J Obes Metab Syndr 2023; 32(1): 46-54
Published online March 30, 2023 https://doi.org/10.7570/jomes23008
Copyright © Korean Society for the Study of Obesity.
1Department of Surgery, School of Medicine, Kyungpook National University, Daegu; 2Department of Surgery, Kyungpook National University Chilgok Hospital, Daegu, Korea
Ji Yeon Park
Department of Surgery, Kyungpook National University Chilgok Hospital, 807 Hoguk-ro, Buk-gu, Daegu 41404, Korea
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Metabolic/bariatric surgery is currently the most effective measure to treat morbid obesity and obesity-related comorbidities such as type 2 diabetes. It has proven effective not only in terms of short-term weight loss, but also in maintaining the lower body weight for several decades. Such weight loss improves patient quality of life and extends life expectancy. It is crucial for patients to understand the likely results of a given bariatric procedure so that they can make an informed decision about whether to undergo surgery. The amount of weight loss after metabolic/bariatric surgery is usually the most important outcome of interest to patients considering surgical treatment. It is also the most common primary endpoint for healthcare providers. Patients undergoing surgery want tangible and realistic expectations about how much weight they could lose after surgery, and healthcare professionals need to determine at each follow-up visit after surgery whether patients are on track to reach their weight loss target so they can provide timely intervention to patients with insufficient weight loss or weight regain. Weight loss after metabolic/bariatric surgery is influenced by many clinical variables, including initial body mass index, age, gender, ethnicity, and type of surgery. A well-validated chronological weight loss prediction model would enable patient-centered counseling and goal setting. This review summarizes and compares several publicly available prediction models.
Keywords: Bariatric surgery, Obesity, morbid, Weight loss
The prevalence of obesity, defined as a body mass index (BMI) higher than 30 kg/m2, has gradually increased around the globe, from 21% in 2010 to 24% in 2016 among adults older than 30 years, according to 2019 Organisation for Economic Co-operation and Development health statistics, and that number is projected to increase further by 2030.1 Obesity and its related diseases are expected to reduce life expectancy by 0.9 years to 4.2 years depending on the country. Furthermore, the health problems related to obesity pose a great economic burden not only through the cost of management, but also through their negative effects on productivity.
The prevalence of those with BMI >30 kg/m2, which is an internationally accepted cut-off for class I obesity, was reported to be 5.2% in Korea in 2018, lower than in other countries. However, due to physiological differences, a different definition of obesity (BMI ≥25 kg/m2) is appropriate for the Asian population; using that standard, the proportion of the Korean population that was obese in 2018 was not much different from that in the Western world, approximately 38.5%, according to the 2020 Obesity Fact Sheet released by the Korean Society for the Study of Obesity.2 The prevalence of obesity in all obesity classes has gradually increased during the past decade in Korea, and class III obesity, which is a BMI above 35 kg/m2 for Asians, has almost tripled in that time, reaching more than 0.8% in 2018. A closer look at the data according to age reveals that the prevalence of obesity is increasing particularly rapidly among people younger than 40 years. Strikingly, the increase in class III obesity is even faster in that age group, with a prevalence of 1.6% in 2018, three or more times higher than the prevalence in older generations. Therefore, the number of patients eligible for metabolic/bariatric surgery (MBS) is increasing rapidly in Korea.
