The review separates four related but non-interchangeable domains. Muscle quantity describes the amount of muscle or lean tissue; muscle strength measures force-generating capacity; physical performance captures the integrated ability to perform mobility-related tasks; and muscle composition describes structural properties such as fat infiltration and radiodensity. An international Delphi process involving 107 experts from 29 countries and regions reached consensus that muscle mass, muscle strength and muscle-specific strength are distinct components of sarcopenia, while reduced physical performance was regarded primarily as an outcome. This distinction means that a dual-energy X-ray absorptiometry scan or bioelectrical impedance analysis cannot substitute for direct strength testing or assessment of physical performance.

Diagnostic criteria themselves can materially alter the number of people classified as having sarcopenia. The Asian Working Group for Sarcopenia’s 2025 framework expanded its applicable age range to include adults aged 50 to 64 and defined sarcopenia through the coexistence of low muscle mass and low muscle strength, with physical performance positioned primarily as an outcome. In the 754-person KAMOGAWA-A cohort of Japanese participants with type 2 diabetes, applying the revised framework increased prevalence from 15.0% to 23.6%. Most newly identified cases were in people younger than 65 or with a BMI of at least 23 kg/m² who met the revised appendicular skeletal muscle-to-BMI criterion. Both frameworks were associated with diabetic kidney disease, with fully adjusted odds ratios of 2.25 for AWGS 2019 and 1.78 for AWGS 2025, but the study did not establish superior diagnostic accuracy or show that the additional cases faced greater future risks of disability, cardiovascular disease or death.

Population estimates also depend heavily on definitions, populations and measurement methods. A meta-analysis of 15 studies found 55% higher odds of sarcopenia among people with type 2 diabetes than among those with normal glucose metabolism, with deficits in strength and physical performance more consistent than differences in muscle mass. A synthesis of 43 Asian observational studies estimated a pooled prevalence of 17% in people with type 2 diabetes, although individual estimates ranged from 4% to 50% and reached 28% in Southeast Asia. Functional testing can also reshape results: among 100 community-dwelling older adults with type 2 diabetes, the proportion classified as having impaired muscle function ranged from 1% with handgrip strength to 92% with the chair-stand test. These tests capture related but different aspects of muscle function, so pooled prevalence estimates of roughly 17% to 18% are population-level benchmarks rather than universal rates.

Imaging studies indicate that abnormal muscle composition may be more consistently associated with type 2 diabetes than reduced muscle size. In a cross-sectional analysis of 20,986 adults undergoing abdominal computed tomography, participants with type 2 diabetes did not have less total abdominal or skeletal muscle area, but they had less normal-attenuation muscle and a lower normal-attenuation-to-total-muscle ratio. From the lowest to the highest quartile of that index, type 2 diabetes prevalence declined from 19.3% to 9.5% in men and from 12.3% to 3.0% in women. In another cross-sectional study of 1,225 adults aged at least 65, type 2 diabetes was associated with lower T4-level skeletal muscle density but not skeletal muscle index, while HbA1c correlated inversely with muscle density. CT and MRI can quantify fat infiltration that DXA and BIA do not directly measure, and ultrasound offers repeatable, radiation-free structural assessment, although operator dependence and a lack of standardized thresholds limit comparisons.

Several interacting mechanisms may connect metabolic disease with declining muscle health. Insulin resistance can weaken IRS-1/PI3K/Akt signaling, reduce muscle glucose uptake and protein synthesis, and relieve FoxO-mediated inhibition of protein breakdown. Chronic hyperglycemia and advanced glycation end products may add oxidative and endoplasmic-reticulum stress, while inflammation, lipotoxicity and ectopic fat can impair mitochondrial oxidation and insulin signaling. Evidence for advanced glycation end products remains mostly associative in humans, although animal experiments provide a plausible mechanism involving PERK/FOXO1 signaling. Peripheral neuropathy can reduce contractile capacity through motor-nerve injury, denervation and loss of motor units, while pain, balance impairment and reduced activity can add disuse-related losses. A prospective hospital cohort also found that sarcopenia was associated with incident diabetic kidney disease and that baseline kidney disease was associated with later sarcopenia, a temporal pattern consistent with bidirectional interactions but not proof of causation.

Continuous glucose monitoring is emerging as a research tool for examining dynamic metabolic exposure alongside muscle phenotypes, but it is not a diagnostic test for sarcopenia. Among 310 hospitalized adults aged at least 60 with type 2 diabetes, each 10-percentage-point increase in time in range was associated with lower odds of sarcopenia, with an odds ratio of 0.83 and 95% confidence interval of 0.74 to 0.93. Time in range was also associated with skeletal muscle index and grip strength, but not with physical performance. A separate study of 303 older adults with type 2 diabetes associated greater glycemic variability, including a higher coefficient of variation, with a lower pectoralis muscle index. These observations do not show that increasing time in range or reducing glycemic variability prevents sarcopenia. Physical activity, nutrition and underlying health may influence both glycemic patterns and muscle status, while reverse causation remains possible.

Exercise trials provide the most direct randomized evidence that metabolic and muscle outcomes can improve together. A meta-analysis of 43 randomized controlled trials involving 2,012 adults aged at least 50 with type 2 diabetes found that resistance training reduced HbA1c by 0.55 percentage points, HOMA-IR by 1.15 and fasting glucose by 6.99 mg/dL, while increasing muscle mass by 0.89 kg. Upper- and lower-extremity strength improved by standardized mean differences of 2.28 and 2.02, respectively. The review notes that functional gains do not always track changes in muscle mass, meaning trials relying only on skeletal muscle index or lean mass may underestimate improvements in strength and mobility. Protein recommendations for generally healthy older adults are approximately 1.0 to 1.2 grams per kilogram of body weight daily, but these targets should not be applied uniformly in type 2 diabetes because kidney function, nutritional status, obesity and the pace of intentional weight loss alter individual needs.

