Weight-related changes in MRI-derived measures of body composition and liver steatosis: a large-scale analysis for obesity trial design

Weight-related changes in MRI-derived measures of body composition and liver steatosis: a large-scale analysis for obesity trial design | 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 Article Weight-related changes in MRI-derived measures of body composition and liver steatosis: a large-scale analysis for obesity trial design Helena Thomaides Brears, Magdalena Nowak, Luis Nunez, Tim Pagliaro, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6656623/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2026 Read the published version in International Journal of Obesity → Version 1 posted 9 You are reading this latest preprint version Abstract Background/Objectives : Amid rising global obesity rates and advances in weight-loss therapies, monitoring body composition and ectopic fat could refine trial design. We quantified weight-related changes in body composition and liver steatosis prior to widespread adoption of incretin treatments. Subjects/Methods : Adults (N=3,071) from the UK Biobank with repeat abdominal MRI scans were included. Percent weight change from baseline was categorised: stable (0±2%), mild change (2-5% weight gain/loss), moderate change (5-10% weight gain/loss), or large change (10-15% weight gain/loss). Intervention/Methods : MRI data were processed automatically from two visits, spaced 2.7 years apart, to derive volumetric visceral (VAT), subcutaneous adipose tissue (SAT), skeletal muscle volume (SM, or indexed SM), fat infiltration (MFI SM ), and psoas muscle cross-sectional area (CSA) in the abdominal region. Liver fat content (LFC) was assessed using LiverMultiScan. Dual-energy x-ray absorptiometry (DXA) measurements were compared. Results : Weight gain occurred in 28% of all subjects (N=3071, age 63 years, male 49%, 13% with obesity, 43% with overweight). Moderate or large weight gain increased LFC, VAT, SAT, MFI SM and psoas CSA (all p<.001). Weight loss also occurred in 28%. Decreases were observed with moderate or large weight loss: LFC -20% or -33%, VAT -22 or -38%, SAT -17 or -30%, SM -3 or -5%, SMI -3 or -4%, psoas CSA -4 or -5%, respectively (all p<.001). MFI SM reduced with large weight loss, by -4%. For every 5% drop in weight, there was -16% reduction in VAT, -11% in SAT, -24% in liver fat, -1.5% in SM (or -1.4% in SMI) and -2.1% in psoas muscle, in those with obesity or overweight. DXA changes in lean mass correlated weakly with changes in SM volume (rho 0.28-0.47). Conclusions : Using MRI, relative changes in body composition and liver steatosis resulting from weight loss can inform clinical trials, including placebo arm design and power estimations. Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity Health sciences/Medical research/Clinical trial design/Clinical trials/Biostatistics Obesity steatosis skeletal muscle visceral adipose tissue subcutaneous adipose tissue muscle fat infiltration metabolic dysfunction-associated steatotic liver disease MRI Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Obesity is a chronic disease currently affecting an estimated 2 billion adults globally and expected to double by 2050 1,2 . The precise manifestation of this disease is influenced by genotype, demographics and environmental factors 3 . The liver is often an early site of free fatty acid accumulation; metabolic dysfunction-associated steatotic liver disease (MASLD) is found in 75% of people with obesity 4 . People with fat accumulation as visceral adipose tissue (VAT), in hepatocytes and in skeletal muscle (SM) are at increased risk of poor outcomes 5 – 8 . In parallel, there has been an explosion in therapeutic advancements for obesity that lead to weight loss and provide additional management options beyond lifestyle and surgical intervention 9 . These drugs can have pleiotropic cardiometabolic benefits and this is associated with a reduction in ectopic fat and/or VAT 10 – 13 , including in populations with comorbidities like MASLD. There are however growing concerns about concurrent loss of skeletal muscle mass and function 14 , 15 , which correlates with poor outcomes 16 , 17 and warrants further investigation. A clinical workflow for obesity that includes direct measurement of body fat - not BMI - and signs, symptoms or tests for organ dysfunction or substantial limitations of daily activities was recently recommended by the Lancet Diabetes & Endocrinology Commission on Clinical Obesity 2 . This commission recommended measurement of body fat by serum tests (triglyceride (TGA) or cholesterol), which lack organ-specificity, or by dual-energy x-ray absorptiometry (DXA), which estimates three-dimensional mass from coronal two-dimensional images but has limited accuracy for VAT 18 , 19 . MRI is the gold standard for volumetric assessment of VAT, SAT and SM, with single-slice area methods being applied for clinical care 20 , 21 . Use of multiparametric MRI to measure liver fat content is recognized as the most accurate non-invasive method for liver steatosis staging 22 , 23 . We sought to provide a comprehensive evaluation of the relative changes in body composition and liver steatosis resulting from general weight loss to inform clinical trial design and clinical management. We compared changes in multiparametric MRI and DXA parameters in individuals with a broad cardiometabolic profile from the UK Biobank, after weight gain and after weight loss prior to widespread adoption of incretin therapies. Materials and Methods Study design and participants This prospective cohort study involved participants enrolled in the UK Biobank imaging sub-study between May 2014 and March 2020, who attended and completed 2 imaging visits within 3 years, by March 2022 21 . Individuals aged 40 to 69 years old were invited for MRI examination (heart and abdomen), including quantitative mapping, as previously described 24 . Some participants were invited for a follow-up if the baseline scan was of sufficient quality, and they lived within certain proximity to an imaging center. UK Biobank has approval from Northwest Multi-Centre Research Ethics Committee and obtained written informed consent from all participants prior to the study. Data were extracted under access application 9914. Those with complete imaging data for MRI VAT, SAT, abdominal SM, abdominal SM index (SMI), abdominal SM fat infiltration (MFI SM ), psoas muscle (PM) and liver fat content were included in the analysis. DXA measurements from the same participants that had previously been processed with automated methods 25 were also included for comparisons without requiring further analysis; the available measurements were trunk lean mass (Field ID 23285) and total lean mass (Field ID 23280). DXA-derived VAT estimates (Field ID 22407) were excluded due to inconsistent quality. Data collection Clinical and biochemical data Clinical data that included demographics (age, sex, ethnicity, weight, BMI, smoking status), and prior history of diabetes, hypertension, recent cancer (within 2 years of imaging visit) and reported medications, were collected at both imaging visits (from ICD-10 codes; Supplementary methods ). Blood biomarker data (TGA, total cholesterol) collected at the baseline visit was also accessed. MRI data acquisition All participants had been scanned on two occasions at one of the UK Biobank imaging centers, on a Siemens Aera 1.5T scanner (Siemens Healthineers, Erlangen, Germany), with both a dual-echo Dixon VIBE protocol for body composition and the LiverMultiScan imaging protocol that are encompassed within the UK Biobank imaging protocol 24 , 26 . Body composition measurements from MRI Body composition measurements were derived using fully automated volumetric analyses, as described previously 21 ( Supplementary Methods ). The region from the center of the T9 vertebra to the top of the lower of the two femoral heads was selected for volumetric analyses. Volumes for VAT, SAT, abdominal SM and MFI SM were calculated based on the number of voxels. MFI SM was computed as the mean signal fat fraction value of the muscle tissue voxels. Semi-automated single-slice area analyses at the 3rd lumbar vertebra (L3) level were also conducted, as described previously 21 ( Supplementary Methods ). Axial slices passing through the centre of mass of the L3 vertebra were automatically extracted from the whole-body volumes. The cross sectional area (CSA) for psoas muscle was calculated based on the number of pixels. All analyses were performed by trained MR technologists and radiographers blinded to all clinical data. Analysis of liver measurements from MRI Liver MRI data analysis was performed automatically using LiverMultiScan® software (Perspectum, Oxford, UK) 27 , and every case was manually reviewed by trained analysts, blinded to all clinical data. Statistical analysis All statistical analyses were performed in R (version 4.3, R Project for Statistical Computing, Vienna, Austria). Participants were categorized according to percentage weight change from baseline into the following groups: stable weight (0 ± 2%), mild change (2–5% weight gain/loss), moderate change (5–10% weight gain/loss), or large change (10–15% weight gain/loss) ( Supplementary