Pancreatic cancer (PanCa) is a highly fatal malignancy with few modifiable risk and prognostic factors. This study investigates the association between cola, diet cola, and non-cola soft drink consumption and PanCa risk and mortality. A retrospective study was conducted using data from the Patient Epidemiology Data System (1982–1998) at Roswell Park Comprehensive Cancer Center (Buffalo, NY, USA), including 213 PanCa patients and 852 cancer-free controls. Data were collected using a self-administered questionnaire, including a 46-item food frequency questionnaire (FFQ). Multivariable logistic regression was used to estimate odds ratio (OR) and 95% confidence interval (CI) of cola, diet cola, and non-cola soft drink consumption and PanCa risk. Cox proportional hazard regression was used to estimate hazard ratios (HR) and 95% CIs of cola, diet cola, and non-cola soft drink consumption and PanCa mortality. Stratified analyses were conducted by sex, body mass index (BMI), and smoking status. We observed significant 55% increased odds of PanCa among patients consuming ≥1 regular cola per day (OR: 1.55, 95% CI: 1.01–2.39). We also observed non-significant 38% increased hazard of mortality among patients consuming ≥1 regular cola per day (HR: 1.38, 95% CI: 0.91–2.07). We conclude that regular cola consumption is a modifiable lifestyle that may be associated with PanCa risk and mortality following diagnosis.

Background: The association of body composition with epithelial ovarian carcinoma (EOC) mortality is poorly understood. To date, evidence suggests that high adiposity is associated with decreased mortality (an obesity paradox), but the impact of muscle on this association has not been investigated. Herein, we define associations of muscle and adiposity joint-exposure body composition phenotypes with EOC mortality. Methods: Body composition from 500 women in the Body Composition and Epithelial Ovarian Cancer Survival Study was dichotomized as normal or low skeletal muscle index (SMI), a proxy for sarcopenia, and high or low adiposity. Four phenotypes were classified as fit (normal SMI and low adiposity; reference; 16.2%), overweight or obese (normal SMI and high adiposity; 51.2%), sarcopenia and overweight or obese (low SMI and high adiposity; 15.6%), and sarcopenia or cachexia (low SMI and low adiposity; 17%). We used multivariable Cox models to estimate associations of each phenotype with mortality for EOC overall and high-grade serous ovarian carcinoma (HGSOC). Results: Overweight or obesity was associated with up to 51% and 104% increased mortality in EOC and HGSOC [Hazard Ratio (HR)] = 1.51, 95% CI = 1.05 to 2.19 and HR = 2.04, 95% CI = 1.29 to 3.21). Sarcopenia and overweight or obesity was associated with up to 66% and 67% increased mortality in EOC and HGSOC (HR = 1.66, 95% CI = 1.13 to 2.45 and HR = 1.67, 95% CI = 1.05 to 2.68). Sarcopenia or cachexia was associated with up to 73% and 109% increased mortality in EOC and HGSOC (HR = 1.73, 95% CI = 1.14 to 2.63 and HR = 2.09, 95% CI = 1.25 to 3.50). Conclusions: Overweight or obesity, sarcopenia and overweight or obesity, and sarcopenia or cachexia phenotypes were each associated with increased mortality in EOC and HGSOC. Exercise and dietary interventions could be leveraged as ancillary treatment strategies for improving outcomes in the most fatal gynecological malignancy with no previously established modifiable prognostic factors.

Background: Muscle loss influences pancreatic ductal adenocarcinoma (PDAC) outcomes, but treatment-related edema may cause overestimation of total skeletal muscle area (tSMA), confounding our understanding of muscle changes. However, no studies have quantified the impact of edema on tSMA and psoas skeletal muscle area (pSMA) changes. Thus, we sought to (i) assess the impact of edema on tSMA and pSMA changes between diagnosis and follow-up and (ii) explore the utility of pSMA as a clinically relevant measure of muscle and muscle loss among patients with PDAC. Methods: Body composition was measured using CT scans at diagnosis and follow-up from 95 patients enrolled in the Florida Pancreas Collaborative cohort study. Edema was assessed by opacifications in subcutaneous fat, and tSMA and pSMA changes were expressed as percent change between diagnosis and follow-up. We used multivariable generalized linear models to estimate mean tSMA and pSMA changes overall and by edema status. Spearman correlation was used to measure interrelationships of tSMA and pSMA. Results: tSMA increased between diagnosis and follow-up (Δ = 0.66) but only in patients with edema (Δ = 3.35) whereas non-edematous patients lost tSMA (Δ = −2.03). Conversely, pSMA decreased regardless of edema status. Furthermore, tSMA and pSMA were strongly correlated overall (r = 0.75) and in non-edematous patients (r = 0.83). Conclusions: Edema inflated estimates of tSMA at follow-up in patients with PDAC, but pSMA was impervious to edema and may represent a suitable proxy for tSMA. Impact: pSMA is a reliable measure of muscle and muscle loss and should be considered in future studies assessing muscle loss in patients with PDAC.

