Obésité Sévère cohort, the study uses SGLT1 genotyping, oral glucose tolerance tests, and jejunal biopsies to measure SGLT1 expression. A loss-of-function SGLT1 haplotype serves as the instrumental variable, with intestinal SGLT1 expression as the exposure and the change in 30-min postload glycemia from fasting glycemia (Δ30 glucose) as the outcome. Results show that 12.8% of the 1,342 genotyped patients carried the SGLT1 loss-of-function haplotype, associated with a mean Δ30 glucose reduction of -0.41 mmol/L and a significant decrease in intestinal SGLT1 expression. The observational study links a 1-SD decrease in SGLT1 expression to a Δ30 glucose reduction of -0.097 mmol/L. MR analysis parallels these findings, associating a statistically significant reduction in genetically instrumented intestinal SGLT1 expression with a Δ30 glucose decrease of -0.353. In conclusion, the MR analysis provides genetic evidence that reducing intestinal SGLT1 expression causally lowers early postload glucose response. This finding has a potential translational impact on managing early glucose response to prevent or treat type 2 diabetes.

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.

Performance of non-invasive tests for liver fibrosis resolution after bariatric surgery.

BACKGROUND & AIMS: The value of non-invasive tests for monitoring the resolution of significant liver fibrosis after treatment is poorly investigated. We compared the performances of six non-invasive tests to predict the resolution of significant fibrosis after bariatric surgery. METHODS: Participants were individuals with obesity submitted to needle liver biopsy at the time of bariatric surgery, and 12 and/or 60 months after surgery. We calculated the fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS), AST to platelet ratio index (APRI), Hepatic fibrosis score (HFS), Fibrotic NASH index (FNI), and Liver risk score (LRS) at each time point, and compared their performances for predicting significant fibrosis (F ≥ 2) and its resolution following surgery. RESULTS: At baseline, 2436 patients had liver biopsy, including 261 (10.7 %) with significant fibrosis. Overall, 672 patients had pre- and post-operative biopsies (564 at M12 and 328 at M60). The fibrosis stage decreased at M12 and M60 (p < 0.001 vs M0). Resolution of significant fibrosis occurred in 58/121 (47.9 %) at M12 and 32/50 (64 %) at M60. The mean value of all tests decreased after surgery, except for FIB-4. Performances for predicting fibrosis resolution was higher at M60 than at M12 for all tests, and maximal at M60 for FNI and LRS: area under the curve 0.843 (95%CI 0.71-0.95) and 0.92 (95%CI 0.84-1.00); positive likelihood ratio 3.75 (95 % CI 1.33-10.59) and 4.58 (95 % CI 1.65-12.70), respectively. CONCLUSIONS: Results showed the value and limits of non-invasive tests for monitoring the evolution of liver fibrosis after an intervention. Following bariatric surgery, the best performances to predict the resolution of significant fibrosis were observed at M60 with tests combining liver and metabolic traits, namely FNI and LRS.

Combining diabetes, sex, and menopause as meaningful clinical features associated with NASH and liver fibrosis in individuals with class II and III obesity: A retrospective cohort study.

OBJECTIVE: Steatotic liver disease (SLD) is frequent in individuals with obesity. In this study, type 2 diabetes (T2D), sex, and menopausal status were combined to refine the stratification of obesity regarding the risk of advanced SLD and gain further insight into disease physiopathology. METHODS: This study enrolled 1446 participants with obesity from the ABOS cohort (NCT01129297), who underwent extensive phenotyping, including liver histology and transcriptome profiling. Hierarchical clustering was applied to classify participants. The prevalence of metabolic disorders associated with steatohepatitis (NASH) and liver fibrosis (F ≥ 2) was determined within each identified subgroup and aligned to clinical and biological characteristics. RESULTS: The prevalence of NASH and F ≥ 2 was, respectively, 9.5% (N = 138/1446) and 11.7% (N = 159/1365) in the overall population, 20.3% (N = 107/726) and 21.1% (N = 106/502) in T2D patients, and 3.4% (N = 31/920) and 6.1% (N = 53/863) in non-T2D patients. NASH and F ≥ 2 prevalence was 15.4% (33/215) and 15.5% (32/206) among premenopausal women with T2D vs. 29.5% (33/112) and 30.3% (N = 36/119) in postmenopausal women with T2D (p < 0.01); and 21.0% (21/100) / 27.0% (24/89) in men with T2D ≥ age 50 years and 17.9% (17/95) / 18.5% (17/92) in men with T2D < age 50 years (NS). The distinct contribution of menopause was confirmed by the interaction between sex and age with respect to NASH among T2D patients (p = 0.048). Finally, several NASH-associated biological traits (lower platelet count; higher serum uric acid; gamma-glutamyl transferase; aspartate aminotransferase) and liver expressed genes AKR1B10 and CCL20 were significantly associated with menopause in women with T2D but not with age in men with T2D. CONCLUSIONS: This study unveiled a remarkably high prevalence of advanced SLD after menopause in women with T2D, associated with a dysfunctional biological liver profile.