Modern glucose-lowering and weight-loss medicines further expose the limits of monitoring a single muscle metric. A meta-analysis of 25 randomized trials involving 2,286 participants found that SGLT2 inhibitors were associated with mean reductions of 0.80 kg in lean mass and 0.38 kg in skeletal muscle mass, but fluid loss and BIA’s hydration sensitivity complicate interpretation, while strength and performance data were sparse. In the post hoc MRI substudy of SURPASS-3, 246 participants with evaluable data at 52 weeks showed a 0.64-liter reduction in thigh muscle volume, close to the change predicted by weight loss, while muscle fat infiltration fell by 0.36 percentage points, more than predicted. Because the substudy was industry funded and sarcopenia, strength and physical performance were not primary outcomes, it neither demonstrates treatment-related muscle injury nor establishes prevention of sarcopenia. Human evidence for GLP-1 receptor agonists likewise remains limited for strength, physical performance and rigorously defined sarcopenia.

The review argues that progress depends on harmonized definitions, standardized measurements and prospective evidence. Long-term cohorts should follow middle-aged as well as older adults with type 2 diabetes across muscle quantity, strength, composition and physical performance, relating changes to mortality, cardiovascular events, falls and diabetic kidney disease. The field also needs a core outcome set for muscle health, with tests of whether myosteatosis, bioelectrical impedance phase angle and CGM-derived metrics add prognostic information beyond HbA1c and conventional clinical variables. Trials of potent weight-loss therapies, including GLP-1 receptor agonists and tirzepatide, should prespecify muscle quantity, intramuscular fat, strength and physical performance alongside body composition. Until such evidence is available, sarcopenia is best regarded as an informative high-risk phenotype rather than a proven independent cause of adverse outcomes, and muscle-centered intervention research must evaluate quantity, composition and function together.

Subject of Research: Multidimensional skeletal muscle health and sarcopenia in type 2 diabetes

Article Title: Sarcopenia in type 2 diabetes mellitus: from muscle quantity to multidimensional muscle health

Article References: Wang, Z., Zhu, L., Zhang, F., Liu, Z., Lu, J., Lei, T., & Sha, W. (2026). Sarcopenia in type 2 diabetes mellitus: from muscle quantity to multidimensional muscle health. Frontiers in Endocrinology, 17, Article 1980238. https://doi.org/10.3389/fendo.2026.1980238

Image Credits: AI Generated

DOI: 10.3389/fendo.2026.1980238

Keywords: type 2 diabetes, sarcopenia, skeletal muscle, muscle strength, physical performance, myosteatosis, muscle composition, continuous glucose monitoring, sarcopenic obesity, resistance training, weight-loss therapy, type

The study, published in BMC Medical Imaging, set out to answer a deceptively simple question: which clinical and radiological factors are independently associated with fatty pancreas disorder on CT? The researchers retrospectively analyzed 389 adult patients who underwent endoscopic retrograde cholangiopancreatography, or ERCP, between September 2014 and June 2023 and who had undergone a prior CT scan. Because every patient already had both a CT and an ERCP on record, the dataset offered an unusually rich window into pancreatic anatomy and physiology, allowing the team to measure not only fat inside the gland itself but also the surrounding landscape of visceral fat, subcutaneous fat, skeletal muscle and muscle density.

The technical backbone of the study is the Hounsfield unit, the standardized density scale of CT imaging. Fat reads as dark, or low attenuation, on CT, while healthy soft tissue reads brighter. The researchers defined fatty pancreas disorder as unenhanced pancreatic attenuation of 15 Hounsfield units or less, a cutoff approximating the lowest quartile of the attenuation distribution in their cohort. In plain terms, a pancreas that darkens to this degree on a scan contains enough fat to stand apart from the majority of glands. Using this definition, fatty pancreas disorder was present in 106 of the 389 patients, or 27.2 percent, a strikingly high proportion that underscores how common the condition may be in populations undergoing pancreatic evaluation.

What makes the study more than a simple prevalence count is the multivariable analysis the team performed. Rather than looking at each factor in isolation, they used logistic regression to determine which variables remained independently associated with a fatty pancreas after accounting for the others. Four predictors survived this statistical gauntlet. Older age carried an odds ratio of 1.06 per year, meaning each additional year of life modestly increased the odds of a fatty gland. The absence of a smooth pancreatic contour, meaning an irregular or lobulated gland outline, was associated with fatty pancreas disorder. A smaller main pancreatic duct diameter also predicted the condition, with an odds ratio of 0.65 per unit increase in duct size, and lower mesenteric fat attenuation, a marker of fat quality in the abdominal cavity, rounded out the picture with an odds ratio of 0.96.

Some of the raw group comparisons are equally telling. Patients with fatty pancreas disorder were significantly older than those without it, averaging 81.1 years against 71.5 years, a difference that was highly statistically significant. They also carried more visceral fat, the metabolically active fat wrapped around internal organs, with a mean visceral fat area of 242.1 square centimeters compared with 200.2 square centimeters in patients whose pancreas remained lean. Visceral fat area is measured directly on CT by tracing the fat compartment inside the abdominal cavity, and it is widely regarded as a more meaningful marker of metabolic risk than body mass index, which cannot distinguish muscle from fat or subcutaneous from visceral deposits.

The correlation analysis adds a layer of physiological coherence to the findings. Pancreatic attenuation, treated as a continuous variable, showed a negative correlation with age, with a correlation coefficient of minus 0.34, and a negative correlation with visceral fat area at minus 0.25. In other words, the older the patient and the larger the visceral fat store, the darker and fattier the pancreas appeared. Conversely, pancreatic attenuation correlated positively with psoas skeletal muscle density at plus 0.26 and with main pancreatic duct diameter at plus 0.26. Dense, healthy muscle and a wider duct traveled together with a leaner gland, painting a picture of fatty pancreas as part of a broader systemic phenotype rather than an isolated organ quirk.

That systemic dimension is where the mesenteric fat finding becomes intriguing. Mesenteric fat attenuation reflects not just how much fat surrounds the intestines but what kind of fat it is; lower attenuation suggests fat that is denser on the CT scale, which in this context the researchers found associated with a fatty pancreas. The idea that the quality of fat elsewhere in the abdomen mirrors the fat content of the pancreas hints at shared metabolic pathways, possibly involving inflammation, insulin resistance and ectopic lipid storage, the process by which fat spills into organs never designed to hold it. The same logic underlies the study’s interest in skeletal muscle density, since fatty infiltration of muscle, known as myosteatosis, is another recognized marker of metabolic dysfunction.

