Exploration of Digestive Diseases

Table 2. Clinical and population-based studies of glycemic variability in MASLD and CLD.

Study	Country/Population	Sample size	Study design	Glycemic assessment	Main findings	Key limitations
Hashiba M et al. (2013) [14]	Japan	169 patients underwent 75-g oral glucose tolerance test (OGTT) (fibrosis staged F0–F3 by biopsy); CGMS subgroup n = 20 (mild fibrosis F0–2 n = 10 vs. severe fibrosis F3–4 n = 10).	Cross-sectional observational study evaluating predictors of fibrosis severity using OGTT-derived metabolic parameters plus a CGM substudy with standardized inpatient diet.	75-g OGTT (glucose/insulin at 0, 30, 60, 90, 120, 180 minutes), HbA1c, fasting insulin, HOMA-IR, insulinogenic index, 1,5-AG, AUC-PG, and AUC-IRI. CGM (≈ 30 hours) in a subset to quantify short-term glycemic variability: median glucose, SD of glucose, maximum glucose, and Δmin–max glucose (with standardized meals).	With increasing fibrosis stage (F0→F3), prevalence of T2DM increased, and NGT decreased; HbA1c and HOMA-IR rose while 1,5-AG declined. In multivariable logistic regression for advanced fibrosis (F3 vs. F0–2), lower 1,5-AG was the only independent factor (suggesting postprandial hyperglycemia/greater excursions not captured by fasting glucose alone). CGMS substudy showed markedly higher glycemic variability in severe fibrosis vs. mild fibrosis: higher SD, higher maximum glucose, and higher Δmin–max, with prominent postprandial hyperglycemia; minimum glucose did not differ significantly.	Single center; mostly cross-sectional (fibrosis severity, not prospective progression); CGM performed in a small subset (n = 20) and for a relatively short monitoring window; potential confounding by age/BMI (both increase with fibrosis); limited generalizability to treated diabetes (participants were not on antidiabetic drugs/insulin).
Honda F et al. (2018) [16]	Japan	105 CLD patients with T2DM CH n = 51; LC n = 54; Child–Pugh A/B/C: 31/18/5. Most were on glucose-lowering therapy (87/105).	Single-center retrospective observational study; inpatient CGM with individualized fixed-calorie diets; cross-sectional analysis of glycemic variability metrics by liver functional reserve and by HbA1c strata.	CGM performed for 72 hours. Glycemic variability metrics included MBG, ΔBG (max–min glucose), MAGE, SD, and time spent above 140 mg/dL (AUC ≥ 140) and below 70 mg/dL (AUC < 70). Postprandial hyperglycemia was defined as a max glucose ≥ 200 mg/dL, nocturnal hypoglycemia as a nighttime min ≤ 70 mg/dL, and high MAGE as ≥ 77.4 mg/dL.	Across worsening hepatic functional reserve (CH→Child A→Child B/C), MBG and ΔBG increased significantly, and AUCgluc ≥ 140 rose stepwise, indicating greater hyperglycemic exposure with declining liver function, despite no clear differences in fasting glucose, insulin, or HOMA-IR across groups. Postprandial hyperglycemia was extremely common (92%), and nocturnal hypoglycemia occurred in 22%, even under inpatient monitoring. Importantly, among non-anemic patients with HbA1c < 7.0%, LC patients had a higher prevalence of high MAGE (≥ 77.4 mg/dL) and elevated MBG (> 145 mg/dL) than CH patients, demonstrating clinically meaningful “hidden” glycemic instability despite apparently acceptable HbA1c. In multivariable models, LC independently predicted higher MBG, and both LC and IR (HOMA-IR > 2.5) independently predicted high MAGE in the non-anemic HbA1c < 7% subgroup.	Retrospective cross-sectional design; no CLD non-diabetic comparator; inpatient setting and fixed diet may limit generalizability to free-living glycemic variability; heterogeneity of CLD etiologies not fully evaluated; LC partly defined using APRI > 1 when biopsy/imaging unavailable; treatment heterogeneity (multiple diabetes agents) may influence CGM patterns despite analyses suggesting no major differences by medication class.
Yoo JH et al. (2021) [100]	South Korea	21,123 patients; NGT 12,838; prediabetes 6,650; diabetes 1,635.	Longitudinal cohort; ≥ 5 annual screening checkups; recruitment 2005–2016; median follow-up 57 months.	Visit-to-visit HbA1c variability derived from three consecutive HbA1c measurements.	Mean HbA1c was independently associated with incident MASLD across glycemic strata, with risk increasing from HbA1c ~4.9%, even in NGT. HbA1c visit-to-visit variability predicted incident MASLD only in participants with established diabetes, independent of mean HbA1c. The glycemic variability-MASLD association in diabetes was significant, particularly among those with an increasing post-baseline HbA1c. No significant association between HbA1c variability and incident NAFLD was observed in NGT or prediabetes.	Single-center; visit-to-visit variability based on only three HbA1c measurements.
Yoo JJ et al. (2021) [101]	South Korea	674,178 patients with diabetes had ≥ 3 health examinations within 5 years prior to index (2009–2010).	Nationwide population-based longitudinal cohort; 1-year lag period; median follow-up 6.7 years.	Visit-to-visit FPG variability assessed across serial health examinations.	Higher visit-to-visit glycemic variability independently predicted incident HCC, with a clear dose-response relationship across glycemic variability quartiles. The highest glycemic variability quartile was associated with ~27% higher HCC risk compared with the lowest quartile, independent of baseline fasting glucose, diabetes duration, BMI, LC, viral hepatitis, alcohol intake, and other confounders. The association was consistent across all glycemic variability metrics and broadly across clinical subgroups.	Glycemic variability limited to long-term visit-to-visit FPG; HbA1c unavailable; limited fibrosis characterization; observational design; findings limited to patients with diabetes.
