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.

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.

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).

Impact of Robotic Assistance on Complications in Bariatric Surgery at Expert Laparoscopic Surgery Centers: A Retrospective Comparative Study With Propensity Score.

OBJECTIVE: To investigate the way robotic assistance affected rate of complications in bariatric surgery at expert robotic and laparoscopic surgery facilities. BACKGROUND: While the benefits of robotic assistance were established at the beginning of surgical training, there is limited data on the robot's influence on experienced bariatric laparoscopic surgeons. METHODS: We conducted a retrospective study using the BRO clinical database (2008-2022) collecting data of patients operated on in expert centers. We compared the serious complication rate (defined as a Clavien score≥3) in patients undergoing metabolic bariatric surgery with or without robotic assistance. We used a directed acyclic graph to identify the variables adjustment set used in a multivariable linear regression, and a propensity score matching to calculate the average treatment effect (ATE) of robotic assistance. RESULTS: The study included 35,043 patients [24,428 sleeve gastrectomy (SG); 10,452 Roux-en-Y gastric bypass (RYGB); 163 single anastomosis duodenal-ileal bypass with sleeve gastrectomy (SADI-S)], with 938 operated on with robotic assistance (801 SG; 134 RYGB; 3 SADI-S), among 142 centers. Overall, we found no benefit of robotic assistance regarding the risk of complications (average treatment effect=-0.05, P =0.794), with no difference in the RYGB+SADI group ( P =0.322) but a negative trend in the SG group (more complications, P =0.060). Length of hospital stay was decreased in the robot group (3.7±11.1 vs 4.0±9.0 days, P <0.001). CONCLUSIONS: Robotic assistance reduced the length of stay but did not statistically significantly reduce postoperative complications (Clavien score≥3) following either GBP or SG. A tendency toward an elevated risk of complications following SG requires more supporting studies.

Morbid obesity increases death and dropout from the liver transplantation waiting list: A prospective cohort study.

Liver transplant (LT) candidates with a body mass index (BMI) over 40 kg/m2 have lower access to a liver graft without clear explanation. Thus, we studied the impact of obesity on the waiting list (WL) and aimed to explore graft proposals and refusal. METHOD: Data between January 2007 and December 2017 were extracted from the French prospective national database: CRISTAL. Competing risk analyses were performed to evaluate predictors of receiving LT. Competitive events were (1) death/WL removal for disease aggravation or (2) improvement. The link between grade obesity, grafts propositions, and reason for refusal was studied. RESULTS: 15,184 patients were analysed: 10,813 transplant, 2847 death/dropout for aggravation, 748 redirected for improvement, and 776 censored. Mortality/dropout were higher in BMI over 35 (18% vs. 14% 1 year after listing) than in other candidates. In multivariate analysis, BMI>35, age, hepatic encephalopathy, and ascites were independent predictors of death/dropout. Candidates with a BMI ≥ 35 kg/m2 had reduced access to LT, without differences in graft proposals. However, grafts refusal was more frequent especially for 'morphological incompatibility' (14.9% vs. 12.7% p < 0.01). CONCLUSION: BMI over 35 kg/m2 reduces access to LT with increased risk of dropout and mortality. Increased mortality and dropout could be due to a lower access to liver graft secondary to increased graft refusal for morphological incompatibility.

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.

A tool to predict progression of non-alcoholic fatty liver disease in severely obese patients.

BACKGROUND & AIMS: Severely obese patients are a growing population at risk of non-alcoholic fatty liver disease (NAFLD). Considering the increasing burden, a predictive tool of NAFLD progression would be of interest. Our objective was to provide a tool allowing general practitioners to identify and refer the patients most at risk, and specialists to estimate disease progression and adapt the therapeutic strategy. METHODS: This predictive tool is based on a Markov model simulating steatosis, fibrosis and non-alcoholic steatohepatitis (NASH) evolution. This model was developped from data of 1801 severely obese, bariatric surgery candidates, with histological assessment, integrating duration of exposure to risk factors. It is then able to predict current disease severity in the absence of assessment, and future cirrhosis risk based on current stage. RESULTS: The model quantifies the impact of sex, body-mass index at 20, diabetes, age of overweight onset, on progression. For example, for 40-year-old severely obese patients seen by the general practitioners: (a) non-diabetic woman overweight at 20, and (b) diabetic man overweight at 10, without disease assessment, the model predicts their current risk to have NASH or F3-F4: for (a) 5.7% and 0.6%, for (b) 16.1% and 10.0% respectively. If those patients have been diagnosed F2 by the specialist, the model predicts the 5-year cirrhosis risk: 1.8% in the absence of NASH and 6.0% in its presence for (a), 10.3% and 26.7% respectively, for (b). CONCLUSIONS: This model provides a decision-making tool to predict the risk of liver disease that could help manage severely obese patients.

A Model to Identify Heavy Drinkers at High Risk for Liver Disease Progression.

BACKGROUND & AIMS: Alcohol-related liver disease (ALD) causes chronic liver disease. We investigated how information on patients' drinking history and amount, stage of liver disease, and demographic feature can be used to determine risk of disease progression. METHODS: We collected data from 2334 heavy drinkers (50 g/day or more) with persistently abnormal results from liver tests who had been admitted to a hepato-gastroenterology unit in France from January 1982 through December 1997; patients with a recorded duration of alcohol abuse were assigned to the development cohort (n=1599; 75% men) or the validation cohort (n=735; 75% men), based on presence of a liver biopsy. We collected data from both cohorts on patient history and disease stage at the time of hospitalization. For the development cohort, severity of the disease was scored by the METAVIR (due to the availability of liver histology reports); in the validation cohort only the presence of liver complications was assessed. We developed a model of ALD progression and occurrence of liver complications (hepatocellular carcinoma and/or liver decompensation) in association with exposure to alcohol, age at the onset of heavy drinking, amount of alcohol intake, sex and body mass index. The model was fitted to the development cohort and then evaluated in the validation cohort. We then tested the ability of the model to predict disease progression for any patient profile (baseline evaluation). Patients with a 5-y weighted risk of liver complications greater than 5% were considered at high risk for disease progression. RESULTS: Model results are given for the following patient profiles: men and women, 40 y old, who started drinking at an age of 25 y, drank 150 g/day, and had a body mass index of 22 kg/m2 according to the disease severity at baseline evaluation. For men with baseline F0-F2 fibrosis, the model estimated the probabilities of normal liver, steatosis, or steatohepatitis at baseline to be 31.8%, 61.5% and 6.7%, respectively. The 5-y weighted risk of liver complications was 1.9%, ranging from 0.2% for men with normal liver at baseline evaluation to 10.3% for patients with steatohepatitis at baseline. For women with baseline F0-F2 fibrosis, probabilities of normal liver, steatosis, or steatohepatitis at baseline were 25.1%, 66.5% and 8.4%, respectively; the 5-y weighted risk of liver complications was 3.2%, ranging from 0.5% for women with normal liver at baseline to 14.7% for patients with steatohepatitis at baseline. Based on the model, men with F3-F4 fibrosis at baseline have a 24.5% 5-y weighted risk of complications (ranging from 20.2% to 34.5%) and women have a 30.1% 5-y weighted risk of complications (ranging from 24.7% to 41.0%). CONCLUSIONS: We developed a Markov model that integrates data on level and duration of alcohol use to identify patients at high risk of liver disease progression. This model might be used to adapt patient care pathways.