A crucial strength of the work lies in its sensitivity analyses, which tested whether the findings depended on the arbitrary choice of a single attenuation cutoff. The researchers re-ran their models using a pancreas-to-spleen attenuation ratio, an alternative absolute threshold of 30 Hounsfield units or less, and pancreatic attenuation modeled as a continuous variable. The association with irregular pancreatic contour proved robust across all these alternative definitions, suggesting it is a genuine morphological companion of pancreatic steatosis. The link with main pancreatic duct diameter, by contrast, was definition-dependent, meaning it appeared under some definitions but not others, a nuance the authors transparently report and one that future studies will need to resolve.

Reproducibility received attention as well. In a random subsample of 50 patients, the team assessed how consistently different observers classified the pancreatic contour, an essential quality check for any imaging feature proposed as a biomarker. If two radiologists cannot agree on whether a contour is smooth or irregular, the feature is of little clinical use, and the reproducibility testing in this study addresses that concern directly. The broader context also matters: the study was conducted in accordance with the Declaration of Helsinki, approved by the institutional review board of CHU de Saint-Etienne, and carried out without external funding, with the authors declaring no competing interests.

Why should ordinary readers care about a dark pancreas on a CT scan? The pancreas performs two jobs essential to life: it secretes digestive enzymes into the gut and it releases insulin and glucagon into the bloodstream to regulate blood sugar. Fat crowding pancreatic tissue has been linked in other research to impaired insulin secretion and elevated risk of type 2 diabetes, and it may interact with pancreatic inflammation and even cancer risk, though this study was designed to identify imaging associations rather than outcomes. What the Saint-Etienne team has delivered is a practical observation: fatty pancreas disorder travels with a distinct metabolic and morphological phenotype, one that a radiologist can begin to recognize on scans already being performed for other reasons. As CT scanners continue to generate vast archives of abdominal images, findings like these suggest that the pancreas, long a silent bystander in the obesity epidemic, may be quietly recording the same story of aging and visceral fat that the liver has been telling all along, and that the tools to read that story are already in every hospital.

Subject of Research: Radiological and clinical predictors of fatty pancreas disorder on computed tomography

Article Title: Fatty pancreas disorder on CT: associations with mesenteric fat attenuation, irregular contour, and reduced main pancreatic duct diameter

Article References: Boumaiza, M., Prouvot, C., Grange, S., Magand, N., Lutz, N., Phelip, J.-M., Roblin, X., Boutet, C., Williet, N., & Grange, R. (2026). Fatty pancreas disorder on CT: associations with mesenteric fat attenuation, irregular contour, and reduced main pancreatic duct diameter. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02925-9

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02925-9

Keywords: fatty pancreas disorder, computed tomography, pancreatic steatosis, visceral fat, mesenteric fat attenuation, main pancreatic duct, Hounsfield units, body composition, myosteatosis, ERCP, pancreatic contour, metabolic dysfunction

The scale of the clinical associations is striking. Excessive intramuscular fat has been linked not only to sarcopenia in older adults but also to coronary microvascular dysfunction, where it independently predicts adverse cardiovascular outcomes. It is tightly connected to insulin resistance and type 2 diabetes, with intermuscular adipose tissue shown to directly impair the insulin sensitivity of muscle tissue. In metabolic dysfunction-associated steatotic liver disease, greater intramuscular fat correlates with disease severity, visceral adiposity and even gut dysbiosis. Worsening muscle quality is associated with increased mortality in chronic kidney disease, poorer outcomes in cirrhosis, and complications after transplantation. In oncology, myosteatosis has emerged as an independent prognostic marker across multiple malignancies, predicting survival in ways that simple muscle volume measurements cannot capture.

Part of the appeal of intramuscular fat as a biomarker lies in its sensitivity to change. While ageing may cause roughly five to ten per cent muscle loss over a decade, the fat fraction within muscle can double over the same period, explaining functional decline far better than muscle mass alone. Sedentary behaviour or immobilisation quickly increases intramuscular fat content, whereas regular physical activity helps maintain lean muscle quality. Fatty infiltration in the thigh and calf muscles of older people correlates with poorer balance and increased falls risk, and in neuromuscular disorders such as muscular dystrophies, MRI fat fraction mapping has already become an invaluable outcome measure for tracking disease progression.

Yet the term myosteatosis conceals a biological complexity that the review works hard to untangle. At the microscopic level, intramyocellular lipids are stored as droplets within muscle fibres, serving as a readily mobilised energy reserve. When accumulation exceeds the storage capacity of fibres — a state known as lipotoxicity — insulin signalling is impaired and inflammatory pathways are activated. Crucially, other lipid species such as diacylglycerols and ceramides are metabolically bioactive, suggesting that lipid composition, not merely quantity, matters when interpreting the biomarker. Extramyocellular lipids occupy the space between fibres and fascicles, while intermuscular adipose tissue lies between muscle groups beneath the deep fascia. Each compartment carries different biological meaning, and the relative contribution of each to a measured fat signal depends on the imaging modality and its resolution.

That measurement challenge sits at the heart of the field. Magnetic resonance imaging offers a spectrum of approaches, from subjective visual grading systems such as the Goutallier scale, still widely used in orthopaedic surgical planning, to quantitative Dixon-based techniques that exploit the chemical shift between water and fat protons. Multi-echo Dixon sequences correct for field inhomogeneities, relaxation effects and spectral complexity, yielding the proton density fat fraction — a standardised measure validated against spectroscopy and increasingly applied in clinical trials. Magnetic resonance spectroscopy remains the gold standard for biochemical specificity, uniquely distinguishing intramyocellular from extramyocellular lipids, and phosphorus spectroscopy can probe mitochondrial energetics through high-energy phosphate metabolism. However, spectroscopy suffers from low spatial resolution, voxel placement variability and labour-intensive acquisition, confining it largely to mechanistic research.

Computed tomography offers a pragmatic alternative. It infers fat content from tissue radiographic density expressed in Hounsfield units, with lean muscle typically measuring around 50 to 60 HU and fat-infiltrated muscle appearing progressively less dense. Standardised assessment at the third lumbar vertebral level, using thresholds such as mean density below 41 HU for individuals with a body mass index under 25, has proven effective in predicting cancer survival and mortality in kidney and liver disease. Artificial intelligence, particularly convolutional neural networks, now enables fully automated segmentation of muscle and adipose tissue from routine clinical scans, reducing observer variability and opening the door to large-scale population studies. But CT carries ionising radiation, cannot separate lipid compartments, and is confounded by fluid retention and glycogen variation, which can lower muscle density independently of fat.

