Exponential decay modeling can define parameters of weight loss trajectory after laparoscopic Roux-en-Y gastric bypass

Early Post-operative Weight Change After Roux-en-Y Gastric Bypass Predicts Weight Loss at 12-Month Follow-up

INTRODUCTION

Sub-optimal weight loss following Roux-en-Y gastric bypass (RYGB) represents an important clinical challenge in a significant number of patients. Early identification of such patients would be advantageous, as it could aid in the selective implementation of targeted adjunct interventions during the first post-operative year.

METHODS

Clinical audit data from 1137 patients undergoing RYGB between 2013 and 2016 at the Instituto Sallet in Brazil were prospectively registered in an online database (BOLD) and analyzed.

RESULTS

Forty-eight percent of patients achieving less than 5% total weight loss after the first post-operative month achieved a 20% total weight loss at 1 year (n = 626; OR = 0.6 CI = 95%). Eighty-three percent of patients losing between 5 and 10% at 1 month and 95% of patients losing greater than 10% at 1 month had lost at least 20% of total body weight after the first post-operative year. Forty-four percent of patients achieving less than 10% total weight loss after the third post-operative month achieved 20% total weight loss at 1 year (n = 494; OR = 0.3 CI = 95%).

CONCLUSION

Total bodyweight reduction after RYGB of < 5% at 1 month and < 10% at 3 months is associated with suboptimal weight loss at 1 year. These results reinforce findings from other studies reporting that patients tend to follow a common weight loss trajectory. Identifying the patients with weight trajectory requiring adjunct therapies early on is crucial so appropriate adjustments can be made to post-operative care.

Weight Loss Trajectories After Bariatric Surgery for Obesity: Mathematical Model and Proof-of-Concept Study

BACKGROUND

Obesity surgery has proven its effectiveness in weight loss. However, after a loss phase of about 12 to 18 months, between 20% and 40% of patients regain weight. Prediction of weight evolution is therefore useful for early detection of weight regain.

OBJECTIVE

This proof-of-concept study aimed to analyze the postoperative weight trajectories and to identify "curve families" for early prediction of weight regain.

METHODS

This was a monocentric retrospective study with calculation of the weight trajectory of patients having undergone gastric bypass surgery. Data on 795 patients after a 2-year follow-up allowed modeling of weight trajectories according to a hierarchical cluster analysis (HCA) tending to minimize the intragroup distance according to Ward. Clinical judgement was used to finalize the identification of clinically relevant representative trajectories. This modeling was validated on a group of 381 patients for whom the observed weight at 18 months was compared to the predicted weight.

RESULTS

Two successive HCA produced 14 representative trajectories, distributed among 4 clinically relevant families: Of the 14 weight trajectories, 6 decreased systematically over time or decreased and then stagnated; 4 decreased, increased, and then decreased again; 2 decreased and then increased; and 2 stagnated at first and then began to decrease. A comparison of observed weight and that estimated by modeling made it possible to correctly classify 98% of persons with excess weight loss (EWL) >50% and more than 58% of persons with EWL between 25% and 50%. In the category of persons with EWL >50%, weight data over the first 6 months were adequate to correctly predict the observed result.

CONCLUSIONS

This modeling allowed correct classification of persons with EWL >50% and could identify early after surgery the patients with potentially less that optimal weight loss. Further studies are needed to validate this model in other populations, with other types of surgery, and with other medical-surgical teams.

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT): Recommendations from the Biological Domain

BACKGROUND

The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) project provides a framework for how obesity researchers, working collectively, can generate the evidence base needed to guide the development of tailored, and potentially more effective, strategies for obesity treatment.

OBJECTIVES

The objective of the ADOPT biological domain subgroup is to create a list of high-priority biological measures for weight-loss studies that will advance the understanding of individual variability in response to adult obesity treatments. This list includes measures of body composition, energy homeostasis (energy intake and output), brain structure and function, and biomarkers, as well as biobanking procedures, which could feasibly be included in most, if not all, studies of obesity treatment. The recommended high-priority measures are selected to balance needs for sensitivity, specificity, and/or comprehensiveness with feasibility to achieve a commonality of usage and increase the breadth and impact of obesity research.

SIGNIFICANCE

The accumulation of data on key biological factors, along with behavioral, psychosocial, and environmental factors, can generate a more precise description of the interplay and synergy among them and their impact on treatment responses, which can ultimately inform the design and delivery of effective, tailored obesity treatments.