Published online Oct 15, 2026. doi: 10.4239/wjd.125355
Revised: July 27, 2026
Accepted: September 20, 2026
Published online: October 15, 2026
Processing time: 100 Days and 0.2 Hours
Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent in patients with type 2 diabetes mellitus (T2DM), with insulin resistance as its core pathophysiological driver. The estimated glucose disposal rate (eGDR) is a simple surrogate for systemic insulin resistance, yet its association with MASLD in T2DM and the related gender heterogeneity remain unclear.
To investigate the association between the eGDR and MASLD in patients with T2DM, and to analyse the moderating effect of gender on this association.
A cross-sectional study design was adopted, and 648 hospitalised patients with T2DM were consecutively enrolled. A multivariable logistic regression model was used to analyse the independent association between eGDR and MASLD; a restricted cubic spline (RCS) model was employed to analyse the dose-response relationship between the two; and the moderating effects of factors such as gender were assessed through stratified analysis and interaction tests.
The eGDR levels in patients with MASLD were significantly lower than those in patients without MASLD (P < 0.001). Multivariate logistic regression analysis showed that, with the lowest eGDR quartile (Q1) as the reference, patients in the highest quartile (Q4) had a 58% reduced risk of MASLD [odds ratio (OR) = 0.42, 95% confidence interval (CI): 0.25-0.70, P < 0.001], and a significant dose-de
It has been demonstrated that eGDR is independently and negatively associated with the risk of MASLD in patients with T2DM, and that this association shows significant gender differences, being more pronounced in male patients.
Core Tip: This cross-sectional study demonstrates that estimated glucose disposal rate is independently and inversely associated with metabolic dysfunction-associated steatotic liver disease risk in type 2 diabetes mellitus patients in a linear dose-dependent pattern. The association presents significant gender heterogeneity, being more pronounced in males. Estimated glucose disposal rate may serve as a simple practical indicator for metabolic dysfunction-associated steatotic liver disease risk stratification in clinical practice.
- Citation: Yin JT, Lu YX, Li ZM, Niu S. Association between estimated glucose disposal rate and metabolic dysfunction-associated steatotic liver disease in type 2 diabetes mellitus patients. World J Diabetes 2026; 17(10): 125355
- URL: https://www.wjgnet.com/1948-9358/full/v17/i10/125355.htm
- DOI: https://dx.doi.org/10.4239/wjd.125355
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most preva
The glucose clamp technique is widely regarded as the gold standard for the assess
Recent studies have identified significant gender disparities in the prevalence of MASLD, with the incidence of the disease being considerably higher in men than in women, and the progression of the disease being faster in men than in women. These findings suggest that gender may have a modifying effect on the association between metabolic ab
Using a continuous sampling method, patients with T2DM hospitalised in the Department of Endocrinology at Shijiazhuang People’s Hospital between January 2024 and December 2025 were selected as the study subjects.
Inclusion criteria: (1) Compliance with the 1999 World Health Organisation (WHO) diagnostic criteria for T2DM; and (2) Complete clinical records, including abdominal ultrasound results and relevant biochemical indicators.
Exclusion criteria: (1) Other types of liver disease, such as viral hepatitis, drug-induced liver disease, autoimmune liver disease and alcoholic liver disease; (2) Patients with severe hepatic or renal insufficiency, malignant tumours, severe infections or acute diabetic complications; (3) Patients who had recently used medications affecting hepatic lipid metabolism or insulin sensitivity; and (4) Incomplete clinical records.
This study was approved by the hospital’s Ethics Committee, and all participants signed informed consent forms, approval No. 2024-177.
The clinical data of the study participants was collected through the retrieval of information from electronic health records and the conducting of face-to-face consultations at the time of admission. All anthropometric measurements and laboratory tests were carried out in accordance with standardised procedures. The general clinical data collected included gender, age, duration of diabetes, smoking history and history of HT. HT was defined as a previous medical diagnosis or a systolic blood pressure (SBP) of ≥ 140 mmHg and/or a diastolic blood pressure (DBP) of ≥ 90 mmHg at rest. Smoking was defined as smoking ≥ 1 cigarette per day for a duration of at least one year. All participants underwent a series of anthropometric assessments, including height, weight, waist circumference (WC), hip circumference, and blood pressure, while fasting and at rest. Their body mass index (BMI) was subsequently calculated. Venous blood samples were collected from participants after an 8-12-hour fast, and standardised laboratory tests were performed, specifically including fasting blood glucose (FBG) and glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), alanine transaminase (ALT), aspartate transaminase (AST), blood urea nitrogen, serum creatinine, uric acid (UA), homocysteine, red blood cell (RBC) count, haemoglobin (Hb), white blood cell (WBC) count and platelet (PLT) count.
