Postprandial Glycemic Dynamics (PGD): Integrating Nutritional and Behavioral Modulation of Glycemic Curves
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2025
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| _version_ | 1866901647234433024 |
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| author | Marciu, Vicentiu Bogdan Ion |
| author_facet | Marciu, Vicentiu Bogdan Ion |
| contents | <p><strong>Postprandial Glycemic Dynamics (PGD)</strong> introduces a practical and mathematically consistent framework for describing individual glycemic responses after meals.<br>Unlike traditional static indicators such as the Glycemic Index (GI) or Glycemic Load (GL), PGD integrates both the <em>amplitude</em> of the glycemic excursion (ΔG) and its <em>duration</em> (Δt) into a single proportional measure of postprandial exposure — the incremental area under the curve (AUC).</p> <p>The study demonstrates that PGD is directly proportional to AUC under both theoretical and real-life physiological conditions, remaining robust even when the glycemic curve is irregular due to external or behavioral factors (e.g., emotional stress, movement, temperature, or smoking).<br>This property allows PGD to serve as a dynamic and intuitive indicator for metabolic adaptability, nutrition-behavior interaction, and cardiovascular risk modeling.</p> <p>PGD can be integrated into AI-based models such as the <strong>MaRS (Marciu Risk Score)</strong> framework to enhance personalized metabolic monitoring, continuous glucose data interpretation, and preventive lifestyle interventions.</p> <p><strong><em>Sometimes the math route is the shortest path to physiology.</em></strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17304294 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Postprandial Glycemic Dynamics (PGD): Integrating Nutritional and Behavioral Modulation of Glycemic Curves Marciu, Vicentiu Bogdan Ion PGD Postprandial Glycemic Dynamic Metabolic Modeling Metabolic Health Personalized Nutrition Continuous Glucose Monitoring CGM Cardiovascular Risk MaRS Risk Score Fast Science Open Research <p><strong>Postprandial Glycemic Dynamics (PGD)</strong> introduces a practical and mathematically consistent framework for describing individual glycemic responses after meals.<br>Unlike traditional static indicators such as the Glycemic Index (GI) or Glycemic Load (GL), PGD integrates both the <em>amplitude</em> of the glycemic excursion (ΔG) and its <em>duration</em> (Δt) into a single proportional measure of postprandial exposure — the incremental area under the curve (AUC).</p> <p>The study demonstrates that PGD is directly proportional to AUC under both theoretical and real-life physiological conditions, remaining robust even when the glycemic curve is irregular due to external or behavioral factors (e.g., emotional stress, movement, temperature, or smoking).<br>This property allows PGD to serve as a dynamic and intuitive indicator for metabolic adaptability, nutrition-behavior interaction, and cardiovascular risk modeling.</p> <p>PGD can be integrated into AI-based models such as the <strong>MaRS (Marciu Risk Score)</strong> framework to enhance personalized metabolic monitoring, continuous glucose data interpretation, and preventive lifestyle interventions.</p> <p><strong><em>Sometimes the math route is the shortest path to physiology.</em></strong></p> |
| title | Postprandial Glycemic Dynamics (PGD): Integrating Nutritional and Behavioral Modulation of Glycemic Curves |
| topic | PGD Postprandial Glycemic Dynamic Metabolic Modeling Metabolic Health Personalized Nutrition Continuous Glucose Monitoring CGM Cardiovascular Risk MaRS Risk Score Fast Science Open Research |
| url | https://doi.org/10.5281/zenodo.17304294 |