Early Postoperative Weight Loss Trajectories Predict Weight Regain at 18 Months Following Bariatric Surgery

Abstract

Background:

Early postoperative total weight loss percentage (TWL%) shows heterogeneous predictive value for subsequent outcomes, yet targeted management strategies remain challenging. Identifying distinct early weight loss trajectories could characterize high-risk populations for weight regain, enabling proactive interventions to optimize long-term outcomes.

Methods:

This prospective observational study was conducted from April 2023 to December 2024. Dynamic weight data were recorded at 1, 3, 6, and 18 months postoperatively; TWL% was calculated. Latent Growth Curve Modeling (LGCM) was used to construct early weight loss trajectory models. Logistic regression analyzed associations between early weight loss trajectories and weight regain risk.

Results:

A total of 215 patients were included in the study. Weight regain incidence was 23.26%. LGCM identified two TWL% trajectory groups: rapid weight loss group and slow weight loss group. Taking the slow weight loss group as reference, rapid weight loss group had significant lower risk in the incidences of weight regain [Odds ratio (OR) = 0.441, p = 0.044].

Conclusion:

Two distinct early postoperative weight loss trajectories were identified, with the rapid weight loss group demonstrating significantly lower weight regain at 18 months. Early weight loss patterns are correlated with medium-term outcomes through more effective resetting of body weight set points.

Keywords

bariatric surgery early weight loss trajectory total weight loss percentage weight regain

Introduction

Obesity represents a chronic metabolic disorder characterized by pathological expansion and altered distribution of adipose tissue. This condition significantly elevates the risk for developing various comorbidities, including type 2 diabetes mellitus, cardiovascular disease, and metabolic syndrome.¹ Bariatric surgery, including sleeve gastrectomy (SG) and Roux-en-Y gastric bypass, is considered an important and effective treatment for severe obesity.² A systematic review and meta-analysis demonstrated that surgical interventions resulted in significantly greater weight reduction (range, 14.4–24 kg) than nonsurgical treatment during 1 to 2 years of follow-up.³ Substantial weight reduction confers clinically meaningful improvements or complete resolution of obesity-associated conditions such as type 2 diabetes mellitus, hypertension, and dyslipidemia, thereby contributing to reduce mortality and extend life expectancy.^4–7

Despite the significant weight loss achieved through metabolic surgery, it is important to recognize that there is still a risk of regaining weight after bariatric surgery. In a retrospective cohort study conducted by Hatami, investigators followed 2,567 patients who underwent bariatric surgery and documented weight regain in approximately 10–30% of subjects beginning at 18 months postoperatively,⁸ indicating that this critical period may represent an inflection point for weight trajectory following bariatric procedures. Weight regain following bariatric surgery not only increases the risk of recurrent obesity-related comorbidities but also imposes substantial psychological and financial burdens on affected individuals, significantly compromising both physical and mental well-being.⁹ In a prospective controlled investigation conducted by Sjöström, weight regain following bariatric intervention was associated with the recurrence of metabolic derangements, including hypertriglyceridemia, reduced high-density lipoprotein cholesterol levels, type 2 diabetes mellitus, hypertension, and hyperuricemia.¹⁰ Early identification of high-risk populations for postoperative weight regain is crucial for sustaining long-term weight loss maintenance and reducing the likelihood of weight regain following bariatric surgery.

