Application of Adiposity Indices for Body Fat and Metabolic Health Assessment Before and After Roux-en-Y Gastric Bypass

Application of Adiposity Indices for Body Fat and Metabolic Health Assessment Before and After Roux-en-Y Gastric Bypass | 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 Application of Adiposity Indices for Body Fat and Metabolic Health Assessment Before and After Roux-en-Y Gastric Bypass Mariana Luna, Silvia Pereira, Carlos Saboya, Andrea Ramalho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4843683/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Visceral fat is determinant for obesity metabolic disturbances. Gold standard methods for its assessment are unfeasible in clinical practice. Adiposity indices have been proposed to provide a more feasible evaluation. The study aims to assess body fat distribution through adiposity indices, before and 6 months after Roux-en-Y gastric bypass(RYGB), and their correlation with body and biochemical variables. Methods prospective longitudinal study with adults undergoing RYGB, evaluated before(T0) and 6 months after surgery(T1). Weight, height, waist circumference(WC), BMI, waist-to-height ratio(WHtR), total cholesterol(TC), HDL-c, LDL-c, triglycerides(TG), insulin, glucose, HbA1c and HOMA-IR were evaluated. Visceral adiposity index(VAI), conicity index(CI), lipid accumulation product(LAP), Clinica Universidad de Navarra-Body adiposity estimator(CUN-BAE) and a body shape index(ABSI) were calculated. Results 62 individuals, 80% women, mean age 42.8 ± 11.13 years. There was significant improvement in adiposity and all biochemical variables, even with more than 90% still in overweight/obesity class and/or inadequate WC and WHtR. Only 40% of individuals had VAI inadequacy at T1. Individuals with elevated VAI had higher HOMA-IR, TG and LAP at T0, compared to those with adequate VAI. Although this difference was not maintained at T1, after surgery, VAI was the only index that correlated with HOMA-IR. Pre-surgical ABSI, CI, WC and WHtR correlated with post-surgical HOMA-IR, insulin and TC. Only the post-surgical variation in ABSI correlated with HOMA-IR reduction.BMI did not correlate with post-surgical metabolic outcome. Conclusion The findings reinforce the ability of RYGB to reduce visceral adiposity, and the applicability of the indices to assess metabolic health, with emphasis on VAI and ABSI. Bariatric surgery obesity visceral fat adiposity Keypoints Adiposity indices are promising to assess the impact of RYGB on body adiposity 6 months post RYGB, 90% of individuals remain in WC or WHtR inadequacy/overweight At this same time, there is a reduction in visceral adiposity, observed using VAI After surgery, VAI was the only index that correlated with insulin sensitivity Abdominal obesity reduction, assessed by ABSI, is correlated to post-RYGB HOMA-IR improvement 1. INTRODUCTION As a result of the exponential increase in obesity, bariatric surgery has been increasingly performed worldwide, with greater efficacy than other interventions[ 1 ]. In addition to substantial weight loss, it promotes significant metabolic improvements, with early and sustainable mitigation/remission of comorbidities, regardless of weight loss, reducing obesity health risks[ 1 , 2 ]. Body fat distribution is more important than weight or total fat mass in determining health risk. Adipocytes, depending on its location, have distinct metabolic and inflammatory characteristics. Greater visceral fat accumulation, in detriment of subcutaneous fat, is associated with insulin resistance, dyslipidemias, cardiovascular risk and metabolic syndrome[ 3 – 5 ]. However, Body Mass Index (BMI) is still the most used parameter for obesity diagnosis and evaluation, and even for bariatric surgery indication and follow-up [ 1 , 5 ]. It is unable to assess body fat accumulation and its distribution and depending on the region where adipose tissue deposition predominates, individuals within the same BMI range may present different degrees of health impairment [ 5 ]. To reduce these limitations, it is recommended to associate BMI with waist circumference (WC) and waist-to-height ratio (WHtR). Both measures, although useful for abdominal obesity assessment, are also unable to distinguish between subcutaneous and visceral abdominal fat content[ 6 , 7 ]. The main objective of bariatric surgery is health improvement. Knowing that visceral fat plays a key role in this context, both indication and post-surgical follow-up should include tools that assess this adipose compartment, in order to select individuals at greater risk and to evaluate its effectiveness. However, gold standard methods, are invasive and expensive, unfeasible for clinical practice and epidemiological studies[ 8 ]. Therefore, studies have proposed accurate measurements that are easily applicable in clinical practice[ 7 , 9 – 12 ]. They show good correlation with cardiovascular risk and/or adiposity assessment by gold standard methods [ 6 , 10 , 11 , 13 , 14 ]. However, their application in bariatric context is still scarce, and it is not clear whether they are useful for evaluating metabolic health of these individuals or even for predicting post-surgery outcomes. Therefore, the present study aims to evaluate body fat composition and distribution through different adiposity indices (BMI, WC, WHtR, Clinical University of Navarra-body adiposity estimator(CUN-BAE), visceral adiposity index (VAI), conicity index (CI), lipid accumulation product (LAP) and a body shape index (ABSI)) in individuals undergoing RYGB, both before and after surgery, as well as their correlation with body and biochemical variables. 2. METHODS 2.1. Study design and population Prospective longitudinal analytical study, with individuals undergoing RYGB in a Multidisciplinary Center for Bariatric and Metabolic Surgery, Rio de Janeiro, Brazil, from January 2019 to September 2020. Sample selection occurred by convenience and all individuals attended during the study period were invited to take part in this research. Were included individuals between 20 and 60 years of age, men and women, with indication for RYGB. Previous malabsorptive and restrictive surgeries, intestinal malabsorptive syndromes, neoplasms, use of drugs for weight loss, alcohol consumption greater than 20g/day for women and 40g/day for men, pregnancy or lactation, renal failure (defined by estimated glomerular filtration rate < 60 mL/min/1.73m²), liver diseases (except non-alcoholic fatty liver disease), acute or chronic infections, high serum calcium levels, endocrinopathies (hyperparathyroidism, hypothyroidism, hypercortisolemia) and use of anticonvulsant medications were considered as exclusion criteria. Participants who met inclusion and exclusion criteria were evaluated preoperatively (T0) and 6 months (T1) after surgery. All procedures performed were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The present study was approved by the Research Ethics Committee of Hospital Clementino Fraga Filho/UFRJ. Informed consent was obtained from all individual participants included in the study.. 2.2. Roux em Y Gastric Bypass All patients underwent RYGB via videolaparoscopy and were assisted by the multidisciplinary team at that clinic, both before and after the procedure. 2.3. Data collection: anthropometric, clinical and biochemical variables In a scheduled consultation, as part of the clinic's routine care, sex, age and anthropometric variables (weight, height, BMI and WC) were collected at T0 and T1, by a single specialized evaluator. BMI was calculated (weight(kg)/height(m)²), and categorized according to WHO, 2000[ 15 ]. WC was measured in accordance with WHO recommendations. The cutoff point adopted were: WC ≥ 94cm for men and ≥ 80cm for women[ 15 ]. By dividing WC by height, the WHtR was calculated, with a cutoff point of 0.5. For post-surgical weight assessment, %Weight loss (%WL) and %excess weight loss (%EWL) were calculated at T1. For %WL, the following formula was used: [(Weight T0 (Kg)) – (Weight T1 (Kg))] / [(Weight T0 (Kg))]*100, and for %EWL: [(BMI T0) – (BMI T1)] / [(BMI T0) – (BMI 25)]. For biochemical evaluation, 5mL blood samples were collected from the participants after 12 hours of fasting, in a heparinized anticoagulant tube. Total cholesterol (TC), HDL-c, LDL-c, triglycerides (TG), fasting blood glucose, glycated hemoglobin (HbA1c) and insulin were measured. To assess insulin resistance, the homoeostasis model assessment for insulin resistance (HOMA-IR) was calculated [ 17 ]. The analysis method used for the lipid profile was enzymatic colorimetric, for glycemia and glycated hemoglobin, enzymatic, and for insulinemia, chemiluminescence. To evaluate the adequacy of biochemical parameters, the following cutoff points were applied: CT < 190mg/dL, LDL-c 40 mg/dL, TG < 150mg/dL[ 18 ], glucose < 100mg/dL; glycated hemoglobin < 5,7%, insulin 2,0–17 mcU/mL and HOMA-IR ≥ 2,5[ 16 , 17 ]. 2.4. Body adiposity assessment From anthropometric and biochemical variables obtained, the indices: VAI, LAP, CI, ABSI and CUN-BAE were calculated for T0 and T1, using the following formulas: VAI[ 12 ]: Men: \(\:\left(\frac{WC\:}{39.68+\left(1.88xBMI\right)}\right)*\left(\frac{TG}{1.03}\right)*\left(\frac{1.31}{HDL}\right)\) ; Women: \(\:\left(\frac{WC\:}{36.58+\left(1.88xBMI\right)}\right)*\left(\frac{TG}{0.81}\right)*\left(\frac{1.52}{HDL}\right)\) CI[ 9 ]: \(\:\frac{WC\:\left(m\right)}{\left(\text{0,109}*\sqrt{\frac{Weight\left(kg\right)}{Height\left(m\right)}}\right)}\) ABSI[ 10 ]: \(\:\frac{WC\:\left(m\right)}{{BMI}^{2/3}*{Height}^{1/2}}\) Sex: men = 0 ; women = 1 LAP[ 11 ]: Men: \(\:\left(WC-65\right)*TG\) ; Women: \(\:\left(WC-58\right)*TG\) VAI presents cut-off point for inadequacy, proposed by its creators and it was applied to identify the presence of dysfunctional visceral adipose tissue [ 19 ]. 2.5. Statistical analyzes For sample characterization, quantitative variables were expressed as means and standard deviations. Categorical variables were expressed as percentages. Data normality was assessed using the Kolmogorov-Smirnov test. For comparisons of numerical variables before and after surgery, the Wilcoxon test was used. To compare the means of biochemical and body variables according to VAI classification before and after surgery, the Mann Whitney test was used. To evaluate the correlation between body variables and biochemical parameters before and after surgery, Spearmann's correlation was used. To test the homogeneity of proportions between categorical variables, the Chi-square test or Fisher's exact test were applied. For statistical significance, a p-value < 0.05 was adopted, and the statistical program used was SPSS for Windows®, version 20.0. 3. RESULTS 3.1. Sample characterization and post-surgical outcome 62 individuals were included, mean age of 42.8 ±11.13 years and 80% women (n=48). Six months after surgery, a %WL of 23.35 ± 7.45 and a %EWL of 60.52 ± 22.00 were achieved. Table 1 shows body and biochemical variables before and 6 months after surgery and the variation between the two periods. RYGB promoted significant changes in all parameters evaluated, including body adiposity indices, with the exception of ABSI (Table 1). Tabela 1 . Body and biochemical variables of individuals with obesity before and 6 months after roux-en-Y gastric bypass VARIABLES T0 (n=62) Mean± SD T1(n=62) Mean± SD Variation p-value BMI (Kg/m²) 41.58 ±3.92 31.86 ±4.18 -9.73 ± 3.15 0.000 WC (cm) 119.68 ±11.81 97.79 ± 9.14 -21.95 ± 8.00 0.000 WtHR 0.72 ±0.06 0.58±0.05 -0.13 ± 0.05 0.000 Glucose (mg/dL) 99.20 ± 20.2 89.12 ± 10.24 -10.08 ±17.40 0.000 HbA1c (%) 6.23 ± 5.78 3.72 ±1.20 -2.51 ± 5.68 0.000 HOMA-IR 3.44 ± 1.90 0.64 ±0.79 -2.89 ± 1.90 0.000 Insulin (mCu/mL) 15.12 ±8.47 3.39 ± 3.90 -11.84 ± 7.99 0.000 TC (mg/dL) 206.08 ± 58.08 131.04 ± 39.50 -75.04±54.46 0.000 LDL-c (mg/dL) 123.62 ± 47.48 91.28 ± 29.39 -32.34 ± 38.95 0.000 HDL-c (mg/dL) 43.32 ±11.37 46.83 ±13.74 3.51 ±14.08 0.030 TG (mg/dL) 149.01 ±73.52 121.12±66.00 -31.33±44.71 0.000 VAI 2.88±1.61 2.05±1.21 -0.84 ±1.11 0.000 CI 1.32 ±0.94 1.24 ±0.10 -0.08 ±0.09 0.000 ABSI 0.077 ± 0.01 0.075 ±0.01 -0.001 ± 0.006 0.080 CUN-BAE 50.74 ± 5.15 41.78 ±7.67 -8.98 ±3.90 0.000 LAP 200.81±101.97 120.79 ± 50.07 -69.17 ±65.12 0.000 SD: standard deviation; T0: before surgery; T1: 6 months after surgery; BMI: Body mass index; WC: Waist circumference; WtHR: Waist to height ratio; TC: Total cholesterol; LDL-c: low-density lipoprotein; HDL-c: high-density lipoprotein; TG: triglycerides; VAI: visceral adiposity index; CI: conicity index; LAP: lipid accumulation product; ABSI: body shape index; Although approximately 94% of the sample (n=58) was still classified with abdominal obesity according to WtHR and WC 6 months after surgery, according to VAI, only 40% (n=24) remained with excess visceral fat. At the same time, 95% (n=59) was also overweight, according to BMI (overweight: 33%; class I obesity: 40%, class II obesity: 20%, class III obesity:1.7%). Regarding glycemic metabolism, at T0, 36.7% (22), 56.9% (33) and 15.0% (9) of the individuals had elevations in blood glucose, HOMA-IR and HbA1c, respectively. At T1, rates reduced to 6.7% (4), 6.7% (4) and 0.0% (0). For lipid profile, at T0, 48.3% (29), 35.0% (21), 45.0% (27), 46.7% (28) of the individuals had inadequate TC, LDL-c, HDL-c and TG, respectively. These prevalences reduced to 1.7% (1), 8.3% (5), 21.7% (13), 23.3% (14) at T1. 