Fat mass index predicts the effect of weight loss and quality of life early after laparoscopic sleeve gastrectomy

Fat mass index predicts the effect of weight loss and quality of life early after laparoscopic sleeve gastrectomy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fat mass index predicts the effect of weight loss and quality of life early after laparoscopic sleeve gastrectomy Yi-Ming Jiang, Qing Zhong, Zhi-Xin Shang-Guan, Guang-Tan Lin, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4590701/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Sep, 2024 Read the published version in Obesity Surgery → Version 1 posted 9 You are reading this latest preprint version Abstract Background Fat mass index (FMI) is a body composition indicator that reflects body fat content. Laparoscopic sleeve gastrectomy (LSG) is widely performed in patients with obesity. Objective This study aimed to evaluated the value of the FMI in predicting weight loss effect and quality of life early after LSG. Material and Methods From January 2014 to July 2022, the clinical data and computed tomography (CT) images of patients underwent LSG at a tertiary referral teaching hospital were analyzed. Body composition indicators were calculated using the SliceOmatic software. Achieving initial body mass index within 6 months postoperatively was defined as early eligible weight loss (EEWL). The relationship between body composition and EEWL was analyzed. Results A total of 243 patients were included. Receiver operating characteristic (ROC) curve analysis showed that the predictive value of the FMI for EEWL in patients after LSG was higher than that of other indicators (all P <0.05; area under the curve = 0.813). The best FMI cut-off point was 13.662. Accordingly, the patients were divided into the high-FMI group and low-FMI group. The %EWL and BMI of patients in the low-FMI group at 1, 3, 6, 9, 12 and 24 months after surgery were better than those in the high-FMI group (all P <0.001). Patients in the low-FMI group had higher BAROS (Bariatric Analysis and Reporting Outcome System) scores than those in the high-FMI group ( P <0.001). Conclusion Compared with other body composition indicators, FMI can effectively predict the early effect of weight loss and quality of life after LSG. LSG body composition weight loss effect QoL Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Bariatric surgery is the only effective treatment for patients with severe obesity for long-term and substantial weight loss, remission of obesity-related comorbidities, improvement in quality of life (QoL), and longer life expectancy [1–8]. Laparoscopic sleeve gastrectomy (LSG) is the most common bariatric and metabolic surgical procedure, accounting for approximately 60% of all bariatric procedures, both globally and in the US [9, 10]. Accumulating evidence has revealed that LSG can effectively reduce weight, alleviate comorbidities, and improve the QoL in patients with obesity. However, the change in %EWL in patients with severe obesity after LSG remains an important topic for surgeons [1, 2, 3]. Currently, a consensus on how to provide targeted postoperative guidance is lacking. Moreover, only few studies have confirmed the baseline factors that can predict the effect of weight loss early after surgery. Among the various parameters reflecting body size, body mass index (BMI) is a widely used index, and its association with patient outcomes has been well described [11, 12]. However, the BMI cannot distinguish between different body tissues such as muscles and fat, or their proportions. [13, 14, 15]. The impact of individual tissue components on weight loss cannot be described by BMI. Therefore, to predict the effects of weight loss more accurately, use of indicators that can distinguish between different body components is necessary. According to existing guidelines, we routinely preformed abdominal CT scan before LSG to evaluate the patient's abdominal condition and exclude the presence of other complications. We can obtain body composition indicators conveniently and quickly by SliceOmatic version 5.0(TomoVision) based on CT images [16]. Some body composition indicators have been applied in patients received metabolic and bariatric surgery (MBS) [17, 18]. Fat mass index (FMI) has shown significant prognostic value for determining nutrition-related outcomes and its use has been widely accepted for the treatment of nutrition-related diseases [19–22]. To date, few studies had explored the correlation between body composition and weight loss after LSG. Therefore, this study aimed to evaluate the predictive efficacy of FMI for weight loss and QoL early after LSG. Materials and Methods Study population and data collection We retrospectively analyzed the clinical data and CT images of 260 patients with obesity who underwent LSG at Fujian Medical University Union Hospital between January 2014 and July 2022. Informed consent was obtained from all individual participants included in the study. All patients at these institutions who met the following inclusion criteria below were included in the study: (a) BMI ≥ 32.5, or 27.5 ≤ BMI < 32.5 but suffering from type 2 diabetes mellitus with uncontrollable complications despite lifestyle changes and drug treatment, (b) underwent abdominal CT scan preoperatively, (c) LSG recipient. The exclusion criteria were as follows: (a) major abdominal surgery (excluding laparoscopic cholecystectomy), (b) severe gastroesophageal reflux disease (despite receiving medication), (c) pregnancy within 1 year after surgery, and (d) loss to follow-up. Finally, 243 patients were included in the analysis (Fig. 1 ). This study passed the Fujian Medical University Union Hospital institutional review board (IRB number:2023KY176). Due to the retrospective and observational design, the IRB waived the need for informed consent for this study. Body composition A single CT image at the third lumbar vertebra (L3) was selected to quantify muscle and adipose characteristics because this anatomical location is strongly associated with whole-body volume. One researcher, unaware of the effect of weight loss, received training on obtaining the L3 level and segmenting the muscle and adipose tissues. All CT images without any patient information were analyzed using SliceOmatic (version 5.0; TomoVision) (eFigure 1.). The skeletal muscle density (SMD) was directly measured using this software. According to the standard Hounsfield unit (HU) range, skeletal muscle cross-sectional area (SMA, -29–150 HU), visceral adipose tissue (VAT, -150 – -50 HU), subcutaneous adipose tissue (SAT, -190 – -30 HU), and intramuscular adipose tissue (IMAT, -190 – -30 HU) were quantified [23, 24, 25]. Fat mass (FM) was calculated as 0.042 × (SAT + VAT + IMAT) + 11.2. Fat-free mass (FFM) was calculated as 0.3 × SMA + 6.06 [26]. The measured value of each body component (cm 3 ) divided by the square of the height (m 2 ) was converted into an index, that included the skeletal muscle index (SMI), visceral adipose index (VAI), subcutaneous adipose index (SAI), fat-free mass index (FFMI), and FMI [23, 24, 25]. BMI was calculated by dividing body weight (kg) by height squared (m 2 ). Definition of early eligible weight loss (EEWL) According to the recommendations of the American Society of Metabolic and Bariatric Surgery (ASMBS), the ideal BMI for patients with obesity who undergo LSG is 25 (kg/m 2 ) [1, 2, 27, 28]. Based on this, the ideal weight for each patient who undergoes LSG could be calculated easily base on it. The %EWL was calculated as follows: (initial weight - follow-up weight)/(initial weight - ideal weight) × 100 [1–4]. A previous study has reported that weight loss is relatively significant and stable at 1 year postoperatively.[29]. The %EWL at 6 months after LSG is an independent factor that influences long-term weight loss effect after MBS[30]. Therefore, this study innovatively defined early weight loss effect as achieving the ideal BMI (%EWL ≥ 100) at 6 months postoperatively. To improve the readability of the content of the article, we chose its initials to determine the abbreviation of this definition (early eligible weight loss (EEWL)). Surgery procedures and postoperative treatment All participating surgeons were experienced laparoscopists (Z.C.H and L.J.X). The LSG procedure was performed using five trocars. Using a 32-36F bougie, the stomach was trans-fected from the antrum to the His angle with multiple staple lines. The first staple line was at the antrum,which was 2–6 cm from the pylorus. All the patients started a liquid diet on the first postoperative day and decided to abdominal drainage tube was removed based on the postoperative gastrointestinal radiography. They were instructed to consume sufficient water and protein daily. In addition, all the patients received sufficient long-term supplementation with multiple vitamins and trace elements. Follow-up investigation The final follow-up evaluation was conducted in January 2023. Follow-up assessments were performed at 1, 3, 6, 9 and 12 months postoperatively and then every 6 months thereafter. Most routine follow-up appointments included a physical examination, weight measurement, BMI measurement, routine blood examination, biochemical blood examination, vitamin intake measurement, and oral glucose tolerance test. The outcomes of comorbidities included aggravated, unchanged, improved and remission statuses. The specific definitions of the outcomes of comorbidities were shown in eTable 1. In addition, we administered the Moorehead–Ardelt Quality of Life Questionnaire II 6 months postoperatively. To evaluate the therapeutic effect of LSG, the Bariatric Analysis and Reporting Outcome System (BAROS) that four items (weight loss points, medical condition points, QoL points, and complication points) was used [31, 32, 33]. The determination method is described in detail in eFigure 2. The first three items were summed, and the complication points were deduced to obtain the BAROS score. The BAROS grade was determined based on the score range. All parameters were evaluated 6 months postoperatively. Statistical Analysis Normally distributed variables are described as absolute numbers and percentages, means, and standard deviations. Normally distributed measurement data were compared using two independent samples t-tests, and the counting data were compared using the chi-squared test or categorical Fisher’s exact tests. Receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA), C-index, and chi-square likelihood ratio were used to compare the predictive values of the body composition indicators. Youden's J statistical analysis was used to determine the cutoff value for the best indicator. The patients were then divided into the high- and low-FMI groups. Radar images were used to reveal the outcomes of comorbidities in both groups. Univariate and multivariate logistic regression analyses were conducted to determine the independent factors influencing EEWL. The interaction between weight and low FMI in predicting EEWL was also examined. Statistical analyses were performed using R (version 3.6.1) and SPSS (version 22.0; SPSS Inc., Chicago, IL). Significance was set at P < 0.05. Result Demographic and Clinical Characteristics Altogether, there were a total of 243 patients with obesity comprised the discovery cohort. And 82 patients (33.7%) achieved EEWL. The number of patients was 48 (19.8%) in the early stage of this study, 195 patients (80.2%) in the past 3 years. The mean age of the patients