Due to regulatory and logistical challenges, traditional hands-on endocrine labs can be difficult to implement. Here, we provide a flexible, dry-lab/classroom data analysis activity that eliminates the need for direct blood sampling and instead focuses on teaching analytical skills and theoretical knowledge. This article presents a dry-lab/classroom-ready dataset and teaching approach that allows students to analyze the endocrine regulation of metabolism following the consumption of foods predominantly composed of fat, protein, or carbohydrates. By examining real data on blood glucose and insulin responses, students gain a deeper understanding of how macronutrient intake influences metabolic pathways. A pilot set of data (originally collected with appropriate ethical approval) is provided, showing blood glucose and insulin levels from 15 participants randomly assigned to consume a food primarily composed of either fat, protein, or carbohydrates. This dataset is intended for in-class data analysis, where students predict and interpret changes in blood glucose and insulin using statistical tests. Postprandial glucose and insulin levels increased most dramatically after carbohydrate intake, whereas protein and fat intake produced more modest increases with minimal insulin changes. These findings align with expected endocrine responses and provide a rich dataset for student exploration of metabolic regulation. Shifting from direct laboratory work to data-driven classroom analysis offers an accessible way to teach endocrine metabolism. By using real-world data, students can practice experimental design skills, interpret statistical findings, and better understand how diet influences blood glucose and insulin levels.