3.2. Biochemical variables and metabolic inadequacies according to visceral adiposity index before and 6 months after RYGB Considering that, among the body adiposity indices, the VAI presents a well-established cutoff point1[19], the sample was subdivided according to VAI into adequate and inadequate, both before and after surgery, in order to evaluate the biochemical difference in obesity associated with visceral adipose tissue dysfunction. Individuals with elevated VAI before surgery had higher means of HOMA-IR, TG and LAP at T0 (Table 2) and a higher prevalence of insulin resistance and elevated triglycerides (Table 3), compared to those with adequate VAI. However, the difference for HOMA-IR (Table 2) and prevalence of insulin resistance did not remain significant after surgery (Table 3). Table 2. Body and biochemical variables of individuals with obesity before and 6 months after roux-en-Y gastric by-pass, according to Visceral Adiposity Index classification VARIABLES T0 p T1 p Adequate VAI (n=22) Inadequate VAI (n=38) Adequate VAI (n=36) Inadequate VAI (n=24) BMI (Kg/m²) 41.54±4.08 41.60±3.87 0.800 31.49±4.59 32.40±4.47 0.377 WC (cm) 122.55±12.09 118.02±10.95 0.186 99.33±9.68 95.33±7.85 0.061 WtHR 0.72±0.06 0.72±0.06 0.591 0.58±0.05 0.59±0.05 0.751 Glucose (mg/dL) 100.77±25.44 98.30±16.66 0.747 90.11±10.10 87.63±10.47 0.597 HbA1c (%) 5.38±0.53 6.72±7.24 0.478 3.90±10.09 3.44±1.31 0.141 HOMA-IR 2.68±1.33 3.86±2.05 0.029 0.86±0.99 0.59±0.77 0.155 Insulin (mCu/mL) 13.07±8.02 16.33±8.61 0.078 3.80±4.11 2.76±3.55 0.277 TC (mg/dL) 190.00±40.36 215.39±64.89 0.192 134.46±39.84 125.91±39.21 0.469 LDL-c (mg/dL) 113.45±32.54 129.50±53.82 0.172 89.25±22.52 94.33±37.77 0.763 HDL-c (mg/dL) 46.04±13.41 41.73±9.85 0.243 52.22±10.65 38.74±14.06 0.000 TG (mg/dL) 90.27±29.86 185.94±68.66 0.000 87.50±22.34 173.74±77.11 0.000 VAI 1.55±0.45 3.73±1.51 0.000 1.29±0.41 3.42±0.91 0.000 LAP 126.42±49.07 247.56±99.04 0.000 98.80±29.64 160.38±55.43 0.000 CI 1.34±0.10 1.31±0.10 0.253 1.25±0.11 1.21±0.09 0.110 ABSI 0.08±0.01 0.08±0.01 0.412 0.08±0.01 0.07±0.01 0.124 CUNBAE 49.50±5.93 51.46±4.57 0.170 39.99±8.94 44.44±4.07 0.088 SD: standard deviation; T0: before surgery; T1: 6 months after surgery; BMI: Body mass index; WC: Waist circumference; WtHR: Waist to height ratio; TC: Total cholesterol; LDL-c: low-density lipoprotein; HDL-c: high-density lipoprotein; TG: triglycerides; VAI: visceral adiposity index; CI: conicity index; LAP: lipid accumulation product; ABSI: body shape index; Table 3. Prevalence of metabolic inadequacies of individuals with obesity before and 6 months after roux-en-Y gastric by-pass, according to Visceral Adiposity Index classification METABOLIC INADEQUACIES T0 T1 Adequate VAI (n=22) Inadequate VAI (n=38) p Adequate VAI (n=36) Inadequate VAI (n=24) p Elevated glucose 27.3% (6) 42.1% (16) 0.251 16.7% (6) 4.2% (1) 0.225 Elevated HbA1c 4.5% (1) 21.1% (8) 0.135 0.0% (0) 0.0% (0) - Elevated HOMA-IR 45.5% (10) 73.0% (27) 0.035 5.6% (2) 12.5% (3) 0.380 Elevated TC 36.4% (8) 55.3% (21) 0.158 2.8% (1) 0.0% (0) 0.410 Elevated LDL 27.3% (6) 39.5% (15) 0.340 2.8% (1) 16.7% (4) 0.147 Low HDL 36.4% (8) 50.0% (19) 0.306 11.1% (4) 37.5% (9) 0.015 Elevated TG 4.5% (1) 71.1% (27) 0.000 0.0% (0) 58.3% (14) 0.000 T0: baseline; VAI: visceral adiposity index; HOMA-IR: Homeostasis model assessment for insulin resistance; LDL-c: low-density lipoprotein; HDL-c: high-density lipoprotein; TG: triglycerides; 3.3. Correlation between body and biochemical variables before and after surgery Before surgery, a correlation between BMI, WC, CUN-BAE and ABSI with lipid profile variables was observed and, after, only BMI and WC maintained this relationship (Table 4). Regarding glycemic homeostasis, before surgery, LAP and WC correlated with HbA1c and, after surgery, VAI correlated with HOMA-IR. Table 4. Correlation between pre- and post-surgery body and biochemical variables in individuals undergoing Roux en Y Gastric Bypass PRE-SURGERY BMI WC WtH VAI LAP CI ABSI CUN-BAE Glucose -0.099 0.005 -0.025 0.020 0.147 0.050 0.051 -0.071 HbA1c -0.018 -0.263* -0.250 -0.148 -0.271* -0.224 -0.202 0.057 HOMA-IR -0.048 0.108 0.010 -0.185 -0.047 0.145 0.146 -0.173 Insulin -0.017 0.104 0.014 -0.180 -0.072 0.129 0.125 -0.144 TC -0.176 -0.283* -0.214 -0.121 -0.002 -0.158 -0.107 -0.083 LDL -0.294* -0.096 -0.118 -0.113 0.082 0.051 0.120 -0.299* HDL -0.102 0.091 -0.093 -0.568* -0.115 0.048 0.053 -0.184 TG 0.259* -0.097 0.094 0.694* 0.962* -0.221 -0.256* 0.241 POST-SURGERY BMI WC WtH VAI LAP CI ABSI CUN-BAE Glucose -0.221 -0.046 -0.065 0.044 0.000 0.123 0.183 -0.135 HbA1c -0.122 -0.047 -0.039 0.145 0.073 0.087 0.130 -0.054 HOMA-IR -0.242 -0.128 -0.029 0.278* 0.145 -0.062 -0.006 -0.113 Insulin -0.111 -0.121 0.011 0.204 0.139 -0.040 -0.011 0.008 TC -0.225 -0.270* -0.291 0.227 0.057 -0.189 -0.155 -0.092 LDL -0.279* -0.359* -0.226 0.168 0.206 -0.181 -0.125 -0.158 HDL 0.072 -0.105 0.019 -0.232 0.133 -0.102 -0.119 0.177 TG 0.160 -0.102 0.004 0.847* 0.965* -0.142 -0.151 0;083 T0: baseline; BMI: body mass index; WC: waist circumference; WtH: waist to hip ratio; VAI: visceral adiposity index; LAP: lipid accumulation index; CI: conicity index; ABSI: a body shape index; * p<0.05 according to Spearmann correlation test 3.4. Correlation between pre-surgical body variables and post-surgical biochemical variables In order to assess the potential predictive nature of post-surgical outcomes, the correlation between body variables at T0 and biochemical variables at T1 was also evaluated. Pre-surgical WC, WHtR, CI and ABSI correlated with post-surgical HOMA-IR, insulin and TC, while BMI did not correlate with any of the variables analyzed (Table 5). Table 5. Correlation between pre-surgery body and post-surgery biochemical variables in individuals undergoing Roux em Y Gastric Bypass BMI WC WtH VAI LAP CI ABSI CUN-BAE Glucose -0.174 -0.143 -0.096 0.022 -0.058 -0.009 0.074 -0.057 HbA1c -0.036 -0.150 -0.173 -0.056 -0.020 -0.174 -0.192 -0.027 HOMA-IR 0.038 0.376* 0.351* -0.145 -0.015 0.493* 0.511* -0.213 Insulin 0.080 0.377* 0.350* -0.163 -0.024 0.443* 0.438* -0.121 TC -0.145 -0.328* -0.296* 0.114 0.127 -0.320* -0.314* -0.074 LDL -0.246 -0.098 -0.099 0.081 0.047 0.038 0.097 -0.201 HDL -0.066 0.094 -0.090 -0.341* -0.110 0.016 0.010 -0.162 TG 0.092 -0.111 -0.031 0.713* 0.738* -0.098 -0.098 0.149 T0: baseline; BMI: body mass index; WC: waist circumference; WtH: waist to hip ratio; VAI: visceral adiposity index; LAP: lipid accumulation index; CI: conicity index; ABSI: a body shape index; * p<0.05 according to Spearmann correlation test 3.5. Correlation between variation of body and biochemical variables 6 months after RYGB When analyzing the relationship between variation in post-surgical body and biochemical variables, it was observed that both EWL and the reduction in CUN-BAE correlated with glycemia reduction. Furthermore, the reduction in ABSI correlated with the variation in HOMA-IR and insulinemia after surgery. Variations in VAI and LAP correlated with variables included in their calculation formulas, such as TG and HDL. Variations in BMI, WC, WHtR and CI did not correlate with any changes in biochemical variables (Table 6). Table 6 . Correlation between variation of body and biochemical variables of individuals 6 months after Roux em Y Gastric Bypass ΔBMI ΔWC EWL ΔWtH ΔVAI ΔLAP ΔCI ΔABSI ΔCUN-BAE ΔGlucose 0.211 0.053 -0.307* 0.009 -0.058 -00.033 -0.136 -0.191 -0.291* ΔHbA1c -0.041 0.004 -0.060 0.004 0.024 0.018 -0.064 -0.083 0.093 ΔHOMA-IR 0.145 -0.158 -0.232 -0.151 -0.090 0.062 -0.238 -0.307* 0.173 ΔInsulin 0.118 -0.184 -0.171 -0.161 -0.120 0.040 -0.237 -0.279* 0.105 ΔTC -0.052 -0.087 -0.138 -0.120 0.114 0.058 -0.157 -0.154 0.058 ΔLDL-c -0.167 -0.032 0.040 -0.012 0.043 0.169 0.097 0.093 -0.118 ΔHDL-c -0.178 -0.157 0.230 -0.096 -0.515* 0.078 0.049 0.090 -0.254 ΔTG 0.117 0.212 -0.055 0.202 0.566* 0.877* 0.157 0.139 0.072 BMI: body mass index; WC: waist circumference; EWL: excess weight loss; WtH: waist to hip ratio; VAI: visceral adiposity index; LAP: lipid accumulation index; CI: conicity index; ABSI: a body shape index; CUN-BAE: Clinica Universidad de Navarra-Body adiposity Estimator; TC: total cholesterol; LDL: low density lipoprotein-cholesterol; HDL-c: high density lipoprotein-cholesterol; TG: triglycerides * p<0.05 according to Spearmann correlation test 4. Discussion Six months post-RYGB there was a significant reduction not only in measures classically used for obesity assessment, such as weight, BMI, WC and WHtR, but also in adiposity indices, with emphasis on VAI, CI, CUN-BAE, and LAP. These findings highlight the impact of surgery beyond body weight, but also on reducing abdominal fat content and itsvisceral depot. It was previously observed by studies that applied gold standard methods to quantify this tissue[ 6 , 20 ]. It suggests that the aforementioned indices are promising tools for clinical practice, allowing a more accurate and effective assessment, with reduced cost and easy applicability. Even with 95% of individuals still classified with overweight or obesity according to BMI, and 94% with inadequate WC or WHtR, there was significant improvement all biochemical variables 6 months post-RYGB. However, only 40% still had inadequate VAI at the same time. These data reinforce the limitation of BMI, WC and WHtR to detect visceral adiposity disfunction in this population. It highlights that RYGB promotes health benefits regardless of changes in body weight, achieving adequacy of metabolic variables even when the BMI, WC and WHtR criteria have not yet been satisfactorily met. Previous studies that applied gold standard methods to assess visceral adiposity also observed similar results [ 6 , 20 ]. Although most studies apply gold standard methods, a previous study observed that 1 year after RYGB or sleeve VAI reached levels similar to those of people in eutrophy, even though the average BMI still was in the obesity range [ 2 ]. Fat distribution is more important than weight or total body fat for health risk in obesity. Greater visceral fat represents greater metabolic and cardiovascular impairment[ 3 , 4 ]. It occurs mainly due to its relationship with insulin resistance (IR), a key factor in the development of metabolic syndrome and chronic non-communicable diseases [ 3 , 4 , 21 ]. Several mechanisms can explain this relationship, such as the distinct metabolic and inflammatory adipocytes behavior, depending on the region in which they are located. Visceral adipocytes are more metabolically active than subcutaneous, which results in greater lipolysis and constant release of free fatty acids (FFA) into the circulation, making visceral adipose tissue (VAT) highly pathogenic [ 3 , 4 ].Excessive FFA release promotes local IR, but also has systemic effects. FFA reaches the liver and trigger changes in lipid and glucose metabolism, resulting in greater production of TG and glucose, favouring IR, hyperinsulinemia and hyperglycemia. They also reach circulation, promoting accumulation of ectopic fat in skeletal muscle (impairing glucose uptake), pancreas, heart and kidneys, impairing the functionality of these organs[ 3 ]. Visceral obesity is also associated with reduced levels of adiponectin, which has anti-inflammatory, cardioprotective