methods ). Initial analyses of variance (ANOVA) were performed to examine overall differences across weight change categories. Subsequently, within the stable weight reference group, paired sample t-tests were used to assess longitudinal changes in body composition from baseline to follow-up to quantify natural temporal variations. Between-group analyses using independent two-sample t-tests were used to compare each weight change category against the stable weight reference group to isolate effects specifically attributable to weight change. All statistical tests were conducted after confirming that parametric test assumptions were met. To control for multiple comparisons, p-values were adjusted using the Bonferroni method. Spearman’s rank sum test was used for correlation analyses (rho), with the Benjamini-Hochberg procedure applied to control the false discovery rate (FDR) due to multiple comparisons. Linear regression was performed to define rate of change in individual parameters after weight loss. Data are presented as mean ± standard deviation (SD) throughout the text. All significance tests were two-tailed, and p < 0.05 was considered statistically significant. For figures illustrating mean changes in metrics across weight loss/gain groups, standard error of the mean (SEM) is displayed to improve visualization of between-group differences. The World Health Organisation standards for BMI stratification were applied 28 . Terminology and diagnostic criteria for what was previously “non-alcoholic fatty liver disease” have changed recently to metabolic dysfunction-associated liver disease and metabolic dysfunction-associated steatoepatitis and are adopted herein 29 , 30 . MASLD was defined as the presence of 1 cardiometabolic risk factor (diabetes, obesity, hypertension or hyperlipidaemia) and concurrent elevation in liver fat content (> 5%), in the absence of high consistent alcohol intake 31 . Results Study population 3071 individuals enrolled in the UK Biobank between 2014 and 2020 who had repeat MRI imaging over a mean 32 ± 13 months follow up interval were included (Fig. 1 ). At the first imaging visit, heir mean age was 62 ± 8 years, and their BMI was 26 ± 4 kg/m 2 . 97% were White, 49% were male and 5% were smokers ( Table 1 ). 13% of individuals were living with obesity, 43% with overweight and 15% with metabolic syndrome, 21% had MASLD. Only 5% had type 2 diabetes and 1.8% had a recent cancer diagnosis. 13% were on statins or other treatment for hypertension. Over the follow-up period, body weight was stable in 43% (1333 of 3071). 864 individuals (28%) gained > 2% of baseline weight over the same period. There was mild weight gain in 577 individuals (19%), gaining 2–5% of their baseline weight), with 7.4% (227 individuals) gaining moderate weight, and only 60 individuals (2%) gaining 10–15% of their baseline weight. Additionally, 18% (538 of 3071) lost 2–5% of their baseline weight and 8.3% (255 individuals) lost moderate weight, while 81 (3%) lost 10–15% of their original weight. Individuals who lost weight were more likely to have been overweight or obese and have high waist circumference at baseline (p < 0.001). Large weight loss was not associated with prior history of cancer or new cancer diagnoses (both p = 0.2). Body fat, muscle and liver MRI measurements in individuals maintaining stable weight During the follow-up period, people who maintained their weight showed relatively small increase in VAT (5 ± 13%, p < 0.001) and SAT (2.3 ± 12%, p < 0.001) compared to baseline ( Supplementary Table 1 , Figs. 2 – 3 ). SM volume and psoas muscle CSA remained relatively stable (-1.2 ± 3% and − 0.6 ± 16% change, respectively; p < 0.001 for both). Fat infiltration in the abdominal SM (MFI SM ) was higher at follow-up by 3 ± 3% (p < 0.001). In the liver, fat content showed relatively small increases (relative % change of 5.5 ± 29%, p < 0.001). Body fat, muscle and liver MRI measurements in individuals gaining weight People who gained weight showed significant changes in body composition compared to those who maintained stable weight. Substantial increases were observed in VAT (ranging from 20–58% change, p < 0.001), in SAT (12–36% change, p < 0.001), and in MFI SM (4.1–8% change, p < 0.001) (Fig. 2 ). In contrast, the volume of abdominal SM remained relatively stable (-0.4% to -1.2% change, p < 0.001). Mean percent changes in psoas muscle CSA showed statististically significant increases in two larger weight gain groups (4–6% change, p < 0.05) but all groups exhibited high inter-individual variability. Liver fat content increased (ranging from 22–72% change, p < 0.001). Changes were equivalent when individuals on anti-hypertensive medications were excluded ( Supplementary Table 2 ). Body fat, muscle and liver MRI measurements in individuals losing weight Those who lost weight during the follow-up period also had the highest baseline values for VAT, SAT and MFI SM across all groups ( Table 1 ). Individuals who lost mild amounts of weight (2–5% of their baseline weight) showed significant but not clinically meaningful changes in all metrics (Fig. 3 ). In individuals with moderate weight loss (5–10% baseline weight lost), more substantial decreases were observed in VAT (-22 ± 13%, p < 0.001) and SAT (-17 ± 10%, p < 0.001). SM volume also reduced by -3 ± 4% (p < 0.001), as did psoas muscle CSA (by -4 ± 14%, p < 0.001). However, substantial decreases in MFI SM (-4 ± 6% change, p < 0.001) only occurred with large weight loss (10–15% of baseline weight). In these individuals substantial decreases were also observed in VAT (-38 ± 15%), in SAT (-30% ± 12%), in skeletal (-4.5 ± 4.8%) and psoas muscles (-5 ± 5%), all p < 0.001. In the liver, fat content decreased (-33 ± 29%). Loss of psoas muscle was higher in males ( Supplementary Tables 3–4 ). In individuals with obesity or overweight, for every 5% drop in weight, there was a decrease of -16% in VAT, -11% in SAT, -24% in liver fat, -1.5% in SM (or -1.4% in SMI) and − 2.1% in psoas muscle ( Supplementary Table 5 ). Correlations between MRI measures of fat and muscle At baseline MFI SM correlated most strongly with SAT (rho = 0.58), but change in MFI SM after the follow-up showed highest correlation with change in VAT (rho = 0.60) and change in SAT (rho = 0.46) ( Supplementary Figs. 1–2 ). Neither abdominal SM nor psoas muscle CSA demonstrated correlations with any other MRI measure at baseline or with longitudinal changes at follow-up. DXA assessment of lean mass during weight loss and gain During follow-up, participants with stable weight showed minimal but statistically significant changes in total lean mass (-0.6 ± 2.1%, p 0.05) compared to baseline. Total lean mass decreased with moderate/large weight loss (-2.3 ± 2.5% to -2.6 ± 3.2%, p < 0.05) and increased modestly with weight gain (0.4 ± 2.3% to 2.2 ± 2.8%, all p 0.05). There were strong correlations between baseline DXA-derived lean mass measurements with MRI measurement of abdominal SM volume (rho = 0.95 for trunk lean mass and rho = 0.96 for total lean mass, p < 0.001 ( Supplementary Figs. 1–2 ). However, the correlations were weak or moderate when longitudinal changes in these measures were considered over the follow-up period (rho = 0.28 for trunk lean mass and rho = 0.47 for total lean mass, p < 0.001) (Fig. 4 , Supplementary Fig. 2 ). Discussion In this study of 3071 individuals from the general population evaluated over a 3-year follow-up period with multi-parametric MRI, we present quantitative changes in body composition and liver fat according to different degrees of weight gain or loss. There were three key findings. Firstly, substantial increases in adipose tissue (VAT, SAT, MFI SM, liver fat) occurred with just moderate weight gain. Secondly, all metrics of fat except muscle fat infiltration (MFI SM ) decreased with moderate weight loss. Thirdly, both skeletal and psoas muscles decreased with weight loss, with reductions in SM evident even after mild weight loss. The values reported here provide reference data of real-world weight changes in the pre-incretin drug era. The changes in body composition may have direct applicability for powering clinical trials and design of placebo arms. In clinical guidelines for obesity and diabetes, large weight loss (> 10%) is recommended 32 , 33 , due to expected improvements in cardiometabolic outcomes 34 – 36 ; milder effects are observed with moderate (5–10%) weight loss. Diabetes remission improved 1.7-fold with moderate weight loss compared to 5-fold with large weight loss in a study of 15,211 people with recent type 2 diabetes diagnoses 34 . Incretin-based drugs, in particular, can achieve 15–25% weight loss within 1.5 years 37 – 41 . Encouragingly, in our study of ageing individuals, those with higher BMI and waist circumference lost the most weight, in keeping with current clinical recommendations for weight management. This work preceded licensing of GLP-1RA for obesity in the UK (first approval in 2020 42 ), and only 5% of the study population had diabetes (first approval for diabetes in 2007 43 ). Nevertheless, the decreases in both VAT and SAT after moderate weight loss herein match the range of changes reported with GLP1-RA over shorter follow-up periods (-15% for VAT and − 