Is It Possible to Predict Weight Loss After Bariatric Surgery?-External Validation of Predictive Models

Development of predictive model for predicting postoperative BMI and optimize bariatric surgery: a single center pilot study

BACKGROUND

The pilot study addresses the challenge of predicting postoperative outcomes, particularly body mass index (BMI) trajectories, following bariatric surgery. The complexity of this task makes preoperative personalized obesity treatment challenging.

OBJECTIVES

To develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction.

SETTING

The study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453.

METHODS

We conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m2.

RESULTS

This study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making.

CONCLUSION

This study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies.

Performance of a predictive weight loss model in terms of rapid detection of inadequate weight loss after Roux-en-Y gastric bypass

BACKGROUND

Predictive weight loss models can help patients meet their expectations after bariatric surgery and assist physicians in responding to deviations from the predicted weight. A model published by Seyssel et al. appears to accurately predict postoperative body mass index.

OBJECTIVES

We aimed to demonstrate this model's performance in terms of rapid detection of insufficient weight loss (surgical nonresponse).

SETTING

Cantonal Hospital, Switzerland.

METHODS

We retrospectively analyzed weight and body mass index deviations at 2 years postoperatively, based on values predicted by the model of Seyssel et al. The primary outcome was the timing of detection of surgical nonresponse. The secondary outcome was how patients' weight loss expectations influenced their real weight loss.

RESULTS

Between 2016 and 2019, 190 patients underwent Roux-en-Y gastric bypass. Of these patients, 36 were lost to follow-up and 154 were included in this study. At 24 months, 16 patients had surgical nonresponse, defined as a real weight of +1 standard deviation higher than that predicted. Among these patients, 44% had a weight of ≥+1 standard deviation higher than predicted at 3 months, and 63% at 12 months. The positive and negative predictive values at 12 months were 59% and 95%, respectively. Patients with a lower hypothetically wanted weight (38.5%) exhibited greater weight loss (P < .05).

CONCLUSIONS

The predictive weight loss model of Seyssel et al. enables rapid detection of surgical nonresponse, allowing physicians to react as early as 3 months postsurgery. Patients' overestimation of postoperative weight loss was positively correlated with the actual weight loss achieved.

Prediction Model for Chronological Weight Loss After Bariatric Surgery in Korean Patients

Purpose

This study aimed to develop a predictive model for monitoring chronological weight loss during the early postoperative period following bariatric surgery in Korean patients with morbid obesity.

Materials and Methods

The baseline characteristics and postoperative weight loss outcomes were collected for up to 24 months after surgery in patients who underwent sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB). The factors influencing weight loss outcomes were analyzed, and longitudinal percentile charts were plotted using quantile regression models adjusted for the identified independent factors.

Results

The analysis included 491 and 274 patients who underwent SG and RYGB, respectively, of whom 225 (29.4%) were men. A positive association was found between the maximum percentage of total weight loss (%TWL) and female sex, body mass index (BMI) ≥40, and age <40 years. Among patients who reached nadir BMI or had at least 12 months of follow-up data (n=304), 7.6% exhibited inadequate weight loss (TWL <20%). The predictors of insufficient weight loss were older age (>40 years), male sex, and psychological problems. Centile charts were generated for the entire cohort, incorporating age, sex, and the type of procedure as covariates.

Conclusion

The percentile charts proposed in the present study can assist surgeons and healthcare providers in gauging patients' progress toward their weight loss goals and determining the timing of adjunctive intervention in poor responders during early postoperative follow-up.

Developing a Decision Aid for Clinical Obesity Services in the Real World: the DACOS Nationwide Pilot Study

PURPOSE

The purpose of this study is to develop a decision aid tool using "real-world" data within the Australian health system to predict weight loss after bariatric surgery and non-surgical care.

MATERIALS AND METHODS

We analyzed patient record data (aged 16+years) from initial review between 2015 and 2020 with 6-month (n=219) and 9-/12-month (n=153) follow-ups at eight clinical obesity services. Primary outcome was percentage total weight loss (%TWL) at 6 months and 9/12 months. Predictors were selected by statistical evidence (p<0.20), effect size (±2%), and clinical judgment. Multiple linear regression and bariatric surgery were used to create simple predictive models. Accuracy was measured using percentage of predictions within 5% of the observed value, and sensitivity and specificity for predicting target weight loss of 5% (non-surgical care) and 15% (bariatric surgery).