and insulin-sensitizing effects[ 4 ]. VAT expansion is accompanied by inflammation, with greater release of IL-6 and TNF-a[ 4 ]. This inflammation is promoted by VAT resident immune cells, which naturally contain a greater number of macrophages than subcutaenous adipose tissue, even in individuals without excessive body fat [ 4 , 22 ]. This chronic low-grade inflammation worsens IR and increases cardiovascular risk[ 4 ]. In individuals undergoing bariatric surgery and evaluated by CT before and 12 months after, there was a positive correlation between VAT volume and CRP levels, an important inflammatory marker, regardless of BMI, while the same correlation was not observed when considering SAT [ 20 ]. Among body adiposity indices, VAI presents a well-established cutoff point [ 19 ]. When segmenting the sample of the present study, it was observed that individuals with elevated VAI, especially before surgery, presented higher IR and TG, both in mean values and in prevalence of these changes. Furthermore, they also presented higher means of CI and LAP, indices that also reflect visceral adiposity[ 9 , 11 ], and VAI was the only post-surgical index that correlated with HOMA-IR. These findings reinforce the negative impact of dysfunctional adipose tissue on metabolic health[ 23 ], the heterogeneity of obesity and the usefulness of VAI for a more assertive assessment in this population. This index considers anthropometric components (BMI and WC) and biochemical data (HDL-c and TG) in a sex-dependent manner, which makes it a more accurate indicator of dysfunctional VAT[ 12 ]. Validated for visceral adiposity assessment using MRI, VAI is an independent predictor of cardiovascular risk, surpassing BMI, WC and even LAP [ 2 , 23 ]. Besides that, it is closely associated with IR, highlighting its relevance in health assessment and monitoring. The ABSI was the only index that did not suffer a reduction after surgery, as also previously observed [ 24 ]. This index aims to evaluate fat accumulation in the central region, independently of WC or BMI. An important aspect of this index that may have influenced this result is that, according to previous evidence, lean mass content is related to ABSI value in obesity[ 25 – 27 ]. High ABSI values ​​may indicate not only greater visceral adiposity, but also significant loss of muscle mass, being an indicator of sarcopenic obesity. Considering that during the first 6 months post-RYGB the individual is in high catabolism, including loss of lean mass, changes in body composition may have influenced this result[ 28 ]. Corroborating this hypothesis, in the present study, although ABSI did not undergo significant change when comparing pre- and post-surgery, its modification correlated with the reduction in HOMA-IR after surgery, reinforcing the body composition impact on insulin sensitivity. Musculoskeletal mass is essential for insulin sensitivity maintenance. Significant decrease in muscle mass, as well as excess visceral adiposity, is associated with IR and cardiometabolic complications[ 28 – 30 ]. Therefore, we recommend further investigation into this index, which appears to provide important information about body composition and its relationship with metabolic health in obesity, especially in post-surgical follow-up. The predictive capacity of pre-surgical visceral adiposity in improving insulin sensitivity after surgery has recently been discussed in the literature and is still not well understood. Few studies apply adiposity indices in the context of RYGB and mainly investigate their predictive nature for diabetes mellitus remission after intervention. Some have observed that LAP and VAI are independent predictors of this outcome[ 8 , 24 ]. In contrast, a multicentric study, did not observe a prospective correlation between VAI or LAP at baseline and HOMA-IR 12 months after bariatric surgery[ 21 ]. However, HOMA-IR at baseline correlated with VAI at follow-up. The authors suggested that the improvement in IR precedes visceral fat loss and that the reduction of this adipose compartment might be a consequence of increased post-surgical insulin sensitivity, and not the opposite, as also suggested by Other previous studies[ 21 , 31 , 32 ]. In the present study, there was no correlation between pre-surgical VAI or LAP with post-surgical HOMA-IR. However, Other pre-surgical variables indicative of central adiposity, such as WC, WHtR, ABSI and CI, were correlated with HOMA-IR and insulinemia after surgery. Among the variations after BGYR, only the ABSI correlated with the HOMA-IR variation. This highlights the importance of additional studies to understand the impact of central adiposity on post-surgical metabolic improvement and the usefulness of adiposity indices in predicting this outcome and the causal relationship. The lack of correlation between pre-surgical BMI and post-surgical biochemical variables reinforces the inability of this indicator to predict health outcomes after surgery, although it is still the main criterion used for its indication. Thus it reinforces that, in addition to not reflecting the change in fat distribution, BMI also do not reflect properly the cardiometabolic benefits resulting from the RYGB[ 6 ]. Despite limitations related to sample size, the present study adopted statistical analyzes appropriate to our sample. We are pioneers in simultaneously evaluating different body adiposity indices in patients undergoing RYGB, establishing correlations with biochemical variables and comparing pre- and post-surgical scenarios. However, we emphasize the importance of obtaining long-term results in order to provide a more comprehensive understanding of the trajectories of these indices after RYGB, improving our knowledge about the effects of bariatric surgery on body adiposity and metabolic health. Our findings reinforce the importance of evaluating visceral adiposity in obesity and in individuals undergoing RYGB, and the applicability of adiposity indices, with emphasis on VAI and ABSI. Declarations Conflict of Interest: The authors declare that they have no conflict of interest. Funding: This work was supported by National Council for Scientific and Technological Development (CNPq) (301479/2022-4) and FAPERJ (E-26/200.876/20210) Author Contribution Author contribution: ML conceptualization, data curation, formal analysis, methodology, validation, visualization, writing – original draft and editing; SP and CS data curation, investigation, methodology, resources, validation, writing – review; AR conceptualization, funding acquisition, methodology, project administration, resourves, supervision, visualization, writing - review and editing. Data Availability Data availability: The data that support the findings of this study are not publicly available due to privacy. The data are, however, available from the authors upon reasonable request. References Azagury DE, Morton JM. Bariatric Surgery. Endocrinol Metab Clin North Am. 2016;45:647–56. Voglino C, Tirone A, Ciuoli C, Benenati N, Paolini B, Croce F, et al. Cardiovascular Benefits and Lipid Profile Changes 5 Years After Bariatric Surgery: A Comparative Study Between Sleeve Gastrectomy and Roux-en-Y Gastric Bypass. J Gastrointest Surg Off J Soc Surg Aliment Tract. 2020;24:2722–9. Lopes HF, Corrêa-Giannella ML, Consolim-Colombo FM, Egan BM. Visceral adiposity syndrome. Diabetol Metab Syndr. 2016;8:40. Cesaro A, De Michele G, Fimiani F, Acerbo V, Scherillo G, Signore G, et al. Visceral adipose tissue and residual cardiovascular risk: a pathological link and new therapeutic options. Front Cardiovasc Med. 2023;10:1187735. Alves LB, Mattiello R, Todescatto AD, Sarria EE, Mottin CC, Padoin AV. Bariatric patient’s body composition: An option to BMI? Clin Nutr ESPEN. 2020;40:121–4. Mizrahi I, Beglaibter N, Simanovsky N, Lioubashevsky N, Mazeh H, Ghanem M, et al. Ultrasound Evaluation of Visceral and Subcutaneous Fat Reduction in Morbidly Obese Subjects Undergoing Laparoscopic Gastric Banding, Sleeve Gastrectomy, and Roux-en-Y Gastric Bypass: A Prospective Comparison Study. Obes Surg. 2015;25:959–66. Krakauer NY, Krakauer JC. A New Body Shape Index Predicts Mortality Hazard Independently of Body Mass Index. PLoS ONE. 2012;7:e39504. Ke Z, Li F, Gao Y, Tan D, Sun F, Zhou X, et al. The Use of Visceral Adiposity Index to Predict Diabetes Remission in Low BMI Chinese Patients After Bariatric Surgery. Obes Surg. 2021;31:805–12. Valdez R. A simple model-based index of abdominal adiposity. J Clin Epidemiol. 1991;44:955–6. Vinknes KJ, Nurk E, Tell GS, Sulo G, Refsum H, Elshorbagy AK. The relation of CUN-BAE index and BMI with body fat, cardiovascular events and diabetes during a 6-year follow-up: the Hordaland Health Study. Clin Epidemiol. 2017;9:555–66. Kahn HS. The “lipid accumulation product” performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison. BMC Cardiovasc Disord. 2005;5:26. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, et al. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33:920–2. Ji M, Zhang S, An R. Effectiveness of A Body Shape Index (ABSI) in predicting chronic diseases and mortality: a systematic review and meta-analysis. Obes Rev Off J Int Assoc Study Obes. 2018;19:737–59. Costa A, Konieczna J, Reynés B, Martín M, Fiol M, Palou A, et al. CUN-BAE Index as a Screening Tool to Identify Increased Metabolic Risk in Apparently Healthy Normal-Weight Adults and Those with Obesity. J Nutr. 2021;151:2215–25. WHO | Obesity: preventing and managing the global epidemic [Internet]. WHO. World Health Organization; [cited 2020 Jul 8]. Available from: http://www.who.int/entity/nutrition/publications/obesity/WHO_TRS_894/en/index.html Diagnóstico do diabetes e rastreamento do diabetes tipo 2 [Internet]. Dir. Soc. Bras. Diabetes - Ed 2022. 2021 [cited 2022 Jun 8]. Available from: https://diretriz.diabetes.org.br/diagnostico-e-rastreamento-do-diabetes-tipo-2/ Durward CM, Hartman TJ, Nickols-Richardson SM. All-Cause Mortality Risk of Metabolically Healthy Obese Individuals in NHANES III. J Obes. 2012;2012:460321. Faludi A, Izar M, Saraiva J, Chacra A, Bianco H, Afiune Neto A, et al. ATUALIZAÇÃO DA DIRETRIZ BRASILEIRA DE DISLIPIDEMIAS E PREVENÇÃO DA ATEROSCLEROSE – 2017. Arq Bras Cardiol [Internet]. 2017 [cited 2022 Jun 8];109. Available from: http://www.gnresearch.org/doi/ 10.5935/abc.20170121 Amato MC, Giordano C, Pitrone M, Galluzzo A. Cut-off points of the visceral adiposity index (VAI) identifying a visceral adipose dysfunction associated with cardiometabolic risk in a Caucasian Sicilian population. Lipids Health Dis. 2011;10:183. Torriani M, Oliveira AL, Azevedo DC, Bredella MA, Yu EW. Effects of Roux-en-Y gastric bypass surgery on visceral and subcutaneous fat density by computed tomography. Obes Surg. 2015;25:381–5. Tang H, Ling J, Meng H, Wu L, Zhu L, Zhu S. Temporal Relationship Between Insulin Resistance and Lipid Accumulation After Bariatric Surgery: a Multicenter Cohort Study. Obes Surg. 2023;33:1720–9. Cinkajzlová A, Mráz M, Haluzík M. Adipose tissue immune cells in obesity, type 2 diabetes mellitus and cardiovascular diseases. J Endocrinol. 2021;252:R1–22. Zakerkish M, Hoseinian A, Alipour M, Payami SP. The Association between Cardio-metabolic and hepatic indices and anthropometric measures with metabolically obesity phenotypes: a cross-sectional study from the Hoveyzeh Cohort Study. BMC Endocr Disord. 2023;23:122. Yin Q, Yan X, Cao Y, Zheng J. Evaluation of novel obesity- and lipid-related indices as predictors of abnormal glucose tolerance in Chinese women with polycystic ovary syndrome. BMC Endocr Disord. 2022;22:272. Biolo G, Di Girolamo FG, Breglia A, Chiuc M, Baglio V, Vinci P, et al. Inverse relationship between “a body shape index” (ABSI) and fat-free mass in women and men: Insights into mechanisms of sarcopenic obesity. Clin Nutr Edinb Scotl. 2015;34:323–7. Gomez-Peralta F, Abreu C, Cruz-Bravo M, Alcarria E, Gutierrez-Buey G, Krakauer NY, et al. Relationship between “a body shape index (ABSI)” and body composition in obese patients with type 2 diabetes. Diabetol Metab Syndr. 