BACKGROUND/OBJECTIVES:Maternal anthropometric changes are rapid during pregnancy and reflect increments in maternal and fetal tissues. These dynamic changes in body composition and shape during pregnancy are associated with maternal and fetal outcomes and are often monitored with simple tools such as a flexible tape for quantifying selected circumferences. The current study aim was to evaluate the hypothesis that circumferential measures of maternal body size and shape acquired with a 3D-optical imaging system will correlate, be accurate, and agree closely with ground-truth estimates made with a flexible tape by trained staff. SUBJECTS/METHODS:3D-optical scans and flexible tape measurements were acquired at 15-, 25-, and 35-weeks of gestation in 57, 41, and 35 participants, respectively. 3D avatars obtained at each time point were analyzed for waist (two sites), hip, mid-upper arm, mid-thigh, and calf circumferences. 3D-optical and flexible tape measurements were compared using linear regression analyses (r, r 2s), mean absolute errors (MAEs), root-mean square errors (RMSEs), concordance correlation coefficients (CCCs), and Bland-Altman plots. RESULTS:Overall, associations between 3D and conventional anthropometric measurements were strong (univariate linear regression r values, 0.80-0.98; p's all <0.001) with good corresponding accuracy and agreement at all five anatomic sites (MAEs, -8.5 to 0.8 cm; RMSEs, 0.61-9.94 cm; CCCs 0.6-1.0; small significant (p < 0.05) bias was present at some sites/timepoints for some measures). Post hoc analyses revealed a potential basis for impact of advancing pregnancy on between-method differences. CONCLUSIONS:Strong associations, good accuracy, and close agreement with flexible tape circumference measurements, combined with feasible implementation, supports further development of 3D-optical technology as an alternative to conventional anthropometry for evaluating and monitoring body size, shape, and composition over the course of pregnancy.

BACKGROUND:Dual-energy X-ray absorptiometry (DXA) is widely used to assess body composition, but differences in device calibration, software, and scan modes across manufacturers hinder data harmonization in multi-site studies. OBJECTIVE:To develop and validate regression models that convert body composition measurements among three DXA systems, GE iDXA, Hologic Horizon A, and Hologic Discovery A, and deploy these models in an accessible web-based tool. METHODS:A cohort of 101 adults completed same-day whole-body scans on all three machines. Fat mass (FM), lean soft tissue (LST), fat-free mass (FFM), bone mineral content (BMC), and % fat body fat were extracted. Pearson correlations quantified measurement agreement. For each device pair, we developed unadjusted, age-adjusted, and age and sex regression models and evaluated generalizability using leave-one-out cross-validation (Q²). Bland-Altman analyses assessed bias and limits of agreement. A user-friendly web-based application was developed deploy the conversions. RESULTS:Correlations between DXA machines were high across all body composition measures (% body fat: r = 0.96-0.98; FM: r = 0.98-0.99; LST: r = 0.97-0.99). Adjusted R² values exceeded 0.95 for nearly all regression models, with lower performance observed for FFM conversions involving the Horizon device (adjusted R² = 0.84-0.86). Bland-Altman analysis of % body fat conversions revealed mean bias ranging from 1.02 percentage points for iDXA-to-Discovery to 4.24 percentage points for Discovery-to-Horizon. The Discovery-to-Horizon conversion exhibited a 95% confidence interval entirely above zero [ 1.56,6.91 ] , indicating systematic overestimation of Horizon % body fat. In contrast, the iDXA-to-Discovery conversion showed narrower limits of agreement [ - 0.25,4.52 ] with no evident proportional error. Several conversions demonstrated proportional bias, including Horizon-to-Discovery and Discovery-to-iDXA % body fat predictions, characterized by overestimation at lower adiposity and underestimation at higher adiposity levels. CONCLUSIONS:Same-day, same-subject measurements across three DXA systems enabled development of machine-to-machine conversion equations with high predictive accuracy for most body composition variables and greater variability for FFM. The accompanying web-based tool provides a practical resource for applying these equations at scale and reducing inter-device measurement differences in multi-site datasets.

