8  Closing words

In this book we have (hopefully) learnt essential concepts and practical tools for analyzing continuous glucose monitoring (CGM) data using R. We began with the fundamentals—objects, classes, data structures—and built up to importing and cleaning real‑world CGM files. We then explored the rich landscape of glycemic metrics, from basic summaries (mean, SD, CV) to advanced indices (MAGE, MODD, CONGA, GRADE, LBGI, HBGI, ADRR). We learned to visualize glucose patterns with time‑series plots, AGP, lasagna plots, and episode profiles. We delved into the new frontier of glucotype and glucodensity, which promise to extract even deeper insights from biosensors data in diabetes research.

More importantly, we have equipped you with the skills to turn raw sensor exports into actionable clinical knowledge. You now know how to:

This is not an end, but a beginning. CGM technology continues to evolve, and the analytical tools will keep expanding. The greatest value, however, lies in your clinical judgment and your ability to connect these numbers to the people they represent.

Keep in mind: the best analysis is the one that leads to better decisions for your patients.

About the Author

Óscar Lado Baleato (aka myself) is a bio-statistician and professor (substitute) at the University of Oviedo, Spain. His research specializes in developing advanced statistical methodologies for complex biomedical data, particularly through the design of innovative statistical models and the statiscally-rigorous analysis of real-world clinical data. He is the author of several (well, two) R packages for conditional reference regions (refreg, TwoDiRef). His research interests span regression modeling, conditional reference regions, distributional (conditional & functional) data analysis, and high quality reproducible research in health sciences.

Selected Publications on CGM and Glycemic Markers

  • Diabetes risk assessment in adult population without diabetes employing continuous glucose monitoring: A novel approach
    Diabetes Research and Clinical Practice, 2025
    Co-author – Pazos-Couselo M, Lado-Baleato Ó, Izquierdo V, Moreno-Fernández J, Alonso-Sampedro M, Fernández-Merino C, Gude F

  • Divergent hypoglycemic and hyperglycemic responses to the components of evening meals. A general adult population study in individuals without diabetes (AEGIS study)
    Clinical Nutrition, 2024
    Co-author – González-Vidal T, Calvo-Malvar M, Fernández-Merino C, Sánchez-Castro J, Lado-Baleato Ó, Díaz-Louzao C, Pazos-Couselo M, Alonso-Sampedro M, Matabuena M, Gude F

  • Age, Sex, BMI, Meal Timing, and Glycemic Response to Meal Glycemic Load
    JAMA Network Open, 2025
    Co-author – Calvo-Malvar M, Lado-Baleato Ó, Ríos AC, Fernández CP, Benítez-Calvo A, Fernandez-Merino C, Sánchez-Castro J, Wagner R, Matabuena M, Gude F

  • Modeling conditional reference regions: Application to glycemic markers
    Statistics in Medicine, 2021
    Co-author – Lado-Baleato Ó, Cadarso-Suárez C, Gude F, Roca-Pardiñas J

  • Optimal Cut‑Point Estimation for functional digital biomarkers: Application to Continuous Glucose Monitoring
    arXiv preprint (close to appear published), 2024
    Co-author – Lado-Baleato Ó, Matabuena M, Díaz-Louzao C, Gude F

For a complete list, please visit Google Scholar.

This book was built using R Markdown and bookdown, and it’s free to access online (though I suspect you’re already familiar with its availability 🤗)

NoteStay connected

If you have questions, feedback, or would like to discuss further applications of these methods, please feel free to reach out.

I am always happy to hear from readers and collaborators.

Contact

Happy analyzing!