Metabolic adaptation following gastric bypass surgery: Results from a 2-year observational study.

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Ruth Price, Fathimath Naseer, Shu-Dong Zhang, Alexander Miras, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3789295/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Sep, 2024 Read the published version in International Journal of Obesity → Version 1 posted 8 You are reading this latest preprint version Abstract Background/Objectives: Metabolic adaptation is the lowering of basal metabolic rate (BMR) beyond what is predicted from changes in fat mass (FM) and fat-free mass (FFM) and may hamper weight-loss progression. It is unclear whether metabolic adaptation occurs following gastric bypass surgery (GBP) and if it persists. The aim of this study was to evaluate the reduction in BMR that is not explained by changes in body composition in patients following GBP compared to a weight-stable comparator group. Subjects : Thirty-one patients [77.4% female; mean BMI 45.5(SD 7.0) kg/m 2 ; age 47.4 (11.6)y] who underwent GBP, and 32 time-matched comparators [50% female; BMI 27.2(4.6) kg/m 2 ; age 41.8(13.6)y) were evaluated at 1-month pre-surgery, 3-, 12- and 24-months post-surgery. Methods : BMR was measured under standardised residential conditions using indirect calorimetry and body composition using DXA. Linear regression analyses assessed metabolic adaptation post-surgery. Results: After surgery, patients lost a quarter of their body weight [-25.6%(1.8%); p<0.0001] consisting mainly of FM (4:1 FM to FFM loss ratio) at 24-months post-surgery. Absolute BMR (MJ/d) reduced by 25.7% at 24-months post-surgery with values becoming similar to the comparator group from 3-months post-surgery. Positive associations were observed between changes in BMR and changes in FFM and FM (P<0.03). Metabolic adaptation was present in patients during the 1) rapid weight loss phase (6.9kg/month at 3-months post-surgery)(p=0.011), 2) slower weight loss phase (1.6kg/month from 3 to 12-months post-surgery)(p<0.0001), and, 3) weight maintenance phase (24-months post-surgery)(p=0.00073). However, the degree of metabolic adaptation observed in GBP patients was similar to the weight-stable comparator group (no metabolic adaptation) from 12-months post-surgery onwards (3-months; p=0.01, 12-months; p=0.26, 24-months post-surgery; p=0.70). Conclusion : These results suggest that there is a potential biological mechanism of surgery that attenuates the expected postoperative downregulation in BMR thus helping GBP patients maintain weight loss. Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity Health sciences/Health care/Weight management Figures Figure 1 Figure 2 Figure 3 Introduction Gastric Bypass Surgery (GBP) is one of the most frequently performed bariatric surgeries ( 1 ). It yields significant weight loss of approximately 60–70% of excess body weight at 1-year post-surgery with improvements in associated health complications and quality of life outcomes( 2 ). There are multiple mediators involved in weight loss following GBP that cannot be explained by restrictive and malabsorptive mechanisms ( 3 , 4 ). Identifying such mediators is essential for both advancing current understanding of the biology of obesity and providing evidence-based clinical advice for managing weight loss and related clinical outcomes ( 5 ). During negative energy balance and weight loss, metabolic adaptation leads to downregulation in measured BMR that cannot be explained exclusively by a reduction in fat-mass (FM) and fat-free mass (FFM) ( 6 – 8 ). It is thought to favour resistance to weight loss and contribute to weight recividism ( 9 ). In a retrospective study of metabolic adaptation, individuals who lost weight through non-surgical interventions exhibited a higher degree of metabolic adaptation (at 7-months post-intervention) compared to individuals after GBP (at 6-months post-surgery) ( 10 ). Another observational study noted that despite GBP patients losing significantly more weight and lean body mass than gastric banding patients, the degree of metabolic adaptation between groups was similar at 6-months post-surgery ( 11 )Taken together, these data suggest a potential biological mechanism that may help explain, at least in part, the substantial and sustained weight loss after GBP. Additionally, it has been demonstrated that the observed metabolic adaptation dissipated at follow-ups of > 6 months post-surgery ( 10 , 12 , 13 ). However, no study to date has evaluated longer term (≥ 2 years) changes in metabolic adaptation following GBP. Therefore, the aim of this study was to evaluate the prospectively measured reduction in BMR that is not explained by changes in FM and FFM in patients undergoing GBP and compare with a non-surgical weight stable comparator group at 3-, 12- and 24-months post-surgery. It was hypothesised that the expected down regulation of BMR observed after weight-loss, over and above that explained by changes in FM and FFM, would be attenuated in patients by 3-month post -surgery. Materials/Subjects and Methods Study design The hypothesis for this paper was tested as a secondary hypothesis within a wider study investigating changes in energy intake following GBP, which is described in detail elsewhere ( 14 , 15 ). Owing to the novel study protocol, the sample size was estimated from the patient population recruited for a randomised controlled trial ( 16 ) that detected significant differences in self-reported energy intake between Vertical Banded Gastrectomy (VBG (n = 7) and GBP (n = 9) participants at 6 years post-surgery. The SD associated with the change in dietary fat intake (% energy) from pre- to post-surgery and a 95% confidence interval was applied as follows: $$n= {\left(\frac{confidence level x SD}{margin of error}\right)}^{2}$$ $$n= {\left(\frac{1.96 x 1.9}{1}\right)}^{2}$$ $$n= 14$$ It was estimated that at least 16 participants are required for the present study based on a 14% attrition rate reported by another similar intake study ( 17 ). However, as the proposed study protocol was intensive for study participants, 32 GBP patients and a similar number of weight-stable comparators were recruited to account for a potentially higher attrition rate. In brief, in this study, all participants were required to complete four fully residential study assessments at 1-month pre-surgery, 3, 12- and 24-months post-surgery at the Human Intervention Studies Unit (HISU) at Ulster University. HISU consists of en-suite bedrooms, a communal sitting room, a metabolic kitchen (closed access to participants) and communal dining room. Participants arrived at HISU at approximately 6pm on day 1 for an initial acclimation period where no measurements were performed. Following a standard meal of Spaghetti Bolognese for dinner (if requested), participants fasted from 10pm. Measurements started early on day 2 (approx. 7am) and lasted until bedtime (approx. 11pm) on day 2. Participants remained sedentary throughout but were free to engage in light activities such as reading, crafts and watching television. Ethical approval was granted by the West of Scotland Research Ethics Service (WoSRES) (REC 16/WS/0056, IRAS 200567). The study was registered as a clinical trial (NCT03113305) (clinicaltrials.gov) and was conducted according to the principles of the Helsinki declaration. The primary outcome of the work was changes in dietary energy intake, with the work presented in this study included as secondary outcomes. Prior to the start of the study written informed consent was obtained from all participants. Patients were recruited from four sites in the United Kingdom (Phoenix Health NHS, Phoenix Health Private, London Imperial Weight Centre and North Bristol NHS Trust) and one site in the Republic of Ireland (Letterkenny University Hospital). Inclusion criterion were ≥ 18 years old with a scheduled GBP. Weight-stable (> 6 months) comparators were time-matched and recruited by posters, email circulations, radio, and social media platforms. The purpose of the comparator group was to account for external factors which could potentially impact GBP patients over the study period, as well as any change in behaviour in the residential unit over the four time points. Inclusion criterion were ≥ 18 years old with no plans to alter current body weight. Exclusion criteria for all participants were: presence of physical or psychological conditions affecting food intake; strict dietary restrictions, food allergies and pregnant or lactating women. Total body weight, body mass index (BMI), FM, FFM, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were assessed under standardised conditions using the total body GE Lunar iDXA scan (GE Healthcare, USA). Height was measured during the initial visit to the nearest 0.1cm using a wall-mounted stadiometer (Seca Ltd, Hamburg, Germany (%CV = 0.23%). If the participant’s body width exceeded the standard dimension of the DXA’s scanning area, they were positioned such that the right half of the body was fully within the scan field. Half scans have shown satisfactory validity ( 18 ). DXA measurements were conducted by trained researchers and verified by a qualified health care professional. Percentage total weight loss (%TWL) was calculated as \(\%TWL=\frac{\left(weight prior to surgery - follow-up weight\right)}{weight prior to surgery} x 100 .\) Postoperative weight loss was also expressed as a percentage excess of weight loss (%EWL) following the formula: \(EWL=\frac{(\text{w}\text{e}\text{i}\text{g}\text{h}\text{t} \text{p}\text{r}\text{i}\text{o}\text{r} \text{t}\text{o} \text{s}\text{u}\text{r}\text{g}\text{e}\text{r}\text{y} - \text{f}\text{o}\text{l}\text{l}\text{o}\text{w}-\text{u}\text{p} \text{w}\text{e}\text{i}\text{g}\text{h}\text{t})}{(\text{w}\text{e}\text{i}\text{g}\text{h}\text{t} \text{p}\text{r}\text{i}\text{o}\text{r} \text{t}\text{o} \text{s}\text{u}\text{r}\text{g}\text{e}\text{r}\text{y} - \text{w}\text{e}\text{i}\text{g}\text{h}\text{t} \text{c}\text{o}\text{r}\text{r}\text{e}\text{s}\text{p}\text{o}\text{n}\text{d}\text{i}\text{n}\text{g} \text{t}\text{o} \text{B}\text{M}\text{I} = 25 \text{k}\text{g}/{\text{m}}^{2})} x 100\) BMR was measured under standardised conditions following an overnight fast (from 10pm) using open-circuit portable indirect calorimetry (ECAL, Metabolic Health Solutions) by a trained researcher. Each participant was awakened at approximately 7am in the morning to empty their bladder and return to rest for at least 30 minutes in a quiet, darkened and thermoneutral room before the measurement was made. Distractions such as use of mobile phones were not permitted. Data were recorded for a minimum of 8-minutes and was terminated after readings had been stable for 45-seconds. The first 2-minutes of the measurement period were automatically discarded by the ECAL software, with any other anomalous recordings (e.g., coughing, removal of mouthpiece) also discarded as ‘false’ readings. BMR values were calculated using the Weir formula ( 19 ). To determine the magnitude of metabolic adaptation following GBP, this study used the gold standard methodology ( 6 , 7 , 10 , 11 , 20 , 21 ). The baseline BMR (dependent variable) for both patient and comparator groups was used to generate a linear regression model with multiple predictor variables (independent variables) that may affect BMR values - baseline FM, FFM, age, gender, medications, group (participants) and medical conditions. This model was used to predict the BMR (pBMR) at 3-,12- and 24-months post-surgery. $$\text{p}\text{B}\text{M}\text{R} \left(\text{M}\text{J}/\text{d}\text{a}\text{y}\right)= 3.529-\left(1.509 \text{x} \text{P}\text{a}\text{r}\text{t}\text{i}\text{c}\text{i}\text{p}\text{a}\text{n}\text{t}\text{s}\right)+\left(0.511 \text{x} \text{G}\text{e}\text{n}\text{d}\text{e}\text{r}\right) - \left(0.001 \text{x} \text{A}\text{g}\text{e} \text{i}\text{n} \text{y}\text{e}\text{a}\text{r}\text{s}\right) + \left(0.022 \text{x} \text{F}\text{M} \text{i}\text{n} \text{k}\text{g}\right) + \left(0.088 \text{x} \text{F}\text{F}\text{M} \text{i}\text{n} \text{k}\text{g}\right) + \left(0.936 \text{x} \text{M}\text{e}\text{d}\text{i}\text{c}\text{a}\text{t}\text{i}\text{o}\text{n} \text{t}\text{h}\text{a}\text{t} \text{a}\text{f}\text{f}\text{e}\text{c}\text{t} \text{B}\text{M}\text{R}\right) - \left(0.513 \text{x} \text{D}\text{i}\text{s}\text{e}\text{a}\text{s}\text{e} \text{t}\text{h}\text{a}\text{t} \text{a}\text{f}\text{f}\text{e}\text{c}\text{t} \text{B}\text{M}\text{R}\right)$$ Participants (1 for Patient, 2 for Comparator), Gender (1 Female, 2 Male), Medications that affect BMR (1 Prescribed, 2 Not prescribed), Diseases that affect BMR (1 Present, 2 Absent). Finally, the residual BMR (resBMR) is defined as the difference between the observed BMR (as measured by indirect calorimetry) from the predicted BMR based on the above linear regression equation. $$BMR residual =(\text{m}\text{e}\text{a}\text{s}\text{u}\text{r}\text{e}\text{d} \text{B}\text{M}\text{R} - \text{p}\text{r}\text{e}\text{d}\text{i}\text{c}\text{t}\text{e}\text{d} \text{B}\text{M}\text{R})$$ And the presence of metabolic adaptation is defined as resBMR being significantly different from zero. Statistical analysis Statistical analyses were performed using IBM SPSS for windows (UK, version 26.0) and R (version 4.2). Baseline summary statistics are expressed as mean (SD) for continuous variables, or as numbers (percentage) for categorical variables. Results from linear mixed models were presented as least squares mean (SEM). At each time-point, there were some random missing values due to missed appointments (Fig. 1 ) and, in a few cases, technical issues with measuring equipment. Given that it is reasonable to assume that such values were missing purely at random, mixed effects linear models were fitted for the main outcome measures of interests (Weight, BMI, FM, FFM, LBM, VAT, SAT, and BMR). In each of these linear mixed models, participant IDs were fitted as random effects, with participant group (patient or comparator), time and the interaction between group and time as fixed effects. For such linear mixed modelling, no imputation of missing values was conducted as this was unnecessary. From the fitted linear mixed models, the estimated means and standard errors of the outcome measure were then obtained for all group and time point combinations. Where applicable and deemed interesting, comparative analysis between different time points per group, or between the two groups per time point were conducted by testing the corresponding general linear hypothesis. Mixed model analysis was applied to the residual BMR to determine the presence or absence of metabolic adaptation. Metabolic adaptation was considered to have occurred if BMR residual (magnitude of metabolic adaptation) was significantly different from zero (p ≤ 0.05). Pearson correlation coefficients were used to study associations between changes in FM, FFM, %FFM/ weight and BMR in patients. P-values of ≤ 0.05 were considered as statistically significant. Results Sixty-six participants attended the baseline study appointment (Fig. 1 ). Three of the patients were subsequently excluded as they did not receive GBS (Sleeve Gastrectomy surgery, n 2; medical issues, n 1). Of the remaining 63 participants, two individuals from the comparator group were uncontactable after the first appointment, leaving 31 patients and 30 comparators (Fig. 1 ). Following the COVID-19 pandemic lock down period only patients were followed up for their final 24-month appointment. The patient group had a higher proportion of females and were more likely to present with diabetes mellitus pre-surgery. Body composition A reduction in all anthropometric variables was observed in patients by 3-months post-surgery, with stability in changes from pre-surgery achieved at 12- and 24-months (Table 1 ). Over the 24-month study period patients had lost over a quarter of their total mean weight − 25.6% (SD 1.8)% from pre-surgery (P < 0.001), while the comparator group remained weight-stable throughout the study period (p = 0.96). Table 1 Mean anthropometric measures and basal metabolic rate at baseline (1-month pre-surgery) and at 3-, 12- and 24-months post-surgery 1-month pre-surgery 3-months post-surgery 12-months post-surgery 24-months post-surgery ANOVA P values for Weight (kg) Patients Comparator group 122.90(3.08) 78.01 (3.13) 102.31 (3.13) 78.25 (3.14) 87.92 (3.08) 78.63(3.13) 89.86 (3.13) 79.78 (3.34) Group < 0.0001 Time < 0.0001 Group:Time < 0.0001 BMI (kg/m 2 ) Patients Comparator group 45.47 (0.92) 27.22 (0.94) 37.81 (0.94) 27.25 (0.95) 32.38 (0.92) 27.39 (0.94) 33.06 (0.94) 27.64 (1.03) Group < 0.0001 Time < 0.0001 Group:Time < 0.0001 Fat mass (kg) Patients Comparator group 62.07 (2.00) 26.17 (2.04) 46.78 (2.06) 26.55 (2.05) 33.73 (2.00) 26.62 (2.04) 35.98 (2.06) 27.00 (2.26) Group < 0.0001 Time < 0.0001 Group:Time < 0.0001 Fat-free mass (kg) Patients Comparator group 60.83 (1.90) 51.84 (1.94) 55.54 (1.91) 51.70 (1.94) 54.19 (1.90) 52.01 (1.94) 53.84 (1.91) 52.80 (1.96) Group 0.14 Time < 0.0001 Group:Time < 0.0001 LBM (kg) Patients Comparator group 58.04 (1.83) 49.13 (1.86) 52.79 (1.83) 48.99 (1.86) 51.52 (1.83) 49.30 (1.86) 51.09 (1.83) 50.10 (1.89) Group 0.13 Time < 0.0001 Group:Time < 0.0001 VAT (kg) Patients Comparator group 3.10 (0.19) 1.00 (0.20) 1.93 (0.20) 1.02 (0.20) 1.29 (0.19) 1.06 (0.20) 1.20 (0.20) 1.05 (0.21) Group 0.018 Time < 0.0001 Group:Time < 0.0001 SAT (kg) Patients Comparator group 58.96 (1.91) 25.17 (1.94) 44.85 (1.97) 25.53 (1.95) 32.44 (1.91) 25.55 (1.94) 34.78 (1.97) 25.95 (2.15) Group < 0.0001 Time < 0.0001 Group:Time < 0.0001 BMR (MJ/day) Patients Comparator group 9.93 (0.38) 7.03 (0.40) 7.72 (0.41) 7.52 (0.38) 7.18 (0.37) 6.52 (0.39) 7.38 (0.39) 6.57 (0.46) Group 0.0077 Time < 0.0001 Group:Time < 0.0001 Data presented as mean (SE) based on the linear mixed model estimate. In each of these linear mixed models, participants were fitted as random effects, while group, time and the interaction between group and time as fixed effects. For all the variables listed in this table, from Weight to BMR, ANOVA tests suggest that the interaction between Group and Time is highly significant, meaning that the difference between the two Groups depends on Time. (The time trajectory curves for the two groups are far from parallel based on the linear mixed models, see Supplementary figures). BMI Body Mass Index, BMR Basal Metabolic Rate, LBM Lean Body Mass, SAT Subcutaneous Adipose Tissue, VAT Visceral Adipose Tissue. At 24-months post-surgery 71% (n = 22) of patients had achieved successful weight loss (> 50%EWL), with three patients regaining weight ( 50%EWL) from their 12-month measurement. The majority of weight loss following GBP was accounted for by a decrease in FM. On average, patients lost 40% of pre-surgery FM and 11% of pre-surgery FFM at 24-months post-surgery, a ratio of roughly 4:1 FM to FFM in terms of percentage loss. By 3 months post-surgery FFM was similar in both the patient and comparator groups, with p-values being 0.68, 0.97, and 1.0 respectively for Month 3, 12 and 24 post surgery. On the other hand, comparing patient versus comparator groups in terms of FM, the p-values were < 0.0001 for the 3rd month, 0.10 for 12th month, and 0.028 for 24-months post-surgery. Therefore, patients’ FM largely remained higher than comparators and as a result, the mean total body %FM decreased at each timepoint from 50.0% pre-surgery to 40.0% 24-months after surgery (Fig. 2 ). Basal Metabolic Rate In the linear mixed modelling for all outcome variables from weight to BMR, ANOVA tests suggest that the interaction between group and time is highly significant – meaning that the difference between groups depend on time. The time course curves of the two groups are therefore not parallel (See supplementary figures). Based on the estimates from the linear mixed models, absolute BMR (MJ/d) was 29% higher in patients than the comparator group pre-surgery [9.93(0.38) vs 7.03(0.40) MJ/d for patients and comparator group respectively, p < 0.0001] but was similar to the comparator group at all post-surgery time-points despite a significant reduction in BMR post-surgery (< 22%). Absolute BMR values remained stable in the comparator group at all three post-surgery assessments in comparison to pre-surgery (<+/-7%) (p = 0.86, 0.86, and 0.94 respectively). In the linear mixed modelling analysis for resBMR (the metabolic adaptation measure), ANOVA test indicated that the group time interaction is at the margin of statistical significance (p = 0.052) - suggesting that a simpler model without the group time interaction could be used. However, a log likelihood ratio test comparing the models with and without the interaction, indicated that the full model (with interaction) is still better than that model without the interaction (P = 0.045). Therefore, for the measure of metabolic adaptation, the full model was retained to obtain the least square means and standard errors for all the group time point combinations. Based on the obtained linear mixed model, the hypotheses regarding whether resBMR estimate at each time point per group is different from 0 (p ≤ 0.05) were tested as shown in Fig. 3 . Metabolic adaptation was present post-surgery for patients only (p = 0.011 at 3 months post -GBP; p < 0.0001 at 12-months and p = 0.00073 at 24-months post-surgery. Figure 3 shows the time trajectory plot for the degree of metabolic adaptation for patient compared to the comparator group at 3-months, 12-months and 24-months following GBP. A significant difference between groups was noted at 3-months post-surgery (P = 0.014) and the degree of metabolic adaptation was similar between groups pre-surgery (p = 1.00), 12-months post-surgery (p = 0.26) and 24-months post-surgery (p = 0.70). Finally, in patients, a positive correlation was observed between changes in FFM (kg) and changes in absolute BMR at 12-months (r = 0.45, p = 0.025) and 24 months post-surgery (r = 0. 50 , p = 0.012) (Table 2 ). Similarly, a positive correlation was observed between changes in %FFM/weight and changes in absolute BMR at 12-months (r = 0.50, p = 0.010) and 24-months post-surgery (r = 0.50, p = 0.011). Changes in FM (kg) and changes in absolute BMR were positively correlated at 12-months post-surgery (r = 0.65, p = 0.00041) and 24-months post-surgery (r = 0.64, p = 0.00053). No other associations were observed. Table 2 Associations between changes in body composition and basal metabolic rate for patients and comparator group at 3-months, 12-months and 24-months post-surgery. 3-months post-surgery 12-months post-surgery 24-months post-surgery Patients (n 20) Comparator group (n 24) Patients (n 25) Comparator group (n 21) Patients (n 25) Comparator group (n 17) ΔBMR (MJ/day) ΔBMR (MJ/day) ΔBMR (MJ/day) ΔFM (kg) 0.20 (95%CI: -0.27 to 0.59; p = 0.40) 0.23 (95%CI: -0.19 to 0.58; p = 0.28) 0.65 a (95%CI: 0.35 to 0.83; p = 0.00041) 0.25 (95%CI: -0.21 to 0.61; p = 0.28) 0.64 a (95%CI: 0.33 to 0.83; p = 0.00053) 0.04 (95%CI: -0.45 to 0.51; p = 0.89) ΔFFM (kg) 0.33 (95%CI: -0.14 to 0.67; p = 0.16) -0.20 (95%CI: -0.56 to 0.23; p = 0.36) 0.45 a (95%CI: 0.06 to 0.72; p = 0.025) -0.13 (95%CI: -0.53 to 0.32; p = 0.60) 0.50 a (95%CI: 0.13 to 0.74; p = 0.012) -0.02 (95%CI: -0.49 to 0.47; p = 0.94) Δ%FFM/weight kg -0.11 (95%CI: -0.53 to 0.35; p = 0.64) 0.24 (95%CI: -0.18 to 0.58; p = 0.27) 0.50 a (95%CI: 0.13 to 0.75; p = 0.010) 0.26 (95%CI: -0.20 to 0.62; p = 0.26) 0.50 a (95%CI: 0.13 to 0.75; p = 0.011) 0.09 (95%CI: -0.41 to 0.55; p = 0.72) Associations analysed using Pearson’s correlation. Data presented as r (correlation coefficient) with 95% confidence interval (CI). a denotes P < 0.05 indicating a statistically significant correlation value. BMR Basal Metabolic Rate, FM Fat Mass, FFM Fat-Free Mass, Δ Change values from baseline Discussion This is the first prospective study to measure BMR and body composition using standardised gold-standard methodology at 3-, 12- and 24-months in patients after GBP surgery and time-matched comparators. It was hypothesized that the expected down regulation of BMR observed after weight-loss, over and above that explained by changes in FM and FFM, would be attenuated in patients 3-months following GBP. Following weight loss, there was a significant decline in absolute BMR levels - largely attributable to the decline in FFM. Metabolic adaptation (the change in BMR that is greater than would be predicted from changes in body composition alone during negative energy balance) was observed in patients at 3-months post-surgery when the magnitude of weight loss is greatest (approx. 