MR Imaging Body composition as a non-invasive tool to detect endogenous hypercortisolism: an exploratory study

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Up to 73% of CS patients also meet the criteria for metabolic syndrome, further blurring the clinical picture and delaying diagnosis. We aim to compare the body composition of two groups—healthy volunteers and patients with endogenous hypercortisolism—using magnetic resonance imaging (MRI). Our objective was to identify a specific anthropometric phenotypic signature in patients with endogenous hypercortisolism that could facilitate earlier clinical suspicion. Methods 30 patients with a recent diagnosis of endogenous hypercortisolism (77% adrenal origin, 10% pituitary origin, 13% other origin), were matched with 15 healthy controls using a propensity score analysis, considering age, sex, and body mass index. We acquired the abdominal and thigh images using a 3T Philips Ingenia MRI scanner and a quantitative Dixon sequence. Results Robust Principal Component Analysis (RPCA) and cluster analysis showed a sensitivity of 93% and specificity of 73% for classifying control and endogenous hypercortisolism patients using only body composition information. Logistic regression analysis showed that belonging to the cluster found by RPCA has an OR of 38.5, p < 0.001, for presenting endogenous hypercortisolism. Conclusions MRI-based body composition may offer a novel approach for the early suspicion of endogenous hypercortisolism in overweight and obese patients, and the muscle-to-liver fat fraction ratio is a specific anthropometric phenotypic signature. Endogenous Hypercortisolism Cushing's syndrome MRI Body composition Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cushing's syndrome (CS) is a complex endocrine disorder resulting from prolonged exposure to hypercortisolism. When excluding exogenous sources, this condition is referred to as endogenous CS, which includes various forms such as adrenal CS, and the most prevalent form, Cushing's disease secondary to pituitary benign tumour [ 1 , 2 ]. The annual incidence of overt CS ranges from 0.2 to 5 cases per million people, with 1.2 to 1.7 cases per million attributed to Cushing’s disease. Women are more frequently affected, as indicated by a female-to-male ratio of approximately 3:1 [ 3 , 4 , 5 ]. From this point forward, we will use the term endogenous hypercortisolism (EH) to encompass all cases, including adrenal Cushing's syndrome, pituitary Cushing's syndrome (also known as Cushing's disease), as well as mild autonomous cortisol secretion (MACS) that does not exhibit the overt signs of Cushing's syndrome but still reflects endogenous hypercortisolism. EH is characterized by a broad spectrum of clinical manifestations, including central obesity, hypertension, easy bruising, red stretch marks, buffalo hump, and proximal myopathy with atrophy [ 6 ]. These features, however, often overlap with those of more common conditions such as metabolic syndrome, obesity, dyslipidaemia, and insulin resistance, which complicates early diagnosis [ 7 , 8 , 9 ]. Notably, up to 73% of EH patients also meet the criteria for metabolic syndrome, further blurring the clinical picture and delaying diagnosis [ 2 , 10 ]. One significant concern with EH is the diagnostic delay, as the average time from the onset of symptoms to diagnosis ranges from 34 to 38 months, with particularly prolonged delays in cases of pituitary CS [ 11 , 12 ]. This delay is not only due to the nonspecific nature of symptoms but also to the complexity of distinguishing EH from other common clinical conditions [ 7 , 13 ]. The prolonged exposure to high cortisol levels increases the risk of severe complications, including osteoporosis, hypertension, diabetes mellitus, heart failure, cerebrovascular accidents, thrombosis, and infections due to immunosuppression [ 14 – 17 ]. Current diagnostic methods include the 24-hour urinary free cortisol test, the 1 mg dexamethasone suppression test, and late-night salivary cortisol, all of which are widely used but are not without limitations. These tests may produce false-positive or false-negative results requiring careful interpretation and often necessitating further evaluation [ 1 , 11 ]. Moreover, there is no single screening test that is entirely reliable for all patients, making early diagnosis particularly challenging [ 1 ]. Early clinical suspicion that allows the identification of patients who will need these diagnostic tests continues to be the main barrier to early diagnosis [ 10 ]. Currently, body composition analysis based on the measurement of different tissue compartments is receiving attention for clinical and research applications. Numerous studies have shown that body composition correlates with cardiometabolic risk, and the prognosis of different medical and surgical conditions [ 18 , 19 ]. Among imaging modalities for body composition analysis, magnetic resonance imaging (MRI) has emerged as the most impactful technique, owing to its excellent ability to separate different tissues, the capacity to estimate fat infiltration in organs such as the liver and muscle, and its favourable safety profile, as it does not involve ionizing radiation [ 20 – 22 ]. In this exploratory study, we aim to compare the body composition of two groups—healthy volunteers and patients with recent diagnosis of endogenous hypercortisolism (EH)—using MRI technology. Our objective is to identify a distinct anthropometric phenotypic signature in patients with EH that could facilitate earlier clinical suspicion. Previous studies have described body composition changes in this population using Dual-energy X-ray Absorptiometry or isolated MRI measures [ 23 – 26 ]. Resmini et al. reported increased truncal fat and reduced lean mass, while Díaz-Manera et al. [ 25 ] demonstrated greater intramuscular fat infiltration on MRI, associated with lower physical performance. However, no study to date has simultaneously assessed abdominal adiposity, hepatic fat content, and skeletal muscle fat infiltration using MRI in this context. Our study addresses this gap by proposing an integrated imaging-based phenotypic signature to aid earlier EH detection, thereby contributing to the development of more precise diagnostic tools and ultimately improving patient outcomes through earlier intervention. Methods 1. Patient cohort This prospective, exploratory observational study was conducted at the University Hospital of the Pontificia Universidad Católica de Chile in Santiago, Chile. Participants were recruited from the Endocrinology Department between September 2023 and March 2025. The study included adult recently diagnosed (less than 1 month) with endogenous hypercortisolism (EH). EH diagnosis was defined by serum cortisol levels exceeding 1.8 µg/dL following a nighttime 1 mg dexamethasone suppression test (1 mg DST), measured using Electrochemiluminescence Immunoassay (ECLIA). Adrenocorticotropic hormone (ACTH) levels were measured using a chemiluminescent immunoassay, with levels greater than 20 pg/mL used to differentiate between pituitary and adrenal sources of hypercortisolism, indicating pituitary Cushing's syndrome. A control group of healthy volunteers, matched by age, sex, and BMI, was also included. Patients were recruited from the Program of Pituitary Disease and the Program for Adrenal Disorders of the Pontificia Universidad Catolica de Chile, while controls were selected from the general population through a public call. Individuals under 18 years old, recent exogenous glucocorticoid use, pregnant women, those with pacemakers, claustrophobia, or who did not sign informed consent were excluded. This study was approved by the Scientific Ethics Committee of Pontificia Universidad Católica de Chile (ID 230108002), and all participants provided written informed consent before their inclusion in the study. 