Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are heterogeneous tumors with rising incidence, necessitating precise preoperative grading for treatment planning. Existing imaging techniques and endoscopic biopsies often fall short due to insufficient markers and tissue samples. Body composition influences tumor biology, yet traditional 2D assessments are time-consuming and lack objectivity. This study aimed to develop a rapid non-invasive predictive model by integrating automatic segmented abdominal volumetric body composition with machine learning to differentiate between low-grade and high-grade GEP-NENs. This multicenter retrospective cohort study enrolled 633 patients with GEP-NENs from three institutions. Patients were divided into: Training set (n=403) and internal validation (n=174) (7:3 ratio from Hospital 1); test set (n= 56 from 2 other hospitals). An nnUNetv2-based automatic segmentation algorithm for abdominal fat tissue and skeletal muscle on arterial-phase CT was applied. Visceral fat index, subcutaneous fat index, intermuscular fat index and skeletal muscle index were calculated. Features with a P-value < 0.05 were selected using univariate logistic regression and included in the prediction model built using the extreme gradient boosting algorithm. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were performed to evaluate the utility of the model. SHapley Additive exPlanations (SHAP) was conducted to enhance model interpretability and visualization. The automatic segmentation achieved a Dice coefficient of 0.98. For pathological grading, the model built using body composition parameters achieved an AUC of 0.863 in the training set, 0.750 in the validation set, and 0.717 in the test set. SHAP analysis revealed that the relative intermuscular adipose tissue (rIMAT) contributed the most among the body composition parameters to the model decision-making, and rIMAT levels were higher in P53-mutant and CK19-positive cases compared to negative cases. Auto-segmented abdominal body composition combined with a machine learning-based model could provide an assisted, non-invasive tool for predicting pathological grade in GEP-NENs.

Sheep (Ovis aries) exhibit significant diversity in adipose tissue deposition, which influences meat quality, environmental adaptation, and economic value. Tail fat, in particular, varies widely among breeds, yet the transcriptomic basis of this variation remains incompletely understood. This study aims to systematically compare the transcriptional profiles of five adipose depots across five sheep breeds to identify molecular mechanisms underlying fat deposition and tail phenotype divergence. We analyzed 250 publicly available RNA-seq samples from five adipose tissues (caul, subcutaneous, perirenal, intermuscular, and tail fat) of five sheep breeds (Altay, Tibetan, Merino, Wadi, Small-tailed Han). Data were processed using FastQC, STAR, and featureCounts. Differential expression analysis was performed with DESeq2, followed by GO and KEGG enrichment analyses. Breeds were grouped into three tail phenotypes: fat-tailed, short fat-tailed, and thin-tailed. Cross-tissue and phenotype-specific pathway analyses were conducted to identify key regulatory genes. Transcriptional divergence was most pronounced in subcutaneous and intermuscular fat, while tail fat exhibited both conserved and phenotype-specific pathways. Fat-tailed breeds showed enrichment in mitochondrial oxidative phosphorylation and lipid biosynthesis genes (TAFAZZIN, GPAM, COQ family). Short fat-tailed breeds were characterized by extracellular matrix remodeling genes (MMP9, MMP12, MMP19). Thin-tailed sheep lacked these pro-lipogenic and structural remodeling pathways. A dual-axis model of tail fat development is proposed to explain phenotypic diversity. This study reveals that distinct molecular mechanisms underpin tail fat phenotypes in sheep: fat-tailed breeds prioritize metabolic efficiency, short fat-tailed breeds rely on ECM remodeling, and thin-tailed breeds lack these enhancements. The identified candidate genes may serve as potential targets for molecular breeding strategies aimed at optimizing fat deposition and adaptive traits in sheep.