20% for SAT with tirzepatide in people with diabetes after 1.5 years 11 ; -13% decrease in VAT after 36 weeks of liraglutide in individuals with obesity 44 ). Adverse muscle composition, characterized by elevated muscle fat combined with low muscle volume, has been linked to poor function and is a strong and independent predictor of all-cause mortality 45 . Even small changes in skeletal muscle composition affect muscle function 46 and insulin resistance 47 . The rapid weight loss as a result of incretin treatment has raised concerns about adverse effects on muscle mass and function 14 , 15 . Using DXA, trial findings indicate that 10–40% of the weight lost in incretin trials is adjusted fat-free mass 14 , 48 , 49 , estimated to consist of ‘approximately’ 50% skeletal muscle 14 , 50 , 51 . Using MRI, impact of incretin mimetics on thigh skeletal muscle has very recently been investigated in randomized control trials. Treatment with liraglutide over 40 weeks was associated with a reduction of thigh muscle MFI (~ 3%) and 5% weight loss among individuals with obesity 52 . In SURPASS-3 trial, there was a 6–7% reduction in thigh muscle volume and 4–5% reduction in thigh MFI after 52 weeks of tirzepatide in people with type 2 diabetes 53 . Our study evaluated MFI SM and SM in the abdomen rather than the thigh, because these muscle groups serve as strong markers of whole-body SM mass 54 , while also providing operational advantages due to shorter scan duration and fewer hardware requirements 21 . Observed reductions in abdominal SM and MFI SM with large weight loss herein are comparable. The parabolic relationship that MFI SM showed with weight loss in our study may indicate a redistribution of fat from other deposits to skeletal muscle following low level weight loss, consistent with changes in fat depots after lifestyle intervention 55 . Clinically meaningful increases in fat occurred only with moderate to large weight gain. In the liver, 30% relative fat changes are considered clinically meaningful in individuals with MASLD, although smaller effects (> 1% absolute change) can be monitored and are significant 27 , 56 . The gains in liver fat were most pronounced among all fat measurement changes and correlated less with changes in VAT, SAT and MFI SM , consistent with previous findings 57 . This may be suggestive of differential temporal patterns of fat mobilization or redistribution after weight gain. The high repeatability of measurements using multiparametric MRI 20 , 27 is evident in the minimal changes we found in those with stable weight. The availability of DXA has resulted in its use for cross-sectional body composition assessment 58 , with recent recommendations endorsing its application for direct measurement of fat 2 in obesity definition. In clinical trials the reduced accuracy of DXA compared to MRI volumetric measurement 19 , is a hindrance when measuring smaller changes, particularly for detecting longitudinal changes in muscle mass. While less accurate, DXA may be warranted in trials where bone mineral density is a relevant endpoint. However, the weak correlation we found between SM and lean mass changes may reflect its inability to differentiate between SM and MFI SM compartments 59 , 60 . This prevents retrospective extrapolations of SM from DXA datasets and indicates that lean mass is a poor surrogate for tracking longitudinal changes in SM. Almost all UK Biobank participants are White, highlighting the need for comprehensive research across multi-ethnic populations. We explored the impact of prior cancer and disease history and of treatments for hypertension, but other treatments affecting weight changes were not investigated due to lack of detailed medical history in the UK Biobank registry. Nevertheless, the granularity of changes in body composition provided by MRI in this large-scale population study is a timely and useful reference for exploring the impact of drug candidates that induce weight loss in clinical trials. Of relevance to clinical trial design is that for every 5% drop in weight, there was a 16% reduction in VAT,, -11% in SAT, -24% in liver fat, 1.5% reduction in SM (or 1.4% in SMI) and 2.1% reduction in psoas muscle, in those who were obese or overweight. Amid rising global obesity rates, the need to optimize weight-loss therapies and personalize management options is vital, requiring accurate tools to measure adverse effects and stratify response to treatment. Our findings provide data to inform sample size calculations for future clinical trials and support label claims, while also offering a ‘virtual placebo’ arm or population-level estimate 53 . Declarations Acknowledgements We wish to acknowledge the UK Biobank participants. Author Contributions Conceptualization, H.T.B. and M.N.; methodology, L.N. and M.D.R.; formal analysis, M.N. and L.N.; data curation, M.N.; writing—original draft preparation, H.T.B.; writing—review and editing, M.N., L.N., T.P., C.D., S.K., L.K., S.H., M.D.R., H.T.B., E.L.T. and J.D.B.; supervision, H.T.B. and L.T. All authors have read and agreed to the published version of the manuscript. Competing interests This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Magdalena Nowak, Luis Núñez, Tim Pagliaro, Matthew D. Robson and Helena Thomaides Brears are employees for Perspectum, a company that has developed LiverMultiScan, a UKCA and FDA(510k)-cleared medical device for liver health measurements that was used in the imaging sub-study of the UK Biobank. Helena Thomaides Brears and Matthew D. Robson are also shareholders of Perspectum. Louise E. Thomas and Jimmy D. Bell are consultants for Perspectum. Lee M. Kaplan is a consultant for Altimmune, Amgen, AstraZeneca, Boehringer Ingelheim, Cytoki, Helicore, Johnson & Johnson, Kallyope, Eli Lilly & Company, MetaVia, Neurogastrx, Novo Nordisk, Oxford Medical Products, Perspectum, Pfizer, Skye Bioscience and Zealand. The remaining authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Statement Participant data was obtained through UK Biobank Access Application number 9914. UK Biobank has approval from Northwest Multi-Centre Research Ethics Committee and obtained written informed consent from all participants prior to the study. Summary data is included in the manuscript or uploaded as online supplemental information. Anonymized individual patient data can be shared upon request or as required by law and/or regulation and/or governance by and within the rules of UK Biobank access with qualified external researchers. 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Expert Panel Report: Guidelines (2013) for the management of overweight and obesity in adults. Obesity (Silver Spring) 2014; 22 Suppl 2: S41-410. Morieri ML, Rigato M, Frison V, D’Ambrosio M, Sartore G, Avogaro A et al. Early weight loss, diabetes remission and long-term trajectory after diagnosis of type 2 diabetes: a retrospective study. Diabetologia 2025. doi: 10.1007/s00125-025-06402-w . Ryan DH, Yockey SR. Weight Loss and Improvement in Comorbidity: Differences at 5%, 10%, 15%, and Over. Curr Obes Rep 2017; 6: 187–194. Wing RR, Lang W, Wadden TA, Safford M, Knowler WC, Bertoni AG et al. Benefits of Modest Weight Loss in Improving Cardiovascular Risk Factors in Overweight and Obese Individuals With Type 2 Diabetes. Diabetes Care 2011; 34: 1481–1486. Kadouh H, Chedid V, Halawi H, Burton DD, Clark MM, Khemani D et al. GLP-1 Analog Modulates Appetite, Taste Preference, Gut Hormones, and Regional Body Fat Stores in Adults with Obesity. J Clin Endocrinol Metab 2020; 105: 1552–1563. Silver HJ, Olson D, Mayfield D, Wright P, Nian H, Mashayekhi M et al. Effect of the glucagon-like peptide-1 receptor agonist liraglutide, compared to caloric restriction, on appetite, dietary intake, body fat distribution and cardiometabolic biomarkers: A randomized trial in adults with obesity and prediabetes. Diabetes Obes Metab 2023; 25: 2340–2350. Blundell J, Finlayson G, Axelsen M, Flint A, Gibbons C, Kvist T et al. Effects of once-weekly semaglutide on appetite, energy intake, control of eating, food preference and body weight in subjects with obesity. Diabetes Obes Metab 2017; 19: 1242–1251. Heise T, DeVries JH, Urva S, Li J, Pratt EJ, Thomas MK et al. Tirzepatide Reduces Appetite, Energy Intake, and Fat Mass in People With Type 2 Diabetes. Diabetes Care 2023; 46: 998–1004. Wilding JPH, Batterham RL, Calanna S, Davies M, Van Gaal LF, Lingvay I et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med 2021; 384: 989–1002. Ansari S, Mazaheri T, O’Donnell K, Waite M, Cann A, Abdel-Malek M et al. Time to unshackle the medical treatment of obesity in the NHS. Clin Med (Lond) 2024; 24: 100206. Thong K y, Gupta PS, Cull ML, Adamson KA, Dove DS, Rowles SV et al. GLP-1 receptor agonists in type 2 diabetes - NICE guidelines versus clinical practice. British Journal of Diabetes 2014; 14: 52–59. Neeland IJ, Marso SP, Ayers CR, Lewis B, Oslica R, Francis W et al. Effects of liraglutide on visceral and ectopic fat in adults with overweight and obesity at high cardiovascular risk: a randomised, double-blind, placebo-controlled, clinical trial. The Lancet Diabetes & Endocrinology 2021; 9: 595–605. Linge J, Petersson M, Forsgren MF, Sanyal AJ, Dahlqvist Leinhard O. Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study. Journal of Cachexia, Sarcopenia and Muscle 2021; 12: 1513–1526. Whitcher B, Thanaj M, Cule M, Liu Y, Basty N, Sorokin EP et al. Precision MRI phenotyping enables detection of small changes in body composition for longitudinal cohorts. Sci Rep 2022; 12: 3748. Maltais A, Alméras N, Lemieux I, Tremblay A, Bergeron J, Poirier P et al. Trunk muscle quality assessed by computed tomography: Association with adiposity indices and glucose tolerance in men. Metabolism - Clinical and Experimental 2018; 85: 205–212. Mechanick JI, Butsch WS, Christensen SM, Hamdy O, Li Z, Prado CM et al. Strategies for minimizing muscle loss during use of incretin-mimetic drugs for treatment of obesity. Obes Rev 2025; 26: e13841. Stefanakis K, Kokkorakis M, Mantzoros CS. The impact of weight loss on fat-free mass, muscle, bone and hematopoiesis health: Implications for emerging pharmacotherapies aiming at fat reduction and lean mass preservation. Metabolism 2024; 161: 156057. Tinsley GM, Heymsfield SB. Fundamental Body Composition Principles Provide Context for Fat-Free and Skeletal Muscle Loss With GLP-1 RA Treatments. J Endocr Soc 2024; 8: bvae164. Magkos F, Fraterrigo G, Yoshino J, Luecking C, Kirbach K, Kelly SC et al. Effects of Moderate and Subsequent Progressive Weight Loss on Metabolic Function and Adipose Tissue Biology in Humans with Obesity. Cell Metab 2016; 23: 591–601. Pandey A, Patel KV, Segar MW, Ayers C, Linge J, Leinhard OD et al. Effect of liraglutide on thigh muscle fat and muscle composition in adults with overweight or obesity: Results from a randomized clinical trial. Journal of Cachexia, Sarcopenia and Muscle 2024; 15: 1072–1083. Sattar N, Neeland IJ, Leinhard OD, Landó LF, Bray R, Linge J et al. Tirzepatide and muscle composition changes in people with type 2 diabetes (SURPASS-3 MRI): a post-hoc analysis of a randomised, open-label, parallel-group, phase 3 trial. The Lancet Diabetes & Endocrinology 2025; 0. doi: 10.1016/S2213-8587(25)00027-0 . Lee SJ, Janssen I, Heymsfield SB, Ross R. Relation between whole-body and regional measures of human skeletal muscle. Am J Clin Nutr 2004; 80: 1215–1221. Yaskolka Meir A, Shelef I, Schwarzfuchs D, Gepner Y, Tene L, Zelicha H et al. Intermuscular adipose tissue and thigh muscle area dynamics during an 18-month randomized weight loss trial. J Appl Physiol (1985) 2016; 121: 518–527. Alkhouri N, Beyer C, Shumbayawonda E, Andersson A, Yale K, Rolph T et al. Decreases in cT1 and liver fat content reflect treatment-induced histological improvements in MASH. J Hepatol 2025; 82: 438–445. Mátis D, Hegyi P, Teutsch B, Tornai T, Erőss B, Pár G et al. Improved body composition decreases the fat content in non-alcoholic fatty liver disease, a meta-analysis and systematic review of longitudinal studies. Front Med 2023; 10. doi: 10.3389/fmed.2023.1114836 . Kim D, Lee J, Park R, Oh C-M, Moon S. Association of low muscle mass and obesity with increased all-cause and cardiovascular disease mortality in US adults. J Cachexia Sarcopenia Muscle 2024; 15: 240–254. Basty N, Thanaj M, Whitcher B, Bell JD, Thomas EL. Comparing DXA and MRI body composition measurements in cross-sectional and longitudinal cohorts. 2024;: 2024.12.12.24318943. Dubin RL, Heymsfield SB, Ravussin E, Greenway FL. Glucagon-like peptide-1 receptor agonist-based agents and weight loss composition: Filling the gaps. Diabetes, Obesity and Metabolism 2024; 26: 5503–5518. Tables Table 1 is available in the Supplementary Files section. Additional Declarations Yes there is potential conflict of interest. Supplementary Files 2025NowakWeightlossinUKBBTable1.xlsx Table 1 2025NowakWeightlossinUKBBIJObesitySUPPL.pdf Weight-related changes in MRI-derived measures of body composition and liver steatosis: a large-scale analysis for obesity trial design_Supplementary Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2026 Read the published version in International Journal of Obesity → Version 1 posted Editorial decision: revise 10 Oct, 2025 Review # 2 received at journal 04 Sep, 2025 Reviewer # 2 agreed at journal 15 Aug, 2025 Review # 1 received at journal 07 Jun, 2025 Reviewer # 1 agreed at journal 20 May, 2025 Reviewers invited by journal 19 May, 2025 Submission checks completed at journal 14 May, 2025 Editor assigned by journal 13 May, 2025 First submitted to journal 13 May, 2025 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. 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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-6656623","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":458437383,"identity":"98d68b3e-3dae-4628-827e-455eedadd37c","order_by":0,"name":"Helena Thomaides Brears","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0774-6983","institution":"Perspectum","correspondingAuthor":true,"prefix":"","firstName":"Helena","middleName":"Thomaides","lastName":"Brears","suffix":""},{"id":458437384,"identity":"a2fabfab-67ea-4021-81b2-395936f76260","order_by":1,"name":"Magdalena Nowak","email":"","orcid":"","institution":"Perspectum","correspondingAuthor":false,"prefix":"","firstName":"Magdalena","middleName":"","lastName":"Nowak","suffix":""},{"id":458437385,"identity":"bd5a3327-199a-41b3-bf95-3c2f404eab3e","order_by":2,"name":"Luis Nunez","email":"","orcid":"","institution":"Perspectum","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Nunez","suffix":""},{"id":458437386,"identity":"294e2d77-3f84-4f10-a9b5-3e0c61638675","order_by":3,"name":"Tim Pagliaro","email":"","orcid":"","institution":"Perspectum","correspondingAuthor":false,"prefix":"","firstName":"Tim","middleName":"","lastName":"Pagliaro","suffix":""},{"id":458437387,"identity":"292e2fa7-a9e7-49ef-af21-a8c4fe6f075e","order_by":4,"name":"Matthew Robson","email":"","orcid":"","institution":"Perspectum","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Robson","suffix":""},{"id":458437388,"identity":"47ee1119-28ff-4c93-9e8f-3e0da35d2afa","order_by":5,"name":"Carlos Duncker","email":"","orcid":"","institution":"Pinnacle Clinical Research","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Duncker","suffix":""},{"id":458437389,"identity":"e26d06b2-0629-4500-bcf1-168ceee63840","order_by":6,"name":"Lee Kaplan","email":"","orcid":"","institution":"Dartmouth College","correspondingAuthor":false,"prefix":"","firstName":"Lee","middleName":"","lastName":"Kaplan","suffix":""},{"id":458437390,"identity":"9731bbff-e949-44e5-afbb-66bcc98d4e6c","order_by":7,"name":"Steven Heymsfield","email":"","orcid":"https://orcid.org/0000-0003-1127-9425","institution":"Pennington Biomedical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Steven","middleName":"","lastName":"Heymsfield","suffix":""},{"id":458437391,"identity":"c0d35bc0-4d86-4247-9869-bd89e2f9ca9e","order_by":8,"name":"Jimmy Bell","email":"","orcid":"","institution":"University of Westminster","correspondingAuthor":false,"prefix":"","firstName":"Jimmy","middleName":"","lastName":"Bell","suffix":""},{"id":458437392,"identity":"b74050bf-80db-4690-9f83-77e907a1a27d","order_by":9,"name":"E. Louise Thomas","email":"","orcid":"https://orcid.org/0000-0003-4235-4694","institution":"University of Westminster","correspondingAuthor":false,"prefix":"","firstName":"E.","middleName":"Louise","lastName":"Thomas","suffix":""}],"badges":[],"createdAt":"2025-05-13 14:40:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6656623/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6656623/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41366-026-02037-y","type":"published","date":"2026-03-03T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83213861,"identity":"7fb327e3-6de9-4ef3-9f70-614fdc45cb9c","added_by":"auto","created_at":"2025-05-21 08:49:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":336074,"visible":true,"origin":"","legend":"\u003cp\u003eStudy population\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6656623/v1/b5a667a7c829e2581736e4aa.jpg"},{"id":83213863,"identity":"11ac449f-c99b-46b9-b9d8-466d9ccdc640","added_by":"auto","created_at":"2025-05-21 08:49:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155534,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in MRI-derived measurements of body fat, muscle, and liver across weight gain groups. Relative percent changes (mean ± SE) in: A. Fat metrics (VAT, SAT); B. Muscle metrics (SM, SMI, psoas CSA, MFI\u003csub\u003eSM\u003c/sub\u003e); C. Liver fat content. Note that x-axis range differs between sub-figures. * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6656623/v1/bcf894263cf829b16258cdc8.jpg"},{"id":83213865,"identity":"e59c170a-7dab-4917-927a-d626a72f23aa","added_by":"auto","created_at":"2025-05-21 08:49:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141226,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in MRI-derived measurements of body fat, muscle, and liver steatosis across weight loss groups. Relative percent changes (mean ± SEM) in: A. Fat metrics (VAT, SAT); B. Muscle metrics (SM, SMI, psoas CSA, MFI\u003csub\u003eSM\u003c/sub\u003e); C. Liver fat content. Note that x-axis range differs between sub-figures. * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6656623/v1/926550496dd383763c9b5454.jpg"},{"id":83215213,"identity":"13072639-3d00-4545-b920-dde955e3ab17","added_by":"auto","created_at":"2025-05-21 08:57:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":91343,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between relative % change in MRI-derived abdominal skeletal muscle (SM) volume and DXA-derived trunk lean mass (A) and total lean mass (B) over the follow-up period across the entire cohort.