2. Study design This study uses MRI to analyse body composition differences between subjects. To minimize selection bias and balance observed characteristics between the groups with and without EH, a Propensity Score Matching (PSM) analysis was performed. Propensity Scores were calculated using a logistic regression model, incorporating gender, age, and body mass index (BMI) as covariates. The imaging data collected were analysed to identify potential radiological markers that could distinguish between these two groups. 3. MRI Acquisition A Philips Ingenia 3T MRI scanner was used to acquire both abdominal and thigh muscle images. All images were obtained at the University Hospital of the Pontificia Universidad Católica de Chile. Participants were scanned in a feet-first supine position. The fat fraction was estimated using the mDixonQuant package (Philips Healthcare), which provides quantitative maps of proton density fat fraction (PDFF). The abdominal acquisition protocol included parameters such as TR/TE1/ΔTE = 6/0.98/0.8 ms, flip angle = 3°, FOV RL/AP/FH = 460/250/400 mm, Acquisition Matrix = 232x125, with a slice thickness of 6 mm. We evaluated the fat fraction and volume of the thigh muscle, following the methodology used by Martel-Duguech et al. [ 27 ]. The muscular acquisition protocol used TR/TE1/ΔTE = 6.4/1.07/0.8 ms, flip angle = 3°, FOV RL/AP/FH = 220/180/240 mm, Acquisition Matrix = 112x87, with a slice thickness of 6 mm. For muscle fat quantification, two regions of interest (ROIs) were manually placed on each vastus lateralis muscle (right and left) within the acquired volumetric dataset. The ROIs were selected on representative axial slices, and their values were averaged to estimate the overall fat fraction across the muscle volume. The acquisition protocol lasted 45 minutes. Abdominal adipose tissue compartments were manually segmented from DICOM images using OsiriX MD software (version 12.0, Pixmeo, Geneva, Switzerland) by a single blinded evaluator who was unaware of group assignment. Total abdominal adipose tissue was calculated by adding subcutaneous adipose tissue and visceral adipose tissue. Patients were instructed not to consume food or drinks for 2 hours before the exam. 4. Analysis The independent variables included patient characteristics such as age, sex, height, and body mass index (BMI), as well as quantitative metrics derived from MRI image analysis: muscle fat fraction (MFF), liver fat fraction (LFF), thigh area, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and total adipose tissue (TAT). All quantitative variables were treated as continuous and summarized using descriptive statistics for both groups. Additional variables derived from the primary data included the ratio of muscle fat fraction to liver fat fraction, and the ratio of thigh area to height. The dependent variable was the diagnosis of EH or healthy individual. For muscle fat quantification, two regions of interest (ROIs) were manually placed on each vastus lateralis muscle (right and left) within the acquired volumetric dataset. The ROIs were selected on representative axial slices, and their values were averaged to estimate the overall fat fraction across the muscle volume. For liver fat quantification, three circular regions of interest (ROIs) were manually placed on representative axial slices throughout the liver volume—two in the right lobe and one in the left lobe—avoiding large vessels, bile ducts, and artifacts. The fat fraction values from these ROIs were averaged to obtain the final liver fat fraction (LFF) for each participant. Abdominal adipose compartments (SAT and VAT) were manually segmented in the full volumetric acquisition, and total adipose tissue was calculated as the sum of SAT and VAT. To minimize bias, both EH patients and healthy volunteers’ groups were matched by age, sex, and BMI, thus controlling for confounding variables. Additionally, the same imaging protocols, segmentation methods, and measurement procedures, ensuring data comparability. Finally, data acquisition and image analysis were performed by trained personnel using standardized equipment and software to ensure consistency and reducing observer-introduced variability. 5. Statistical methods: Mann–Whitney test was used to compare differences between groups, with a p-value < 0.05 considered statistically significant. Pearson correlation of determination (R²) was calculated to quantify the proportion of variability in the presence of EH that could be explained by the independent variable. All analyses were conducted using Prism 9 (GraphPad Software Inc., La Jolla, CA). Robust Principal Component Analysis (RPCA) was applied for dimensionality reduction and clustering observations, aiding in the identification of underlying patterns in the data. This method is robust to outliers and allows for more accurate classification of subjects into homogeneous groups [ 28 ]. The K-Nearest Neighbours (KNN) algorithm was employed for classifying subjects based on the characteristics identified by RPCA. This classification method was used to predict the occurrence of EH by evaluating the similarity of observations to the closest neighbours in the feature space. We used logistic regression analysis to evaluate the predictive value of the identified clusters to predict EH. RPCA and cluster analysis were performed with the statistical package R v4.0.2 ( www.r-project.org/ ) and the libraries: rpca, class and caret. Results A total of 33 patients with endogenous hypercortisolism (EH) were initially considered eligible for the study. Three were excluded due to claustrophobia, leaving 30 participants who met the inclusion criteria. Additionally, 15 healthy volunteers were included as a control group. The demographic characteristics, such as age and sex, along with anthropometric data, including body mass index (BMI), were matched across the groups, showing no significant differences (Table 1 ). The mean Propensity Score was 0.5063 (SD = 0.0675) for the EH group and 0.4937 (SD = 0.0398) for the control group. The similarity in scores suggests a good balance between covariates. The Mann-Whitney test showed no statistically significant difference (p = 0.565). Table 1 Demographic and clinical characteristics of control and endogenous hypercortisolism groups. The mean values are presented with their standard deviations, and the p-values indicate the statistical significance of differences between the control and hypercortisolism group. Control Endogenous Hypercortisolism P-value Number of participants 15 30 Females (%) 80 80 Age (years) 55.5 ± 15.5 56.8 ± 15.0 0.80 Weight (kilograms) 76.3 ± 14.5 76.0 ± 13.0 0.94 Height (meters) 1.61 ± 0.08 1.59 ± 0.09 0.63 BMI (kilograms/meters²) 29.5 ± 5.1 29.9 ± 4.8 0.79 Within the EH patients included, 23 had an adrenal origin (including 7 with mild autonomous cortisol secretion (MACS)), 3 had a pituitary origin, 3 had adrenal carcinoma and one had an ectopic ACTH-secreting carcinoma. No significant differences were observed in the SAT and VAT to total adipose tissue ratio, VAT to SAT ratio, Liver-FF and Muscle FF (Fig. 1 .A-E) between both groups. However, control group showed larger muscle area and muscle area to height ratio (Fig. 1 .F-G) than the EH group. Conversely, the EH group exhibited a significantly higher muscle-to-liver fat fraction ratio (Fig. 1 .H) compared with control group. Since BMI is a widely used parameters to evaluate overweighted patients, we studied the relationship between BMI and the body composition parameters in both groups (Fig. 2 ). For most parameters, the linear regression indicates minimal variability explained by BMI with low R 2 values. The strongest correlation with BMI was found with the Liver FF in the control group (R 2 = 0.32, Fig. 2 .D) and the Muscle-to-Liver FF ratio in the EH group (R 2 = 0.21, Fig. 2 .H). Our results showed that EH group has a consistent tendency to have lower muscle area and muscle area to height ratio, and higher muscle-to-liver fat fraction ratio adjusted by BMI than the control group. Robust Principal Component Analysis (RPCA) was applied using all MRI-derived body composition variables. Figure 3 A-B shows the distribution of individuals in our sample along Robust Principal Components 1 and 2. The KNN algorithm identified two independent clusters in our sample (Fig. 3 C). Cluster 1 included 15% EH patients, whereas Cluster 2 included 94% EH patients. The characteristics of the individuals grouped in each cluster are shown in Fig. 4 . Significant differences were found in the muscle-to-liver fat fraction ratio, muscle area, and muscle area-to-height ratio, with p-values < 0.05. Cluster classification identified by RPCA and the KNN algorithm showed a sensitivity of 93% (CI95%:77.9%-99.2%), specificity of 73% (CI95%:45.0%-92.2%), a positive Likelihood Ratio of 3.5 (CI95%:1.5–8.2), and a negative Likelihood Ratio of 0.09 (CI95%:0.02–0.36) to differentiate between both groups using the MR derived body composition parameters. Logistic regression analysis showed that belonging to the cluster found by RPCA has an OR of 38.5 (p < 0.001) and AUC of 0.83 (p < 0.001) to correctly identify endogenous hypercortisolism patients based in these parameters (Fig. 4 ). Discussion This exploratory study provides a new perspective on the use of MRI for assessing body composition in the early suspicion of EH. Our study provides a novel contribution to the field by advancing the use of MRI for detailed, organ-specific assessment of body composition in metabolic disease. Through quantitative imaging biomarkers, it enhances current understanding of ectopic fat distribution—particularly in the liver and muscle—and supports the role of MRI as a non-invasive tool for phenotyping and monitoring metabolic dysfunction. The RPCA and the cluster analysis using KNN identified two differentiated groups within our sample: one predominantly composed of control subjects (Cluster 1) and the other with EH patients (Cluster 2). With a sensitivity of 93% and specificity of 73% for classifying these groups based solely on body composition, our study highlights the potential of this specific anthropometric phenotypic signature to enhance diagnostic accuracy in a clinical setting. Analysis of the clusters identified by RPCA showed that thigh area and fatty infiltration of the thigh and its relationship with fatty infiltration of the liver are key markers for differentiating between patients with EH and control subjects. Muscle fat infiltration exhibits a distinct anthropometric phenotypic signature for EH, which may be linked to specific morphological alterations detectable by MRI. Proximal muscle weakness is a common presentation in EH, affecting 60–82% of patients. This myopathy typically involves the thigh muscles and significantly impacts quality of life [ 29 ]. The underlying mechanism of EH myopathy is complex, involving protein degradation, intramuscular fat accumulation, and muscle atrophy due to inactivity [ 30 ]. Researchers have thoroughly investigated myopathy in EH using various techniques to better understand muscle health and function. These efforts include quantitative muscle ultrasonography to monitor myopathy progression [ 31 ], whole-body MRI to examine intramuscular fat accumulation [ 32 ], and CT to assess body composition changes post-adrenalectomy [ 33 ]. Gallagher et al. emphasize the need for accurate adipose tissue quantification [ 34 ]. Muscle biopsies and nerve conduction studies have revealed key pathological features, such as type II fiber atrophy and impaired muscle protein levels [ 34 , 35 ], with additional studies examining additional muscle parameters [ 36 ]. Our results align with previous findings suggesting the relevance of body composition in the diagnosis of EH. MRI-based studies, such as that by Martel-Duguech et al. [ 27 ], have demonstrated increased fat fraction in thigh compartments among EH patients in remission, with associations to impaired physical performance. However, their analysis was limited to muscle tissue. In contrast, our study simultaneously quantified fat in abdominal, hepatic, and thigh compartments using standardized ROIs, and incorporated advanced data analysis methods, including RPCA and KNN. Although our analysis did not reveal significant group differences in muscle fat fraction, the muscle-to-liver fat fraction ratio proved to be the most discriminative marker, with significantly higher values in EH patients. Using this multiregional approach, our model achieved 93% sensitivity, 73% specificity, and an AUC of 0.83 in distinguishing EH from controls. Regarding the behaviour of hypercortisolism according to its pathophysiological origin, Fig. 3 D illustrates its subclassification in the RPCA graph. The three groups of hypercortisolism showed a distributed location along the cluster identified as hypercortisolism, with a slight tendency for cases of pituitary origin to cluster more closely together. This pattern may be attributed to the preservation of muscle mass due to the relatively maintained androgen levels in pituitary hypercortisolism, making these cases more like the control group without solid evidence of myopathy. However, a larger and more balanced sample across the different types of hypercortisolism will be required to clearly validate a specific subclassification. We acknowledge that this study has some limitations. As an exploratory study, the sample size is relatively small and for a single institution, which could affect the generalizability of the results. All segmentations were performed by a single observer, potentially introducing observer bias. Imaging data were obtained from a specific MRI system and vendor (Philips Ingenia 3T), which may affect reproducibility across platforms. Additionally, while our findings are promising, a longitudinal study with a larger sample size and a validation cohort are needed to validate these non-invasive imaging markers in future clinical practice. Other techniques such as magnetic resonance spectroscopy (MRS) could offer complementary information. Future research could focus on integrating this body-composition phenotypic signature with other clinical and biochemical data to develop a more robust multiparametric diagnostic tool for EH. Additionally, longitudinal studies could explore how these body composition changes evolve over time and their relationship with treatment and disease progression. In conclusion, this study suggests that MRI-based body composition analysis, combined with advanced data analysis methods, may offer a new approach for the early clinical suspicion of EH. Identifying specific anthropometric phenotypic patterns associated with EH could not only improve early diagnosis but also open new opportunities for more personalized and