6.8 kg mean weight loss per month from baseline). This finding is in line with previous studies that observed an adaptive response with approximately 5.5kg mean weight loss per month at ≤ 6 months post-surgery ( 10 , 13 , 22 ). This body composition independent reduction in BMR is hypothesized to be an evolutionary biological process that “slows down metabolism” during periods of food scarcity or significant negative energy balance to increase chances of survival( 20 , 21 ). It appears to be induced by a collection of physiological and neuroendocrine shifts, such as a reduction in plasma insulin levels and associated lower glycogen levels to sustain the brain and body’s energy requirements ( 23 ). At 12-months post-surgery, mean weight loss decreased by 57.4% per month (approx. 2.9kg mean weight loss per month) and this was also accompanied by a significant degree of metabolic adaptation. This finding is at variance with Knuth et al’s (2014) data that demonstrated a lack of metabolic adaptation with approx. 3.4kg mean weight loss per month at 12-months post-surgery. They also used standardised indirect calorimetry to measure absolute BMR and the recommended linear regression method to assess the degree of metabolic adaptation. However, the small sample size (n 13) could have driven a statistically nonsignificant result (type 2 error). Conversely, despite an even smaller sample size (n 5), Tam et al. ( 24 ) reported the same finding as the present paper where metabolic adaptation was observed at 12-months following GBP. However, they defined metabolic adaptation as a “negative residual value” rather than the recommended approach of assessing whether the residual is significantly different from zero ( 25 ). Therefore, studies with adequate sample sizes to detect clinically relevant differences and appropriate statistical techniques to assess metabolic adaptation are required to confirm that the subsequent decline in BMR was not attributed solely to the reduction in FM and FFM levels 12-months following surgical weight loss. Nevertheless, the degree of metabolic adaptation observed in the GBP group was only statistically different from the values obtained by the weight-stable comparator group at 3-months post-surgery. From 12-months post-surgery, the values were similar between groups. This suggests that although metabolic adaptation was present in the surgery group – it appears to be attenuated in the longer-term post-surgery - and this may positively impact weight loss and limit weight recidivism. The underlying biological mechanism of this phenomenon is unclear but it is possible that during a slower rate of weight loss and/or during the weight loss maintenance phase, the therapeutic effects from adipocentric signals such as enhanced leptin sensitivity (owing to significant FM reduction) may aid in attenuating the degree of metabolic adaptation through its actions on triidodothyronine (T3) balance and the mitochondrial content and coupling alterations ( 26 ). The observed increase in overall %FFM per body weight may contribute as well. A moderate positive correlation was observed between changes in FFM (kg) and changes in absolute BMR at 12- and 24- months post-surgery. As mentioned above, the reduction in mean FFM explains the consequent reduction in absolute BMR values in patients. Nevertheless, similar to the degree of metabolic adaptation, the mean absolute BMR values were similar between patients and the comparator group post-surgery; despite the comparator group maintaining their weight and FFM levels, suggesting again that the usual compensatory metabolic response which minimises weight loss during periods of energy deficit ( 27 ) appears to be blunted in patients following GBP. Similarly, because BMR is also dependent on fat-mass, a positive correlation was observed between changes in FM and BMR at 12- and 24-months post-surgery ( 28 , 29 ). As discussed above, the significant reduction in FM levels with concurrent enhanced leptin bioavailability may potentially contribute to attenuating the expected reduction in BMR and metabolic adaptation following surgical weight loss ( 26 ). Limitations of the methodology include the absence of randomisation and matching the comparator group for BMI and sex. Physical activity level was not measured in this study. However, BMR measurements within this study were taken at complete rest > 8 hours after the last meal to avoid all possibility of physical activity and meal induced thermogenesis. The participants engaged in only sedentary activity while residing in HISU. As such, BMR measurement should not be impacted by physical activity, apart from indirectly by body composition changes. Finally, although this study included a larger sample size compared to existing similar studies –the sample size calculation was based on energy intake (the primary outcome). Nevertheless, this study is unique by investigating changes in BMR in GBP patients up to 24-months post-surgery with a concurrent weight-stable comparator group in a residential setting using highly controlled gold-standard protocols. This included taking BMR measurements on awakening after a controlled fast to avoid activity and meal-induced thermogenesis which is often cited as a study limitation ( 30 ). In this study, linear regression analysis was used to assess improvements in metabolic compensation following weight loss instead of the ratio method (i.e., BMR/weight) as the latter changes could be a direct result of an increase in FFM:FM ratio per kilogram of weight following weight loss ( 7 , 10 ). Future controlled intervention human studies are required to clarify the kinetic changes in plasma levels of T3, insulin and leptin and its impact on metabolic adaptation during the rapid weight loss and weight maintenance phase following GBP. It might be useful to study metabolic adaptation in associated physiological responses such as heart rate and glomerular filtration rate too ( 23 ). The degree of metabolic adaptation should be assessed using standardised mathematical modelling as discussed above. It is also worthwhile investigating whether standardising the variables used in the linear regression analysis could aid in comparing future studies that investigate metabolic adaptation following GBP. As the highly metabolically active organs and skeletal muscle are considered major sites of metabolic adaptation( 31 ), medical imaging techniques can be used to measure the volume and mass of FFM components i.e. liver that reduces in size significantly following weight loss. A weight-matched control group, losing weight via nutrition therapy and a similar activity level as the GBP group, is recommended albeit difficult to execute. Finally, as inter-individual post-operative weight loss and clinical response vary considerably( 4 ) and remain poorly understood, it should prompt further research in understanding the predictors (neuroendocrine, gender, age, stress, activity level) and mechanisms of metabolic adaptation following weight loss. In conclusion, the outcomes of this prospective study suggest that metabolic adaptation is present during the rapid weight loss phase (at least 6.9kg mean weight loss per month) and weight maintenance phase (from 12-months onwards) following GBP. Therefore, the downregulation in BMR was not fully explained by changes in FM and FFM. However, it appears that the degree of metabolic adaptation was attenuated in the surgical group from 12-months onwards and this may potentially contribute to sustained weight loss and limit weight recidivism. Understanding the underlying mechanisms and predictors that attenuate metabolic adaptation following GBP could potentially help the development of treatments to aid weight loss maintenance after non-surgical weight loss or even weight regain after surgery. Abbreviations Basal Metabolic Rate (BMR), Dual-Energy X-Ray Absorptiometry (DXA), Fat Mass (FM), Fat-Free Mass (FFM), Gastric Bypass Surgery (GBP). Declarations Funding Support: Research supported by the US-Ireland Research and Development Partnership program though the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (R01DK106112), the Health and Social Care R&D Division of Northern Ireland (STL/5062/14) and the Medical Research Council (MC_PC_16017), and the Health Research Board of the Republic of Ireland (USIRL-2006-2). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. Author contributions: CWLR, MBEL, RKP were responsible for funding acquisition and designing the research; FN, TR, AB, AM, ZB, DK, DJP, CWLR recruited the participants, FN, TR, MM, AB, MBEL, RKP conducted the research; FN, SDZ, TR, MM, AB, HS, MBEL, RKP analysed the data; FN, AM, SDZ, CWLR, MBEL, RKP wrote the original draft; All authors read and approved the final manuscript. Competing Interests: CLW reports; grants from Science Foundation Ireland, grants from Health Research Board, during the conduct of the study; other from NovoNordisk, other from GI Dynamics, personal fees from Eli Lilly, grants and personal fees from Johnson and Johnson, personal fees from Sanofi Aventis, personal fees from Astra Zeneca, personal fees from Janssen, personal fees from Bristol-Myers Squibb, personal fees from Boehringer-Ingelheim, outside the submitted work. Data availability statement: Data described in the manuscript, including de-identified individual participant data, code book, and analytic code will be made available upon request pending application and approval. References Douglas IJ, Bhaskaran K, Batterham RL, Smeeth L. Bariatric Surgery in the United Kingdom: A Cohort Study of Weight Loss and Clinical Outcomes in Routine Clinical Care. PLoS Med. 2015; 12(12). Versteegden DPA, van himbeeck MJJ, Nienhuijs SW. Improvement in quality of life after bariatric surgery: sleeve versus bypass. Surgery for Obesity and Related Diseases. 2018; 14(2):170–4. Ionut V, Bergman RN. Mechanisms Responsible for Excess Weight Loss after Bariatric Surgery. J Diabetes Sci Technol. 2011; 5(5):1263–82. Ruiz-Lozano T, Vidal J, de Hollanda A, Scheer FAJL, Garaulet M, Izquierdo-Pulido M. Timing of food intake is associated with weight loss evolution in severe obese patients after bariatric surgery. Clinical Nutrition. 2016; 35(6):1308–14. Cadena-Obando D, Ramírez-Rentería C, Ferreira-Hermosillo A, Albarrán-Sanchez A, Sosa-Eroza E, Molina-Ayala M, et al. Are there really any predictive factors for a successful weight loss after bariatric surgery? BMC Endocr Disord. 2020; 20(1). Johannsen DL, Knuth ND, Huizenga R, Rood JC, Ravussin E, Hall KD. Metabolic slowing with massive weight loss despite preservation of fat-free mass. J Clin Endocrinol Metab. 2012; 97(7):2489–96. Browning MG, Khoraki J, Campos GM. Regression-based approach is needed to compare predicted and measured resting metabolic rate after weight loss and body composition changes. Surg Obes Relat Dis. 2018; 14(6):807–9. Hopkins M, Finlayson G, Duarte C, Whybrow S, Ritz P, Horgan GW, et al. Modelling the associations between fat-free mass, resting metabolic rate and energy intake in the context of total energy balance. Int J Obes. 2016; 40(2):312–8. Martins C, Gower BA, Hunter GR. Metabolic adaptation delays time to reach weight loss goals. Obesity. 