2018;10:21. Chung W, Park JH, Chung HS, Yu JM, Kim DS, Moon S. Utility of the Z-score of log-transformed A Body Shape Index (LBSIZ) in the assessment for sarcopenic obesity and cardiovascular disease risk in the United States. Sci Rep. 2019;9:9292. Cho H-W, Chung W, Moon S, Ryu O-H, Kim MK, Kang JG. Effect of Sarcopenia and Body Shape on Cardiovascular Disease According to Obesity Phenotypes. Diabetes Metab J. 2021;45:209–18. Liu Z-J, Zhu C-F. Causal relationship between insulin resistance and sarcopenia. Diabetol Metab Syndr. 2023;15:46. Kim TN, Choi KM. The implications of sarcopenia and sarcopenic obesity on cardiometabolic disease. J Cell Biochem. 2015;116:1171–8. Fabbrini E, Tamboli RA, Magkos F, Marks-Shulman PA, Eckhauser AW, Richards WO, et al. Surgical removal of omental fat does not improve insulin sensitivity and cardiovascular risk factors in obese adults. Gastroenterology. 2010;139:448–55. Douros JD, Niu J, Sdao S, Gregg T, Fisher-Wellman K, Bharadwaj M, et al. Sleeve gastrectomy rapidly enhances islet function independently of body weight. JCI Insight. 2019;4:e126688, 126688. Additional Declarations No competing interests reported. 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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-4843683","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348880181,"identity":"55818786-c4ce-4fa7-bb53-ae425d55b5cc","order_by":0,"name":"Mariana Luna","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYJCCA0AsA8SMj/9UAClm5gaitPCA1BrwnAFRjIS1MEC1sEnwtoHYBLTotp99eODnDjseg9vNzyQk59VG87cDtfyo2IZTi9mZdIODvWeSeQzuHDO2MNx2PHfGYcYGxp4zt3FrOZDGcIC3jZnH4EaC4Y3EbcdyG4BamBnb8Gg5/4zh4N+2eqCW9A8SB+ccy51PUMuNNIbDvG2HgVpyjCQbG2pyNxDW8ozhsGzbcR7JGznFxgzHDuRuBGo5iNcv59OYP75tq5bju5G+8TFDTV3uvPOHDz74UYFbCzo4DCYPEK0eCOpIUTwKRsEoGAUjBAAAtrhgdWzpca8AAAAASUVORK5CYII=","orcid":"","institution":"Postgraduate Program in Internal Medicine, Medical School, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Rio de Janeiro State, Brazil","correspondingAuthor":true,"prefix":"","firstName":"Mariana","middleName":"","lastName":"Luna","suffix":""},{"id":348880182,"identity":"b6e914b1-f6b6-4a9e-a9be-fb51c085be1d","order_by":1,"name":"Silvia Pereira","email":"","orcid":"","institution":"Multidisciplinary Center for Bariatric and Metabolic Surgery, Rio de Janeiro, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Pereira","suffix":""},{"id":348880185,"identity":"863ef544-0a6a-447a-a9d5-8bce98966bc8","order_by":2,"name":"Carlos Saboya","email":"","orcid":"","institution":"Multidisciplinary Center for Bariatric and Metabolic Surgery, Rio de Janeiro, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Saboya","suffix":""},{"id":348880188,"identity":"2e795501-41db-4b79-88a0-b4b9f98d0fcb","order_by":3,"name":"Andrea Ramalho","email":"","orcid":"","institution":"Social Applied Nutrition Department, Institute of Nutrition, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Ramalho","suffix":""}],"badges":[],"createdAt":"2024-08-01 17:17:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4843683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4843683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68777670,"identity":"8a273991-54a9-4a44-956d-d0cbc733eef6","added_by":"auto","created_at":"2024-11-12 01:54:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":918225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4843683/v1/c54c6c4c-f5c5-4a19-b6f4-67048021f10b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of Adiposity Indices for Body Fat and Metabolic Health Assessment Before and After Roux-en-Y Gastric Bypass","fulltext":[{"header":"Keypoints","content":"\u003cul\u003e\n \u003cli\u003eAdiposity indices are promising to assess the impact of RYGB on body adiposity\u003c/li\u003e\n \u003cli\u003e6 months post RYGB, 90% of individuals remain in WC or WHtR inadequacy/overweight\u003c/li\u003e\n \u003cli\u003eAt this same time, there is a reduction in visceral adiposity, observed using VAI\u003c/li\u003e\n \u003cli\u003eAfter surgery, VAI was the only index that correlated with insulin sensitivity\u003c/li\u003e\n \u003cli\u003eAbdominal obesity reduction, assessed by ABSI, is correlated to post-RYGB HOMA-IR improvement\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. INTRODUCTION","content":"\u003cp\u003eAs a result of the exponential increase in obesity, bariatric surgery has been increasingly performed worldwide, with greater efficacy than other interventions[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition to substantial weight loss, it promotes significant metabolic improvements, with early and sustainable mitigation/remission of comorbidities, regardless of weight loss, reducing obesity health risks[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBody fat distribution is more important than weight or total fat mass in determining health risk. Adipocytes, depending on its location, have distinct metabolic and inflammatory characteristics. Greater visceral fat accumulation, in detriment of subcutaneous fat, is associated with insulin resistance, dyslipidemias, cardiovascular risk and metabolic syndrome[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, Body Mass Index (BMI) is still the most used parameter for obesity diagnosis and evaluation, and even for bariatric surgery indication and follow-up [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is unable to assess body fat accumulation and its distribution and depending on the region where adipose tissue deposition predominates, individuals within the same BMI range may present different degrees of health impairment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. To reduce these limitations, it is recommended to associate BMI with waist circumference (WC) and waist-to-height ratio (WHtR). Both measures, although useful for abdominal obesity assessment, are also unable to distinguish between subcutaneous and visceral abdominal fat content[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe main objective of bariatric surgery is health improvement. Knowing that visceral fat plays a key role in this context, both indication and post-surgical follow-up should include tools that assess this adipose compartment, in order to select individuals at greater risk and to evaluate its effectiveness. However, gold standard methods, are invasive and expensive, unfeasible for clinical practice and epidemiological studies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, studies have proposed accurate measurements that are easily applicable in clinical practice[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. They show good correlation with cardiovascular risk and/or adiposity assessment by gold standard methods [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, their application in bariatric context is still scarce, and it is not clear whether they are useful for evaluating metabolic health of these individuals or even for predicting post-surgery outcomes.\u003c/p\u003e \u003cp\u003eTherefore, the present study aims to evaluate body fat composition and distribution through different adiposity indices (BMI, WC, WHtR, Clinical University of Navarra-body adiposity estimator(CUN-BAE), visceral adiposity index (VAI), conicity index (CI), lipid accumulation product (LAP) and a body shape index (ABSI)) in individuals undergoing RYGB, both before and after surgery, as well as their correlation with body and biochemical variables.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design and population\u003c/h2\u003e \u003cp\u003eProspective longitudinal analytical study, with individuals undergoing RYGB in a Multidisciplinary Center for Bariatric and Metabolic Surgery, Rio de Janeiro, Brazil, from January 2019 to September 2020. Sample selection occurred by convenience and all individuals attended during the study period were invited to take part in this research.\u003c/p\u003e \u003cp\u003eWere included individuals between 20 and 60 years of age, men and women, with indication for RYGB. Previous malabsorptive and restrictive surgeries, intestinal malabsorptive syndromes, neoplasms, use of drugs for weight loss, alcohol consumption greater than 20g/day for women and 40g/day for men, pregnancy or lactation, renal failure (defined by estimated glomerular filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73m\u0026sup2;), liver diseases (except non-alcoholic fatty liver disease), acute or chronic infections, high serum calcium levels, endocrinopathies (hyperparathyroidism, hypothyroidism, hypercortisolemia) and use of anticonvulsant medications were considered as exclusion criteria. Participants who met inclusion and exclusion criteria were evaluated preoperatively (T0) and 6 months (T1) after surgery.\u003c/p\u003e \u003cp\u003e All procedures performed were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The present study was approved by the Research Ethics Committee of Hospital Clementino Fraga Filho/UFRJ. Informed consent was obtained from all individual participants included in the study..\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Roux em Y Gastric Bypass\u003c/h2\u003e \u003cp\u003eAll patients underwent RYGB via videolaparoscopy and were assisted by the multidisciplinary team at that clinic, both before and after the procedure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data collection: anthropometric, clinical and biochemical variables\u003c/h2\u003e \u003cp\u003eIn a scheduled consultation, as part of the clinic's routine care, sex, age and anthropometric variables (weight, height, BMI and WC) were collected at T0 and T1, by a single specialized evaluator.\u003c/p\u003e \u003cp\u003eBMI was calculated (weight(kg)/height(m)\u0026sup2;), and categorized according to WHO, 2000[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. WC was measured in accordance with WHO recommendations. The cutoff point adopted were: WC\u0026thinsp;\u0026ge;\u0026thinsp;94cm for men and \u0026ge;\u0026thinsp;80cm for women[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. By dividing WC by height, the WHtR was calculated, with a cutoff point of 0.5.\u003c/p\u003e \u003cp\u003eFor post-surgical weight assessment, %Weight loss (%WL) and %excess weight loss (%EWL) were calculated at T1. For %WL, the following formula was used: [(Weight T0 (Kg)) \u0026ndash; (Weight T1 (Kg))] / [(Weight T0 (Kg))]*100, and for %EWL: [(BMI T0) \u0026ndash; (BMI T1)] / [(BMI T0) \u0026ndash; (BMI 25)].\u003c/p\u003e \u003cp\u003eFor biochemical evaluation, 5mL blood samples were collected from the participants after 12 hours of fasting, in a heparinized anticoagulant tube. Total cholesterol (TC), HDL-c, LDL-c, triglycerides (TG), fasting blood glucose, glycated hemoglobin (HbA1c) and insulin were measured. To assess insulin resistance, the \u003cem\u003ehomoeostasis model assessment for insulin resistance\u003c/em\u003e (HOMA-IR) was calculated [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The analysis method used for the lipid profile was enzymatic colorimetric, for glycemia and glycated hemoglobin, enzymatic, and for insulinemia, chemiluminescence.