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 study, led by Dagyeong Lee of Wonkwang University Sanbon Hospital and Sungkyunkwan University, together with biostatistician Kyungdo Han of Soongsil University and Dong Wook Shin of Samsung Medical Center, exploited a uniquely rich data infrastructure. South Korea’s National Health Insurance Service requires nearly all residents to attend periodic health screenings, during which body measurements and blood tests are collected, and the national cancer registry captures virtually every diagnosed malignancy in the country. By linking these systems, the team assembled a cohort of 635,867 adults who had been diagnosed with cancer and who had body composition data available around the time of diagnosis, allowing mortality outcomes to be tracked with unusual statistical power.

The technical backbone of the analysis was the appendicular skeletal muscle mass index, or ASMI, an estimate of the muscle contained in the arms and legs normalized to body size. Rather than relying on expensive imaging for every participant, the researchers used validated prediction equations that incorporate anthropometric measurements, serum creatinine levels and lifestyle factors, an approach previously validated in Korean adults. Patients falling in the lowest quartile of ASMI were classified as having low muscle mass, a definition aligned with the framework used by the Asian Working Group for Sarcopenia. Obesity was assessed two ways: a body mass index of 25 kilograms per square meter or higher, following Asia-Pacific criteria, and abdominal obesity defined as a waist circumference of at least 90 centimeters in men and 85 centimeters in women.

Using Cox proportional hazards regression, the team calculated adjusted hazard ratios that accounted for a broad range of potential confounders, including age, sex, smoking status, alcohol consumption, physical activity, income, comorbid conditions and cancer characteristics. Compared with patients in the highest ASMI quartile, those with low muscle mass showed a 25 percent higher risk of all-cause mortality, a 21 percent higher risk of dying specifically from cancer, a 49 percent higher risk of cardiovascular death and a startling 126 percent higher risk of respiratory death. The dose-response pattern was consistent: the less muscle a patient carried, the greater the mortality risk across every category examined.

Perhaps the most provocative result concerned the interaction between muscle and fat. Obesity, which is often assumed to be uniformly harmful in cancer patients, was actually associated with modestly lower mortality than non-obese status in this cohort, echoing the so-called obesity paradox reported in several cancer populations. But when low muscle mass and obesity coexisted, the protective veneer vanished. Patients with both conditions had the highest risks of all, with a 22 percent elevation in all-cause mortality and a 22 percent elevation in cancer-specific mortality compared with their counterparts. In other words, carrying extra fat did not rescue patients who lacked muscle; it appeared to compound their vulnerability.

This combination, often called sarcopenic obesity, has been recognized as a distinct clinical entity by international consensus statements from ESPEN and EASO in Europe and by an Asia-Oceania consortium, but its prognostic weight has been difficult to quantify because most prior studies were small, single-center or limited to specific tumor types. Meta-analyses of sarcopenia in solid tumors, including work in pancreatic, esophageal, lung and breast cancers, have consistently flagged poor outcomes, yet the new study is among the first to dissect cause-specific mortality at nationwide scale. By separating deaths due to cancer, cardiovascular disease and respiratory disease, the researchers revealed that muscle depletion is not merely a marker of advanced malignancy but a systemic risk factor operating across multiple organ systems.

The biological explanations are plausible and varied. Skeletal muscle is not an inert reservoir of protein; it is a metabolically active tissue that regulates glucose disposal, secretes anti-inflammatory myokines during contraction and serves as the body’s main amino acid store during illness. Cancer cachexia, the syndrome of muscle wasting that accompanies many malignancies, disrupts mitochondrial dynamics and promotes inflammation within muscle fibers, and low muscle mass is closely associated with elevated inflammatory markers such as erythrocyte sedimentation rate and low albumin. Depleted muscle also alters the pharmacokinetics of chemotherapy, potentially increasing toxicity, and predicts postoperative complications including pulmonary failure after esophagectomy and poor long-term outcomes in rectal cancer.

The respiratory findings deserve particular attention. Sarcopenia affects the diaphragm and intercostal muscles, compromising ventilatory capacity and cough strength, which helps explain why patients with low muscle mass were more than twice as likely to die from respiratory causes. Studies of cancer cachexia in animal models have documented diaphragm and ventilatory dysfunction, and clinical work has linked low muscle mass to severe dysphagia, raising aspiration risk. Meanwhile, the cardiovascular signal aligns with a growing literature showing that cardiovascular disease is a leading cause of death among long-term cancer survivors, driven partly by shared risk factors and partly by cardiotoxic treatments such as anthracycline chemotherapy, whose effects may be amplified in patients with depleted physiological reserve.