2022; 30(2):400–6. Knuth ND, Johannsen DL, Tamboli RA, Marks-Shulman PA, Huizenga R, Chen KY, et al. Metabolic adaptation following massive weight loss is related to the degree of energy imbalance and changes in circulating leptin. Obesity. 2014; 22(12):2563–9. Browning MG, Rabl C, Campos GM. Blunting of adaptive thermogenesis as a potential additional mechanism to promote weight loss after gastric bypass. Surgery for Obesity and Related Diseases. 2017; 13(4):669–73. Dirksen C, Jørgensen NB, Bojsen-Møller KN, Kielgast U, Jacobsen SH, Clausen TR, et al. Gut hormones, early dumping and resting energy expenditure in patients with good and poor weight loss response after Roux-en-Y gastric bypass. Int J Obes. 2013; 37(11):1452–9. Schmidt JB, Pedersen SD, Gregersen NT, Vestergaard L, Nielsen MS, Ritz C, et al. Effects of RYGB on energy expenditure, appetite and glycaemic control: A randomized controlled clinical trial. Int J Obes. 2016; 40(2):281–90. Livingstone MBE, Redpath T, Naseer F, Boyd A, Martin M, Finlayson G, et al. Food intake following gastric bypass surgery: patients eat less but do not eat differently. J Nutr. 2022; 152(11):2319–2332 Redpath T, Naseer F, Price RK, Boyd A, Martin M, le Roux CW, et al. Evaluation of the impact of gastric bypass surgery on eating behaviour using objective methodologies under residential conditions: Rationale and study protocol. Contemp Clin Trials Commun. 2021; 24:100846. le Roux CW, Bueter M, Theis N, Werling M, Ashrafian H, Lowenstein C, et al. Gastric bypass reduces fat intake and preference. Am J Physiol Regul Integr Comp Physiol. 2011; 301(4):R1057–66. Kenler HA, Brolin RE, Cody RP. Changes in eating behavior after horizontal gastroplasty and Roux-en-Y gastric bypass. AJCN. 1990; 52:87–92. Rothney MP, Brychta RJ, Schaefer E v., Chen KY, Skarulis MC. Body composition measured by dual-energy x-ray absorptiometry half-body scans in obese adults. Obesity. 2009; 17(6):1281–6. Weir JB de v. New methods for calculating metabolic rate with special reference to protein metabolism. Journal of physiology. 1949; 109:1–9. Tremblay A, Chaput JP. Short communication: Adaptive reduction in thermogenesis and resistance to lose fat in obese men. BJN; 102(4):488–92. Karl JP, Roberts SB, Schaefer EJ, Gleason JA, Fuss P, Rasmussen H, et al. Effects of carbohydrate quantity and glycemic index on resting metabolic rate and body composition during weight loss. Obesity. 2015; 23(11):2190–8. Carrasco F, Papapietro K, Csendes A, Salazar G, Echenique C, Lisboa C, et al. Changes in resting energy expenditure and body composition after weight loss following Roux-en-Y gastric bypass. Obes Surg. 2007; 17(5):608–16. Müller MJ, Bosy-Westphal A, Muller MJ, Bosy-Westphal A, Müller MJ, Bosy-Westphal A. Adaptive thermogenesis with weight loss in humans. Obesity. 2013; 21(2):218–28. Tam CS, Rigas G, Heilbronn LK, Matisan T, Probst Y, Talbot M. Energy Adaptations Persist 2 Years After Sleeve Gastrectomy and Gastric Bypass. Obes Surg. 2016; 26(2):459–63. Galgani JE, Santos JL. Insights about weight loss-induced metabolic adaptation. Obesity. 2016; 24(2):277–8. Guijarro A, Osei-Hyiaman D, Harvey-White J, Kunos G, Suzuki S, Nadtochiy S, et al. Sustained weight loss after Roux-en-Y gastric bypass is characterized by down regulation of endocannabinoids and mitochondrial function. Ann Surg. 2008; 247(5):779–90. Westerterp KR. Metabolic adaptations to over - and underfeeding - Still a matter of debate? Eur J Clin Nutr. 2013; 67:443–5. Johnstone A. Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. AJCN. 2005; 82:941–8. Galgani JE, Santos JL. Insights about weight loss-induced metabolic adaptation. Obesity. Blackwell Publishing Inc.; 2016; 24:277–8. Westerterp KR. Predicting resting energy expenditure: a critical appraisal. Eur J Clin Nutr. 2023; 77:953–958 Müller MJ, Enderle J, Bosy-Westphal A. Changes in Energy Expenditure with Weight Gain and Weight Loss in Humans. Curr Obes Rep. 2016; 5:413–23. Additional Declarations (Not answered) Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 03 Sep, 2024 Read the published version in International Journal of Obesity → Version 1 posted Editorial decision: revise 02 Apr, 2024 Review # 1 received at journal 22 Jan, 2024 Reviewer # 2 agreed at journal 19 Jan, 2024 Reviewer # 1 agreed at journal 10 Jan, 2024 Reviewers invited by journal 09 Jan, 2024 Submission checks completed at journal 22 Dec, 2023 First submitted to journal 21 Dec, 2023 Editor assigned by journal 21 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3789295","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":266147973,"identity":"cc6fd233-a65b-47b5-8cda-067c19929f76","order_by":0,"name":"Ruth Price","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACHhBhwMDAz8DcABdkbMChGkWLZANQ3QHitYB0HSBWi3nPAbbHFQV2csY3Eps/f6i4x8DffoBNcgYeLTJnG9gNzxgkG5vdSGyTOHCmmEHiTAKb5AY8WiT4GdgkGwwOJG4DamE42JbAwHADKPKACC31m2ckNn84+C+BQZ6gFt4GsJYEA4nEBomDDQkMBiAteB3Gc7DdsMEg2XDGmYdtEmeOJfAYnklstsTnfQme5GMPG/7YyfO3Jx/+UFGTICd3/PDBmz14tADjoA2Fy0MoIkGAjZCCUTAKRsEoGOkAAFPQSx0rgTmqAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8765-2842","institution":"Ulster University","correspondingAuthor":true,"prefix":"","firstName":"Ruth","middleName":"","lastName":"Price","suffix":""},{"id":266147974,"identity":"dcf7fbfb-e8ee-4ece-9781-28f16fd70833","order_by":1,"name":"Fathimath Naseer","email":"","orcid":"","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Fathimath","middleName":"","lastName":"Naseer","suffix":""},{"id":266147975,"identity":"4a75cd16-bab8-452a-a126-cb353b6e4e0c","order_by":2,"name":"Shu-Dong Zhang","email":"","orcid":"https://orcid.org/0000-0002-7721-0167","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Shu-Dong","middleName":"","lastName":"Zhang","suffix":""},{"id":266147976,"identity":"e4e1d5c8-01a9-4c2c-86b0-df9c6951ad7e","order_by":3,"name":"Alexander Miras","email":"","orcid":"https://orcid.org/0000-0003-3830-3173","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Miras","suffix":""},{"id":266147977,"identity":"37aad342-5c68-4d6e-9189-8e58060dc808","order_by":4,"name":"Tamsyn Redpath","email":"","orcid":"","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Tamsyn","middleName":"","lastName":"Redpath","suffix":""},{"id":266147978,"identity":"15bf04f1-63c5-406c-97cd-855f8de02a66","order_by":5,"name":"Melanie Martin","email":"","orcid":"","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Martin","suffix":""},{"id":266147979,"identity":"15b1e1a3-4213-49b0-89e7-64c019e6941b","order_by":6,"name":"Adele Boyd","email":"","orcid":"","institution":"Ulster University","correspondingAuthor":false,"prefix":"","firstName":"Adele","middleName":"","lastName":"Boyd","suffix":""},{"id":266147980,"identity":"290876b9-f3cb-41bb-9f46-a0882054c9c5","order_by":7,"name":"Heather Spence","email":"","orcid":"https://orcid.org/0000-0001-7318-5358","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Heather","middleName":"","lastName":"Spence","suffix":""},{"id":266147981,"identity":"5c4e427f-445d-4380-b546-946352af7eb7","order_by":8,"name":"Dimitri Pournaras","email":"","orcid":"","institution":"National Health Service","correspondingAuthor":false,"prefix":"","firstName":"Dimitri","middleName":"","lastName":"Pournaras","suffix":""},{"id":266147982,"identity":"5160bb3a-4be8-4790-9ce4-77ca2e3e0bc2","order_by":9,"name":"David Kerrigan","email":"","orcid":"","institution":"Phoenix health","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Kerrigan","suffix":""},{"id":266147983,"identity":"2a8b9e06-53aa-4a22-a76e-1214c6f8f808","order_by":10,"name":"Zsolt Bodnar","email":"","orcid":"","institution":"Letterkenny University Hospital. Donegal","correspondingAuthor":false,"prefix":"","firstName":"Zsolt","middleName":"","lastName":"Bodnar","suffix":""},{"id":266147984,"identity":"782b324d-867f-43cd-ac86-33a275a1630f","order_by":11,"name":"Carel Le Roux","email":"","orcid":"https://orcid.org/0000-0001-5521-5445","institution":"Conway Institute","correspondingAuthor":false,"prefix":"","firstName":"Carel","middleName":"Le","lastName":"Roux","suffix":""},{"id":266147985,"identity":"b8621d6d-2e46-4e00-a275-ab232f7038d1","order_by":12,"name":"M Livingstone","email":"","orcid":"","institution":"University of Ulster","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"","lastName":"Livingstone","suffix":""}],"badges":[],"createdAt":"2023-12-22 01:10:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3789295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3789295/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41366-024-01585-5","type":"published","date":"2024-09-03T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49494529,"identity":"2da57ea9-d756-47fc-91ad-430a876c4cb0","added_by":"auto","created_at":"2024-01-11 19:19:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70247,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of participant attendance through the study.\u003c/p\u003e\n\u003cp\u003e*Post-prandial syndrome, medications and body weight collected via phone. \u003cem\u003eGBP\u003c/em\u003e Gastric Bypass Surgery, \u003cem\u003eTP\u003c/em\u003eTime-point\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3789295/v1/eee6e2ce56444aa128fab109.png"},{"id":49494531,"identity":"8f313a90-7271-420a-a30b-7b9dfdb564b9","added_by":"auto","created_at":"2024-01-11 19:19:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean %proportion of fat mass (FM) and fat-free mass (FFM) per bodyweight for participants at 1-month pre-surgery and at 3-, 12- and 24-months post-surgery.\u003c/strong\u003e Data presented as arithmetic mean (SEM) of data available per group at each time point. n: (TP1: patients 31, comparators 30, TP2: patients 26, comparator 29, TP3: patients 31, comparators 30, TP4: patients 26, comparator 17).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3789295/v1/b16364d7360255384f41e2b6.png"},{"id":49494530,"identity":"9c445780-60a1-48bf-997e-c21dd4831b53","added_by":"auto","created_at":"2024-01-11 19:19:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64316,"visible":true,"origin":"","legend":"\u003cp\u003eTime trajectory plot of residual BMR(resBMR) or magnitude of metabolic adaptation for patient and comparator groups. Errors represents the 95% confidence intervals of the estimated means based on the fitted linear mixed model. This plot demonstrates the trends/changes in BMR residual for patients and comparator group at 3-months, 12-months and 24-months after Gastric Bypass Surgery. resBMR is defined as the difference between the observed BMR (as measured by indirect calorimetry) from the predicted BMR based on the linear regression equations. Difference between groups: 1-month pre-surgery (p=1.00), 3-months post-surgery (P=0.014), 12-months post-surgery (p=0.26) and 24-months post-surgery (p=0.70).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3789295/v1/0ec2e69cd2168a71a6dbb36d.png"},{"id":63951841,"identity":"b608f029-ce99-48bd-9501-c307d219def1","added_by":"auto","created_at":"2024-09-04 07:08:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":800829,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3789295/v1/daed1e2d-9036-4486-8d2d-336c3967db22.pdf"},{"id":49494532,"identity":"c7ca97ea-a12f-4fbe-a27a-8fac3053a5db","added_by":"auto","created_at":"2024-01-11 19:19:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":566833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3789295/v1/b0968b02994c609dada4c7e1.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Metabolic adaptation following gastric bypass surgery: Results from a 2-year observational study.