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6656623/v1/c0535ac89b652eba5337591c.jpg"},{"id":103810831,"identity":"0379121f-d29c-4657-87a0-90dc979e7800","added_by":"auto","created_at":"2026-03-03 08:13:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1592459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6656623/v1/25e7ccdc-bb50-4ef8-978e-313009c7890d.pdf"},{"id":83213860,"identity":"51ade4f5-75d4-4399-8c34-341d9456108d","added_by":"auto","created_at":"2025-05-21 08:49:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13787,"visible":true,"origin":"","legend":"Table 1","description":"","filename":"2025NowakWeightlossinUKBBTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6656623/v1/093dabc56ae028770b32835b.xlsx"},{"id":83213867,"identity":"63387f17-f8bf-4d00-81cb-efa01304c3f2","added_by":"auto","created_at":"2025-05-21 08:49:47","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":380127,"visible":true,"origin":"","legend":"Weight-related changes in MRI-derived measures of body composition and liver steatosis: a large-scale analysis for obesity trial design_Supplementary","description":"","filename":"2025NowakWeightlossinUKBBIJObesitySUPPL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6656623/v1/d49898c80add583c1e2527b6.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Weight-related changes in MRI-derived measures of body composition and liver steatosis: a large-scale analysis for obesity trial design","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eObesity is a chronic disease currently affecting an estimated 2\u0026nbsp;billion adults globally and expected to double by 2050 \u003csup\u003e1,2\u003c/sup\u003e. The precise manifestation of this disease is influenced by genotype, demographics and environmental factors\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The liver is often an early site of free fatty acid accumulation; metabolic dysfunction-associated steatotic liver disease (MASLD) is found in 75% of people with obesity \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. People with fat accumulation as visceral adipose tissue (VAT), in hepatocytes and in skeletal muscle (SM) are at increased risk of poor outcomes \u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn parallel, there has been an explosion in therapeutic advancements for obesity that lead to weight loss and provide additional management options beyond lifestyle and surgical intervention \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These drugs can have pleiotropic cardiometabolic benefits and this is associated with a reduction in ectopic fat and/or VAT \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, including in populations with comorbidities like MASLD. There are however growing concerns about concurrent loss of skeletal muscle mass and function \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, which correlates with poor outcomes \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and warrants further investigation.\u003c/p\u003e \u003cp\u003eA clinical workflow for obesity that includes direct measurement of body fat -\u003cem\u003enot BMI\u003c/em\u003e- and signs, symptoms or tests for organ dysfunction or substantial limitations of daily activities was recently recommended by the Lancet Diabetes \u0026amp; Endocrinology Commission on Clinical Obesity \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This commission recommended measurement of body fat by serum tests (triglyceride (TGA) or cholesterol), which lack organ-specificity, or by dual-energy x-ray absorptiometry (DXA), which estimates three-dimensional mass from coronal two-dimensional images but has limited accuracy for VAT \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMRI is the gold standard for volumetric assessment of VAT, SAT and SM, with single-slice area methods being applied for clinical care \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Use of multiparametric MRI to measure liver fat content is recognized as the most accurate non-invasive method for liver steatosis staging \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe sought to provide a comprehensive evaluation of the relative changes in body composition and liver steatosis resulting from general weight loss to inform clinical trial design and clinical management. We compared changes in multiparametric MRI and DXA parameters in individuals with a broad cardiometabolic profile from the UK Biobank, after weight gain and after weight loss prior to widespread adoption of incretin therapies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis prospective cohort study involved participants enrolled in the UK Biobank imaging sub-study between May 2014 and March 2020, who attended and completed 2 imaging visits within 3 years, by March 2022 \u003csup\u003e21\u003c/sup\u003e. Individuals aged 40 to 69 years old were invited for MRI examination (heart and abdomen), including quantitative mapping, as previously described \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Some participants were invited for a follow-up if the baseline scan was of sufficient quality, and they lived within certain proximity to an imaging center. UK Biobank has approval from Northwest Multi-Centre Research Ethics Committee and obtained written informed consent from all participants prior to the study. Data were extracted under access application 9914. Those with complete imaging data for MRI VAT, SAT, abdominal SM, abdominal SM index (SMI), abdominal SM fat infiltration (MFI\u003csub\u003eSM\u003c/sub\u003e), psoas muscle (PM) and liver fat content were included in the analysis.\u003c/p\u003e\u003cp\u003eDXA measurements from the same participants that had previously been processed with automated methods \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e were also included for comparisons without requiring further analysis; the available measurements were trunk lean mass (Field ID 23285) and total lean mass (Field ID 23280). DXA-derived VAT estimates (Field ID 22407) were excluded due to inconsistent quality.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eClinical and biochemical data\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eClinical data that included demographics (age, sex, ethnicity, weight, BMI, smoking status), and prior history of diabetes, hypertension, recent cancer (within 2 years of imaging visit) and reported medications, were collected at both imaging visits (from ICD-10 codes; \u003cb\u003eSupplementary methods\u003c/b\u003e). Blood biomarker data (TGA, total cholesterol) collected at the baseline visit was also accessed.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eMRI data acquisition\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll participants had been scanned on two occasions at one of the UK Biobank imaging centers, on a Siemens Aera 1.5T scanner (Siemens Healthineers, Erlangen, Germany), with both a dual-echo Dixon VIBE protocol for body composition and the LiverMultiScan imaging protocol that are encompassed within the UK Biobank imaging protocol \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBody composition measurements from MRI\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBody composition measurements were derived using fully automated volumetric analyses, as described previously \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Methods\u003c/b\u003e). The region from the center of the T9 vertebra to the top of the lower of the two femoral heads was selected for volumetric analyses. Volumes for VAT, SAT, abdominal SM and MFI\u003csub\u003eSM\u003c/sub\u003e were calculated based on the number of voxels. MFI\u003csub\u003eSM\u003c/sub\u003e was computed as the mean signal fat fraction value of the muscle tissue voxels. Semi-automated single-slice area analyses at the 3rd lumbar vertebra (L3) level were also conducted, as described previously \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Methods\u003c/b\u003e). Axial slices passing through the centre of mass of the L3 vertebra were automatically extracted from the whole-body volumes. The cross sectional area (CSA) for psoas muscle was calculated based on the number of pixels.\u003c/p\u003e\u003cp\u003eAll analyses were performed by trained MR technologists and radiographers blinded to all clinical data.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAnalysis of liver measurements from MRI\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLiver MRI data analysis was performed automatically using LiverMultiScan\u0026reg; software (Perspectum, Oxford, UK) \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e, and every case was manually reviewed by trained analysts, blinded to all clinical data.