Early postoperative weight loss demonstrates significant yet complex predictive value for medium-term outcomes following bariatric surgery. Lisa et al.’s retrospective study demonstrated that adolescents experience most of their weight loss within the first year following bariatric surgery, with higher percentage of excess weight loss (%EWL) at 3 months postoperatively predicting greater weight reduction at both 12 and 24 months.¹¹ However, Pokala et al.¹² demonstrated a paradoxical association wherein greater initial weight loss at 6 months (OR, 1.20, 95% CI: 1.08,1.33; p = 0.001) and 1 year postoperatively (OR, 1.14; 95% CI: 1.06,1.23; P < 0.001) significantly predicted higher risks of long-term weight regain at 4-year follow-up. Meanwhile, a retrospective cohort study¹³ pointed out that total weight loss percentage (%TWL) at 3 months (B = −0.066, p = 0.649) and 6 months (B = 0.312, p = 0.072) was not a significant predictor of weight regain at 2 years of follow-up. Traditionally, success following metabolic and bariatric surgery has been evaluated using static, single time-point metrics. While informative, this conventional approach inherently treats patients as a homogenous group, thereby obscuring the significant interindividual heterogeneity in their weight loss trajectories. This substantial variability in individual weight loss patterns, rather than a single, uniform response, suggests that the predictive utility of early postoperative timepoints for medium-term weight outcomes may be significantly influenced by these underlying diverse trajectories.

Trajectory analysis provides critical insights into individual heterogeneity by dynamically modeling weight loss fluctuations over time, overcoming the limitations of cross-sectional metrics that capture static time-point estimates. The primary advantage of this method is its ability to move beyond population averages and capture the dynamic nature of postoperative weight change, revealing how different groups of patients progress through the early recovery phases. This temporal granularity enhances predictive validity for long-term outcomes while informing personalized intervention strategies tailored to dynamic recovery phases. Despite these clear advantages, the application of trajectory analysis to the early postoperative weight loss period remains limited. However, there is a paucity of literature delineating early postoperative (6-month) TWL% trajectories following bariatric surgery, and the relationship of different trajectories of TWL% with the incidences of weight regain at midterm follow-up (18 months postsurgery) is still unclear. Therefore, this study aims to explore the status of the trajectories of TWL% and the associations between the trajectories of TWL% and the incidences of weight regain.

Materials and Methods

Study design, setting, and participants

This study enrolled all patients who underwent any type of bariatric surgery between April 2023 and December 2024 at the Department of Gastrointestinal Surgery of the First Affiliated Hospital of Soochow University and Suzhou Jiulong Hospital. Study participants included all patients who adhered to the scheduled 6-month postoperative follow-up protocol and had completed medical documentation (1-month and 3-month postoperative assessments). Patients were included if they were aged 18–65 years, had undergone any type of bariatric procedure within the previous 6 months, could accurately document their nadir postoperative weight, possessed adequate cognitive and communication abilities, and provided informed consent. We excluded patients taking weight-modifying medications (antidepressants, corticosteroids, insulin), those who were pregnant or lactating, individuals with serious comorbidities (malignancies, severe hepatic/renal dysfunction), patients with prior revision surgeries or endoscopic interventions for weight regain, and those simultaneously enrolled in other clinical studies.

Variables and data sources/measurement

Study variables comprised comprehensive demographic characteristics, detailed clinical parameters, and anthropometric measurements. Demographic data included sex, age, marital status, smoking, alcohol consumption, physical activity patterns, and sleep quality. Clinical parameters encompassed height, preoperative weight, preoperative body mass index (BMI), type of surgery, comorbidities, medication profile, family history of obesity, fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol.

Body weight was measured using a calibrated digital scale with participants in a fasting state, after voiding bowel and bladder, wearing light clothing, and without footwear. Participants were instructed to stand centrally on the scale platform with weight evenly distributed on both feet until the digital reading stabilized. Weight measurements were recorded to the nearest 0.1 kg. Body weight measurements were obtained at standardized time points: preoperatively (baseline) and at 1, 3, 6, and 18 months postoperatively. BMI is calculated as weight (kg) divided by height squared (m²). According to World Health Organization classification standards,¹⁴ overweight is defined as 25≤BMI <30 kg/m²; Class I obesity as 30≤BMI ≤34.9 kg/m²; Class II obesity as 35≤BMI ≤39.9 kg/m²; and Class III obesity as BMI ≥40 kg/m². Postoperative nadir weight was defined as three consecutive weight measurements without decrease and subsequent fluctuations ≤0.5 kg over 1 month.¹⁵ Bariatric surgical outcomes were evaluated using %TWL, calculated according to the following formula: TWL% = (initial weight−postoperative weight)/(initial weight) × 100. Weight regain was defined as an increase in body weight exceeding 10% of the maximum weight loss achieved after surgery, as measured at the 18-month postoperative follow-up.¹⁶

