Beta Cell Function as a Baseline Predictor of Weight Loss After Bariatric Surgery

Are weight loss and metabolic outcomes of bariatric surgery influenced by candidate glucocorticoid receptor gene polymorphisms? A prospective study

BACKGROUND

Bariatric surgery is the most effective treatment for severe obesity. There can be variation in the degree of weight reduction following bariatric surgery. It is unknown whether single nucleotide polymorphisms (SNPs) in the glucocorticoid receptor locus (GRL) affect postoperative weight loss and metabolic outcomes.

MATERIALS/METHODS

We studied the association between selected candidate SNPs and postoperative weight loss and metabolic outcomes in patients with severe obesity undergoing bariatric surgery. The polymorphisms rs41423247 (Bcl1), rs56149945 (N363S) and rs6189/rs6190 (ER22/23EK) were analysed.

RESULTS

The 139 participants included 95 women (68.3%) and had a median (interquartile range) age of 53.0 (46.0-60.0) years and mean (SD) weight of 140.8 (28.8) kg and body mass index of 50.3 (8.6) kg/m2. At baseline, 59 patients had type 2 diabetes (T2D), 60 had hypertension and 35 had obstructive sleep apnoea syndrome treated with continuous positive airway pressure (CPAP). 84 patients (60.4%) underwent gastric bypass and 55 (39.6%) underwent sleeve gastrectomy. There were no significant differences in weight loss, glycated haemoglobin (HbA1c) or lipid profile categorized by genotype status, sex or median age. There was significant weight reduction after bariatric surgery with a postoperative BMI of 34.1 (6.8) kg/m2 at 24 months (p < 0.001).

CONCLUSION

While GRL polymorphisms with a known deleterious effect on adipose tissue mass and function may have a small, additive effect on the prevalence of obesity and related metabolic disorders in the population, we suggest that the relatively weak biological influence of these SNPs is readily overcome by bariatric surgery.

Baseline phenotypes with preserved β-cell function and high insulin concentrations have the best improvements in glucose tolerance after weight loss: results from the prospective DEXLIFE and EGIR-RISC studies

BACKGROUND

Weight loss and lifestyle intervention improve glucose tolerance delaying the onset of type 2 diabetes (T2D), but individual responses are highly variable. Determining the predictive factors linked to the beneficial effects of weight loss on glucose tolerance could provide tools for individualized prevention plans. Thus, the aim was to investigate the relationship between pre-intervention values of insulin sensitivity and secretion and the improvement in glucose metabolism after weight loss.

METHODS

In the DEXLIFE cohort (373 individuals at high risk of T2D, assigned 3:1 to a 12-week lifestyle intervention or a control arm, Trial Registration: ISRCTN66987085), K-means clustering and logistic regression analysis were performed based on pre-intervention indices of insulin sensitivity, insulin secretion (AUC-I), and glucose-stimulated insulin response (ratio of incremental areas of insulin and glucose, iAUC I/G). The response to the intervention was evaluated in terms of reduction of OGTT-glucose concentration. Clusters' validation was done in the prospective EGIR-RISC cohort (n = 1538).

RESULTS

Four replicable clusters with different glycemic and metabolomic profiles were identified. Individuals had similar weight loss, but improvement in glycemic profile and β-cell function was different among clusters, highly depending on pre-intervention insulin response to OGTT. Pre-intervention high insulin response was associated with the best improvement in AUC-G, while clusters with low AUC-I and iAUC I/G showed no beneficial effect of weight loss on glucose control, as also confirmed by the logistic regression model.

CONCLUSIONS

Individuals with preserved β-cell function and high insulin concentrations at baseline have the best improvement in glucose tolerance after weight loss.

The Impact of Early-Stage Chronic Kidney Disease on Weight Loss Outcomes After Gastric Bypass

PURPOSE

Weight loss achieved through bariatric metabolic surgery was demonstrated to be effective at reversing chronic kidney dysfunction associated with obesity-related glomerulopathy. However, robust data on how pre-operative kidney status impacts on bariatric metabolic surgery weight loss outcomes is still lacking. The aim of this study was to evaluate the impact of kidney dysfunction on weight loss outcomes after bariatric metabolic surgery.

METHODS

Patients with obesity to be submitted to gastric bypass surgery underwent a pre-operative evaluation of creatinine clearance, estimated glomerular filtration rate (eGFR), proteinuria, and albuminuria in 24-hour urine. Body mass index (BMI), % total weight loss (%TWL), and % excess BMI loss (%EBMIL) were assessed at 6 and 12 months after surgery.