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll statistical analyses were performed in R (version 4.3, R Project for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003cp\u003eParticipants were categorized according to percentage weight change from baseline into the following groups: stable weight (0\u0026thinsp;\u0026plusmn;\u0026thinsp;2%), mild change (2\u0026ndash;5% weight gain/loss), moderate change (5\u0026ndash;10% weight gain/loss), or large change (10\u0026ndash;15% weight gain/loss) (\u003cb\u003eSupplementary methods\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eInitial analyses of variance (ANOVA) were performed to examine overall differences across weight change categories. Subsequently, within the stable weight reference group, paired sample t-tests were used to assess longitudinal changes in body composition from baseline to follow-up to quantify natural temporal variations. Between-group analyses using independent two-sample t-tests were used to compare each weight change category against the stable weight reference group to isolate effects specifically attributable to weight change. All statistical tests were conducted after confirming that parametric test assumptions were met. To control for multiple comparisons, p-values were adjusted using the Bonferroni method. Spearman\u0026rsquo;s rank sum test was used for correlation analyses (rho), with the Benjamini-Hochberg procedure applied to control the false discovery rate (FDR) due to multiple comparisons. Linear regression was performed to define rate of change in individual parameters after weight loss. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) throughout the text. All significance tests were two-tailed, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. For figures illustrating mean changes in metrics across weight loss/gain groups, standard error of the mean (SEM) is displayed to improve visualization of between-group differences.\u003c/p\u003e \u003cp\u003eThe World Health Organisation standards for BMI stratification were applied \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. Terminology and diagnostic criteria for what was previously \u0026ldquo;non-alcoholic fatty liver disease\u0026rdquo; have changed recently to metabolic dysfunction-associated liver disease and metabolic dysfunction-associated steatoepatitis and are adopted herein \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e. MASLD was defined as the presence of 1 cardiometabolic risk factor (diabetes, obesity, hypertension or hyperlipidaemia) and concurrent elevation in liver fat content (\u0026gt;\u0026thinsp;5%), in the absence of high consistent alcohol intake \u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e3071 individuals enrolled in the UK Biobank between 2014 and 2020 who had repeat MRI imaging over a mean 32\u0026thinsp;\u0026plusmn;\u0026thinsp;13 months follow up interval were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the first imaging visit, heir mean age was 62\u0026thinsp;\u0026plusmn;\u0026thinsp;8 years, and their BMI was 26\u0026thinsp;\u0026plusmn;\u0026thinsp;4 kg/m\u003csup\u003e2\u003c/sup\u003e. 97% were White, 49% were male and 5% were smokers (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). 13% of individuals were living with obesity, 43% with overweight and 15% with metabolic syndrome, 21% had MASLD. Only 5% had type 2 diabetes and 1.8% had a recent cancer diagnosis. 13% were on statins or other treatment for hypertension.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOver the follow-up period, body weight was stable in 43% (1333 of 3071). 864 individuals (28%) gained\u0026thinsp;\u0026gt;\u0026thinsp;2% of baseline weight over the same period. There was mild weight gain in 577 individuals (19%), gaining 2\u0026ndash;5% of their baseline weight), with 7.4% (227 individuals) gaining moderate weight, and only 60 individuals (2%) gaining 10\u0026ndash;15% of their baseline weight.\u003c/p\u003e \u003cp\u003eAdditionally, 18% (538 of 3071) lost 2\u0026ndash;5% of their baseline weight and 8.3% (255 individuals) lost moderate weight, while 81 (3%) lost 10\u0026ndash;15% of their original weight. Individuals who lost weight were more likely to have been overweight or obese and have high waist circumference at baseline (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Large weight loss was not associated with prior history of cancer or new cancer diagnoses (both p\u0026thinsp;=\u0026thinsp;0.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBody fat, muscle and liver MRI measurements in individuals maintaining stable weight\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDuring the follow-up period, people who maintained their weight showed relatively small increase in VAT (5\u0026thinsp;\u0026plusmn;\u0026thinsp;13%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SAT (2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to baseline (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). SM volume and psoas muscle CSA remained relatively stable (-1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3% and \u0026minus;\u0026thinsp;0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;16% change, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). Fat infiltration in the abdominal SM (MFI\u003csub\u003eSM\u003c/sub\u003e) was higher at follow-up by 3\u0026thinsp;\u0026plusmn;\u0026thinsp;3% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the liver, fat content showed relatively small increases (relative % change of 5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;29%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBody fat, muscle and liver MRI measurements in individuals gaining weight\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePeople who gained weight showed significant changes in body composition compared to those who maintained stable weight. Substantial increases were observed in VAT (ranging from 20\u0026ndash;58% change, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), in SAT (12\u0026ndash;36% change, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and in MFI\u003csub\u003eSM\u003c/sub\u003e (4.1\u0026ndash;8% change, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, the volume of abdominal SM remained relatively stable (-0.4% to -1.2% change, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mean percent changes in psoas muscle CSA showed statististically significant increases in two larger weight gain groups (4\u0026ndash;6% change, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but all groups exhibited high inter-individual variability. Liver fat content increased (ranging from 22\u0026ndash;72% change, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eChanges were equivalent when individuals on anti-hypertensive medications were excluded (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eBody fat, muscle and liver MRI measurements in individuals losing weight\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThose who lost weight during the follow-up period also had the highest baseline values for VAT, SAT and MFI\u003csub\u003eSM\u003c/sub\u003e across all groups (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e). Individuals who lost mild amounts of weight (2\u0026ndash;5% of their baseline weight) showed significant but not clinically meaningful changes in all metrics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In individuals with moderate weight loss (5\u0026ndash;10% baseline weight lost), more substantial decreases were observed in VAT (-22\u0026thinsp;\u0026plusmn;\u0026thinsp;13%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SAT (-17\u0026thinsp;\u0026plusmn;\u0026thinsp;10%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). SM volume also reduced by -3\u0026thinsp;\u0026plusmn;\u0026thinsp;4% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as did psoas muscle CSA (by -4\u0026thinsp;\u0026plusmn;\u0026thinsp;14%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eHowever, substantial decreases in MFI\u003csub\u003eSM\u003c/sub\u003e (-4\u0026thinsp;\u0026plusmn;\u0026thinsp;6% change, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) only occurred with large weight loss (10\u0026ndash;15% of baseline weight). In these individuals substantial decreases were also observed in VAT (-38\u0026thinsp;\u0026plusmn;\u0026thinsp;15%), in SAT (-30% \u0026plusmn; 12%), in skeletal (-4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8%) and psoas muscles (-5\u0026thinsp;\u0026plusmn;\u0026thinsp;5%), all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. In the liver, fat content decreased (-33\u0026thinsp;\u0026plusmn;\u0026thinsp;29%). Loss of psoas muscle was higher in males (\u003cb\u003eSupplementary Tables\u0026nbsp;3\u0026ndash;4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn individuals with obesity or overweight, for every 5% drop in weight, there was a decrease of -16% in VAT, -11% in SAT, -24% in liver fat, -1.5% in SM (or -1.4% in SMI) and \u0026minus;\u0026thinsp;2.1% in psoas muscle (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between MRI measures of fat and muscle\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAt baseline MFI\u003csub\u003eSM\u003c/sub\u003e correlated most strongly with SAT (rho\u0026thinsp;=\u0026thinsp;0.58), but change in MFI\u003csub\u003eSM\u003c/sub\u003e after the follow-up showed highest correlation with change in VAT (rho\u0026thinsp;=\u0026thinsp;0.60) and change in SAT (rho\u0026thinsp;=\u0026thinsp;0.46) (\u003cb\u003eSupplementary Figs.