The formula for calculating eGDR is based on previous classic validation studies: EGDR = 21.158 - (0.551 × HbA1c) - (3.407 × HT) - (0.09 × WC), where the value assigned for HT status is: 1 for those with HT and 0 for those without[12]. The determination of hypertensive status in the formula is consistent with the definition of HT used in this study, namely a history of HT based on a previous clinical diagnosis, or a SBP of ≥ 140 mmHg and/or a DBP of ≥ 90 mmHg at rest upon admission; a value of 1 is assigned if either criterion is met, and a value of 0 is assigned if neither is met.
Diagnostic criteria for MASLD, based on the 2023 International MASLD Expert Consensus: Hepatic steatosis confirmed by transabdominal ultrasound, accompanied by at least one of the following indicators of metabolic dysfunction: (1) Overweight/obesity (BMI ≥ 25 kg/m2); (2) T2DM; and (3) abnormalities in metabolic risk factors such as HT, dyslipidaemia and hyperuricaemia. All ultrasound examinations were performed by experienced radiologists using standardised diagnostic criteria[13].
The statistical analysis was conducted utilising the R version 4.2.1 software. In the context of statistical analysis, the presentation of quantitative data is typically characterised by the use of mean ± SD (x ± s), where x denotes the observed data and s denotes the standard deviation. The comparison of two groups is typically conducted using an independent samples t-test, whereas the comparison of multiple groups is performed using one-way analysis of variance. In the event of non-normally distributed continuous variables, the median (interquartile range) [M (P25, P75)] is employed for presentation, with comparisons between groups conducted using the Mann-Whitney U test or the Kruskal-Wallis H test. Categorical variables are presented as counts (percentages), with comparisons between groups performed using the χ2 test.
The participants of the study were divided into four groups (Q1-Q4) according to the quartiles of eGDR. Utilising the Q1 group as a reference point, multivariable logistic regression models were constructed to analyse the association between eGDR and MASLD. Four adjusted models were established: Model 1 was an unadjusted crude model; model 2 was adjusted for sex, age and duration of diabetes; model 3 was further adjusted for TC, TG, LDL-C and HDL-C on top of model 2; and model 4 was further adjusted for ALT, AST, blood urea nitrogen, serum creatinine, UA, Hcy, WBC and PLT based on model 3.
The dose-response relationship between eGDR and MASLD was modelled using a restricted cubic spline (RCS) model. Stratified analyses were performed by sex, age, smoking history, history of HT, duration of diabetes and BMI. The statistical significance of interactions was assessed by including interaction terms. All tests were two-sided, with a significance level of α = 0.05. A P value of < 0.05 was considered to be statistically significant. The present study was a retrospective cross-sectional study based on a sample of consecutively enrolled inpatients, with a final sample size of 648 participants. A post-hoc calculation of statistical power, at a significance level of α = 0.05, was conducted to determine the sample size of this study. It was found that the study’s sample size is sufficient to detect an effect size of a more than 40% reduction in the risk of MASLD in the highest quartile of eGDR compared with the lowest quartile [odds ratio (OR) = 0.6]. Additionally, the study demonstrated a statistical power of over 90 per cent, thereby meeting the statistical power requirements for the primary analysis of this study.
The present study comprised a total of 648 patients diagnosed with T2DM, of whom 375 were male (57.87%) and 273 were female (42.13%), with a mean age of (57.19 ± 13.07) years (Figure 1). A total of 260 patients were diagnosed with co-morbid MASLD. In comparison with the non-MASLD group, patients in the MASLD group were found to be younger and had a shorter duration of diabetes. They also exhibited higher levels of BMI, WC, HP, DBP, TC, TG, LDL-C, ALT, AST, FBG, UA, RBC, Hb, WBC and PLT, and lower levels of HDL-C. Moreover, the proportion of males and the prevalence of HT were higher in the MASLD group, with all differences being statistically significant (P < 0.05). The eGDR levels in the MASLD group were found to be significantly lower than those in the non-MASLD group (P < 0.001) (Table 1).