Ultrasound rounds out the toolkit as a portable, inexpensive, radiation-free option. Increased echogenicity indicates higher fat or fibrous tissue content, and quantitative echo intensity correlates with MRI-estimated intramuscular fat and with muscle performance. Ultrasound also captures muscle architecture — fascicle length and pennation angle — in a single bedside examination. Its weaknesses are reproducibility and specificity: measurements are sensitive to operator technique, probe pressure, transducer frequency and machine settings, and ultrasound cannot distinguish fat from fibrosis or resolve individual lipid compartments. The SARCUS consensus has defined anatomical landmarks for 39 muscles to improve acquisition consistency, and a cadaver-validated three-point grading system for sarcopenia has shown excellent reliability, but inter-device variability remains a barrier to multicentre trials.

Perhaps the most provocative finding in the review is that ‘normal’ intramuscular fat is anything but uniform. Even in healthy individuals, different muscles harbour inherently different fat content: the gluteus maximus typically contains around fifteen per cent fat in young men and twenty per cent in young women, while neighbouring hip abductor muscles hold only eight to ten per cent. Ageing amplifies these differences dramatically — quadriceps muscles in older men contain four to five times more fat than those of young men, despite only modest differences in muscle cross-sectional area. Sex differences appear partly mediated by conditioning: recreational cyclists of both sexes showed equivalent gluteal fat of roughly twelve per cent once body size and activity were accounted for. Ethnicity adds further complexity, with South Asian children carrying higher intramyocellular lipid than their peers, and African American and Hispanic adults accumulating more intramuscular fat for a given body size — patterns that may partly explain population differences in insulin resistance and that render European-derived reference values non-universal.

The athlete’s paradox crystallises why context is everything. Endurance athletes can carry high intramyocellular lipid yet remain exquisitely insulin sensitive, because their lipid droplets sit within oxidative fibres, are smaller, and are closely apposed to mitochondria, supporting rapid turnover. The same lipid levels in sedentary individuals signal insulin resistance and frailty. This U-shaped relationship — high fat in insulin-sensitive athletes, low fat in insulin-sensitive non-athletes, high fat again in insulin-resistant states — means intramuscular fat cannot be treated as a uniform risk factor. The review suggests composite indices, such as a muscle quality index integrating fat with strength or power measures, and points to emerging mass spectrometry imaging studies that map lipid species and their microregional organisation within muscle histology.

Encouragingly, intramuscular fat is modifiable. A single bout of cycling depletes intramyocellular lipid by roughly twenty-four per cent in exercised muscles. Twelve months of moderate physical activity in older adults prevented the thigh muscle fat gain seen in sedentary controls, who accumulated around eighteen per cent more fat while the active group gained only two per cent — and the exercisers avoided the strength loss their sedentary peers suffered. Meta-analyses of randomised trials show combined aerobic and resistance training reduces fat infiltration most robustly when paired with weight loss. Caloric restriction and bariatric surgery consistently reduce ectopic fat depots, though rapid weight loss risks lean mass loss unless protected by resistance training and adequate protein. Most recently, glucagon-like peptide-1 receptor agonists such as liraglutide and semaglutide have shown modest but significant reductions in intramuscular fat, though concerns persist because these drugs also reduce absolute muscle mass, raising sarcopenic obesity risks in older or frail patients. Before intramuscular fat can join bone density and liver fat as a routine clinical measure, the field needs standardised protocols, multi-ethnic longitudinal cohorts to define age-, sex- and ethnicity-specific reference ranges, and consensus on what constitutes a clinically meaningful change. If those hurdles fall, clinicians may one day stratify risk across sarcopenia, cirrhosis and cancer cachexia — and track whether an intervention genuinely improves muscle quality rather than merely mass.

Subject of Research: Intramuscular fat accumulation (myosteatosis) as a biomarker of skeletal muscle health and chronic disease

Article Title: Intramuscular Fat as a Biomarker of Muscle Health: Strengths, Limitations and Open Challenges

Article References: Spence, C., Belzunce, M. A., Di Laura, A., Henckel, J., & Hart, A. J. (2026). Intramuscular Fat as a Biomarker of Muscle Health: Strengths, Limitations and Open Challenges. Journal of Cachexia, Sarcopenia and Muscle, 17(5), Article e70389. https://doi.org/10.1002/jcsm.70389

Image Credits: AI Generated

DOI: 10.1002/jcsm.70389

Keywords: intramuscular fat, myosteatosis, sarcopenia, muscle quality, Dixon MRI, magnetic resonance spectroscopy, computed tomography, ultrasound, insulin resistance, athlete's paradox, GLP-1 receptor agonists, biomarker standardisation

Colorectal cancer is the third most common cancer among men and women in the United States, and roughly 55,000 patients are expected to die of the disease by the end of 2026. Surgery remains the most common primary treatment, but it carries a substantial burden: postoperative complications occur in about 35 percent of patients, mortality ranges from 1 to 16.4 percent, and adverse outcomes prolong hospitalization. Identifying who is at highest risk before the first incision is made has therefore become a pressing clinical goal, and the skeletal muscle has emerged as an unexpected prognostic organ, thanks to its secretory and metabolic properties.

The technique at the heart of the study is opportunistic imaging analysis. Using preoperative abdominal CT scans, the researchers measured two quantities at the level of the third lumbar vertebra. The skeletal muscle index, or SMI, quantifies muscle quantity by dividing the cross-sectional area of muscle by the square of the patient’s height. The skeletal muscle density, or SMD, expressed in Hounsfield units, captures the average radiation attenuation of the muscle—essentially a proxy for how much fat has infiltrated the tissue. Healthy muscle attenuates more radiation; fat-invaded muscle reads darker and lower. Segmentation was performed with semi-automated software integrated into the hospital’s picture archiving system, using the standard Hounsfield unit window of −29 to +150, and the measurements proved remarkably repeatable, with an intraclass correlation coefficient of 0.99.