effective interventions in the management of this complex endocrine entity. Abbreviations BMI: Body mass index CS: Cushing's syndrome EH: Endogenous hypercortisolism KNN: K-Nearest Neighbours LFF: Liver fat fraction MACS: Mild autonomous cortisol secretion MFF: Muscle fat MRI: Magnetic resonance imaging ROI: Region of interest RPCA: Robust Principal Component Analysis SAT: Subcutaneous adipose tissue TAT: Total adipose tissue VAT: Visceral adipose tissue Declarations Ethics approval and consent to participate : This study was conducted in accordance with the Declaration of Helsinki and was approved by the Scientific Ethics Committee of Pontificia Universidad Católica de Chile (ID 230108002). All participants provided written informed consent before their inclusion in the study. Consent for publication : All participants provided written informed consent for result publication before their inclusion in the study. Availability of data and materials : The datasets generated and analysed during the current study are not publicly available due privacy and ethical restrictions but are available from the corresponding author on reasonable request. Competing interests : The authors have no conflicts of interest to declare. Funding: Chilean Agency for Research and Development: FONDECYT 1220922 and Millennium Science Initiative Program – ICN2021_004 Authors' contributions: CP, MEA: have made substantial contributions to the conception and design of the work; acquisition, analysis and interpretation of data; and have drafted the work. RB, TU, FG, AX, CB: have made substantial contributions to the conception and design of the work; interpretation of data; and substantively revised the manuscript. AX, LM, MV: have made substantial contributions in the acquisition, analysis and interpretation of data and have substantively revised the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated and analysed during the current study are not publicly available due privacy and ethical restrictions but are available from the corresponding author on reasonable request. References Nieman LK, Biller BMK, Findling JW, et al. The diagnosis of Cushing's syndrome: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2008;93(5):1526–40. 10.1210/jc.2008-0125 . Bolland MJ, Holdaway IM, Berkeley JE et al. Mortality and morbidity in Cushing's syndrome in New Zealand. Clin Endocrinol (Oxf). 2011;75(4):436–442. 10.1111/j.1365-2265.2011.04124 . x. Lindholm J, Juul S, Jørgensen JO, et al. Incidence and late prognosis of Cushing's syndrome: a population-based study. J Clin Endocrinol Metab. 2001;86(1):117–23. 10.1210/jcem.86.1.7093 . 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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-8398852","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576152844,"identity":"8f774e92-de80-4dd3-b99a-cb2ff4d34401","order_by":0,"name":"Claudia Pazmiño","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Pazmiño","suffix":""},{"id":576152845,"identity":"37e73304-1311-467d-83d3-53c8790c30b9","order_by":1,"name":"Rene Baudrand","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Rene","middleName":"","lastName":"Baudrand","suffix":""},{"id":576152847,"identity":"e4da4264-2fea-4dd3-a2d4-1b0d88b3fdf7","order_by":2,"name":"Thomas Uslar","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Uslar","suffix":""},{"id":576152850,"identity":"89c65aa4-78f9-456b-892e-530e02245117","order_by":3,"name":"Francisco Guarda","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Guarda","suffix":""},{"id":576152854,"identity":"900becb5-7ba8-4167-b86b-052a59544c4b","order_by":4,"name":"Aline Xavier","email":"","orcid":"","institution":"University of Santiago 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09:06:10","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94009,"visible":true,"origin":"","legend":"","description":"","filename":"bcf8c0382ee742b980e8d94ae60b20c11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8398852/v1/12d8ae3e083aff4b4568d383.xml"},{"id":100663473,"identity":"67764dec-ea2d-4b3b-b11d-38390cff9a53","added_by":"auto","created_at":"2026-01-20 09:07:19","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106433,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8398852/v1/f18705854aae89f405b2d889.html"},{"id":100663580,"identity":"0b6dd8df-b860-4e0b-830c-c4acb2acc849","added_by":"auto","created_at":"2026-01-20 09:08:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":202927,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of body composition parameters between control and endogenous hypercortisolism groups. SAT= subcutaneous adipose tissue, VAT= visceral adipose tissue, EH=endogenous hypercortisolism.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8398852/v1/b66437e2477bd718ac6712b6.png"},{"id":100663547,"identity":"3b343356-e2b6-4673-b1a6-b47be32134a1","added_by":"auto","created_at":"2026-01-20 09:07:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":232536,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between BMI (kg/m²) and various measured variables in control and Endogenous Hypercortisolism groups. SAT= subcutaneous adipose tissue, VAT= visceral adipose tissue, EH=endogenous hypercortisolism, BMI= body mass index.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8398852/v1/e202b8e1996a22d3ff441100.png"},{"id":100663357,"identity":"fb6869ae-708a-4e4f-bdf7-4bd18bf98596","added_by":"auto","created_at":"2026-01-20 09:06:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158854,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of the Robust Principal Component 1 and 2 (3.A-B); KNN Classification clusters boundaries corresponding to the control (blue) and Endogenous Hypercortisolism (red) groups (3.C). Scatter plot showing the control subjects and hypercortisolism patients according to its pathophysiological origin: control (blue), Adrenal EH (red), Pituitary EH (green), and Other EH (purple) (3.D). RCPA= robust principal component analysis, KNN= K-nearest neighbour.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8398852/v1/6840b5dcdc235b99adabaa73.png"},{"id":100663109,"identity":"cf236cf1-c065-46e8-bb86-31ef4db811be","added_by":"auto","created_at":"2026-01-20 09:05:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":203988,"visible":true,"origin":"","legend":"\u003cp\u003eTable summarizes the variables included in each cluster, highlighting significant differences in muscle-to-liver fat ratio, muscle area, and muscle area-to-height ratio. Contingency table of the classification performance of the clusters identify by RPCA and KNN and its ROC Curve for EH patient identification. BMI= body mass index, RPCA= robust principal component analysis, KNN= K-nearest neighbour.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8398852/v1/7eaae3638cec5efc95fdf06f.png"},{"id":100667603,"identity":"d740aa31-ca74-4780-b959-00b526200c70","added_by":"auto","created_at":"2026-01-20 09:48:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1259605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8398852/v1/45a566bf-cc34-493b-bf1b-6d701597cbde.