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric Bypass Surgery (GBP) is one of the most frequently performed bariatric surgeries (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It yields significant weight loss of approximately 60\u0026ndash;70% of excess body weight at 1-year post-surgery with improvements in associated health complications and quality of life outcomes(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). There are multiple mediators involved in weight loss following GBP that cannot be explained by restrictive and malabsorptive mechanisms (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Identifying such mediators is essential for both advancing current understanding of the biology of obesity and providing evidence-based clinical advice for managing weight loss and related clinical outcomes (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring negative energy balance and weight loss, metabolic adaptation leads to downregulation in measured BMR that cannot be explained exclusively by a reduction in fat-mass (FM) and fat-free mass (FFM) (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). It is thought to favour resistance to weight loss and contribute to weight recividism (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In a retrospective study of metabolic adaptation, individuals who lost weight through non-surgical interventions exhibited a higher degree of metabolic adaptation (at 7-months post-intervention) compared to individuals after GBP (at 6-months post-surgery) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Another observational study noted that despite GBP patients losing significantly more weight and lean body mass than gastric banding patients, the degree of metabolic adaptation between groups was similar at 6-months post-surgery (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)Taken together, these data suggest a potential biological mechanism that may help explain, at least in part, the substantial and sustained weight loss after GBP. Additionally, it has been demonstrated that the observed metabolic adaptation dissipated at follow-ups of \u0026gt;\u0026thinsp;6 months post-surgery (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, no study to date has evaluated longer term (\u0026ge;\u0026thinsp;2 years) changes in metabolic adaptation following GBP.\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study was to evaluate the prospectively measured reduction in BMR that is not explained by changes in FM and FFM in patients undergoing GBP and compare with a non-surgical weight stable comparator group at 3-, 12- and 24-months post-surgery. It was hypothesised that the expected down regulation of BMR observed after weight-loss, over and above that explained by changes in FM and FFM, would be attenuated in patients by 3-month post -surgery.\u003c/p\u003e"},{"header":"Materials/Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe hypothesis for this paper was tested as a secondary hypothesis within a wider study investigating changes in energy intake following GBP, which is described in detail elsewhere (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Owing to the novel study protocol, the sample size was estimated from the patient population recruited for a randomised controlled trial (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) that detected significant differences in self-reported energy intake between Vertical Banded Gastrectomy (VBG (n\u0026thinsp;=\u0026thinsp;7) and GBP (n\u0026thinsp;=\u0026thinsp;9) participants at 6 years post-surgery. The SD associated with the change in dietary fat intake (% energy) from pre- to post-surgery and a 95% confidence interval was applied as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$n= {\\left(\\frac{confidence level x SD}{margin of error}\\right)}^{2}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$n= {\\left(\\frac{1.96 x 1.9}{1}\\right)}^{2}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$n= 14$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIt was estimated that at least 16 participants are required for the present study based on a 14% attrition rate reported by another similar intake study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, as the proposed study protocol was intensive for study participants, 32 GBP patients and a similar number of weight-stable comparators were recruited to account for a potentially higher attrition rate.\u003c/p\u003e \u003cp\u003eIn brief, in this study, all participants were required to complete four fully residential study assessments at 1-month pre-surgery, 3, 12- and 24-months post-surgery at the Human Intervention Studies Unit (HISU) at Ulster University. HISU consists of en-suite bedrooms, a communal sitting room, a metabolic kitchen (closed access to participants) and communal dining room. Participants arrived at HISU at approximately 6pm on day 1 for an initial acclimation period where no measurements were performed. Following a standard meal of Spaghetti Bolognese for dinner (if requested), participants fasted from 10pm. Measurements started early on day 2 (approx. 7am) and lasted until bedtime (approx. 11pm) on day 2. Participants remained sedentary throughout but were free to engage in light activities such as reading, crafts and watching television.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003ewas granted by the West of Scotland Research Ethics Service (WoSRES) (REC 16/WS/0056, IRAS 200567). The study was registered as a clinical trial (NCT03113305) (clinicaltrials.gov) and was conducted according to the principles of the Helsinki declaration. The primary outcome of the work was changes in dietary energy intake, with the work presented in this study included as secondary outcomes. Prior to the start of the study written informed consent was obtained from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePatients were recruited from four sites in the United Kingdom (Phoenix Health NHS, Phoenix Health Private, London Imperial Weight Centre and North Bristol NHS Trust) and one site in the Republic of Ireland (Letterkenny University Hospital). Inclusion criterion were \u0026ge;\u0026thinsp;18 years old with a scheduled GBP. Weight-stable (\u0026gt;\u0026thinsp;6 months) comparators were time-matched and recruited by posters, email circulations, radio, and social media platforms. The purpose of the comparator group was to account for external factors which could potentially impact GBP patients over the study period, as well as any change in behaviour in the residential unit over the four time points. Inclusion criterion were \u0026ge;\u0026thinsp;18 years old with no plans to alter current body weight. Exclusion criteria for all participants were: presence of physical or psychological conditions affecting food intake; strict dietary restrictions, food allergies and pregnant or lactating women.\u003c/p\u003e \u003cp\u003eTotal body weight, body mass index (BMI), FM, FFM, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were assessed under standardised conditions using the total body GE Lunar iDXA scan (GE Healthcare, USA). Height was measured during the initial visit to the nearest 0.1cm using a wall-mounted stadiometer (Seca Ltd, Hamburg, Germany (%CV\u0026thinsp;=\u0026thinsp;0.23%). If the participant\u0026rsquo;s body width exceeded the standard dimension of the DXA\u0026rsquo;s scanning area, they were positioned such that the right half of the body was fully within the scan field. Half scans have shown satisfactory validity (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). DXA measurements were conducted by trained researchers and verified by a qualified health care professional.\u003c/p\u003e \u003cp\u003ePercentage total weight loss (%TWL) was calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\%TWL=\\frac{\\left(weight prior to surgery - follow-up weight\\right)}{weight prior to surgery} x 100 .\\)\u003c/span\u003e\u003c/span\u003ePostoperative weight loss was also expressed as a percentage excess of weight loss (%EWL) following the formula:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(EWL=\\frac{(\\text{w}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t} \\text{p}\\text{r}\\text{i}\\text{o}\\text{r} \\text{t}\\text{o} \\text{s}\\text{u}\\text{r}\\text{g}\\text{e}\\text{r}\\text{y} - \\text{f}\\text{o}\\text{l}\\text{l}\\text{o}\\text{w}-\\text{u}\\text{p} \\text{w}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t})}{(\\text{w}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t} \\text{p}\\text{r}\\text{i}\\text{o}\\text{r} \\text{t}\\text{o} \\text{s}\\text{u}\\text{r}\\text{g}\\text{e}\\text{r}\\text{y} - \\text{w}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t} \\text{c}\\text{o}\\text{r}\\text{r}\\text{e}\\text{s}\\text{p}\\text{o}\\text{n}\\text{d}\\text{i}\\text{n}\\text{g} \\text{t}\\text{o} \\text{B}\\text{M}\\text{I} = 25 \\text{k}\\text{g}/{\\text{m}}^{2})} x 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eBMR was measured under standardised conditions following an overnight fast (from 10pm) using open-circuit portable indirect calorimetry (ECAL, Metabolic Health Solutions) by a trained researcher. Each participant was awakened at approximately 7am in the morning to empty their bladder and return to rest for at least 30 minutes in a quiet, darkened and thermoneutral room before the measurement was made. Distractions such as use of mobile phones were not permitted. Data were recorded for a minimum of 8-minutes and was terminated after readings had been stable for 45-seconds. The first 2-minutes of the measurement period were automatically discarded by the ECAL software, with any other anomalous recordings (e.g., coughing, removal of mouthpiece) also discarded as \u0026lsquo;false\u0026rsquo; readings. BMR values were calculated using the Weir formula (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo determine the magnitude of metabolic adaptation following GBP, this study used the gold standard methodology (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The baseline BMR (dependent variable) for both patient and comparator groups was used to generate a linear regression model with multiple predictor variables (independent variables) that may affect BMR values - baseline FM, FFM, age, gender, medications, group (participants) and medical conditions. This model was used to predict the BMR (pBMR) at 3-,12- and 24-months post-surgery.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\text{p}\\text{B}\\text{M}\\text{R} \\left(\\text{M}\\text{J}/\\text{d}\\text{a}\\text{y}\\right)= 3.529-\\left(1.509 \\text{x} \\text{P}\\text{a}\\text{r}\\text{t}\\text{i}\\text{c}\\text{i}\\text{p}\\text{a}\\text{n}\\text{t}\\text{s}\\right)+\\left(0.511 \\text{x} \\text{G}\\text{e}\\text{n}\\text{d}\\text{e}\\text{r}\\right) - \\left(0.001 \\text{x} \\text{A}\\text{g}\\text{e} \\text{i}\\text{n} \\text{y}\\text{e}\\text{a}\\text{r}\\text{s}\\right) + \\left(0.022 \\text{x} \\text{F}\\text{M} \\text{i}\\text{n} \\text{k}\\text{g}\\right) + \\left(0.088 \\text{x} \\text{F}\\text{F}\\text{M} \\text{i}\\text{n} \\text{k}\\text{g}\\right) + \\left(0.936 \\text{x} \\text{M}\\text{e}\\text{d}\\text{i}\\text{c}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n} \\text{t}\\text{h}\\text{a}\\text{t} \\text{a}\\text{f}\\text{f}\\text{e}\\text{c}\\text{t} \\text{B}\\text{M}\\text{R}\\right) - \\left(0.513 \\text{x} \\text{D}\\text{i}\\text{s}\\text{e}\\text{a}\\text{s}\\text{e} \\text{t}\\text{h}\\text{a}\\text{t} \\text{a}\\text{f}\\text{f}\\text{e}\\text{c}\\text{t} \\text{B}\\text{M}\\text{R}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eParticipants (1 for Patient, 2 for Comparator), Gender (1 Female, 2 Male), Medications that affect BMR (1 Prescribed, 2 Not prescribed), Diseases that affect BMR (1 Present, 2 Absent).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFinally, the residual BMR (resBMR) is defined as the difference between the observed BMR (as measured by indirect calorimetry) from the predicted BMR based on the above linear regression equation.