was 29.7 years (range, 22.1–37.3). The mean preoperative weight and BMI were 107.2 kg (range, 83.9–130.5) and 38.4 kg/m 2 (range, 32.3–44.5). The mean abdominal girth was 115.2 cm (range, 99.0–131.4). The mean SMD, SMI, VAI, SAI, FFMI, and FMI were 34.8 (range, 33.3–46.3), 26.8 cm 2 /m 2 (range, 21.1–32.5), 72.2 cm 2 /m 2 (range, 41.1–103.3), 163.1 cm 2 /m 2 (range, 101.1–225.1), 10.2 cm 2 /m 2 (range, 8.4–12.0), and 14.1 cm 2 /m 2 (range, 10.8–17.4), respectively. The average operation time was 107.1 min (range, 83.0–131.2), and the average intraoperative blood loss was 12.6 ml (range, 5.4–19.8). The mean length of postoperative hospital stay was 5.1 days (range, 2.8–7.4). Six (2.5%) patients experienced early postoperative complications, including three (1.3%) gastric leaks, two (0.8%) abdominal infections, and one (0.4%) stenosis. None of the patients experienced late complications. The clinical characteristics of the study cohort are presented in Supplementary eTable 2. The postoperative changes in %EWL and BMI of all the patients are shown in eFigure 3. Body composition predicting EEWL The average %EWL of the patients at 1, 3, 6, 9 and 12 months postoperatively were 42.9, 68.1, 88.5, 101.0, and 106.2, respectively. The mean BMI of the patients at 6 and 12 months postoperatively were 28.0 and 25.7, respectively. The ROC analysis showed that the predictive value of FMI was superior to that of the other five indicators (AUC value: FMI: 0.813; SMD: 0.711 [ P = 0.007]; SMI: 0.667 [P < 0.001]; VAI: 0.707 [ P < 0.001]; SAI: 0.778 [ P = 0.031], FFMI: 0.664 [ P < 0.001]). The ROC curve was shown in Fig. 2 . ROC analysis showed that FMI had similar performance in achieving early eligible weight loss in patients early in the study and in the past three years (AUC value: early in the study vs. in the past 3 years: 0.808 vs. 0.812) (eFigure 4.). DCA demonstrated that FMI could provide a greater net benefit and clinical value than the other five body composition indicators (Fig. 3 ). In addition, C-index and likelihood ratio chi-square revealed that FMI had better predictive value than the other indicators (C-index: FMI = 0.747; SMD = 0.500 [ P < 0.001]; SMI = 0.657 [ P = 0.017]; VAI = 0.694 [ P = 0.012]; SAI = 0.629 [ P < 0.001]; FFMI = 0.660 [ P = 0.023]; likelihood ratio chi-square: FMI = 80.487; SMD = 28.280 [ P < 0.001]; SMI = 20.840 [ P < 0.001]; VAI = 34.812 [ P = 0.001]; SAI = 48.988 [ P = 0.004]; FFMI = 10.776 [ P < 0.001])(eTable 3). Baseline characteristics and intraoperative and postoperative conditions by groups According to Youden's J statistical analysis, the cut-off value for the FMI was 13.662. Patients with an FMI of ≥ 13.662 were included in the high-FMI group, whereas the others were included in the low-FMI group. Overall, 123 (50.6%) patients were in the high-FMI group and 120 (49.4%) were in the low-FMI group. Compared with the high-FMI group, the low-FMI group had fewer males (30.1% vs. 16.0%; P = 0.008), lower preoperative weight (118.0 ± 24.6 vs. 96.0 ± 15.9; P < 0.002) and BMI (42.2 ± 5.7 vs. 34.5 ± 3.4; P < 0.001), and higher high-density lipoprotein cholesterol (HDL-C) (1.1 ± 0.2 vs. 1.2 ± 0.3; P = 0.012). The baseline characteristics by groups were shown in Table 1 . No significant differences in operation time, intraoperative blood loss, total cost, early or late complications were observed between the two groups. However, compared with the high-FMI group, the low-FMI group had a shorter postoperative length of hospital stay (5.4 ± 2.7 vs. 4.7 ± 1.7; P = 0.015). The intraoperative and postoperative outcomes by groups were shown in eTable 4. In addition, compared to the high-FMI group, the low-FMI group had significantly higher %EWL and lower BMI at 1, 3, 6, 9, and 12 months after surgery (all P 0.05) (eTable 5). Table 1 Participants’ Baseline Characteristics by Groups Characteristics Mean ± SD/N(%) P- value High-FMI group Low-FMI group Age, y 29.8 ± 7.6 29.6 ± 7.2 0.850 Gender 0.008* Male 38(30.1%) 19(16.0%) Female 87(69.6) 100(84.0%) Weight, kg 118.0 ± 24.6 96.0 ± 15.9 < 0.001* BMI, kg/m2 42.2 ± 5.7 34.5 ± 3.4 < 0.001* Blood index GLU, mg/L 7.8 ± 10.4 5.9 ± 2.5 0.152 HbA1c, % 6.7 ± 1.6 6.3 ± 1.5 0.223 TBIL, umol/L 10.8 ± 4.3 11.4 ± 5.2 0.350 DBIL, umol/L 2.7 ± 1.2 2.7 ± 1.4 0.993 HDL-C, mmol/L 1.1 ± 0.2 1.2 ± 0.3 0.012* LDL-C, mmol/L 3.3 ± 0.8 3.2 ± 0.9 0.530 CHOL, mmol/L 4.8 ± 1.1 5.0 ± 1.0 0.375 TG, mmol/L 2.0 ± 1.6 2.3 ± 2.3 0.240 CRP, mg/L 10.3 ± 6.1 20.3 ± 24.5 0.333 FMI = fat mass index; BMI = body mass index; GLU = glocose; TBIL = total bilirubin; DBIL = direct bilirubin; HDL-C = High-Density Lipoprotein Cholesterol; LDL-C = Low-Density Lipoprotein Cholesterol; CHOL = cholesterol; TG = triglyceride Table 2 BAROS score of patients by Group Item High-FMI group Low-FMI group P value Score Scoring range Score Scoring range QoL points 0.7 ± 1.1 (-1.7) ~ 2.6 1.0 ± 1.04 (-1.7) ~ 2.8 0.001* Medical condition points 2.3 ± 1.01 0 ~ 3 2.2 ± 1.0 0 ~ 3 0.097 Weight loss points 2.1 ± 0.8 (-1) ~ 3 2.8 ± 0.5 1 ~ 3 < 0.001* BAROS score 4.9 ± 1.8 (-1.9) ~ 8.4 6.2 ± 1.6 2.1 ~ 8.7 < 0.001* BAROS = Bariatric Analysis and Reporting Outcome System; QoL = quality of life Improvement of postoperative complications and QoL Compared with the high-FMI group, the low-FMI group had a better BAROS grade ( P < 0.001)(eTable 6). The low-FMI group also had significantly higher QoL points (0.7 ± 1.1 vs. 1.0 ± 1.04; P = 0.001), weight loss points (2.1 ± 0.8 vs. 2.8 ± 0.5; P < 0.001) and BAROS score (4.9 ± 1.8 vs. 6.2 ± 1.6; P < 0.001) than the high-FMI group (Table 3 ). In addition, the low-FMI group demonstrated significantly better hyperuricemia and hyperglycemia in the low-FMI group were significantly better than those in the high-FMI group (all P < 0.001). No significant differences in the outcomes of other complications were observed between the two groups (Table 4 ). Table 3 Univariate and Multivariate Analysis of influence factors for Eligiable Early Weight Loss in patients receiving LSG Variables Univariate analysis Multivariable analysis OR 95%CI P- value OR 95CI% P- value Age 0.992 0.960–1.026 0.653 Gender Male Ref Ref Female 2.346 1.267–4.345 0.007* 0.872 0.369–2.063 0.756 Abdominal girth 1.0246 0.993–1.103 0.088 Blood index GLU 0.944 0.865–1.029 0.188 HbA1c 1.045 0.961–1.136 0.303 TBIL 1.03 0.969–1.094 0.343 DBIL 0.999 0.805–1.240 0.993 HDL-C 2.143 0.762–6.026 0.149 LDL-C 0.778 0.524–1.155 0.212 CHOL 1.126 0.865–1.485 0.377 TG 1.098 0.939–1.283 0.241 Weight 0.944 0.928–0.960 < 0.001* 0.977 0.959–0.995 0.014* Comorbidity Fatty Liver 0.639 0.329–1.239 0.185 OSAS 0.227 0.051–1.01 0.052 Hyperuricemia 0.758 0.394–1.459 0.407 Hyperglycemia 0.726 0.366–1.439 0.359 Hyeperlipidemia 0.551 0.276-1.1 0.091 Hypertension 0.851 0.406–1.781 0.668 FMI group High-FMI group Ref Ref Low-FMI group 4.167 2.434–7.132 < 0.001* 2.636 1.424–4.879 0.002* FMI = fat mass index; GLU = glocose; TBIL = total bilirubin; DBIL = direct bilirubin; HDL-C = High-Density Lipoprotein Cholesterol; LDL-C = Low-Density Lipoprotein Cholesterol; CHOL = cholesterol; TG = triglyceride; OSAS = sleep apnea syndrome. Table 4 Changes in comorbidities by group Variable Patients with commorbidities P value high FMI group low FMI group Unchanged Improved Remission Unchanged Improved Remission Fatty liver 7(9.1%) 24(31.1%) 46(59.7%) 5(9.4%) 13(24.5%) 35(66.0%) 0.708 Hyperuricemia 0(0%) 4(25.0%) 12(75.0%) 0(0%) 0(0%) 7(100.0%) < 0.001* Hyperglycemia 8(15.7%) 14(27.6%%) 29(56.9%) 1(4.8%) 8(38.1%) 12(57.1%) < 0.001* Hyperlipidemia 3(20.0%) 1(6.7%) 11(73.3%) 3(20.0%) 3(20.0%) 9(60.0%) 0.662 Hypertension 4(19.0%) 7(33.3%) 10(47.6%) 0(0%) 5(45.5%) 6(55.5%) 0.39 OSAS 0(0%) 6(66.7%) 3(33.3%) 0(0%) 1(50.0%) 1(50.0%) 0.618 PCOS 1(7.7%) 9(69.2%) 3(23.1%) 0(0%) 5(55.6%) 4(44.4%) 0.644 Abbreviation: OSAS = sleep apnea syndrome; PCOS = polycystic ovary syndrome FMI Related to EEWL The univariate logistic regression analysis revealed that preoperative weight (odds ratio [OR]: 0.944, 95% confidence interval [CI]: 0.928–0.960, P < 0.001), female sex (OR: 2.346, 95% CI: 1.267–4.345, P = 0.007) and low FMI (OR: 4.167, 95% CI: 2.434–7.132, P < 0.001) were related to EEWL. Further multivariate analysis revealed that high preoperative weight (OR: 0.977, 95% CI: 0.959–0.995, P = 0.014) and low FMI (OR: 2.636, 95%CI: 1.424–4.879, P = 0.002) as independent positive influencing factors for EEWL (Table 4 ). Additionally, the interaction analysis that preoperative weight and low-FMI exhibited an interaction (OR = 1.023, 95%CI: 1.016–1.029, P < 0.001). Discussion LSG is mainly achieved by reducing gastric volume. Removing the fundus and creating a greater stomach curvature while maintaining the anatomical structure of the original gastrointestinal tract can alter the levels of some gastrointestinal hormones involved in optimal glucose metabolism and other metabolic indicators in patients with obesity. Weight loss and the improvement of comorbidities is the most important value of LSG [34]. Robert et al. have reported that preoperative FFM could affect weight loss after MBS[17]. However, this study did not compare FFM with other body composition indicators. Therefore, this study aimed to evaluate the predictive value of body composition indicators for LSG efficacy. FMI was determined as a convenient indicator that can accurately predict the effects of weight loss and QoL immediately after LSG. Patients with low FMI had significantly better weight-loss effects and QoL than those with high FMI. BMI can be used to quickly assess the degree of overweight in patients with obesity. However, patients with similar BMI may not achieve similar weight loss effects owing to factors such as age, sex, and race, which cause differences in body fat and muscle proportions. Compared with traditional BMI, body composition can reflect muscle and fat proportions [25]. Mastino et al. have reported that bariatric surgery was effective in patients with sarcopenia and obesity. However, the effects of weight loss were similar immediately after surgery [35]. Therefore, indicators that can accurately predict early weight loss after bariatric surgery must be identified. Yin et al. have identified low FMI as a valuable predictor of cancer survival [36]. This may be related to the low-fat body composition and high fat metabolism rates in patients with a low FMI. Similarly, these may lead to greater weight loss after LSG. Through univariate and multivariate logistic analyses, we found that preoperative weight and low-FMI were two independent positive influencing factors influencing EEWL. Moreover, the interaction analysis showed that lower preoperative weight and low-FMI exhibited an interaction. For patients with a low-FMI, each kilogram of weight reduction in weight was associated with a 0.023-fold increase in the probability of achieving EEWL. In clinical practice, we should combine low-FMI and weight to evaluate the weight loss effect of patients who undergo LSG in order to make the appropriate clinical decisions. FMI also performed well in predicting the resolution of some comorbidities, such as hyperuricemia and hyperglycemia. Owing to our small sample size and differences from the Western population, patients who underwent LSG in China had fewer comorbidities, which may explain why why the improvement of most comorbidities did not differ between the two groups. We further developed radar charts to reveal the improvements in comorbidities (eFigure 5.). The results showed that although there was no statistically significant difference in the improvement of most comorbidities between