\u003c/p\u003e \u003cp\u003eTo evaluate the adequacy of biochemical parameters, the following cutoff points were applied: CT\u0026thinsp;\u0026lt;\u0026thinsp;190mg/dL, LDL-c\u0026thinsp;\u0026lt;\u0026thinsp;130 mg/dL, HDL-c\u0026thinsp;\u0026gt;\u0026thinsp;40 mg/dL, TG\u0026thinsp;\u0026lt;\u0026thinsp;150mg/dL[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], glucose\u0026thinsp;\u0026lt;\u0026thinsp;100mg/dL; glycated hemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;5,7%, insulin 2,0\u0026ndash;17 mcU/mL and HOMA-IR\u0026thinsp;\u0026ge;\u0026thinsp;2,5[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Body adiposity assessment\u003c/h2\u003e \u003cp\u003eFrom anthropometric and biochemical variables obtained, the indices: VAI, LAP, CI, ABSI and CUN-BAE were calculated for T0 and T1, using the following formulas:\u003c/p\u003e \u003cp\u003eVAI[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]: Men: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{WC\\:}{39.68+\\left(1.88xBMI\\right)}\\right)*\\left(\\frac{TG}{1.03}\\right)*\\left(\\frac{1.31}{HDL}\\right)\\)\u003c/span\u003e\u003c/span\u003e ; Women: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{WC\\:}{36.58+\\left(1.88xBMI\\right)}\\right)*\\left(\\frac{TG}{0.81}\\right)*\\left(\\frac{1.52}{HDL}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eCI[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{WC\\:\\left(m\\right)}{\\left(\\text{0,109}*\\sqrt{\\frac{Weight\\left(kg\\right)}{Height\\left(m\\right)}}\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eABSI[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{WC\\:\\left(m\\right)}{{BMI}^{2/3}*{Height}^{1/2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"595\" height=\"67\"\u003e\u003c/p\u003e\n \u003cp\u003eSex: men\u0026thinsp;=\u0026thinsp;0 ; women\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003cp\u003eLAP[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]: Men: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(WC-65\\right)*TG\\)\u003c/span\u003e\u003c/span\u003e ; Women: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(WC-58\\right)*TG\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eVAI presents cut-off point for inadequacy, proposed by its creators and it was applied to identify the presence of dysfunctional visceral adipose tissue [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical analyzes\u003c/h2\u003e \u003cp\u003eFor sample characterization, quantitative variables were expressed as means and standard deviations. Categorical variables were expressed as percentages. Data normality was assessed using the Kolmogorov-Smirnov test. For comparisons of numerical variables before and after surgery, the Wilcoxon test was used. To compare the means of biochemical and body variables according to VAI classification before and after surgery, the Mann Whitney test was used. To evaluate the correlation between body variables and biochemical parameters before and after surgery, Spearmann's correlation was used. To test the homogeneity of proportions between categorical variables, the Chi-square test or Fisher's exact test were applied. For statistical significance, a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was adopted, and the statistical program used was SPSS for Windows\u0026reg;, version 20.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cem\u003e3.1. \u0026nbsp; \u0026nbsp; \u0026nbsp;Sample characterization and post-surgical outcome\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e62 individuals were included, mean age of 42.8 \u0026plusmn;11.13 years and 80% women (n=48). Six months after surgery, a %WL of 23.35 \u0026plusmn; 7.45 and a %EWL of 60.52 \u0026plusmn; 22.00 were achieved.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 shows body and biochemical variables before and 6 months after surgery and the variation between the two periods. RYGB promoted significant changes in all parameters evaluated, including body adiposity indices, with the exception of ABSI (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTabela 1\u003c/strong\u003e. Body and biochemical variables of individuals with obesity before and 6 months after roux-en-Y gastric bypass\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVARIABLES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT0 (n=62)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1(n=62)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMean\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (Kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e41.58 \u0026plusmn;3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e31.86 \u0026plusmn;4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-9.73 \u0026plusmn; 3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e119.68 \u0026plusmn;11.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e97.79 \u0026plusmn; 9.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-21.95 \u0026plusmn; 8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eWtHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e0.72 \u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-0.13 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e99.20 \u0026plusmn; 20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e89.12 \u0026plusmn; 10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-10.08 \u0026plusmn;17.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e6.23 \u0026plusmn; 5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e3.72 \u0026plusmn;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-2.51 \u0026plusmn; 5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e3.44 \u0026plusmn; 1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e0.64 \u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-2.89 \u0026plusmn; 1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin (mCu/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e15.12 \u0026plusmn;8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e3.39 \u0026plusmn; 3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-11.84 \u0026plusmn; 7.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eTC (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e206.08 \u0026plusmn; 58.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e131.04 \u0026plusmn; 39.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-75.04\u0026plusmn;54.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-c (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e123.62 \u0026plusmn; 47.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e91.28 \u0026plusmn; 29.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-32.34 \u0026plusmn; 38.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-c (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e43.32 \u0026plusmn;11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e46.83 \u0026plusmn;13.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e3.51 \u0026plusmn;14.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eTG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e149.01 \u0026plusmn;73.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e121.12\u0026plusmn;66.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-31.33\u0026plusmn;44.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e2.88\u0026plusmn;1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e2.05\u0026plusmn;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-0.84 \u0026plusmn;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eCI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.32 \u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e1.24 \u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-0.08 \u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e0.077 \u0026plusmn; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e0.075 \u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-0.001 \u0026plusmn; 0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eCUN-BAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e50.74 \u0026plusmn; 5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e41.78 \u0026plusmn;7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-8.98 \u0026plusmn;3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.69747899159664%\" valign=\"top\"\u003e\n \u003cp\u003eLAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.865546218487395%\" valign=\"top\"\u003e\n \u003cp\u003e200.81\u0026plusmn;101.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e120.79 \u0026plusmn; 50.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e-69.17 \u0026plusmn;65.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.092436974789916%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eSD: standard deviation; T0: before surgery; T1: 6 months after surgery; BMI: Body mass index; WC: Waist circumference; WtHR: Waist to height ratio; TC: Total cholesterol; LDL-c: low-density lipoprotein; HDL-c: high-density lipoprotein; TG: triglycerides; VAI: visceral adiposity index; CI: conicity index; LAP: lipid accumulation product; ABSI: body shape index;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAlthough approximately 94% of the sample (n=58) was still classified with abdominal obesity according to WtHR and WC 6 months after surgery, according to VAI, only 40% (n=24) remained with excess visceral fat. At the same time, 95% (n=59) was also overweight, according to BMI (overweight: 33%; class I obesity:\u0026nbsp;40%, class II obesity: 20%, class III obesity:1.7%).\u003c/p\u003e\n\u003cp\u003eRegarding glycemic metabolism, at T0, 36.7% (22), 56.9% (33) and 15.0% (9) of the individuals had elevations in blood glucose, HOMA-IR and HbA1c, respectively. At T1, rates reduced to 6.7% (4), 6.7% (4) and 0.0% (0). For lipid profile, at T0, 48.3% (29), 35.0% (21), 45.0% (27), 46.7% (28) of the individuals had inadequate TC, LDL-c, HDL-c and TG, respectively. These prevalences reduced to 1.7% (1), 8.3% (5), 21.7% (13), 23.3% (14) at T1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2. \u0026nbsp; \u0026nbsp; \u0026nbsp;Biochemical variables and metabolic inadequacies according to visceral adiposity index before and 6 months after RYGB\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConsidering that, among the body adiposity indices, the VAI presents a well-established cutoff point1[19], the sample was subdivided according to VAI into adequate and inadequate, both before and after surgery, in order to evaluate the biochemical difference in obesity associated with visceral adipose tissue dysfunction.\u003c/p\u003e\n\u003cp\u003eIndividuals with elevated VAI before surgery had higher means of HOMA-IR, TG and LAP at T0 (Table 2) and a higher prevalence of insulin resistance and elevated triglycerides (Table 3), compared to those with adequate VAI. However, the difference for HOMA-IR (Table 2) and prevalence of insulin resistance did not remain significant after surgery (Table 3).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e Body and biochemical variables of individuals with obesity before and 6 months after roux-en-Y gastric by-pass, according to Visceral Adiposity Index classification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVARIABLES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.73913043478261%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;T0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.304347826086957%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.908045977011493%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdequate VAI (n=22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.436781609195403%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInadequate VAI (n=38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.908045977011493%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdequate VAI (n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.74712643678161%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInadequate VAI (n=24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (Kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e41.54\u0026plusmn;4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e41.60\u0026plusmn;3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e31.49\u0026plusmn;4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e32.40\u0026plusmn;4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eWC (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e122.55\u0026plusmn;12.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e118.02\u0026plusmn;10.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e99.33\u0026plusmn;9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e95.33\u0026plusmn;7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eWtHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e0.72\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.72\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e0.59\u0026plusmn;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e100.77\u0026plusmn;25.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e98.30\u0026plusmn;16.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e90.11\u0026plusmn;10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e87.63\u0026plusmn;10.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e5.38\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e6.72\u0026plusmn;7.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e3.90\u0026plusmn;10.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e3.44\u0026plusmn;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e2.68\u0026plusmn;1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.86\u0026plusmn;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u0026plusmn;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e0.59\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin (mCu/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e13.07\u0026plusmn;8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e16.33\u0026plusmn;8.