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.

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 ≥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 follow-up 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. FINDINGS: 10 231 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 (75·3%) were female, 2530 (24·7%) 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 2·8 kg/m2 (95% CI 2·6-3·0) and mean RMSE BMI was 4·7 kg/m2 (4·4-5·0), and the mean difference between predicted and observed BMI was -0·3 kg/m2 (SD 4·7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. INTERPRETATION: We developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions. FUNDING: SOPHIA Innovative Medicines Initiative 2 Joint Undertaking, supported by the EU's Horizon 2020 research and innovation programme, the European Federation of Pharmaceutical Industries and Associations, Type 1 Diabetes Exchange, and the Juvenile Diabetes Research Foundation and Obesity Action Coalition; Métropole Européenne de Lille; Agence Nationale de la Recherche; Institut national de recherche en sciences et technologies du numérique through the Artificial Intelligence chair Apprenf; Université de Lille Nord Europe's I-SITE EXPAND as part of the Bandits For Health project; Laboratoire d'excellence European Genomic Institute for Diabetes; Soutien aux Travaux Interdisciplinaires, Multi-établissements et Exploratoires programme by Conseil Régional Hauts-de-France (volet partenarial phase 2, project PERSO-SURG).

Oral metformin transiently lowers post-prandial glucose response by reducing the apical expression of sodium-glucose co-transporter 1 in enterocytes.

Metformin (MET) is the most prescribed antidiabetic drug, but its mechanisms of action remain elusive. Recent data point to the gut as MET's primary target. Here, we explored the effect of MET on the gut glucose transport machinery. Using human enterocytes (Caco-2/TC7 cells) in vitro, we showed that MET transiently reduced the apical density of sodium-glucose transporter 1 (SGLT1) and decreased the absorption of glucose, without changes in the mRNA levels of the transporter. Administered 1 h before a glucose challenge in rats (Wistar, GK), C57BL6 mice and mice pigs, oral MET reduced the post-prandial glucose response (PGR). This effect was abrogated in SGLT1-KO mice. MET also reduced the luminal clearance of 2-(18F)-fluoro-2-deoxy-D-glucose after oral administration in rats. In conclusion, oral metformin transiently lowers post-prandial glucose response by reducing the apical expression of SGLT1 in enterocytes, which may contribute to the clinical effects of the drug.

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.

Apolipoprotein F is reduced in humans with steatosis and controls plasma triglyceride-rich lipoprotein metabolism.

BACKGROUND: NAFLD affects nearly 25% of the global population. Cardiovascular disease (CVD) is the most common cause of death among patients with NAFLD, in line with highly prevalent dyslipidemia in this population. Increased plasma triglyceride (TG)-rich lipoprotein (TRL) concentrations, an important risk factor for CVD, are closely linked with hepatic TG content. Therefore, it is of great interest to identify regulatory mechanisms of hepatic TRL production and remnant uptake in the setting of hepatic steatosis. APPROACH AND RESULTS: To identify liver-regulated pathways linking intrahepatic and plasma TG metabolism, we performed transcriptomic analysis of liver biopsies from two independent cohorts of obese patients. Hepatic encoding apolipoprotein F ( APOF ) expression showed the fourth-strongest negatively correlation with hepatic steatosis and the strongest negative correlation with plasma TG levels. The effects of adenoviral-mediated human ApoF (hApoF) overexpression on plasma and hepatic TG were assessed in C57BL6/J mice. Surprisingly, hApoF overexpression increased both hepatic very low density lipoprotein (VLDL)-TG secretion and hepatic lipoprotein remnant clearance, associated a ~25% reduction in plasma TG levels. Conversely, reducing endogenous ApoF expression reduced VLDL secretion in vivo , and reduced hepatocyte VLDL uptake by ~15% in vitro . Transcriptomic analysis of APOF -overexpressing mouse livers revealed a gene signature related to enhanced ApoB-lipoprotein clearance, including increased expression of Ldlr and Lrp1 , among others. CONCLUSION: These data reveal a previously undescribed role for ApoF in the control of plasma and hepatic lipoprotein metabolism by favoring VLDL-TG secretion and hepatic lipoprotein remnant particle clearance.

