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Breast cancer responds heterogeneously to treatment. This study evaluated the efficacy of image registration-based lesion tracking for assessing lesion-wise response in metastatic breast cancer (MBC). [18F]FDG PET/CT images from 15 patients with MBC from the multi-center IMPACT-MBC study were analyzed. Manual segmentation identified 644 lesions in baseline scans and 804 lesions in 2-week post-treatment scans. A deformable image registration method, validated for whole-body PET/CT scans of patients with cancer, was applied. Lesion tracking was performed using spatial overlap resulting from within-subject image registration, yielding a precision of 0.93 ± 0.07, a sensitivity of 0.87 ± 0.17, and an F1 score of 0.89 ± 0.10. Performance was consistent across lesion sites and lesion count-based disease burden. Sensitivity decreased for lesions under 1 mL. Image registration direction did not affect results. Between-subject registration enabled quantitative visualization and analysis of metabolic response patterns in bone lesions across the cohort. Results demonstrate the potential for automated, accurate lesion-wise response assessment, which may improve treatment monitoring in MBC.

BACKGROUND: Obesity has been associated with onset and progression of chronic kidney disease (CKD) but causal relationship remains uncertain. This study investigated how obesity causally affects estimated glomerular filtration rate.

METHODS: Cross-sectional and magnetic resonance imaging (MRI) data analyses were performed within the Prospective Investigation of Obesity, Energy, and Metabolism (POEM) study (502 participants, all aged 50 years). Additionally Mendelian randomization was performed using published summary data. Outcomes were creatinine- and cystatin C-based eGFR. Body mass index (BMI) and waist circumference (WC) were used as exposure variables in the cross-sectional and Mendelian randomization analyses. In the imaging data analyses, eGFR was regressed non-parametrically on tissue volume for each 3D voxel and visualized as a correlation "Imiomics" map.

RESULTS: Negative correlations were shown between cystatin C-based eGFR and BMI [beta = -0.190 (95% CI: -0.280 to -0.100)] and WC [beta = -0.160 (95% CI: -0.250 to -0.060)] in an adjusted model. In contrast, a positive association was found for creatinine-based eGFR [BMI beta = 1.20 (95% CI: 0.030 to 0.210) and WC beta = 0.160 (95% CI: 0.070 to 0.260)]. Similar patterns were found using MRI analysis (Imiomics map). Mendelian randomization implied a negative causal effect of obesity-related measures on cystatin C-based eGFR [BMI beta = -0.031 (95% CI: -0.037 to -0.026) and WC beta = -0.038 (95% CI: -0.045 to -0.031)], but no statistically significant effect was found for creatinine-based eGFR.

CONCLUSION: This study suggests a causal negative effect of obesity on cystatin C-based, but not creatinine-based eGFR. These findings warrant further research regarding estimations of kidney function when assessing obesity and CKD.

Aims:

In addition to weight loss, obesity surgery (OS) leads to metabolic improvements that seem at least partly independent of weight loss and are also mediated by various endocrine pathways and the brain. For the first time, we compared the short-term effects of weight loss achieved by either OS or a low-energy diet (LED) on several hormonal systems, at fasting and upon an oral glucose challenge.

Materials and Methods:

This study presents sub-analyses from a randomized controlled trial including 24 participants with obesity but without diabetes (BMI 35-45 kg/m(2)), randomized 2:1 to either OS or 4-week LED leading to comparable weight loss. Circulating levels of gut, pituitary, adrenal, thyroid hormones, glucagon, insulin-like growth factor-1 and sex hormone-binding globulin were measured at baseline and 4 weeks after either intervention, both at fasting and during an oral glucose tolerance test (OGTT).

Results:

At 4 weeks, similar weight loss was achieved for the two interventions (7.7 for OS vs. 7.4% for LED). glucagon-like peptide-1 and peptide YY secretion during the OGTT increased after OS (p < 0.001 for OGTT(AUC) for both hormones), but not LED, while glucagon secretion remained unaffected. Adrenocorticotropin, cortisol and prolactin levels during OGTT were increased after OS (p = 0.04, p < 0.001, p = 0.002, respectively), while parathyroid hormone levels were decreased (p = 0.007). Fasting triiodothyronine levels were reduced after OS (p = 0.01). Fasting sex hormone-binding globulin levels decreased after both interventions (p < 0.01).