The authors emphasize that the practical message is not simply to gain weight but to build and preserve muscle. Clinical guidelines from the American Cancer Society already recommend that survivors maintain healthy weight through nutrition and physical activity, and randomized trials of combined aerobic and resistance training, along with dietary interventions, have shown meaningful improvements in body composition and cardiometabolic risk in patients with cancer. What the new study adds is a rationale for making muscle mass itself a routine clinical measurement at diagnosis, rather than an afterthought. Because the ASMI estimate can be derived from simple measurements already collected in health screenings, the barrier to implementation is low, particularly in health systems with structured screening programs.

Limitations remain. The cohort was exclusively Korean, and muscle mass thresholds and obesity criteria differ across populations, so the absolute risks may not translate directly to other ethnic groups. The prediction equations used to estimate ASMI, while validated, are less precise than direct imaging with computed tomography or dual-energy X-ray absorptiometry, and residual confounding by cancer stage, treatment intensity and unmeasured lifestyle factors cannot be excluded. The observational design means causality cannot be proven. Still, with more than 635,000 patients and consistent, graded associations across every cause of death examined, the study makes a compelling case that the scale tells only half the story. For oncologists and survivors alike, the takeaway is increasingly clear: in the fight against cancer, muscle is not optional equipment, and protecting it may be one of the most actionable steps available for extending survival.

Subject of Research: The association of low muscle mass and obesity with cause-specific mortality in cancer patients

Article Title: Low muscle mass, obesity, and cause-specific mortality in cancer patients: a nationwide cohort study

Article References: Lee, D., Kim, B., Jung, K.-W., Nam, G. E., Rhee, S. Y., Kim, S., Chun, S., Cho, I. Y., Han, K., & Shin, D. W. (2026). Low muscle mass, obesity, and cause-specific mortality in cancer patients: a nationwide cohort study. Cancer Causes & Control, 37(10), Article 156. https://doi.org/10.1007/s10552-026-02244-y

Image Credits: AI Generated

DOI: 10.1007/s10552-026-02244-y

Keywords: low muscle mass, sarcopenia, sarcopenic obesity, cancer mortality, obesity paradox, body composition, cancer survivors, cardiovascular mortality, respiratory mortality, nationwide cohort study, ASMI, cancer prognosis

The study drew on two prospective, multicenter Korean registries: the Korea Heart Failure Registry, which enrolled 3,200 patients hospitalized for acute heart failure at 24 tertiary centers between 2004 and 2009, and the Korean Acute Heart Failure Registry, which captured 5,625 patients across 10 tertiary centers between 2011 and 2014. After excluding 442 patients with missing body mass index or diabetes data, the researchers analyzed 8,383 individuals with a mean age of 68.2 years, 52 percent of whom were men. Importantly, the team applied an Asian-specific threshold for overweight, classifying patients as lean if their body mass index fell below 23 kilograms per square meter and overweight if it met or exceeded that value—a cutoff grounded in evidence that cardiometabolic risk rises at lower body weights in Asian populations than in Western ones. Roughly a third of the cohort had diabetes, and the patients were sorted into four groups: overweight non-diabetic, overweight diabetic, lean non-diabetic, and lean diabetic.

The baseline profiles of these groups told a striking story before any outcome data were even examined. Overweight patients without diabetes had the most favorable characteristics: they were younger, less likely to have ischemic heart disease, and displayed lower creatinine, C-reactive protein, and natriuretic peptide levels, along with better echocardiographic measures of cardiac function. At the opposite extreme, lean patients with diabetes carried the heaviest burden of disease, with more advanced heart failure symptoms, greater renal dysfunction, and a higher prevalence of ischemic etiology. That gradient—healthiest at the heavier, non-diabetic end of the spectrum and sickest at the lean, diabetic end—set the stage for outcome differences that were as dramatic as they were consistent.

During the index hospitalization, 5.3 percent of all patients died, with in-hospital mortality peaking at 6.9 percent among lean diabetic patients and falling as low as 4.2 percent among overweight diabetic patients. But it was the post-discharge picture that revealed the paradox in its sharpest form. Over one year of follow-up, 14.9 percent of patients died overall, yet the mortality gradient across the four groups was steep: 9.3 percent among overweight non-diabetic patients, 12.7 percent among overweight diabetic patients, 16.4 percent among lean non-diabetic patients, and a sobering 20.7 percent among lean diabetic patients. Diabetes alone raised the one-year mortality rate from 13.8 to 17.1 percent, but leanness exerted an even larger penalty, lifting mortality from 11.1 to 18.8 percent.