Primary Graft Function and 5 Year Insulin Independence After Pancreas and Islet Transplantation for Type 1 Diabetes: A Retrospective Parallel Cohort Study.

In islet transplantation (ITx), primary graft function (PGF) or beta cell function measured early after last infusion is closely associated with long term clinical outcomes. We investigated the association between PGF and 5 year insulin independence rate in ITx and pancreas transplantation (PTx) recipients. This retrospective multicenter study included type 1 diabetes patients who underwent ITx in Lille and PTx in Nantes from 2000 to 2022. PGF was assessed using the validated Beta2-score and compared to normoglycemic control subjects. Subsequently, the 5 year insulin independence rates, as predicted by a validated PGF-based model, were compared to the actual rates observed in ITx and PTx patients. The study enrolled 39 ITx (23 ITA, 16 IAK), 209 PTx recipients (23 PTA, 14 PAK, 172 SPK), and 56 normoglycemic controls. Mean[SD] PGF was lower after ITx (ITA 22.3[5.2], IAK 24.8[6.4], than after PTx (PTA 38.9[15.3], PAK 36.8[9.0], SPK 38.7[10.5]), and lower than mean beta-cell function measured in normoglycemic control: 36.6[4.3]. The insulin independence rates observed at 5 years after PTA and PAK aligned with PGF predictions, and was higher after SPK. Our results indicate a similar relation between PGF and 5 year insulin independence in ITx and solitary PTx, shedding new light on long-term transplantation outcomes.

Functional genomics uncovers the transcription factor BNC2 as required for myofibroblastic activation in fibrosis.

Tissue injury triggers activation of mesenchymal lineage cells into wound-repairing myofibroblasts, whose unrestrained activity leads to fibrosis. Although this process is largely controlled at the transcriptional level, whether the main transcription factors involved have all been identified has remained elusive. Here, we report multi-omics analyses unraveling Basonuclin 2 (BNC2) as a myofibroblast identity transcription factor. Using liver fibrosis as a model for in-depth investigations, we first show that BNC2 expression is induced in both mouse and human fibrotic livers from different etiologies and decreases upon human liver fibrosis regression. Importantly, we found that BNC2 transcriptional induction is a specific feature of myofibroblastic activation in fibrotic tissues. Mechanistically, BNC2 expression and activities allow to integrate pro-fibrotic stimuli, including TGFß and Hippo/YAP1 signaling, towards induction of matrisome genes such as those encoding type I collagen. As a consequence, Bnc2 deficiency blunts collagen deposition in livers of mice fed a fibrogenic diet. Additionally, our work establishes BNC2 as potentially druggable since we identified the thalidomide derivative CC-885 as a BNC2 inhibitor. Altogether, we propose that BNC2 is a transcription factor involved in canonical pathways driving myofibroblastic activation in fibrosis.

Data-driven subgroups of type 2 diabetes, metabolic response, and renal risk profile after bariatric surgery: a retrospective cohort study.