MBS is now undisputedly the most effective measure to treat morbid obesity and obesity-related comorbidities. Many clinical trials have shown that surgical treatment achieves significantly better weight loss outcomes than medical treatment, as well as better resolution or improvement of obesity-related comorbidities, such as type 2 diabetes, dyslipidemia, and hypertension.3-7 MBS has proven effective not only in terms of short-term weight loss, but also in maintaining a lower body weight for several decades, which improves patient quality of life and prolongs life expectancy.8-10 For those reasons, the Korean National Health Insurance plan began to cover MBS at the beginning of 2019. Patients with a BMI above 35 kg/m2 or a BMI above 30 kg/m2 with obesity-related comorbidities now qualify for insurance coverage for MBS. Those with a BMI above 27.5 kg/m2 and poorly controlled diabetes also qualify for MBS with partial insurance coverage in Korea.11
It is important for patients to understand the likely results of any given bariatric procedure so they can make an informed decision about whether to undergo surgery. Weight loss after MBS is usually the most important outcome for patients considering such surgery, and it is also the most common primary endpoint for medical providers. Patients undergoing MBS need tangible and realistic goals about weight loss after surgery, and at each follow-up visit after surgery, healthcare professionals need to determine whether the patient is on track to reach their weight loss target so they can provide timely intervention for patients with insufficient weight loss or weight regain.
To understand the literature reporting weight loss after MBS, it is necessary to understand the weight loss reporting systems commonly used. The percentage of excess weight loss (%EWL) is calculated from the excess weight beyond the ideal body weight. The ideal body weight was originally based on a Metropolitan Life Insurance Company table released in 1983; in 2005, bariatric societies recommended using a BMI of 25 kg/m2 as a criterion for calculating weight loss. Therefore, since 2005, %EWL and percentage of excess BMI loss (%EBMIL) have represented the same measure.
Another metric of interest is % total weight loss (%TWL), which is the most commonly used metric in the non-surgical literature about obesity (Table 1).
Traditionally, many surgeons have reported weight loss outcomes as %EWL or %EBMIL and change in BMI. However, some have argued in recent years that the %EWL or %EBMIL metric should be abandoned. When the dynamic percentile charts of nadir BMI, %EWL, and %TWL are plotted against initial BMI using bariatric weight loss outcomes, the absolute BMI values and %EWL results are significantly influenced by initial BMI, unlike %TWL.12,13 In other words, super-obese patients find it very difficult to reach the target BMI of 35 kg/m2 because they have much more weight to lose than less obese people. Similarly, %EWL is lower if the patient is heavier before surgery, which can cause bias in data interpretation.
Therefore, since 2015, the International Bariatric Society has strongly recommended using absolute change in BMI and %TWL in reporting weight loss outcomes, as in the non-surgical literature.14,15 When comparing the weight loss results in earlier publications, outcomes in %EWL should be interpreted in consideration of the patient’s initial BMI.
Bariatric surgery candidates usually have much higher weight loss expectations than are clinically reasonable. A study of 84 sleeve gastrectomy (SG) candidates revealed that almost all of them believed that they would achieve their dream (%EWL of 88.7%), happy (76.4%), or at least acceptable (68.2%) weight loss expectations by 1 year after surgery, and all of those expectations were significantly higher than the clinically expected %EWL of 56.1%. Those patients reported that they would be disappointed with a %EWL of 40.6%, which roughly corresponds to 20% TWL, whereas surgeons might consider that a successful weight loss outcome.16 That study showed the large gap between patient expectations and actual weight loss outcomes after MBS.
Because unrealistic weight-loss expectations could negatively affect treatment adherence, causing dropouts and negatively influencing weight loss outcomes, it is necessary to offer proper patient education with accurate prediction models to establish reasonable weight loss goals.
Weight loss outcomes show wide variability across patients, even using the %TWL metric, which is the least likely to be influenced by baseline BMI. A retrospective study from two European centers of 918 Roux-en-Y gastric bypass (RYGB) and 538 SG patients showed that almost two-thirds of the patients reached their maximal weight loss between 12 and 18 months after surgery, and the median maximal %TWL was 32.9% and 26.2%, respectively, indicating slightly better weight loss with RYGB.17 Nonetheless, the maximal %TWL showed wide variability, ranging from less than 5% to 60% for both procedures. This variability indicates that weight loss mechanisms are complex, and it is difficult to accurately predict the weight loss outcomes of individual bariatric patients.
It is important to understand what causes the weight-loss differences among bariatric patients so that individual patients can be given realistic weight loss goals. These factors include genetic background; baseline characteristics; medical, psychosocial, and environmental factors; and type of surgical procedure (Figure 1).