\u0026nbsp;1\u0026ndash;2\u003c/b\u003e). Neither abdominal SM nor psoas muscle CSA demonstrated correlations with any other MRI measure at baseline or with longitudinal changes at follow-up.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDXA assessment of lean mass during weight loss and gain\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDuring follow-up, participants with stable weight showed minimal but statistically significant changes in total lean mass (-0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) while trunk lean mass remained essentially unchanged (0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6%, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) compared to baseline. Total lean mass decreased with moderate/large weight loss (-2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5% to -2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and increased modestly with weight gain (0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3% to 2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8%, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Trunk lean mass remained statistically unchanged across all weight loss and gain groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThere were strong correlations between baseline DXA-derived lean mass measurements with MRI measurement of abdominal SM volume (rho\u0026thinsp;=\u0026thinsp;0.95 for trunk lean mass and rho\u0026thinsp;=\u0026thinsp;0.96 for total lean mass, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (\u003cb\u003eSupplementary Figs.\u0026nbsp;1\u0026ndash;2\u003c/b\u003e). However, the correlations were weak or moderate when longitudinal changes in these measures were considered over the follow-up period (rho\u0026thinsp;=\u0026thinsp;0.28 for trunk lean mass and rho\u0026thinsp;=\u0026thinsp;0.47 for total lean mass, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this study of 3071 individuals from the general population evaluated over a 3-year follow-up period with multi-parametric MRI, we present quantitative changes in body composition and liver fat according to different degrees of weight gain or loss. There were three key findings. Firstly, substantial increases in adipose tissue (VAT, SAT, MFI\u003csub\u003eSM,\u003c/sub\u003e liver fat) occurred with just moderate weight gain. Secondly, all metrics of fat except muscle fat infiltration (MFI\u003csub\u003eSM\u003c/sub\u003e) decreased with moderate weight loss. Thirdly, both skeletal and psoas muscles decreased with weight loss, with reductions in SM evident even after mild weight loss. The values reported here provide reference data of real-world weight changes in the pre-incretin drug era. The changes in body composition may have direct applicability for powering clinical trials and design of placebo arms.\u003c/p\u003e\u003cp\u003eIn clinical guidelines for obesity and diabetes, large weight loss (\u0026gt;\u0026thinsp;10%) is recommended \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, due to expected improvements in cardiometabolic outcomes \u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e; milder effects are observed with moderate (5\u0026ndash;10%) weight loss. Diabetes remission improved 1.7-fold with moderate weight loss compared to 5-fold with large weight loss in a study of 15,211 people with recent type 2 diabetes diagnoses \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Incretin-based drugs, in particular, can achieve 15\u0026ndash;25% weight loss within 1.5 years \u003csup\u003e\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Encouragingly, in our study of ageing individuals, those with higher BMI and waist circumference lost the most weight, in keeping with current clinical recommendations for weight management.\u003c/p\u003e\u003cp\u003eThis work preceded licensing of GLP-1RA for obesity in the UK (first approval in 2020 \u003csup\u003e42\u003c/sup\u003e), and only 5% of the study population had diabetes (first approval for diabetes in 2007 \u003csup\u003e43\u003c/sup\u003e). Nevertheless, the decreases in both VAT and SAT after moderate weight loss herein match the range of changes reported with GLP1-RA over shorter follow-up periods (-15% for VAT and \u0026minus;\u0026thinsp;20% for SAT with tirzepatide in people with diabetes after 1.5 years \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e; -13% decrease in VAT after 36 weeks of liraglutide in individuals with obesity\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eAdverse muscle composition, characterized by elevated muscle fat combined with low muscle volume, has been linked to poor function and is a strong and independent predictor of all-cause mortality \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Even small changes in skeletal muscle composition affect muscle function \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and insulin resistance \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The rapid weight loss as a result of incretin treatment has raised concerns about adverse effects on muscle mass and function \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Using DXA, trial findings indicate that 10\u0026ndash;40% of the weight lost in incretin trials is adjusted fat-free mass \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, estimated to consist of \u0026lsquo;approximately\u0026rsquo; 50% skeletal muscle \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Using MRI, impact of incretin mimetics on thigh skeletal muscle has very recently been investigated in randomized control trials. Treatment with liraglutide over 40 weeks was associated with a reduction of thigh muscle MFI (~\u0026thinsp;3%) and 5% weight loss among individuals with obesity \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In SURPASS-3 trial, there was a 6\u0026ndash;7% reduction in thigh muscle volume and 4\u0026ndash;5% reduction in thigh MFI after 52 weeks of tirzepatide in people with type 2 diabetes \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Our study evaluated MFI\u003csub\u003eSM\u003c/sub\u003e and SM in the abdomen rather than the thigh, because these muscle groups serve as strong markers of whole-body SM mass \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, while also providing operational advantages due to shorter scan duration and fewer hardware requirements \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Observed reductions in abdominal SM and MFI\u003csub\u003eSM\u003c/sub\u003e with large weight loss herein are comparable. The parabolic relationship that MFI\u003csub\u003eSM\u003c/sub\u003e showed with weight loss in our study may indicate a redistribution of fat from other deposits to skeletal muscle following low level weight loss, consistent with changes in fat depots after lifestyle intervention \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eClinically meaningful increases in fat occurred only with moderate to large weight gain. In the liver, 30% relative fat changes are considered clinically meaningful in individuals with MASLD, although smaller effects (\u0026gt;\u0026thinsp;1% absolute change) can be monitored and are significant \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The gains in liver fat were most pronounced among all fat measurement changes and correlated less with changes in VAT, SAT and MFI\u003csub\u003eSM\u003c/sub\u003e, consistent with previous findings \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. This may be suggestive of differential temporal patterns of fat mobilization or redistribution after weight gain.\u003c/p\u003e\u003cp\u003eThe high repeatability of measurements using multiparametric MRI \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e is evident in the minimal changes we found in those with stable weight. The availability of DXA has resulted in its use for cross-sectional body composition assessment \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, with recent recommendations endorsing its application for direct measurement of fat \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e in obesity definition. In clinical trials the reduced accuracy of DXA compared to MRI volumetric measurement \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, is a hindrance when measuring smaller changes, particularly for detecting longitudinal changes in muscle mass. While less accurate, DXA may be warranted in trials where bone mineral density is a relevant endpoint. However, the weak correlation we found between SM and lean mass changes may reflect its inability to differentiate between SM and MFI\u003csub\u003eSM\u003c/sub\u003e compartments \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. This prevents retrospective extrapolations of SM from DXA datasets and indicates that lean mass is a poor surrogate for tracking longitudinal changes in SM.