RESULTS

Observed %TWL with bariatric surgery vs. non-surgical care was 19% vs. 5% at 6 months and 22% vs. 5% at 9/12 months. Predictors at 6 months with intercept (non-surgical care) of 6% include bariatric surgery (+11%), BMI>60 (-3%), depression (-2%), anxiety (-2%), and eating disorder (-2%). Accuracy, sensitivity, and specificity were 58%, 69%, and 56%. Predictors at 9/12 months with intercept of 5% include bariatric surgery (+15%), type 2 diabetes (+5%), eating disorder (+4%), fatty liver (+2%), atrial fibrillation (-4%), osteoarthritis (-3%), sleep/mental disorders (-2-3%), and ≥10 alcohol drinks/week (-2%). Accuracy, sensitivity, and specificity were 55%, 86%, and 53%.

CONCLUSION

Clinicians may use DACOS to discuss potential weight loss predictors with patients after surgery or non-surgical care.

Effect of preoperative intragastric balloon treatment on perioperative and postoperative outcomes after laparoscopic sleeve gastrectomy: A retrospective cohort study

<b><br>Introduction:</b> Intragastric balloon (IGB) insertion is used as a bridging therapy in patients with body mass index (BMI) ≥ 50 kg/m2 . We arranged a retrospective study to evaluate whether pre-operative IGB treatment influences perioperative and postoperative weight loss outcomes after laparoscopic sleeve gastrectomy (SG), and especially to evaluate the impact of post - IGB percentage of excessive weight loss (%EWL) on postoperative %EWL.</br> <b><br>Materials and methods:</b> Patients who underwent IGB placement followed by laparoscopic SG were divided into the following groups considering %EWL after IGB: Group 1 <=10.38%; Group 2 >10.38% and <=17.27%; Group 3 >17.27% and <=24.86%; Group 4 >24.86%. 1 year after SG data were collected. The following parameters were compared between groups: operative time, total blood loss, length of stay and weight, BMI, percentage of total weight loss (%TWL), %EWL.</br> <b><br>Results:</b> There were no statistically significant differences between groups in perioperative results. Post-SG %EWL was the highest in intermediate groups: 2 and 3. Post-treatment results were observed: body weight and BMI were the lowest in Group 4 and the highest in Group 1. Post-treatment %EWL was the highest in Group 4, the lowest in Group 1 and grew gradually in subsequent groups.</br> <b><br>Discussion:</b> The study confirmed the impact of weight loss on IGB on postoperative results. The study showed that %EWL after the IGB treatment influences %EWL after SG and most of all affects definitive %EWL after two-stage treatment and it could be a foreshadowing factor of these outcomes.</br> <b><br>Importance:</b> The importance of research for the development of the field %EWL after IGB influences the final BMI and final weight, which means that patients with the greatest %EWL after IGB are more likely to have the greatest postoperative weight loss and overall weight loss.</br>.

Machine learning models to predict success of endoscopic sleeve gastroplasty using total and excess weight loss percent achievement: a multicentre study

BACKGROUND

The large amount of heterogeneous data collected in surgical/endoscopic practice calls for data-driven approaches as machine learning (ML) models. The aim of this study was to develop ML models to predict endoscopic sleeve gastroplasty (ESG) efficacy at 12 months defined by total weight loss (TWL) % and excess weight loss (EWL) % achievement. Multicentre data were used to enhance generalizability: evaluate consistency among different center of ESG practice and assess reproducibility of the models and possible clinical application. Models were designed to be dynamic and integrate follow-up clinical data into more accurate predictions, possibly assisting management and decision-making.

METHODS

ML models were developed using data of 404 ESG procedures performed at 12 centers across Europe. Collected data included clinical and demographic variables at the time of ESG and at follow-up. Multicentre/external and single center/internal and temporal validation were performed. Training and evaluation of the models were performed on Python's scikit-learn library. Performance of models was quantified as receiver operator curve (ROC-AUC), sensitivity, specificity, and calibration plots.

RESULTS

Multicenter external validation: ML models using preoperative data show poor performance. Best performances were reached by linear regression (LR) and support vector machine models for TWL% and EWL%, respectively, (ROC-AUC: TWL% 0.87, EWL% 0.86) with the addition of 6-month follow-up data. Single-center internal validation: Preoperative data only ML models show suboptimal performance. Early, i.e., 3-month follow-up data addition lead to ROC-AUC of 0.79 (random forest classifiers model) and 0.81 (LR models) for TWL% and EWL% achievement prediction, respectively. Single-center temporal validation shows similar results.

CONCLUSIONS

Although preoperative data only may not be sufficient for accurate postoperative predictions, the ability of ML models to adapt and evolve with the patients changes could assist in providing an effective and personalized postoperative care. ML models predictive capacity improvement with follow-up data is encouraging and may become a valuable support in patient management and decision-making.