| Variables | Total (n = 648) | Non-MASLD (n = 380) | MASLD (n = 260) | P value |
| Age (year) | 57.19 ± 13.07 | 60.68 ± 11.79 | 52.25 ± 13.23 | < 0.001 |
| Duration (year) | 5.00 (2.00, 14.00) | 8.00 (2.75, 15.00) | 3.00 (1.00, 9.25) | < 0.001 |
| SBP (mmHg) | 139.68 ± 18.44 | 139.37 ± 19.41 | 140.10 ± 16.98 | 0.611 |
| DBP (mmHg) | 82.89 ± 12.36 | 80.77 ± 11.96 | 85.88 ± 12.31 | < 0.001 |
| BMI (kg/m2) | 26.41 ± 4.51 | 25.11 ± 3.76 | 28.26 ± 4.84 | < 0.001 |
| WC (cm) | 96.44 ± 12.15 | 93.33 ± 11.16 | 100.85 ± 12.16 | < 0.001 |
| HC (cm) | 102.25 ± 10.28 | 99.88 ± 8.95 | 105.60 ± 11.09 | < 0.001 |
| LDL-C (mmol/L) | 3.09 ± 0.94 | 3.03 ± 0.92 | 3.19 ± 0.96 | 0.034 |
| HDL-C (mmol/L) | 1.23 ± 0.30 | 1.26 ± 0.29 | 1.19 ± 0.31 | 0.003 |
| TG (mmol/L) | 2.20 ± 2.65 | 1.81 ± 2.28 | 2.74 ± 3.03 | < 0.001 |
| TC (mmol/L) | 4.95 ± 1.38 | 4.80 ± 1.29 | 5.17 ± 1.48 | 0.001 |
| ALT (U/L) | 23.00 (16.00, 35.00) | 19.00 (14.00, 28.25) | 29.00 (20.00, 45.00) | < 0.001 |
| AST (U/L) | 19.00 (15.00, 26.00) | 17.00 (14.00, 22.00) | 22.00 (17.00, 33.00) | < 0.001 |
| FBG (mmol/L) | 9.37 ± 3.36 | 9.14 ± 3.51 | 9.69 ± 3.11 | 0.043 |
| HbA1c (%) | 8.64 ± 2.06 | 8.58 ± 2.17 | 8.73 ± 1.89 | 0.367 |
| BUN (mmol/L) | 5.69 ± 2.44 | 5.77 ± 2.08 | 5.56 ± 2.87 | 0.273 |
| UA (mmol/L) | 341.55 ± 101.79 | 325.64 ± 95.85 | 364.10 ± 105.81 | < 0.001 |
| Scr (mmol/L) | 64.88 ± 24.26 | 65.53 ± 25.09 | 63.96 ± 23.05 | 0.416 |
| Hcy (μmol/L) | 14.17 ± 7.35 | 13.74 ± 6.23 | 14.76 ± 8.67 | 0.082 |
| RBC (× 1012/L) | 4.66 ± 0.58 | 4.56 ± 0.59 | 4.80 ± 0.52 | < 0.001 |
| Hb (g/L) | 142.18 ± 19.37 | 138.74 ± 19.37 | 147.05 ± 18.32 | < 0.001 |
| WBC (× 109/L) | 6.20 ± 1.86 | 5.98 ± 1.69 | 6.51 ± 2.04 | < 0.001 |
| PLT (× 109/L) | 224.57 ± 60.02 | 218.11 ± 58.58 | 233.73 ± 60.95 | 0.001 |
| eGDR | 5.85 ± 2.46 | 6.30 ± 2.43 | 5.21 ± 2.36 | < 0.001 |
| Sex | - | - | - | 0.025 |
| Male | 375 (57.87) | 206 (54.21) | 169 (63.06) | - |
| Female | 273 (42.13) | 174 (45.79) | 99 (36.94) | - |
| Smoking | - | - | - | 0.362 |
| No | 200 (30.86) | 112 (29.47) | 88 (32.84) | - |
| Yes | 448 (69.14) | 268 (70.53) | 180 (67.16) | - |
| HT | - | - | - | 0.030 |
| No | 289 (44.60) | 183 (48.16) | 106 (39.55) | - |
| Yes | 359 (55.40) | 197 (51.84) | 162 (60.45) | - |
| eGDR quantile | - | - | - | < 0.001 |
| Q1 | 162 (25.00) | 65 (17.11) | 97 (36.19) | - |
| Q2 | 162 (25.00) | 99 (26.05) | 63 (23.51) | - |
| Q3 | 162 (25.00) | 101 (26.58) | 61 (22.76) | - |
| Q4 | 162 (25.00) | 115 (30.26) | 47 (17.54) | - |
Patients were divided into four groups according to eGDR quartiles. As the eGDR quartile increased, patient age exhibited an overall upward trend, whilst levels of BMI, WC, hip circumference, SBP, DBP, TC, LDL-C, ALT, AST, FBG, HbA1c, UA, Hcy, RBC, Hb, and WBC gradually decreased, whilst the prevalence of HT and MASLD gradually declined; all differences were statistically significant (P < 0.05). It is noteworthy that the prevalence of MASLD was 59.88% in the Q1 group and fell to 29.01% in the Q4 group, indicating a significant downward trend (Table 2).