Roux-en-Y gastric bypass (RYGB) surgery is known to improve lipid profiles in individuals with obesity, but the degree of improvement varies. Emerging evidence suggests a role for sphingolipids in modulating cholesterol metabolism. This study investigated the relationship between plasma sphingolipid changes and cholesterol improvements in women with obesity and Type 2 diabetes mellitus (T2DM) following RYGB. Patients from the SURMetaGIT study were evaluated before and 3 months after surgery. Biochemical and sphingolipid analyses, anthropometric measures, and body composition were assessed. A nonhierarchical cluster analysis assessed distinct phenotypes in response to cholesterol after surgery. The p value from multiple comparison tests was adjusted using the false discovery rate (FDR) method. A metabolite fold change [log2 (postoperative mean/preoperative mean)] of 1.2 and an adjusted p value < 5% were considered statistically significant. A linear regression model was used to investigate the relationship between sphingolipids and cholesterol levels. Finally, a binary logistic regression model was developed to identify the predictive value of specific discriminative sphingolipids in the lipid improvement after RYGB. Distinct sphingolipid remodeling patterns were observed, with specific sphingolipids, including Cer(d18:1/24:0), SM(d18:1/22:0), and SM(d18:1/24:0), presenting a strong positive correlation with cholesterol levels, independent of weight loss and T2DM remission. The binary logistic regression showed preoperative Cer(d18:1/24:0) could be the strongest candidate predictor of plasma lipid improvement after surgery (accuracy of 78%; AUC 0.87). These findings suggest that specific sphingolipids, notably preoperative Cer(d18:1/24:0), seemed to have a potential role in predicting cholesterol improvement after RYGB. External validation in independent cohorts is warranted before clinical applicability can be established. Trial Registration: ClinicalTrials.gov identifier: NCT01251016.

All animals perform physical activity, but humans engage in a special kind of physical activity - exercise, defined as discretionary physical activity for health and fitness. However, the effects of physical activity on whole-organism metabolism and health are unresolved, partly because it is difficult to measure the three major components of metabolism: active energy expenditure (AEE), resting energy expenditure (REE) and dietary induced thermogenesis (DIT), which together equal total energy expenditure (TEE). Three competing models make different predictions about the effects of AEE on REE and TEE. Whereas the traditional 'additive' model of energy balance predicts that AEE is independent of REE, the 'stress' model hypothesizes that AEE temporarily increases REE partly because of transient effects of excess post-exercise oxygen consumption (EPOC). In contrast, the 'constrained energy' model predicts that increases in AEE cause compensatory decreases in REE to maintain a constant TEE. Here, we discuss how different analytical models, measurements, experimental designs and statistical methods affect tests of these three models' hypotheses. After accounting for spurious correlations, we find that longitudinal and cross-sectional data provide most support for the additive model. However, more and better data are needed to test these hypotheses rigorously. To conclude, we also review the evidence, mostly from humans, that increased levels of physical activity slow aging and reduce vulnerability to disease by diverting energy away from processes that improve reproductive success at the expense of long-term health and by increasing energy allocation to repair, maintenance and capacity building.

The American College of Sports Medicine has included a description of the components of physical fitness (PF) in their publications for 40 yr. Because new scientific evidence has emerged, the American College of Sports Medicine convened a scientific roundtable to reexamine the components of PF. The scientific roundtable agreed upon standardized definitions and an updated evidence-informed model of PF consisting of five interconnected components of PF: 1) cardiorespiratory fitness, 2) muscular fitness, 3) body composition, 4) neuromotor fitness, and 5) flexibility, with muscular fitness, body composition, and neuromotor fitness further separated into subcomponents. These components met four inclusion criteria: they 1) are changeable by exercise, 2) affect the ability to participate in physical activity or exercise, 3) contribute to health, and 4) can be feasibly assessed in professional practice. Additional overriding themes that emerged from the scientific roundtable included the complexity of PF and the interrelated nature of the components. Furthermore, it was agreed that when applied in practice, PF is best addressed in an individualized manner. Therefore, the components of PF should be considered dynamic as the focus on any given component can shift when considering individual needs, goals, health status, and priorities/interests.