Anthropometric measurements included preoperative skeletal muscle mass and preoperative body fat percentage. Body composition was assessed using the BCA-2A eight-electrode multifrequency bioelectrical impedance analysis system(Tsinghua Tongfan, China). Subjects were required to meet the following measurement conditions: fasting for 2 h prior to testing, avoiding exercise and bathing, emptying bladder, cleaning body surface, females avoiding menstrual periods, and laboratory temperature strictly controlled between 20°C and 25°C. The measurement procedure was as follows: basic data, including age, gender, and height, were first entered into the system. Subjects then stood barefoot on the detection platform, holding electrode handles with all five fingers in complete contact with the electrode plates, arms extended at a 15° angle from the trunk. The system performed five-segment impedance detection through four limbs (four electrodes each on hands and feet) and completed a comprehensive analysis of 13 body composition parameters (including lean body mass, body fat percentage, visceral fat level, and regional fat distribution) within 120 s. The measurement data were encrypted and transmitted to the terminal workstation for storage.

Eating behavior patterns were assessed using the psychometrically validated Dutch Eating Behavior Questionnaire (DEBQ) at 6 months postoperatively. This instrument assesses three conceptually distinct eating behavior constructs: restrained eating (deliberate caloric restriction aimed at weight control or loss), emotional eating (food consumption in response to negative emotional states rather than physiological hunger cues), and external eating (heightened responsiveness to environmental food stimuli independent of internal satiety signals). Participants select their answers using a five-point Likert scale (1 = never, 5 = very often). The Chinese version of DEBQ has demonstrated robust internal consistency (Cronbach’s α = 0.82–0.88) and construct validity across diverse clinical and nonclinical populations.¹⁷ The Cronbach’s α coefficient in this study was 0.92.

Statistical analyses

Latent growth curve modeling (LGCM) was performed using Mplus 8.0 software to identify heterogeneous subgroups based on %TWL trajectories at three early postoperative time points (1, 3, and 6 months). The optimal number of trajectory classes was determined through a systematic process. We began by fitting a one-class model and sequentially added classes up to a five-class model. Each model was evaluated based on a combination of statistical fit indices and theoretical considerations, as recommended by the GRoLTS-Checklist.¹⁸ Trajectory class selection was guided by multiple criteria: minimization of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values; entropy exceeding 0.80, indicating strong classification precision; high diagonal values (approximately 0.80) in the classification probability matrix; and each class containing at least 5% of participants to ensure statistical power for comparisons.^19,20 Finally, clinical interpretability was a key consideration. This was defined as the extent to which the resulting trajectory classes represented distinct patterns. The final model was selected only if it provided a clear, theoretically sound, and clinically relevant classification of patient trajectories.

Baseline characteristics were summarized as counts (percentages) for categorical variables and as means ± standard deviations or medians (interquartile ranges) for normally or nonnormally distributed continuous variables, respectively. Participants were categorized into weight regain and weight loss maintenance groups based on 18-month postoperative outcomes. Between-group differences in demographic characteristics, clinical parameters, trajectory subgroups, and eating behavior scores were compared using t-test, chi-squared tests or Kruskal–Wallis tests as appropriate. Multivariable binary logistic regression was subsequently performed to identify independent predictors of postoperative weight regain, incorporating significant variables from univariate analyses.

A two-tailed p-value <0.05 was considered statistically significant. Statistical analyses were conducted using SPSS version 27.0 (SPSS, Chicago, IL, USA), and Latent Class Growth Analysis (LCGA) was conducted using Mplus 8.0 software.

