Journal Article

Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants

Zhang, Ke, Lin, Lishan, Chen, Jingnan, Chen, Yu-ming, Zhong, Haili, Yan, Yan, Liang, Xinxiu, Zheng, Ju-Sheng, Chen, Hanzu, Miao, Zelei, Shen, Luqi

2024

Overview

Background Continuous 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. Methods This 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. Results Mean 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. Conclusions Higher 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.

• Publisher: BioMed Central,Springer Nature B.V,BMC
• Subject: Age Factors / Aged / Angiology / Biomarkers / Biomarkers - blood / Blood Glucose - metabolism / Blood Glucose Self-Monitoring - instrumentation / Body mass index / Cardiology / China - epidemiology / Continues glucose monitoring / Continuous Glucose Monitoring / Cross-Sectional Studies / Daytime / Diabetes / East Asian People / Exercise / Fatty liver / Fatty Liver - blood / Fatty Liver - diagnosis / Fatty Liver - epidemiology / Female / Glucose monitoring / Glycemic index / Glycemic variability / Health care / Hepatic steatosis / Humans / Lipids / Liver / Liver diseases / Machine Learning / Magnetic resonance imaging / Male / Mean glucose level / Medicine / Medicine & Public Health / Middle age / Middle Aged / Nighttime / Non-alcoholic Fatty Liver Disease - blood / Non-alcoholic Fatty Liver Disease - diagnosis / Non-alcoholic Fatty Liver Disease - epidemiology / Performance evaluation / Population studies / Predictive Value of Tests / Regression analysis / Risk Assessment / Risk Factors / Severity of Illness Index / Steatosis / Time Factors / Time in range