Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to follow or not impactful. While recent advances leverage continuous glucose monitoring (CGM) and machine learning to predict glycemic responses, these approaches are largely predictive and lack actionable guidance. Moreover, recommendation systems are often misaligned with user goals and require extensive input. We present MetaPlate, a counterfactual explanation (CF) guided, context-aware decision-support framework that generates personalized meal recommendations to mitigate postprandial glucose excursions in healthy adults. MetaPlate integrates multimodal data, including CGM readings, wearable-derived physiological signals, and user-provided meal inputs from $25$ individuals to model pre-meal context. A machine learning model predicts glucose response, while a CF optimization module adjusts meal composition modifying macronutrient amounts to maintain glucose levels within a target range ($\leq 140$ mg/dL). An LLM-based retrieval-augmented generation (RAG) layer enhances interpretability by producing human-readable recommendations using constrained search of the USDA food database. We evaluate MetaPlate via a structured expert-in-the-loop assessment with registered dietitians (RDs), comparing performance before and after prompt refinement. Results show improvements in meal realism, portion suitability, and recommendation likelihood, with expert feedback indicating a shift from clinically implausible outputs to actionable, contextually appropriate recommendations. Our findings emphasize the importance of domain knowledge and structured constraints in LLM-driven systems and highlight the potential of MetaPlate as a real-time personalized dietary decision-support tool.

翻译：餐后高血糖是代谢紊乱的关键风险因素；然而，现有的饮食指导往往静态、不切实际且个性化不足，所提供的建议难以遵循或缺乏实际效果。虽然近期研究利用连续血糖监测（CGM）和机器学习来预测血糖反应，但这些方法主要为预测性，缺乏可执行的指导。此外，推荐系统常与用户目标不一致，且需要大量输入。我们提出MetaPlate——一种基于反事实解释（CF）的上下文感知决策支持框架，可生成个性化膳食推荐以减轻健康成年人的餐后血糖波动。MetaPlate整合了来自25名个体的多模态数据，包括CGM读数、可穿戴设备衍生的生理信号以及用户提供的膳食输入，以建模餐前上下文。机器学习模型预测血糖反应，而CF优化模块通过调整膳食组成（修改宏量营养素含量）来维持血糖水平在目标范围内（≤140 mg/dL）。基于大语言模型的检索增强生成（RAG）层通过利用USDA食物数据库的约束搜索生成人类可读的推荐，从而提升可解释性。我们通过注册营养师（RD）参与的结构化专家评估流程来评估MetaPlate，比较提示词优化前后的性能。结果显示，膳食真实性、份量适宜性和推荐接受度均有提升，专家反馈表明输出从临床不可行的方案转变为可执行且上下文合理的推荐。我们的研究强调了领域知识与结构化约束在基于大语言模型的系统中的重要性，并凸显了MetaPlate作为实时个性化膳食决策支持工具的潜力。