Sample size calculation

The BIC was selected as the primary model selection index, which requires a sample size greater than 200.²¹ Accounting for an anticipated 20% attrition rate, the final target sample size was determined to be at least 250 participants.

Results

Demographic and clinical characteristics

At baseline, 270 participants met the inclusion criteria and were enrolled in the study. Six patients did not attend their 6-month postoperative follow-up appointment. Early postoperative (1, 3, and 6 months) weight data and eating behavior questionnaires were successfully collected from 264 participants. At the 18-month postoperative time point, 49 participants were excluded from analysis due to failure to accurately record their nadir postoperative weight (n = 10) or refusal to respond to follow-up telephone calls (n = 39). Thus, the completed 18-month postoperative weight data were available for 215 participants. The attrition rate was 18.56%. We compared the baseline characteristics of the 215 participants included in the final analysis with the 55 participants who dropped out. Our analysis revealed no statistically significant differences between the two groups in any of these key baseline variables (all p > 0.05). The more details, see Supplementary Table S1. The mean age of the patients was 31.49 ± 7.66 years. A total of 71.2% of patients were female. The mean preoperative BMI was 37.51 ± 6.22 kg/m², with approximately 4.19% of patients classified as overweight, 34.88% as Class I obesity, and 60.93% as Class II obesity or above. Among the participants, 126 patients (58.6%) underwent SG, while 89 patients (41.4%) underwent gastric bypass surgery. Family history of obesity was presented in 77 participants (35.8%). Table 1 shows the demographic and clinical characteristics of the patients.

Table 1.

The Demographic and Clinical Characteristics of All the Patients

Variables	$\bar{x}$ ± s / M (P25, P75)/n (%)
Age (year)	31.49 ± 7.66
Sex (female)	153 (71.17)
Marital status
Married	126 (58.60)
Unmarried/divorced	89 (41.40)
Profession
Working	193 (89.77)
Student/unemployed/retired	22 (10.23)
Smoking(yes)	44 (20.47)
Drink (yes)	28 (13.02)
Sleep
Good	125 (58.14)
Normal	75 (34.88)
Bad/very bad	15 (6.98)
Exercise
Never	143 (66.51)
Occasionally	65 (30.23)
Regularly and quantitatively	7 (3.26)
Type of surgery
Sleeve gastrectomy	126 (58.60)
Gastric bypass	89 (41.40)
Comorbidities (yes)	149 (69.30)
Medication (yes)	43 (20.00)
Family history of obesity (yes)	77 (35.81)
FBG (mmol/L)	5.58 (5.12,6.25)
TC (mmol/L)	4.93 (4.26,5.56)
LDL-C (mmol/L)	3.27 ± 0.85
HDL-C (mmol/L)	1.08 ± 0.24
TG (mmol/L)	1.87 ± 1.32
BMI
Overweight	9 (4.19)
Grade I obesity	75 (34.88)
Grade II obesity and above	131 (60.93)

Notes: BMI, preoperative body mass index; FBG, preoperative fasting blood glucose; HDL-C, preoperative high-density lipoprotein cholesterol; LDL-C, preoperative low-density lipoprotein cholesterol; TC, preoperative total cholesterol; TG, preoperative triglycerides.

TWL% trajectories

The TWL% values at 1, 3, and 6 months postoperatively were 10.92 ± 2.74%, 19.85 ± 4.10%, and 26.05 ± 5.25%, respectively. The fit statistics of trajectory model are reported in Table 2. The model fit indices demonstrated progressive improvement across Class-1 to Class-4, with sequentially decreasing AIC, BIC, and aBIC values. All entropy values exceeded 0.8, indicating high classification accuracy. Significant Lo-Mendell-Rubin likelihood ratio and bootstrap likelihood ratio test results ( p < 0.05) were observed for Class-2, Class-3, and Class-4. However, the smallest subgroups in both Class-3 and Class-4 constituted less than 5% of the cohort, compromising interpretability and generalizability. Based on parsimony and model stability criteria, Class-2 provided the optimal balance between statistical fit and clinical utility, was therefore selected as the final trajectory model, and was named rapid weight loss group and slow weight loss group (see Fig. 1). Detailed %TWL of the two groups at different time points after surgery are shown in Table 3.