Reduced cardiorespiratory fitness is common among breast cancer survivors and, although traditionally attributed to cardiac dysfunction, might also be related to peripheral skeletal muscle abnormalities. We examined peak and submaximal plantar-flexion exercise and recovery kinetics for lower-leg oxygen uptake ( ), blood flow and arteriovenous O2 difference in 35 older, female long-term breast cancer survivors (age, 70±5years; 14±6 years post-treatment) and 19 age- and sex-matched healthy control subjects using MRI. The calf intermuscular fat to skeletal muscle ratio was evaluated using fat- and water-separated MRI to quantify myosteatosis. No significant differences were found between groups for lower-leg , blood flow or arteriovenous O2 difference at peak or during submaximal plantar-flexion exercise. Recovery kinetics for these measures were similar between groups (all P>0.05). No differences were found in calf muscle mass between breast cancer survivors and control subjects. A higher intermuscular fat to skeletal muscle ratio was significantly associated with reduced peak (cycle exercise) pulmonary (r=-0.37, P=0.01), lower plantar-flexion power output (r=-0.325, P=0.012) and delayed muscle recovery kinetics in both groups (r=-0.325, P=0.015). Older, female long-term breast cancer survivors have comparable lower-limb skeletal muscle oxidative capacity to control subjects. However, myosteatosis represents a key determinant of exercise performance and recovery kinetics regardless of breast cancer history. Interventions targeting skeletal muscle quality might be effective in improving both submaximal and maximal exercise tolerance and recovery kinetics in older women with or without a breast cancer diagnosis.

The distribution of adipose depots in different body parts affects pig production value and human health, governed by complex epigenomic mechanisms. Limited studies on pig adipose depots have hindered the genetic improvement of fat-related economic traits and their biomedical applications. To address this issue, we generated epigenomic maps for backfat, belly fat, groin fat, and intermuscular fat (IMF) in Meishan pigs, integrating ChIP-seq, ATAC-seq, RNA-seq, Hi–C, and public whole-genome sequencing data. Our results reveal that belly/backfat share similar chromatin states, while groin fat/IMF exhibit distinct H3K27ac modification, super-enhancer (SE) dynamics, and open chromatin landscapes compared to belly/backfat. The spatially specific expressions of adipogenic transcription factors (TFs), such as lipid synthesis-related TFs PPARA and SOX6, which are highly expressed in back/belly fat, and adipocyte differentiation TF KLF4 was driven by a groin fat specific SE, underlie these chromatin state disparities. These results also suggest enhanced lipid synthesis in belly/backfat and adipocyte differentiation in groin fat. Moreover, candidate functional variants identified in IMF-gained H3K27ac peaks are primarily associated with meat quality traits. Genes linked to pig backfat thickness may also serve as candidate genes for human obesity due to the conserved cis-regulatory elements and gene expression patterns between humans and pigs. Overall, our epigenomic landscape enhances understanding of adipose depot regulation in mammals, facilitating cross-species insights and precision breeding.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13578-025-01513-8.

This study determined the relationship between kidney fat thickness (KFT), measured by ultrasound and body fat depots in Dorper ewe lambs. Twenty ewe lambs of six months of age (body weight 36.53±25 kg) were used. The subcutaneous fat thickness (SFT), depth (LTD, cm), width (LTW, cm), and area (LTMA, cm2) of the longissimus thoracis muscle and KFT were measured by ultrasound 24 h before slaughter. After slaughtering, the carcass weight (HCW) was recorded. The carcass was then chilled for 24 hours at a temperature of 1°C and reweighed (CCW) halved along the dorsal midline. The left half of the carcass was dissected into muscle, fat (subcutaneous fat plus intermuscular fat, CF), and bone, and each of these tissues was weighed separately and were adjusted to give the total weight of the carcass. Internal fat (IF) was removed, weighed, and categorized as mesenteric (MF), omental (GO), or perirenal (GP) fat deposits. Total IF weight was the sum of these depots. The total weight of total body fat (TBF) was calculated as the sum of the IF plus the CF. KFT ranged from 0.27 to 0.80 cm. Similarly, the TBF ranged from 2.41 to 6.38 kg. KFT was moderately associated with internal fat stores (0.65 ≤ r ≤ 0.79, P &lt; 0.001). In addition, PLT and ALT had a low level of association with CF. The results showed that KFT had a moderate association with internal body fat (0.65 ≤ r ≤ 0.79, P &lt; 0.001). However, it was weakly associated with CF (r=0.42). All equations had low to moderate precision (0.57 ≤ r2 ≤ 0.81). However, all equations had high accuracy (bias correction factor &gt; 0.94). It is also important to note that Dorper lambs in this study tended to store more fat in the carcass than in the internal depots. The results of this study support the use of KFT as a non-invasive method for predicting body fatness in hair sheep.