Hong SH et al. (2021) [15]	South Korea	57,636 adults aged ≥ 45 years without T2DM or MASLD.	Nationwide population-based retrospective longitudinal cohort; ≥ 3 health examinations over 5 years.	Long-term visit-to-visit FPG variability was assessed by CV, SD, VIM, and ASV.	Higher long-term FPG variability was independently associated with incident MASLD, even after adjustment for mean FPG and cardiometabolic risk factors. Individuals in the highest quartile of FPG-CV had a 15% higher risk of MASLD compared with the lowest quartile. The association was consistent across all glycemic variability metrics and persisted in subgroup and sensitivity analyses. The effect was particularly pronounced in normal-weight individuals, suggesting that glycemic variability confers liver risk beyond adiposity and average glycemia.	Hepatic steatosis is defined using the fatty liver index rather than imaging or histology; liver fibrosis is not assessed.
Ogawa Y et al. (2022) [71]	Japan	335 patients with CLD and glucose intolerance; CH 51%, cirrhosis (Child–Pugh A–C) 49%; diabetes in 76%.	Single-center observational cohort; inpatient evaluation; CGM performed between 2013–2017; cross-sectional analysis with external validation cohort (n = 231).	CGM over 72 hours; assessment of mean glucose, postprandial hyperglycemia, nocturnal hypoglycemia; estimated HbA1c derived from CGM mean glucose and compared with measured HbA1c.	CGM revealed substantial underestimation of glycemic burden by HbA1c in CLD, particularly with worsening hepatic functional reserve. As liver function declined, mean glucose levels and glycemic variability increased despite lower HbA1c values. CGM detected frequent postprandial hyperglycemia and nocturnal hypoglycemia not captured by fasting glucose or HbA1c. The discrepancy between CGM-derived estimated HbA1c and measured HbA1c widened progressively from CH to Child–Pugh C cirrhosis, leading to missed or delayed diabetes diagnosis in a substantial proportion of patients.	Cross-sectional design; CGM performed during hospitalization for liver dysfunction or liver cancer; no longitudinal liver or cardiovascular outcomes assessed; predominantly diabetic population; single-country cohort.
Zhou H et al. (2022) [102]	United States	2,467 Black and White adults; aged 18–30 years at baseline (1985–1986); followed for 25 years; MASLD prevalence at midlife 9.8%.	Prospective population-based cohort; long-term follow-up over 25 years with repeated examinations.	Visit-to-visit FPG variability was assessed as CV, SD, and ARV across serial measurements.	Higher visit-to-visit FPG-glycemic variability in early adulthood was independently associated with prevalent MASLD in middle age, irrespective of mean fasting glucose levels. Participants in the highest quartile of FPG variability had a 2.8-to-3.8-fold higher odds of MASLD compared with the lowest quartile, consistent across CV, SD, and ARV metrics. These findings suggest that long-term glycemic instability, even within largely nondiabetic ranges, contributes to NAFLD risk later in life.	Steatosis was not measured at baseline, limiting temporality; relatively low MASLD prevalence; the cohort was limited to Black and White participants.
Zhong H et al. (2024) [21]	China	1,180 participants (healthy n = 698; mild steatosis n = 242; moderate/severe steatosis n = 240).	Cross-sectional analysis (CGM worn for 2 weeks; liver MRI performed within a few days after CGM).	CGM-based glycemic assessment (2 weeks; blinded): mean glucose metrics, glycemic variability, and time-in-range metrics computed for all-day, daytime, and nighttime windows.	Higher mean glucose and higher short-term glycemic variability were independently associated with higher steatosis degree. Lower daytime TIR was linked to higher steatosis degree, while nighttime TIR showed the opposite-direction association in their models. Prediction models: nighttime and daytime CGM features outperformed all-day features for classifying moderate/severe steatosis; the combined day + night model performed best (AUROC ~0.73), and nighttime MODD emerged as the most important predictor.	Cross-sectional design; limited generalizability.
Barbieri E et al. (2025) [103]	United States	27 youth with obesity (11 girls); median age 15 years; BMI percentile ~99th; racially/ethnically diverse (non-Hispanic Black and Hispanic reported).	Cross-sectional, mechanistic clinical study; 10-day blinded CGM plus 3-hour OGTT; liver fat quantified by MRI-PDFF.	Daily glucose variability by CGM (10 days): mean sensor glucose, SD (STDEV), and CV; complemented by OGTT-derived beta-cell function and insulin sensitivity.	Higher daily glycemic variability was associated with greater intrahepatic fat content, even when fasting glucose was not informative. Mean sensor glucose, STDEV, and CV each correlated with PDFF; youths meeting MASLD criteria (PDFF ≥ 5.5%) had higher average glucose and higher STDEV (and a trend toward higher CV). Glucose variability was also associated with lower insulin sensitivity and with indices of beta-cell responsiveness/insulin secretion, linking glycemic instability to early beta-cell compensation in obesity. Variability metrics related to post-load glycemia (2-hour OGTT glucose) and STDEV related to fasting plasma lactate.	Small sample size; cross-sectional design; limited generalizability.
Wang Y et al. (2025) [13]	China	2,897 T2DM inpatients (1,057 T2DM + MASLD matched 1:1 with 1,057 T2DM without MASLD).	Retrospective age- and sex-matched case–control study (June 2019–March 2022).	Short-term glycemic variability derived from inpatient multi-point capillary glucose measurements (≥ 2 days): SD, CV, MAGE; HbA1c, FPG, 2-hour PG; IR and β-cell function assessed by HOMA-IR and HOMA-β.	Among T2DM patients, MASLD was associated with lower glycemic variability and lower hypoglycemia incidence, despite higher IR and hyperinsulinemia. The inverse association between glycemic variability and MASLD was consistent across BMI and HbA1c strata. Within the T2DM + MASLD group, higher fibrosis risk (FIB-4 ≥ 1.3) was associated with higher SD and MAGE, suggesting that glycemic variability increases with advancing liver disease severity. HbA1c was the strongest positive determinant of glycemic variability, whereas postprandial C-peptide showed a negative association with glycemic variability, supporting a role of hyperinsulinemia in dampening glucose fluctuations.	Retrospective single-center inpatient cohort; MASLD diagnosed by ultrasound; glycemic variability derived from capillary glucose; lack of detailed data on diabetes duration and antidiabetic medication.

