Visceral adipose tissue area and proportion provide distinct reflections of cardiometabolic outcomes in weight loss; Pooled analysis of MRI-assessed CENTRAL and DIRECT PLUS dietary randomized controlled trials

Visceral adipose tissue area and proportion provide distinct reflections of cardiometabolic outcomes in weight loss; Pooled analysis of MRI-assessed CENTRAL and DIRECT PLUS dietary randomized controlled trials | 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 Visceral adipose tissue area and proportion provide distinct reflections of cardiometabolic outcomes in weight loss; Pooled analysis of MRI-assessed CENTRAL and DIRECT PLUS dietary randomized controlled trials Hadar Klein, Hila Zelicha, Anat Yaskolka Meir, Ehud Rinott, Gal Tsaban, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4323673/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2025 Read the published version in BMC Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Background Visceral adipose tissue (VAT) is well established as a pathogenic fat depot, while superficial subcutaneous adipose tissue (SAT) is associated with an improved or no association with the cardiovascular state. However, it is unclear to what extent VAT area (VATcm 2 ) and its proportion of total abdominal adipose tissue (VAT%) are distinguished in predicting cardiometabolic status and clinical outcomes during weight loss. Methods We integrated magnetic resonance imaging (MRI) measurements of VAT, deep-SAT, and superficial-SAT from two 18-month lifestyle weight loss clinical trials, CENTRAL and DIRECT-PLUS (n = 572). Results At baseline, the mean VATcm 2 was 134.8cm 2 and VAT%=28.2%; over 18-months, participants lost 28cm 2 VATcm 2 (-22.5%), and 1.3 VAT% units. Baseline VATcm 2 and VAT% were similarly associated with metabolic syndrome, hypertension, and diabetes status, while VAT% better classified hypertriglyceridemia. Conversely, higher VATcm 2 was associated with elevated high-sensitivity C-reactive protein (hsCRP), while VAT% was not. After 18 months of lifestyle intervention, both VATcm 2 and VAT% loss were significantly associated with decreased triglycerides, HbA1c, chemerin, ferritin, and liver enzymes, and increased HDL-c levels beyond weight loss (FDR < 0.05). Only VATcm 2 loss was correlated with decreased HOMA-IR and leptin levels. Conclusions Although increased VATcm 2 and VAT% exhibit similar clinical manifestations, it might be preferable to examine VAT% when exploring lipid status, while VATcm 2 may better reflect inflammatory and glycemic states. Trial registration: CENTRAL (Clinical-trials-identifier: NCT01530724); DIRECT-PLUS (Clinical-trials-identifier: NCT03020186) Visceral adipose tissue Subcutaneous adipose tissue Weight loss Diabetes Metabolic syndrome Figures Figure 1 Figure 2 Figure 3 Background Although visceral adipose tissue (VAT) is widely recognized as a pathogenic fat depot, superficial subcutaneous adipose tissue (SAT) has been linked to improved indicators of cardiovascular health [ 1 – 4 ]. This contrast contributed to the controversy surrounding the distinction between the visceral adipose tissue (VAT) area and its proportion of the total abdominal adipose tissue (%). While some reports explore metabolic complications of obesity with measures of VAT absolute area or volume [ 5 – 8 ], others refer to VAT proportion or other normalized indices of VAT as ratios that may better reflect cardiometabolic status and clinical outcomes [ 9 – 15 ]. Notably, some reports found VAT absolute quantity to be superior in predicting specific features independently (e.g., fasting insulin) and inferior in predicting others (e.g., fasting glucose) compared to the VAT ratio to subcutaneous adipose tissue (SAT) [ 16 , 17 ]. This controversy is further increased by the impracticality of assessing abdominal fat depots in routine clinical settings with costly techniques such as dual-energy X-ray absorptiometry (DEXA), computed tomography (CT), and magnetic resonance imaging (MRI) [ 4 , 18 , 19 ]. To address this quantification challenge, several surrogate indices have been developed to serve as alternative estimators for assessing VAT quantity. Some of these indices are strictly anthropometric, such as the body mass index (BMI) and waist circumference (WC) [ 18 , 20 – 22 ], and others incorporate additional characteristics such as demographic data (e.g., age, sex), plasma biomarkers (e.g., glucose, cholesterol, and amino acids) and bioelectrical impedance analysis (BIA) parameters [ 23 – 28 ]. However, compared to the numerous surrogate indices of VAT area, estimations of VAT proportion and changes in VAT area and proportion are lacking [ 4 , 29 ]. In this study, we utilized pooled data from two 18-month lifestyle, randomized weight loss clinical trials, CENTRAL and DIRECT-PLUS, with 572 participants and MRI-assessed fat depots. We hypothesized that either the absolute area or proportion of VAT would better reflect obesity complications and developed novel predictors to assess the VAT state and changes following lifestyle interventions. Methods Study design This is a pooled analysis of two 18-month lifestyle intervention clinical trials, the CENTRAL (Clinical-trials-identifier: NCT01530724) and the DIRECT-PLUS (Clinical-trials-identifier: NCT03020186) trials. The CENTRAL (n = 278, 2012–2014) and DIRECT-PLUS (n = 294, 2017–2019) clinical trials were conducted in the same research center workplace in Dimona, Israel. The retention rates at 18 months were 86.3% and 89.8%, respectively, as previously described in detail [ 30 , 31 ]. Accordingly, this pooled analysis included data on 572 participants at baseline and 528 participants who completed the trials. Of 572 participants from both trials, almost all MRI scans at baseline were eligible for quantification of the VAT area (n = 564; 99%) and VAT proportion (n = 553; 97%). Losses were due to technical reasons. Both trials had similar inclusion criteria: WC > 102 cm for men and > 88 cm for women or dyslipidemia (serum triglycerides (TG) > 150 mg/dL and high-density-lipoprotein cholesterol (HDLc) < 40 mg/dL for men and < 50 mg/dL for women). DIRECT-PLUS was limited to those over age 30. Both trials had nearly identical exclusion criteria, as fully described in S1. Both studies were approved and monitored by the Medical Ethics Board and Helsinki Committee of the Soroka University Medical Center. All participants provided written informed consent and received no financial compensation or gifts. Randomization and Interventions All diets aimed for moderate, long-term weight loss with restricted consumption of trans fats and refined carbohydrates and increased intake of vegetables. Lunch, commonly the primary meal in this population, was tailored to meet the specific dietary requirements of each group and was provided through the workplace cafeteria. All lifestyle education programs were provided to all groups by physicians, clinical dietitians, and fitness instructors at the same intensity. Randomization was performed with an equal allocation ratio across all treatment groups, stratified by sex and work site. The participants were aware of their assigned intervention (open-label). Study investigators assessing outcomes were blinded to the group assignments. In the CENTRAL trial, the diet groups were low-fat or Mediterranean (MED)/low-carbohydrate. These groups were further divided after 6 months into groups with added physical activity (PA) or no added PA for the last 12 months of intervention. In the DIRECT-PLUS trial, the diet groups included healthy dietary guidelines, MED, and green-MED, all of which were combined with PA. The characteristics of each lifestyle intervention group are fully detailed in previous publications [ 30 , 31 ]. Outcome measures Abdominal fat deposits were evaluated at baseline and 18 months later using 3-T MRI (Philips Ingenia 3.0T). Abdominal fat depots were quantified using MATLAB-based semiautomatic software [ 30 , 31 ]. The scans included 2–3 axial slices—L5-S1, L4-L5, and L2-L3—in the CENTRAL trial and L5-S1 and L4-L5 in the DIRECT-PLUS trial. For this pooled analysis, only the common L5-S1 and L4-L5 axial slices from both trials were used to calculate the mean VAT area and proportion for uniformity. Quantification of the fat mass regions included both the absolute area of each fat type and its proportion (percentage) of the total area of both fat types [VAT/(VAT + SAT)*100]. The entire protocol is reported in S2. Anthropometric parameters and blood biomarkers were measured at baseline and after 18 months of intervention (S3). Statistical analysis A common coprimary outcome of both trials was VAT change following nutritional interventions. This report presents continuous variables as the means (standard deviations) or medians (interquartile ranges), depending on the variables’ normal distribution. Nominal variables are expressed as numbers and percentages. Histograms of each variable were inspected to determine whether the variables were normally distributed. Nonnormal distributions were natural logarithm (ln) transformed. Changes in VAT, anthropometrics, and biomarkers were computed as changes relative to baseline [(time 18 – time 0)/time 0 × 100]. Differences between two groups of baseline characteristics were tested using the chi-square test for nominal variables and two sample t-test or Wilcoxon rank sum test for continuous variables. Sex-specific deciles of VAT parameters were calculated per trial. The Kendall tau trend test was used to examine demographic and anthropometric measurements and blood biomarkers across adipose tissue deciles and groups of similar and opposite VAT areas and proportion medians via partial correlations. The ANCOVA test was also used to test demographic and anthropometric measurements and blood biomarkers across groups of similar and opposite VAT areas and proportion medians. The correlation between VAT area and proportion was tested using Spearman’s correlation analysis. The metabolic syndrome criteria were assessed based on the National Cholesterol Education Program Adult Treatment Panel III criteria [ 32 ]. The Youden index was used to determine the optimal VAT cutoffs for metabolic morbidities [ 33 ]. We used generalized linear regression models (GLMs) to classify participants with metabolic abnormalities and evaluated their performance using the C-statistic, also known as the concordance statistic or the area under the receiver operating characteristic curve (AUC-ROC) [ 34 ]. We used the robust likelihood test to test whether a nonnested model fit better than a reference model. Least absolute shrinkage and selection operator (LASSO) regression was used to identify and evaluate predictors of VAT parameters at baseline and their changes following nutritional intervention. Models were constructed for both genders and for men only. The models were trained on 80% of the DIRECT PLUS data, tested on the remaining 20%, and validated on the CENTRAL data. Random splits of observations into training and testing sets were stratified by sex and VAT area or proportion quartiles, depending on the predicted VAT measure. Predictor variables included anthropometrics, demographics, and blood biomarkers available from both the CENTRAL and DIRECT PLUS trials (including glycemic and lipidic profiles, liver enzymes, inflammatory markers, and adipokines). The predictors were centered and scaled prior to model