Glucotypes reveal new patterns of glucose dysregulation
Open accessAbstract
Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called "glucotypes" that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.
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Examination of users' time-in-range compared to their weight led to observation of several individuals who self-reported as "healthy" but exhibited lower TIR, which is typically more characteristic of individuals with prediabetes or T2D.
“Examination of users’ time-in-range compared to their weight led to observation of several individuals who self-reported as “healthy” but nonetheless exhibit lower TIR, which is typically more characteristic of individuals with prediabetes or T2D; a feature noted previously19.”
From Impact of digital health interventions on glycemic control and weight management. (2025) · cites this paper as [19]
The results align with existing literature emphasizing the clinical benefits of higher TIR, including reduced complications and improved overall health outcomes for individuals with diabetes.
“These results align with existing literature that emphasizes the clinical benefits of higher TIR, including reduced complications and improved overall health outcomes for individuals with diabetes19,22–24.”
From Impact of digital health interventions on glycemic control and weight management. (2025) · cites this paper as [19]
The Introduction (page unavailable) discusses glycemic variability, postprandial glucose peaks, and cardiovascular risk, but does not address whether higher time in range (TIR) reduces complications or improves overall health outcomes in people with diabetes. Searches for TIR, time in range, complications, and health outcomes found no passage establishing the attributed relationship. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThe magnitude of improvements observed, particularly in TIR and weight loss metrics, aligns with outcomes from controlled studies of similar digital health interventions.
“the magnitude of improvements observed, particularly in TIR and weight loss metrics, align with outcomes from controlled studies of similar digital health interventions19.”
From Impact of digital health interventions on glycemic control and weight management. (2025) · cites this paper as [19]
The Methods describe glucose monitoring during a period of stable lifestyle, not a controlled digital-health intervention. The Introduction reports improved postmeal glucose responses from a separate personalized-nutrition study, but our copy provides no TIR or weight-loss outcomes to compare with the magnitude claimed (searched: TIR, time in range, weight loss, intervention). (LLM, conf=0.78)
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Glycemic Patterns Are Distinct in Post-Bariatric Hypoglycemia After Gastric Bypass (PBH-RYGB)
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Self-citationNon-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept
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Self-citationImpact of digital health interventions on glycemic control and weight management.
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Improvement of skeletal muscle insulin sensitivity by 1 week of SGLT2 inhibitor use
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A Data-Driven Approach to Classifying Daily Continuous Glucose Monitoring (CGM) Time Series
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Sensitive enzymatic determination of neurotransmitters in artificial sweat
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Digital Resilience Biomarkers for Personalized Health Maintenance and Disease Prevention
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Current State and Future Perspectives on Personalized Metabolomics
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Metabolic syndrome and Growth Differentiation Factor 15 in older adults
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Diabetes and Parkinson’s Disease: Understanding Shared Molecular Mechanisms
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Towards Personalized AI-Based Diabetes Therapy: A Review
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High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases
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The Need for Precision Medicine to be Applied to Diabetes
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An Aptamer-based Electrochemical-Sensing Implant for Continuous Therapeutic- Drug Monitoring in vivo
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A Digital-First Model of Diabetes Care
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The Exposome in the Era of the Quantified Self
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Self-citationEvolving Use of Continuous Glucose Monitoring Beyond Intensive Insulin Treatment
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- Authors
- Hall, H., Perelman, D., Breschi, A., Limcaoco, P., Kellogg, R., McLaughlin, T., & Snyder, M.
- Affiliations
- Institute for Stem Cell Biology and Regenerative Medicine (US)
- Stanford University (US)
- Stanford Medicine (US)
- Corresponding author
- Snyder, Michael
- Venue
- PLOS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-07-24
- Volume
- 16
- Issue
- 7
- Pages
- e2005143
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- PMC
- PMC6057684
- OpenAlex
- W2883478711
- Cited by
- 326
- References
- 45
- Citations by year
- 2026: 52 · 2025: 59 · 2024: 38 · 2023: 41 · 2022: 37 · 2021: 42 · 2020: 34 · 2019: 19 · 2018: 4
- FWCI
- 19.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK110186, U54DK102556, P30DK116074)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
- Keywords
- glucose dysregulation, continuous glucose monitoring, glycemic variability, postprandial blood glucose, prediabetes, type 2 diabetes, insulin resistance, insulin secretion, individual heterogeneity
- MeSH terms
- Adult, Aged, Blood Glucose, Diabetes Mellitus, Type 2, Female, Homeostasis, Humans, Hyperglycemia, Insulin, Male, Middle Aged, Blood Glucose Self-Monitoring, Cohort Studies, Internet, Carbohydrate Metabolism, Meals
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