the two groups early after LSG, the low-FMI group still had a better trend in the improvement of comorbidities. This study spanned a long time (2014–2022), and the number of our patients demonstrated a ramping up trend. Most of the patients included in this study were concentrated in the past three years, especially in 2022. Considering the relatively long duration of the research, there may be inevitable biases. However, the ROC analysis showed that FMI had similar performance in achieving EEWL in patients early in the study and in the past three years. Overall, this study compared multiple widely used body component indicators and found that FMI performed well in predicting early weight loss and QoL after LSG, which was valuable for guiding clinical decision-making and follow-up. Additionally, the complication outcomes and QoL after LSG are important for bariatric surgeons. Ursula et al. have reported that a high FMI significantly increased the length of hospital stay [37]. This may have been attributable to the surplus body fat content. Severe nutritional risks increase the incidence of obesity, comorbidities, and complications during hospitalization. In this study, patients with a low FMI performed better than those with a high FMI in terms of postoperative hospital stay and EW, QoL, and BAROS scores, indicating a better weight loss effect and QoL. This may be due to the lower body fat content in these patients. The surgeon could dissect more accurately during surgery, making it less likely to cause damage, thereby improving the quality of the surgery and achieving better weight loss and QoL. Similarly, compared to patients with a high FMI, those with a low FMI had a lower nutritional risk, which reduces the risk of postoperative complications. This may be related to a shorter postoperative hospital stay and better fat metabolism levels in patients with a low FMI after surgery. Thus, patients with a low FMI recovered better in multiple aspects after LSG than those with a high FMI. This also suggests that for patients with a high FMI, postoperative attention, rehabilitation guidance, and follow-up work should be strengthened so that more patients can achieve EEWL and obtain better postoperative QoL. This study had some limitations. Firstly, this was a retrospective eastern small-sample study with a short-term follow-up that explored the relationship between body composition indicators and EEWL for the first time. Hence, bias may have been inevitable. We will conduct relevant prospective studies with larger sample sizes, multiple centers and long-term follow-ups to further explore the relationship between body composition indicators and EEWL. Secondly, the calculation of FMI was based on abodominal CT scan before surgery, which increases the complexity of the evaluation to a certain extent. But due to the value of FMI in predicting weight loss effect and QoL early after LSG, we think it still worthed to be included in the evolution system of LSG. Thirdly, for most patients, we did not routinely perform abdominal CT scans to measure body composition indicators after surgery. We will conduct relevant research in the future to explore the impact of postoperative body composition indicators on the weight loss effect of LSG. Forthly, The average age of patients included in this study was 29.7 ± 7.6 years old, which is similar to the average age of patients studied in other Chinese centers [38, 39]. Meanwhile, in the classic study of The SLEEVEPASS Randomized Clinical Trial in a Western center, the average age of patients underwent LSG was 48.5 ± 9.6 years old [2, 3, 4]. As you mentioned, Our patients were significantly younger than the patients in North American or European centers. This may be because of the higher acceptance of MBS among young Chinese patients. Therefore, lower in our center than in North American or European centers. We will further validating the application of FMI in older patients using international multicenter data. Lastly, total weight loss (%TWL) and %EWL are frequently used metrics to assess the effectiveness of weight loss following LSG. %TWL is a more objective and less biased measure for evaluating weight loss outcomes. However, as the most commonly used metric for evaluating weight loss outcomes, %EWL also has its advantages. So far, many important studies had used %EWL as the main indicator to evaluate weight loss effectiveness, including the renowned randomized controlled trial research in the field of weight loss metabolic surgery (the SLEEVEPASS Randomized Clinical Trial) [1–6]. Existing guidelines also recognize the use of %EWL for evaluating weight loss outcomes [40]. Additionally, the BAROS also used %EWL as the standard for evaluating weight loss point [31]. Due to the higher acceptance of LSG among Chinese patients, the preoperative BMI of Chinese patients who underwent LSG was lower than western patients. Many of them could achieve ideal BMI after surgery. Therefore, this study selected% EWL as the indicator to evaluate the weight loss effect after LSG. In the future, we hope to conduct research to explore which indicator is more suitable for low BMI patients receiving LSG in Chinese centers. Nevertheless, this study obtained different body composition indicators through widely-used and convenient CT images and compared their predictive performance for weight loss and quality of life early after LSG. To the best of our knowledge, this is the first study to discover the predictive value of FMI in this area, which supplements existing indicators and provide additional references for decision-making around clinical treatment. Conclusion In this study, compared with other body composition indicators, FMI can effectively predict the effects of weight loss and QoL early after LSG. For patients with a high FMI, postoperative rehabilitation guidance and health management should be emphasized. This study requires a large-sample, prospective, multicenter study for verification. Declarations Ethics Approval This article does not contain any studies with human participants or animals performed by any of the authors. Conflict of Interest The authors declare no competing interests. Funding Financial support and sponsorship: This study was supported by the Financial Support for the Construction of "Dual High" Medical Services in Fujian Province" (Min Wei Yi Zheng [2021] No. 76). Author Contribution # Yi-Ming Jiang, Qing Zhong, and Zhi-Xin Shang-Guan contributed equally to this work and should be considered co-first authors.Yi-ming Jiang: Substantial contributions to the conception design of the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Qing Zhong: Substantial contributions to the conception interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Zhi-Xin Shang-Guan: Substantial contributions to the conception the acquisition, analysis; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Guang-Tan Lin: Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Xiao-Jing Guo: Substantial contributions to the collection of data and follow-up; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Ze-Ning Huang: Substantial contributions to the conception design of the work; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Jun-Lu: Substantial contributions to the conception design of the work; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Chang-Ming Huang: Substantial contributions to the conception design of the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Jian-Xian Lin: Substantial contributions to the conception interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Chao-Hui Zheng: Substantial contributions to the conception or design of the work and the acquisition, analysis; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. References Peterli R, Wölnerhanssen BK, Peters T, et al. Effect of laparoscopic sleeve gastrectomy vs laparoscopic Roux-en-Y gastric bypass on weight loss in patients with morbid obesity: the SM-BOSS randomized clinical trial. JAMA . 018;319(3):255-265.doi:10.1001/jama.2017.20897 Salminen P, Helmiö M, Ovaska J, et al. Effect of laparoscopic sleeve gastrectomy vs laparoscopic Roux-en-Y gastric bypass on weight loss at 5 years among patients with morbid obesity: the SLEEVEPASS randomized clinical trial. JAMA . 2018;319(3):241-254.doi:10.1001/jama.2017.20313 Grönroos S, Helmiö M, Juuti A, et al. Effect of laparoscopic sleeve gastrectomy vs Roux-en-Y gastric bypass on weight loss and quality of life at 7 years in patients with morbid obesity: the SLEEVEPASS randomized clinical trial. JAMA Surg .2021;156(2):137-146. doi:10.1001/ jamasurg.2020.5666 Salminen P, Grönroos S, Helmiö M, et al. Effect of Laparoscopic Sleeve Gastrectomy vs Roux-en-Y Gastric Bypass on Weight Loss, Comorbidities, and Reflux at 10 Years in Adult Patients With Obesity: The SLEEVEPASS Randomized Clinical Trial [J]. JAMA Surg, 2022, 157(8): 656-66. Adams TD, Davidson LE, Hunt SC. Weight and metabolic outcomes 12 years after gastric bypass. N Engl J Med . 2018;378(1):93-96. doi:10.1056/ NEJMc1714001 Adams TD, Gress RE, Smith SC, et al. Long-term mortality after gastric bypass surgery. N Engl J Med . 2007;357(8):753-761. doi:10.1056/NEJMoa066603 Arterburn DE, Olsen MK, Smith VA, et al. Association between bariatric surgery and long-term survival. JAMA . 2015;313(1):62-70. doi: 10.1001/jama.2014.16968 Schauer PR, Bhatt DL, Kirwan JP, et al; STAMPEDE Investigators. Bariatric surgery versus intensive medical therapy for diabetes—5-year outcomes. N Engl J Med .2017;376(7):641-651. doi: 10.1056/NEJMoa1600869 Wölnerhanssen BK, Peterli R, Hurme S, et al. Laparoscopic Roux-en-Y gastric bypass versus laparoscopic sleeve gastrectomy: 5-year outcomes of merged data from two randomized clinical trials (SLEEVEPASS and SM-BOSS). Br J Surg . 2021;108 (1):49-57. doi:10.1093/bjs/znaa011 Angrisani L, Santonicola A, Iovino P, et al. IFSO Worldwide Survey 2016: primary, endoluminal, and revisional procedures. Obes Surg. 2018;28(12): 3783-3794.doi:10.1007/s11695-018-3450-2 Campos GM, Khoraki J, Browning MG, Pessoa BM, Mazzini GS, Wolfe L. Changes in utilization of bariatric surgery in the United States from 1993 to 2016. Ann Surg . 2020;271(2):201-209. doi:10.1097/ SLA.0000000000003554 Kroenke CH, Neugebauer R, Meyerhardt J, Prado CM, Weltzien E, Kwan ML, et al. Analysis of body mass index and mortality in patients with colorectal cancer using causal diagrams. JAMA Oncol 2016;2:1137e45. Chan DSM, Vieira AR, Aune D, Bandera EV, Greenwood DC, McTiernan A, et al. Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies. Ann Oncol 2014;25:1901e14. Gonzalez MC, Pastore CA, Orlandi SP, Heymsfifield SB. Obesity paradox in cancer: new insights provided by body composition. Am J Clin Nutr 2014;99: 999e1005. Nagai M, Kuriyama S, Kakizaki M, Ohmori-Matsuda K, Sugawara Y, Sone T, et al. Effect of age on the association between body mass index and all-cause mortality: the Ohsaki cohort study. J Epidemiol 2010;20:398e407. Oh H, Kwak S Y, Jo G, et al. Adiposity and mortality in Korean adults: a population-based prospective cohort study [J]. Am J Clin Nutr, 2021, 113(1): 142-53. Mourtzakis M, Prado C, Lieffers JR, Reiman T, Mccargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab (2008) 33:997–1006. Robert M, Pelascini E, Disse E, et al. Preoperative fat-free mass: a predictive factor of weight loss after gastric bypass [J]. Obes Surg, 2013, 23(4): 446-55. Liu P, Ma F, Lou H, et al. The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome [J]. BMC Public Health, 2013, 13(629). Cederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition - a consensus report from the global clinical nutrition community. Clin Nutr 2019;38:1e9. Brunani A, Perna S, Soranna D, et al. Body composition assessment using bioelectrical impedance analysis (BIA) in a wide cohort of patients affected with mild to severe obesity [J]. Clin Nutr, 2021, 40(6): 3973-81. van Beijsterveldt I, van der Steen M, de Fluiter K S, et al. Body composition and bone mineral density by Dual Energy X-ray Absorptiometry: Reference values for young children [J]. Clin Nutr, 2022, 41(1): 71-9. Cereda E, Pedrazzoli P, Lobascio F, Masi S, Crotti S, Klersy C, et al. The prognostic impact of BIA-derived fat-free mass index in patients with cancer. Clin Nutr 2021;40:3901e7. Feliciano E, Popuri K, Cobzas D, Baracos VE, Caan BJ. Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients. Journal of Cachexia, Sarcopenia and Muscle. 2020. Kvist H, Chowdhury B, Grangrd U, Tylén U, Sjstrm L. Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. American Journal of Clinical Nutrition. 1988;48(6):1351-1361. Rosenberg IH. Sarcopenia: origins and clinical relevance. The Journal of nutrition. 1997;127(5):990S-991S. Lee J, Jeong W K, Kim J H, et al. Serial Observations of Muscle and Fat Mass as Prognostic Factors for Deceased Donor Liver Transplantation [J]. Korean J Radiol, 2021, 22(2): 189-97. Brethauer SA, Kim J, el Chaar M, et al; ASMBS Clinical Issues Committee. Standardized outcomes reporting in metabolic and bariatric surgery. Surg Obes Relat Dis. 2015;11(3):489-506. Brethauer SA, Kim J, El Chaar M, et al; ASMBS Clinical Issues Committee. Standardized outcomes reporting in metabolic and bariatric surgery. Obes Surg. 2015;25(4):587-606. van de Laar AW, Nienhuijs SW, Apers JA, van Rijswijk AS, de Zoete JP, Gadiot RP (2019) The Dutch bariatric weight loss chart: A multicenter tool to assess weight outcome up to 7 years after sleeve gastrectomy and laparoscopic Roux-en-Y gastric bypass. Surg Obes Relat Dis 15:200-210. Wang L, Tian C, Xu G, et al. Long-Term Weight Loss Outcome of Laparoscopic Sleeve Gastrectomy Predicted by the Percentage of Excess Weight Loss at 6 Months in Chinese Patients with Body Mass Index ≥ 32.5 Kg/m(2) [J]. Diabetes Metab Syndr Obes, 2022, 15(2235-47. Oria HE, Moorehead MK. Bariatric analysis and reporting outcome system (BAROS). Obes Surg. 1998;8(5):487-499. Peterli R, Hurme S, Bueter M, et al. Standardized Assessment of Metabolic Bariatric Surgery Outcomes: Secondary Analysis of 2 Randomized Clinical Trials [J]. JAMA Surg, 2024, 159(3): 306-14. Oria H E, Moorehead M K. Updated Bariatric Analysis and Reporting Outcome System (BAROS) [J]. Surg Obes Relat Dis, 2009, 5(1): 60-6. Brethauer SA, Kim J, el Chaar M, et al; ASMBS Clinical Issues Committee. Standardized outcomes reporting in metabolic and bariatric surgery. Surg Obes Relat Dis. 2015;11(3):489-506. Mastino D, Robert M, Betry C, et al. Bariatric Surgery Outcomes in Sarcopenic Obesity [J]. Obes Surg, 2016, 26(10): 2355-62. Yin L, Song C, Cui J, et al. Low fat mass index outperforms handgrip weakness and GLIM-defined malnutrition in predicting cancer survival: Derivation of cutoff values and joint analysis in an observational cohort [J]. Clin Nutr, 2022, 41(1): 153-64. Kyle U G, Pirlich M, Lochs H, et al. Increased length of hospital stay in underweight and overweight patients at hospital admission: a controlled population study [J]. Clin Nutr, 2005, 24(1): 133-42. Lu G, Dong Z, Huang B, et al. Determination of weight loss effectiveness evaluation indexes and establishment of a nomogram for forecasting the probability of effectiveness of weight loss in bariatric surgery: a retrospective cohort [J]. Int J Surg, 2023, 109(4): 850-60. Zhao J, Jiang Y, Qian J, et al. A nomogram model based on the combination of the systemic immune-inflammation index and prognostic nutritional index predicts weight regain after laparoscopic sleeve gastrectomy [J]. Surg Obes Relat Dis, 2023, 19(1): 50-8 Busetto L, Dixon J, De Luca M, et al. Bariatric surgery in class I obesity : a Position Statement from the International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO) [J]. Obes Surg, 2014, 24(4): 487-519. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 28 Sep, 2024 Read the published version in Obesity Surgery → Version 1 posted Editorial decision: Revision requested 26 Jul, 2024 Reviews received at journal 21 Jul, 2024 Reviews received at journal 20 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers invited by journal 08 Jul, 2024 Editor assigned by journal 02 Jul, 2024 Submission checks completed at journal 20 Jun, 2024 First submitted to journal 16 Jun, 2024 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-4590701","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324064014,"identity":"3a4d7d43-28a0-45e9-9eee-883e83ef0299","order_by":0,"name":"Yi-Ming Jiang","email":"","orcid":"","institution":"Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Yi-Ming","middleName":"","lastName":"Jiang","suffix":""},{"id":324064016,"identity":"9fa41668-6023-4c50-baf5-b027ca2524c9","order_by":1,"name":"Qing 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18:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4590701/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4590701/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11695-024-07518-5","type":"published","date":"2024-09-28T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60604585,"identity":"bd183c03-cdc5-4ef0-abf4-f44989f617d2","added_by":"auto","created_at":"2024-07-18 16:37:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":254517,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart in this study\u003c/p\u003e","description":"","filename":"FIg1.png","url":"https://assets-eu.researchsquare.com/files/rs-4590701/v1/87356245ee2eaa6862515386.png"},{"id":60605299,"identity":"f88f1bf3-3d86-49a7-bc3a-7324758b917d","added_by":"auto","created_at":"2024-07-18 16:45:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140598,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ROC curves analysis to evaluate the predictive value of each body composition for EEWL in patients after LSG\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4590701/v1/33f1c162548a20040022518d.png"},{"id":60604587,"identity":"8bf450a1-bc16-439f-afca-db01d677bd79","added_by":"auto","created_at":"2024-07-18 16:37:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126856,"visible":true,"origin":"","legend":"\u003cp\u003eDCA for EEWL. The y-axis measures the net beneft.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4590701/v1/ae9af4f30ac2764c6175fd05.png"},{"id":60604583,"identity":"607f0e97-cbdb-4441-9ce4-67090c8bafd9","added_by":"auto","created_at":"2024-07-18 16:37:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67661,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage Excess Weight Loss (%EWL) and BMI by Group After Laparoscopic Sleeve Gastrectomy (LSG)\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4590701/v1/82a8a53fc427824b0efc1bab.png"},{"id":65627358,"identity":"232b0a67-6133-4f72-a00d-a2e4fb7b91a3","added_by":"auto","created_at":"2024-09-30 16:15:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1310782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4590701/v1/999981cf-cdf4-47c6-9c07-f5bb9fcf1a85.pdf"},{"id":60605300,"identity":"67a0c47d-b199-436d-811e-ac7f81238757","added_by":"auto","created_at":"2024-07-18 16:45:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1921119,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4590701/v1/475432be6cc3287c28832df5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fat mass index predicts the effect of weight loss and quality of life early after laparoscopic sleeve gastrectomy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBariatric surgery is the only effective treatment for patients with severe obesity for long-term and substantial weight loss, remission of obesity-related comorbidities, improvement in quality of life (QoL), and longer life expectancy [1\u0026ndash;8]. Laparoscopic sleeve gastrectomy (LSG) is the most common bariatric and metabolic surgical procedure, accounting for approximately 60% of all bariatric procedures, both globally and in the US [9, 10]. Accumulating evidence has revealed that LSG can effectively reduce weight, alleviate comorbidities, and improve the QoL in patients with obesity. However, the change in %EWL in patients with severe obesity after LSG remains an important topic for surgeons [1, 2, 3]. Currently, a consensus on how to provide targeted postoperative guidance is lacking. Moreover, only few studies have confirmed the baseline factors that can predict the effect of weight loss early after surgery.\u003c/p\u003e \u003cp\u003eAmong the various parameters reflecting body size, body mass index (BMI) is a widely used index, and its association with patient outcomes has been well described [11, 12]. However, the BMI cannot distinguish between different body tissues such as muscles and fat, or their proportions. [13, 14, 15]. The impact of individual tissue components on weight loss cannot be described by BMI. Therefore, to predict the effects of weight loss more accurately, use of indicators that can distinguish between different body components is necessary. According to existing guidelines, we routinely preformed abdominal CT scan before LSG to evaluate the patient's abdominal condition and exclude the presence of other complications. We can obtain body composition indicators conveniently and quickly by SliceOmatic version 5.0(TomoVision) based on CT images [16]. Some body composition indicators have been applied in patients received metabolic and bariatric surgery (MBS) [17, 18]. Fat mass index (FMI) has shown significant prognostic value for determining nutrition-related outcomes and its use has been widely accepted for the treatment of nutrition-related diseases [19\u0026ndash;22]. To date, few studies had explored the correlation between body composition and weight loss after LSG. Therefore, this study aimed to evaluate the predictive efficacy of FMI for weight loss and QoL early after LSG.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and data collection\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed the clinical data and CT images of 260 patients with obesity who underwent LSG at Fujian Medical University Union Hospital between January 2014 and July 2022. Informed consent was obtained from all individual participants included in the study. All patients at these institutions who met the following inclusion criteria below were included in the study: (a) BMI\u0026thinsp;\u0026ge;\u0026thinsp;32.5, or 27.