\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$BMR residual =(\\text{m}\\text{e}\\text{a}\\text{s}\\text{u}\\text{r}\\text{e}\\text{d} \\text{B}\\text{M}\\text{R} - \\text{p}\\text{r}\\text{e}\\text{d}\\text{i}\\text{c}\\text{t}\\text{e}\\text{d} \\text{B}\\text{M}\\text{R})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAnd the presence of metabolic adaptation is defined as resBMR being significantly different from zero.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS for windows (UK, version 26.0) and R (version 4.2). Baseline summary statistics are expressed as mean (SD) for continuous variables, or as numbers (percentage) for categorical variables. Results from linear mixed models were presented as least squares mean (SEM).\u003c/p\u003e \u003cp\u003eAt each time-point, there were some random missing values due to missed appointments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and, in a few cases, technical issues with measuring equipment. Given that it is reasonable to assume that such values were missing purely at random, mixed effects linear models were fitted for the main outcome measures of interests (Weight, BMI, FM, FFM, LBM, VAT, SAT, and BMR). In each of these linear mixed models, participant IDs were fitted as random effects, with participant group (patient or comparator), time and the interaction between group and time as fixed effects. For such linear mixed modelling, no imputation of missing values was conducted as this was unnecessary. From the fitted linear mixed models, the estimated means and standard errors of the outcome measure were then obtained for all group and time point combinations. Where applicable and deemed interesting, comparative analysis between different time points per group, or between the two groups per time point were conducted by testing the corresponding general linear hypothesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMixed model analysis was applied to the residual BMR to determine the presence or absence of metabolic adaptation. Metabolic adaptation was considered to have occurred if BMR residual (magnitude of metabolic adaptation) was significantly different from zero (p\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003ePearson correlation coefficients were used to study associations between changes in FM, FFM, %FFM/ weight and BMR in patients. P-values of \u0026le;\u0026thinsp;0.05 were considered as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSixty-six participants attended the baseline study appointment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Three of the patients were subsequently excluded as they did not receive GBS (Sleeve Gastrectomy surgery, n 2; medical issues, n 1). Of the remaining 63 participants, two individuals from the comparator group were uncontactable after the first appointment, leaving 31 patients and 30 comparators (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Following the COVID-19 pandemic lock down period only patients were followed up for their final 24-month appointment.\u003c/p\u003e \u003cp\u003eThe patient group had a higher proportion of females and were more likely to present with diabetes mellitus pre-surgery.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBody composition\u003c/h2\u003e \u003cp\u003eA reduction in all anthropometric variables was observed in patients by 3-months post-surgery, with stability in changes from pre-surgery achieved at 12- and 24-months (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Over the 24-month study period patients had lost over a quarter of their total mean weight \u0026minus;\u0026thinsp;25.6% (SD 1.8)% from pre-surgery (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the comparator group remained weight-stable throughout the study period (p\u0026thinsp;=\u0026thinsp;0.96).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean anthropometric measures and basal metabolic rate at baseline (1-month pre-surgery) and at 3-, 12- and 24-months post-surgery\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-month pre-surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-months post-surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12-months post-surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24-months post-surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eANOVA P values for\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eComparator group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122.90(3.08)\u003c/p\u003e \u003cp\u003e78.01 (3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.31 (3.13)\u003c/p\u003e \u003cp\u003e78.25 (3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.92 (3.08)\u003c/p\u003e \u003cp\u003e78.63(3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.86 (3.13)\u003c/p\u003e \u003cp\u003e79.78 (3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eTime\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eGroup:Time\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eComparator group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.47 (0.92)\u003c/p\u003e \u003cp\u003e27.22 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.81 (0.94)\u003c/p\u003e \u003cp\u003e27.25 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.38 (0.92)\u003c/p\u003e \u003cp\u003e27.39 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.06 (0.94)\u003c/p\u003e \u003cp\u003e27.64 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eTime\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eGroup:Time\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFat mass (kg)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eComparator group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.07 (2.00)\u003c/p\u003e \u003cp\u003e26.17 (2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.78 (2.06)\u003c/p\u003e \u003cp\u003e26.55 (2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.73 (2.00)\u003c/p\u003e \u003cp\u003e26.62 (2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.98 (2.06)\u003c/p\u003e \u003cp\u003e27.00 (2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eTime\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eGroup:Time\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFat-free mass (kg)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eComparator group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.83 (1.90)\u003c/p\u003e \u003cp\u003e51.84 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.54 (1.91)\u003c/p\u003e \u003cp\u003e51.70 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.19 (1.90)\u003c/p\u003e \u003cp\u003e52.01 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.84 (1.91)\u003c/p\u003e \u003cp\u003e52.80 (1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup 0.14\u003c/p\u003e \u003cp\u003eTime\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eGroup:Time\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBM (kg)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eComparator group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.04 (1.83)\u003c/p\u003e \u003cp\u003e49.13 (1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.79 (1.83)\u003c/p\u003e \u003cp\u003e48.99 (1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.52 (1.83)\u003c/p\u003e \u003cp\u003e49.30 (1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.09 (1.83)\u003c/p\u003e \u003cp\u003e50.10 (1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup 0.13\u003c/p\u003e \u003cp\u003eTime\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eGroup:Time\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVAT (kg)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eComparator group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10 (0.19)\u003c/p\u003e \u003cp\u003e1.00 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93 (0.20)\u003c/p\u003e \u003cp\u003e1.02 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29 (0.19)\u003c/p\u003e \u003cp\u003e1.06 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20 (0.20)\u003c/p\u003e \u003cp\u003e1.05 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup 0.018\u003c/p\u003e \u003cp\u003eTime\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eGroup:Time\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSAT (kg)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eComparator group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.96 (1.91)\u003c/p\u003e \u003cp\u003e25.17 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.85 (1.97)\u003c/p\u003e \u003cp\u003e25.53 (1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.44 (1.91)\u003c/p\u003e \u003cp\u003e25.55 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.78 (1.97)\u003c/p\u003e \u003cp\u003e25.95 (2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eTime\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eGroup:Time\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMR (MJ/day)\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eComparator group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.93 (0.38)\u003c/p\u003e \u003cp\u003e7.03 (0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.72 (0.41)\u003c/p\u003e \u003cp\u003e7.52 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.18 (0.37)\u003c/p\u003e \u003cp\u003e6.52 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.38 (0.39)\u003c/p\u003e \u003cp\u003e6.57 (0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup 0.0077\u003c/p\u003e \u003cp\u003eTime\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003cp\u003eGroup:Time\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData presented as mean (SE) based on the linear mixed model estimate. In each of these linear mixed models, participants were fitted as random effects, while group, time and the interaction between group and time as fixed effects. For all the variables listed in this table, from Weight to BMR, ANOVA tests suggest that the interaction between Group and Time is highly significant, meaning that the difference between the two Groups depends on Time. (The time trajectory curves for the two groups are far from parallel based on the linear mixed models, see Supplementary figures). \u003cem\u003eBMI\u003c/em\u003e Body Mass Index, \u003cem\u003eBMR\u003c/em\u003e Basal Metabolic Rate, \u003cem\u003eLBM\u003c/em\u003e Lean Body Mass, \u003cem\u003eSAT\u003c/em\u003e Subcutaneous Adipose Tissue, \u003cem\u003eVAT\u003c/em\u003e Visceral Adipose Tissue.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt 24-months post-surgery 71% (n\u0026thinsp;=\u0026thinsp;22) of patients had achieved successful weight loss (\u0026gt;\u0026thinsp;50%EWL), with three patients regaining weight (\u0026lt;\u0026thinsp;50%EWL) and two patients continuing to lose weight (achieving\u0026thinsp;\u0026gt;\u0026thinsp;50%EWL) from their 12-month measurement.\u003c/p\u003e \u003cp\u003eThe majority of weight loss following GBP was accounted for by a decrease in FM. On average, patients lost 40% of pre-surgery FM and 11% of pre-surgery FFM at 24-months post-surgery, a ratio of roughly 4:1 FM to FFM in terms of percentage loss. By 3 months post-surgery FFM was similar in both the patient and comparator groups, with p-values being 0.68, 0.97, and 1.0 respectively for Month 3, 12 and 24 post surgery. On the other hand, comparing patient versus comparator groups in terms of FM, the p-values were \u0026lt;\u0026thinsp;0.0001 for the 3rd month, 0.10 for 12th month, and 0.028 for 24-months post-surgery. Therefore, patients\u0026rsquo; FM largely remained higher than comparators and as a result, the mean total body %FM decreased at each timepoint from 50.0% pre-surgery to 40.0% 24-months after surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBasal Metabolic Rate\u003c/h2\u003e \u003cp\u003eIn the linear mixed modelling for all outcome variables from weight to BMR, ANOVA tests suggest that the interaction between group and time is highly significant \u0026ndash; meaning that the difference between groups depend on time. The time course curves of the two groups are therefore not parallel (See supplementary figures). Based on the estimates from the linear mixed models, absolute BMR (MJ/d) was 29% higher in patients than the comparator group pre-surgery [9.93(0.38) \u003cem\u003evs\u003c/em\u003e 7.03(0.40) MJ/d for patients and comparator group respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001] but was similar to the comparator group at all post-surgery time-points despite a significant reduction in BMR post-surgery (\u0026lt;\u0026thinsp;22%). Absolute BMR values remained stable in the comparator group at all three post-surgery assessments in comparison to pre-surgery (\u0026lt;+/-7%) (p\u0026thinsp;=\u0026thinsp;0.86, 0.86, and 0.94 respectively).