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e3.80\u0026plusmn;4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e2.76\u0026plusmn;3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eTC (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e190.00\u0026plusmn;40.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e215.39\u0026plusmn;64.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e134.46\u0026plusmn;39.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e125.91\u0026plusmn;39.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-c (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e113.45\u0026plusmn;32.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e129.50\u0026plusmn;53.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e89.25\u0026plusmn;22.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e94.33\u0026plusmn;37.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-c (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e46.04\u0026plusmn;13.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e41.73\u0026plusmn;9.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e52.22\u0026plusmn;10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e38.74\u0026plusmn;14.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eTG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e90.27\u0026plusmn;29.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e185.94\u0026plusmn;68.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e87.50\u0026plusmn;22.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e173.74\u0026plusmn;77.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e1.55\u0026plusmn;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.73\u0026plusmn;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e1.29\u0026plusmn;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e3.42\u0026plusmn;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eLAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e126.42\u0026plusmn;49.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e247.56\u0026plusmn;99.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e98.80\u0026plusmn;29.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e160.38\u0026plusmn;55.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e1.34\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.31\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e1.25\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e1.21\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eABSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u0026plusmn;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.71014492753623%\" valign=\"top\"\u003e\n \u003cp\u003eCUNBAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e49.50\u0026plusmn;5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e51.46\u0026plusmn;4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.391304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.072463768115941%\" valign=\"top\"\u003e\n \u003cp\u003e39.99\u0026plusmn;8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.231884057971016%\" valign=\"top\"\u003e\n \u003cp\u003e44.44\u0026plusmn;4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.855072463768115%\" valign=\"top\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eSD: standard deviation; T0: before surgery; T1: 6 months after surgery; BMI: Body mass index; WC: Waist circumference; WtHR: Waist to height ratio; TC: Total cholesterol; LDL-c: low-density lipoprotein; HDL-c: high-density lipoprotein; TG: triglycerides; VAI: visceral adiposity index; CI: conicity index; LAP: lipid accumulation product; ABSI: body shape index;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Prevalence of metabolic inadequacies of individuals with obesity before and 6 months after roux-en-Y gastric by-pass, according to Visceral Adiposity Index classification\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.038880248833593%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMETABOLIC INADEQUACIES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.19129082426127%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.769828926905134%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.542619542619544%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdequate VAI\u0026nbsp;\u003cbr\u003e\u0026nbsp;(n=22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.037422037422036%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInadequate VAI\u003cbr\u003e\u0026nbsp;(n=38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.602910602910603%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.83991683991684%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdequate VAI\u0026nbsp;\u003cbr\u003e\u0026nbsp;(n=36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.542619542619544%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInadequate VAI\u003cbr\u003e\u0026nbsp;(n=24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.434511434511435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003eElevated glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e27.3% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"top\"\u003e\n \u003cp\u003e42.1% (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.94392523364486%\" valign=\"top\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.616822429906541%\" valign=\"top\"\u003e\n \u003cp\u003e16.7% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e4.2% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.566978193146417%\" valign=\"top\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003eElevated HbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e4.5% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"top\"\u003e\n \u003cp\u003e21.1% (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.94392523364486%\" valign=\"top\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.616822429906541%\" valign=\"top\"\u003e\n \u003cp\u003e0.0% (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e0.0% (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.566978193146417%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003eElevated HOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e45.5% (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"top\"\u003e\n \u003cp\u003e73.0% (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.94392523364486%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.616822429906541%\" valign=\"top\"\u003e\n \u003cp\u003e5.6% (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e12.5% (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.566978193146417%\" valign=\"top\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003eElevated TC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e36.4% (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"top\"\u003e\n \u003cp\u003e55.3% (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.94392523364486%\" valign=\"top\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.616822429906541%\" valign=\"top\"\u003e\n \u003cp\u003e2.8% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e0.0% (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.566978193146417%\" valign=\"top\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003eElevated LDL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e27.3% (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"top\"\u003e\n \u003cp\u003e39.5% (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.94392523364486%\" valign=\"top\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.616822429906541%\" valign=\"top\"\u003e\n \u003cp\u003e2.8% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e16.7% (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.566978193146417%\" valign=\"top\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003eLow HDL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e36.4% (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"top\"\u003e\n \u003cp\u003e50.0% (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.94392523364486%\" valign=\"top\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.616822429906541%\" valign=\"top\"\u003e\n \u003cp\u003e11.1% (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e37.5% (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.566978193146417%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.077881619937695%\" valign=\"top\"\u003e\n \u003cp\u003eElevated TG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e4.5% (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.510903426791277%\" valign=\"top\"\u003e\n \u003cp\u003e71.1% (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.94392523364486%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.616822429906541%\" valign=\"top\"\u003e\n \u003cp\u003e0.0% (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.641744548286605%\" valign=\"top\"\u003e\n \u003cp\u003e58.3% (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.566978193146417%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eT0: baseline; VAI: visceral adiposity index; HOMA-IR: Homeostasis model assessment for insulin resistance; LDL-c: low-density lipoprotein; HDL-c: high-density lipoprotein; TG: triglycerides;\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e3.3. \u0026nbsp; \u0026nbsp; \u0026nbsp;Correlation between body and biochemical variables before and after surgery\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eBefore surgery, a correlation between BMI, WC, CUN-BAE and ABSI with lipid profile variables was observed and, after, only BMI and WC maintained this relationship (Table 4). Regarding glycemic homeostasis, before surgery, LAP and WC correlated with HbA1c and, after surgery, VAI correlated with HOMA-IR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Correlation between pre- and post-surgery body and biochemical variables in individuals undergoing Roux en Y Gastric Bypass\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRE-SURGERY\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWtH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eABSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUN-BAE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n 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width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.283*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n 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width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.568*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e0.259*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.694*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.962*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.256*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOST-SURGERY\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n 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width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.278*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.270*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.279*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.359*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.847*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.965*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e0;083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eT0: baseline; BMI: body mass index; WC: waist circumference; WtH: waist to hip ratio; VAI: visceral adiposity index; LAP: lipid accumulation index; CI: conicity index; ABSI: a body shape index; * p\u0026lt;0.05 according to Spearmann correlation test\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e3.4. \u0026nbsp; \u0026nbsp; \u0026nbsp;Correlation between pre-surgical body variables and post-surgical biochemical variables\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eIn order to assess the potential predictive nature of post-surgical outcomes, the correlation between body variables at T0 and biochemical variables at T1 was also evaluated. Pre-surgical WC, WHtR, CI and ABSI correlated with post-surgical HOMA-IR, insulin and TC, while BMI did not correlate with any of the variables analyzed (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Correlation between pre-surgery