Background/Objectives: Sarcopenia and muscle composition have emerged as significant indicators in the fields of musculoskeletal and metabolic research. The objective of this study was to develop and validate a fully automated, deep learning-based method for segmenting thigh muscles into three functional groups (extensor, flexor, and adductor) using non-contrast computed tomography (CT) images and to quantitatively evaluate the thigh muscles. Methods: In order to ascertain the most efficacious architecture for automated thigh muscle segmentation, three deep learning models (Dense U-Net, MANet, and SegFormer) were implemented and subsequently compared. Each model was trained using 136 manually labeled non-contrast thigh CT scans and externally validated with 40 scans from another institution. The performance of the segmentation was evaluated using the Dice similarity coefficient (DSC), sensitivity, specificity, and accuracy. Quantitative indices, including total muscle volume, lean muscle volume, and intra-/intermuscular fat volumes, were automatically calculated and compared with manual measurements. Results: All three models exhibited high segmentation accuracy, with the mean DSC exceeding 96%. The MANet model demonstrated optimal performance in internal validation, while the SegFormer model exhibited superior volumetric agreement in external validation, as indicated by an intraclass correlation coefficient (ICC) of at least 0.995 and a p-value less than 0.01. Conclusions: A CT-based deep learning framework enables accurate and reproducible segmentation of functional thigh muscle groups. A comparative evaluation of convolutional attention- and transformer-based architectures supports the feasibility of CT-based quantitative muscle assessment for sarcopenia and musculoskeletal research.

Background/Objective: Aging is associated with a decline in metabolic health, including impaired glucose regulation. Both diet and biological sex impact metabolic health, yet sexual heterogeneity in diet response is understudied. We report on exploratory analyses of sex-specific associations between diet and insulin sensitivity, insulin resistance, and android and intermuscular fat composition in older adults. Methods: This secondary analysis uses baseline data from a previously completed clinical trial (n = 96), the MASTERS study. An oral glucose tolerance test (OGTT) was used to calculate insulin resistance and insulin sensitivity as measures of metabolic function, while dual-energy x-ray absorptiometry and computed tomography were used to assess body composition. Univariate analyses were used to identify sex-specific associations between metabolic health and single nutrients, as well as other dietary components. Feasible solutions algorithm (FSA) modeling was employed to identify food groups that were most associated with insulin sensitivity. Results: In men, greater intakes of vegetable protein (p < 0.0001) and whole grains (p = 0.001) were associated with higher insulin sensitivity, while refined grains (p = 0.003) and conjugated linoleic acids (p < 0.001) were negatively associated. In women, insulin sensitivity was positively associated with alcohol (p < 0.001) and xylitol (p = 0.007). FSA modeling identified whole grains, nuts, and seeds as food groups that predicted higher insulin sensitivity in men, while alcohol remained the strongest predictor in women. Conclusions: Men showed higher insulin sensitivity with plant-based diets, while alcohol intake was the dietary factor most associated with insulin sensitivity in women. The findings of these exploratory analyses support the need for sex-specific clinical trials and dietary guidance for aging populations.