Here are some factors identified in the previous literature to be significantly associated with postoperative weight loss outcomes.
Preoperative BMI can greatly influence weight loss outcomes. Previous studies have demonstrated a clear correlation between initial BMI before surgery and final BMI after surgery.12,18 Patients with a higher initial BMI ended up with a higher BMI even if they lost many more kilograms than those with a lower BMI. Still et al.19 reported that, among 12 preoperative variables that were independently associated with weight nadir after RYGB, baseline BMI had the largest impact. Therefore, surgeons and physicians need to be cautious and present the expected weight goals to surgical candidates based on initial BMI.
Age at surgery also affects weight loss outcomes. Still et al.19 retrospectively reviewed 109 RYGB and 228 SG patients and divided them into two age groups, those younger than 45 years and those older than 45 years. They compared the 1-year weight loss outcomes between those age groups and demonstrated that patient age was negatively associated with weight loss, with the older group showing a significantly higher incidence of insufficient weight loss (14.9% vs. 2.6%), defined as %EWL <50%. Scozzari et al.20 showed similar results with a longer follow-up period of more than 5 years in their retrospective analysis of 489 RYGB patients. When divided into four age groups, the youngest group showed a significantly higher BMI and larger proportion of super-obese patients preoperatively. However, the younger patients experienced a significantly greater and more prolonged BMI decrease during the entire follow-up period, whereas the oldest group (older than 52 years) showed smaller weight loss and a greater tendency for weight regain in the long-term follow-up. These results suggest that less satisfactory weight loss should be considered in surgical options for older patients.
Gender is another clinical factor that might affect weight loss outcomes. Many have reported that being a woman was a favorable factor for greater weight loss.21-23 Coleman et al.21 reported that women had higher %EWL than men up to 3 years after SG or RYGB procedure. Similarly, Kitamura et al.23 also found that being a woman was an independent predictor of greater %EBMIL for up to 10 years. However, Andersen et al.24 showed that being a woman was an unfavorable predictor of weight loss 2 years after SG and also suggested that predictors for weight loss differed between the genders. Thus, the effects of gender on weight loss are not yet conclusive, in part because the published results are not consistent and in part because the samples in most previous studies were predominantly women.
The effects of ethnicity on postoperative weight loss have been the subject of constant discussion. The study by Coleman et al.21, which involved more than 20,000 patients of various ethnic backgrounds in the United States, found differences in weight loss outcomes according to ethnicity. For example, they found that weight loss after RYGB was highest in Caucasians throughout their entire 3-year follow-up period and lowest in non-Hispanic Black patients. Interestingly, however, after SG, the weight loss response did not differ much among the ethnic groups.
In terms of the influence of Asian ethnicities on surgical outcomes, Koh et al.25 conducted a retrospective study of 2,150 SG patients from 14 representative centers in Asia. Most of the subjects were Asian (Chinese, Indians, Koreans, Malays, and Japanese), but some were Caucasians for reference. Compared with the Caucasians, the Japanese showed the best %TWL (+3.90% in %TWL) at 3 years, and Malaysians had the worst outcomes (–4.42% in %TWL). Chinese and Korean subjects showed similar %TWL (–1.89% and –2.61%, respectively), which was worse than that of the Caucasians. Thus, differences in weight loss outcomes after MBS occur even among Asian ethnicities. The ethnic differences in weight loss outcomes might be multifactorial, partly due to genetics but mostly attributable to cultural factors such as lifestyles and dietary habits.
Many studies have shown that type 2 diabetes is a prognostic factor for poor weight loss after MBS.19,26-28 Campos et al.26 found that diabetes was independently associated with poor weight loss (%EWL <40%) 1 year after RYGB, particularly in those using insulin before surgery. Still et al.19 confirmed that diabetes was independently associated with weight loss outcomes for up to 36 months after RYGB. Eghbali et al.28 recently reported that the mean %TWL was 11.9% (95% confidence interval [CI], 6.84 to 16.9) higher for non-diabetic patients than for those with diabetes in mid-term results (36 to 60 months) following RYGB after adjusting for other clinical factors. Preoperative insulin use was associated with weight gain, and its anabolic effect might hamper weight loss after MBS. Other possible explanations for poorer weight loss in diabetic patients include frequent “protective” calorie intake to prevent hypoglycemic symptoms and a reduction in urinary glucose losses after surgery.