Conclusion:

Rapid and extensive hormonal changes occur after OS, but not LED, despite similar weight loss. Of note, few differences were seen in the fasting state, whereas multiple endocrine pathways were affected during the oral glucose challenge. The findings suggest altered responses to oral glucose after OS in several hypothalamus-pituitary endocrine axes and peripheral endocrine glands.

Aims: In addition to weight loss, obesity surgery (OS) leads to metabolic improvements that seem at least partly independent of weight loss and are also mediated by various endocrine pathways and the brain. For the first time, we compared the short-term effects of weight loss achieved by either OS or a low-energy diet (LED) on several hormonal systems, at fasting and upon an oral glucose challenge.

Materials and Methods: This study presents sub-analyses from a randomized controlled trial including 24 participants with obesity but without diabetes (BMI 35-45 kg/m(2)), randomized 2:1 to either OS or 4-week LED leading to comparable weight loss. Circulating levels of gut, pituitary, adrenal, thyroid hormones, glucagon, insulin-like growth factor-1 and sex hormone-binding globulin were measured at baseline and 4 weeks after either intervention, both at fasting and during an oral glucose tolerance test (OGTT).

Results: At 4 weeks, similar weight loss was achieved for the two interventions (7.7 for OS vs. 7.4% for LED). glucagon-like peptide-1 and peptide YY secretion during the OGTT increased after OS (p < 0.001 for OGTT(AUC) for both hormones), but not LED, while glucagon secretion remained unaffected. Adrenocorticotropin, cortisol and prolactin levels during OGTT were increased after OS (p = 0.04, p < 0.001, p = 0.002, respectively), while parathyroid hormone levels were decreased (p = 0.007). Fasting triiodothyronine levels were reduced after OS (p = 0.01). Fasting sex hormone-binding globulin levels decreased after both interventions (p < 0.01).

Conclusion: Rapid and extensive hormonal changes occur after OS, but not LED, despite similar weight loss. Of note, few differences were seen in the fasting state, whereas multiple endocrine pathways were affected during the oral glucose challenge. The findings suggest altered responses to oral glucose after OS in several hypothalamus-pituitary endocrine axes and peripheral endocrine glands.

Background & Aims: A quarter of the world population is estimated to have metabolic dysfunction-associated steatotic liver disease. Here, we aim to understand the impact of liver trait-associated genetic variants on fat content and tissue volume across organs and body compartments and on a large set of biomarkers.

Methods: Genome-wide association analyses were performed on liver fat and liver volume estimated with magnetic resonance imaging in up to 27,243 unrelated European participants from the UK Biobank. Identified variants were assessed for associations with fat fraction and tissue volume in >2 million 'Imiomics' image elements in 22,261 individuals and with circulating biomarkers in 310,224 individuals.

Results: We confirmed four liver fat and nine liver volume previously reported genetic variants (p values <5 x 10(-8)). We further found evidence suggestive of a novel liver volume locus, ADH4, where each additional T allele increased liver volume by 0.05 SD (SE = 0.01, p value = 3.3 x 10(-8)). The Imiomics analyses showed that liver fat-increasing variants were specifically associated with fat fraction of the liver tissue (p values <2.8 x 10(-3)) and with higher inflammation, liver and renal injury biomarkers, and lower lipid levels. Associations of liver volume variants with fat content, tissue volume, and biomarkers were more heterogeneous, for example the liver volume-increasing alleles at CENPW and PPP1R3B were associated with higher skeletal muscle volumes and were more pronounced in men, whereas the GCKR variant was negatively associated with lower skeletal muscle volumes in women (p values <2.8 x 10(-3)).

Conclusions: Liver fat-increasing variants were mostly linked to fat fraction of the liver and were positively associated with some adverse metabolic biomarkers and negatively with lipids. In contrast, liver volume-associated variants showed a less consistent pattern across organs and biomarkers. (c) 2025 The Authors. Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Background: To investigate the effects of increased weight-loading on body weight, body composition, fat mass distribution, physical activity and energy balance in individuals with obesity.