FIG. 1.

Trajectory of %TWL at three early postoperative time points (1, 3, and 6 months). %TWL, total weight loss percentage.

Table 2.

Summary of Model Fits for TWL% Trajectory

Trajectories	AIC	BIC	aBIC	Entropy	VLMR	BLRT	Percentage of persons in groups
Class-1	3606.324	3623.177	3607.333	—	—	—	1
Class-2	3418.494	3445.459	3420.108	0.802	0.004	<0.001	0.340/0.660
Class-3	3344.674	3381.751	3346.894	0.852	0.008	<0.001	0.563/0.400/0.037
Class-4	3284.704	3331.893	3287.530	0.855	0.009	<0.001	0.526/0.037/0.297/0.140

Notes: %EWL, percentage of excess weight loss; TWL%, total weight loss percentage; aBIC, sample size-adjusted bayesian information criterion; AIC, akaike information criterion; BIC, bayesian information criterion; BLRT, bootstrap likelihood ratio test; VLMR, Lo-Mendell-Rubin likelihood ratio test.

Table 3.

Status of %TWL in the Groups with Different TWL% Trajectories

Variables	1M	3M	6M
Rapid weight loss group	12.99 ± 2.83	24.30 ± 2.74	31.65 ± 3.00
Slow weight loss group	9.86 ± 1.99	17.57 ± 2.50	23.17 ± 3.56

Notes: TWL%, total weight loss percentage.

Table 4.

Incidences of Weight Regain in the Groups with Different TWL% Trajectory

Variables	Weight regain group (n = 50, n, %)	Weight loss maintenance group (n = 165, n, %)	χ2	P
Rapid weight loss group	11 (22.00)	62 (37.58)	4.145	0.042^*
Slow weight loss group	39 (78.00)	103 (62.42)	4.145	0.042^*

^*p < 0.05.

Notes: TWL%, total weight loss percentage.

Incidences of weight regain in the different groups with TWL% trajectories

As outlined in the methodology, we categorized the enrolled patients into two groups: weight regain group and weight loss maintenance group. The overall incidence of weight regain at the 18-month postoperative follow-up was 23.25%. As shown in Table 4, rapid weight loss group demonstrated significantly lower weight regain rate (11 cases, 22%) compared with that of the slow weight loss group (39 cases, 78%), with this difference reaching statistical significance in Chi-square analysis (χ² = 4.145, p = 0.0 42).

Associations between TWL% trajectories and weight regain at 18 months postsurgery

Table 5 presents baseline characteristics of participants stratified by weight regain status at 18 months postsurgery. Demographic characteristics, clinical parameters, and eating behavior patterns were compared between patients who experienced weight regain and those who maintained weight loss. Variables demonstrating statistically significant differences ( p < 0.05) between the groups were subsequently incorporated into binomial logistic regression models. Significant between-group differences were observed for age, marital status, sleep quality, exercise habits, and preoperative body fat percentage. Among the evaluated eating behavior patterns, external eating behavior demonstrated significant differences between the groups, while restrained eating and emotional eating did not.

Table 5.

Demographic Characteristics, Clinical Parameters, and Eating Behavior Patterns of Study Participants Stratified by Weight Regain Status at 18 Months Post-Bariatric Surgery