What distinguishes this study from much of the prior literature is its treatment of these variables. Most earlier work dichotomized patients into low versus normal muscle categories using fixed cutoffs, which is clinically convenient but obscures how graded changes in muscle characteristics translate into graded changes in risk. Here, the team modeled SMI and SMD both as continuous predictors, expressed per 5-unit decrease, and as binary variables using two widely applied definitions drawn from Martin and colleagues and from Xiao and colleagues. The cohort of 415 patients had a mean age of 63.7 years, a mean body mass index of 28.2 kilograms per square meter, and underwent laparoscopic, robotic-assisted, or open resection in roughly equal measure.

The results were strikingly one-sided. Each 5-unit decrease in SMD was associated with a 6 percent higher incidence of hospital days after surgery, an association that held whether muscle density was modeled continuously or through clinical cutoffs. Muscle quantity, by contrast, lost its apparent univariate association with length of stay once age, sex, comorbidity burden, tumor stage, neoadjuvant treatment, and surgical approach were adjusted for. A similar pattern emerged for severe complications, defined as grade 3 or higher on the Clavien–Dindo classification: each 5-unit drop in SMD raised the odds by 25 percent, while SMI showed no significant adjusted association. These findings persisted in sensitivity analyses restricted to patients scanned within two months of surgery, a window that best captures muscle status closest to the operation.

Perhaps the most consequential findings concerned where patients went after leaving the hospital. Of 409 patients with discharge data, 30 were sent to a skilled nursing facility, inpatient rehabilitation, or long-term care rather than home. Each 5-unit decrease in SMD was associated with 40 percent greater odds of non-home discharge, and patients classified as having low SMD faced roughly three-and-a-half to eight-and-a-half times the odds of institutional discharge depending on the cutoff definition used. Non-home discharge matters beyond convenience: it is linked to diminished quality of life and higher healthcare costs across surgical populations, making it a meaningful marker of functional recovery.

Mortality data reinforced the same hierarchy. Twelve patients died within 90 days of surgery, and although the small number of events precluded multivariable analysis, each 5-unit decrease in SMD was associated with a 36 percent higher hazard of early death in univariate analysis. Over a median follow-up of 31.6 months, 75 patients—18.1 percent of the cohort—died of any cause. In adjusted Cox regression, each 5-unit decrease in SMD carried a 26 percent higher risk of overall mortality, and low SMD per the Martin criteria was associated with a 93 percent higher risk compared with normal density. Kaplan–Meier curves confirmed significantly worse survival for patients with low values of either measure, but only SMD survived full statistical adjustment.

Why would fat inside muscle outpredict muscle size? The authors point to the biology of myosteatosis, which appears to compound age-related declines in muscle strength and function more aggressively than simple atrophy. Across studies of older adults and patients with cancer, muscle density correlates more strongly than muscle quantity with strength, physical performance, frailty, and comorbidity burden. Myosteatosis is also entwined with systemic inflammation and insulin resistance, mechanisms plausibly relevant to surgical stress, wound healing, and immune defense. Encouragingly, the condition may be modifiable: meta-analytic evidence shows that aerobic and resistance training improve SMD in adults, raising the prospect that prehabilitation programs before cancer surgery could measurably densify muscle and blunt operative risk, though such trials in surgical oncology remain scarce.

The study has limits the authors acknowledge. The cohort was modest, and events for complications, discharge location, and early mortality were few, forcing cautious interpretation and simplified models. Nearly all scans were contrast-enhanced, which modestly overestimates SMD and may underdiagnose myosteatosis, and the researchers lacked data on physical activity, nutrition, and frailty over time. Still, the central message is hard to dismiss: a measurement already sitting inside nearly every preoperative CT scan, requiring no extra radiation, cost, or patient burden, may identify the colorectal surgery patients most likely to struggle. The authors argue that preoperative SMD could guide risk stratification, treatment decisions, and perioperative care, and they call for trials testing whether prehabilitation can improve muscle density before the operating room doors swing open.

Subject of Research: CT-derived skeletal muscle density and mass as predictors of postoperative outcomes after colorectal cancer surgery

Article Title: Associations of CT‐Derived Skeletal Muscle Index and Density With Postoperative Outcomes Following Colorectal Cancer Surgery

Article References: Papadopoulos, E., Irving, B. A., Spielmann, G., Lin, M., & Finan, K. R. (2026). Associations of CT‐Derived Skeletal Muscle Index and Density With Postoperative Outcomes Following Colorectal Cancer Surgery. Journal of Cachexia, Sarcopenia and Muscle, 17(5), Article e70384. https://doi.org/10.1002/jcsm.70384

Image Credits: AI Generated

DOI: 10.1002/jcsm.70384

Keywords: colorectal cancer, skeletal muscle density, myosteatosis, sarcopenia, CT imaging, postoperative complications, length of stay, mortality, prehabilitation, surgical oncology, body composition, risk stratification

The research, published in the European Journal of Nuclear Medicine and Molecular Imaging, examined whether body composition measured on routine CT scans could predict which patients would experience early progression after PRRT. The idea rests on a growing body of evidence in oncology: it is not merely how much muscle a patient carries, but the quality of that muscle, that shapes outcomes across a striking range of cancers. Previous studies have linked fatty infiltration of muscle, a condition called myosteatosis, to poorer survival after pancreatic surgery, worse responses to chemoembolization in liver cancer, and altered activity of immunotherapies in metastatic melanoma.

Myosteatosis is best understood as muscle that has been quietly invaded by fat at the microscopic level. On a CT scan, healthy skeletal muscle appears relatively dark because it contains abundant water-dense contractile tissue. When fat infiltrates the muscle fibers, the tissue’s average density drops, which radiologists measure in Hounsfield units on the standard gray-scale of CT imaging. Crucially, researchers can extract these measurements from virtually any abdominal CT, including the CT component of a PET/CT scan that every PRRT patient already receives, simply by analyzing a slice at the level of the third and fourth lumbar vertebrae, where the psoas muscles anchor the spine.

The Munich team, led by first authors Elma Dema and Christian Dascalescu under the senior guidance of Rudolf Werner and Sophie Siegmund, screened 321 consecutive patients who underwent PRRT for somatostatin receptor-positive gastroenteropancreatic neuroendocrine tumors. Complete imaging and follow-up data were available for 301 of them. Using BMI-adjusted cut-offs for psoas muscle attenuation, the investigators defined which patients had myosteatosis at baseline, before a single radioactive dose had been administered. Their primary endpoint was deliberately stringent: early progression as seen on somatostatin receptor-targeted PET imaging after up to four consecutive treatment cycles, dichotomized as progressive disease versus disease control.