Abstract Description Background Immune checkpoint inhibitors (ICIs) targeting the negative regulatory checkpoints PD-1 and CTLA-4 have improved outcomes in advanced cutaneous melanoma, yet effective biomarkers to predict ICI response are lacking. Objective: To develop predictive biomarkers of ICI response in advanced melanoma patients treated with ICIs in the first-line setting. Materials and Methods This retrospective study included 157 advanced melanoma patients (mean age: 63.3 years; 65.6% male) who underwent pre-treatment CT imaging and received ICIs. AI algorithms were utilized to compute CT image body composition features, which are standardized across abdominal, chest, pelvic regions, and spinal vertebrae. Associations between CT/clinical features and durable progression-free survival (PFS≥12 months) were assessed using univariate analysis and ML models. Prediction performance was evaluated using the area under the receiver operating curve (AUROC) using stratified 10-fold cross-validation. Results Increased visceral fat density (p = 0.038) was negatively associated with PFS≥12, while increased subcutaneous fat (p = 0.001), muscle (p = 0.005), intermuscular fat (p = 0.007), and bone volumes (p = 0.001) were favorably associated with PFS≥12. An ML model combining three CT-derived features and two clinical features achieved a mean AUROC of 0.83 (95% CI: 0.72-0.94). Conclusion CT-derived body composition features are associated with improved response to ICI therapy in advanced cutaneous melanoma. Funding Sources This work was supported in part by Developmental Research funding through the University of Pittsburgh Melanoma and Skin Cancer SPORE NIH P50 CA254865 and NIH R01CA237277. Topic Categories Translational and Interventional Immunology (TI)

BackgroundEctopic fat distribution reflects patient’s metabolic profile, shaping the tumor microenvironment, which may be a key prognostic indicator. Our study pursues to explore the independent and combined effects of muscle atrophy and abnormal fat distribution on survival in gastric cancer patients.MethodThis retrospective cohort study analyzed body composition in 283 gastric cancer patients from Center Hospital, Tianjin University (June 2016–May 2025) using pretreatment CT at L3, assessing muscle mass, intermuscular, and visceral fat. Ectopic fat phenotypes included visceral obesity (increased visceral adipose tissue, VAT) and myosteatosis (reduced skeletal muscle density, SMD). Associations between body composition parameters/phenotypes and progression-free survival (PFS), overall survival (OS), and cachexia-related survival (CRS) were evaluated using Kaplan–Meier analysis with log-rank tests and Cox proportional hazards regression.ResultsOf the 283 patients, 32 patients (11.3%) were BMI-define obese, 139 patients (49.1%) showed visceral obesity, and 176 patients (62.2%) presented with myosteatosis. Sarcopenia was present in 234 patients (82.7%), and 151 patients (53.4%) presented with a combination of sarcopenia and myosteatosis. During a median follow-up of 41 months (2–108 months), 53 patients developed metastasis, 6 relapse, and 142 died. Skeletal muscle mass (Normal Attenuation Muscle Area, NAMA, HR = 0.989, 95% CI 0.980–0.998, P = 0.022, for PFS) and quality (SMD, HR = 0.975, 95% CI 0.955–0.995, P = 0.013, for PFS) serve as protective prognostic factors after multiple covariate adjustments. Multivariate analyses identified all adiposity-related parameters, including Low Attenuation Muscle Area (LAMA, HR = 1.015, 95% CI 1.003–1.026, P = 0.012, for PFS), intermuscular adipose tissue (IMAT, HR = 1.018, 95% CI 1.002–1.034, P = 0.027, for PFS), and VFI (HR = 1.008, 95% CI 1.001–1.015, P = 0.042) as negative indicators for survival. Sensitivity analyses confirmed these associations. Besides, IMAT, LAMA, and VFI significantly influence prognosis in advanced patients (TNM stage III–IV). Kaplan–Meier survival analysis revealed sarcopenic visceral obesity markedly reduces survival; when combined with myosteatosis (SMVO), this combined phenotype further exacerbated CRS (5-year CRS: 74.7% in SMVO, P = 0.019) beyond the effects of individual phenotypes.ConclusionSarcopenic obesity (myosteatosis/visceral obesity) can identify poorer prognosis in gastric cancer patients, especially male patients. It is crucial for obese gastric cancer patients to evaluate their body composition, specifically focusing on fat distribution.Trial registration: Registered in August 2016, NCT02873676 at www.clinicaltrials.gov.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40001-025-03251-6.