fitting. The LASSO penalty tuning parameter λ with the minimal average root mean square error (RMSE) across 10-fold cross-validation and 10 repeats was chosen to compute the final model on the complete training data. All the statistical analyses were computed using R version 4.2.0, with the use of the following packages: “gtsummary”, “cutpointr”, “pROC”, “performance”, “nonnest2”, “caret”, “dplyr”, “ggplot2”, “corrplot” and “glmnet”, “interactions” [ 35 – 45 ]. Results Baseline characteristics The participants (Table 1 ) had a mean age of 49.5 ± 10.1 years, with an average BMI of 30.9 ± 3.9 kg/m² and WC of 108.2 ± 9.7 cm (109.1 ± 9.1 cm for males and 101.4 ± 11.0 cm for females). Most participants were men (88.5%), 10.9% of participants had diabetes, and 59.4% had metabolic syndrome. Following the 18-month lifestyle intervention, participants lost − 2.6 ± 5.6 kg body weight and 4.8 ± 5.9 cm of their WC. The changes in abdominal adipose depots were − 17.5 cm 2 (-35.4 – -4.0) for superficial SAT (-16.7% (-26.4 – -4.0)), -41.3 cm 2 (-76.7 – -9.9) for deep SAT (-19.9% (-31.2 – -4.6)) and − 27.9 cm 2 (-52.6 – -6.4) for VAT (-22.5% (-35.9 – -5.7)). The change in VAT proportion absolute units was − 1.3 ± 3.4% (-3.8% (-11.5–3.6)). Table 1 Sex-specific baseline characteristics of the CENTRAL and DIRECT PLUS clinical trials participants. Characteristic 1 N Overall, N = 572 2 Male, N = 506 2 Female, N = 66 2 p value 3 q-value 4 Age 572 49.5 ± 10.1 49.3 ± 10.1 51.1 ± 10.1 0.17 0.24 Weight, kg 572 92.6 ± 13.9 94.3 ± 13.1 79.4 ± 13.0 < 0.001 < 0.001 BMI, kg/m² 572 30.9 ± 3.9 30.9 ± 3.7 31.0 ± 5.2 0.87 0.87 Waist circumference, cm 571 108.2 ± 9.7 109.1 ± 9.1 101.4 ± 11.0 < 0.001 < 0.001 Diabetes 568 62 (10.9) 58 (11.5) 4 (6.2) 0.19 0.24 Metabolic syndrome 559 332 (59.4) 305 (61.7) 27 (41.5) 0.002 0.003 SSAT area, cm² 553 119.7 (91.5–162.9) 113.6 (87.9–151.9) 197.0 (159.0–256.2) < 0.001 < 0.001 DSAT area, cm² 560 228.2 (179.9–290.2) 228.5 (178.5–293.9) 227.3 (187.4–268.4) 0.69 0.77 VAT area, cm² 564 134.8 (103.2–174.3) 139.3 (108.2–178.1) 105.4 (77.8–138.4) < 0.001 < 0.001 VAT proportion, % 553 28.2 ± 9.0 29.3 ± 8.7 19.8 ± 6.7 < 0.001 < 0.001 1 BMI, body mass index; SSAT, superficial subcutaneous adipose tissue; DSAT, deep subcutaneous adipose tissue; VAT, visceral adipose tissue. 2 Values are presented as either the median (p25, p75) or the mean ± standard deviation for continuous variables, depending on their distribution, or as number (%) for categorical variables. 3 Two Sample t-test; Pearson's Chi-squared test; Wilcoxon rank sum test 4 False discovery rate correction for multiple testing Baseline VAT area and proportion sex-specific deciles showed parallel direct and significant correlation trends with age, blood pressure, and most blood biomarkers (FDR < 0.05) (Table S1 and Figure S1 ). Specifically, fasting glucose and insulin, homeostatic model assessment of insulin resistance (HOMA-IR), HbA1c, TG, TG/HDLc, and gamma-glutamyl transferase (GGT) were found to increase with higher VAT area and proportion deciles. Conversely, VAT area and proportion exhibited dissimilar associations with anthropometric measurements and specific blood biomarkers. VAT area was positively associated with WC (tau = 0.33, FDR < 0.001), chemerin (tau = 0.18, FDR < 0.001), high-sensitivity C reactive protein (hsCRP) (tau = 0.0.16, FDR < 0.001), alkaline phosphatase (ALKP) and alanine transaminase (ALT) (tau = 0.09, FDR = 0.04 for both). However, the VAT proportion was not associated with these markers. Additionally, while VAT area demonstrated an increasing trend with body weight (tau = 0.21, FDR < 0.001) and leptin (tau = 0.19, FDR < 0.001), VAT proportion presented a decreasing trend with these measurements (tau=-0.13 and − 0.12, FDR < 0.001). These trends remained following adjustment for age, weight, and intervention trial (CENTRAL and DIRECT-PLUS). In a partial correlation analysis adjusted for these covariates, sex-specific deciles of superficial SAT area, deep SAT area, VAT area, and VAT proportion exhibited distinct associations with anthropometrics and blood biomarkers (Fig. 1 A). All fat depot areas were directly and significantly associated with WC, leptin, chemerin, and hsCRP (FDR < 0.01). In contrast to VAT area, which was negatively associated with HDLc (tau=-0.08, FDR < 0.01) and positively associated with TG and TG/HDLc (tau = 0.11, FDR < 0.001), superficial and deep SAT exhibited positive correlations with HDLc (tau = 0.08 and 0.09, FDR < 0.001) and inverse correlations with TG (tau=-0.07 and − 0.06, FDR < 0.05) and TG/HDLc (tau=-0.09 and − 0.08, FDR < 0.01). The VAT proportion association with TG and HDLc (tau = 0.15 and − 0.13, FDR < 0.001) appeared stronger than that of the VAT area (tau = 0.11 and − 0.08, FDR < 0.001). Deep SAT and VAT areas were directly correlated with insulin and HOMA-IR (FDR < 0.05), while superficial SAT was not. The VAT area was positively correlated with systolic and diastolic blood pressure, fasting glucose, HbA1c, ferritin, GGT, and ALKP (FDR < 0.05), while no significant associations were found between superficial and deep SAT and these markers. Superficial SAT was inversely associated with ALT levels (tau=-0.07, FDR < 0.05), whereas VAT was positively associated with ALT levels (tau = 0.09, FDR < 0.01). The VAT area and proportion showed similar trends with most biomarkers, except for leptin and hsCRP. While VAT area had a positive correlation with both hsCRP and leptin (FDR < 0.001), VAT proportion did not correlate with hsCRP and was negatively correlated with leptin (FDR < 0.001). Similarly, in contrast to the direct association of VAT area with WC (FDR < 0.001), VAT proportion was not associated with WC (FDR = 0.99). Baseline VAT area and proportion in relation to obesity complications Sex-specific cutoff values of VAT area and proportion were calculated for metabolic syndrome and diabetes status (Table S2, Figure S2). The cutoff values for metabolic syndrome at baseline were 120.72 cm 2 VAT area (AUC = 0.69) and 27.84% VAT proportion (AUC = 0.62) for men and 114.8 cm 2 VAT area (AUC = 0.82) and 24.39% VAT proportion for women (AUC = 0.74). The cutoff values for diabetes status at baseline were 114.10 cm 2 VAT area (AUC = 0.62) and 35.41% VAT proportion (AUC = 0.60) for men and 90 cm 2 VAT area (AUC = 0.70) and 25.74% VAT proportion for women (AUC = 0.81). We further compared the VAT area and proportion prediction performances in classifying states of metabolic dysfunction at baseline, in adjustment for trial type, sex, age, and baseline weight (Fig. 2 ). VAT area and proportion seemed to similarly predict states of metabolic syndrome (AUC = 0.75 for both, p = 0.49), hypertension (AUC = 0.76 for both, p = 0.25), and diabetes (AUC = 0.71, p = 0.48). However, VAT proportion performed better at classifying participants with hypertriglyceridemia (AUC = 0.66) compared to VAT area (AUC = 0.62) (p = 0.01). VAT area and proportion were correlated with each other (r = 0.68, p < 0.001). Nevertheless, distinct phenotypes of visceral adiposity could be classified for participants whose VAT area was above the median (men = 139 cm 2 , female = 105 cm 2 ) and whose VAT proportion was below the median (men = 29%, female = 19%), and vice versa. Participants with higher VAT area and decreased VAT proportion had, by definition, higher SAT and increased weight. In multivariable analyses of groups with similar and opposite sex-specific VAT area and proportion medians, participants (13%) characterized by a top-median VAT area and low-median VAT proportion exhibited increased diastolic blood pressure, HbA1c, fasting insulin, HOMA-IR, ALT, AST, leptin, chemerin, and hsCRP, after controlling for weight and deep SAT, compared to the other groups (FDR < 0.05 for all). Alternatively, participants (14.1%) with low-median VAT areas and top-median VAT proportions presented similar adverse lipid profiles to those with higher VAT areas (Supplementary Material Table S3). Eighteen-month changes in abdominal adipose tissue depots Despite the opposite associations at baseline between SAT and VAT regarding lipids profile, the loss of each fat compartment was associated with an improved lipids profile, even after adjustment for age, overall weight loss, and intervention trial (Fig. 1 B). Similarly, all fat depot area reductions were associated with reduced WC, fasting insulin, HOMA-IR, and leptin, and none were associated with changes in blood pressure, hsCRP, ALKP, or AST. Both SAT subcompartments’ losses were related to reductions in glucose, but VAT loss was not. The opposite was true for the reductions in HbA1c, ferritin, GGT, and ALT, which were directly associated only with the loss of VAT area. Deep SAT and VAT losses were associated with chemerin reduction, while superficial SAT loss was not. Changes in VAT area and proportion were both directly and significantly correlated with reduced WC, HbA1c, dyslipidemia, chemerin, ferritin, GGT, and ALT (FDR < 0.05). However, some contrasts were noted; while VAT area loss was correlated with reduced insulin (tau = 0.11), HOMA-IR (tau = 0.10), and leptin (tau = 0.12), FDR < 0.01 for all, VAT proportion loss was not (tau = 0.04–0.06, FDR = 0.10–0.30). Alternatively, VAT proportion loss was correlated with reduced AST (tau = 0.09, FDR = 0.01), while VAT area loss was not (tau = 0.06, FDR = 0.12). Prediction models of VAT baseline and 18-month change Prediction models were developed for VAT area and proportion baseline and changes, utilizing either anthropometric measurements and demographic data, blood biomarkers, or a combination of both. Each model’s selected variables and performance metrics are presented in Supplementary Material Tables S4-S7. The best-performing prediction model for baseline VAT area included a combination of anthropometrics, demographics, and blood biomarkers (Supplementary Material Table S4). It was trained on data from 227 DIRECT-PLUS participants, tested on 55 DIRECT-PLUS participants, and validated on 259 CENTRAL participants. Participants assigned to the training data had similar characteristics to those assigned to the testing data (Supplementary Material Table S8). The cross-validation models for choosing the optimal hyperparameters for the final model had RMSE of 0.27 and R 2 of 0.44. The final model was applied to the testing and validation datasets, with RMSEs of 0.26 and 0.40 and R 2 of 0.53 and 0.50, respectively. This model selected both anthropometric, demographic, and blood biomarkers mesurments, including WC, MAP, age, TG/HDLc, HbA1c, HOMA-IR, glucose, GGT, ALKP, and chemerin (Fig. 3 A). Similarly, the best-performing model for the estimation of baseline VAT proportion also included a combination of anthropometric, demographic and blood biomarkers predictors (Supplementary Material Table S5). The model was trained on n = 218 participants (RMSE = 6.87, R 2 = 0.37), tested on n = 53 participants (RMSE = 6.55, R 2 = 0.51), and validated on n = 142 participants (RMSE = 6.7, R 2 = 0.39). The selected variables were similar to those predictive of VAT area, with additional selected variables: sex, ALT, fetuin-A, ferritin, and leptin (the latter negatively contributing to VAT proportion). Furthermore, female sex was a predictor of a lower VAT proportion (Fig. 3 B). In contrast to the VAT area prediction, WC was not selected for the prediction of VAT proportion. However, higher weight was selected