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MR Imaging Body composition as a non-invasive tool to detect endogenous hypercortisolism: an exploratory study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCushing's syndrome (CS) is a complex endocrine disorder resulting from prolonged exposure to hypercortisolism. When excluding exogenous sources, this condition is referred to as endogenous CS, which includes various forms such as adrenal CS, and the most prevalent form, Cushing's disease secondary to pituitary benign tumour [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The annual incidence of overt CS ranges from 0.2 to 5 cases per million people, with 1.2 to 1.7 cases per million attributed to Cushing\u0026rsquo;s disease. Women are more frequently affected, as indicated by a female-to-male ratio of approximately 3:1 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. From this point forward, we will use the term endogenous hypercortisolism (EH) to encompass all cases, including adrenal Cushing's syndrome, pituitary Cushing's syndrome (also known as Cushing's disease), as well as mild autonomous cortisol secretion (MACS) that does not exhibit the overt signs of Cushing's syndrome but still reflects endogenous hypercortisolism.\u003c/p\u003e \u003cp\u003eEH is characterized by a broad spectrum of clinical manifestations, including central obesity, hypertension, easy bruising, red stretch marks, buffalo hump, and proximal myopathy with atrophy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These features, however, often overlap with those of more common conditions such as metabolic syndrome, obesity, dyslipidaemia, and insulin resistance, which complicates early diagnosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, up to 73% of EH patients also meet the criteria for metabolic syndrome, further blurring the clinical picture and delaying diagnosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne significant concern with EH is the diagnostic delay, as the average time from the onset of symptoms to diagnosis ranges from 34 to 38 months, with particularly prolonged delays in cases of pituitary CS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This delay is not only due to the nonspecific nature of symptoms but also to the complexity of distinguishing EH from other common clinical conditions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The prolonged exposure to high cortisol levels increases the risk of severe complications, including osteoporosis, hypertension, diabetes mellitus, heart failure, cerebrovascular accidents, thrombosis, and infections due to immunosuppression [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent diagnostic methods include the 24-hour urinary free cortisol test, the 1 mg dexamethasone suppression test, and late-night salivary cortisol, all of which are widely used but are not without limitations. These tests may produce false-positive or false-negative results requiring careful interpretation and often necessitating further evaluation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, there is no single screening test that is entirely reliable for all patients, making early diagnosis particularly challenging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Early clinical suspicion that allows the identification of patients who will need these diagnostic tests continues to be the main barrier to early diagnosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, body composition analysis based on the measurement of different tissue compartments is receiving attention for clinical and research applications. Numerous studies have shown that body composition correlates with cardiometabolic risk, and the prognosis of different medical and surgical conditions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Among imaging modalities for body composition analysis, magnetic resonance imaging (MRI) has emerged as the most impactful technique, owing to its excellent ability to separate different tissues, the capacity to estimate fat infiltration in organs such as the liver and muscle, and its favourable safety profile, as it does not involve ionizing radiation [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this exploratory study, we aim to compare the body composition of two groups\u0026mdash;healthy volunteers and patients with recent diagnosis of endogenous hypercortisolism (EH)\u0026mdash;using MRI technology. Our objective is to identify a distinct anthropometric phenotypic signature in patients with EH that could facilitate earlier clinical suspicion. Previous studies have described body composition changes in this population using Dual-energy X-ray Absorptiometry or isolated MRI measures [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Resmini et al. reported increased truncal fat and reduced lean mass, while D\u0026iacute;az-Manera et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] demonstrated greater intramuscular fat infiltration on MRI, associated with lower physical performance. However, no study to date has simultaneously assessed abdominal adiposity, hepatic fat content, and skeletal muscle fat infiltration using MRI in this context. Our study addresses this gap by proposing an integrated imaging-based phenotypic signature to aid earlier EH detection, thereby contributing to the development of more precise diagnostic tools and ultimately improving patient outcomes through earlier intervention.\u003c/p\u003e"},{"header":"Methods","content":"\n\u003ch3\u003e1. Patient cohort\u003c/h3\u003e\n\u003cp\u003eThis prospective, exploratory observational study was conducted at the University Hospital of the Pontificia Universidad Cat\u0026oacute;lica de Chile in Santiago, Chile. Participants were recruited from the Endocrinology Department between September 2023 and March 2025. The study included adult recently diagnosed (less than 1 month) with endogenous hypercortisolism (EH). EH diagnosis was defined by serum cortisol levels exceeding 1.8 \u0026micro;g/dL following a nighttime 1 mg dexamethasone suppression test (1 mg DST), measured using Electrochemiluminescence Immunoassay (ECLIA). Adrenocorticotropic hormone (ACTH) levels were measured using a chemiluminescent immunoassay, with levels greater than 20 pg/mL used to differentiate between pituitary and adrenal sources of hypercortisolism, indicating pituitary Cushing's syndrome. A control group of healthy volunteers, matched by age, sex, and BMI, was also included. Patients were recruited from the Program of Pituitary Disease and the Program for Adrenal Disorders of the Pontificia Universidad Catolica de Chile, while controls were selected from the general population through a public call. Individuals under 18 years old, recent exogenous glucocorticoid use, pregnant women, those with pacemakers, claustrophobia, or who did not sign informed consent were excluded. This study was approved by the Scientific Ethics Committee of Pontificia Universidad Cat\u0026oacute;lica de Chile (ID 230108002), and all participants provided written informed consent before their inclusion in the study.\u003c/p\u003e\n\u003ch3\u003e2. Study design\u003c/h3\u003e\n\u003cp\u003eThis study uses MRI to analyse body composition differences between subjects. To minimize selection bias and balance observed characteristics between the groups with and without EH, a Propensity Score Matching (PSM) analysis was performed. Propensity Scores were calculated using a logistic regression model, incorporating gender, age, and body mass index (BMI) as covariates. The imaging data collected were analysed to identify potential radiological markers that could distinguish between these two groups.\u003c/p\u003e\n\u003ch3\u003e3. MRI Acquisition\u003c/h3\u003e\n\u003cp\u003eA Philips Ingenia 3T MRI scanner was used to acquire both abdominal and thigh muscle images. All images were obtained at the University Hospital of the Pontificia Universidad Cat\u0026oacute;lica de Chile. Participants were scanned in a feet-first supine position.