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;32.5 but suffering from type 2 diabetes mellitus with uncontrollable complications despite lifestyle changes and drug treatment, (b) underwent abdominal CT scan preoperatively, (c) LSG recipient. The exclusion criteria were as follows: (a) major abdominal surgery (excluding laparoscopic cholecystectomy), (b) severe gastroesophageal reflux disease (despite receiving medication), (c) pregnancy within 1 year after surgery, and (d) loss to follow-up. Finally, 243 patients were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study passed the Fujian Medical University Union Hospital institutional review board (IRB number:2023KY176). Due to the retrospective and observational design, the IRB waived the need for informed consent for this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBody composition\u003c/h2\u003e \u003cp\u003eA single CT image at the third lumbar vertebra (L3) was selected to quantify muscle and adipose characteristics because this anatomical location is strongly associated with whole-body volume. One researcher, unaware of the effect of weight loss, received training on obtaining the L3 level and segmenting the muscle and adipose tissues. All CT images without any patient information were analyzed using SliceOmatic (version 5.0; TomoVision) (eFigure 1.). The skeletal muscle density (SMD) was directly measured using this software. According to the standard Hounsfield unit (HU) range, skeletal muscle cross-sectional area (SMA, -29\u0026ndash;150 HU), visceral adipose tissue (VAT, -150 \u0026ndash; -50 HU), subcutaneous adipose tissue (SAT, -190 \u0026ndash; -30 HU), and intramuscular adipose tissue (IMAT, -190 \u0026ndash; -30 HU) were quantified [23, 24, 25]. Fat mass (FM) was calculated as 0.042 \u0026times; (SAT\u0026thinsp;+\u0026thinsp;VAT\u0026thinsp;+\u0026thinsp;IMAT)\u0026thinsp;+\u0026thinsp;11.2. Fat-free mass (FFM) was calculated as 0.3 \u0026times; SMA\u0026thinsp;+\u0026thinsp;6.06 [26]. The measured value of each body component (cm\u003csup\u003e3\u003c/sup\u003e) divided by the square of the height (m\u003csup\u003e2\u003c/sup\u003e) was converted into an index, that included the skeletal muscle index (SMI), visceral adipose index (VAI), subcutaneous adipose index (SAI), fat-free mass index (FFMI), and FMI [23, 24, 25]. BMI was calculated by dividing body weight (kg) by height squared (m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of early eligible weight loss (EEWL)\u003c/h2\u003e \u003cp\u003eAccording to the recommendations of the American Society of Metabolic and Bariatric Surgery (ASMBS), the ideal BMI for patients with obesity who undergo LSG is 25 (kg/m\u003csup\u003e2\u003c/sup\u003e) [1, 2, 27, 28]. Based on this, the ideal weight for each patient who undergoes LSG could be calculated easily base on it. The %EWL was calculated as follows: (initial weight - follow-up weight)/(initial weight - ideal weight) \u0026times; 100 [1\u0026ndash;4]. A previous study has reported that weight loss is relatively significant and stable at 1 year postoperatively.[29]. The %EWL at 6 months after LSG is an independent factor that influences long-term weight loss effect after MBS[30]. Therefore, this study innovatively defined early weight loss effect as achieving the ideal BMI (%EWL\u0026thinsp;\u0026ge;\u0026thinsp;100) at 6 months postoperatively. To improve the readability of the content of the article, we chose its initials to determine the abbreviation of this definition (early eligible weight loss (EEWL)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSurgery procedures and postoperative treatment\u003c/h2\u003e \u003cp\u003eAll participating surgeons were experienced laparoscopists (Z.C.H and L.J.X). The LSG procedure was performed using five trocars. Using a 32-36F bougie, the stomach was trans-fected from the antrum to the His angle with multiple staple lines. The first staple line was at the antrum,which was 2\u0026ndash;6 cm from the pylorus. All the patients started a liquid diet on the first postoperative day and decided to abdominal drainage tube was removed based on the postoperative gastrointestinal radiography. They were instructed to consume sufficient water and protein daily. In addition, all the patients received sufficient long-term supplementation with multiple vitamins and trace elements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up investigation\u003c/h2\u003e \u003cp\u003eThe final follow-up evaluation was conducted in January 2023. Follow-up assessments were performed at 1, 3, 6, 9 and 12 months postoperatively and then every 6 months thereafter. Most routine follow-up appointments included a physical examination, weight measurement, BMI measurement, routine blood examination, biochemical blood examination, vitamin intake measurement, and oral glucose tolerance test. The outcomes of comorbidities included aggravated, unchanged, improved and remission statuses. The specific definitions of the outcomes of comorbidities were shown in eTable 1. In addition, we administered the Moorehead\u0026ndash;Ardelt Quality of Life Questionnaire II 6 months postoperatively. To evaluate the therapeutic effect of LSG, the Bariatric Analysis and Reporting Outcome System (BAROS) that four items (weight loss points, medical condition points, QoL points, and complication points) was used [31, 32, 33]. The determination method is described in detail in eFigure 2. The first three items were summed, and the complication points were deduced to obtain the BAROS score. The BAROS grade was determined based on the score range. All parameters were evaluated 6 months postoperatively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eNormally distributed variables are described as absolute numbers and percentages, means, and standard deviations. Normally distributed measurement data were compared using two independent samples t-tests, and the counting data were compared using the chi-squared test or categorical Fisher\u0026rsquo;s exact tests. Receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA), C-index, and chi-square likelihood ratio were used to compare the predictive values of the body composition indicators. Youden's J statistical analysis was used to determine the cutoff value for the best indicator. The patients were then divided into the high- and low-FMI groups. Radar images were used to reveal the outcomes of comorbidities in both groups. Univariate and multivariate logistic regression analyses were conducted to determine the independent factors influencing EEWL. The interaction between weight and low FMI in predicting EEWL was also examined. Statistical analyses were performed using R (version 3.6.1) and SPSS (version 22.0; SPSS Inc., Chicago, IL). Significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eAltogether, there were a total of 243 patients with obesity comprised the discovery cohort. And 82 patients (33.7%) achieved EEWL. The number of patients was 48 (19.8%) in the early stage of this study, 195 patients (80.2%) in the past 3 years. The mean age of the patients was 29.7 years (range, 22.1\u0026ndash;37.3). The mean preoperative weight and BMI were 107.2 kg (range, 83.9\u0026ndash;130.5) and 38.4 kg/m\u003csup\u003e2\u003c/sup\u003e (range, 32.3\u0026ndash;44.5). The mean abdominal girth was 115.2 cm (range, 99.0\u0026ndash;131.4). The mean SMD, SMI, VAI, SAI, FFMI, and FMI were 34.8 (range, 33.3\u0026ndash;46.3), 26.8 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e (range, 21.1\u0026ndash;32.5), 72.2 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e (range, 41.1\u0026ndash;103.3), 163.1 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e (range, 101.1\u0026ndash;225.1), 10.2 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e (range, 8.4\u0026ndash;12.0), and 14.1 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e (range, 10.8\u0026ndash;17.4), respectively. The average operation time was 107.1 min (range, 83.0\u0026ndash;131.2), and the average intraoperative blood loss was 12.6 ml (range, 5.4\u0026ndash;19.8). The mean length of postoperative hospital stay was 5.1 days (range, 2.8\u0026ndash;7.4). Six (2.5%) patients experienced early postoperative complications, including three (1.3%) gastric leaks, two (0.8%) abdominal infections, and one (0.4%) stenosis. None of the patients experienced late complications. The clinical characteristics of the study cohort are presented in Supplementary eTable 2. The postoperative changes in %EWL and BMI of all the patients are shown in eFigure 3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBody composition predicting EEWL\u003c/h2\u003e \u003cp\u003eThe average %EWL of the patients at 1, 3, 6, 9 and 12 months postoperatively were 42.9, 68.1, 88.5, 101.0, and 106.2, respectively. The mean BMI of the patients at 6 and 12 months postoperatively were 28.0 and 25.7, respectively. The ROC analysis showed that the predictive value of FMI was superior to that of the other five indicators (AUC value: FMI: 0.813; SMD: 0.711 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007]; SMI: 0.667 [P\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; VAI: 0.707 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; SAI: 0.778 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031], FFMI: 0.664 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]). The ROC curve was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. ROC analysis showed that FMI had similar performance in achieving early eligible weight loss in patients early in the study and in the past three years (AUC value: early in the study vs. in the past 3 years: 0.808 vs. 0.812) (eFigure 4.). DCA demonstrated that FMI could provide a greater net benefit and clinical value than the other five body composition indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, C-index and likelihood ratio chi-square revealed that FMI had better predictive value than the other indicators (C-index: FMI\u0026thinsp;=\u0026thinsp;0.747; SMD\u0026thinsp;=\u0026thinsp;0.500 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; SMI\u0026thinsp;=\u0026thinsp;0.657 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017]; VAI\u0026thinsp;=\u0026thinsp;0.694 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012]; SAI\u0026thinsp;=\u0026thinsp;0.629 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; FFMI\u0026thinsp;=\u0026thinsp;0.660 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023]; likelihood ratio chi-square: FMI\u0026thinsp;=\u0026thinsp;80.487; SMD\u0026thinsp;=\u0026thinsp;28.280 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; SMI\u0026thinsp;=\u0026thinsp;20.840 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; VAI\u0026thinsp;=\u0026thinsp;34.812 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001]; SAI\u0026thinsp;=\u0026thinsp;48.988 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004]; FFMI\u0026thinsp;=\u0026thinsp;10.776 [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001])(eTable 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics and intraoperative and postoperative conditions by groups\u003c/h2\u003e \u003cp\u003eAccording to Youden's J statistical analysis, the cut-off value for the FMI was 13.662. Patients with an FMI of \u0026ge;\u0026thinsp;13.662 were included in the high-FMI group, whereas the others were included in the low-FMI group. Overall, 123 (50.6%) patients were in the high-FMI group and 120 (49.4%) were in the low-FMI group. Compared with the high-FMI group, the low-FMI group had fewer males (30.1% vs. 16.0%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), lower preoperative weight (118.0\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6 vs. 96.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.002) and BMI (42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7 vs. 34.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher high-density lipoprotein cholesterol (HDL-C) (1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 vs. 