OBJECTIVE:This study aimed to establish new body mass index (BMI) cutoff points for detecting excess body fat in 18-y-old males and females, using air displacement plethysmography (ADP, BodPod®) as the reference for measuring percent (%) fat mass. METHODS:A cross-sectional analysis was conducted using data from the 2004 Pelotas Birth Cohort (Brazil), including 3052 individuals at 18 y of age. Body composition was measured using ADP, with excess adiposity defined as body fat percentage ≥20% for males and ≥33% for females. BMI was calculated using the formula, body weight/height2. Cutoff points were analyzed using the receiver operating characteristic curve (ROC) and the Youden index, comparing sensitivity, specificity, and predictive values of traditional versus proposed cutoff points. RESULTS:The optimal BMI cutoff points for detecting excess body fat were ≥24.0 kg/m2 for males and ≥23.5 kg/m2 for females, showing similar accuracy to the traditionally adopted cutoff for overweight (≥25.0 kg/m2) and greater accuracy than the obesity cutoff (≥30.0 kg/m2). The sensitivity of the new cutoff was 2.4 times greater than that of the ≥30.0 kg/m2 cutoff among males (75.8% vs. 31.9%) and 2.6 times greater among females (82.2% vs. 31.0%). The findings indicate that a BMI ≥30.0 kg/m2 accurately detects fewer than one-third of young men and women with excess body fat. CONCLUSIONS:The newly proposed BMI cutoff points (≥24.0 kg/m2 for males and ≥23.5 kg/m2 for females) optimize the detection of excess body fat and are more effective than traditional thresholds. These findings may facilitate early detection and support the implementation of public health interventions.

Background:Ectopic fat infiltration in skeletal muscle, particularly in the thigh, may impair muscle function and contribute to metabolic dysfunction. However, its predictive value for incident type 2 diabetes mellitus remains understudied. Methods:We conducted a prospective cohort study of 2,129 East Asian adults (mean age, 57.4±16.9 years; body mass index, 25.0±3.9 kg/m2; 48.5% male) with one or more cardiometabolic risk factors but no diabetes at baseline. Low-density muscle (LDM) area was quantified using non-contrast computed tomography at the midthigh level, with attenuation values of 0-30 Hounsfield units defined as LDM. The association between LDM area and incident diabetes was assessed over a median followup of 7.4 years. Results:During follow-up, 201 males (19.5%) and 156 females (14.1%) developed diabetes. Those who developed diabetes had higher baseline glycosylated hemoglobin levels (6.1%±0.3% vs. 5.7%±0.4%) and larger LDM areas (48.8±14.2 cm2 vs. 38.1±13.2 cm2, both P<0.05). In adjusted Cox models, a large LDM area was independently associated with incident diabetes (hazard ratio [HR], 2.36; 95% confidence interval [CI], 1.52-3.67 for males and HR, 2.15; 95% CI, 1.28-3.61 for females). Incorporating LDM area into models with traditional risk factors improved predictive accuracy (area under the curve increased from 0.810 to 0.838 in males and from 0.893 to 0.908 in females). Conclusion:LDM area, an indicator of thigh muscle quality and intramuscular fat infiltration, is an independent predictor of incident diabetes.

BACKGROUND:Advances in health technology have enabled body composition assessments using smartphone photos, offering an accessible, cost-efficient, and portable alternative that can also be used by non-experts. However, it is essential to provide clarity on their technical development and estimation process for clinicians, researchers, and users. AIM:Here, we aimed to provide a technical description and guidance on the use and interpretation of a selected artificial intelligence (AI)-based app for body composition estimation. METHODS:We selected one app as a representative for in-depth technical analysis, based on a non-systematic review of scientific databases, developer websites, search engines, and digital marketplaces, to generate insights relevant to similar tools. RESULTS:MeThreeSixty® app was selected due to its availability and validation for several body composition measures (body fat, fat mass, fat-free mass, and appendicular lean mass). The app integrates advanced technologies, such as three-dimensional (3D) imaging and AI, which improves its accuracy with potential for refinement. It also features a self-assessment function to enhance user accessibility. Early findings indicate the app provides reliable group-level results for body circumference and composition estimations, with refinements needed for individual assessments. CONCLUSION:MeThreeSixty app used 3D imaging and AI with acceptable group-level accuracy for estimating body circumference and composition, but limited precision at the individual level requires cautious interpretation. Further prospective validation and model refinement are needed, especially in diverse populations, and using longitudinal datasets before supporting personalized nutrition and broader health platform integration.