Comparative Analysis of Various Weight Loss Success Criteria Models After Bariatric Metabolic Surgery in Korean Morbid Obese Patients

Purpose

To identify weight loss prediction models by validating previous models using weight loss success criteria.

Materials and Methods

Patients with morbid obesity from 4 hospitals were retrospectively analyzed between Jan 2019 and 2022. Preoperative demographics, postoperative data, and 1-year follow-up weight loss outcomes were compared between 2 groups who underwent laparoscopic sleeve gastrectomy (LSG) and laparoscopic Roux-en-Y gastric bypass (LRYGB). Additionally, the predictive factors for the success of excess weight loss (EWL) (>50%) and total weight loss (TWL) (>25%) were analyzed.

Results

Of the 162 patients, 137 were enrolled during the study period, 75 underwent LSG, and 62 underwent LRYGB. The >50% EWL and >25% TWL 1 year after surgery were 61.3% and 43.1%, respectively. Diabetes mellitus medication use was reduced in 94.8% of patients with type 2 diabetes mellitus. Male sex and body mass index (BMI) were independent risk factors for successful weight loss (SWL) or >50% EWL (odds ratio [OR] for BMI 0.830, 95% confidence interval [CI] 0.764-0.902), whereas achieving >25% TWL was not affected by sex or BMI (OR for BMI 1.010, 95% CI 0.957-1.065). External validation of the prediction models showed an acceptable range of accuracy (adjusted R2 66.5-71.3%).

Conclusion

LSG and LRYGB are feasible and effective bariatric procedures for SWL in Korean patients with morbid obesity. The TWL model was a more appropriate criterion than EWL, and weight loss prediction models may help assess the 1-year outcomes of bariatric surgery.

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).

Novel Insights into the Physiology of Nutrient Sensing and Gut-Brain Communication in Surgical and Experimental Obesity Therapy

Despite standardized surgical technique and peri-operative care, metabolic outcomes of bariatric surgery are not uniform. Adaptive changes in brain function may play a crucial role in achieving optimal postbariatric weight loss. This review follows the anatomic-physiologic structure of the postbariatric nutrient-gut-brain communication chain through its key stations and provides a concise summary of recent findings in bariatric physiology, with a special focus on the composition of the intestinal milieu, intestinal nutrient sensing, vagal nerve-mediated gastrointestinal satiation signals, circulating hormones and nutrients, as well as descending neural signals from the forebrain. The results of interventional studies using brain or vagal nerve stimulation to induce weight loss are also summarized. Ultimately, suggestions are made for future diagnostic and therapeutic research for the treatment of obesity.

How Good is Stratification and Prediction Model Analysis Between Primary and Revisional Roux-en-Y Gastric Bypass Surgery? A Multi-center Study and Narrative Review

INTRODUCTIONS

Revision surgery because of weight recurrence is performed in 2.5-33% of primary vertical banded gastroplasty (VBG), laparoscopic sleeve gastrectomy (LSG), and gastric band (GB) cases. These cases qualify for revisional Roux-en-Y gastric bypass (RRYGB).

METHODS

This retrospective cohort study analyzed data from 2008 to 2019. A stratification analysis and multivariate logistic regression for prediction modeling compared the possibility of sufficient % excess weight loss (%EWL) ≥ 50 or insufficient %EWL < 50 between three different RRYGB procedures, with primary Roux-en-Y gastric bypass (PRYGB) as the control during 2 years of follow-up. A narrative review was conducted to test the presence of prediction models in the literature and their internal and external validity.

RESULTS

A total of 558 patients underwent PRYGB, and 338 underwent RRYGB after VBG, LSG, and GB, and completed 2 years of follow-up. Overall, 32.2% of patients after RRYGB had a sufficient %EWL ≥ 50 after 2 years, compared to 71.3% after PRYGB (p ≤ 0.001). The total %EWL after the revision surgeries for VBG, LSG, and GB was 68.5%, 74.2%, and 64.1%, respectively (p ≤ 0.001). After correcting for confounding factors, the baseline odds ratio (OR) or sufficient %EWL ≥ 50 after PRYGB, LSG, VBG, and GB was 2.4, 1.45, 0.29, and 0.32, respectively (p ≤ 0.001). Age was the only significant variable in the prediction model (p = 0.0016). It was impossible to develop a validated model after revision surgery because of the differences between stratification and the prediction model. The narrative review showed only 10.2% presence of validation in the prediction models, and 52.5% had external validation.