RESULTS

Before surgery, patients (N=127) had a mean BMI of 39.6 ± 3.0 kg/m2, and 56.7% (n=72) had a creatinine clearance > 130 mL/min, 23.6% (n= 30) presented proteinuria > 150 mg/24h, and 15.0% (n= 19) presented albuminuria > 30 mg/24h. After surgery, the mean BMI was 27.7 kg/m2 and 25.0 kg/m2 at 6 and 12 months, respectively (p<0.0001). The %TWL was lower in patients with pre-operative eGFR < percentile 25 (34.4 ± 5.8% vs 39.4 ± 4.9%, p=0.0007, at 12 months). There were no significant correlations between weight loss metrics and pre-operative creatinine clearance rate, proteinuria, or albuminuria.

CONCLUSION

Early-stage chronic kidney disease (G2) has a negative impact on short-term weight loss outcomes after bariatric metabolic surgery, albeit in a magnitude inferior to the clinically relevant threshold.

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).

Development of a New Index Based on Preoperative Serum Lipocalin 2 to Predict Post-LSG Weight Reduction

Background

Bariatric surgery is the most effective therapy for obesity, but targeted weight reduction is not always achieved. Serum lipocalin-2 (LCN2) is closely associated with obesity, but its impact on weight loss after surgery is unknown. We aimed to access the reliability of LCN2 levels and other parameters as effective predictors of excellent weight loss (≥ 75% excess weight loss (EWL)) 1 year after bariatric surgery.

Methods

This retrospective study evaluated 450 patients (aged 18–65 years) with obesity at 3 months and 1 year after laparoscopic sleeve gastrectomy (LSG) surgery. Seventy-four patients who underwent LSG surgery and met the inclusion and exclusion criteria were included in this study. Serum LCN2, thyroid function, and metabolic and anthropometric parameters were assessed. Weight reduction was expressed as %EWL and percent total weight loss (%TWL) at 3 months and 1 year post surgery. Multivariable logistic regression analysis and receiver operating characteristic (ROC) curve analysis were used to evaluate predictors of ≥ 75%EWL.

Results

In our cohort, %EWL and %TWL were both strongly associated with preoperative serum LCN2 levels. The binary logistic regression analysis showed that preoperative LCN2, waist circumference, and glycated hemoglobin were independent predictors of excellent weight loss.

Conclusions

Based on these results, we determined a new P index with better predictive value for excellent weight reduction (≥ 75%EWL) 1 year after LSG surgery.

Graphical abstract

Serum Fibrinogen-Like Protein 1 Levels in Obese Patients Before and After Laparoscopic Sleeve Gastrectomy: A Six-Month Longitudinal Study

PURPOSE

Fibrinogen-like protein (FGL)-1 is an original hepatokine with a critical role in developing hepatic steatosis. This study intends to examine the pre- and postoperative serum FGL-1 levels in bariatric patients and identify its relationship with other clinical indicators.

PATIENTS AND METHODS

Ninety-two individuals (60 bariatric patients and 32 people with normal weight) were enrolled in this research between July 2018 and April 2021. All bariatric patients finished follow-up visits 6 months after laparoscopic sleeve gastrectomy (LSG). Clinical data, anthropometric parameters, biochemical variables, FibroScan, and serum FGL-1 levels were collected at baseline and 6 months after LSG.

RESULTS

FGL-1 levels in patients with obesity (44.66±20.03 ng/mL) were higher than in individuals with normal weight (20.73±9.73 ng/mL, p < 0.001). After LSG, FGL-1 levels were significantly decreased (27.53±11.45 ng/mL, p < 0.001). Besides, body mass index (BMI), liver enzyme levels, glucose metabolism, lipid metabolism, uric acid (UA), controlled attenuation parameter (CAP), and liver stiffness measurement (LSM) were significantly improved. After adjusting possible confounders, FGL-1 levels at baseline were negatively associated with changes in LSM levels; changes in FGL-1 levels showed positive correlations with changes in alanine aminotransferase (ALT), aspartate aminotransferase (AST) and UA levels at 6 months after surgery.

CONCLUSION

Serum FGL-1 levels were significantly decreased following LSG in patients with obesity. The preoperative serum FGL-1 levels could be a predictor of postoperative liver fibrosis improvement. Furthermore, the decreased FGL-1 levels were associated with improved liver enzymes and UA but not with bodyweight or glucolipid metabolism.