body and post-surgery biochemical variables in individuals undergoing Roux em Y Gastric Bypass\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWtH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eABSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCUN-BAE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e0.376*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e0.351*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e0.493*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e0.511*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e0.377*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e0.350*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e0.443*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e0.438*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.328*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.296*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.320*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.314*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.341*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.709969788519638%\" valign=\"top\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.969788519637461%\" valign=\"top\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.19939577039275%\" valign=\"top\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.120845921450151%\" valign=\"top\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.713*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.610271903323262%\" valign=\"top\"\u003e\n \u003cp\u003e0.738*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.459214501510575%\" valign=\"top\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.48036253776435%\" valign=\"top\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.83987915407855%\" valign=\"top\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eT0: baseline; BMI: body mass index; WC: waist circumference; WtH: waist to hip ratio; VAI: visceral adiposity index; LAP: lipid accumulation index; CI: conicity index; ABSI: a body shape index; * p\u0026lt;0.05 according to Spearmann correlation test\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e3.5. \u0026nbsp; \u0026nbsp; \u0026nbsp;Correlation between variation of body and biochemical variables 6 months after RYGB\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eWhen analyzing the relationship between variation in post-surgical body and biochemical variables, it was observed that both EWL and the reduction in CUN-BAE correlated with glycemia reduction. Furthermore, the reduction in ABSI correlated with the variation in HOMA-IR and insulinemia after surgery. Variations in VAI and LAP correlated with variables included in their calculation formulas, such as TG and HDL. Variations in BMI, WC, WHtR and CI did not correlate with any changes in biochemical variables (Table 6).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"728\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"10\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e. Correlation between variation of body and biochemical variables of individuals 6 months after Roux em Y Gastric Bypass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.266117969821673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.053497942386832%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;BMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.89437585733882%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;WC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEWL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;WtH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;VAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;LAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.681755829903978%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.425240054869684%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;ABSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.659807956104252%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;CUN-BAE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.266117969821673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;Glucose\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.053497942386832%\" valign=\"top\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.89437585733882%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.307*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e-00.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.681755829903978%\" valign=\"top\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.425240054869684%\" valign=\"top\"\u003e\n \u003cp\u003e-0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.659807956104252%\" valign=\"top\"\u003e\n \u003cp\u003e-0.291*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.266117969821673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;HbA1c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.053497942386832%\" valign=\"top\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.89437585733882%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.681755829903978%\" valign=\"top\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.425240054869684%\" valign=\"top\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.659807956104252%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.266117969821673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;HOMA-IR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.053497942386832%\" valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.89437585733882%\" valign=\"top\"\u003e\n \u003cp\u003e-0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.681755829903978%\" valign=\"top\"\u003e\n \u003cp\u003e-0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.425240054869684%\" valign=\"top\"\u003e\n \u003cp\u003e-0.307*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.659807956104252%\" valign=\"top\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.266117969821673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;Insulin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.053497942386832%\" valign=\"top\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.89437585733882%\" valign=\"top\"\u003e\n \u003cp\u003e-0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e-0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.681755829903978%\" valign=\"top\"\u003e\n \u003cp\u003e-0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.425240054869684%\" valign=\"top\"\u003e\n \u003cp\u003e-0.279*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.659807956104252%\" valign=\"top\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.266117969821673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;TC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.053497942386832%\" valign=\"top\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.89437585733882%\" valign=\"top\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.681755829903978%\" valign=\"top\"\u003e\n \u003cp\u003e-0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.425240054869684%\" valign=\"top\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.659807956104252%\" valign=\"top\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.266117969821673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;LDL-c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.053497942386832%\" valign=\"top\"\u003e\n \u003cp\u003e-0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.89437585733882%\" valign=\"top\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.681755829903978%\" valign=\"top\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.425240054869684%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.659807956104252%\" valign=\"top\"\u003e\n \u003cp\u003e-0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.266117969821673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;HDL-c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.053497942386832%\" valign=\"top\"\u003e\n \u003cp\u003e-0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.89437585733882%\" valign=\"top\"\u003e\n \u003cp\u003e-0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e-0.515*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.681755829903978%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.425240054869684%\" valign=\"top\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.659807956104252%\" valign=\"top\"\u003e\n \u003cp\u003e-0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.266117969821673%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;TG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.053497942386832%\" valign=\"top\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.89437585733882%\" valign=\"top\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.190672153635116%\" valign=\"top\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.566*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.818930041152264%\" valign=\"top\"\u003e\n \u003cp\u003e0.877*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.681755829903978%\" valign=\"top\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.425240054869684%\" valign=\"top\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.659807956104252%\" valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003eBMI: body mass index; WC: waist circumference; EWL: excess weight loss; WtH: waist to hip ratio; VAI: visceral adiposity index; LAP: lipid accumulation index; CI: conicity index; ABSI: a body shape index; CUN-BAE: Clinica Universidad de Navarra-Body adiposity Estimator; TC: total cholesterol; LDL: low density lipoprotein-cholesterol; HDL-c: high density lipoprotein-cholesterol; TG: triglycerides * p\u0026lt;0.05 according to Spearmann correlation test\u003c/sup\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSix months post-RYGB there was a significant reduction not only in measures classically used for obesity assessment, such as weight, BMI, WC and WHtR, but also in adiposity indices, with emphasis on VAI, CI, CUN-BAE, and LAP. These findings highlight the impact of surgery beyond body weight, but also on reducing abdominal fat content and itsvisceral depot. It was previously observed by studies that applied gold standard methods to quantify this tissue[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It suggests that the aforementioned indices are promising tools for clinical practice, allowing a more accurate and effective assessment, with reduced cost and easy applicability.\u003c/p\u003e \u003cp\u003eEven with 95% of individuals still classified with overweight or obesity according to BMI, and 94% with inadequate WC or WHtR, there was significant improvement all biochemical variables 6 months post-RYGB. However, only 40% still had inadequate VAI at the same time. These data reinforce the limitation of BMI, WC and WHtR to detect visceral adiposity disfunction in this population. It highlights that RYGB promotes health benefits regardless of changes in body weight, achieving adequacy of metabolic variables even when the BMI, WC and WHtR criteria have not yet been satisfactorily met.\u003c/p\u003e \u003cp\u003ePrevious studies that applied gold standard methods to assess visceral adiposity also observed similar results [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although most studies apply gold standard methods, a previous study observed that 1 year after RYGB or sleeve VAI reached levels similar to those of people in eutrophy, even though the average BMI still was in the obesity range [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFat distribution is more important than weight or total body fat for health risk in obesity. Greater visceral fat represents greater metabolic and cardiovascular impairment[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It occurs mainly due to its relationship with insulin resistance (IR), a key factor in the development of metabolic syndrome and chronic non-communicable diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Several mechanisms can explain this relationship, such as the distinct metabolic and inflammatory adipocytes behavior, depending on the region in which they are located.