ABSTRACT Objective This study aimed to estimate extreme obesity prevalence and association with disease burden and outcomes in patients undergoing metabolic and bariatric surgery (MBS). Methods Cross‐sectional data from the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP, 2015–2023) were analyzed ( n = 1,795,127). Preoperative disease burden and 30‐day outcomes were compared across BMI categories (< 40, 40.0–49.9, 50.0–59.9, 60.0–69.9, 70.0–79.9, and ≥ 80 kg/m 2 ), emphasizing extreme obesity compared to lower BMI categories. Results Extreme obesity represented 5.3% of the eligible sample, though even higher BMI values (i.e., ≥ 70 kg/m 2 ) were consistently present in 1.1% of cases annually (13,677 cases/9 years). Relative to BMI < 40 kg/m 2 , patients with the highest BMI values (≥ 70 kg/m 2 ) had significantly greater frequency of hypertension (56.7% vs. 46.9%), sleep apnea (59.6% vs. 34.6%), COPD (2.6% vs. 1.1%), and functional dependence (4.5% vs. 0.5%). Increasing BMI category was associated with increased serious complications (2.6% vs. 0.7%; p < 0.001) and mortality (0.35% vs. 0.05%; p < 0.001). Conclusions Extreme obesity is common in MBS, and greater BMI is associated with progressively higher comorbidity burden and complications. Despite the higher frequency of complications, absolute events are low and do not contraindicate multidisciplinary obesity treatment. Strategies to reduce preoperative BMI among patients with the highest BMI values warrant evaluation.

BACKGROUND:Glucagon-like peptide-1 (GLP-1) receptor agonists have transformed obesity treatment by inducing clinically significant weight loss. However, long-term use can lead to a plateau that may trigger discontinuation and weight regain. OBJECTIVE:Use mathematical modeling to test the hypothesis that the weight-loss plateau observed during long-term GLP-1 receptor agonist use reflects predictable changes in energy dynamics. DESIGN:Secondary mathematical modeling applying Hall's human metabolism model to estimate changes in energy intake and expenditure over 176 weeks of treatment and 17 weeks after discontinuation. PARTICIPANTS:Modeling was anchored to a single representative phenotype using average baseline demographics and weight dispersions from a clinical trial. MAIN OUTCOME MEASURES:Percent change in body weight, body mass index (BMI), energy intake, energy expenditure. STATISTICAL ANALYSIS:Monte Carlo methods captured variability. Modeled trajectories of energy intake and expenditure illustrated energy balance dynamics. RESULTS:Modeled weight loss peaked at 24.0% (95% confidence interval [CI], 22.6-25.4) by week 96, reducing weight from 108 kg (BMI, 40.0) to 82.9 kg (BMI, 33.0). A plateau persisted for ∼78 weeks despite continued treatment. Energy intake decreased 32.1% during the first 4 weeks, then rose to match energy expenditure by week 98 (2500 kcal, vs 2508 kcal, respectively). After discontinuation (week 176), energy intake exceeded baseline, contributing to a 5.3% weight loss reversal. Energy expenditure declined 9.2% from baseline by week 98 and stabilized. CONCLUSION:Although GLP-1 receptor agonists achieve unprecedented weight loss, many individuals plateau but remain with overweight/obesity. This plateau reflects the narrowing of the energy intake and expenditure gap during long-term treatment. To attenuate this, systematic integration of nutrition and behavioral therapy could be tested as adjuncts, particularly approaches that support nutrient adequacy and prevent excess energy intake. Whether such strategies can sustain or extend GLP-1 receptor agonist-induced weight loss warrants future study.

An abundance of publications is drawing attention to the decrements in skeletal muscle mass accompanying weight loss mediated by glucagon-like peptide-1 receptor agonists. Herein we advance three suggestions for improving the clarity of these reports that will lead to their improved interpretation by the scientific community.

BACKGROUND:Recent consensus statements from the European Association for the Study of Obesity and the 2024 Lancet Obesity Commission have renewed the debate on how obesity should be defined and clinically characterized. Although both frameworks acknowledge the limitations of body mass index (BMI), they continue to rely largely on anthropometric measures, including BMI and waist circumference, for diagnosis and risk stratification. Body composition analysis (BCA), however, has received comparatively little attention. Obesity is a heterogeneous condition characterized not only by excess adiposity but also by differences in fat distribution, ectopic fat accumulation, and skeletal muscle mass. Advances in imaging and body composition methodology now enable a more detailed assessment of these components and their metabolic consequences. SUMMARY:This perspective examines the potential role of BCA in the assessment of preclinical and clinical obesity. Rather than focusing on what can technically be measured, we address the clinically relevant questions of what should be measured and in which patients. Specifically, we discuss whether fat mass should replace BMI for diagnosing obesity, whether visceral adipose tissue should replace waist circumference for cardiometabolic risk assessment, whether liver fat should be routinely assessed, and whether skeletal muscle mass should be evaluated in clinical practice. KEY MESSAGES:BCA provides clinically relevant information that complements, rather than necessarily replaces, traditional anthropometric measures. When used selectively, BCA may improve risk stratification, facilitate the monitoring of treatment responses, and support more individualized management strategies for patients with obesity.