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). The baseline characteristics by groups were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences in operation time, intraoperative blood loss, total cost, early or late complications were observed between the two groups. However, compared with the high-FMI group, the low-FMI group had a shorter postoperative length of hospital stay (5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7 vs. 4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). The intraoperative and postoperative outcomes by groups were shown in eTable 4. In addition, compared to the high-FMI group, the low-FMI group had significantly higher %EWL and lower BMI at 1, 3, 6, 9, and 12 months after surgery (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).No significant differences in vitamin intake and other biochemical parameters were observed between the two groups at 6 months postoperatively (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (eTable 5).\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\u003eParticipants\u0026rsquo; Baseline Characteristics by Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD/N(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-FMI group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-FMI group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87(69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(84.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118.0\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.3\u0026thinsp;\u0026plusmn;\u0026thinsp;24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eFMI\u0026thinsp;=\u0026thinsp;fat mass index; BMI\u0026thinsp;=\u0026thinsp;body mass index; GLU\u0026thinsp;=\u0026thinsp;glocose; TBIL\u0026thinsp;=\u0026thinsp;total bilirubin; DBIL\u0026thinsp;=\u0026thinsp;direct bilirubin; HDL-C\u0026thinsp;=\u0026thinsp;High-Density Lipoprotein Cholesterol; LDL-C\u0026thinsp;=\u0026thinsp;Low-Density Lipoprotein Cholesterol; CHOL\u0026thinsp;=\u0026thinsp;cholesterol; TG\u0026thinsp;=\u0026thinsp;triglyceride\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \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\u003eBAROS score of patients by Group\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=\"char\" char=\"\u0026plusmn;\" 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=\"char\" char=\"\u0026plusmn;\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHigh-FMI group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLow-FMI group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScoring range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScoring range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQoL points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.7)\u0026thinsp;~\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-1.7)\u0026thinsp;~\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical condition points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1)\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026thinsp;~\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAROS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(-1.9)\u0026thinsp;~\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u0026thinsp;~\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBAROS\u0026thinsp;=\u0026thinsp;Bariatric Analysis and Reporting Outcome System; QoL\u0026thinsp;=\u0026thinsp;quality of life\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImprovement of postoperative complications and QoL\u003c/h2\u003e \u003cp\u003eCompared with the high-FMI group, the low-FMI group had a better BAROS grade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(eTable 6). The low-FMI group also had significantly higher QoL points (0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 vs. 1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), weight loss points (2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 vs. 2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BAROS score (4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 vs. 6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than the high-FMI group (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, the low-FMI group demonstrated significantly better hyperuricemia and hyperglycemia in the low-FMI group were significantly better than those in the high-FMI group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences in the outcomes of other complications were observed between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Analysis of influence factors for Eligiable Early Weight Loss in patients receiving LSG\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95CI%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.960\u0026ndash;1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.267\u0026ndash;4.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.369\u0026ndash;2.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal girth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.993\u0026ndash;1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.865\u0026ndash;1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.961\u0026ndash;1.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.969\u0026ndash;1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.805\u0026ndash;1.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.762\u0026ndash;6.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.524\u0026ndash;1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.865\u0026ndash;1.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.939\u0026ndash;1.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.928\u0026ndash;0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.959\u0026ndash;0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty Liver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.329\u0026ndash;1.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperuricemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.394\u0026ndash;1.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperglycemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.366\u0026ndash;1.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyeperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.276-1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.406\u0026ndash;1.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-FMI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-FMI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.434\u0026ndash;7.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.424\u0026ndash;4.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFMI\u0026thinsp;=\u0026thinsp;fat mass index; GLU\u0026thinsp;=\u0026thinsp;glocose; TBIL\u0026thinsp;=\u0026thinsp;total bilirubin; DBIL\u0026thinsp;=\u0026thinsp;direct bilirubin; HDL-C\u0026thinsp;=\u0026thinsp;High-Density Lipoprotein Cholesterol; LDL-C\u0026thinsp;=\u0026thinsp;Low-Density Lipoprotein Cholesterol; CHOL\u0026thinsp;=\u0026thinsp;cholesterol; TG\u0026thinsp;=\u0026thinsp;triglyceride; OSAS\u0026thinsp;=\u0026thinsp;sleep apnea syndrome.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in comorbidities by group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003ePatients with commorbidities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ehigh FMI group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003elow FMI group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\" colname=\"c2\"\u003e \u003cp\u003eUnchanged\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRemission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnchanged\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eImproved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRemission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty liver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(59.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13(24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35(66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperuricemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7(100.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperglycemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(27.6%%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8(38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12(57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(73.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9(60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5(45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6(55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5(55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4(44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviation: OSAS\u0026thinsp;=\u0026thinsp;sleep apnea syndrome; PCOS\u0026thinsp;=\u0026thinsp;polycystic ovary syndrome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFMI Related to EEWL\u003c/h2\u003e \u003cp\u003eThe univariate logistic regression analysis revealed that preoperative weight (odds ratio [OR]: 0.944, 95% confidence interval [CI]: 0.928\u0026ndash;0.960, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), female sex (OR: 2.346, 95% CI: 1.267\u0026ndash;4.345, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and low FMI (OR: 4.167, 95% CI: 2.434\u0026ndash;7.132, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were related to EEWL. Further multivariate analysis revealed that high preoperative weight (OR: 0.977, 95% CI: 0.959\u0026ndash;0.995, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and low FMI (OR: 2.636, 95%CI: 1.424\u0026ndash;4.879, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) as independent positive influencing factors for EEWL (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additionally, the interaction analysis that preoperative weight and low-FMI exhibited an interaction (OR\u0026thinsp;=\u0026thinsp;1.023, 95%CI: 1.016\u0026ndash;1.029, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLSG is mainly achieved by reducing gastric volume. Removing the fundus and creating a greater stomach curvature while maintaining the anatomical structure of the original gastrointestinal tract can alter the levels of some gastrointestinal hormones involved in optimal glucose metabolism and other metabolic indicators in patients with obesity. Weight loss and the improvement of comorbidities is the most important value of LSG [34]. Robert et al. have reported that preoperative FFM could affect weight loss after MBS[17]. However, this study did not compare FFM with other body composition indicators. Therefore, this study aimed to evaluate the predictive value of body composition indicators for LSG efficacy. FMI was determined as a convenient indicator that can accurately predict the effects of weight loss and QoL immediately after LSG. Patients with low FMI had significantly better weight-loss effects and QoL than those with high FMI.