Methods: This single-centre non-blinded randomised controlled trial was conducted from August 1, 2021, through February 28, 2022. Adults with obesity class 1 (body mass index, BMI 30-35 kg/m2) were assigned to wear either a heavy (high load; 11% of body weight, n = 28) or light (low load; 1% of body weight, n = 30) weight vest for 8 h per day over 5 weeks.

Results: High-load treatment reduced fat mass (mean difference - 2.60%; 95% CI - 3.79, - 1.41) and increased lean mass (mean difference 1.40%; 95% CI 0.37, 2.42), with no significant effect on body weight. Fat mass reductions were primarily observed in weight-loaded regions but not in the non-weight-bearing regions such as the arms. Waist circumference decreased (mean difference - 2.26%; 95% CI - 3.81, - 0.71) in the high-load group compared to the low-load group. Despite these beneficial changes, sedentary time was higher in the high-load group (mean difference 4.69%; 95% CI 0.98, 8.39) compared to the low-load group, while energy expenditure and energy intake remained unchanged.

Conclusions: Increased weight-loading reduced fat mass and increased lean mass, resulting in a healthier body composition. These effects were achieved despite no increase in physical activity. The fat mass-reducing effect was primarily seen in weight-loaded regions, implying local adaptation to the increased loading.Trial registrationRegistered at ClinicalTrials.gov (NCT04697238) in 2021.

Background and Aims The relationship between skeletal muscle features and hepatic fat infiltration remains understudied. To address this knowledge gap, a cross-sectional observational study was conducted using data from two ancillary studies of the SCAPIS cohort.

Method The study aimed to examine the relationship between skeletal thigh muscle radiodensity (Hounsfield Units, HU) and area (cm2), and metabolic dysfunction-associated steatotic liver disease (MASLD) and hepatic radiodensity (HU). Multivariable linear regression analyses were applied to data from 4620 participants (52% women) with a mean age of 57.9 years. Adjustments for confounders were computed in four theoretical models.

Results Results showed a positive significant association between thigh muscle area and MASLD (OR = 1.29, 95% CI 1.02, 1.62, p = 0.033), and a negative association with hepatic radiodensity (B = -0.76, 95% CI -1.19, -0.34, p = 0.001), independent of muscle radiodensity. Additionally, a significant association was observed between muscle radiodensity and hepatic radiodensity (B = 0.37, 95% 0.09, 0.64, p = 0.008). Finally, sex differences were notable in the association between thigh muscle area and MASLD (F-test = 0.10). Specifically, we observed statistically significant associations between thigh muscle features and liver density/MASLD in men, but not in women.

Conclusion Conclusively, increased thigh muscle volume was associated with greater odds of MASLD and hepatic steatosis, independent of muscle radiodensity. Yet, greater thigh muscle radiodensity was associated with decreased odds of hepatic steatosis, regardless of the muscle volume. Furthermore, a sex difference was observed in our study, underscoring the importance of considering sex-specific factors on the development of MASLD.

Background: Assessment of glucose uptake by PET imaging under hyperinsulinemic euglycemic clamp (HEC) is an insightful tool for quantification of insulin resistance, a hallmark of diabetes and an area of interest in drug development. To enable the use of the method in metabolic trials, the repeatability of dynamic whole-body PET/MRI assessments of the tissue-specific glucose uptake and the total body glucose utilisation were investigated. The study included participants with type 2 diabetes mellitus (T2DM) and overweight/obesity, for two repeated examinations in standardised conditions. All participants signed informed consent, and the study plan was approved by the Swedish Ethical Review Authority (#2020-04140). After an overnight fast, HEC was established and a series of [¹⁸F]FDG-PET/MRI scans were performed. Total body glucose utilisation (M-value) was calculated for the duration of the scan and the tissue-specific metabolic rates of glucose uptake (MRGlu) were calculated using Patlak model. The repeatability was assessed by calculating the intraclass correlation coefficient (ICC).

Results: Repeatability was assessed in per protocol set of 12 participants (PPS, defined by a consistent HEC) and in full analysis set (FAS n = 16). The measured M-values and tissue MRGlu demonstrated varying levels of insulin resistance. M-value ICC was 0.95 (95% CI 0.86-0.99) for PPS, indicating excellent repeatability. Tissue-specific MRGlu repeatability was excellent for skeletal muscle (ICC 0.94), and good to at least fair for SAT, VAT, myocardium, and brain. The FAS had lower, but at least fair repeatability, emphasising the importance of standardisation in metabolic assessments.