The headline result was, in one sense, a negative one. Traditional quantity-based measures of body composition, including skeletal muscle area and related indices that simply quantify how much muscle tissue surrounds the spine, showed no association whatsoever with early progression. Muscle bulk alone, it seems, tells clinicians nothing useful about whether a neuroendocrine tumor will shrug off PRRT within the first treatment window. This finding matters because muscle quantity is still the most commonly reported body composition parameter in oncology literature, often treated as a stand-in for overall nutritional reserve.

The quality-based measure told a more intriguing story. Only eight of the 301 patients, a mere 2.7 percent, met the criteria for baseline myosteatosis, yet those eight appeared to fare strikingly worse. In univariable analysis, myosteatosis was associated with roughly an eight-fold increase in the odds of early PET-based progression, with an odds ratio of 7.8 and a 95 percent confidence interval running from 1.74 to 34.78, yielding a nominal p-value of 0.0073. In a separately specified model adjusted for baseline clinical characteristics, the association held steady at an odds ratio of 8.1. But here the statistical plot thickens considerably.

When the researchers applied the Benjamini-Hochberg procedure, a standard correction that controls the false discovery rate when multiple hypotheses are tested simultaneously, the p-value drifted to 0.1073, crossing above the conventional significance threshold. The association was also attenuated when the number of completed PRRT cycles was added as a post-baseline treatment exposure variable, landing at an odds ratio of 5.7 with a confidence interval that narrowly grazed 1.0 and a p-value of 0.0538, just shy of the 0.05 benchmark. With only eight affected patients, the confidence intervals around these estimates were wide, which is the statistical signature of a hypothesis that cannot yet be called a finding.

The authors are admirably blunt about this limitation. They conclude that the results should be regarded as exploratory and hypothesis-generating, and that they do not establish any clinical utility for myosteatosis in individual risk stratification. In plain terms, no one should yet order a CT-based muscle analysis to decide who gets PRRT or how closely they should be monitored. The study also reveals a practical puzzle: myosteatosis was far rarer in this cohort than the 30 to 50 percent prevalence reported in some other cancer populations treated with PRRT, suggesting that the BMI-adjusted thresholds used here may flag only the most severe cases, or that neuroendocrine tumor patients differ fundamentally in their metabolic profiles.

Why might fat-infiltrated muscle and treatment resistance be connected at all? The mechanisms remain speculative, but several plausible pathways have been described in the wider literature. Myosteatosis is considered a marker of systemic metabolic derangement, reflecting chronic inflammation, insulin resistance, and altered mitochondrial function within muscle tissue. These same processes could plausibly influence tumor biology, modulate the delivery of radiopharmaceuticals, or shape how the body tolerates and responds to radiation. Some researchers have even proposed myosteatosis as an early and targetable biomarker of cancer prognosis, a measurable vulnerability that nutritional or exercise interventions might one day reverse before treatment begins.

For the field of nuclear medicine, the study is a careful lesson in statistical humility and a genuine invitation to dig deeper. Early progression after PRRT occurs in roughly 8 percent of patients, and identifying this minority before therapy would allow clinicians to consider intensified surveillance, alternative sequencing, or combination approaches for those at highest risk. The Munich data suggest that the search should move beyond how much muscle a patient has and toward the finer architecture of that muscle, ideally with larger, prospective cohorts and standardized measurement protocols. Until then, the radioactive missile remains a remarkably effective weapon, even if the secrets of the rare patients it fails are still written, faintly, in their own flesh.

Subject of Research: Association between CT-based body composition and early progression after peptide receptor radionuclide therapy in gastroenteropancreatic neuroendocrine tumors

Article Title: Baseline body composition and early PRRT outcome in GEP-NETs: an exploratory analysis

Article References: Dema, E., Dascalescu, C., Holzgreve, A., Cyran, C. C., Widjaja, L., Gildehaus, F. J., Herr, F. L., Spitzweg, C., Auernhammer, C. J., Ingrisch, M., Brendel, M., Ricke, J., Werner, R. A., & Siegmund, S. C. (2026). Baseline body composition and early PRRT outcome in GEP-NETs: an exploratory analysis. European Journal of Nuclear Medicine and Molecular Imaging. https://doi.org/10.1007/s00259-026-08182-w

Image Credits: AI Generated

DOI: 10.1007/s00259-026-08182-w

Keywords: PRRT, neuroendocrine tumors, body composition, myosteatosis, lutetium-177, PET/CT, sarcopenia, radiomics, nuclear medicine, cachexia, psoas muscle, tumor progression

The study, led by Hye Ju Yeo and Ha Lim Kim of Pusan National University Yangsan Hospital in South Korea, together with colleagues including Woo Hyun Cho, examined 1,107 adults with sepsis who had undergone abdominal CT imaging as part of their clinical care. Rather than relying on height and weight, the team deployed an automated segmentation pipeline built on the nnU-Net framework, a self-configuring deep learning architecture that has become a standard tool for biomedical image analysis, supplemented by the TotalSegmentator software capable of delineating more than one hundred anatomic structures in a single scan. The algorithms measured the cross-sectional areas of subcutaneous adipose tissue, the fat layer beneath the skin, and visceral adipose tissue, the fat packed around internal organs, at the level of the third and fourth lumbar vertebrae, a standard landmark in body composition research.

The technical rationale for this approach is straightforward. Body mass index cannot distinguish between fat stored under the skin and fat stored deep within the abdomen, yet these two depots behave in profoundly different ways biologically. Subcutaneous adipose tissue acts largely as a passive energy reservoir and, in some contexts, appears metabolically protective, sequestering lipids away from the liver, muscle, and bloodstream. Visceral adipose tissue, by contrast, drains directly into the portal circulation, is richly innervated and hormonally active, and is strongly associated with insulin resistance, systemic inflammation, and adverse outcomes across a range of acute and chronic illnesses. The idea that these depots might carry opposite prognostic signals during sepsis is not new, but measuring them reliably has traditionally required painstaking manual tracing of CT images, which is impractical at scale.