\u003c/p\u003e \u003cp\u003eVisceral adipocytes are more metabolically active than subcutaneous, which results in greater lipolysis and constant release of free fatty acids (FFA) into the circulation, making visceral adipose tissue (VAT) highly pathogenic [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Excessive FFA release promotes local IR, but also has systemic effects. FFA reaches the liver and trigger changes in lipid and glucose metabolism, resulting in greater production of TG and glucose, favouring IR, hyperinsulinemia and hyperglycemia. They also reach circulation, promoting accumulation of ectopic fat in skeletal muscle (impairing glucose uptake), pancreas, heart and kidneys, impairing the functionality of these organs[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVisceral obesity is also associated with reduced levels of adiponectin, which has anti-inflammatory, cardioprotective and insulin-sensitizing effects[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. VAT expansion is accompanied by inflammation, with greater release of IL-6 and TNF-a[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This inflammation is promoted by VAT resident immune cells, which naturally contain a greater number of macrophages than subcutaenous adipose tissue, even in individuals without excessive body fat [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This chronic low-grade inflammation worsens IR and increases cardiovascular risk[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In individuals undergoing bariatric surgery and evaluated by CT before and 12 months after, there was a positive correlation between VAT volume and CRP levels, an important inflammatory marker, regardless of BMI, while the same correlation was not observed when considering SAT [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong body adiposity indices, VAI presents a well-established cutoff point [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. When segmenting the sample of the present study, it was observed that individuals with elevated VAI, especially before surgery, presented higher IR and TG, both in mean values and in prevalence of these changes. Furthermore, they also presented higher means of CI and LAP, indices that also reflect visceral adiposity[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and VAI was the only post-surgical index that correlated with HOMA-IR. These findings reinforce the negative impact of dysfunctional adipose tissue on metabolic health[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the heterogeneity of obesity and the usefulness of VAI for a more assertive assessment in this population.\u003c/p\u003e \u003cp\u003eThis index considers anthropometric components (BMI and WC) and biochemical data (HDL-c and TG) in a sex-dependent manner, which makes it a more accurate indicator of dysfunctional VAT[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Validated for visceral adiposity assessment using MRI, VAI is an independent predictor of cardiovascular risk, surpassing BMI, WC and even LAP [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Besides that, it is closely associated with IR, highlighting its relevance in health assessment and monitoring.\u003c/p\u003e \u003cp\u003eThe ABSI was the only index that did not suffer a reduction after surgery, as also previously observed [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This index aims to evaluate fat accumulation in the central region, independently of WC or BMI. An important aspect of this index that may have influenced this result is that, according to previous evidence, lean mass content is related to ABSI value in obesity[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. High ABSI values ​​may indicate not only greater visceral adiposity, but also significant loss of muscle mass, being an indicator of sarcopenic obesity.\u003c/p\u003e \u003cp\u003eConsidering that during the first 6 months post-RYGB the individual is in high catabolism, including loss of lean mass, changes in body composition may have influenced this result[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Corroborating this hypothesis, in the present study, although ABSI did not undergo significant change when comparing pre- and post-surgery, its modification correlated with the reduction in HOMA-IR after surgery, reinforcing the body composition impact on insulin sensitivity.\u003c/p\u003e \u003cp\u003eMusculoskeletal mass is essential for insulin sensitivity maintenance. Significant decrease in muscle mass, as well as excess visceral adiposity, is associated with IR and cardiometabolic complications[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, we recommend further investigation into this index, which appears to provide important information about body composition and its relationship with metabolic health in obesity, especially in post-surgical follow-up.\u003c/p\u003e \u003cp\u003eThe predictive capacity of pre-surgical visceral adiposity in improving insulin sensitivity after surgery has recently been discussed in the literature and is still not well understood. Few studies apply adiposity indices in the context of RYGB and mainly investigate their predictive nature for diabetes mellitus remission after intervention. Some have observed that LAP and VAI are independent predictors of this outcome[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, a multicentric study, did not observe a prospective correlation between VAI or LAP at baseline and HOMA-IR 12 months after bariatric surgery[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, HOMA-IR at baseline correlated with VAI at follow-up. The authors suggested that the improvement in IR precedes visceral fat loss and that the reduction of this adipose compartment might be a consequence of increased post-surgical insulin sensitivity, and not the opposite, as also suggested by Other previous studies[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, there was no correlation between pre-surgical VAI or LAP with post-surgical HOMA-IR. However, Other pre-surgical variables indicative of central adiposity, such as WC, WHtR, ABSI and CI, were correlated with HOMA-IR and insulinemia after surgery. Among the variations after BGYR, only the ABSI correlated with the HOMA-IR variation. This highlights the importance of additional studies to understand the impact of central adiposity on post-surgical metabolic improvement and the usefulness of adiposity indices in predicting this outcome and the causal relationship.\u003c/p\u003e \u003cp\u003eThe lack of correlation between pre-surgical BMI and post-surgical biochemical variables reinforces the inability of this indicator to predict health outcomes after surgery, although it is still the main criterion used for its indication. Thus it reinforces that, in addition to not reflecting the change in fat distribution, BMI also do not reflect properly the cardiometabolic benefits resulting from the RYGB[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite limitations related to sample size, the present study adopted statistical analyzes appropriate to our sample. We are pioneers in simultaneously evaluating different body adiposity indices in patients undergoing RYGB, establishing correlations with biochemical variables and comparing pre- and post-surgical scenarios. However, we emphasize the importance of obtaining long-term results in order to provide a more comprehensive understanding of the trajectories of these indices after RYGB, improving our knowledge about the effects of bariatric surgery on body adiposity and metabolic health.\u003c/p\u003e \u003cp\u003eOur findings reinforce the importance of evaluating visceral adiposity in obesity and in individuals undergoing RYGB, and the applicability of adiposity indices, with emphasis on VAI and ABSI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by National Council for Scientific and Technological Development (CNPq) (301479/2022-4) and FAPERJ (E-26/200.876/20210)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contribution: ML conceptualization, data curation, formal analysis, methodology, validation, visualization, writing \u0026ndash; original draft and editing; SP and CS data curation, investigation, methodology, resources, validation, writing \u0026ndash; review; AR conceptualization, funding acquisition, methodology, project administration, resourves, supervision, visualization, writing - review and editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData availability: The data that support the findings of this study are not publicly available due to privacy. The data are, however, available from the authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAzagury DE, Morton JM. Bariatric Surgery. Endocrinol Metab Clin North Am. 2016;45:647\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoglino C, Tirone A, Ciuoli C, Benenati N, Paolini B, Croce F, et al. Cardiovascular Benefits and Lipid Profile Changes 5 Years After Bariatric Surgery: A Comparative Study Between Sleeve Gastrectomy and Roux-en-Y Gastric Bypass. J Gastrointest Surg Off J Soc Surg Aliment Tract. 2020;24:2722\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopes HF, Corr\u0026ecirc;a-Giannella ML, Consolim-Colombo FM, Egan BM. Visceral adiposity syndrome. Diabetol Metab Syndr. 2016;8:40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCesaro A, De Michele G, Fimiani F, Acerbo V, Scherillo G, Signore G, et al. 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JCI Insight. 2019;4:e126688, 126688.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bariatric surgery, obesity, visceral fat, adiposity","lastPublishedDoi":"10.21203/rs.3.rs-4843683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4843683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eVisceral fat is determinant for obesity metabolic disturbances. Gold standard methods for its assessment are unfeasible in clinical practice. Adiposity indices have been proposed to provide a more feasible evaluation. The study aims to assess body fat distribution through adiposity indices, before and 6 months after Roux-en-Y gastric bypass(RYGB), and their correlation with body and biochemical variables.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eprospective longitudinal study with adults undergoing RYGB, evaluated before(T0) and 6 months after surgery(T1). Weight, height, waist circumference(WC), BMI, waist-to-height ratio(WHtR), total cholesterol(TC), HDL-c, LDL-c, triglycerides(TG), insulin, glucose, HbA1c and HOMA-IR were evaluated. Visceral adiposity index(VAI), conicity index(CI), lipid accumulation product(LAP), Clinica Universidad de Navarra-Body adiposity estimator(CUN-BAE) and a body shape index(ABSI) were calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e62 individuals, 80% women, mean age 42.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.13 years. There was significant improvement in adiposity and all biochemical variables, even with more than 90% still in overweight/obesity class and/or inadequate WC and WHtR. Only 40% of individuals had VAI inadequacy at T1. Individuals with elevated VAI had higher HOMA-IR, TG and LAP at T0, compared to those with adequate VAI. Although this difference was not maintained at T1, after surgery, VAI was the only index that correlated with HOMA-IR. Pre-surgical ABSI, CI, WC and WHtR correlated with post-surgical HOMA-IR, insulin and TC. Only the post-surgical variation in ABSI correlated with HOMA-IR reduction.BMI did not correlate with post-surgical metabolic outcome.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings reinforce the ability of RYGB to reduce visceral adiposity, and the applicability of the indices to assess metabolic health, with emphasis on VAI and ABSI.\u003c/p\u003e","manuscriptTitle":"Application of Adiposity Indices for Body Fat and Metabolic Health Assessment Before and After Roux-en-Y Gastric Bypass","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-06 11:53:52","doi":"10.21203/rs.3.rs-4843683/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88b84961-a151-42a6-9096-a8da524d03a6","owner":[],"postedDate":"September 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-12T01:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-06 11:53:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4843683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4843683","identity":"rs-4843683","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}