\u003c/p\u003e \u003cp\u003eThe fat fraction was estimated using the mDixonQuant package (Philips Healthcare), which provides quantitative maps of proton density fat fraction (PDFF). The abdominal acquisition protocol included parameters such as TR/TE1/ΔTE\u0026thinsp;=\u0026thinsp;6/0.98/0.8 ms, flip angle\u0026thinsp;=\u0026thinsp;3\u0026deg;, FOV RL/AP/FH\u0026thinsp;=\u0026thinsp;460/250/400 mm, Acquisition Matrix\u0026thinsp;=\u0026thinsp;232x125, with a slice thickness of 6 mm. We evaluated the fat fraction and volume of the thigh muscle, following the methodology used by Martel-Duguech et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The muscular acquisition protocol used TR/TE1/ΔTE\u0026thinsp;=\u0026thinsp;6.4/1.07/0.8 ms, flip angle\u0026thinsp;=\u0026thinsp;3\u0026deg;, FOV RL/AP/FH\u0026thinsp;=\u0026thinsp;220/180/240 mm, Acquisition Matrix\u0026thinsp;=\u0026thinsp;112x87, with a slice thickness of 6 mm. For muscle fat quantification, two regions of interest (ROIs) were manually placed on each vastus lateralis muscle (right and left) within the acquired volumetric dataset. The ROIs were selected on representative axial slices, and their values were averaged to estimate the overall fat fraction across the muscle volume. The acquisition protocol lasted 45 minutes. Abdominal adipose tissue compartments were manually segmented from DICOM images using OsiriX MD software (version 12.0, Pixmeo, Geneva, Switzerland) by a single blinded evaluator who was unaware of group assignment. Total abdominal adipose tissue was calculated by adding subcutaneous adipose tissue and visceral adipose tissue. Patients were instructed not to consume food or drinks for 2 hours before the exam.\u003c/p\u003e\n\u003ch3\u003e4. Analysis\u003c/h3\u003e\n\u003cp\u003eThe independent variables included patient characteristics such as age, sex, height, and body mass index (BMI), as well as quantitative metrics derived from MRI image analysis: muscle fat fraction (MFF), liver fat fraction (LFF), thigh area, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and total adipose tissue (TAT). All quantitative variables were treated as continuous and summarized using descriptive statistics for both groups. Additional variables derived from the primary data included the ratio of muscle fat fraction to liver fat fraction, and the ratio of thigh area to height. The dependent variable was the diagnosis of EH or healthy individual.\u003c/p\u003e \u003cp\u003eFor muscle fat quantification, two regions of interest (ROIs) were manually placed on each vastus lateralis muscle (right and left) within the acquired volumetric dataset. The ROIs were selected on representative axial slices, and their values were averaged to estimate the overall fat fraction across the muscle volume.\u003c/p\u003e \u003cp\u003eFor liver fat quantification, three circular regions of interest (ROIs) were manually placed on representative axial slices throughout the liver volume\u0026mdash;two in the right lobe and one in the left lobe\u0026mdash;avoiding large vessels, bile ducts, and artifacts. The fat fraction values from these ROIs were averaged to obtain the final liver fat fraction (LFF) for each participant. Abdominal adipose compartments (SAT and VAT) were manually segmented in the full volumetric acquisition, and total adipose tissue was calculated as the sum of SAT and VAT.\u003c/p\u003e \u003cp\u003eTo minimize bias, both EH patients and healthy volunteers\u0026rsquo; groups were matched by age, sex, and BMI, thus controlling for confounding variables. Additionally, the same imaging protocols, segmentation methods, and measurement procedures, ensuring data comparability. Finally, data acquisition and image analysis were performed by trained personnel using standardized equipment and software to ensure consistency and reducing observer-introduced variability.\u003c/p\u003e\n\u003ch3\u003e5. Statistical methods:\u003c/h3\u003e\n\u003cp\u003eMann\u0026ndash;Whitney test was used to compare differences between groups, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Pearson correlation of determination (R\u0026sup2;) was calculated to quantify the proportion of variability in the presence of EH that could be explained by the independent variable. All analyses were conducted using Prism 9 (GraphPad Software Inc., La Jolla, CA).\u003c/p\u003e \u003cp\u003eRobust Principal Component Analysis (RPCA) was applied for dimensionality reduction and clustering observations, aiding in the identification of underlying patterns in the data. This method is robust to outliers and allows for more accurate classification of subjects into homogeneous groups [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The K-Nearest Neighbours (KNN) algorithm was employed for classifying subjects based on the characteristics identified by RPCA. This classification method was used to predict the occurrence of EH by evaluating the similarity of observations to the closest neighbours in the feature space. We used logistic regression analysis to evaluate the predictive value of the identified clusters to predict EH. RPCA and cluster analysis were performed with the statistical package R v4.0.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.r-project.org/\" target=\"_blank\"\u003ewww.r-project.org/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and the libraries: rpca, class and caret.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 33 patients with endogenous hypercortisolism (EH) were initially considered eligible for the study. Three were excluded due to claustrophobia, leaving 30 participants who met the inclusion criteria. Additionally, 15 healthy volunteers were included as a control group.\u003c/p\u003e \u003cp\u003eThe demographic characteristics, such as age and sex, along with anthropometric data, including body mass index (BMI), were matched across the groups, showing no significant differences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean Propensity Score was 0.5063 (SD\u0026thinsp;=\u0026thinsp;0.0675) for the EH group and 0.4937 (SD\u0026thinsp;=\u0026thinsp;0.0398) for the control group. The similarity in scores suggests a good balance between covariates. The Mann-Whitney test showed no statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.565).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of control and endogenous hypercortisolism groups. The mean values are presented with their standard deviations, and the p-values indicate the statistical significance of differences between the control and hypercortisolism group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEndogenous Hypercortisolism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemales (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kilograms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (meters)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kilograms/meters\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWithin the EH patients included, 23 had an adrenal origin (including 7 with mild autonomous cortisol secretion (MACS)), 3 had a pituitary origin, 3 had adrenal carcinoma and one had an ectopic ACTH-secreting carcinoma.