Skeletal muscle is a crucial facilitator of many of the effects of exercise on metabolic health. Intrinsic myocellular mechanisms, exercise-induced myokine secretion, and crosstalk between multiple organ systems contribute to the maintenance of energy homeostasis, cardiovascular health, strength, cognition, and quality of life. Investigating the molecular underpinnings of the skeletal muscle response to exercise from multiple perspectives, including the genetic, physiological, and environmental factors leading to metabolic dysfunction, has advanced our understanding of disease risk and helped identify avenues for the prevention and treatment of metabolic disorders and chronic diseases. The National Institutes of Health-funded Boston Area Nutrition Obesity Research Center, in partnership with the Harvard Medical School Division of Nutrition, hosted its 26th Annual Symposium, Muscle Movement and Metabolism: Exercise and Skeletal Muscle as Mediators of Health," in June of 2025. Speakers presented novel research and unique perspectives on exercise and skeletal muscle as key determinants of health. This manuscript synthesizes the symposium's major themes: 1) physiological and molecular mechanisms of exercise, 2) clinical implications of physical inactivity and reduced muscle function, and 3) individual variability and personalized medicine. By bridging mechanistic and clinical insights with principles of personalized medicine, the symposium provided key insights into the current landscape of treatments for metabolic diseases and evidence-based strategies for disease prevention.

The assessment of body composition has long been a fundamental component of research and is gaining increasing adoption in clinical practice. This growing interest has drawn new professionals to the field and increased emphasis on its clinical relevance and applications. However, the diversity of assessment techniques and inconsistent terminology create challenges, highlighting the urgent need for harmonized approaches across research and healthcare settings. Commonly employed methods include bioelectrical impedance approaches, dual-energy X-ray absorptiometry, and computerized tomography, with ultrasound emerging as an increasingly prominent tool. These methods are featured in guidelines for diagnosing conditions such as low muscle mass, malnutrition, sarcopenia, and sarcopenic obesity, among others. This second narrative review in a series, developed by an international panel of experts, focuses on these widely accessible assessment tools that align with clinical recommendations. It presents foundational knowledge, discusses validity and reliability considerations, and offers practical advice on terminology, measurement protocols, data interpretation, and longitudinal monitoring. The report also addresses current limitations and identifies areas needing further research. Our goal is to provide clear, evidence-based guidance that is useful for both experienced practitioners and those newly engaging with body composition assessment. We urge organizations, journals, and stakeholders across the body composition field to adopt the proposed principles and standards to support consistency, transparency, and scientific rigor in both research and clinical care.

INTRODUCTION:Weight loss results in reduced energy expenditure (EE) due to body composition alterations (e.g., fat-free mass and fat mass losses) and mass-independent adaptations in EE (e.g., hormones). Glucagon-like peptide-1 receptor agonists (GLP-1RA) are indicated for obesity management; however, their effects on EE remain unclear. METHODS:In this scoping review, we searched MEDLINE, EMBASE, CINAHL, Web of Science, ProQuest, and Cochrane Library (inception to October 2025) for studies that investigated the effects of GLP-1RA (mono or combination therapy) on EE in humans. RESULTS:Twenty-three studies were included, 10 assessed GLP-1RA monotherapy (4 exenatide, 4 liraglutide, 1 semaglutide, 1 beinaglutide) and 13 combination therapy (11 dual, 2 triple agonists); drug regimen heterogeneity was high. Most studies assessed resting metabolic rate (RMR); 4 used 24-h whole-room indirect calorimetry; and none applied doubly labeled water. Eight studies (34.8%) concluded that GLP-1RA mono or combination therapy had non-significant effects on EE. Combination with glucagon produced varied impacts on EE components (RQ [n = 3], RMR [n = 1], and sleep metabolic rate [n = 1]), whereas combination with glucose-dependent insulinotropic polypeptide (GIP) decreased RQ and increased fat utilization (n = 1). Eleven studies (47.8%) produced inconclusive results due to the applied statistical analyses. CONCLUSION:Acute or chronic GLP-1RA monotherapy does not appear to impact EE independent of weight loss. Combining GLP-1RA with glucagon or GIP may impact EE in different ways, requiring further exploration.