\u003c/p\u003e \u003cp\u003eBMI can be used to quickly assess the degree of overweight in patients with obesity. However, patients with similar BMI may not achieve similar weight loss effects owing to factors such as age, sex, and race, which cause differences in body fat and muscle proportions. Compared with traditional BMI, body composition can reflect muscle and fat proportions [25]. Mastino et al. have reported that bariatric surgery was effective in patients with sarcopenia and obesity. However, the effects of weight loss were similar immediately after surgery [35]. Therefore, indicators that can accurately predict early weight loss after bariatric surgery must be identified. Yin et al. have identified low FMI as a valuable predictor of cancer survival [36]. This may be related to the low-fat body composition and high fat metabolism rates in patients with a low FMI. Similarly, these may lead to greater weight loss after LSG. Through univariate and multivariate logistic analyses, we found that preoperative weight and low-FMI were two independent positive influencing factors influencing EEWL. Moreover, the interaction analysis showed that lower preoperative weight and low-FMI exhibited an interaction. For patients with a low-FMI, each kilogram of weight reduction in weight was associated with a 0.023-fold increase in the probability of achieving EEWL. In clinical practice, we should combine low-FMI and weight to evaluate the weight loss effect of patients who undergo LSG in order to make the appropriate clinical decisions. FMI also performed well in predicting the resolution of some comorbidities, such as hyperuricemia and hyperglycemia. Owing to our small sample size and differences from the Western population, patients who underwent LSG in China had fewer comorbidities, which may explain why why the improvement of most comorbidities did not differ between the two groups. We further developed radar charts to reveal the improvements in comorbidities (eFigure 5.). The results showed that although there was no statistically significant difference in the improvement of most comorbidities between the two groups early after LSG, the low-FMI group still had a better trend in the improvement of comorbidities. This study spanned a long time (2014\u0026ndash;2022), and the number of our patients demonstrated a ramping up trend. Most of the patients included in this study were concentrated in the past three years, especially in 2022. Considering the relatively long duration of the research, there may be inevitable biases. However, the ROC analysis showed that FMI had similar performance in achieving EEWL in patients early in the study and in the past three years. Overall, this study compared multiple widely used body component indicators and found that FMI performed well in predicting early weight loss and QoL after LSG, which was valuable for guiding clinical decision-making and follow-up.\u003c/p\u003e \u003cp\u003eAdditionally, the complication outcomes and QoL after LSG are important for bariatric surgeons. Ursula et al. have reported that a high FMI significantly increased the length of hospital stay [37]. This may have been attributable to the surplus body fat content. Severe nutritional risks increase the incidence of obesity, comorbidities, and complications during hospitalization. In this study, patients with a low FMI performed better than those with a high FMI in terms of postoperative hospital stay and EW, QoL, and BAROS scores, indicating a better weight loss effect and QoL. This may be due to the lower body fat content in these patients. The surgeon could dissect more accurately during surgery, making it less likely to cause damage, thereby improving the quality of the surgery and achieving better weight loss and QoL. Similarly, compared to patients with a high FMI, those with a low FMI had a lower nutritional risk, which reduces the risk of postoperative complications. This may be related to a shorter postoperative hospital stay and better fat metabolism levels in patients with a low FMI after surgery. Thus, patients with a low FMI recovered better in multiple aspects after LSG than those with a high FMI. This also suggests that for patients with a high FMI, postoperative attention, rehabilitation guidance, and follow-up work should be strengthened so that more patients can achieve EEWL and obtain better postoperative QoL.\u003c/p\u003e \u003cp\u003eThis study had some limitations. Firstly, this was a retrospective eastern small-sample study with a short-term follow-up that explored the relationship between body composition indicators and EEWL for the first time. Hence, bias may have been inevitable. We will conduct relevant prospective studies with larger sample sizes, multiple centers and long-term follow-ups to further explore the relationship between body composition indicators and EEWL. Secondly, the calculation of FMI was based on abodominal CT scan before surgery, which increases the complexity of the evaluation to a certain extent. But due to the value of FMI in predicting weight loss effect and QoL early after LSG, we think it still worthed to be included in the evolution system of LSG. Thirdly, for most patients, we did not routinely perform abdominal CT scans to measure body composition indicators after surgery. We will conduct relevant research in the future to explore the impact of postoperative body composition indicators on the weight loss effect of LSG. Forthly, The average age of patients included in this study was 29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6 years old, which is similar to the average age of patients studied in other Chinese centers [38, 39]. Meanwhile, in the classic study of The SLEEVEPASS Randomized Clinical Trial in a Western center, the average age of patients underwent LSG was 48.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6 years old [2, 3, 4]. As you mentioned, Our patients were significantly younger than the patients in North American or European centers. This may be because of the higher acceptance of MBS among young Chinese patients. Therefore, lower in our center than in North American or European centers. We will further validating the application of FMI in older patients using international multicenter data. Lastly, total weight loss (%TWL) and %EWL are frequently used metrics to assess the effectiveness of weight loss following LSG. %TWL is a more objective and less biased measure for evaluating weight loss outcomes. However, as the most commonly used metric for evaluating weight loss outcomes, %EWL also has its advantages. So far, many important studies had used %EWL as the main indicator to evaluate weight loss effectiveness, including the renowned randomized controlled trial research in the field of weight loss metabolic surgery (the SLEEVEPASS Randomized Clinical Trial) [1\u0026ndash;6]. Existing guidelines also recognize the use of %EWL for evaluating weight loss outcomes [40]. Additionally, the BAROS also used %EWL as the standard for evaluating weight loss point [31]. Due to the higher acceptance of LSG among Chinese patients, the preoperative BMI of Chinese patients who underwent LSG was lower than western patients. Many of them could achieve ideal BMI after surgery. Therefore, this study selected% EWL as the indicator to evaluate the weight loss effect after LSG. In the future, we hope to conduct research to explore which indicator is more suitable for low BMI patients receiving LSG in Chinese centers. Nevertheless, this study obtained different body composition indicators through widely-used and convenient CT images and compared their predictive performance for weight loss and quality of life early after LSG. To the best of our knowledge, this is the first study to discover the predictive value of FMI in this area, which supplements existing indicators and provide additional references for decision-making around clinical treatment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, compared with other body composition indicators, FMI can effectively predict the effects of weight loss and QoL early after LSG. For patients with a high FMI, postoperative rehabilitation guidance and health management should be emphasized. This study requires a large-sample, prospective, multicenter study for verification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics Approval\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eFinancial support and sponsorship: This study was supported by the Financial Support for the Construction of \u0026quot;Dual High\u0026quot; Medical Services in Fujian Province\u0026quot; (Min Wei Yi Zheng [2021] No. 76).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003e# Yi-Ming Jiang, Qing Zhong, and Zhi-Xin Shang-Guan contributed equally to this work and should be considered co-first authors.Yi-ming Jiang: Substantial contributions to the conception design of the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Qing Zhong: Substantial contributions to the conception interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Zhi-Xin Shang-Guan: Substantial contributions to the conception the acquisition, analysis; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Guang-Tan Lin: Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Xiao-Jing Guo: Substantial contributions to the collection of data and follow-up; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Ze-Ning Huang: Substantial contributions to the conception design of the work; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Jun-Lu: Substantial contributions to the conception design of the work; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Chang-Ming Huang: Substantial contributions to the conception design of the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Jian-Xian Lin: Substantial contributions to the conception interpretation of data for the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Chao-Hui Zheng: Substantial contributions to the conception or design of the work and the acquisition, analysis; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePeterli R, W\u0026ouml;lnerhanssen BK, Peters T, et al. Effect of laparoscopic sleeve gastrectomy vs laparoscopic Roux-en-Y gastric bypass on weight loss in patients with morbid obesity: the SM-BOSS randomized clinical trial. \u003cem\u003eJAMA\u003c/em\u003e. 018;319(3):255-265.doi:10.1001/jama.2017.20897 \u003c/li\u003e\n\u003cli\u003eSalminen P, Helmi\u0026ouml; M, Ovaska J, et al. Effect of laparoscopic sleeve gastrectomy vs laparoscopic Roux-en-Y gastric bypass on weight loss at 5 years among patients with morbid obesity: the SLEEVEPASS randomized clinical trial. \u003cem\u003eJAMA\u003c/em\u003e. 2018;319(3):241-254.doi:10.1001/jama.2017.20313 \u003c/li\u003e\n\u003cli\u003eGr\u0026ouml;nroos S, Helmi\u0026ouml; M, Juuti A, et al. Effect of laparoscopic sleeve gastrectomy vs Roux-en-Y gastric bypass on weight loss and quality of life at 7 years in patients with morbid obesity: the SLEEVEPASS randomized clinical trial.\u003cem\u003eJAMA Surg\u003c/em\u003e.2021;156(2):137-146. doi:10.1001/ jamasurg.2020.5666 \u003c/li\u003e\n\u003cli\u003eSalminen P, Gr\u0026ouml;nroos S, Helmi\u0026ouml; M, et al. Effect of Laparoscopic Sleeve Gastrectomy vs Roux-en-Y Gastric Bypass on Weight Loss, Comorbidities, and Reflux at 10 Years in Adult Patients With Obesity: The SLEEVEPASS Randomized Clinical Trial [J]. JAMA Surg, 2022, 157(8): 656-66.\u003c/li\u003e\n\u003cli\u003eAdams TD, Davidson LE, Hunt SC. Weight and metabolic outcomes 12 years after gastric bypass. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2018;378(1):93-96. doi:10.1056/ NEJMc1714001 Adams TD, Gress RE, Smith SC, et al. Long-term mortality after gastric bypass surgery. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2007;357(8):753-761. doi:10.1056/NEJMoa066603 \u003c/li\u003e\n\u003cli\u003eArterburn DE, Olsen MK, Smith VA, et al. Association between bariatric surgery and long-term survival.