\u003c/p\u003e \u003cp\u003eNo significant differences were observed in the SAT and VAT to total adipose tissue ratio, VAT to SAT ratio, Liver-FF and Muscle FF (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.A-E) between both groups. However, control group showed larger muscle area and muscle area to height ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.F-G) than the EH group. Conversely, the EH group exhibited a significantly higher muscle-to-liver fat fraction ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.H) compared with control group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince BMI is a widely used parameters to evaluate overweighted patients, we studied the relationship between BMI and the body composition parameters in both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For most parameters, the linear regression indicates minimal variability explained by BMI with low R\u003csup\u003e2\u003c/sup\u003e values. The strongest correlation with BMI was found with the Liver FF in the control group (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.32, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.D) and the Muscle-to-Liver FF ratio in the EH group (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.21, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.H). Our results showed that EH group has a consistent tendency to have lower muscle area and muscle area to height ratio, and higher muscle-to-liver fat fraction ratio adjusted by BMI than the control group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRobust Principal Component Analysis (RPCA) was applied using all MRI-derived body composition variables. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B shows the distribution of individuals in our sample along Robust Principal Components 1 and 2. The KNN algorithm identified two independent clusters in our sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Cluster 1 included 15% EH patients, whereas Cluster 2 included 94% EH patients. The characteristics of the individuals grouped in each cluster are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Significant differences were found in the muscle-to-liver fat fraction ratio, muscle area, and muscle area-to-height ratio, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eCluster classification identified by RPCA and the KNN algorithm showed a sensitivity of 93% (CI95%:77.9%-99.2%), specificity of 73% (CI95%:45.0%-92.2%), a positive Likelihood Ratio of 3.5 (CI95%:1.5\u0026ndash;8.2), and a negative Likelihood Ratio of 0.09 (CI95%:0.02\u0026ndash;0.36) to differentiate between both groups using the MR derived body composition parameters. Logistic regression analysis showed that belonging to the cluster found by RPCA has an OR of 38.5 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and AUC of 0.83 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) to correctly identify endogenous hypercortisolism patients based in these parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis exploratory study provides a new perspective on the use of MRI for assessing body composition in the early suspicion of EH. Our study provides a novel contribution to the field by advancing the use of MRI for detailed, organ-specific assessment of body composition in metabolic disease. Through quantitative imaging biomarkers, it enhances current understanding of ectopic fat distribution\u0026mdash;particularly in the liver and muscle\u0026mdash;and supports the role of MRI as a non-invasive tool for phenotyping and monitoring metabolic dysfunction.\u003c/p\u003e \u003cp\u003eThe RPCA and the cluster analysis using KNN identified two differentiated groups within our sample: one predominantly composed of control subjects (Cluster 1) and the other with EH patients (Cluster 2). With a sensitivity of 93% and specificity of 73% for classifying these groups based solely on body composition, our study highlights the potential of this specific anthropometric phenotypic signature to enhance diagnostic accuracy in a clinical setting.\u003c/p\u003e \u003cp\u003eAnalysis of the clusters identified by RPCA showed that thigh area and fatty infiltration of the thigh and its relationship with fatty infiltration of the liver are key markers for differentiating between patients with EH and control subjects. Muscle fat infiltration exhibits a distinct anthropometric phenotypic signature for EH, which may be linked to specific morphological alterations detectable by MRI. Proximal muscle weakness is a common presentation in EH, affecting 60\u0026ndash;82% of patients. This myopathy typically involves the thigh muscles and significantly impacts quality of life [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The underlying mechanism of EH myopathy is complex, involving protein degradation, intramuscular fat accumulation, and muscle atrophy due to inactivity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearchers have thoroughly investigated myopathy in EH using various techniques to better understand muscle health and function. These efforts include quantitative muscle ultrasonography to monitor myopathy progression [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], whole-body MRI to examine intramuscular fat accumulation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and CT to assess body composition changes post-adrenalectomy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Gallagher et al. emphasize the need for accurate adipose tissue quantification [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Muscle biopsies and nerve conduction studies have revealed key pathological features, such as type II fiber atrophy and impaired muscle protein levels [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], with additional studies examining additional muscle parameters [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our results align with previous findings suggesting the relevance of body composition in the diagnosis of EH.\u003c/p\u003e \u003cp\u003eMRI-based studies, such as that by Martel-Duguech et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], have demonstrated increased fat fraction in thigh compartments among EH patients in remission, with associations to impaired physical performance. However, their analysis was limited to muscle tissue. In contrast, our study simultaneously quantified fat in abdominal, hepatic, and thigh compartments using standardized ROIs, and incorporated advanced data analysis methods, including RPCA and KNN. Although our analysis did not reveal significant group differences in muscle fat fraction, the muscle-to-liver fat fraction ratio proved to be the most discriminative marker, with significantly higher values in EH patients. Using this multiregional approach, our model achieved 93% sensitivity, 73% specificity, and an AUC of 0.83 in distinguishing EH from controls.\u003c/p\u003e \u003cp\u003eRegarding the behaviour of hypercortisolism according to its pathophysiological origin, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD illustrates its subclassification in the RPCA graph. The three groups of hypercortisolism showed a distributed location along the cluster identified as hypercortisolism, with a slight tendency for cases of pituitary origin to cluster more closely together. This pattern may be attributed to the preservation of muscle mass due to the relatively maintained androgen levels in pituitary hypercortisolism, making these cases more like the control group without solid evidence of myopathy. However, a larger and more balanced sample across the different types of hypercortisolism will be required to clearly validate a specific subclassification.