Cancer is associated with accelerated aging, including changes in muscle composition and cognition. However, the relationship between myosteatosis and cognitive function has not been investigated in older cancer survivors. This study evaluated the association between myosteatosis and cognitive function in this population. The sample included 75 cancer survivors (age 76-85; 65% men; 31% Black; 41% prostate cancer; 21% breast cancer) from the Health ABC study who developed cancer within the first five years, completed CT imaging at Year 6, and cognitive testing at Year 10. Thigh intermuscular fat area (myosteatosis) was measured by CT at Year 6. Cognitive function was assessed using the Digit Symbol Substitution Test (DSST) and Modified Mini-Mental Status Exam (3MS) at Years 5 and 10. Multivariable models adjusted for demographics, education, cognitive function, and thigh muscle area at Year 5. Sensitivity analyses adjusted for leg strength, race, dementia risk factors, BMI, abdominal visceral fat, and thigh subcutaneous fat. LASSO regression identified key predictors of DSST and 3MS scores. Greater thigh myosteatosis at Year 6 was associated with lower DSST (B = - 0.212, p < 0.05) and 3MS (B = - 0.145, p < 0.05) scores at Year 10. Associations remained significant after adjustment for strength, dementia risk, and adiposity. LASSO identified race, education, Year 5 cognition, and myosteatosis as key predictors for DSST as well as thigh muscle area and physical activity for 3MS. Thigh myosteatosis is independently associated with lower cognitive performance in older cancer survivors and may represent a rehabilitation target to improve cognitive outcomes.

IntroductionThis study investigated the effects of dietary resveratrol (RES) and β-Hydroxy β-Methylbutyrate (HMB) on immune function, oxidative status, and morphological changes in intermuscular fat of Tibetan sheep. Previous research suggests that RES and HMB may enhance muscle quality and lipid metabolism, but their combined effects on meat flavor, fatty acid composition, and underlying molecular mechanisms remain unclear. Therefore, we employed transcriptomics and lipid metabolomics to explore how RES and HMB synergistically regulate key signaling pathways and lipid metabolites to improve meat quality.MethodsA total of 120 male Tibetan lambs with similar initial body weight (15.5 ± 0.14 kg) were randomly divided into four groups (n = 30 per group): 1) H group (basal diet without RES or HMB); 2) H-RES group (1.5 g/day RES); 3) H-HMB group (1.25 g/day HMB); and 4) H-RES-HMB group (1.5 g/day RES + 1.25 g/day HMB). The experiment lasted 100 days, including a 10-day pre-test period and a 90-day formal trial. Intermuscular fat morphology, fatty acid composition, and flavor compounds were analyzed. Transcriptomic and lipid metabolomic approaches were used to identify differentially expressed genes and lipid metabolites, followed by pathway enrichment analysis to elucidate regulatory mechanisms.ResultsThe H-RES-HMB group exhibited significantly reduced intermuscular adipocyte area and diameter (p < 0.05) but increased cell density. Among medium- and long-chain fatty acids, the H-RES-HMB group showed significantly decreased SFAs (C17:0 and C18:0) (p < 0.05) and significantly increased MUFAs (C15:1N5 and C18:1N9) and PUFAs (C18:2N6, C18:3N6, C18:3N3, C20:3N6, and C20:3N3) (p < 0.05). Additionally, flavor compounds such as 2-Hexanone, 3-Hexanone, 3-Pentanone, and Methyl acetate were significantly elevated in the H-RES-HMB group (p < 0.05). Transcriptomic analysis revealed that RES and HMB synergistically regulated the Calcium (ERBB4, P2RX7, ERBB3, P2RX3, and SLC8A1), Hippo (WNT9A, WNT10B, WNT6, and WNT2B), Estrogen (HSP90AA1, TGFA, and RARA), and Arachidonic acid (PLA2G4A, ALOX12, and PTGDS) signaling pathways, collectively promoting muscle cell proliferation and differentiation, Metabolomics identified key lipid molecules (LPC(20:0/20:1), PC(21:2/37:0/38:5)) and pathways (Glycerophospholipid, Arachidonic acid metabolism) contributing to flavor optimization. Integrated analysis highlighted the PLA2G4A-AA-ALOX12/PTGDS axis as a central hub for flavor regulation.DiscussionThe findings demonstrate that RES and HMB synergistically improve meat quality by modulating lipid metabolism and inflammatory responses. The reduction in SFAs and increase in MUFAs/PUFAs align with enhanced nutritional value, while elevated ketones/esters contribute to favorable flavor profiles. The transcriptomic and metabolomic integration reveals that PLA2G4A hydrolyzes PC(38:5) to release AA, which is metabolized via ALOX12/PTGDS to generate flavor precursors (generating 12-HPETE and PGD2). These mechanisms explain the “reduced off-flavor and enhanced aroma” effect. Future studies should validate these pathways in other livestock to assess broader applicability.