Background Pancreatic ductal adenocarcinoma is projected to become the second leading cause of cancer-related deaths by 2040, with the highest disease burden expected amongst Non-Hispanic Black patients. One of the most significant predictors of poor outcomes is the presence of cancer-associated cachexia (CCa). Yet, race- and ethnicity-specific biomarkers for early CCa diagnosis are lacking. Methods We evaluated a panel of candidate biomarkers of CCa in a diverse cohort of patients with pre-treatment serum using multiplex ELISA-based methods. Results We find that growth/differentiation factor-15 (GDF-15) is associated with cachexia severity, is superior to standard biomarkers at classifying cachexia, and differentiates between non-cachexia and pre-cachexia status, but only among Hispanic/Latinx and non-Hispanic White participants. Furthermore, high GDF-15 levels at diagnosis are associated with a greater weight loss from 3.3% (95%CI = −0.14–6.7) to 8.0% (CI = 5.9–10.1) over the 6 months post-diagnosis. Finally, both ENA-78/CXCL5 and GRO-α/CXCL1 are elevated in non-Hispanic Black individuals in a disease-independent manner (P < 0.001 for both analytes). Conclusions GDF-15 may be a potential biomarker for “pre-cachexia” in the non-Hispanic White and the Hispanic population, but not non-Hispanic Black individuals. These findings underscore the unmet need to enroll non-Hispanic Black participants in clinical trials for CCa.

Loss of skeletal muscle mass in cancer cachexia is associated with poorer survival, reduced treatment tolerance, and diminished quality of life. Routine oncology computed tomography (CT) can yield skeletal muscle area (SMA) and skeletal muscle index (SMI) for early cachexia assessment and prognostication, but manual annotation is labor intensive and existing automated tools often show inconsistent reliability. We developed SMAART-AI (Skeletal Muscle Assessment—Automated and Reliable Tool based on AI), a fully automated pipeline that localizes the third lumbar (L3) vertebral level, segments skeletal muscle, and quantifies prediction uncertainty to flag potentially unreliable outputs. Performance and reliability were evaluated across gastroesophageal, pancreatic, colorectal, and ovarian cancer cohorts, benchmarking against expert annotations and existing tools. SMAART-AI achieved a Dice score of 97.80% ± 0.93% in gastroesophageal cancer and a median SMA deviation of 2.48% from expert annotations across pancreatic, colorectal, and ovarian cohorts. Uncertainty scores correlated strongly with prediction error, enabling identification of high-error cases to support trustworthy deployment. Integrating the SMA/SMI with clinical features and body mass index (BMI) improved survival prediction (concordance index was +2.19% for colorectal, +9.82% for pancreatic, and +2.58% for ovarian cancer) and supported cachexia detection (70.00% accuracy; F1 80.00%). Overall, SMAART-AI provides an uncertainty-aware, clinically translatable framework for scalable CT-based muscle assessment and improved oncologic prognostication.

Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Monitoring skeletal muscle area (SMA) longitudinally through computed tomography (CT) scans, an imaging modality routinely acquired in cancer care, is an effective way to identify and track this condition. However, existing tools often lack full automation and exhibit inconsistent accuracy, limiting their potential for integration into clinical workflows. To address these challenges, we developed SMAART-AI (Skeletal Muscle Assessment-Automated and Reliable Tool-based on AI), an end-to-end automated pipeline powered by deep learning models (nnU-Net 2D) trained on mid-third lumbar level CT images with 5-fold cross-validation, ensuring generalizability and robustness. SMAART-AI incorporates an uncertainty-based mechanism to flag high-error SMA predictions for expert review, enhancing reliability. We combined the SMA, skeletal muscle index, BMI, and clinical data to train a multi-layer perceptron (MLP) model designed to predict cachexia at the time of cancer diagnosis. Tested on the gastroesophageal cancer dataset, SMAART-AI achieved a Dice score of 97.80% ± 0.93%, with SMA estimated across all four datasets in this study at a median absolute error of 2.48% compared to manual annotations with SliceOmatic. Uncertainty metrics — variance, entropy, and coefficient of variation — strongly correlated with SMA prediction errors (0.83, 0.76, and 0.73 respectively). The MLP model predicts cachexia with 79% precision, providing clinicians with a reliable tool for early diagnosis and intervention. By combining automation, accuracy, and uncertainty awareness, SMAART-AI bridges the gap between research and clinical application, offering a transformative approach to managing cancer cachexia.