Statement of Significance While the importance of sleep duration for optimal health is well established, the role of sleep/wake timing consistency remains

Obesity Advancements: Arrowhead’s RNAi Debut, Lilly’s $870M Bet, Novo’s Triple Exit & More In this week’s Obesity Updates, we spotlight […] The post Obesity Weekly News – June 10th 2025 appeared first on LucidQuest Ventures.

PCOS is more than a reproductive disorder. Learn how ADHD, cardiometabolic health, and mental health intersect, and why holistic care is crucial for women at risk.

Increasing overnight fasting by three hours to align with sleep patterns improved cardiometabolic health in middle-aged and older adults at higher risk of cardiometabolic disease. ...

The deal gives Lilly exclusive global rights to incretin products based on Camurus’ technology

Background: Effective management of cardiometabolic conditions requires sustained positive nutrition habits, often hindered by complex and individualized barriers. Direct human management is simply not scalable, while deterministic automated approaches to nutrition coaching may lack the personalization needed to address these diverse challenges. Objective: We report the development and validation of a novel large language model (LLM)-powered agentic workflow designed to provide personalized nutrition coaching by directly identifying and mitigating patient-specific barriers. Methods: We used behavioral science principles to create a comprehensive workflow that can map nutrition-related barriers to corresponding evidence-based strategies. First, a specialized LLM agent intentionally probes for and identifies root causes of a patient’s dietary struggles. Subsequently, a separate LLM agent delivers tailored tactics designed to overcome those specific barriers. We conducted a user study with individuals with cardiometabolic conditions (N=16) to inform our workflow design and then validated our approach through an additional user study (n=6). We also conducted a large-scale simulation study, grounding on real patient vignettes and expert-validated metrics, where human experts evaluated the system’s performance across multiple scenarios and domains. Results: In our user study, the system accurately identified barriers and provided personalized guidance. Five out of 6 participants agreed that the LLM agent helped them recognize obstacles preventing them from being healthier, and all participants strongly agreed that the advice felt personalized to their situation. In our simulation study, experts agreed that the LLM agent accurately identified primary barriers in more than 90% of cases. Additionally, experts determined that the workflow delivered personalized and actionable tactics empathetically, with average ratings of 4.17-4.79 on a 5-point Likert scale. Conclusions: Our findings demonstrate the potential of this LLM-powered agentic workflow to improve nutrition coaching by providing personalized, scalable, and behaviorally-informed interventions. Clinical Trial: NA