Integrating computer vision and machine learning technologies for model building to quantify intermuscular fat content in salmonid fillets

Baseline physiologic characteristics of participants in the APOLLO ( Personalized rehAbilitation PrOgram in aLLOgeneic Bone Marrow Transplantation) study assessed by MRI: preliminary results

MRI study of long-term body composition alterations in HER2+ breast cancer survivors

Preoperative myosteatosis and intermuscular adiposity as CT-Derived nutritional prognostic markers in colorectal cancer: A multicenter development-validation study.

Background Sarcopenia is prevalent in Crohn’s disease (CD) patients, but the prognostic value of skeletal muscle-to-intermuscular adipose tissue (IMAT) balance in penetrating CD remains unexplored. Objective To determine whether skeletal muscle-IMAT imbalance predicts non-surgical treatment failure in CD. Methods This retrospective study included CD patients undergoing computed tomography enterography (CTE, 2013–2022), stratified by disease behavior (penetrating vs. non-penetrating). Automated CTE segmentation algorithm quantified skeletal muscle and IMAT volumes at baseline. Skeletal muscle ratio was calculated as skeletal muscle/(skeletal muscle + IMAT). Sarcopenia was defined by the skeletal muscle area at the third lumbar vertebra. Patients received ≥1 year of non-surgical therapy, with outcomes categorized as ‘maintenance therapy’ or ‘treatment escalation’. Cox proportional hazards analysis identified predictors of escalation; mediation analysis evaluated inflammatory-nutritional pathways. Results Among 157 patients (penetrating: n = 42; non-penetrating: n = 115), treatment escalation rates were 64.3% (27/42) and 53.0% (61/115) respectively, without significant intergroup difference (p = 0.21). Skeletal muscle ratio predicted escalation in both cohorts (penetrating: AUC = 0.822 [0.673, 0.923], non-penetrating: AUC = 0.922 [0.857, 0.964]), outperforming conventional sarcopenia metrics (p = 0.002 and p < 0.001). Cox regression confirmed skeletal muscle ratio as an independent protective factor (penetrating: HR = 0.098 [0.014 − 0.680], p = 0.02; non-penetrating: HR = 0.597 [0.442 − 0.804], p = 0.001; combined: HR = 0.637 [0.493 − 0.823], p = 0.001), while penetrating disease as risk factor (HR = 3.778 [1.281 − 11.14], p = 0.02). Body mass index mediated 6.9% of the skeletal muscle ratio-treatment escalation relationship in non-penetrating CD. Conclusions Skeletal muscle-IMAT imbalance independently predicts treatment escalation in CD, offering superior prognostic utility to traditional sarcopenia measures.