as a predictor of a lower VAT proportion in a model developed with only anthropometrics and demographic variables. As for VAT area´s change, the best-performing predictor on the testing data included only anthropometric measurements (RMSE = 15.33, R 2 = 0.59). In contrast, the best performance for the validation data incorporated variables of both anthropometrics and blood biomarkers (RMSE = 52.1, R 2 = 0.52). The predictors were trained on 180 participants, tested on 46 participants, and validated on 207–212 participants. Both predictors included changes in weight and WC, with the former including change in MAP and the latter including change in leptin (Fig. 3 C, Supplementary Material Table S6). Lastly, the best-performing predictor of the change in VAT proportion was developed using only anthropometric and demographic data (weight, WC, MAP, age and sex). The model was trained on data from 172 participants (RMSE = 11.32, R 2 = 0.15), tested on data from 43 participants (RMSE = 11.80, R 2 = 0.24) and validated on data from 212 participants (RMSE = 18.72, R 2 = 0.16) (Fig. 3 D, Supplementary Material Table S7). Due to insufficient data, sex-specific prediction formulas could not be developed for females (11.5%). However, sex-specific male predictors are available in Supplementary Material Figure S3. Discussion This pooled analysis of two 18-month nutritional randomized controlled trials (n = 572) revealed notable differences in the parameters of abdominal VAT distribution. VAT area and VAT proportion are associated with similar metabolic indicators, with higher values corresponding to a worsened cardiometabolic state. However, VAT proportion was more strongly associated with lipid status, whereas VAT area was more strongly linked to glucose metabolism and inflammation biomarkers. Several limitations should be acknowledged. First, although multislice volume imaging is considered the gold standard for measuring adipose tissue [ 46 ], we measured abdominal adipose tissue area rather than volume. However, we calculated these areas as the means of two images at the L4-L5 and L5-S1 intervertebral spaces. Additionally, we observed high inter- and intraclass correlations (r > 0.96; p < 0.001), supporting their reproducibility. Second, due to the nature of the workplace environment where the trials were held, 88.5% of the participants were men. Hence, we identified predictors for both sexes, accounting for sex, and for men only, but not for women only. Third, total fat mass values were not available in our MRI measurements, restricting our ability to quantify the VAT proportion of total body fat. Hence, only the VAT proportion of total abdominal fat is discussed here. The strengths of the analysis include its large sample size and high retention rates within two relatively large and long clinical trials conducted in the same workplace for the same duration with similar inclusion and exclusion criteria. Furthermore, both trials measured VAT parameters using the same 3-T MRI analysis . The VAT area and proportion were closely correlated and similarly associated with various cardiometabolic biomarkers, including hypertension, impaired glycemic and lipidic profiles, liver dysfunction, and elevated chemerin. However, while both VAT parameters were positively correlated with TG and negatively correlated with HDLc, SAT had opposite associations with these biomarkers. Specifically, both superficial and deep SATs presented beneficial associations with lipids profile, with a direct association with HDLc and an inverse association with TG. These findings are in accordance with the well-established association of superficial SAT with improved indicators of cardiovascular health [ 1 – 3 ]. As a result, the correlations of VAT proportion with TG and HDLc were stronger than those of VAT area. VAT area and proportion had near identical performance in independently predicting metabolic syndrome, hypertension, and diabetes status. However, the VAT proportion better predicted hypertriglyceridemia state than the absolute VAT amount (p = 0.01). More discrepancies have been noted, with body weight and WC presenting different trends across VAT area and proportion. While VAT area was positively correlated with weight and WC, VAT proportion was inversely related to weight and had no significant association with WC. These findings are attributed to the stronger association of weight and WC with SAT rather than with VAT and are consistent with previous reports that found VAT area to be greater in patients with obesity than in patients without obesity, in contrast to VAT proportion, which was similar in these groups [ 9 ]. The heterogeneous phenotypes of visceral obesity were classified to further explore the associations of VAT area and proportion with adverse health indicators. Naturally, participants presenting both increased (above median) VAT and SAT areas (i.e., low VAT proportion) were characterized by higher SAT, WC and weight. As deep SAT was found to be independently associated with increased insulin resistance [ 30 ], we performed multivariable analyses between the visceral adiposity phenotype groups, controlling for weight and deep SAT. Participants with a high VAT area and low VAT proportion (n = 72) presented a worsened metabolic state compared to those with a low VAT area and high VAT proportion (n = 78). Specifically, they had higher insulin resistance and increased HbA1c levels (FDR < 0.01). This finding is in accordance with previous reports that VAT area is superior to VAT/SAT for predicting fasting insulin [ 16 ]. Moreover, this group presented an extensive decline in metabolic health, involving elevated blood pressure, liver enzymes, adipokines, and inflammatory state (FDR < 0.05). These findings are consistent with the observed distinct associations of VAT area, but not its proportion, with chemerin, hsCRP, ALKP and ALT (FDR < 0.05). However, the diverse visceral adiposity phenotypes presented similar adverse lipid profiles, repeatedly revealing the strong association of increased VAT proportion with a poor lipidic state, even in the presence of a relatively low VAT area. We found some differences in the relations of VAT area and proportion with biomarkers at baseline compared to changes in those two measures with weight loss over time. In particular, reduced SAT was associated with an improved lipids profile, despite their beneficial association at baseline. In addition, despite WC having no association with VAT proportion at baseline, it was correlated with its change. This is probably due to VAT’s greater sensitivity to weight reduction than SAT’s [ 47 , 48 ]. Variables selected for LASSO prediction formulas reflected the distinct associations of VAT area and proportion with anthropometric, demographic, and blood biomarkers measurements at baseline and of their changes. Specifically, older age was highly predictive of both increased VAT area and proportion, in agreement with other reports [ 7 , 47 , 49 ]. However, WC was predictive of VAT area, but not VAT proportion. Alternatively, male sex and lower levels of leptin were predictive of a higher VAT proportion but not of its area [ 4 , 47 , 49 , 50 ]. The latter is explained by leptin’s higher secretion rates in SAT than in VAT [ 51 ]. Although indicators of poor lipid and glycemic profiles, along with elevated levels of liver enzymes and several adipokines, were important predictors of increased baseline VAT area and proportion, both of their changes were mainly predicted by a combination of anthropometric measurements. Conclusions Although VAT area and proportion are highly correlated, each parameter holds distinct attributes of cardiometabolic state. While the VAT proportion is more strongly associated with a poor lipid state, the VAT area better reflects the inflammatory state and glycemic profile during weight loss. These findings indicate the complexity of VAT dynamics and emphasize the relevance of personalized approaches in targeting visceral adiposity for cardiometabolic health improvement. Abbreviations ALKP Alkaline Phosphatase ALT Alanine Transaminase AST Aspartate Transaminase BMI Body Mass Index CT Computed Tomography DBP Diastolic Blood Pressure DEXA Dual Energy X-ray absorptiometry FDR False Discovery Rate GGT Gamma-Glutamyl Transferase HDG Healthy Dietary Guidelines HDLc High-Density Lipoprotein Cholesterol HOMA-IR Homeostatic Model Assessment of Insulin Resistance hsCRP High Sensitivity C Reactive Protein LASSO Least Absolute Shrinkage and Selection Operator MAP Mean Arterial Pressure MED Mediterranean MRI Magnetic Resonance Imaging PA Physical Activity SAT Subcutaneous Adipose Tissue SBP Systolic Blood Pressure TG Triglycerides VAT Visceral Adipose Tissue WC Waist Circumference. Declarations Ethics approval and consent to participate The Soroka University Medical Centre Medical Ethics Board and the Institutional Review Board approved the study protocols for the CENTRAL and DIRECT PLUS trials. All participants provided written informed consent and received no financial compensation. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by grants from the German Research Foundation (DFG), German Research Foundation - project number 209933838 - SFB 1052; B11 to I. Shai (SFB-1052/B11) and to M. Blüher; Israel Ministry of Health grant 87472511 (to I Shai); Israel Ministry of Science and Technology grant 3-13604 (to I Shai); and the California Walnuts Commission (to I Shai). None of the funding providers were involved in any stage of the design, conduct, or analysis of the study, and they had no access to the study results before publication. Author Contribution HK and HZ had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: IS; Conduct of the study: HZ, AYM, GT, AK, ER, IS; Collection, management, analysis, and interpretation of the data: All authors; Review and approval of the manuscript: All authors; Statistical analysis: HK and HZ; Supervision: IS. All authors read and approved the final manuscript. Acknowledgement We thank the CENTRAL and DIRECT-PLUS participants for their valuable contributions. We thank the California Walnut Commission, Wissotzky Tea Company, and Hinoman, Ltd., for kindly supplying food items for these studies. We thank Dr. Dov Brikner, Efrat Pupkin, Eyal Goshen, Avi Ben Shabat, Benjamin Sarusi, and Evyatar Cohen from the Nuclear Research Center Negev and Prof. Assaf Rudich and Liz Shabtai from Ben-Gurion University of the Negev for their valuable contributions to these studies. Data Availability The majority of results corresponding to the current studies are included in the article or uploaded as supplementary material. No further data are available. References Golan R, Shelef I, Rudich A, Gepner Y, Shemesh E, Chassidim Y, Harman-Boehm I, Henkin Y, Schwarzfuchs D, Ben Avraham S, et al. Abdominal superficial subcutaneous fat: a putative distinct protective fat subdepot in type 2 diabetes. Diabetes Care. 2012;35(3):640–7. Zelicha H, Kloting N, Kaplan A, Yaskolka Meir A, Rinott E, Tsaban G, Chassidim Y, Bluher M, Ceglarek U, Isermann B, et al. The effect of high-polyphenol Mediterranean diet on visceral adiposity: the DIRECT PLUS randomized controlled trial. BMC Med. 2022;20(1):327. Wu SE, Chen WL. Not the enemy: potential protective benefits of superficial subcutaneous adipose tissue. Pol Arch Intern Med. 2022;132:7–8. Neeland IJ, Ross R, Despres JP, Matsuzawa Y, Yamashita S, Shai I, Seidell J, Magni P, Santos RD, Arsenault B, et al. 