Among the 1,107 patients in the cohort, 300, or 27.1 percent, died within 28 days of their sepsis diagnosis during the index hospitalization. When the researchers fed their imaging-derived measurements into Cox proportional hazards models adjusted for clinically selected covariates, a clear and internally consistent picture emerged. Greater subcutaneous adipose tissue area was associated with lower mortality, with an adjusted hazard ratio of 0.83 per one standard deviation increase, a statistically significant protective association. Obesity itself, defined here as a body mass index of 25 kilograms per square meter or higher in keeping with thresholds commonly used in Asian populations, was also associated with lower mortality, with an adjusted hazard ratio of 0.62. But the most telling variable was the ratio of visceral to subcutaneous fat: patients with a higher VAT/SAT ratio had significantly higher mortality, with an adjusted hazard ratio of 1.14 per standard deviation increase.

That pattern reframes the obesity paradox in mechanistic terms. It suggests that what has appeared to be a survival advantage of obesity in sepsis may actually reflect the composition of body fat rather than its total quantity. A patient whose excess weight is dominated by subcutaneous fat may be metabolically better buffered against the catabolic storm of critical illness than a leaner patient whose fat is disproportionately visceral. The finding aligns with a growing body of work. A 2016 study in Critical Care Medicine by Pisitsak and colleagues found that an increased visceral-to-subcutaneous adipose tissue ratio in septic patients was associated with adverse outcomes, and subsequent research suggested that the survival benefit of a low ratio depends on the balance of LDL cholesterol clearance versus production during infection. Population-level analyses in the UK Biobank have similarly shown that abdominal fat distribution outperforms traditional anthropometric indices in predicting sepsis outcomes.

The stratified analyses added further texture, though with appropriate statistical caution. In exploratory models stratified by body mass index category, the protective association of subcutaneous fat was most pronounced among underweight patients, where greater subcutaneous adipose tissue area carried an adjusted hazard ratio of 0.37, meaning a substantially lower hazard of death. Conversely, among patients with obesity, a higher visceral-to-subcutaneous ratio was associated with a markedly elevated mortality risk, with an adjusted hazard ratio of 1.48. In other words, not all obesity is created equal: a patient with obesity whose fat is predominantly subcutaneous may carry a very different prognosis from one whose obesity is visceral-dominant. The authors were careful to note, however, that formal interaction tests between these imaging measures and body mass index category did not reach statistical significance, with P values of 0.145 and 0.127, meaning the apparent effect modification could reflect chance and requires confirmation in larger, independent cohorts.

The study also interrogated skeletal muscle, a dimension of body composition that has gained increasing attention in critical care. Sarcopenia, the loss of muscle mass and function, is common in critically ill patients and has been linked to worse long-term outcomes, partly because sepsis itself induces profound muscle wasting through disrupted autophagy and mitochondrial dysfunction. The team quantified psoas muscle measures at the same lumbar level, including the psoas muscle index, a conventional marker of muscle quantity. Strikingly, muscle size itself did not retain an independent association with mortality after adjustment. What did survive the statistical scrutiny was muscle quality: a higher proportion of low-attenuation muscle, which reflects fatty infiltration of the muscle and is a radiographic signature of myosteatosis, was associated with higher mortality, with an adjusted hazard ratio of 1.14. A joint phenotype combining a high visceral-to-subcutaneous ratio with a low psoas muscle index, which the investigators had hypothesized might identify particularly high-risk patients, was not significant after adjustment.

These findings carry real implications for how risk is characterized in the intensive care unit. Abdominal CT scans are already obtained routinely in many sepsis patients to search for the infectious source, meaning the raw imaging data needed for automated body composition analysis often exists before anyone thinks to use it prognostically. A pretrained deep learning pipeline can extract these measurements in seconds without manual labor, and the underlying software is publicly available, making the approach reproducible and potentially deployable as a clinical decision-support tool. If validated externally, a VAT/SAT ratio or a low-attenuation muscle fraction could complement, and in some cases correct, the crude signal provided by body mass index, helping clinicians identify high-risk patients who would be missed by conventional anthropometry, including visceral-dominant patients with obesity and sarcopenic patients of normal weight.

The authors and independent commentators are quick to emphasize the study’s limitations. It was a single-center, retrospective cohort, which raises the possibility of selection bias, since only patients who happened to receive abdominal CT imaging could be included. The subgroup findings, however compelling, rest on exploratory analyses with non-significant interaction tests, and the skeletal muscle results in particular require external validation before they can inform practice. Residual confounding by illness severity, nutritional status, and chronic disease cannot be excluded in any observational design. Still, the convergence of this work with prior mechanistic and epidemiological evidence, and the elegance of using artificial intelligence to turn diagnostic scans that already exist into prognostic information that costs nothing extra, marks a meaningful step forward. The obesity paradox in sepsis has long been an embarrassment to simple models of body size and disease; deep learning analysis of where the body stores its fat may finally explain what the scale never could.

Subject of Research: Deep learning–based CT analysis of abdominal fat distribution and its association with 28-day mortality in sepsis patients

Article Title: Deep learning–derived abdominal adiposity phenotypes and 28-day mortality in sepsis

Article References: Yeo, H. J., Kim, H. L., Kim, K., Jang, J. H., Choi, E., Seol, H. Y., Lee, S. E., & Cho, W. H. (2026). Deep learning–derived abdominal adiposity phenotypes and 28-day mortality in sepsis. International Journal of Obesity. https://doi.org/10.1038/s41366-026-02220-1

Image Credits: AI Generated

DOI: 10.1038/s41366-026-02220-1

Keywords: sepsis, obesity paradox, visceral adipose tissue, subcutaneous adipose tissue, deep learning, nnU-Net, body composition, computed tomography, 28-day mortality, psoas muscle, myosteatosis, critical care

The scale of the problem is considerable. Breast cancer accounted for approximately 2.308 million new cases worldwide in 2022, representing 11.6 percent of all new cancer diagnoses and making it the second most common cancer globally. Chemotherapy remains a cornerstone of treatment for early-stage disease, but its toxicities—ranging from severe neutropenia to peripheral neuropathy—can force dose reductions, treatment interruptions, or complete discontinuation, ultimately compromising prognosis and quality of life.

The review, led by researchers at The First Affiliated Hospital of Jinzhou Medical University in China, synthesizes evidence on how body composition indicators relate to chemotherapy toxicity. The authors trace the evolution of assessment methods from crude anthropometric surrogates—such as the five percent weight-loss threshold once used to mark cachexia in the 1970s—to today’s sophisticated imaging tools. Computed tomography, magnetic resonance imaging, dual-energy X-ray absorptiometry (DXA), and bioelectrical impedance analysis (BIA) now allow clinicians to quantify skeletal muscle, visceral fat, subcutaneous fat, and muscle quality with remarkable precision, and artificial intelligence is increasingly automating these analyses.