BACKGROUND: A novel data-driven classification of type 2 diabetes has been proposed to personalise anti-diabetic treatment according to phenotype. One subgroup, severe insulin-resistant diabetes (SIRD), is characterised by mild hyperglycaemia but marked hyperinsulinaemia, and presents an increased risk of diabetic nephropathy. We hypothesised that patients with SIRD could particularly benefit from metabolic surgery. METHODS: We retrospectively related the newly defined clusters with the response to metabolic surgery in participants with type 2 diabetes from independent cohorts in France (the Atlas Biologique de l'Obésite Sévère [ABOS] cohort, n=368; participants underwent Roux-en-Y gastric bypass or sleeve gastrectomy between Jan 1, 2006, and Dec 12, 2017) and Brazil (the metabolic surgery cohort of the German Hospital of San Paulo, n=121; participants underwent Roux-en-Y gastric bypass between April 1, 2008, and March 20, 2016). The study outcomes were type 2 diabetes remission and improvement of estimated glomerular filtration rate (eGFR). FINDINGS: At baseline, 34 (9%) of 368 patients, 314 (85%) of 368 patients, and 17 (5%) of 368 patients were classified as having SIRD, mild obesity-related diabetes (MOD), and severe insulin deficient diabetes (SIDD) in the ABOS cohort, respectively, and in the São Paulo cohort, ten (8%) of 121 patients, 83 (69%) of 121 patients, and 25 (21%) of 121 patients were classified as having SIRD, MOD, and SIDD, respectively. At 1 year, type 2 diabetes remission was reported in 26 (81%) of 32 and nine (90%) of ten patients with SIRD, 167 (55%) of 306 and 42 (51%) of 83 patients with MOD, and two (13%) of 16 and nine (36%) of 25 patients with SIDD, in the ABOS and São Paulo cohorts, respectively. The mean eGFR was lower in patients with SIRD at baseline and increased postoperatively in these patients in both cohorts. In multivariable analysis, SIRD was associated with more frequent type 2 diabetes remission (odds ratio 4·3, 95% CI 1·8-11·2; p=0·0015), and an increase in eGFR (mean effect size 13·1 ml/min per 1·73 m2, 95% CI 3·6-22·7; p=0·0070). INTERPRETATION: Patients in the SIRD subgroup had better outcomes after metabolic surgery, both in terms of type 2 diabetes remission and renal function, with no additional surgical risk. Data-driven classification might help to refine the indications for metabolic surgery. FUNDING: Agence Nationale de la Recherche, Investissement d'Avenir, Innovative Medecines Initiative, Fondation Cœur et Artères, and Fondation Francophone pour la Recherche sur le Diabète.

Maternal Glycemic Dysregulation During Pregnancy and Neonatal Blood DNA Methylation: Meta-analyses of Epigenome-Wide Association Studies.

OBJECTIVE: Maternal glycemic dysregulation during pregnancy increases the risk of adverse health outcomes in her offspring, a risk thought to be linearly related to maternal hyperglycemia. It is hypothesized that changes in offspring DNA methylation (DNAm) underline these associations. RESEARCH DESIGN AND METHODS: To address this hypothesis, we conducted fixed-effects meta-analyses of epigenome-wide association study (EWAS) results from eight birth cohorts investigating relationships between cord blood DNAm and fetal exposure to maternal glucose (Nmaximum = 3,503), insulin (Nmaximum = 2,062), and area under the curve of glucose (AUCgluc) following oral glucose tolerance tests (Nmaximum = 1,505). We performed lookup analyses for identified cytosine-guanine dinucleotides (CpGs) in independent observational cohorts to examine associations between DNAm and cardiometabolic traits as well as tissue-specific gene expression. RESULTS: Greater maternal AUCgluc was associated with lower cord blood DNAm at neighboring CpGs cg26974062 (ß [SE] -0.013 [2.1 × 10-3], P value corrected for false discovery rate [PFDR] = 5.1 × 10-3) and cg02988288 (ß [SE]-0.013 [2.3 × 10-3], PFDR = 0.031) in TXNIP. These associations were attenuated in women with GDM. Lower blood DNAm at these two CpGs near TXNIP was associated with multiple metabolic traits later in life, including type 2 diabetes. TXNIP DNAm in liver biopsies was associated with hepatic expression of TXNIP. We observed little evidence of associations between either maternal glucose or insulin and cord blood DNAm. CONCLUSIONS: Maternal hyperglycemia, as reflected by AUCgluc, was associated with lower cord blood DNAm at TXNIP. Associations between DNAm at these CpGs and metabolic traits in subsequent lookup analyses suggest that these may be candidate loci to investigate in future causal and mediation analyses.

PSD3 downregulation confers protection against fatty liver disease.

