Temporal Trends in Body Composition and Metabolic Markers Prior to Diagnosis of Pancreatic Ductal Adenocarcinoma.

BACKGROUND & AIMS: Changes in body composition and metabolic factors may serve as biomarkers for the early detection of pancreatic ductal adenocarcinoma (PDAC). The aim of this study was to capture the longitudinal changes in body composition and metabolic factors before diagnosis of PDAC. METHODS: We performed a retrospective cohort study in which all patients (≥18 years) diagnosed with PDAC from 2002 to 2021 were identified. We collected all abdominal computed tomography scans and 10 different blood-based biomarkers up to 36 months before diagnosis. We applied a fully automated abdominal segmentation algorithm previously developed by our group for 3-dimensional quantification of body composition on computed tomography scans. Longitudinal trends of body composition and blood-based biomarkers before PDAC diagnosis were estimated using linear mixed models, compared across different time windows, and visualized using spline regression. RESULTS: We included 1690 patients in body composition analysis, of whom 516 (30.5%) had ≥2 prediagnostic computed tomography scans. For analysis of longitudinal trends of blood-based biomarkers, 3332 individuals were included. As an early manifestation of PDAC, we observed a significant decrease in visceral and subcutaneous adipose tissue (ß = -1.94 [95% confidence interval (CI), -2.39 to -1.48] and ß = -2.59 [95% CI, -3.17 to -2.02]) in area (cm2)/height (m2) per 6 months closer to diagnosis, accompanied by a decrease in serum lipids (eg, low-density lipoprotein [ß = -2.83; 95% CI, -3.31 to -2.34], total cholesterol [ß = -2.69; 95% CI, -3.18 to -2.20], and triglycerides [ß = -1.86; 95% CI, -2.61 to -1.11]), and an increase in blood glucose levels. Loss of muscle tissue and bone volume was predominantly observed in the last 6 months before diagnosis. CONCLUSIONS: This study identified significant alterations in a variety of soft tissue and metabolic markers that occur in the development of PDAC. Early recognition of these metabolic changes may provide an opportunity for early detection.

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05-4.27; P = 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85-3.62; P = 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576.