{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T17:46:36Z","timestamp":1791740796530,"version":"4.3.4"},"reference-count":34,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"abstract":"<jats:p>Continuous glucose monitoring (CGM) provides information about real-time glycemic exposure that is not captured by measurements of fasting plasma glucose (FPG), changes in glycemia during oral glucose tolerance testing (OGTT), or Hemoglobin A1c (HbA1c). We propose that (1) CGM can identify dysglycemia earlier than existing approaches, (2) CGM may serve as an adjunctive diagnostic tool, and (3) CGM may eventually support independent diagnostic criteria. The strongest case is for Stage 2 type 1 diabetes (T1D), because this stage represents a critical opportunity for intervention before progression to Stage 3 disease. CGM as a diagnostic tool could identify individuals at risk of progression to Stage 3 T1D, particularly those with islet autoantibodies, by detecting progression of dysglycemia, supporting disease staging, and identifying candidates for confirmatory testing, closer monitoring, or future prevention trials. Beyond T1D, CGM might also prove to be a reliable diagnostic test for type 2 diabetes (T2D) in selected populations. Overall, there is a need for prospective clinical studies to compare the value of using CGM-derived metrics directly with existing diagnostic standards, including OGTT and HbA1c. For both T1D and T2D, earlier diagnosis by CGM has the potential to improve patient outcomes by creating opportunities for prevention and treatment early in the natural history of the disease. This article presents an outcome-based framework for deriving and validating CGM-based diagnostic thresholds. The framework emphasizes that future diagnostic criteria should be based on clinically meaningful outcomes, rather than concordance with existing tests.<\/jats:p>","DOI":"10.1177\/19322968261493542","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T10:50:05Z","timestamp":1791456605000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["The Case for Evaluating Continuous Glucose Monitoring as a Diagnostic Tool for Diabetes"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6394-6862","authenticated-orcid":false,"given":"David C.","family":"Klonoff","sequence":"first","affiliation":[{"name":"Sutter Health Research Institute, Emeryville, CA, USA"},{"name":"Department of Diabetes &amp; Endocrinology, Sutter Mills Peninsula Medical Center, San Mateo, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3827-7107","authenticated-orcid":false,"given":"Viral N.","family":"Shah","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Indiana University School of Medicine, Indianapolis, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3252-5026","authenticated-orcid":false,"given":"Guillermo E.","family":"Umpierrez","sequence":"additional","affiliation":[{"name":"Emory University School of Medicine, Atlanta, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5885-9959","authenticated-orcid":false,"given":"Curtiss B.","family":"Cook","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Mayo Clinic, Scottsdale, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6549-0288","authenticated-orcid":false,"given":"G. 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