Use of Continuous Glucose Monitoring with Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes

• URL: http://arxiv.org/abs/2511.03986v1
• Date: Thu, 06 Nov 2025 02:15:08 GMT
• Title: Use of Continuous Glucose Monitoring with Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes
• Authors: Ahmed A. Metwally, Heyjun Park, Yue Wu, Tracey McLaughlin, Michael P. Snyder,
• Abstract summary: The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity.<n>We show that continuous glucose monitoring and wearable technologies enable a paradigm shift towards non-invasive, dynamic metabolic phenotyping.
• Score: 4.643854266548864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily driven by insulin resistance (IR), beta-cell dysfunction, and incretin deficiency. This review demonstrates that continuous glucose monitoring and wearable technologies enable a paradigm shift towards non-invasive, dynamic metabolic phenotyping. We show evidence that machine learning models can leverage high-resolution glucose data from at-home, CGM-enabled oral glucose tolerance tests to accurately predict gold-standard measures of muscle IR and beta-cell function. This personalized characterization extends to real-world nutrition, where an individual's unique postprandial glycemic response (PPGR) to standardized meals, such as the relative glucose spike to potatoes versus grapes, could serve as a biomarker for their metabolic subtype. Moreover, integrating wearable data reveals that habitual diet, sleep, and physical activity patterns, particularly their timing, are uniquely associated with specific metabolic dysfunctions, informing precision lifestyle interventions. The efficacy of dietary mitigators in attenuating PPGR is also shown to be phenotype-dependent. Collectively, this evidence demonstrates that CGM can deconstruct the complexity of early dysglycemia into distinct, actionable subphenotypes. This approach moves beyond simple glycemic control, paving the way for targeted nutritional, behavioral, and pharmacological strategies tailored to an individual's core metabolic defects, thereby paving the way for a new era of precision diabetes prevention.

Related papers

• Transformer-Based Multi-Modal Temporal Embeddings for Explainable Metabolic Phenotyping in Type 1 Diabetes [7.06459235706998]
  Type 1 diabetes (T1D) is a highly metabolically heterogeneous disease that cannot be adequately characterized by conventional biomarkers.<n>This study proposes an explainable deep learning framework that integrates continuous glucose monitoring (CGM) data with laboratory profiles to learn multimodal temporal embeddings of individual metabolic status.
  arXiv  Detail & Related papers  (2026-01-07T18:01:12Z)
• Integrating Neural Differential Forecasting with Safe Reinforcement Learning for Blood Glucose Regulation [51.12307713554633]
  TSODE is a safety-aware controller that integrates Thompson RL Sampling with a Neural Ordinary Differential Equation forecaster.<n>In the FDA-approved UVa/Padova simulator (adult cohort), TSODE achieved 87.9% time-in-range with less than 10% time below 70 mg/dL.
  arXiv  Detail & Related papers  (2025-11-16T02:11:33Z)
• Insulin Resistance Prediction From Wearables and Routine Blood Biomarkers [44.735350554750916]
  We remotely recruited the largest dataset to date across the US to study insulin resistance.<n>Deep neural network models were developed to predict insulin resistance based on readily available digital and blood biomarkers.<n>Model showed 93% sensitivity and 95% adjusted specificity in obese and sedentary participants.
  arXiv  Detail & Related papers  (2025-04-30T16:10:20Z)
• LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet [6.292642131180376]
  Postprandial hyperglycemia is marked by the blood glucose level exceeding the normal range after consuming a meal.<n>A key metric for understanding blood glucose dynamics after eating is the postprandial area under the curve (AUC)<n>We developed an explainable machine learning solution, GlucoLens, that takes sensor-driven inputs and uses advanced data processing, large language models, and trainable machine learning models.
  arXiv  Detail & Related papers  (2025-03-05T22:10:14Z)
• Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting [6.466206145151128]
  GlucoNet is an AI-powered sensor system for continuously monitoring behavioral and physiological health.<n>We propose a decomposition-based transformer model that incorporates patients' behavioral and physiological data.<n>G GlucoNet achieves a 60% improvement in RMSE and a 21% reduction in the number of parameters, improving RMSE and MAE by 51% and 57%, using data obtained involving 12 participants with T1-Diabetes.
  arXiv  Detail & Related papers  (2024-11-16T05:09:20Z)
• Gene-Metabolite Association Prediction with Interactive Knowledge Transfer Enhanced Graph for Metabolite Production [49.814615043389864]
  We propose a new task, Gene-Metabolite Association Prediction based on metabolic graphs. We present the first benchmark containing 2474 metabolites and 1947 genes of two commonly used microorganisms. Our proposed methodology outperforms baselines by up to 12.3% across various link prediction frameworks.
  arXiv  Detail & Related papers  (2024-10-24T06:54:27Z)
• From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [47.23780364438969]
  We present GluFormer, a generative foundation model for CGM data that learns nuanced glycemic patterns and translates them into predictive representations of metabolic health.<n>GluFormer generalizes to 19 external cohorts spanning different ethnicities and ages, 5 countries, 8 CGM devices, and diverse pathophysiological states.<n>In a longitudinal study of 580 adults with CGM data and 12-year follow-up, GluFormer identifies individuals at elevated risk of developing diabetes more effectively than blood HbA1C%.
  arXiv  Detail & Related papers  (2024-08-20T13:19:06Z)
• NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
  Self-reporting methods are often inaccurate and suffer from substantial bias. Recent work has explored using computer vision prediction systems to predict nutritional information from food images. This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
  arXiv  Detail & Related papers  (2024-05-13T14:56:55Z)
• Toward Short-Term Glucose Prediction Solely Based on CGM Time Series [4.7066018521459725]
  TimeGlu is an end-to-end pipeline for short-term glucose prediction based on CGM time series data. It achieves state-of-the-art performance without the need for additional personal data from patients.
  arXiv  Detail & Related papers  (2024-04-18T06:02:12Z)
• Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates [28.39179475412449]
  A meal's macronutritional content has nuanced effects on the absorption profile, which is difficult to model mechanistically. We use a neural network to predict an individual's glucose absorption rate.
  arXiv  Detail & Related papers  (2023-04-27T16:03:41Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.