Central to the discussion is the distinction between lean body mass and fat-free mass, terms that are chemically similar but historically defined differently—lean body mass includes polar lipids, while fat-free mass does not. The authors recommend prioritizing fat-free mass in research to improve scientific rigor. They also highlight the third lumbar vertebra skeletal muscle index (L3-SMI), calculated from a single CT slice as skeletal muscle area at L3 divided by height squared, which was first reported in 2008 as an independent predictor of chemotherapy toxicity. Notably, DXA-derived appendicular lean mass indices and CT-based L3-SMI correlate only moderately (r = 0.66, p < 0.001), meaning the two metrics are not directly interchangeable—a source of ongoing confusion in the literature.

The mechanistic story is where the review becomes particularly compelling. Muscle is highly vascularized and metabolically active, so patients with greater lean mass tend to metabolize and clear drugs more efficiently. Pharmacokinetic studies bear this out: each additional kilogram of lean body mass increased doxorubicin clearance by roughly 19 percent in an exploratory study, and lower muscle mass was associated with reduced volume of distribution and higher peak plasma concentrations of paclitaxel. Low lean mass can also reduce creatinine production, causing the Cockcroft-Gault formula to overestimate renal function—a hazard flagged by a creatinine clearance to glomerular filtration rate ratio of 1.23 as a warning threshold for carboplatin overdose and severe thrombocytopenia.

Fat tells a different, sometimes paradoxical story. Lipophilic agents such as paclitaxel and docetaxel distribute into adipose compartments, while hydrophilic drugs like fluoruracil and cyclophosphamide prefer water-rich lean tissue. Visceral fat volume was positively correlated with doxorubicin exposure (r² = 0.324, P < 0.001) and grade 4 leukopenia in Asian breast cancer patients. Experimental work suggests adipocytes can increase anthracycline levels by 30 percent by upregulating CBR1 and AKR metabolic enzymes, sustaining the release of toxic metabolites. Visceral fat-derived free fatty acids also reach the liver through the portal circulation, potentially inducing hepatic steatosis and impairing drug metabolism—liver attenuation on CT, inversely related to fat content, predicted epirubicin exposure in one analysis.

The clinical correlations are striking. In early-stage breast cancer patients, higher fat mass increased the risk of toxicity-induced modification of treatment—dose reductions, interruptions, cessation, or regimen changes—while higher relative lean mass reduced that risk. Obese patients (BMI ≥ 30 kg/m²) experienced docetaxel dose reductions at 18 percent versus 5 percent in nonobese patients (p = 0.008), along with lower pathological complete response rates and shorter disease-free survival. Sarcopenia, which affects an estimated 40 to 45 percent of breast cancer patients, independently predicted severe toxicity: sarcopenic patients receiving epirubicin-cyclophosphamide experienced severe laboratory adverse events at 70 percent versus 22.2 percent (OR 7.9, p = 0.004). Myosteatosis—fat infiltration within muscle, visible as lower Hounsfield units on CT—was associated with reduced relative dose intensity and with dose reductions, early treatment interruption, and hospitalization.

Perhaps the most alarming phenotype is sarcopenic obesity, the coexistence of excessive adiposity with reduced muscle mass and impaired function, as defined by the ESPEN-EASO consensus. In early-stage breast cancer patients receiving anthracycline and taxane chemotherapy, sarcopenic obesity independently predicted severe toxicity, tripling the risk of grade 3–4 hematological toxicity and raising the risk of neutropenia 3.5-fold. Prevalence estimates vary widely—from 0.8 to 22.3 percent in general populations—partly because diagnostic thresholds remain inconsistent, with more than 14 sarcopenia cutoffs reported across oncology studies.

The review does not shy away from the field’s contradictions. Adipose tissue can exert bidirectional effects: in one study of 120 patients receiving neoadjuvant chemotherapy, higher fat percentage correlated with reduced neurotoxicity risk, though no significant interaction appeared in platinum-containing regimens. Chemotherapy itself alters body composition over time, and most studies rely only on baseline measurements, potentially underestimating true toxicity risk. The authors also point to the LEANOX randomized controlled trial as proof of concept: lean body mass-based oxaliplatin dosing at 3.09 mg/kg increased the proportion of patients free of grade ≥ 2 peripheral neurotoxicity from 42.1 to 67.2 percent, without compromising long-term survival—evidence that composition-guided dosing is clinically practicable, at least for some drugs.

Looking forward, the authors call for regimen-specific pharmacokinetic modeling across anthracyclines, taxanes, and platinum agents; risk stratification that integrates breast cancer subtypes with visceral-to-subcutaneous fat ratios and muscle indices; prospective trials of nutritional optimization and resistance training in high-risk patients; and international consensus on definitions and cutoffs, potentially enriched with multi-omics biomarkers. They suggest DXA, a low-radiation whole-body scan, could eventually replace CT for routine body composition assessment in early cancer, where L3-level CT scans are not standard. Until prospective, breast cancer-specific studies validate these approaches, body surface area dosing will remain the norm—but the writing is on the wall, and it is written in muscle and fat.

Subject of Research: The relationship between body composition and chemotherapy-related toxicity in early-stage breast cancer

Article Title: Body composition and chemotherapy-related toxicities in early-stage breast cancer: implications for personalized treatment strategies

Article References: Tang, X., Wang, J., Feng, D., Tian, Y., Zhang, X., Zhang, M., Zhang, Y., Yang, B., Zhou, X., Zhao, J., Tao, W., Liu, Y., & Zhai, Z. (2026). Body composition and chemotherapy-related toxicities in early-stage breast cancer: implications for personalized treatment strategies. Holistic Integrative Oncology, 5(1), Article 69. https://doi.org/10.1007/s44178-026-00287-4

Image Credits: AI Generated

DOI: 10.1007/s44178-026-00287-4

Keywords: breast cancer, body composition, chemotherapy toxicity, sarcopenia, sarcopenic obesity, lean body mass, visceral fat, myosteatosis, pharmacokinetics, personalized dosing, DXA, CT imaging