| Variables | eGDR | P value | |||
| Q1 (n = 162) | Q2 (n = 162) | Q3 (n = 162) | Q4 (n = 162) | ||
| Age (year) | 52.25 ± 15.49 | 58.86 ± 13.21 | 57.45 ± 11.48 | 60.21 ± 10.21 | < 0.001 |
| Duration (year) | 4.00 (1.00, 10.00) | 7.00 (2.00, 15.00) | 5.50 (2.00, 13.00) | 6.00 (2.00, 15.00) | 0.037 |
| SBP (mmHg) | 150.88 ± 16.04 | 150.08 ± 16.30 | 132.93 ± 14.44 | 124.81 ± 11.46 | < 0.001 |
| DBP (mmHg) | 90.60 ± 11.91 | 87.71 ± 11.35 | 79.20 ± 9.97 | 74.03 ± 8.23 | < 0.001 |
| BMI (kg/m2) | 29.63 ± 5.28 | 25.94 ± 3.96 | 25.62 ± 3.61 | 24.47 ± 3.18 | < 0.001 |
| WC (cm) | 106.49 ± 12.00 | 96.27 ± 8.17 | 94.59 ± 9.33 | 88.38 ± 11.20 | < 0.001 |
| HC (cm) | 110.04 ± 11.10 | 101.49 ± 7.03 | 100.34 ± 8.57 | 97.11 ± 9.31 | < 0.001 |
| LDL-C (mmol/L) | 3.38 ± 1.01 | 3.01 ± 0.92 | 3.05 ± 0.86 | 2.93 ± 0.92 | < 0.001 |
| HDL-C (mmol/L) | 1.16 ± 0.27 | 1.25 ± 0.31 | 1.24 ± 0.31 | 1.26 ± 0.30 | 0.017 |
| TG (mmol/L) | 2.42 ± 1.95 | 2.24 ± 2.43 | 2.27 ± 3.15 | 1.87 ± 2.91 | 0.284 |
| TC (mmol/L) | 5.30 ± 1.46 | 4.92 ± 1.29 | 4.88 ± 1.38 | 4.71 ± 1.33 | 0.001 |
| ALT (U/L) | 32.50 (19.00, 47.75) | 23.00 (16.00, 34.00) | 21.00 (16.00, 29.75) | 20.00 (14.00, 29.00) | < 0.001 |
| AST (U/L) | 23.00 (16.00, 36.00) | 19.00 (15.25, 25.00) | 18.00 (16.00, 23.00) | 18.00 (16.00, 22.00) | < 0.001 |
| FBG (mmol/L) | 10.60 ± 3.48 | 9.31 ± 3.75 | 9.30 ± 3.08 | 8.27 ± 2.63 | < 0.001 |
| HbA1c (%) | 9.95 ± 2.00 | 8.30 ± 1.90 | 8.81 ± 2.13 | 7.49 ± 1.30 | < 0.001 |
| BUN (mmol/L) | 5.59 ± 3.20 | 6.01 ± 2.43 | 5.56 ± 2.31 | 5.59 ± 1.54 | 0.290 |
| UA (mmol/L) | 362.64 ± 107.67 | 344.64 ± 109.19 | 339.48 ± 98.40 | 319.43 ± 86.51 | 0.002 |
| Scr (mmol/L) | 65.30 ± 22.30 | 67.12 ± 27.16 | 64.83 ± 28.95 | 62.27 ± 16.73 | 0.348 |
| Hcy (μmol/L) | 15.53 ± 7.85 | 14.57 ± 8.32 | 13.17 ± 5.16 | 13.38 ± 7.48 | 0.012 |
| RBC (× 1012/L) | 4.87 ± 0.57 | 4.60 ± 0.60 | 4.66 ± 0.58 | 4.51 ± 0.49 | < 0.001 |
| Hb (g/L) | 146.17 ± 18.78 | 141.71 ± 19.06 | 144.20 ± 20.91 | 136.62 ± 17.41 | < 0.001 |
| WBC (× 109/L) | 6.69 ± 2.04 | 6.38 ± 2.22 | 5.91 ± 1.46 | 5.82 ± 1.49 | < 0.001 |
| PLT (× 109/L) | 230.90 ± 67.82 | 224.08 ± 59.20 | 220.48 ± 57.95 | 222.81 ± 54.36 | 0.440 |
| eGDR | 2.83 ± 0.94 | 4.81 ± 0.39 | 6.69 ± 0.72 | 9.07 ± 1.14 | < 0.001 |
| Sex | - | - | - | - | < 0.001 |