\u003c/p\u003e \u003cp\u003eIn the linear mixed modelling analysis for resBMR (the metabolic adaptation measure), ANOVA test indicated that the group time interaction is at the margin of statistical significance (p\u0026thinsp;=\u0026thinsp;0.052) - suggesting that a simpler model without the group time interaction could be used. However, a log likelihood ratio test comparing the models with and without the interaction, indicated that the full model (with interaction) is still better than that model without the interaction (P\u0026thinsp;=\u0026thinsp;0.045). Therefore, for the measure of metabolic adaptation, the full model was retained to obtain the least square means and standard errors for all the group time point combinations. Based on the obtained linear mixed model, the hypotheses regarding whether resBMR estimate at each time point per group is different from 0 (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) were tested as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Metabolic adaptation was present post-surgery for patients only (p\u0026thinsp;=\u0026thinsp;0.011 at 3 months post -GBP; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 at 12-months and p\u0026thinsp;=\u0026thinsp;0.00073 at 24-months post-surgery.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the time trajectory plot for the degree of metabolic adaptation for patient compared to the comparator group at 3-months, 12-months and 24-months following GBP. A significant difference between groups was noted at 3-months post-surgery (P\u0026thinsp;=\u0026thinsp;0.014) and the degree of metabolic adaptation was similar between groups pre-surgery (p\u0026thinsp;=\u0026thinsp;1.00), 12-months post-surgery (p\u0026thinsp;=\u0026thinsp;0.26) and 24-months post-surgery (p\u0026thinsp;=\u0026thinsp;0.70).\u003c/p\u003e \u003cp\u003eFinally, in patients, a positive correlation was observed between changes in FFM (kg) and changes in absolute BMR at 12-months (r\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;=\u0026thinsp;0.025) and 24 months post-surgery (r\u0026thinsp;=\u0026thinsp;0.\u003cem\u003e50\u003c/em\u003e, p\u0026thinsp;=\u0026thinsp;0.012) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, a positive correlation was observed between changes in %FFM/weight and changes in absolute BMR at 12-months (r\u0026thinsp;=\u0026thinsp;0.50, p\u0026thinsp;=\u0026thinsp;0.010) and 24-months post-surgery (r\u0026thinsp;=\u0026thinsp;0.50, p\u0026thinsp;=\u0026thinsp;0.011). Changes in FM (kg) and changes in absolute BMR were positively correlated at 12-months post-surgery (r\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;=\u0026thinsp;0.00041) and 24-months post-surgery (r\u0026thinsp;=\u0026thinsp;0.64, p\u0026thinsp;=\u0026thinsp;0.00053). No other associations were observed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between changes in body composition and basal metabolic rate for patients and comparator group at 3-months, 12-months and 24-months post-surgery.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3-months post-surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12-months post-surgery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e24-months post-surgery\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients (n 20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComparator group (n 24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients (n 25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComparator group (n 21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePatients (n 25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eComparator group (n 17)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eΔBMR (MJ/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eΔBMR (MJ/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eΔBMR (MJ/day)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔFM (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e(95%CI: -0.27 to 0.59; p\u0026thinsp;=\u0026thinsp;0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003cp\u003e(95%CI: -0.19 to 0.58; p\u0026thinsp;=\u0026thinsp;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95%CI: 0.35 to 0.83; p\u0026thinsp;=\u0026thinsp;0.00041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e(95%CI: -0.21 to 0.61; p\u0026thinsp;=\u0026thinsp;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95%CI: 0.33 to 0.83; p\u0026thinsp;=\u0026thinsp;0.00053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003cp\u003e(95%CI: -0.45 to 0.51; p\u0026thinsp;=\u0026thinsp;0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔFFM (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003cp\u003e(95%CI: -0.14 to 0.67; p\u0026thinsp;=\u0026thinsp;0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003cp\u003e(95%CI: -0.56 to 0.23; p\u0026thinsp;=\u0026thinsp;0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95%CI: 0.06 to 0.72; p\u0026thinsp;=\u0026thinsp;0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003cp\u003e(95%CI: -0.53 to 0.32; p\u0026thinsp;=\u0026thinsp;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95%CI: 0.13 to 0.74; p\u0026thinsp;=\u0026thinsp;0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003cp\u003e(95%CI: -0.49 to 0.47; p\u0026thinsp;=\u0026thinsp;0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ%FFM/weight kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003cp\u003e(95%CI: -0.53 to 0.35; p\u0026thinsp;=\u0026thinsp;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003cp\u003e(95%CI: -0.18 to 0.58; p\u0026thinsp;=\u0026thinsp;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95%CI: 0.13 to 0.75; p\u0026thinsp;=\u0026thinsp;0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003cp\u003e(95%CI: -0.20 to 0.62; p\u0026thinsp;=\u0026thinsp;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(95%CI: 0.13 to 0.75; p\u0026thinsp;=\u0026thinsp;0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003cp\u003e(95%CI: -0.41 to 0.55; p\u0026thinsp;=\u0026thinsp;0.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAssociations analysed using Pearson\u0026rsquo;s correlation. Data presented as r (correlation coefficient) with 95% confidence interval (CI). \u003cb\u003ea\u003c/b\u003e denotes P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating a statistically significant correlation value. \u003cem\u003eBMR\u003c/em\u003e Basal Metabolic Rate, \u003cem\u003eFM\u003c/em\u003e Fat Mass, \u003cem\u003eFFM\u003c/em\u003e Fat-Free Mass, Δ Change values from baseline\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first prospective study to measure BMR and body composition using standardised gold-standard methodology at 3-, 12- and 24-months in patients after GBP surgery and time-matched comparators. It was hypothesized that the expected down regulation of BMR observed after weight-loss, over and above that explained by changes in FM and FFM, would be attenuated in patients 3-months following GBP.\u003c/p\u003e \u003cp\u003eFollowing weight loss, there was a significant decline in absolute BMR levels - largely attributable to the decline in FFM. Metabolic adaptation (the change in BMR that is greater than would be predicted from changes in body composition alone during negative energy balance) was observed in patients at 3-months post-surgery when the magnitude of weight loss is greatest (approx. 6.8 kg mean weight loss per month from baseline). This finding is in line with previous studies that observed an adaptive response with approximately 5.5kg mean weight loss per month at \u0026le;\u0026thinsp;6 months post-surgery (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This body composition independent reduction in BMR is hypothesized to be an evolutionary biological process that \u0026ldquo;slows down metabolism\u0026rdquo; during periods of food scarcity or significant negative energy balance to increase chances of survival(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). It appears to be induced by a collection of physiological and neuroendocrine shifts, such as a reduction in plasma insulin levels and associated lower glycogen levels to sustain the brain and body\u0026rsquo;s energy requirements (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt 12-months post-surgery, mean weight loss decreased by 57.4% per month (approx. 2.9kg mean weight loss per month) and this was also accompanied by a significant degree of metabolic adaptation. This finding is at variance with Knuth et al\u0026rsquo;s (2014) data that demonstrated a lack of metabolic adaptation with approx. 3.4kg mean weight loss per month at 12-months post-surgery. They also used standardised indirect calorimetry to measure absolute BMR and the recommended linear regression method to assess the degree of metabolic adaptation. However, the small sample size (n 13) could have driven a statistically nonsignificant result (type 2 error).\u003c/p\u003e \u003cp\u003eConversely, despite an even smaller sample size (n 5), Tam et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) reported the same finding as the present paper where metabolic adaptation was observed at 12-months following GBP. However, they defined metabolic adaptation as a \u0026ldquo;negative residual value\u0026rdquo; rather than the recommended approach of assessing whether the residual is significantly different from zero (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Therefore, studies with adequate sample sizes to detect clinically relevant differences and appropriate statistical techniques to assess metabolic adaptation are required to confirm that the subsequent decline in BMR was not attributed solely to the reduction in FM and FFM levels 12-months following surgical weight loss.\u003c/p\u003e \u003cp\u003eNevertheless, the degree of metabolic adaptation observed in the GBP group was only statistically different from the values obtained by the weight-stable comparator group at 3-months post-surgery. From 12-months post-surgery, the values were similar between groups. This suggests that although metabolic adaptation was present in the surgery group \u0026ndash; it appears to be attenuated in the longer-term post-surgery - and this may positively impact weight loss and limit weight recidivism. The underlying biological mechanism of this phenomenon is unclear but it is possible that during a slower rate of weight loss and/or during the weight loss maintenance phase, the therapeutic effects from adipocentric signals such as enhanced leptin sensitivity (owing to significant FM reduction) may aid in attenuating the degree of metabolic adaptation through its actions on triidodothyronine (T3) balance and the mitochondrial content and coupling alterations (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The observed increase in overall %FFM per body weight may contribute as well.