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Ross R, Neeland IJ, Yamashita S, Shai I, Seidell J, Magni P, Santos RD, Arsenault B, Cuevas A, Hu FB, et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol. 2020;16(3):177–89. Gepner Y, Shelef I, Schwarzfuchs D, Zelicha H, Tene L, Yaskolka Meir A, Tsaban G, Cohen N, Bril N, Rein M, et al. Effect of Distinct Lifestyle Interventions on Mobilization of Fat Storage Pools: CENTRAL Magnetic Resonance Imaging Randomized Controlled Trial. Circulation. 2018;137(11):1143–57. Yaskolka Meir A, Rinott E, Tsaban G, Zelicha H, Kaplan A, Rosen P, Shelef I, Youngster I, Shalev A, Bluher M, et al. Effect of green-Mediterranean diet on intrahepatic fat: the DIRECT PLUS randomised controlled trial. Gut. 2021;70(11):2085–95. Expert Panel on Detection E. 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R package version. 2015;04:3:p156. Ginestet C. ggplot2: elegant graphics for data analysis. JOURNAL-ROYAL Stat Soc Ser A. 2011;174:245–245. Wei T, Simko V. R package corrplot: Visualization of a Correlation Matrix (Version 0.84). In.: Vienna; 2017. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. Long J. Interactions: comprehensive, user-friendly toolkit for probing interactions. 2019. Scientific Reports 2022, 12:12184. Thiele C, Hirschfeld G. cutpointr: Improved estimation and validation of optimal cutpoints in R. arXiv preprint arXiv:200209209 2020. Lüdecke D, Ben-Shachar MS, Patil I, Waggoner P, Makowski D. performance: An R package for assessment, comparison and testing of statistical models. J Open Source Softw 2021, 6(60). Merkle E, You D, Schneider L, Bae S. nonnest2: tests of non-nested models. R package version 05–5 2020. Kim SH, Chung JH, Song SW, Jung WS, Lee YA, Kim HN. Relationship between deep subcutaneous abdominal adipose tissue and metabolic syndrome: a case control study. Diabetol Metab Syndr. 2016;8:10. Ibrahim MM. Subcutaneous and visceral adipose tissue: structural and functional differences. Obes Rev. 2010;11(1):11–8. Smith SR, Zachwieja JJ. Visceral adipose tissue: a critical review of intervention strategies. Int J Obes Relat Metab Disord. 1999;23(4):329–35. Lee MJ, Wu Y, Fried SK. Adipose tissue heterogeneity: implication of depot differences in adipose tissue for obesity complications. Mol Aspects Med. 2013;34(1):1–11. van den Munckhof ICL, Bahrar H, Schraa K, Brand T, Ter Horst R, van der Graaf M, Dekker HM, Stienstra R, de Graaf J, Joosten LAB et al. Sex-specific association of visceral and subcutaneous adipose tissue volumes with systemic inflammation and innate immune cells in people living with obesity. Int J Obes (Lond) 2023. Van Harmelen V, Reynisdottir S, Eriksson P, Thorne A, Hoffstedt J, Lonnqvist F, Arner P. Leptin secretion from subcutaneous and visceral adipose tissue in women. Diabetes. 1998;47(6):913–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2025 Read the published version in BMC Medicine → Version 1 posted Editorial decision: Revision requested 27 Aug, 2024 Reviews received at journal 23 Aug, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers agreed at journal 03 Jun, 2024 Reviews received at journal 01 Jun, 2024 Reviewers agreed at journal 23 May, 2024 Reviewers invited by journal 08 May, 2024 Editor assigned by journal 26 Apr, 2024 Submission checks completed at journal 26 Apr, 2024 First submitted to journal 25 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4323673","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":297676490,"identity":"5b6df34b-a36b-41bd-8f22-65e20bd34dc0","order_by":0,"name":"Hadar Klein","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Hadar","middleName":"","lastName":"Klein","suffix":""},{"id":297676494,"identity":"b8372c58-aa05-44af-a60f-aa4253bbd5bd","order_by":1,"name":"Hila Zelicha","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Hila","middleName":"","lastName":"Zelicha","suffix":""},{"id":297676498,"identity":"ff80d47e-36e4-42ab-a966-4da3af7370dd","order_by":2,"name":"Anat Yaskolka Meir","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Anat","middleName":"Yaskolka","lastName":"Meir","suffix":""},{"id":297676502,"identity":"656bca44-690e-4c0a-9bae-9047d8540ae6","order_by":3,"name":"Ehud Rinott","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Ehud","middleName":"","lastName":"Rinott","suffix":""},{"id":297676506,"identity":"0ad49fe3-d74d-4235-bd40-8ee024e9d47d","order_by":4,"name":"Gal Tsaban","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Gal","middleName":"","lastName":"Tsaban","suffix":""},{"id":297676511,"identity":"c7ab8710-e75a-4263-8eb2-cf79983c16cb","order_by":5,"name":"Alon Kaplan","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Alon","middleName":"","lastName":"Kaplan","suffix":""},{"id":297676515,"identity":"e4c6b526-1fdb-4757-95be-0efab0fa25f5","order_by":6,"name":"Yoash Chassidim","email":"","orcid":"","institution":"Sapir Academic College","correspondingAuthor":false,"prefix":"","firstName":"Yoash","middleName":"","lastName":"Chassidim","suffix":""},{"id":297676518,"identity":"28077e72-b3d8-4989-8d08-b5d8118d5011","order_by":7,"name":"Yftach Gepner","email":"","orcid":"","institution":"Tel-Aviv University","correspondingAuthor":false,"prefix":"","firstName":"Yftach","middleName":"","lastName":"Gepner","suffix":""},{"id":297676522,"identity":"fc7aaeae-a40e-4b01-8dc2-3461974b97f8","order_by":8,"name":"Matthias Blüher","email":"","orcid":"","institution":"Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Zentrum München at the University of Leipzig and University Hospital Leipzig","correspondingAuthor":false,"prefix":"","firstName":"Matthias","middleName":"","lastName":"Blüher","suffix":""},{"id":297676526,"identity":"af39fe1e-e6f4-4dc1-b1ff-b4719d386975","order_by":9,"name":"Uta Ceglarek","email":"","orcid":"","institution":"University of Leipzig Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Uta","middleName":"","lastName":"Ceglarek","suffix":""},{"id":297676529,"identity":"c4854d7c-5d56-4dae-8825-254947f057fe","order_by":10,"name":"Berend Isermann","email":"","orcid":"","institution":"University of Leipzig Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Berend","middleName":"","lastName":"Isermann","suffix":""},{"id":297676532,"identity":"1c150135-61f8-459e-8ec4-e8ffe25858a2","order_by":11,"name":"Michael Stumvoll","email":"","orcid":"","institution":"University of Leipzig","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Stumvoll","suffix":""},{"id":297676535,"identity":"644d736d-b08f-49ba-bfe0-c7459348f3cb","order_by":12,"name":"Ilan Shelef","email":"","orcid":"","institution":"Soroka University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ilan","middleName":"","lastName":"Shelef","suffix":""},{"id":297676537,"identity":"ff5b0806-797a-4ca7-9a80-82f8ec5e3fcd","order_by":13,"name":"Lu Qi","email":"","orcid":"","institution":"Tulane University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Qi","suffix":""},{"id":297676538,"identity":"e0ae93b1-b511-45c3-ac22-f863112a71dd","order_by":14,"name":"Jun Li","email":"","orcid":"","institution":"Harvard T.H. 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Chan School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Meir","middleName":"J","lastName":"Stampfer","suffix":""},{"id":297676541,"identity":"ee53c89b-23fb-4eff-9e60-5b3f47f3535f","order_by":17,"name":"Iris Shai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYBACNgbGBmaGigNQroEFsVrOwLVIEGcTM2MbTAsDEVr4pA83fy6cd8duwwHmhx8YCojQwsaX2CY9c9uz5A0H2IwliHIYGw9jGzPvtsPJBgcYzIjzC1BL82feOSAt7N+I1tIgzdtw2M7gAA/xtrRJ8xx7liB5mKdYIoEYLfI97I8/89Tcsec73r7xw4c/NoS1wEAiMEIZGBKI18DAYE+K4lEwCkbBKBhhAADQBjDp/a05ugAAAABJRU5ErkJggg==","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":true,"prefix":"","firstName":"Iris","middleName":"","lastName":"Shai","suffix":""}],"badges":[],"createdAt":"2024-04-25 11:04:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4323673/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4323673/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12916-025-03891-9","type":"published","date":"2025-02-04T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55766982,"identity":"08a06fee-ee27-4a76-91ed-4d5b0c24aa72","added_by":"auto","created_at":"2024-05-02 20:13:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":254059,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAbdominal adipose tissue area and proportion across various characteristics (baseline and 18-month changes). \u003c/strong\u003eHeatmaps of Kendall’s tau partial correlations. \u003cstrong\u003eA. \u003c/strong\u003eBaseline characteristics across abdominal adipose tissue sex-specific ranks, adjusted for age, weight and intervention trial (CENTRAL, DIRECT-PLUS). SSAT area, n=553; DSAT area, n=560; VAT area, n=564; VAT proportion, n=533.\u003cstrong\u003e B.\u003c/strong\u003e 18-months change characteristics across abdominal adipose tissue sex-specific ranks change, adjusted for age, weight loss and intervention trial. Δ SAT area n=430, Δ DSAT area n=438, Δ VAT area n=440, Δ VAT proportion n=429.\u003cstrong\u003e \u003c/strong\u003eCorrelations are color-coded with blue=positive correlation and red=negative correlation. Benjamini‒Hochberg correction was used for multiple comparisons (FDR 5%). Asterisks (***, **, *) correspond to FDRs of 0.001, 0.01, and 0.05, respectively. Abbreviations: SSAT, superficial subcutaneous adipose tissue; DSAT, deep subcutaneous adipose tissue; VAT, visceral adipose tissue; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; HOMA-IR, homeostatic model assessment of insulin resistance; hsCRP, high-sensitivity C reactive protein; HDLc, high-density lipoprotein cholesterol; GGT, gamma-glutamyl transferase; ALKP, alkaline phosphatase; AST, aspartate transaminase; ALT, alanine transaminase.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4323673/v1/b44e6ba64e4142216bb6b2c3.png"},{"id":55766981,"identity":"539ed716-a281-4563-937b-88528a0c470e","added_by":"auto","created_at":"2024-05-02 20:13:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisceral adipose tissue area and proportion predictions of obesity complications; receiver operating characteristic curve models. \u003c/strong\u003en=540-551 participants. The ROC curves compare the performance of different logistic regression models in predicting the odds of obesity complications at baseline: metabolic syndrome (A), hypertension (B), diabetes (C), and hypertriglyceridemia (D). Two sets of models are evaluated for each morbidity state prediction: Model 1, represented by the blue curve, uses VAT area as a predictor variable, and Model 2, represented by the red curve, uses VAT proportion as a predictor variable. Both models are adjusted for trial type (CENTRAL and DIRECT PLUS), sex, age, and baseline weight.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4323673/v1/9b954b76781c2ff85775f4f9.png"},{"id":55766983,"identity":"21b94df4-5601-4fa1-9a3e-ece7b347a7bd","added_by":"auto","created_at":"2024-05-02 20:13:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO linear regression models of baseline and change VAT area and proportion. \u003c/strong\u003eThe x-axis displays the variables selected by the LASSO model, and the y-axis represents the estimated β-unstandardized coefficients.