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- Falchi, M., et al.
(författare)
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Low copy number of the salivary amylase gene predisposes to obesity
- 2014
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 46:5, s. 492-497
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Tidskriftsartikel (refereegranskat)abstract
- Common multi-allelic copy number variants (CNVs) appear enriched for phenotypic associations compared to their biallelic counterparts. Here we investigated the influence of gene dosage effects on adiposity through a CNV association study of gene expression levels in adipose tissue. We identified significant association of a multi-allelic CNV encompassing the salivary amylase gene (AMY1) with body mass index (BMI) and obesity, and we replicated this finding in 6,200 subjects. Increased AMY1 copy number was positively associated with both amylase gene expression (P = 2.31 × 10-14) and serum enzyme levels (P < 2.20 × 10-16), whereas reduced AMY1 copy number was associated with increased BMI (change in BMI per estimated copy =-0.15 (0.02) kg/m 2; P = 6.93 × 10-10) and obesity risk (odds ratio (OR) per estimated copy = 1.19, 95% confidence interval (CI) = 1.13-1.26; P = 1.46 × 10-10). The OR value of 1.19 per copy of AMY1 translates into about an eightfold difference in risk of obesity between subjects in the top (copy number > 9) and bottom (copy number < 4) 10% of the copy number distribution. Our study provides a first genetic link between carbohydrate metabolism and BMI and demonstrates the power of integrated genomic approaches beyond genome-wide association studies. © 2014 Nature America, Inc. All rights reserved.
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- Raverdy, Violeta, et al.
(författare)
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Data-driven subgroups of type 2 diabetes, metabolic response, and renal risk profile after bariatric surgery : a retrospective cohort study
- 2022
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Ingår i: The Lancet Diabetes and Endocrinology. - 2213-8587. ; 10:3, s. 167-176
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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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
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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.10231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30602 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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