\u003c/p\u003e \u003cp\u003eA moderate positive correlation was observed between changes in FFM (kg) and changes in absolute BMR at 12- and 24- months post-surgery. As mentioned above, the reduction in mean FFM explains the consequent reduction in absolute BMR values in patients. Nevertheless, similar to the degree of metabolic adaptation, the mean absolute BMR values were similar between patients and the comparator group post-surgery; despite the comparator group maintaining their weight and FFM levels, suggesting again that the usual compensatory metabolic response which minimises weight loss during periods of energy deficit (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) appears to be blunted in patients following GBP. Similarly, because BMR is also dependent on fat-mass, a positive correlation was observed between changes in FM and BMR at 12- and 24-months post-surgery (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). As discussed above, the significant reduction in FM levels with concurrent enhanced leptin bioavailability may potentially contribute to attenuating the expected reduction in BMR and metabolic adaptation following surgical weight loss (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLimitations of the methodology include the absence of randomisation and matching the comparator group for BMI and sex. Physical activity level was not measured in this study. However, BMR measurements within this study were taken at complete rest\u0026thinsp;\u0026gt;\u0026thinsp;8 hours after the last meal to avoid all possibility of physical activity and meal induced thermogenesis. The participants engaged in only sedentary activity while residing in HISU. As such, BMR measurement should not be impacted by physical activity, apart from indirectly by body composition changes. Finally, although this study included a larger sample size compared to existing similar studies \u0026ndash;the sample size calculation was based on energy intake (the primary outcome).\u003c/p\u003e \u003cp\u003eNevertheless, this study is unique by investigating changes in BMR in GBP patients up to 24-months post-surgery with a concurrent weight-stable comparator group in a residential setting using highly controlled gold-standard protocols. This included taking BMR measurements on awakening after a controlled fast to avoid activity and meal-induced thermogenesis which is often cited as a study limitation (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In this study, linear regression analysis was used to assess improvements in metabolic compensation following weight loss instead of the ratio method (i.e., BMR/weight) as the latter changes could be a direct result of an increase in FFM:FM ratio per kilogram of weight following weight loss (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFuture controlled intervention human studies are required to clarify the kinetic changes in plasma levels of T3, insulin and leptin and its impact on metabolic adaptation during the rapid weight loss and weight maintenance phase following GBP. It might be useful to study metabolic adaptation in associated physiological responses such as heart rate and glomerular filtration rate too (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The degree of metabolic adaptation should be assessed using standardised mathematical modelling as discussed above. It is also worthwhile investigating whether standardising the variables used in the linear regression analysis could aid in comparing future studies that investigate metabolic adaptation following GBP. As the highly metabolically active organs and skeletal muscle are considered major sites of metabolic adaptation(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), medical imaging techniques can be used to measure the volume and mass of FFM components i.e. liver that reduces in size significantly following weight loss. A weight-matched control group, losing weight via nutrition therapy and a similar activity level as the GBP group, is recommended albeit difficult to execute. Finally, as inter-individual post-operative weight loss and clinical response vary considerably(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) and remain poorly understood, it should prompt further research in understanding the predictors (neuroendocrine, gender, age, stress, activity level) and mechanisms of metabolic adaptation following weight loss.\u003c/p\u003e \u003cp\u003eIn conclusion, the outcomes of this prospective study suggest that metabolic adaptation is present during the rapid weight loss phase (at least 6.9kg mean weight loss per month) and weight maintenance phase (from 12-months onwards) following GBP. Therefore, the downregulation in BMR was not fully explained by changes in FM and FFM. However, it appears that the degree of metabolic adaptation was attenuated in the surgical group from 12-months onwards and this may potentially contribute to sustained weight loss and limit weight recidivism. Understanding the underlying mechanisms and predictors that attenuate metabolic adaptation following GBP could potentially help the development of treatments to aid weight loss maintenance after non-surgical weight loss or even weight regain after surgery.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBasal Metabolic Rate (BMR), Dual-Energy X-Ray Absorptiometry (DXA), Fat Mass (FM), Fat-Free Mass (FFM), Gastric Bypass Surgery (GBP).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e \u003cstrong\u003eSupport:\u003c/strong\u003e Research supported by the US-Ireland Research and Development Partnership program though the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (R01DK106112), the Health and Social Care R\u0026amp;D Division of Northern Ireland (STL/5062/14) and the Medical Research Council (MC_PC_16017), and the Health Research Board of the Republic of Ireland (USIRL-2006-2). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e CWLR, MBEL, RKP were responsible for funding acquisition and designing the research; FN, TR, AB, AM, ZB, DK, DJP, CWLR recruited the participants, FN, TR, MM, AB, MBEL, RKP conducted the research; FN, SDZ, TR, MM, AB, HS, MBEL, RKP analysed the data; FN, AM, SDZ, CWLR, MBEL, RKP wrote the original draft; All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests: \u0026nbsp;\u003c/strong\u003eCLW reports; grants from Science Foundation Ireland, grants from Health Research Board, during the conduct of the study; other from NovoNordisk, other from GI Dynamics, personal fees from Eli Lilly, grants and personal fees from Johnson and Johnson, personal fees from Sanofi Aventis, personal fees from Astra Zeneca, personal fees from Janssen, personal fees from Bristol-Myers Squibb, personal fees from Boehringer-Ingelheim, outside the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eData described in the manuscript, including de-identified individual participant data, code book, and analytic code will be made available upon request pending application and approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDouglas IJ, Bhaskaran K, Batterham RL, Smeeth L. 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Obesity. 2016; 24(2):277\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuijarro A, Osei-Hyiaman D, Harvey-White J, Kunos G, Suzuki S, Nadtochiy S, et al. Sustained weight loss after Roux-en-Y gastric bypass is characterized by down regulation of endocannabinoids and mitochondrial function. Ann Surg. 2008; 247(5):779\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWesterterp KR. Metabolic adaptations to over - and underfeeding - Still a matter of debate? Eur J Clin Nutr. 2013; 67:443\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnstone A. Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. AJCN. 2005; 82:941\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalgani JE, Santos JL. Insights about weight loss-induced metabolic adaptation. Obesity. 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Curr Obes Rep. 2016; 5:413\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3789295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3789295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objectives:\u003c/strong\u003e Metabolic adaptation is the lowering of basal metabolic rate (BMR) beyond what is predicted from changes in fat mass (FM) and fat-free mass (FFM) and may hamper weight-loss progression. It is unclear whether metabolic adaptation occurs following gastric bypass surgery (GBP) and if it persists. The aim of this study was to evaluate the reduction in BMR that is not explained by changes in body composition in patients following GBP compared to a weight-stable comparator group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjects\u003c/strong\u003e: Thirty-one patients [77.4% female; mean BMI 45.5(SD 7.0) kg/m\u003csup\u003e2\u003c/sup\u003e; age 47.4 (11.6)y] who underwent GBP, and 32 time-matched comparators [50% female; BMI 27.2(4.6) kg/m\u003csup\u003e2\u003c/sup\u003e; age 41.8(13.6)y) were evaluated at 1-month pre-surgery, 3-, 12- and 24-months post-surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: BMR was measured under standardised residential conditions using indirect calorimetry and body composition using DXA. Linear regression analyses assessed metabolic adaptation post-surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAfter surgery, patients lost a quarter of their body weight [-25.6%(1.8%); p\u0026lt;0.0001] consisting mainly of FM (4:1 FM to FFM loss ratio) at 24-months post-surgery. Absolute BMR (MJ/d) reduced by 25.7% at 24-months post-surgery with values becoming similar to the comparator group from 3-months post-surgery. Positive associations were observed between changes in BMR and changes in FFM and FM (P\u0026lt;0.03). Metabolic adaptation was present in patients during the 1) rapid weight loss phase (6.9kg/month at 3-months post-surgery)(p=0.011), 2) slower weight loss phase (1.6kg/month from 3 to 12-months post-surgery)(p\u0026lt;0.0001), and, 3) weight maintenance phase (24-months post-surgery)(p=0.00073). However, the degree of metabolic adaptation observed in GBP patients was similar to the weight-stable comparator group (no metabolic adaptation) from 12-months post-surgery onwards (3-months; p=0.01, 12-months; p=0.26, 24-months post-surgery; p=0.70).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: These results suggest that there is a potential biological mechanism of surgery that attenuates the expected postoperative downregulation in BMR thus helping GBP patients maintain weight loss.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Metabolic adaptation following gastric bypass surgery: Results from a 2-year observational study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 19:19:19","doi":"10.21203/rs.3.rs-3789295/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-04-02T15:11:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-01-22T06:42:22+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-01-19T18:44:27+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-01-10T07:36:59+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-01-09T16:18:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-22T13:23:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Obesity","date":"2023-12-22T01:06:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-22T01:06:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"dbf15e4e-dd97-40eb-b305-6e8acbd74194","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28043003,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"},{"id":28043004,"name":"Health sciences/Health care/Weight management"}],"tags":[],"updatedAt":"2024-09-04T07:08:45+00:00","versionOfRecord":{"articleIdentity":"rs-3789295","link":"https://doi.org/10.1038/s41366-024-01585-5","journal":{"identity":"international-journal-of-obesity","isVorOnly":false,"title":"International Journal of Obesity"},"publishedOn":"2024-09-03 04:00:00","publishedOnDateReadable":"September 3rd, 2024"},"versionCreatedAt":"2024-01-11 19:19:19","video":"","vorDoi":"10.1038/s41366-024-01585-5","vorDoiUrl":"https://doi.org/10.1038/s41366-024-01585-5","workflowStages":[]},"version":"v1","identity":"rs-3789295","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3789295","identity":"rs-3789295","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}