\u003cstrong\u003e \u003c/strong\u003eThe magnitude and direction by which each variable affects the baseline VAT area (A) and proportion (B) and the 18-month relative changes in VAT area (C) and proportion (D) are represented by the color (blue for positive associations and red for negative associations) and length of the bars. Baseline VAT area (A) model was trained on a set of 227participants, tested on 55participants and validated on 259 participants. Baseline VAT proportion (B) model was trained on a set of n=218 participants, tested on n=53 participants and validated on n=143 participants. The VAT area change (C) model was trained on a set of n=180 participants, tested on n=46 participants and validated on n=207 participants. The VAT proportion change (D) model was trained on a set of 172participants, tested on 43participants and validated on 212participants. Abbreviations: LASSO, Least Absolute Shrinkage and Selection Operator; VAT, Visceral Adipose Tissue; WC, Waist Circumference; MAP, Mean Arterial Pressure; TG, Triglycerides; HDLc, High-Density Lipoprotein cholesterol; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; GGT, Gamma-Glutamyl Transferase; hsCRP, High Sensitivity C Reactive Protein; ALKP, Alkaline Phosphatase; hsCRP, High Sensitivity C Reactive Protein; ALT, Alanine Transaminase; AST, Aspartate Transaminase.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4323673/v1/c532dd42ce1530b260d8e133.png"},{"id":75929914,"identity":"9bd90b4c-9da2-4969-a420-a78c75fc1fcb","added_by":"auto","created_at":"2025-02-10 15:59:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1586903,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4323673/v1/b606e436-edba-4be2-9553-981d934f70ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Visceral adipose tissue area and proportion provide distinct reflections of cardiometabolic outcomes in weight loss; Pooled analysis of MRI-assessed CENTRAL and DIRECT PLUS dietary randomized controlled trials","fulltext":[{"header":"Background","content":"\u003cp\u003eAlthough visceral adipose tissue (VAT) is widely recognized as a pathogenic fat depot, superficial subcutaneous adipose tissue (SAT) has been linked to improved indicators of cardiovascular health [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This contrast contributed to the controversy surrounding the distinction between the visceral adipose tissue (VAT) area and its proportion of the total abdominal adipose tissue (%). While some reports explore metabolic complications of obesity with measures of VAT absolute area or volume [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], others refer to VAT proportion or other normalized indices of VAT as ratios that may better reflect cardiometabolic status and clinical outcomes [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Notably, some reports found VAT absolute quantity to be superior in predicting specific features independently (e.g., fasting insulin) and inferior in predicting others (e.g., fasting glucose) compared to the VAT ratio to subcutaneous adipose tissue (SAT) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis controversy is further increased by the impracticality of assessing abdominal fat depots in routine clinical settings with costly techniques such as dual-energy X-ray absorptiometry (DEXA), computed tomography (CT), and magnetic resonance imaging (MRI) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To address this quantification challenge, several surrogate indices have been developed to serve as alternative estimators for assessing VAT quantity. Some of these indices are strictly anthropometric, such as the body mass index (BMI) and waist circumference (WC) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and others incorporate additional characteristics such as demographic data (e.g., age, sex), plasma biomarkers (e.g., glucose, cholesterol, and amino acids) and bioelectrical impedance analysis (BIA) parameters [\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, compared to the numerous surrogate indices of VAT area, estimations of VAT proportion and changes in VAT area and proportion are lacking [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we utilized pooled data from two 18-month lifestyle, randomized weight loss clinical trials, CENTRAL and DIRECT-PLUS, with 572 participants and MRI-assessed fat depots. We hypothesized that either the absolute area or proportion of VAT would better reflect obesity complications and developed novel predictors to assess the VAT state and changes following lifestyle interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis is a pooled analysis of two 18-month lifestyle intervention clinical trials, the CENTRAL (Clinical-trials-identifier: NCT01530724) and the DIRECT-PLUS (Clinical-trials-identifier: NCT03020186) trials. The CENTRAL (n\u0026thinsp;=\u0026thinsp;278, 2012\u0026ndash;2014) and DIRECT-PLUS (n\u0026thinsp;=\u0026thinsp;294, 2017\u0026ndash;2019) clinical trials were conducted in the same research center workplace in Dimona, Israel. The retention rates at 18 months were 86.3% and 89.8%, respectively, as previously described in detail [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Accordingly, this pooled analysis included data on 572 participants at baseline and 528 participants who completed the trials. Of 572 participants from both trials, almost all MRI scans at baseline were eligible for quantification of the VAT area (n\u0026thinsp;=\u0026thinsp;564; 99%) and VAT proportion (n\u0026thinsp;=\u0026thinsp;553; 97%). Losses were due to technical reasons.\u003c/p\u003e \u003cp\u003eBoth trials had similar inclusion criteria: WC\u0026thinsp;\u0026gt;\u0026thinsp;102 cm for men and \u0026gt;\u0026thinsp;88 cm for women or dyslipidemia (serum triglycerides (TG)\u0026thinsp;\u0026gt;\u0026thinsp;150 mg/dL and high-density-lipoprotein cholesterol (HDLc)\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL for men and \u0026lt;\u0026thinsp;50 mg/dL for women). DIRECT-PLUS was limited to those over age 30. Both trials had nearly identical exclusion criteria, as fully described in S1. Both studies were approved and monitored by the Medical Ethics Board and Helsinki Committee of the Soroka University Medical Center. All participants provided written informed consent and received no financial compensation or gifts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRandomization and Interventions\u003c/h2\u003e \u003cp\u003eAll diets aimed for moderate, long-term weight loss with restricted consumption of trans fats and refined carbohydrates and increased intake of vegetables. Lunch, commonly the primary meal in this population, was tailored to meet the specific dietary requirements of each group and was provided through the workplace cafeteria. All lifestyle education programs were provided to all groups by physicians, clinical dietitians, and fitness instructors at the same intensity. Randomization was performed with an equal allocation ratio across all treatment groups, stratified by sex and work site. The participants were aware of their assigned intervention (open-label). Study investigators assessing outcomes were blinded to the group assignments.\u003c/p\u003e \u003cp\u003eIn the CENTRAL trial, the diet groups were low-fat or Mediterranean (MED)/low-carbohydrate. These groups were further divided after 6 months into groups with added physical activity (PA) or no added PA for the last 12 months of intervention. In the DIRECT-PLUS trial, the diet groups included healthy dietary guidelines, MED, and green-MED, all of which were combined with PA. The characteristics of each lifestyle intervention group are fully detailed in previous publications [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome measures\u003c/h2\u003e \u003cp\u003eAbdominal fat deposits were evaluated at baseline and 18 months later using 3-T MRI (Philips Ingenia 3.0T). Abdominal fat depots were quantified using MATLAB-based semiautomatic software [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The scans included 2\u0026ndash;3 axial slices\u0026mdash;L5-S1, L4-L5, and L2-L3\u0026mdash;in the CENTRAL trial and L5-S1 and L4-L5 in the DIRECT-PLUS trial. For this pooled analysis, only the common L5-S1 and L4-L5 axial slices from both trials were used to calculate the mean VAT area and proportion for uniformity. Quantification of the fat mass regions included both the absolute area of each fat type and its proportion (percentage) of the total area of both fat types [VAT/(VAT\u0026thinsp;+\u0026thinsp;SAT)*100]. The entire protocol is reported in S2. Anthropometric parameters and blood biomarkers were measured at baseline and after 18 months of intervention (S3).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStatistical analysis\u003c/span\u003eA common coprimary outcome of both trials was VAT change following nutritional interventions. This report presents continuous variables as the means (standard deviations) or medians (interquartile ranges), depending on the variables\u0026rsquo; normal distribution. Nominal variables are expressed as numbers and percentages. Histograms of each variable were inspected to determine whether the variables were normally distributed. Nonnormal distributions were natural logarithm (ln) transformed. Changes in VAT, anthropometrics, and biomarkers were computed as changes relative to baseline [(time 18 \u0026ndash; time 0)/time 0 \u0026times; 100]. Differences between two groups of baseline characteristics were tested using the chi-square test for nominal variables and two sample t-test or Wilcoxon rank sum test for continuous variables. Sex-specific deciles of VAT parameters were calculated per trial. The Kendall tau trend test was used to examine demographic and anthropometric measurements and blood biomarkers across adipose tissue deciles and groups of similar and opposite VAT areas and proportion medians via partial correlations. The ANCOVA test was also used to test demographic and anthropometric measurements and blood biomarkers across groups of similar and opposite VAT areas and proportion medians. The correlation between VAT area and proportion was tested using Spearman\u0026rsquo;s correlation analysis. The metabolic syndrome criteria were assessed based on the National Cholesterol Education Program Adult Treatment Panel III criteria [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The Youden index was used to determine the optimal VAT cutoffs for metabolic morbidities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We used generalized linear regression models (GLMs) to classify participants with metabolic abnormalities and evaluated their performance using the C-statistic, also known as the concordance statistic or the area under the receiver operating characteristic curve (AUC-ROC) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We used the robust likelihood test to test whether a nonnested model fit better than a reference model. Least absolute shrinkage and selection operator (LASSO) regression was used to identify and evaluate predictors of VAT parameters at baseline and their changes following nutritional intervention. Models were constructed for both genders and for men only. The models were trained on 80% of the DIRECT PLUS data, tested on the remaining 20%, and validated on the CENTRAL data. Random splits of observations into training and testing sets were stratified by sex and VAT area or proportion quartiles, depending on the predicted VAT measure. Predictor variables included anthropometrics, demographics, and blood biomarkers available from both the CENTRAL and DIRECT PLUS trials (including glycemic and lipidic profiles, liver enzymes, inflammatory markers, and adipokines). The predictors were centered and scaled prior to model fitting. The LASSO penalty tuning parameter λ with the minimal average root mean square error (RMSE) across 10-fold cross-validation and 10 repeats was chosen to compute the final model on the complete training data. All the statistical analyses were computed using R version 4.2.0, with the use of the following packages: \u0026ldquo;gtsummary\u0026rdquo;, \u0026ldquo;cutpointr\u0026rdquo;, \u0026ldquo;pROC\u0026rdquo;, \u0026ldquo;performance\u0026rdquo;, \u0026ldquo;nonnest2\u0026rdquo;, \u0026ldquo;caret\u0026rdquo;, \u0026ldquo;dplyr\u0026rdquo;, \u0026ldquo;ggplot2\u0026rdquo;, \u0026ldquo;corrplot\u0026rdquo; and \u0026ldquo;glmnet\u0026rdquo;, \u0026ldquo;interactions\u0026rdquo; [\u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) had a mean age of 49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1 years, with an average BMI of 30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 kg/m\u0026sup2; and WC of 108.