{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T15:51:05Z","timestamp":1793029865248,"version":"4.5.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Emerging evidence suggests that a metabolic mismatch between cerebral energy demand and supply may be a contributing factor to the onset of migraine. Studies have drawn connections between migraine and conditions such as hypoglycemia, fasting, GLUT1 transporter deficiency, insulin resistance, and diabetes, highlighting the role of metabolic dysregulation in migraine susceptibility. Understanding these metabolic patterns could pave the way for personalized migraine treatments targeting glucose regulation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a retrospective cohort analysis of chronic migraine subjects (&amp;gt;15 headache days\/month) using continuous glucose monitoring (CGM;\n                      <jats:italic>n<\/jats:italic>\n                      = 131) and oral glucose tolerance tests (GTTs;\n                      <jats:italic>n<\/jats:italic>\n                      = 247). Continuous glucose monitoring data were analyzed from a prospective cohort of 24 healthy controls using metrics such as inter\/intraday standard deviation (SD), average daily risk range (ADRR), mean amplitude of glycemic excursion (MAGE), mean glucose excursion (MGE), mean of daily differences (MODDs), continuous overall net glycemic action (CONGA24), post-prandial glucose recovery time (PGRT), and post-prandial area under the curve (PP-AUC). Subjects were clustered into three groups based on OGTT responses using K-means clustering to identify possible postprandial phenotypes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared to age- and gender-matched standards of normal GTT response, individuals with chronic migraine had significantly lower glucose values at fasting and 2 h. CGM data further revealed that migraine subjects exhibited greater glucose variability, including increased day-to-day (MODDs) and within-day (CONGA24) glycemic variability, and higher interday and intraday standard deviation during waking hours. Post-prandial dysregulation was also observed in measures of MAGE, MGE, PGRT, and PPAUC, with all metrics except PGRT differing significantly from controls during waking hours. Clustering of glucose tolerance test results identified three distinct phenotypes, each characterized by unique glucose and insulin response profiles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Chronic migraine subjects exhibited postprandial glucose dysregulation and greater glycemic variability than healthy controls. The identified GTT phenotypes reveal distinct glucose regulation patterns, suggesting the existence of different migraine-associated metabolic profiles. Future research will investigate factors contributing to these phenotypes and their implications for migraine pathogenesis. These findings support the potential for developing targeted migraine treatments informed by glucose dysregulation patterns.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2025.1719724","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T14:04:24Z","timestamp":1768399464000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Glucose dysregulation and glycemic phenotyping in chronic migraine"],"prefix":"10.3389","volume":"16","author":[{"given":"Christina A.","family":"Nelson","sequence":"first","affiliation":[{"name":"Noorda College of Osteopathic Medicine","place":["Provo, UT, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyle W.","family":"Reavely","sequence":"additional","affiliation":[{"name":"Noorda College of Osteopathic Medicine","place":["Provo, UT, United 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