Variables	Weight regain group (n = 50)	Weight loss maintenance group (n = 165)	t / χ2 / z	P
Variables	$\bar{x}$ ± s / M (P25,P75)/n(%)	$\bar{x}$ ± s/M (P25,P75)/n(%)	t / χ2 / z	P
Demographic characteristics
Age (year)	33.82 ± 7.74	30.72 ± 7.65	2.507	0.013^*
Sex (female)	37 (74.00)	116 (70.30)	0.256	0.613
Marital status
Married	36 (72.00)	90 (54,55)	4.819	0.028^*
Unmarried/divorced	14 (28.00)	75 (45.45)	4.819	0.028^*
Profession
Working	48 (96.00)	145 (87.88)	2.755	0.097
Student/unemployed/retired	2 (4.00)	20 (12.12)	2.755	0.097
Smoking(yes)	10 (20.00)	34 (20.61)	0.009	0.926
Drink (yes)	3 (6.00)	25 (15.15)	2.837	0.092
Sleep
Good	27 (54.00)	98 (59.39)	57.441	<0.001^***
Normal	19 (38.00)	56 (33.94)
Bad/very bad	4 (8.00)	11 (6.67)
Exercise
Never	33 (66.00)	110 (66.67)	86.881	<0.001^***
Occasionally	14 (28.00)	51 (30.91)
Regularly and quantitatively	3 (6.00)	4 (2.42)
Clinical parameters
Type of surgery
Sleeve gastrectomy	35 (70.00)	91 (55.15)	3.487	0.062
Gastric bypass	15 (30.00)	74 (44.85)	3.487	0.062
Comorbidities (yes)	31 (62.00)	118 (71.52)	1.633	0.201
Medication (yes)	11 (22.00)	32 (18.79)	0.163	0.687
Family history of obesity (yes)	18 (36.00)	59 (35.76)	0.001	0.975
FBG (mmol/L)	5.55 (5.09, 6.52)	5.61 (5.13, 6.22)	−1.153	0.249
TC (mmol/L)	4.70 (4.01, 5.61)	4.97 (4.45, 5.52)	−0.362	0.717
LDL-C (mmol/L)	3.25 ± 1.01	3.27 ± 0.80	−0.202	0.840
HDL-C (mmol/L)	1.08 ± 0.23	1.08 ± 0.24	0.031	0.976
TG (mmol/L)	1.66 ± 0.90	1.94 ± 1.42	−1.278	0.203
SMM (kg)	31.80 ± 7.73	33.92 ± 7.72	−1.694	0.092
BF%	40.83 ± 5.81	44.15 ± 5.67	−3.610	<0.001^***
BMI
Overweight	5 (10.00)	4 (2.42)	1.506	0.220
Grade I obesity	26 (52.00)	49 (29.70)
Grade II obesity and above	19 (38.00)	112 (67.88)
Eating behavior patterns
External earing	2.85 (2.35, 3.50)	2.50 (2.00, 3.00)	2.981	0.004^**
Restrained eating	2.65 (2.08, 3.13)	2.70 (2.00, 3.20)	−0.409	0.732
Emotional eating	1.92 (1.29, 2.71)	1.69 (1.23, 2.38)	0.817	0.263

^*p < 0.05; **p < 0.001; ^***p < 0.001.

Notes: BF%, preoperative body fat percentage; BMI, body mass index; FBG, preoperative fasting blood glucose; HDL-C, preoperative high-density lipoprotein cholesterol; LDL-C, preoperative low-density lipoprotein cholesterol; SMM, preoperative skeletal muscle mass; TC, preoperative total cholesterol; TG, preoperative triglycerides.

Binomial logistic regression analysis was performed to examine the association between TWL% trajectories and weight regain status at 18 months postsurgery (Table 6). Results demonstrated that patients in the rapid weight loss group exhibited significantly lower risk of weight regain compared with that in the slow weight loss group (OR: 0.441, 95% CI: 0.199–0.979). In addition, higher preoperative body fat percentage was associated with reduced likelihood of weight regain (OR: 0.907, 95% CI: 0.850–0.968), and external eating behavior emerged as a significant risk predictor for weight regain (OR: 2.128, 95% CI: 1.318–3.436).

Table 6.