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7 cm (109.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1 cm for males and 101.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0 cm for females). Most participants were men (88.5%), 10.9% of participants had diabetes, and 59.4% had metabolic syndrome. Following the 18-month lifestyle intervention, participants lost \u0026minus;\u0026thinsp;2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 kg body weight and 4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 cm of their WC. The changes in abdominal adipose depots were \u0026minus;\u0026thinsp;17.5 cm\u003csup\u003e2\u003c/sup\u003e (-35.4 \u0026ndash; -4.0) for superficial SAT (-16.7% (-26.4 \u0026ndash; -4.0)), -41.3 cm\u003csup\u003e2\u003c/sup\u003e (-76.7 \u0026ndash; -9.9) for deep SAT (-19.9% (-31.2 \u0026ndash; -4.6)) and \u0026minus;\u0026thinsp;27.9 cm\u003csup\u003e2\u003c/sup\u003e (-52.6 \u0026ndash; -6.4) for VAT (-22.5% (-35.9 \u0026ndash; -5.7)). The change in VAT proportion absolute units was \u0026minus;\u0026thinsp;1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4% (-3.8% (-11.5\u0026ndash;3.6)).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSex-specific baseline characteristics of the CENTRAL and DIRECT PLUS clinical trials participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;572\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale, N\u0026thinsp;=\u0026thinsp;506\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale, N\u0026thinsp;=\u0026thinsp;66\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eq-value\u003csup\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSAT area, cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.7 (91.5\u0026ndash;162.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.6 (87.9\u0026ndash;151.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197.0 (159.0\u0026ndash;256.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSAT area, cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228.2 (179.9\u0026ndash;290.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228.5 (178.5\u0026ndash;293.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227.3 (187.4\u0026ndash;268.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT area, cm\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.8 (103.2\u0026ndash;174.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139.3 (108.2\u0026ndash;178.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.4 (77.8\u0026ndash;138.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAT proportion, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e BMI, body mass index; SSAT, superficial subcutaneous adipose tissue; DSAT, deep subcutaneous adipose tissue; VAT, visceral adipose tissue.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e Values are presented as either the median (p25, p75) or the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables, depending on their distribution, or as number (%) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e Two Sample t-test; Pearson's Chi-squared test; Wilcoxon rank sum test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sup\u003e False discovery rate correction for multiple testing\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBaseline VAT area and proportion sex-specific deciles showed parallel direct and significant correlation trends with age, blood pressure, and most blood biomarkers (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Specifically, fasting glucose and insulin, homeostatic model assessment of insulin resistance (HOMA-IR), HbA1c, TG, TG/HDLc, and gamma-glutamyl transferase (GGT) were found to increase with higher VAT area and proportion deciles. Conversely, VAT area and proportion exhibited dissimilar associations with anthropometric measurements and specific blood biomarkers. VAT area was positively associated with WC (tau\u0026thinsp;=\u0026thinsp;0.33, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), chemerin (tau\u0026thinsp;=\u0026thinsp;0.18, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), high-sensitivity C reactive protein (hsCRP) (tau\u0026thinsp;=\u0026thinsp;0.0.16, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alkaline phosphatase (ALKP) and alanine transaminase (ALT) (tau\u0026thinsp;=\u0026thinsp;0.09, FDR\u0026thinsp;=\u0026thinsp;0.04 for both). However, the VAT proportion was not associated with these markers. Additionally, while VAT area demonstrated an increasing trend with body weight (tau\u0026thinsp;=\u0026thinsp;0.21, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and leptin (tau\u0026thinsp;=\u0026thinsp;0.19, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), VAT proportion presented a decreasing trend with these measurements (tau=-0.13 and \u0026minus;\u0026thinsp;0.12, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThese trends remained following adjustment for age, weight, and intervention trial (CENTRAL and DIRECT-PLUS). In a partial correlation analysis adjusted for these covariates, sex-specific deciles of superficial SAT area, deep SAT area, VAT area, and VAT proportion exhibited distinct associations with anthropometrics and blood biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). All fat depot areas were directly and significantly associated with WC, leptin, chemerin, and hsCRP (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In contrast to VAT area, which was negatively associated with HDLc (tau=-0.08, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and positively associated with TG and TG/HDLc (tau\u0026thinsp;=\u0026thinsp;0.11, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), superficial and deep SAT exhibited positive correlations with HDLc (tau\u0026thinsp;=\u0026thinsp;0.08 and 0.09, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and inverse correlations with TG (tau=-0.07 and \u0026minus;\u0026thinsp;0.06, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and TG/HDLc (tau=-0.09 and \u0026minus;\u0026thinsp;0.08, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The VAT proportion association with TG and HDLc (tau\u0026thinsp;=\u0026thinsp;0.15 and \u0026minus;\u0026thinsp;0.13, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001) appeared stronger than that of the VAT area (tau\u0026thinsp;=\u0026thinsp;0.11 and \u0026minus;\u0026thinsp;0.08, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Deep SAT and VAT areas were directly correlated with insulin and HOMA-IR (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while superficial SAT was not. The VAT area was positively correlated with systolic and diastolic blood pressure, fasting glucose, HbA1c, ferritin, GGT, and ALKP (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no significant associations were found between superficial and deep SAT and these markers. Superficial SAT was inversely associated with ALT levels (tau=-0.07, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas VAT was positively associated with ALT levels (tau\u0026thinsp;=\u0026thinsp;0.09, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The VAT area and proportion showed similar trends with most biomarkers, except for leptin and hsCRP. While VAT area had a positive correlation with both hsCRP and leptin (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), VAT proportion did not correlate with hsCRP and was negatively correlated with leptin (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, in contrast to the direct association of VAT area with WC (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001), VAT proportion was not associated with WC (FDR\u0026thinsp;=\u0026thinsp;0.99).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline VAT area and proportion in relation to obesity complications\u003c/h2\u003e \u003cp\u003eSex-specific cutoff values of VAT area and proportion were calculated for metabolic syndrome and diabetes status (Table S2, Figure S2). The cutoff values for metabolic syndrome at baseline were 120.72 cm\u003csup\u003e2\u003c/sup\u003e VAT area (AUC\u0026thinsp;=\u0026thinsp;0.69) and 27.84% VAT proportion (AUC\u0026thinsp;=\u0026thinsp;0.62) for men and 114.8 cm\u003csup\u003e2\u003c/sup\u003e VAT area (AUC\u0026thinsp;=\u0026thinsp;0.82) and 24.39% VAT proportion for women (AUC\u0026thinsp;=\u0026thinsp;0.74). The cutoff values for diabetes status at baseline were 114.10 cm\u003csup\u003e2\u003c/sup\u003e VAT area (AUC\u0026thinsp;=\u0026thinsp;0.62) and 35.41% VAT proportion (AUC\u0026thinsp;=\u0026thinsp;0.60) for men and 90 cm\u003csup\u003e2\u003c/sup\u003e VAT area (AUC\u0026thinsp;=\u0026thinsp;0.70) and 25.74% VAT proportion for women (AUC\u0026thinsp;=\u0026thinsp;0.81). We further compared the VAT area and proportion prediction performances in classifying states of metabolic dysfunction at baseline, in adjustment for trial type, sex, age, and baseline weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). VAT area and proportion seemed to similarly predict states of metabolic syndrome (AUC\u0026thinsp;=\u0026thinsp;0.75 for both, p\u0026thinsp;=\u0026thinsp;0.49), hypertension (AUC\u0026thinsp;=\u0026thinsp;0.76 for both, p\u0026thinsp;=\u0026thinsp;0.25), and diabetes (AUC\u0026thinsp;=\u0026thinsp;0.71, p\u0026thinsp;=\u0026thinsp;0.48). However, VAT proportion performed better at classifying participants with hypertriglyceridemia (AUC\u0026thinsp;=\u0026thinsp;0.66) compared to VAT area (AUC\u0026thinsp;=\u0026thinsp;0.62) (p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVAT area and proportion were correlated with each other (r\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Nevertheless, distinct phenotypes of visceral adiposity could be classified for participants whose VAT area was above the median (men\u0026thinsp;=\u0026thinsp;139 cm\u003csup\u003e2\u003c/sup\u003e, female\u0026thinsp;=\u0026thinsp;105 cm\u003csup\u003e2\u003c/sup\u003e) and whose VAT proportion was below the median (men\u0026thinsp;=\u0026thinsp;29%, female\u0026thinsp;=\u0026thinsp;19%), and vice versa. Participants with higher VAT area and decreased VAT proportion had, by definition, higher SAT and increased weight. In multivariable analyses of groups with similar