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- Angelini, Giulia, et al.
(författare)
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Small intestinal metabolism is central to whole-body insulin resistance
- 2021
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Ingår i: Gut. - : BMJ. - 0017-5749 .- 1468-3288. ; 70:6, s. 1098-1109
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Tidskriftsartikel (refereegranskat)abstract
- Objective To assess the role of jejunum in insulin resistance in humans and in experimental animals. Design Twenty-four subjects undergoing biliopancreatic diversion (BPD) or Roux-en-Y gastric bypass (RYGB) were enrolled. Insulin sensitivity was measured at baseline and at 1 week after surgery using oral glucose minimal model. We excluded the jejunum from intestinal continuity in pigs and created a jejunal loop with its vascular and nerve supply intact accessible from two cutaneous stomas, and reconnected the bowel with an end-to-end anastomosis. Glucose stable isotopes were given in the stomach or in the jejunal loop. In vitro studies using primary porcine and human hepatocytes or myoblasts tested the effects of plasma on gluconeogenesis or glucose uptake and insulin signalling. Results Whole-body insulin sensitivity (SI∙104: 0.54±0.12 before vs 0.82±0.11 after BPD, p=0.024 and 0.41±0.09 before vs 0.65±0.09/pM/min after RYGB, p=not significant) and Glucose Disposition Index increased only after BPD. In pigs, insulin sensitivity was significantly lower when glucose was administered in the jejunal loop than in the stomach (glucose rate of disappearance (Rd) area under the curve (AUC)/insulin AUC∙10: 1.82±0.31 vs 2.96±0.33 mmol/pM/min, p=0.0017). Metabolomics showed a similar pattern before surgery and during jejunal-loop stimulation, pointing to a higher expression of gluconeogenetic substrates, a metabolic signature of impaired insulin sensitivity. A greater hepatocyte phosphoenolpyruvate-carboxykinase and glucose-6-phosphatase gene expression was elicited with plasma from porcine jejunal loop or before surgery compared with plasma from jejunectomy in pigs or jejunal bypass in humans. Stimulation of myoblasts with plasma from porcine jejunal loop or before surgery reduced glucose uptake, Ser473-Akt phosphorylation and GLUT4 expression compared with plasma obtained during gastric glucose administration after jejunectomy in pigs or after jejunal bypass in humans. Conclusion Proximal gut plays a crucial role in controlling insulin sensitivity through a distinctive metabolic signature involving hepatic gluconeogenesis and muscle insulin resistance. Bypassing the jejunum is beneficial in terms of insulin-mediated glucose disposal in obesity.
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- de Rooij, Susanne R., et al.
(författare)
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Low-Grade Chronic Inflammation in the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) Population Associations with insulin resistance and cardiometabolic risk profile
- 2009
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Ingår i: Diabetes Care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 32:7, s. 1295-1301
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE - Low-grade chronic inflammation has been hypothesized to underlie the constellation of cardiometabolic risk factors, possibly by inducing insulin resistance. In the present study, we investigated associations between inflammation markers, insulin sensitivity (expressed as the ratio of the M value to the mean plasma insulin concentrations measured during the Final 40 min of the clamp [M/I]), and a range of cardiometabolic risk factors in a large, healthy population. RESEARCH DESIGN AND METHODS - The Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) cohort includes 1,326 nondiabetic European men and women, aged between 30 and 60 years. We measured cardiometabolic risk factors and performed a hyperinsulinemic-euglycemic clamp. We determined total white blood cell count (WBC) and erythrocyte sedimentation rate (ESR) as markers of chronic inflammation. RESULTS - WBC and ESR were both Strongly associated with M/I. WBC and ESR were further associated with a range of cardiometabolic risk factors. Associations between WBC and HDL cholesterol, triglycerides, heart rate, fasting C-peptide, and insulin and 2-h insulin in men and women and between WBC and 2-h glucose in women remained significant after adjustment for both M/I and waist circumference. Associations between ESR and HDL cholesterol, heart rate, fasting, and 2-h insulin in men and women and between ESR and fat mass in women remained significant after adjustment for M/I and waist circumference. CONCLUSIONS - This study showed that low-grade chronic inflammation is associated with the cardiometabolic risk profile of a healthy population. Insulin resistance, although Strongly associated with inflammation, does not seem to play a large intermediary role.
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- Manco, Melania, et al.
(författare)
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One-Hour Plasma Glucose Identifies Insulin Resistance and beta-Cell Dysfunction in Individuals With Normal Glucose Tolerance Cross-sectional data from the Relationship between Insulin Sensitivity and Cardiovascular Risk (RISC) study
- 2010
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Ingår i: Diabetes Care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 33:9, s. 2090-2097
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE- Some individuals with normal glucose tolerance (NGT) exhibit a 1-h excursion of plasma glucose during oral glucose tolerance testing as high as that of individuals with impaired glucose tolerance (IGT). The aim of this study was to characterize their metabolic phenotype. RESEARCH DESIGN AND METHODS- A total of 1,205 healthy volunteers (aged 29-61 years) underwent assessment of 1) oral glucose tolerance and 2) insulin sensitivity (standardized euglycemic-hyperinsulinemic clamp), as part of the Relationship between Insulin Sensitivity and Cardiovascular Risk (RISC) study. RESULTS- One-hour plasma glucose correlated better than 2-h plasma glucose with total insulin secretion (r = 0.43), beta-cell glucose sensitivity (r = -0.46), and beta-cell rate sensitivity (r = -0.18). Receiver operating characteristic analysis identified 8.95 mmol/l as the best cutoff value for prediction of IGT from 1-h plasma glucose (sensitivity 77% and specificity 80%). Participants with NGT with 1-h plasma glucose >8.95 mmol/l had larger waist circumference, higher BMI, lower insulin sensitivity, higher fasting glucose, and higher insulin secretion than their counterparts with 1-h plasma glucose <= 8.95 mmol/l (P < 0.001 for all comparisons). Moreover, they exhibited lower beta-cell glucose sensitivity (P < 0.001), beta-cell rate sensitivity (P < 0.001), and potentiation factor (P = 0.026). When compared with conventionally defined IGT, they were not different in waist circumference and BMI, hepatic insulin extraction, beta-cell glucose sensitivity, beta-cell rate sensitivity, and potentiation factor but did have greater insulin sensitivity along with reduced basal (P = 0.001) and total insulin secretion (P = 0.002). CONCLUSIONS- Higher values of 1-h plasma glucose may identify an intermediate condition between NGT and IGT characterized by greater insulin resistance, reduced beta-cell glucose sensitivity, and reduced beta-cell rate sensitivity.
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- Panunzi, Simona, et al.
(författare)
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Determinants of Diabetes Remission and Glycemic Control After Bariatric Surgery.
- 2016
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Ingår i: Diabetes care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 39:1, s. 166-174
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Tidskriftsartikel (refereegranskat)abstract
- Eligibility criteria for bariatric surgery in diabetes include BMI ≥35 kg/m(2) and poorly controlled glycemia. However, BMI does not predict diabetes remission, and thus, predictors need to be identified.
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- Saux, Patrick, et al.
(författare)
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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.
- 2023
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Ingår i: The Lancet. Digital health. - 2589-7500. ; 5:10, s. e692-e702
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Tidskriftsartikel (refereegranskat)abstract
- 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.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.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.We developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.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).
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