ArticleCardiovascular diabetology2024
Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants.
Article in Cardiovascular diabetology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Continuous glucose monitoring metrics based clustering in people living with type 1 diabetes identifies phenotypes associated with higher inflammation, metabolic dysfunction-associated steatotic liver disease risk, and lower insulin sensitivity.Diabetology & metabolic syndrome · 2026Article
- Longitudinal multimorbidity trajectories shape personalized glycaemic patterns.Nature metabolism · 2026Article
- Leveraging Digital Health Technologies to Assess Older Adults' Frailty and Nutritional Status: Two Cross-Sectional Studies.JMIR aging · 2026Observational
- Use of continuous glucose monitoring to stratify individuals without diabetes.Communications medicine · 2026Article
- Association Between Sedentary Behavior Patterns and Glycemic Outcomes in Women with Overweight and Obesity Under Free-Living Conditions.International journal of behavioral medicine · 2025Article
- Glycemic variability in type 2 diabetic patients with metabolic dysfunction-associated steatotic liver disease: a case-control study.Annals of medicine · 2025Article
- Continuous Glucose Monitoring in People at High Risk of Diabetes and Dysglycaemia: Transforming Early Risk Detection and Personalised Care.Life (Basel, Switzerland) · 2025Review
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11 authors.
Funding
Abstract
backgroundContinuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis.
methodsThis cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree.
resultsMean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P < 0.001 for all comparisons). The model combining both nighttime and daytime metrics achieved the highest predictive capacity (0.73), with nighttime MODD emerging as the most important predictor.
conclusionsHigher CGM-derived mean glucose and glycemic variability were linked with higher steatosis degree. CGM-derived metrics during nighttime and daytime provided distinct and complementary insights into hepatic steatosis.
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