and opposite sex-specific VAT area and proportion medians, participants (13%) characterized by a top-median VAT area and low-median VAT proportion exhibited increased diastolic blood pressure, HbA1c, fasting insulin, HOMA-IR, ALT, AST, leptin, chemerin, and hsCRP, after controlling for weight and deep SAT, compared to the other groups (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). Alternatively, participants (14.1%) with low-median VAT areas and top-median VAT proportions presented similar adverse lipid profiles to those with higher VAT areas (Supplementary Material Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEighteen-month changes in abdominal adipose tissue depots\u003c/h2\u003e \u003cp\u003eDespite the opposite associations at baseline between SAT and VAT regarding lipids profile, the loss of each fat compartment was associated with an improved lipids profile, even after adjustment for age, overall weight loss, and intervention trial (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Similarly, all fat depot area reductions were associated with reduced WC, fasting insulin, HOMA-IR, and leptin, and none were associated with changes in blood pressure, hsCRP, ALKP, or AST. Both SAT subcompartments\u0026rsquo; losses were related to reductions in glucose, but VAT loss was not. The opposite was true for the reductions in HbA1c, ferritin, GGT, and ALT, which were directly associated only with the loss of VAT area. Deep SAT and VAT losses were associated with chemerin reduction, while superficial SAT loss was not. Changes in VAT area and proportion were both directly and significantly correlated with reduced WC, HbA1c, dyslipidemia, chemerin, ferritin, GGT, and ALT (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, some contrasts were noted; while VAT area loss was correlated with reduced insulin (tau\u0026thinsp;=\u0026thinsp;0.11), HOMA-IR (tau\u0026thinsp;=\u0026thinsp;0.10), and leptin (tau\u0026thinsp;=\u0026thinsp;0.12), FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for all, VAT proportion loss was not (tau\u0026thinsp;=\u0026thinsp;0.04\u0026ndash;0.06, FDR\u0026thinsp;=\u0026thinsp;0.10\u0026ndash;0.30). Alternatively, VAT proportion loss was correlated with reduced AST (tau\u0026thinsp;=\u0026thinsp;0.09, FDR\u0026thinsp;=\u0026thinsp;0.01), while VAT area loss was not (tau\u0026thinsp;=\u0026thinsp;0.06, FDR\u0026thinsp;=\u0026thinsp;0.12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePrediction models of VAT baseline and 18-month change\u003c/h2\u003e \u003cp\u003ePrediction models were developed for VAT area and proportion baseline and changes, utilizing either anthropometric measurements and demographic data, blood biomarkers, or a combination of both. Each model\u0026rsquo;s selected variables and performance metrics are presented in Supplementary Material Tables S4-S7.\u003c/p\u003e \u003cp\u003eThe best-performing prediction model for baseline VAT area included a combination of anthropometrics, demographics, and blood biomarkers (Supplementary Material Table S4). It was trained on data from 227 DIRECT-PLUS participants, tested on 55 DIRECT-PLUS participants, and validated on 259 CENTRAL participants. Participants assigned to the training data had similar characteristics to those assigned to the testing data (Supplementary Material Table S8). The cross-validation models for choosing the optimal hyperparameters for the final model had RMSE of 0.27 and R\u003csup\u003e2\u003c/sup\u003e of 0.44. The final model was applied to the testing and validation datasets, with RMSEs of 0.26 and 0.40 and R\u003csup\u003e2\u003c/sup\u003e of 0.53 and 0.50, respectively. This model selected both anthropometric, demographic, and blood biomarkers mesurments, including WC, MAP, age, TG/HDLc, HbA1c, HOMA-IR, glucose, GGT, ALKP, and chemerin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, the best-performing model for the estimation of baseline VAT proportion also included a combination of anthropometric, demographic and blood biomarkers predictors (Supplementary Material Table S5). The model was trained on n\u0026thinsp;=\u0026thinsp;218 participants (RMSE\u0026thinsp;=\u0026thinsp;6.87, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.37), tested on n\u0026thinsp;=\u0026thinsp;53 participants (RMSE\u0026thinsp;=\u0026thinsp;6.55, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.51), and validated on n\u0026thinsp;=\u0026thinsp;142 participants (RMSE\u0026thinsp;=\u0026thinsp;6.7, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.39). The selected variables were similar to those predictive of VAT area, with additional selected variables: sex, ALT, fetuin-A, ferritin, and leptin (the latter negatively contributing to VAT proportion). Furthermore, female sex was a predictor of a lower VAT proportion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In contrast to the VAT area prediction, WC was not selected for the prediction of VAT proportion. However, higher weight was selected as a predictor of a lower VAT proportion in a model developed with only anthropometrics and demographic variables.\u003c/p\u003e \u003cp\u003eAs for VAT area\u0026acute;s change, the best-performing predictor on the testing data included only anthropometric measurements (RMSE\u0026thinsp;=\u0026thinsp;15.33, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.59). In contrast, the best performance for the validation data incorporated variables of both anthropometrics and blood biomarkers (RMSE\u0026thinsp;=\u0026thinsp;52.1, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.52). The predictors were trained on 180 participants, tested on 46 participants, and validated on 207\u0026ndash;212 participants. Both predictors included changes in weight and WC, with the former including change in MAP and the latter including change in leptin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Supplementary Material Table S6).\u003c/p\u003e \u003cp\u003eLastly, the best-performing predictor of the change in VAT proportion was developed using only anthropometric and demographic data (weight, WC, MAP, age and sex). The model was trained on data from 172 participants (RMSE\u0026thinsp;=\u0026thinsp;11.32, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.15), tested on data from 43 participants (RMSE\u0026thinsp;=\u0026thinsp;11.80, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.24) and validated on data from 212 participants (RMSE\u0026thinsp;=\u0026thinsp;18.72, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Supplementary Material Table S7). Due to insufficient data, sex-specific prediction formulas could not be developed for females (11.5%). However, sex-specific male predictors are available in Supplementary Material Figure S3.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis pooled analysis of two 18-month nutritional randomized controlled trials (n\u0026thinsp;=\u0026thinsp;572) revealed notable differences in the parameters of abdominal VAT distribution. VAT area and VAT proportion are associated with similar metabolic indicators, with higher values corresponding to a worsened cardiometabolic state. However, VAT proportion was more strongly associated with lipid status, whereas VAT area was more strongly linked to glucose metabolism and inflammation biomarkers.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, although multislice volume imaging is considered the gold standard for measuring adipose tissue [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], we measured abdominal adipose tissue area rather than volume. However, we calculated these areas as the means of two images at the L4-L5 and L5-S1 intervertebral spaces. Additionally, we observed high inter- and intraclass correlations (r\u0026thinsp;\u0026gt;\u0026thinsp;0.96; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting their reproducibility. Second, due to the nature of the workplace environment where the trials were held, 88.5% of the participants were men. Hence, we identified predictors for both sexes, accounting for sex, and for men only, but not for women only. Third, total fat mass values were not available in our MRI measurements, restricting our ability to quantify the VAT proportion of total body fat. Hence, only the VAT proportion of total abdominal fat is discussed here. The strengths of the analysis include its large sample size and high retention rates within two relatively large and long clinical trials conducted in the same workplace for the same duration with similar inclusion and exclusion criteria. Furthermore, both trials measured VAT parameters using the same 3-T MRI analysis .\u003c/p\u003e \u003cp\u003eThe VAT area and proportion were closely correlated and similarly associated with various cardiometabolic biomarkers, including hypertension, impaired glycemic and lipidic profiles, liver dysfunction, and elevated chemerin. However, while both VAT parameters were positively correlated with TG and negatively correlated with HDLc, SAT had opposite associations with these biomarkers. Specifically, both superficial and deep SATs presented beneficial associations with lipids profile, with a direct association with HDLc and an inverse association with TG. These findings are in accordance with the well-established association of superficial SAT with improved indicators of cardiovascular health [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a result, the correlations of VAT proportion with TG and HDLc were stronger than those of VAT area. VAT area and proportion had near identical performance in independently predicting metabolic syndrome, hypertension, and diabetes status. However, the VAT proportion better predicted hypertriglyceridemia state than the absolute VAT amount (p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eMore discrepancies have been noted, with body weight and WC presenting different trends across VAT area and proportion. While VAT area was positively correlated with weight and WC, VAT proportion was inversely related to weight and had no significant association with WC. These findings are attributed to the stronger association of weight and WC with SAT rather than with VAT and are consistent with previous reports that found VAT area to be greater in patients with obesity than in patients without obesity, in contrast to VAT proportion, which was similar in these groups [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe heterogeneous phenotypes of visceral obesity were classified to further explore the associations of VAT area and proportion with adverse health indicators. Naturally, participants presenting both increased (above median) VAT and SAT areas (i.e., low VAT proportion) were characterized by higher SAT, WC and weight. As deep SAT was found to be independently associated with increased insulin resistance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], we performed multivariable analyses between the visceral adiposity phenotype groups, controlling for weight and deep SAT. Participants with a high VAT area and low VAT proportion (n\u0026thinsp;=\u0026thinsp;72) presented a worsened metabolic state compared to those with a low VAT area and high VAT proportion (n\u0026thinsp;=\u0026thinsp;78). Specifically, they had higher insulin resistance and increased HbA1c levels (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This finding is in accordance with previous reports that VAT area is superior to VAT/SAT for predicting fasting insulin [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, this group presented an extensive decline in metabolic health, involving elevated blood pressure, liver enzymes, adipokines, and inflammatory state (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings are consistent with the observed distinct associations of VAT area, but not its proportion, with chemerin, hsCRP, ALKP and ALT (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the diverse visceral adiposity phenotypes presented similar adverse lipid profiles, repeatedly revealing the strong association of increased VAT proportion with a poor lipidic state, even in the presence of a relatively low VAT area.