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1,5-AG: 1,5-anhydroglucitol; ARV: average real variability; ASV: average successive variability; BMI: body mass index; CGM: continuous glucose monitoring; CH: chronic hepatitis; CLD: chronic liver disease; FPG: fasting plasma glucose; HbA1c: glycated hemoglobin; HCC: hepatocellular carcinoma; HOMA-IR: homeostasis model assessment of insulin resistance; IR: insulin resistance; LC: liver cirrhosis; MAGE: mean amplitude of glycemic excursions; MASLD: metabolic dysfunction-associated steatotic liver disease; MBG: mean blood glucose; MODD: mean of daily differences; NAFLD: non-alcoholic fatty liver disease; NGT: normal glucose tolerance; OGTT: oral glucose tolerance test; SD: standard deviation; T2DM: type 2 diabetes mellitus; TIR: time in range; VIM: variability independent of the mean.

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Author contributions

FMMD: Investigation, Writing—original draft. EAJC: Investigation, Writing—original draft. MMRM: Investigation, Visualization, Writing—original draft, Writing—review & editing. SGC: Investigation, Visualization, Writing—review & editing. NMS: Conceptualization, Supervision, Writing—original draft, Writing—review & editing. All authors read and approved the submitted version.

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Nahum Méndez-Sánchez, who is the Associate Editor and Guest Editor of Exploration of Digestive Diseases, was not involved in the decision-making or the review process of this manuscript. The other authors declare no conflicts of interest.

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