\u003cem\u003eJAMA\u003c/em\u003e. 2015;313(1):62-70. doi: 10.1001/jama.2014.16968 \u003c/li\u003e\n\u003cli\u003eSchauer PR, Bhatt DL, Kirwan JP, et al; STAMPEDE Investigators. Bariatric surgery versus intensive medical therapy for diabetes\u0026mdash;5-year outcomes. \u003cem\u003eN Engl J Med\u003c/em\u003e.2017;376(7):641-651. doi: 10.1056/NEJMoa1600869 \u003c/li\u003e\n\u003cli\u003eW\u0026ouml;lnerhanssen BK, Peterli R, Hurme S, et al. Laparoscopic Roux-en-Y gastric bypass versus laparoscopic sleeve gastrectomy: 5-year outcomes of merged data from two randomized clinical trials (SLEEVEPASS and SM-BOSS). \u003cem\u003eBr J Surg\u003c/em\u003e. 2021;108 (1):49-57. doi:10.1093/bjs/znaa011\u003c/li\u003e\n\u003cli\u003eAngrisani L, Santonicola A, Iovino P, et al. IFSO Worldwide Survey 2016: primary, endoluminal, and revisional procedures. Obes Surg. 2018;28(12): 3783-3794.doi:10.1007/s11695-018-3450-2 \u003c/li\u003e\n\u003cli\u003eCampos GM, Khoraki J, Browning MG, Pessoa BM, Mazzini GS, Wolfe L. Changes in utilization of bariatric surgery in the United States from 1993 to 2016. \u003cem\u003eAnn Surg\u003c/em\u003e. 2020;271(2):201-209. doi:10.1097/ SLA.0000000000003554\u003c/li\u003e\n\u003cli\u003eKroenke CH, Neugebauer R, Meyerhardt J, Prado CM, Weltzien E, Kwan ML, et al. Analysis of body mass index and mortality in patients with colorectal cancer using causal diagrams. JAMA Oncol 2016;2:1137e45. \u003c/li\u003e\n\u003cli\u003eChan DSM, Vieira AR, Aune D, Bandera EV, Greenwood DC, McTiernan A, et al. Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies. Ann Oncol 2014;25:1901e14.\u003c/li\u003e\n\u003cli\u003eGonzalez MC, Pastore CA, Orlandi SP, Heymsfifield SB. Obesity paradox in cancer: new insights provided by body composition. Am J Clin Nutr 2014;99: 999e1005. \u003c/li\u003e\n\u003cli\u003eNagai M, Kuriyama S, Kakizaki M, Ohmori-Matsuda K, Sugawara Y, Sone T, et al. Effect of age on the association between body mass index and all-cause mortality: the Ohsaki cohort study. J Epidemiol 2010;20:398e407. \u003c/li\u003e\n\u003cli\u003eOh H, Kwak S Y, Jo G, et al. Adiposity and mortality in Korean adults: a population-based prospective cohort study [J]. Am J Clin Nutr, 2021, 113(1): 142-53. \u003c/li\u003e\n\u003cli\u003eMourtzakis M, Prado C, Lieffers JR, Reiman T, Mccargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab (2008) 33:997\u0026ndash;1006.\u003c/li\u003e\n\u003cli\u003eRobert M, Pelascini E, Disse E, et al. Preoperative fat-free mass: a predictive factor of weight loss after gastric bypass [J]. Obes Surg, 2013, 23(4): 446-55.\u003c/li\u003e\n\u003cli\u003eLiu P, Ma F, Lou H, et al. The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome [J]. BMC Public Health, 2013, 13(629).\u003c/li\u003e\n\u003cli\u003eCederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition - a consensus report from the global clinical nutrition community. Clin Nutr 2019;38:1e9.\u003c/li\u003e\n\u003cli\u003eBrunani A, Perna S, Soranna D, et al. Body composition assessment using bioelectrical impedance analysis (BIA) in a wide cohort of patients affected with mild to severe obesity [J]. Clin Nutr, 2021, 40(6): 3973-81.\u003c/li\u003e\n\u003cli\u003evan Beijsterveldt I, van der Steen M, de Fluiter K S, et al. Body composition and bone mineral density by Dual Energy X-ray Absorptiometry: Reference values for young children [J]. Clin Nutr, 2022, 41(1): 71-9.\u003c/li\u003e\n\u003cli\u003eCereda E, Pedrazzoli P, Lobascio F, Masi S, Crotti S, Klersy C, et al. The prognostic impact of BIA-derived fat-free mass index in patients with cancer. Clin Nutr 2021;40:3901e7.\u003c/li\u003e\n\u003cli\u003eFeliciano E, Popuri K, Cobzas D, Baracos VE, Caan BJ. Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients. Journal of Cachexia, Sarcopenia and Muscle. 2020.\u003c/li\u003e\n\u003cli\u003eKvist H, Chowdhury B, Grangrd U, Tyl\u0026eacute;n U, Sjstrm L. Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. American Journal of Clinical Nutrition. 1988;48(6):1351-1361.\u003c/li\u003e\n\u003cli\u003eRosenberg IH. Sarcopenia: origins and clinical relevance. The Journal of nutrition. 1997;127(5):990S-991S.\u003c/li\u003e\n\u003cli\u003eLee J, Jeong W K, Kim J H, et al. Serial Observations of Muscle and Fat Mass as Prognostic Factors for Deceased Donor Liver Transplantation [J]. Korean J Radiol, 2021, 22(2): 189-97.\u003c/li\u003e\n\u003cli\u003eBrethauer SA, Kim J, el Chaar M, et al; ASMBS Clinical Issues Committee. Standardized outcomes reporting in metabolic and bariatric surgery. Surg Obes Relat Dis. 2015;11(3):489-506.\u003c/li\u003e\n\u003cli\u003eBrethauer SA, Kim J, El Chaar M, et al; ASMBS Clinical Issues Committee. Standardized outcomes reporting in metabolic and bariatric surgery. Obes Surg. 2015;25(4):587-606.\u003c/li\u003e\n\u003cli\u003evan de Laar AW, Nienhuijs SW, Apers JA, van Rijswijk AS, de Zoete JP, Gadiot RP (2019) The Dutch bariatric weight loss chart: A multicenter tool to assess weight outcome up to 7 years after sleeve gastrectomy and laparoscopic Roux-en-Y gastric bypass. Surg Obes Relat Dis 15:200-210.\u003c/li\u003e\n\u003cli\u003eWang L, Tian C, Xu G, et al. Long-Term Weight Loss Outcome of Laparoscopic Sleeve Gastrectomy Predicted by the Percentage of Excess Weight Loss at 6 Months in Chinese Patients with Body Mass Index \u0026ge; 32.5 Kg/m(2) [J]. Diabetes Metab Syndr Obes, 2022, 15(2235-47.\u003c/li\u003e\n\u003cli\u003eOria HE, Moorehead MK. Bariatric analysis and reporting outcome system (BAROS). Obes Surg. 1998;8(5):487-499. \u003c/li\u003e\n\u003cli\u003ePeterli R, Hurme S, Bueter M, et al. Standardized Assessment of Metabolic Bariatric Surgery Outcomes: Secondary Analysis of 2 Randomized Clinical Trials [J]. JAMA Surg, 2024, 159(3): 306-14.\u003c/li\u003e\n\u003cli\u003eOria H E, Moorehead M K. Updated Bariatric Analysis and Reporting Outcome System (BAROS) [J]. Surg Obes Relat Dis, 2009, 5(1): 60-6.\u003c/li\u003e\n\u003cli\u003eBrethauer SA, Kim J, el Chaar M, et al; ASMBS Clinical Issues Committee. Standardized outcomes reporting in metabolic and bariatric surgery. Surg Obes Relat Dis. 2015;11(3):489-506.\u003c/li\u003e\n\u003cli\u003eMastino D, Robert M, Betry C, et al. Bariatric Surgery Outcomes in Sarcopenic Obesity [J]. Obes Surg, 2016, 26(10): 2355-62.\u003c/li\u003e\n\u003cli\u003eYin L, Song C, Cui J, et al. Low fat mass index outperforms handgrip weakness and GLIM-defined malnutrition in predicting cancer survival: Derivation of cutoff values and joint analysis in an observational cohort [J]. Clin Nutr, 2022, 41(1): 153-64.\u003c/li\u003e\n\u003cli\u003eKyle U G, Pirlich M, Lochs H, et al. Increased length of hospital stay in underweight and overweight patients at hospital admission: a controlled population study [J]. Clin Nutr, 2005, 24(1): 133-42.\u003c/li\u003e\n\u003cli\u003eLu G, Dong Z, Huang B, et al. Determination of weight loss effectiveness evaluation indexes and establishment of a nomogram for forecasting the probability of effectiveness of weight loss in bariatric surgery: a retrospective cohort [J]. Int J Surg, 2023, 109(4): 850-60.\u003c/li\u003e\n\u003cli\u003eZhao J, Jiang Y, Qian J, et al. A nomogram model based on the combination of the systemic immune-inflammation index and prognostic nutritional index predicts weight regain after laparoscopic sleeve gastrectomy [J]. Surg Obes Relat Dis, 2023, 19(1): 50-8\u003c/li\u003e\n\u003cli\u003eBusetto L, Dixon J, De Luca M, et al. Bariatric surgery in class I obesity : a Position Statement from the International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO) [J]. Obes Surg, 2014, 24(4): 487-519.\u003c/li\u003e\n\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":"obesity-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"obsu","sideBox":"Learn more about [Obesity Surgery](https://link.springer.com/journal/11695)","snPcode":"11695","submissionUrl":"https://submission.springernature.com/new-submission/11695/3","title":"Obesity Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"LSG, body composition, weight loss effect, QoL","lastPublishedDoi":"10.21203/rs.3.rs-4590701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4590701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eFat mass index (FMI) is a body composition indicator that reflects body fat content. Laparoscopic sleeve gastrectomy (LSG) is widely performed in patients with obesity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eThis study aimed to evaluated the value of the FMI in predicting weight loss effect and quality of life early after LSG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial and Methods \u003c/strong\u003eFrom January 2014 to July 2022, the clinical data and computed tomography (CT) images of patients underwent LSG at a tertiary referral teaching hospital were analyzed. Body composition indicators were calculated using the SliceOmatic software. Achieving initial body mass index within 6 months postoperatively was defined as early eligible weight loss (EEWL). The relationship between body composition and EEWL was analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA total of 243 patients were included. Receiver operating characteristic (ROC) curve analysis showed that the predictive value of the FMI for EEWL in patients after LSG was higher than that of other indicators (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; area under the curve = 0.813). The best FMI cut-off point was 13.662. Accordingly, the patients were divided into the high-FMI group and low-FMI group. The %EWL and BMI of patients in the low-FMI group at 1, 3, 6, 9, 12 and 24 months after surgery were better than those in the high-FMI group (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Patients in the low-FMI group had higher BAROS (Bariatric Analysis and Reporting Outcome System) scores than those in the high-FMI group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eCompared with other body composition indicators,\u003cstrong\u003e \u003c/strong\u003eFMI can effectively predict the early effect of weight loss and quality of life after LSG.\u003c/p\u003e","manuscriptTitle":"Fat mass index predicts the effect of weight loss and quality of life early after laparoscopic sleeve gastrectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 16:37:24","doi":"10.21203/rs.3.rs-4590701/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-26T11:52:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-21T21:21:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-20T13:07:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190974955484679402448175498579103805193","date":"2024-07-10T12:58:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18546865147209754505991422909385868192","date":"2024-07-10T10:42:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-08T09:42:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-02T15:19:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-20T05:41:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Obesity Surgery","date":"2024-06-16T18:36:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"obesity-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"obsu","sideBox":"Learn more about [Obesity Surgery](https://link.springer.com/journal/11695)","snPcode":"11695","submissionUrl":"https://submission.springernature.com/new-submission/11695/3","title":"Obesity Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5171fcf5-07d4-49ca-980e-d220b7553bca","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T16:07:29+00:00","versionOfRecord":{"articleIdentity":"rs-4590701","link":"https://doi.org/10.1007/s11695-024-07518-5","journal":{"identity":"obesity-surgery","isVorOnly":false,"title":"Obesity Surgery"},"publishedOn":"2024-09-28 15:58:07","publishedOnDateReadable":"September 28th, 2024"},"versionCreatedAt":"2024-07-18 16:37:24","video":"","vorDoi":"10.1007/s11695-024-07518-5","vorDoiUrl":"https://doi.org/10.1007/s11695-024-07518-5","workflowStages":[]},"version":"v1","identity":"rs-4590701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4590701","identity":"rs-4590701","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}