After multivariable adjustment for age, sex, systolic blood pressure, symptom severity, hemoglobin, renal function, ejection fraction, ischemic etiology, and other confounders, the survival advantage of overweight status remained statistically robust for one-year mortality. Overweight diabetic patients faced a 35 percent lower risk of death within a year compared with their lean diabetic counterparts, with an adjusted hazard ratio of 0.65 and a 95 percent confidence interval of 0.53 to 0.79. The same directional benefit appeared among overweight patients without diabetes. Critically, when the researchers tested for an interaction between body mass index and diabetes status, none was found—the protective association held with equal force whether or not a patient had diabetes. When body mass index was modeled as a continuous variable, each single-unit increase was associated with a 7 percent reduction in one-year mortality risk, and restricted cubic spline analysis revealed a smooth, monotonic decline in hazard across the entire observed range of body weight.

Intriguingly, the same pattern did not survive scrutiny for in-hospital mortality. Although the raw death rates favored heavier patients during the admission itself, the association weakened and lost statistical significance once the fully adjusted model accounted for the less favorable baseline profiles of lean patients—including their more advanced heart failure and greater comorbidity burden. This divergence suggests that body mass index carries greater prognostic weight over the months following discharge than during the acute phase, when illness severity and body composition may dominate short-term outcomes more than adiposity itself. In other words, the obesity paradox appears to be primarily a phenomenon of medium-term survival rather than of immediate peri-hospitalization risk.

The research team took considerable care to test whether their findings could be an artifact of statistical fragility. Sensitivity analyses using the World Health Organization overweight cutoff of 25 kilograms per square meter produced nearly identical results, with mortality falling monotonically across body mass index strata from 26.9 percent in the underweight range to just 7.0 percent among those with a body mass index of 30 or higher. Excluding patients with a body mass index below 20—who are most likely to suffer from cardiac cachexia, frailty, or severe malnutrition—did not erase the survival advantage of overweight status, arguing against reverse causality as the sole explanation. Registry-stratified analyses showed consistent associations in both cohorts, with no significant registry-by-body-mass-index interaction, and the pattern held across all three heart failure phenotypes: reduced, mildly reduced, and preserved ejection fraction. Only the smaller mid-range ejection fraction subgroup failed to reach statistical significance, most likely due to insufficient sample size.

Still, the investigators are careful to emphasize the limits of what an observational study can prove. Reverse causality—the idea that leanness reflects advanced disease rather than causing poor outcomes—cannot be dismissed entirely, and the registries lacked longitudinal weight trajectories, body composition data, nutritional markers, and measures of skeletal muscle mass that might distinguish protective adiposity from simply the absence of wasting. The cohort was also exclusively Korean, with a low mean body mass index of 23 kilograms per square meter, raising questions about generalizability to Western populations with very different body composition distributions. HbA1c data were missing in more than three-quarters of patients, diabetes type and duration were not captured, and the cohort predates the widespread use of sodium-glucose cotransporter-2 inhibitors, so the interplay between modern weight-lowering drugs and the paradox remains unresolved. The authors frame their conclusions explicitly as associations, not evidence of a causal protective effect of being overweight.

Yet the clinical implications are difficult to ignore, particularly at a moment when guideline-directed heart failure therapy increasingly includes drugs that cause weight loss. Sodium-glucose cotransporter-2 inhibitors can reduce body weight by 3 to 5 percent, and while trial-level evidence from DAPA-HF and EMPEROR-Reduced suggests these therapies benefit patients across the body mass spectrum without modifying the paradox, the new findings add urgency to the question of whether aggressive weight reduction is appropriate for lean patients with heart failure. Notably, a prespecified analysis of the STEP-HFpEF trial showed that semaglutide improved symptoms and function in overweight patients with preserved ejection fraction in proportion to the weight they lost—hinting that there may be a therapeutic sweet spot, a window of body weight beyond which the prognostic advantage of adiposity diminishes. For now, the message from Korea is unambiguous: among patients hospitalized with heart failure, low body weight—especially when combined with diabetes—identifies a strikingly vulnerable phenotype, and the obesity paradox is no respecter of diabetes status.

Subject of Research: The obesity paradox in heart failure patients with and without diabetes mellitus

Article Title: Obesity paradox in heart failure with and without diabetes mellitus: a retrospective cohort study

Article References: Kwon, O., Yoon, M., & Park, J. J. (2026). Obesity paradox in heart failure with and without diabetes mellitus: a retrospective cohort study. Clinical Research in Cardiology. https://doi.org/10.1007/s00392-026-03007-0

Image Credits: AI Generated

DOI: 10.1007/s00392-026-03007-0

Keywords: obesity paradox, heart failure, diabetes mellitus, body mass index, mortality, KorHF, KorAHF, prognosis, HFrEF, HFpEF, SGLT2 inhibitors, cardiology