\u003c/p\u003e \u003cp\u003eWe acknowledge that this study has some limitations. As an exploratory study, the sample size is relatively small and for a single institution, which could affect the generalizability of the results. All segmentations were performed by a single observer, potentially introducing observer bias. Imaging data were obtained from a specific MRI system and vendor (Philips Ingenia 3T), which may affect reproducibility across platforms. Additionally, while our findings are promising, a longitudinal study with a larger sample size and a validation cohort are needed to validate these non-invasive imaging markers in future clinical practice.\u003c/p\u003e \u003cp\u003eOther techniques such as magnetic resonance spectroscopy (MRS) could offer complementary information. Future research could focus on integrating this body-composition phenotypic signature with other clinical and biochemical data to develop a more robust multiparametric diagnostic tool for EH. Additionally, longitudinal studies could explore how these body composition changes evolve over time and their relationship with treatment and disease progression.\u003c/p\u003e \u003cp\u003eIn conclusion, this study suggests that MRI-based body composition analysis, combined with advanced data analysis methods, may offer a new approach for the early clinical suspicion of EH. Identifying specific anthropometric phenotypic patterns associated with EH could not only improve early diagnosis but also open new opportunities for more personalized and effective interventions in the management of this complex endocrine entity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eCS: Cushing\u0026apos;s syndrome\u003c/p\u003e\n\u003cp\u003eEH: Endogenous hypercortisolism\u003c/p\u003e\n\u003cp\u003eKNN: K-Nearest Neighbours\u003c/p\u003e\n\u003cp\u003eLFF: Liver fat fraction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMACS: Mild autonomous cortisol secretion\u003c/p\u003e\n\u003cp\u003eMFF: Muscle fat\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMRI: Magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eROI: Region of interest\u003c/p\u003e\n\u003cp\u003eRPCA: Robust Principal Component Analysis\u003c/p\u003e\n\u003cp\u003eSAT: Subcutaneous adipose tissue\u003c/p\u003e\n\u003cp\u003eTAT: Total adipose tissue\u003c/p\u003e\n\u003cp\u003eVAT: Visceral adipose tissue\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: This study was conducted in accordance with the Declaration of Helsinki and was approved by the Scientific Ethics Committee of Pontificia Universidad Católica de Chile (ID 230108002). All participants provided written informed consent before their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: All participants provided written informed consent for result publication before their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets generated and analysed during the current study are not publicly available due privacy and ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eChilean Agency for Research and Development: FONDECYT 1220922 and Millennium Science Initiative Program – ICN2021_004\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCP, MEA: have made substantial contributions to the conception and design of the work; acquisition, analysis and interpretation of data; and have drafted the work.\u003c/p\u003e\n\u003cp\u003eRB, TU, FG, AX, CB: have made substantial contributions to the conception and design of the work; interpretation of data; and substantively revised the manuscript.\u003c/p\u003e\n\u003cp\u003eAX, LM, MV: have made substantial contributions in the acquisition, analysis and interpretation of data and have substantively revised the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study are not publicly available due privacy and ethical restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNieman LK, Biller BMK, Findling JW, et al. 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Published 2021 Oct 8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endogenous Hypercortisolism, Cushing's syndrome, MRI, Body composition","lastPublishedDoi":"10.21203/rs.3.rs-8398852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8398852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCushing's syndrome (CS) is a complex endocrine disorder resulting from prolonged exposure to endogenous hypercortisolism. Up to 73% of CS patients also meet the criteria for metabolic syndrome, further blurring the clinical picture and delaying diagnosis. We aim to compare the body composition of two groups\u0026mdash;healthy volunteers and patients with endogenous hypercortisolism\u0026mdash;using magnetic resonance imaging (MRI). Our objective was to identify a specific anthropometric phenotypic signature in patients with endogenous hypercortisolism that could facilitate earlier clinical suspicion.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003e30 patients with a recent diagnosis of endogenous hypercortisolism (77% adrenal origin, 10% pituitary origin, 13% other origin), were matched with 15 healthy controls using a propensity score analysis, considering age, sex, and body mass index. We acquired the abdominal and thigh images using a 3T Philips Ingenia MRI scanner and a quantitative Dixon sequence.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRobust Principal Component Analysis (RPCA) and cluster analysis showed a sensitivity of 93% and specificity of 73% for classifying control and endogenous hypercortisolism patients using only body composition information. Logistic regression analysis showed that belonging to the cluster found by RPCA has an OR of 38.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, for presenting endogenous hypercortisolism.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMRI-based body composition may offer a novel approach for the early suspicion of endogenous hypercortisolism in overweight and obese patients, and the muscle-to-liver fat fraction ratio is a specific anthropometric phenotypic signature.\u003c/p\u003e","manuscriptTitle":"MR Imaging Body composition as a non-invasive tool to detect endogenous hypercortisolism: an exploratory study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 08:19:45","doi":"10.21203/rs.3.rs-8398852/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T09:50:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T23:19:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T12:27:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T19:02:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216000809715315299586790692103002589402","date":"2026-01-25T14:50:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81059164323665425060530837014679159504","date":"2026-01-24T18:11:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148951171039065863595840695109034388084","date":"2026-01-24T16:37:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-22T18:37:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130277982214065467748606298440713357382","date":"2026-01-14T17:50:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T17:31:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-24T08:28:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-23T12:03:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-23T12:02:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-12-18T21:02:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5cd940ad-8d36-4ca9-8670-349dfbcd568d","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T16:53:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 08:19:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8398852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8398852","identity":"rs-8398852","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}