BACKGROUND:Dual-energy X-ray absorptiometry (DXA) is often used as the reference method for calibrating and validating other clinically useful body composition devices, although previous studies report DXA system differences in component estimates. This study evaluated the hypothesis that body composition estimates, specifically percent body fat (BF%), acquired with a method such as 3D optical (3DO) imaging might lead to varying conclusions when judged against different DXA systems and software versions. METHODS:BF% was evaluated in healthy adult participants with a 3DO imaging system (Size Stream, Mobile Fit) and three DXA systems (Hologic Horizon A [Apex 4.0.2] and Discovery A [Apex 5.6.1.3]), and GE Lunar iDXA [enCore 13.60.033] on the same day. Agreement between BF% estimates by 3DO and each DXA system was evaluated with multiple statistical procedures. RESULTS:Ninety-nine participants completed all four evaluations. Linear correlations (r and R2s), Bland-Altman analyses, mean differences, mean absolute errors, root-mean square errors, and concordance correlations differed between %BF by 3DO and each of the DXA devices. Relative to BF% by 3DO, agreement measures were best for Discovery A compared to the other two DXA scanners. A linear mixed-effects model with method as a fixed effect and participant as a random intercept revealed a statistically significant effect of assessment method on estimated BF%: Horizon A DXA (X ± SD, 36.4 ± 9.3%) differed significantly from 3DO (33.3 ± 8.8%), iDXA (35.2 ± 10.9%), and Discovery A (34.4 ± 10.0%); and iDXA and Discovery A both differed significantly from 3DO and Horizon A DXA. Significant interactions between method and sex and method and BMI category were also observed, indicating the differences between measurement methods were not uniform across males and females or BMI categories. CONCLUSIONS:Our findings indicate that conclusions related to a clinical body composition method's accuracy depend on the specific DXA device and associated software version chosen to serve as the reference method.

Background Sarcopenic obesity (SO) is characterized by excess adiposity and reduced muscle mass and function. In 2022, the Sarcopenic Obesity Global Leadership Initiative (SOGLI) proposed a diagnostic algorithm to standardize SO identification by integrating screening, diagnostic assessment, and staging. Aim This review evaluates the application of the SOGLI algorithm, identifying strengths, limitations, and potential areas for refinement. Methods A narrative review with systematic citation tracking was conducted (April 2022- August 2025). Literature searches in PubMed, Scopus, and Web of Science identified original studies in adults (≥18 years) explicitly using the SOGLI algorithm. Data on screening, diagnostic tools, staging, prevalence, and comorbidities were synthesized. Results Seventy-two studies applied the SOGLI algorithm, showing heterogeneous approaches across clinical settings. For obesity screening, about half used both body mass index and waist circumference, while sarcopenia screening tools were less frequently reported. For SO diagnosis, bioelectrical impedance analysis was the most common method for body composition assessment, while muscle function was predominantly assessed via hand-grip strength. SO staging was reported in 19% of studies, most often as Stage II. Application of the algorithm consistently confirmed associations between SO and chronic disease burden, functional decline, and increased mortality. Conclusions The SOGLI algorithm represents a major advance, with 72 studies adopting it in two years. Some inconsistencies in screening and staging suggest opportunities for refinement. These findings support its validity, while further standardization and integration of novel biomarkers could enhance its clinical effectiveness.

Roux-en-Y gastric bypass (RYGB) is associated with substantial weight loss and improved obesity-related comorbidities. However, outcomes on body composition, particularly skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) remain inconsistent due to limitations in measurement techniques. Evaluate longitudinal changes in SM, VAT, and SAT volumes (cm3) following RYGB using Data Analysis Facilitation Suite (DAFS), an automated computed tomography (CT) analysis software. In this prospective pilot study, nine female patients underwent low-dose abdominal and pelvic CT imaging at baseline, 3-, and 6-months post-RYGB. Volumetric analysis from the ninth thoracic veterbra (T9) to the sacrum was performed using DAFS. Changes in SM, VAT, and SAT were assessed using paired t-tests. Participants (mean ± SD; age 35 ± 9 years, BMI 48 ± 10 kg/m²) experienced substantial weight loss (14 ± 5