Objectives: To employ artificial intelligence (AI) to automatically measure bone mineral density (BMD) and intramuscular fat in computed tomography (CT) images of patients with fractures and explore the association between these parameters and fracture healing. Methods: This retrospective study included patients who underwent baseline CT scans for rib fracture diagnosis and follow-up CT scans for fracture healing assessment at our hospital between 2012 and 2023. The volumetric BMD of the entire first lumbar vertebra (L1) and the paraspinal intramuscular fat area (PIFA) at the midsection of L1 in the baseline CT were extracted using AI. The primary outcomes, including callus formation, volume increase, and poor healing, and logistic regression were used to analyze the relationships between BMD and PIFA with primary outcomes. Results: Overall, 297 fractures from 53 patients (24 males; mean age: 53.83 ± 10.86 years) were included in this study. In multivariate regression analysis, a 1 standard deviation (SD) decrease in BMD was identified as an independent prognostic factor for reduced callus formation (odds ratio [OR] = 0.70, 95% confidence interval [CI] = 0.50-0.97), diminished volume increase (OR = 0.70, 95% CI = 0.51-0.96), and elevated poor fracture healing at follow-up (OR = 2.08, 95% CI = 1.38-3.13). Similarly, a 1 SD increase in PIFA was an independent prognostic factor for reduced callus formation (OR = 0.24, 95% CI = 0.16-0.37), diminished volume increase (OR = 0.33, 95% CI = 0.23-0.49), and elevated poor fracture healing at follow-up (OR = 2.09, 95% CI = 1.50-2.93). Therefore, a model combining BMD, PIFA, and clinical characteristics significantly outperformed a model that included only clinical characteristics in predicting callus formation, volume increase, and poor fracture healing, with areas under the curve of 0.790, 0.749, and 0.701, respectively (all p < 0.001). Conclusions: BMD and PIFA can be used as early predictors of fracture healing outcomes and can help clinicians select appropriate interventions to prevent poor healing.

Association of Lung Function With Visceral Adiposity and Skeletal Muscle Mass Considering Myosteatosis.

Morphological evaluation of Semimembranosus muscle quality in the Nero di Lomellina pig: a contribution to porcine biodiversity.

More intermuscular fat (IMF) has been associated with lower cognitive performance through mechanisms that are not fully elucidated. The associations of 7628 plasma proteins with IMF were assessed in the Baltimore Longitudinal Study on Aging (n=941, mean age=66.7±15.2) and validated in the Coronary Artery Risk Development in Young Adults Study (n=2451, mean age=50.2±3.6). Processing speed was assessed by Digit Symbol Substitution Test (DSST). Associations between the main exposures, outcome, and mediators were evaluated using linear regression, and mediating effects were assessed by causal mediation analysis. There were 722 plasma proteins associated with IMF in both the discovery and replication cohorts (false discovery rate [FDR] adjusted p≤0.05). Of these, 26 mediated the relationship between IMF and DSST, with effects ranging from 2.8% to 20.9% (p≤0.05). These proteins represented synaptic function and organization, and growth factor binding (FDR adjusted p≤0.05). Reducing IMF may improve processing speed through effects on growth factor and synaptic activity. Higher intermuscular fat is associated with lower processing speed, consistently across populations that represent different demographic characteristics. There is a robust plasma proteomic profile of intermuscular fat that is assessed in the abdominal and thigh skeletal muscle. The proteins associated with intermuscular fat reflect signals that are reflective of synaptic function and organization, as well as other molecular pathways such as growth factor binding. Circulating proteins partially explain the association between higher intermuscular fat and lower processing speed, suggesting that higher intermuscular fat effects processing speed through pathways including synaptic functions and growth factor binding.