\u003c/p\u003e \u003cp\u003eWe found some differences in the relations of VAT area and proportion with biomarkers at baseline compared to changes in those two measures with weight loss over time. In particular, reduced SAT was associated with an improved lipids profile, despite their beneficial association at baseline. In addition, despite WC having no association with VAT proportion at baseline, it was correlated with its change. This is probably due to VAT\u0026rsquo;s greater sensitivity to weight reduction than SAT\u0026rsquo;s [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVariables selected for LASSO prediction formulas reflected the distinct associations of VAT area and proportion with anthropometric, demographic, and blood biomarkers measurements at baseline and of their changes. Specifically, older age was highly predictive of both increased VAT area and proportion, in agreement with other reports [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, WC was predictive of VAT area, but not VAT proportion. Alternatively, male sex and lower levels of leptin were predictive of a higher VAT proportion but not of its area [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The latter is explained by leptin\u0026rsquo;s higher secretion rates in SAT than in VAT [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Although indicators of poor lipid and glycemic profiles, along with elevated levels of liver enzymes and several adipokines, were important predictors of increased baseline VAT area and proportion, both of their changes were mainly predicted by a combination of anthropometric measurements.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAlthough VAT area and proportion are highly correlated, each parameter holds distinct attributes of cardiometabolic state. While the VAT proportion is more strongly associated with a poor lipid state, the VAT area better reflects the inflammatory state and glycemic profile during weight loss. These findings indicate the complexity of VAT dynamics and emphasize the relevance of personalized approaches in targeting visceral adiposity for cardiometabolic health improvement.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALKP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlkaline Phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine Transaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate Transaminase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComputed Tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEXA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDual Energy X-ray absorptiometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGGT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGamma-Glutamyl Transferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealthy Dietary Guidelines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDLc\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHOMA-IR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHomeostatic Model Assessment of Insulin Resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehsCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Sensitivity C Reactive Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean Arterial Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMediterranean\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubcutaneous Adipose Tissue\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVisceral Adipose Tissue\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist Circumference.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The Soroka University Medical Centre Medical Ethics Board and the Institutional Review Board approved the study protocols for the CENTRAL and DIRECT PLUS trials. All participants provided written informed consent and received no financial compensation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the German Research Foundation (DFG), German Research Foundation - project number 209933838 - SFB 1052; B11 to I. Shai (SFB-1052/B11) and to M. Bl\u0026uuml;her; Israel Ministry of Health grant 87472511 (to I Shai); Israel Ministry of Science and Technology grant 3-13604 (to I Shai); and the California Walnuts Commission (to I Shai). None of the funding providers were involved in any stage of the design, conduct, or analysis of the study, and they had no access to the study results before publication.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHK and HZ had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: IS; Conduct of the study: HZ, AYM, GT, AK, ER, IS; Collection, management, analysis, and interpretation of the data: All authors; Review and approval of the manuscript: All authors; Statistical analysis: HK and HZ; Supervision: IS. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the CENTRAL and DIRECT-PLUS participants for their valuable contributions. We thank the California Walnut Commission, Wissotzky Tea Company, and Hinoman, Ltd., for kindly supplying food items for these studies. We thank Dr. Dov Brikner, Efrat Pupkin, Eyal Goshen, Avi Ben Shabat, Benjamin Sarusi, and Evyatar Cohen from the Nuclear Research Center Negev and Prof. Assaf Rudich and Liz Shabtai from Ben-Gurion University of the Negev for their valuable contributions to these studies.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe majority of results corresponding to the current studies are included in the article or uploaded as supplementary material. No further data are available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGolan R, Shelef I, Rudich A, Gepner Y, Shemesh E, Chassidim Y, Harman-Boehm I, Henkin Y, Schwarzfuchs D, Ben Avraham S, et al. Abdominal superficial subcutaneous fat: a putative distinct protective fat subdepot in type 2 diabetes. 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Sex-specific association of visceral and subcutaneous adipose tissue volumes with systemic inflammation and innate immune cells in people living with obesity. Int J Obes (Lond) 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Harmelen V, Reynisdottir S, Eriksson P, Thorne A, Hoffstedt J, Lonnqvist F, Arner P. Leptin secretion from subcutaneous and visceral adipose tissue in women. Diabetes. 1998;47(6):913\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Visceral adipose tissue, Subcutaneous adipose tissue, Weight loss, Diabetes, Metabolic syndrome","lastPublishedDoi":"10.21203/rs.3.rs-4323673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4323673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eVisceral adipose tissue (VAT) is well established as a pathogenic fat depot, while superficial subcutaneous adipose tissue (SAT) is associated with an improved or no association with the cardiovascular state. However, it is unclear to what extent VAT area (VATcm\u003csup\u003e2\u003c/sup\u003e) and its proportion of total abdominal adipose tissue (VAT%) are distinguished in predicting cardiometabolic status and clinical outcomes during weight loss.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe integrated magnetic resonance imaging (MRI) measurements of VAT, deep-SAT, and superficial-SAT from two 18-month lifestyle weight loss clinical trials, CENTRAL and DIRECT-PLUS (n\u0026thinsp;=\u0026thinsp;572).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAt baseline, the mean VATcm\u003csup\u003e2\u003c/sup\u003e was 134.8cm\u003csup\u003e2\u003c/sup\u003e and VAT%=28.2%; over 18-months, participants lost 28cm\u003csup\u003e2\u003c/sup\u003e VATcm\u003csup\u003e2\u003c/sup\u003e (-22.5%), and 1.3 VAT% units. Baseline VATcm\u003csup\u003e2\u003c/sup\u003e and VAT% were similarly associated with metabolic syndrome, hypertension, and diabetes status, while VAT% better classified hypertriglyceridemia. Conversely, higher VATcm\u003csup\u003e2\u003c/sup\u003e was associated with elevated high-sensitivity C-reactive protein (hsCRP), while VAT% was not. After 18 months of lifestyle intervention, both VATcm\u003csup\u003e2\u003c/sup\u003e and VAT% loss were significantly associated with decreased triglycerides, HbA1c, chemerin, ferritin, and liver enzymes, and increased HDL-c levels beyond weight loss (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Only VATcm\u003csup\u003e2\u003c/sup\u003e loss was correlated with decreased HOMA-IR and leptin levels.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAlthough increased VATcm\u003csup\u003e2\u003c/sup\u003e and VAT% exhibit similar clinical manifestations, it might be preferable to examine VAT% when exploring lipid status, while VATcm\u003csup\u003e2\u003c/sup\u003e may better reflect inflammatory and glycemic states.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eCENTRAL (Clinical-trials-identifier: NCT01530724); DIRECT-PLUS (Clinical-trials-identifier: NCT03020186)\u003c/p\u003e","manuscriptTitle":"Visceral adipose tissue area and proportion provide distinct reflections of cardiometabolic outcomes in weight loss; Pooled analysis of MRI-assessed CENTRAL and DIRECT PLUS dietary randomized controlled trials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 20:13:28","doi":"10.21203/rs.3.rs-4323673/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-27T15:28:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-23T12:09:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125162593157248139355043616237790857471","date":"2024-07-29T13:33:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124597047223592323792048676982653336195","date":"2024-06-03T19:18:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-01T11:06:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267504201501447186156273270071372386589","date":"2024-05-23T18:05:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-08T10:12:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-26T08:44:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-26T08:28:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2024-04-25T11:00:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f13abfe4-7240-4ea1-989b-acd179f067c6","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T15:58:34+00:00","versionOfRecord":{"articleIdentity":"rs-4323673","link":"https://doi.org/10.1186/s12916-025-03891-9","journal":{"identity":"bmc-medicine","isVorOnly":false,"title":"BMC Medicine"},"publishedOn":"2025-02-04 15:56:52","publishedOnDateReadable":"February 4th, 2025"},"versionCreatedAt":"2024-05-02 20:13:28","video":"","vorDoi":"10.1186/s12916-025-03891-9","vorDoiUrl":"https://doi.org/10.1186/s12916-025-03891-9","workflowStages":[]},"version":"v1","identity":"rs-4323673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4323673","identity":"rs-4323673","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}