Multivariable Binomial Logistic Regression Analysis of Factors Associated with Weight Regain at 18 Months Post-Bariatric Surgery

Variables	B	S.E.	Waldχ²	P	OR (95% CI)
Trajectory
Slow weight loss group	Ref.
Rapid weight loss group	−0.819	0.407	4.052	0.044*	0.441 (0.199,0.979)
Age	0.042	0.032	1.702	0.192	1.043 (0.979,1.110)
BF%	−0.098	0.033	8.748	0.003**	0.907 (0.850,0.968)
Marital status
Married	Ref.
Unmarried/divorced	−0.179	0.510	0.124	0.725	0.836 (0.307,2.272)
Sleep
Good	Ref.
Normal	0.328	0.381	0.739	0.390	1.388 (0.658,2.929)
Bad/very bad	0.476	0.713	0.445	0.505	1.609 (0.398,6.511)
Exercise
Never	Ref.
Occasionally	−0.255	0.396	0.415	0.519	0.775 (0.357,1.682)
Regularly and quantitatively	0.857	0.865	0.982	0.322	2.357 (0.432,12.845)
External eating	0.755	0.244	9.554	0.002**	2.128 (1.318,3.436)

*p < 0.05; **p < 0.01.

Notes: BF%, preoperative body fat percentage.

Discussion

This study indicated that there were two TWL% trajectory groups named rapid weight loss group and slow weight loss group at early postoperative time points in patients with bariatric surgery. Rapid weight loss group is associated with a lower incidence of weight regain at the 18-month postoperative follow-up, which is consistent with our research hypothesis based on the weight set point theory.

Status of early postoperative TWL%

Various metrics are employed to evaluate weight loss following bariatric surgery, including weight loss, TWL%, excess weight loss percentage (EWL%), and BMI. Current guidelines emphasize TWL% as the preferred metric for reporting postbariatric surgery weight outcomes,²² as it provides a more accurate representation of weight reduction compared with EWL%, which can be influenced by variations in preoperative excess weight.²³ TWL% is not confounded by differences in baseline excess weight, thereby enabling more precise assessment of actual weight loss. Accordingly, this study utilized TWL% at various early postoperative time points to characterize early weight loss trajectories.

Xu et al. reported TWL% values of 11.65 ± 3.11%, 18.63 ± 3.92%, and 23.11 ± 5.88% at 1, 3, and 6 months postoperatively.²⁴ These findings are comparable with our observations at 1 and 3 months postoperatively, though our cohort demonstrated greater weight loss at 6 months (26.05 ± 5.25%), which also exceeds the 6-month %TWL reported by Kavitha et al. (23.83 ± 7.40%).²⁵ This observed difference at 6 months postoperatively may suggest enhanced weight loss efficiency in our cohort between 3 and 6 months, potentially attributable to intensified postoperative behavioral interventions implemented in our clinical protocol.

Incidence of weight regain

Weight regain, expressed as percentage of nadir weight, progressively increased from 5.7% at 1 year postnadir to 10.1%, 12.9%, 14.2%, and 15.0% at years 2, 3, 4, and 5, respectively, which demonstrated that the most substantial weight regain occurs within 2 years of reaching nadir weight. King et al. further reported that during the first year after achieving nadir weight, 604 patients (42.95%) experienced weight regain exceeding 10% of their maximum postoperative weight loss,²⁶ whereas our study observed a lower regain rate of 23.26% (50 of 215 patients) at 18 months postoperatively, likely attributable to our relatively shorter follow-up period during which most patients had not yet reached 1 year postnadir weight.