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAlmost all UK Biobank participants are White, highlighting the need for comprehensive research across multi-ethnic populations. We explored the impact of prior cancer and disease history and of treatments for hypertension, but other treatments affecting weight changes were not investigated due to lack of detailed medical history in the UK Biobank registry. Nevertheless, the granularity of changes in body composition provided by MRI in this large-scale population study is a timely and useful reference for exploring the impact of drug candidates that induce weight loss in clinical trials. Of relevance to clinical trial design is that for every 5% drop in weight, there was a 16% reduction in VAT,, -11% in SAT, -24% in liver fat, 1.5% reduction in SM (or 1.4% in SMI) and 2.1% reduction in psoas muscle, in those who were obese or overweight. Amid rising global obesity rates, the need to optimize weight-loss therapies and personalize management options is vital, requiring accurate tools to measure adverse effects and stratify response to treatment. Our findings provide data to inform sample size calculations for future clinical trials and support label claims, while also offering a \u0026lsquo;virtual placebo\u0026rsquo; arm or population-level estimate \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe wish to acknowledge the UK Biobank participants.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, H.T.B. and M.N.; methodology, L.N. and M.D.R.; formal analysis, M.N. and L.N.; data curation, M.N.; writing\u0026mdash;original draft preparation, H.T.B.; writing\u0026mdash;review and editing, M.N., L.N., T.P., C.D., S.K., L.K., S.H., M.D.R., H.T.B., E.L.T. and J.D.B.; supervision, H.T.B. and L.T. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Magdalena Nowak, Luis N\u0026uacute;\u0026ntilde;ez, Tim Pagliaro, Matthew D. Robson and Helena Thomaides Brears are employees for Perspectum, a company that has developed LiverMultiScan, a UKCA and FDA(510k)-cleared medical device for liver health measurements that was used in the imaging sub-study of the UK Biobank. Helena Thomaides Brears and Matthew D. Robson are also shareholders of Perspectum. Louise E. Thomas and Jimmy D. Bell are consultants for Perspectum. Lee M. Kaplan is a consultant for Altimmune, Amgen, AstraZeneca, Boehringer Ingelheim, Cytoki, Helicore, Johnson \u0026amp; Johnson, Kallyope, Eli Lilly \u0026amp; Company, MetaVia, Neurogastrx, Novo Nordisk, Oxford Medical Products, Perspectum, Pfizer, Skye Bioscience and Zealand. The remaining authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eData availability Statement\u003c/p\u003e\n\u003cp\u003eParticipant data was obtained through UK Biobank Access Application number 9914. UK Biobank has approval from Northwest Multi-Centre Research Ethics Committee and obtained written informed consent from all participants prior to the study. Summary data is included in the manuscript or uploaded as online supplemental information. Anonymized individual patient data can be shared upon request or as required by law and/or regulation and/or governance by and within the rules of UK Biobank access with qualified external researchers. Approval of such requests is at the discretion of the study sponsors and is dependent on the nature of the request, the merit of the research proposed, the availability of the data, and the intended use of the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg M, Gakidou E, Lo J, Abate YH, Abbafati C, Abbas N \u003cem\u003eet al.\u003c/em\u003e Global, regional, and national prevalence of adult overweight and obesity, 1990\u0026ndash;2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. \u003cem\u003eThe Lancet\u003c/em\u003e 2025; 405: 813\u0026ndash;838.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubino F, Cummings DE, Eckel RH, Cohen RV, Wilding JPH, Brown WA \u003cem\u003eet al.\u003c/em\u003e Definition and diagnostic criteria of clinical obesity. \u003cem\u003eThe Lancet Diabetes \u0026amp; Endocrinology\u003c/em\u003e 2025; 0. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2213-8587(24)00316-4\u003c/span\u003e\u003cspan address=\"10.1016/S2213-8587(24)00316-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeed S, Bonnefond A, Froguel P. 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Association of low muscle mass and obesity with increased all-cause and cardiovascular disease mortality in US adults. \u003cem\u003eJ Cachexia Sarcopenia Muscle\u003c/em\u003e 2024; 15: 240\u0026ndash;254.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasty N, Thanaj M, Whitcher B, Bell JD, Thomas EL. Comparing DXA and MRI body composition measurements in cross-sectional and longitudinal cohorts. 2024;: 2024.12.12.24318943.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDubin RL, Heymsfield SB, Ravussin E, Greenway FL. Glucagon-like peptide-1 receptor agonist-based agents and weight loss composition: Filling the gaps. \u003cem\u003eDiabetes, Obesity and Metabolism\u003c/em\u003e 2024; 26: 5503\u0026ndash;5518.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Obesity, steatosis, skeletal muscle, visceral adipose tissue, subcutaneous adipose tissue, muscle fat infiltration, metabolic dysfunction-associated steatotic liver disease, MRI","lastPublishedDoi":"10.21203/rs.3.rs-6656623/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6656623/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objectives\u003c/strong\u003e: Amid rising global obesity rates and advances in weight-loss therapies, monitoring body composition and ectopic fat could refine trial design. We quantified weight-related changes in body composition and liver steatosis prior to widespread adoption of incretin treatments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjects/Methods\u003c/strong\u003e: Adults (N=3,071) from the UK Biobank with repeat abdominal MRI scans were included. Percent weight change from baseline was categorised: stable (0±2%), mild change (2-5% weight gain/loss), moderate change (5-10% weight gain/loss), or large change (10-15% weight gain/loss).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention/Methods\u003c/strong\u003e: MRI data were processed automatically from two visits, spaced 2.7 years apart, to derive volumetric visceral (VAT), subcutaneous adipose tissue (SAT), skeletal muscle volume (SM, or indexed SM), fat infiltration (MFI\u003csub\u003eSM\u003c/sub\u003e), and psoas muscle cross-sectional area (CSA) in the abdominal region. Liver fat content (LFC) was assessed using LiverMultiScan. Dual-energy x-ray absorptiometry (DXA) measurements were compared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Weight gain occurred in 28% of all subjects (N=3071, age 63 years, male 49%, 13% with obesity, 43% with overweight). Moderate or large weight gain increased LFC, VAT, SAT, MFI\u003csub\u003eSM\u003c/sub\u003e and psoas CSA (all p\u0026lt;.001). Weight loss also occurred in 28%. Decreases were observed with moderate or large weight loss: LFC -20% or -33%, VAT -22 or -38%, SAT -17 or -30%, SM -3 or -5%, SMI -3 or -4%, psoas CSA -4 or -5%, respectively (all p\u0026lt;.001). MFI\u003csub\u003eSM\u003c/sub\u003e reduced with large weight loss, by -4%. For every 5% drop in weight, there was -16% reduction in VAT, -11% in SAT, -24% in liver fat, -1.5% in SM (or -1.4% in SMI) and -2.1% in psoas muscle, in those with obesity or overweight. DXA changes in lean mass correlated weakly with changes in SM volume (rho 0.28-0.47).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Using MRI, relative changes in body composition and liver steatosis resulting from weight loss can inform clinical trials, including placebo arm design and power estimations.\u003c/p\u003e","manuscriptTitle":"Weight-related changes in MRI-derived measures of body composition and liver steatosis: a large-scale analysis for obesity trial design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 08:49:42","doi":"10.21203/rs.3.rs-6656623/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-10-10T12:53:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-04T13:30:23+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-08-15T22:28:18+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-06-07T16:56:33+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-05-20T15:40:25+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-05-19T05:05:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-14T10:40:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T14:36:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Obesity","date":"2025-05-13T14:36:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"48eae569-ee95-4825-b626-4c36d8b80439","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48701317,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"},{"id":48701318,"name":"Health sciences/Medical research/Clinical trial design/Clinical trials/Biostatistics"}],"tags":[],"updatedAt":"2026-03-03T08:13:43+00:00","versionOfRecord":{"articleIdentity":"rs-6656623","link":"https://doi.org/10.1038/s41366-026-02037-y","journal":{"identity":"international-journal-of-obesity","isVorOnly":false,"title":"International Journal of Obesity"},"publishedOn":"2026-03-03 05:00:00","publishedOnDateReadable":"March 3rd, 2026"},"versionCreatedAt":"2025-05-21 08:49:42","video":"","vorDoi":"10.1038/s41366-026-02037-y","vorDoiUrl":"https://doi.org/10.1038/s41366-026-02037-y","workflowStages":[]},"version":"v1","identity":"rs-6656623","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6656623","identity":"rs-6656623","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}