Fatty liver disease (FLD) is a growing health issue with burdening unmet clinical needs. FLD has a genetic component but, despite the common variants already identified, there is still a missing heritability component. Using a candidate gene approach, we identify a locus (rs71519934) at the Pleckstrin and Sec7 domain-containing 3 (PSD3) gene resulting in a leucine to threonine substitution at position 186 of the protein (L186T) that reduces susceptibility to the entire spectrum of FLD in individuals at risk. PSD3 downregulation by short interfering RNA reduces intracellular lipid content in primary human hepatocytes cultured in two and three dimensions, and in human and rodent hepatoma cells. Consistent with this, Psd3 downregulation by antisense oligonucleotides in vivo protects against FLD in mice fed a non-alcoholic steatohepatitis-inducing diet. Thus, translating these results to humans, PSD3 downregulation might be a future therapeutic option for treating FLD.

Peroxisomal ß-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 ß-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.

BMI and pneumonia outcomes in critically ill covid-19 patients: An international multicenter study.

OBJECTIVE: Previous studies have unveiled a relationship between the severity of coronavirus disease 2019 (COVID-19) pneumonia and obesity. The aims of this multicenter retrospective cohort study were to disentangle the association of BMI and associated metabolic risk factors (diabetes, hypertension, hyperlipidemia, and current smoking status) in critically ill patients with COVID-19. METHODS: Patients admitted to intensive care units for COVID-19 in 21 centers (in Europe, Israel, and the United States) were enrolled in this study between February 19, 2020, and May 19, 2020. Primary and secondary outcomes were the need for invasive mechanical ventilation (IMV) and 28-day mortality, respectively. RESULTS: A total of 1,461 patients were enrolled; the median (interquartile range) age was 64 years (40.9-72.0); 73.2% of patients were male; the median BMI was 28.1 kg/m2 (25.4-32.3); a total of 1,080 patients (73.9%) required IMV; and the 28-day mortality estimate was 36.1% (95% CI: 33.0-39.5). An adjusted mixed logistic regression model showed a significant linear relationship between BMI and IMV: odds ratio = 1.27 (95% CI: 1.12-1.45) per 5 kg/m2 . An adjusted Cox proportional hazards regression model showed a significant association between BMI and mortality, which was increased only in obesity class III (≥40; hazard ratio = 1.68 [95% CI: 1.06-2.64]). CONCLUSIONS: In critically ill COVID-19 patients, a linear association between BMI and the need for IMV, independent of other metabolic risk factors, and a nonlinear association between BMI and mortality risk were observed.

IFNγ-producing NK cells in adipose tissue are associated with hyperglycemia and insulin resistance in obese women.

BACKGROUND/OBJECTIVES: Innate lymphoid cells (ILCs) play an important role in the maintenance of immune and metabolic homeostasis in adipose tissue (AT). The crosstalk between AT ILCs and adipocytes and other immune cells coordinates adipocyte differentiation, beiging, glucose metabolism and inflammation. Although the metabolic and homeostatic functions of mouse ILCs have been extensively investigated, little is known about human adipose ILCs and their roles in obesity and insulin resistance (IR). SUBJECTS/METHODS: Here we characterized T and NK cell populations in omental AT (OAT) from women (n = 18) with morbid obesity and varying levels of IR and performed an integrated analysis of metabolic parameters and adipose tissue transcriptomics. RESULTS: In OAT, we found a distinct population of CD56-NKp46+EOMES+ NK cells characterized by expression of cytotoxic molecules, pro-inflammatory cytokines, and markers of cell activation. AT IFNγ+ NK cells, but not CD4, CD8 or γδ T cells, were positively associated with glucose levels, glycated hemoglobin (HbA1c) and IR. AT NK cells were linked to a pro-inflammatory gene expression profile in AT and developed an effector phenotype in response to IL-12 and IL-15. Moreover, integrated transcriptomic analysis revealed a potential implication of AT IFNγ+ NK cells in controlling adipose tissue inflammation, remodeling, and lipid metabolism. CONCLUSIONS: Our results suggest that a distinct IFNγ-producing NK cell subset is involved in metabolic homeostasis in visceral AT in humans with obesity and may be a potential target for therapy of IR.