Mental health conditions, particularly those related to eating behaviors, could be another preoperative predictor of weight loss after MBS. A considerable proportion of MBS candidates report having mental illnesses, and the most common conditions are depression (19%; 95% CI, 14% to 25%) and binge eating disorder (17%; 95% CI, 13% to 21%) according to a meta-analysis.29 The association between preoperative mental health conditions and postoperative weight loss has been inconsistent in the literature. Chao et al.30 reported no difference in 1-year %TWL of patients with and without binge eating disorder (21.5% vs. 24.2%), but their 2-year %TWL was statistically different (18.6% vs. 23.9%).30,31 Based on their consecutive prospective study results, they suggested that preoperative binge eating disorder attenuated long-term weight loss after MBS. It appears that weight loss trajectories were not significantly influenced by preoperative depression or binge eating disorder within the first postoperative year; however, significant differences emerged after the honeymoon period of 2 years after surgery, which could correspond to the increased likelihood of mood deterioration as the time since surgery increases.30,32,33
The type of surgery also plays a crucial role in weight loss outcomes. According to two representative randomized controlled trials, SG and RYGB produced comparable weight loss in the early postoperative period.34-36 However, the difference tended to increase over time, favoring RYGB in terms of sustained weight loss. When the data from those two trials were merged, the %TWL after SG versus RYGB was 28.2% versus 30.8% at 1 year and 23.7% versus 27.2% at 5 years.34 Salminen et al.35 recently reported 10-year follow-up results of the Sleeve vs Bypass (SLEEVEPASS) trial and concluded that %EWL after RYGB was greater than after SG, and that the two procedures did not produce equivalent weight loss at 10 years. The median %EWL at 10 years was 43.5% and 50.7% after SG and RYGB, respectively, which corresponded to 23.4% and 26.9% of %TWL.
Consistent efforts have been made to provide an adequate tool for predicting weight loss outcomes after MBS. Table 2 shows some of the previously introduced prediction models for weight loss after MBS, particularly after SG and RYGB. Some researchers have tried to validate the performance of those prediction models for weight reduction after MBS.18,37-41
Most of the existing models were derived from single-center data and predict static weight loss outcomes at a certain time point, such as at 1 year, or the nadir weight. Those models do not provide information about whether a particular patient’s weight-loss trajectory is on track in the early postoperative period compared with other patients. However, the success of MBS can be predicted as early as 3 to 6 months after surgery. Manning et al.17 showed that %TWL at 6 weeks and 3 and 6 months was significantly associated with maximal %TWL in both RYGB and SG patients, and weight loss velocity between 3 and 6 months was a strong predictor of maximal %TWL. They suggested that patients with a weight loss velocity slower than 0.47 kg per week between 3 and 6 months were less likely than others to achieve a maximal %TWL of 20%. Silveira et al.42 also suggested that early postoperative weight change predicted weight loss 12 months after RYGB. They found that patients with %TWL <5% at 1 month and <10% at 3 months were highly likely to demonstrate suboptimal weight loss (%TWL <20%) at 1 year after RYGB. Therefore, it is important to monitor chronological weight changes in the early postoperative period. Such changes can provide patients realistic weight loss anticipation and satisfaction and motivate lifestyle modifications and can help healthcare providers to identify poor responders and determine the proper timing for adjunctive interventions such as behavioral therapy and weight loss medications.