Conclusion: Dynamic [¹⁸F]FDG-PET/MRI provides quantitative information on tissue-specific insulin sensitivity during hyperinsulinemic euglycemic clamp with a reliability comparable to total body glucose utilisation assessment. The method has potential to add value in monitoring and evaluating T2DM treatment effects on glucose uptake and insulin resistance in interventional trials.

Introduction: Hudda-Index is a prediction model for fat mass (FM) based on simple anthropometric measures. FM is a crucial factor in the development of comorbidities, i.e., type 2 diabetes. Hence, Hudda-Index is a promising tool to facilitate the identification of children at risk for metabolic comorbidities. It has been validated against deuterium dilution assessments; however, independent validation against the gold standard for body composition analysis, magnetic resonance imaging (MRI), is lacking. The aim of this study was to validate FM calculated by Hudda-Index against FM measured by MRI. The secondary aim was to compare Hudda-Index to other anthropometric measures including body mass index (BMI), BMI-standard deviation score (BMI-SDS), waist/hip-ratio, waist circumference (WC), and skinfold thickness.

Methods: The study cohort consists of 115 individuals between the age of 9 and 15 years, recruited at Paracelsus Medical University Hospital in Salzburg (Austria) and Uppsala University Children’s Hospital (Sweden). Anthropometry, blood samples, and oral glucose tolerance tests followed standard procedures. MRI examinations were performed to determine visceral adipose tissue (VAT) and subcutaneous adipose tissue.

Results: BMI and WC showed slightly stronger associations with the reference standard VAT (r = 0.72 and 0.70, p < 0.01, respectively) than Hudda-Index (r = 0.67, p < 0.01). There is an almost perfect linear association between BMI and Hudda-Index. Accordingly, BMI and Hudda-Index both showed an acceptable association with cardiometabolic parameters. VAT was strongly associated with markers of liver status (LFF r = 0.59, p < 0.01) and insulin resistance (HOMA-IR r = 0.71, p < 0.01) and predicted metabolic dysfunction-associated steatotic liver disease.

Conclusion: BMI, although an imperfect measure, remains the most reliable tool and estimates cardiometabolic risk more reliably than other anthropometry-based measures.

Background: Accurate tumor detection and quantification are important for optimized therapy planning and evaluation. Total tumor burden is also an appealing biomarker for clinical trials. Manual examination and annotation of oncologic PET/CT is labor-intensive and demands a high level of expertise. One significant challenge is the risk for human error, leading to potential omission of especially small tumors and tumors with low FDG uptake.

Purpose: In this study, we introduced an automated framework with segmentation prior, from a tissue-wise multi-channel multi-angled based approach, to enhance tumor segmentation in whole-body FDG-PET/CT.

Method: The proposed framework utilized a segmentation prior generated from tumor segmentations in tissue-wise multi-channel projections of the standardized uptake value (SUV) from PET. Projections were created from various angles and the tissues were identified based on their CT Hounsfield values. The resulting segmentation masks were subsequently backprojected into a unified 3D volume for creation of the segmentation prior. Finally, the segmentation prior was provided as an additional input channel along with the CT and SUV images to three variants of 3D segmentation networks (3D UNet, dynUNet, nnUNet) to enhance the overall tumor segmentation performance. All the methods were independently evaluated using 5-fold cross-validation on the autoPET dataset and subsequently tested on the U-CAN dataset.

Results: Combining the segmentation prior with the original SUV and CT images improved overall tumor segmentation performance significantly compared to a baseline network. The increase in Dice coefficient for lymphoma, lung cancer, and melanoma across different segmentation networks were: 3D UNet (⁎, ⁎, ⁎), dynUNet (⁎, ⁎, ⁎), and nnUNet (⁎, , ⁎), respectively; *, p-value < 0.05; ns, non-significance.

Conclusion: The increased segmentation accuracy could be attributed to the segmentation prior generated from tissue-wise SUV projections, revealing information from various tissues that was useful for segmentation of tumors. The results from this study highlight the potential of the proposed method as a valuable future tool for time-efficient quantification of tumor burden in oncologic FDG-PET/CT.