CONCLUSION

Overall, 32.2% of all patients after revisional surgery had a sufficient %EWL ≥ 50 after 2 years, compared to PRYGB. LSG had the best outcome in the revisional surgery group in the sufficient %EWL group and the best outcome in the insufficient %EWL group. The skewness between the prediction model and stratification resulted in a partially non-functional prediction model.

A review on the beneficial effects of bariatric surgery in the management of obesity

INTRODUCTION

Obesity is a chronic disease with a complex interplay of multiple factors such as genetic, metabolic, behavioral, and environmental factors. The management of obesity includes; lifestyle modification, psychological therapy, pharmacological therapy, and bariatric surgery. To date, bariatric surgery is the most effective treatment for obesity by offering a long-term reduction in weight, remission of obesity-related complications, and improving quality of life. However, bariatric surgery is not equally effective in all patients. Thus, if we can predict who would benefit most, it will improve the risk versus benefit ratio of having surgery.

AREAS COVERED

In this narrative review, we explore the question on who will benefit the most from bariatric surgery by examining the recent evidence in the literature. In addition, we investigate the predisposing predictors of bariatric surgery response. Finally, we offer the best strategies in the clinic to explain the potential benefits of bariatric surgery to patients.

EXPERT OPINION

Bariatric surgery is an effective obesity management approach. Despite its efficacy, considerable variation of individual response exists. Thus, it is important to recognize patients that will benefit most, but at present very few predictors are available which can be clinically useful.

One Anastomosis Gastric Bypass for Revision of Restrictive Procedures: Mid-Term Outcomes and Analysis of Possible Outcome Predictors

PURPOSE

Revisional one anastomosis gastric bypass (OAGB) for insufficient weight reduction following primary restrictive procedures is still investigated. We report mid-term outcomes and possible outcome predictors.

MATERIALS AND METHODS

Single-center retrospective comparative study of revisional OAGB outcomes (2015-2018) following laparoscopic adjustable gastric banding (LAGB) and sleeve gastrectomy (SG); silastic ring vertical gastroplasty (SRVG) is separately discussed.

RESULTS

In all, 203 patients underwent revisional OAGB following LAGB (n = 125), SG (n = 64), and SRVG (n = 14). Comparing LAGB and SG, body mass index (BMI) at revision were 41.3 ± 6.6 and 42 ± 11.2 kg/m² (p = 0.64), reduced to 31.3 ± 8.3 and 31.9 ± 8.3 (p = 0.64) at mid-term follow-up, respectively. Excess weight loss (EWL) > 50% was achieved in ~ 50%, with EWL of 79.4 ± 20.4% (corresponding total weight loss 38.5 ± 10.4%). SRVG patients had comparable outcomes. Resolution rates of type 2 diabetes (T2D) and hypertension (HTN) were 93.3% and 84.6% in LAGB compared with 100% and 100% in SG patients (p = 0.47 and p = 0.46), respectively. In univariable analysis, EWL > 50% was associated with male gender (p < 0.001), higher weight (p < 0.001), and BMI (p = 0.007) at primary surgery, and higher BMI at revisional OAGB (p < 0.001). In multivariable analysis, independent predictors for EWL > 50% were male gender (OR = 2.8, 95% CI 1.27-6.18; p = 0.01) and higher BMI at revisional OAGB (OR = 1.11, 95% CI 1.03-1.19; p = 0.006).

CONCLUSION

Revisional OAGB for insufficient restrictive procedures results in excellent weight reduction in nearly 50% of patients, with resolution of T2D and HTN at mid-term follow-up. Male gender and higher BMI at revision were associated with EWL > 50% following revisional OAGB. Identification of more predictors could aid judicious patient selection.

Preoperative Prediction of Body Mass Index of Patients with Type 2 Diabetes at 1 Year After Laparoscopic Sleeve Gastrectomy: Cross-Sectional Study