In line with that effort, more comprehensive, practical, and intuitive weight loss calculators have been released recently. One of them is a weight loss outcome calculator developed by the Michigan Bariatric Surgery Collaborative (https://www.mbscsurgery.org) that is publicly available as a smartphone application named “Weigh the odds” (Figure 2). It was derived from a data registry of >45,000 patients and uses more than 30 clinical variables, including surgery type, demographic data, and comorbidities, to provide personalized outcomes about expected weight loss at 1, 2, and 3 years and about the resolution rate of comorbidities. Varban et al.43 compared the early weight loss trajectories after surgery of patients who met or exceeded their predicted weight loss according to this calculator with those who did not. They found that patients who failed to reach 66% of their predicted weight loss calculation by 6 months after surgery were not likely to reach the target weight loss at 1 year.44 The same group asserted that weight loss curves using this calculator could help to identify low outliers (with an observed-to-expected weight loss ratio <0.5 at 1 year) for additional intervention as early as 2 months after MBS.44
The American Society for Metabolic and Bariatric Surgery, in collaboration with the American College of Surgeons, also released an online Bariatric Surgical Risk/Benefit Calculator ( https://www.facs.org/quality-programs/mbsaqip/calculator). It was constructed based on Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQUIP) data from 775,000 bariatric operations in 925 centers in the United States (Figure 3). This calculator predicts the risk of 30-day complications, weight loss trajectory for up to 1 year after surgery, and remission of comorbidities, and has been validated in previous studies.45,46
Weight loss after bariatric surgery is influenced by many clinical variables, including initial BMI, age, gender, and ethnicity. Type of surgery also significantly influences the amount of weight loss, particularly in the long term. Most bariatric patients desire to know beforehand how much weight they will lose, and a well-validated chronological weight loss prediction model that provides realistic expectations for these patients could improve their compliance and motivation. It could also help healthcare professionals determine when and to whom they should offer additional treatment to achieve optimal weight loss outcomes.
Several weight loss prediction models have been suggested, and some of them have been validated for accuracy and efficacy in clinical usage. Unfortunately, no consensus has been reached about a clear definition of “success” and “failure” after MBS. Therefore, it is challenging to provide explicit guidelines for adjunctive intervention after surgery in terms of timing or cut-off values for weight loss outcome measures. Furthermore, most prediction models were derived from the Western population. Because weight loss after MBS differs significantly across ethnicities, Asian healthcare providers need to develop prediction models using data from Asian populations, particularly Korean patients, to properly predict weight loss outcomes after MBS in Korean patients.
The authors declare no conflict of interest.
Calculation of weight loss metrics
|Relative measure = (initial BMI–current BMI)/(initial BMI−a) × 100 (%)|
|Relative measure||Reference value ( = a)|
|%EWL||BMI at “ideal body weight”|
BMI, body mass index; %EWL, percentage of excess weight loss; %EBMIL, percentage of excess BMI loss; %TWL, percentage of total weight loss.
Weight loss prediction models in the literature
|Author||Procedure||No. of patients||Predicted time point (yr)||Equation|
|Baltasar et al. (2011)18||All||7,410||> 3||PBMIall = IBMI × 0.43+11.75|
|SG||128||> 3||PBMIsg = IBMI × 0.43+10.88|
|RYGB||2,083||> 3||PBMIgbp = IBMI × 0.43+10.23|
|Goulart et al. (2016)37||SG||152||1||PBMI = −3.597+0.621 × IBMI+0.135 × age|
|Wise et al. (2016)38||RYGB||647||1||%EBMIL = 137.1+6.4 × woman−6.7 × black race−1.2 × IBMI−3.7 × HTN–6.0 × DM|
|Cottam et al. (2018)39||SG||371||1||BMI reduction = 0.73–(0.0581 × age)+(0.343 × IBMI)–(2.31 × HTN&DM)|
PBMI, predicted BMI; IBMI, initial BMI; SG, sleeve gastrectomy; RYGB, Roux-en-Y gastric bypass; gbp, gastric bypass; %EBMIL, percentage of excess BMI loss; HTN, hypertension; DM, diabetes mellitus; BMI, body mass index.
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