Associations between different trajectories of TWL% and weight regain

Our study found that rapid weight loss may be associated with the lower incidence of weight regain at the midpoint of postbariatric surgery. The rapid weight loss group has 15.07% weight regain, compared with 27.46% of slow weight loss group. These findings are consistent with previous research demonstrating a positive correlation between early postoperative weight loss following bariatric surgery and long-term weight outcomes, wherein more rapid and substantial early weight reduction is associated with superior weight loss maintenance in the medium to long term. Gholizadeh et al. demonstrated that TWL% at 12 months postoperatively serves as a significant predictor of successful weight loss maintenance at both 24 months (B = 0.519, p < 0.001) and 60 months (B = 0.257, p < 0.001) of follow-up.¹³

The body weight set point is established through dynamic interactions between genetic and environmental factors, with regulation of caloric intake and energy expenditure around this set point determining individual obesity propensity.²⁷ Bariatric surgery currently represents the most effective intervention for substantial weight reduction and maintenance, while simultaneously serving as the optimal approach for modifying the body weight set point.²⁸ Bariatric procedures induce weight loss through alterations in gastric capacity, gastrointestinal anatomical configuration, and food emptying rates, thereby remodeling gut hormone secretion profiles and vagal afferent signaling.^29–32 These physiological modifications significantly enhance satiety perception and suppress hypothalamic hunger centers, effectively overriding previous set point limitations to establish a new energy equilibrium. The metabolic plateau phase observed approximately 12 months postoperatively represents the establishment of a stable negative feedback mechanism following neuroendocrine system reset.^33,34 This metabolic homeostasis reconstruction enables postoperative weight stabilization at a new equilibrium substantially below preoperative baseline levels.³⁵ Compared with gradual weight reduction, more rapid weight changes may facilitate more comprehensive downward resetting of the body weight set point, thereby reducing the risk of persistent physiological resistance to weight loss and consequently decreasing the likelihood of weight regain.

These findings have significant clinical implications. Identifying a patient’s trajectory within the first 6 months creates a crucial window of opportunity for personalized intervention. Patients identified as being on the slow weight loss trajectory could be targeted for enhanced behavioral support, dietary counseling, or pharmacological intervention to mitigate the risk of future regain. Theoretically, our findings support the body weight set point theory. The rapid and substantial weight loss achieved by one group may facilitate a more effective downward resetting of this set point, creating a new, stable metabolic equilibrium that is more resistant to regain. In contrast, slower weight loss may represent an ongoing struggle against the body’s original homeostatic mechanisms.

A key strength of this study is the application of LCGA to model weight loss heterogeneity. Unlike traditional methods that analyze population averages, LCGA allowed us to identify clinically relevant subgroups with distinct prognoses, offering a more granular and patient-centered perspective.

This study has several limitations. First, participants were recruited from only two hospitals within the same city, and the sample size constraints precluded stratified analysis by surgical procedure type, potentially limiting the generalizability of our findings. Future multicenter research with larger and more diverse cohorts is needed to validate and extend these findings. Second, while metabolic and bariatric surgery outcomes are optimally evaluated over extended timeframes, our follow-up was limited to 18 months postoperatively due to time constraints. It is acknowledged that prolonged follow-up is the established standard for robustly assessing the ultimate clinical utility of any predictive model for weight regain. Therefore, future investigations with larger, more heterogeneous cohorts and extended follow-up durations are warranted to comprehensively characterize early weight loss trajectories and their subsequent evolution over time.

Conclusion

This study identified two distinct early postoperative weight loss trajectories following bariatric surgery: rapid weight loss group and slow weight loss group. Our findings demonstrate that patients exhibiting more early rapid weight loss had significantly lower incidence of weight regain at 18 months postoperatively compared with those with slower initial weight reduction. These results suggest that early postoperative weight loss patterns may serve as valuable predictor of medium-term weight maintenance outcome.

Authors’ Contributions

F.-y.Y.: Writing—original draft (equal); formal analysis (lead); review and editing (equal); investigation (equal). B.L.: Writing—original draft (equal); writing—review and editing (equal). H.-b.H.: Investigation (equal). C.-j.P.: Methodology (lead); writing—review and editing (equal). J.-N.G.: Methodology. Li-q.M.: Conceptualization (supporting); funding acquisition; writing—review and editing (equal). X.-h.W.: Conceptualization (supporting); writing—review and editing (equal).

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

Nursing research Project of the First Affiliated Hospital of Soochow University, China (HLYJ-2024-04).

Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Supplemental Material

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