Background: Very few models predict weight loss among type 2 diabetes mellitus (T2D) patients after laparoscopic sleeve gastrectomy (LSG). This retrospective study undertook such a task. Materials and Methods: We identified all patients >18 years old with T2D who underwent primary LSG at our institution and had complete data. The training set comprised 107 patients operated upon during the period April 2011 to June 2014; the validation set comprised 134 patients operated upon during the successive chronological period, July 2014 to December 2015. Sex, age, presurgery BMI, T2D duration, number of T2D medications, insulin use, hypertension, and dyslipidemia were utilized as independent predictors of 1-year BMI. We employed regression analysis, and assessed the goodness of fit and "Residuals versus Fits" plot. Paired sample t-tests compared the observed and predicted BMI at 1 year. Results: The model comprised preoperative BMI (β = 0.757, P = 0.026) + age (β = 0.142, P < 0.0001) with adjusted R² of 0.581 (P < 0.0001), and goodness of fit showed an unbiased model with accurate prediction. The equation was: BMI value 1 year after LSG = 1.777 + 0.614 × presurgery BMI (kg/m²) +0.106 × age (years). For validation, the equation exhibited an adjusted R² 0.550 (P < 0.0001), and the goodness of fit indicated an unbiased model. The BMI predicted by the model fell within -3.78 BMI points to +2.42 points of the observed 1-year BMI. Pairwise difference between the mean 1-year observed and predicted BMI was not significant (-0.41 kg/m², P = 0.225). Conclusions: This predictive model estimates the BMI 1 year after LSG. The model comprises preoperative BMI and age. It allows the forecast of patients' BMI after surgery, hence setting realistic expectations which are critical for patient satisfaction after bariatric surgery. An attainable target motivates the patient to achieve it.

Development and validation of a scoring system for pre-surgical and early post-surgical prediction of bariatric surgery unsuccess at 2 years

Bariatric surgery (BS) is an effective treatment for morbid obesity. However, a simple and easy-to-use tool for the prediction of BS unsuccess is still lacking. Baseline and follow-up data from 300 consecutive patients who underwent BS were retrospectively collected. Supervised regression and machine-learning techniques were used for model development, in which BS unsuccess at 2 years was defined as a percentage of excess-weight-loss (%EWL) < 50%. Model performances were also assessed considering the percentage of total-weight-loss (%TWL) as the reference parameter. Two scoring systems (NAG-score and ENAG-score) were developed. NAG-score, comprising only pre-surgical data, was structured on a 4.5-point-scale (2 points for neck circumference ≥ 44 cm, 1.5 for age ≥ 50 years, and 1 for fasting glucose ≥ 118 mg/dL). ENAG-score, including also early post-operative data, was structured on a 7-point-scale (3 points for %EWL at 6 months ≤ 45%, 1.5 for neck circumference ≥ 44 cm, 1 for age ≥ 50 years, and 1.5 for fasting glucose ≥ 118 mg/dL). A 3-class-clustering was proposed for clinical application. In conclusion, our study proposed two scoring systems for pre-surgical and early post-surgical prediction of 2-year BS weight-loss, which may be useful to guide the pre-operative assessment, the appropriate balance of patients' expectations, and the post-operative care.

Microstructural changes in human ingestive behavior after Roux-en-Y gastric bypass during liquid meals

BACKGROUND

Roux-en-Y gastric bypass (RYGB) decreases energy intake and is, therefore, an effective treatment of obesity. The behavioral bases of the decreased calorie intake remain to be elucidated. We applied the methodology of microstructural analysis of meal intake to establish the behavioral features of ingestion in an effort to discern the various controls of feeding as a function of RYGB.

METHODS

The ingestive microstructure of a standardized liquid meal in a cohort of 11 RYGB patients, in 10 patients with obesity, and in 10 healthy-weight adults was prospectively assessed from baseline to 1 year with a custom-designed drinkometer. Statistics were performed on log-transformed ratios of change from baseline so that each participant served as their own control, and proportional increases and decreases were numerically symmetrical. Data-driven (3 seconds) and additional burst pause criteria (1 and 5 seconds) were used.

RESULTS

At baseline, the mean meal size (909.2 versus 557.6 kCal), burst size (28.8 versus 17.6 mL), and meal duration (433 versus 381 seconds) differed between RYGB patients and healthy-weight controls, whereas suck volume (5.2 versus 4.6 mL) and number of bursts (19.7 versus 20.1) were comparable. At 1 year, the ingestive differences between the RYGB and healthy-weight groups disappeared due to significantly decreased burst size (P = 0.008) and meal duration (P = 0.034) after RYGB. The first-minute intake also decreased after RYGB (P = 0.022).

CONCLUSION

RYGB induced dynamic changes in ingestive behavior over the first postoperative year. While the eating pattern of controls remained stable, RYGB patients reduced their meal size by decreasing burst size and meal duration, suggesting that increased postingestive sensibility may mediate postbariatric ingestive behavior.

TRIAL REGISTRATION

NCT03747445; https://clinicaltrials.gov/ct2/show/NCT03747445.

FUNDING

This work was supported by the University of Zurich, the Swiss National Fund (32003B_182309), and the Olga Mayenfisch Foundation. Bálint File was supported by the Hungarian Brain Research Program Grant (grant no. 2017-1.2.1-NKP-2017-00002).