| Male | 106 (65.43) | 97 (59.88) | 102 (62.96) | 70 (43.21) | - |
| Female | 56 (34.57) | 65 (40.12) | 60 (37.04) | 92 (56.79) | - |
| Smoking | - | - | - | - | 0.153 |
| No | 50 (30.86) | 55 (33.95) | 56 (34.57) | 39 (24.07) | - |
| Yes | 112 (69.14) | 107 (66.05) | 106 (65.43) | 123 (75.93) | - |
| HT | - | - | - | - | < 0.001 |
| No | 7 (4.32) | 14 (8.64) | 110 (67.90) | 158 (97.53) | - |
| Yes | 155 (95.68) | 148 (91.36) | 52 (32.10) | 4 (2.47) | - |
| MASLD | - | - | - | - | < 0.001 |
| No | 65 (40.12) | 99 (61.11) | 101 (62.35) | 115 (70.99) | - |
| Yes | 97 (59.88) | 63 (38.89) | 61 (37.65) | 47 (29.01) | - |
A multivariate logistic regression model was constructed with the presence or absence of MASLD as the dependent variable and eGDR quartile groups as the independent variables. In the unadjusted crude model, compared with the Q1 group, the risk of developing MASLD decreased progressively in the Q2, Q3 and Q4 groups, with OR values of 0.43, 0.40 and 0.27, respectively (P < 0.001). After stepwise adjustment for several confounding factors, this negative association remained statistically significant. In the fully adjusted model (model 4), with the Q1 group as the reference, the OR values for MASLD in the Q2, Q3 and Q4 groups were 0.56 [95% confidence interval (CI): 0.35-0.92, P = 0.021], 0.54 (95%CI: 0.33-0.88, P = 0.013) and 0.42 (95%CI: 0.25-0.70, P < 0.001), respectively, showing a significant dose-dependent relationship (Table 3).
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | ||||
| OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | |
| eGDR | ||||||||
| Q1 | 1.00 (reference) | - | 1.00 (reference) | - | 1.00 (reference) | - | 1.00 (reference) | |
| Q2 | 0.43 (0.27-0.67) | < 0.001 | 0.46 (0.29-0.73) | 0.016 | 0.50 (0.31-0.81) | 0.004 | 0.56 (0.35-0.92) | 0.021 |
| Q3 | 0.40 (0.26-0.63) | < 0.001 | 0.41 (0.26-0.66) | < 0.001 | 0.45 (0.28-0.72) | < 0.001 | 0.54 (0.33-0.88) | < 0.013 |
| Q4 | 0.27 (0.17-0.43) | < 0.001 | 0.29 (0.18-0.47) | < 0.001 | 0.33 (0.20-0.55) | < 0.001 | 0.42 (0.25-0.70) | < 0.001 |
The results of the RCS analysis indicated that, when unadjusted for confounding factors, the overall association between eGDR and the risk of developing MASLD was statistically significant (P for overall < 0.001), whilst the non-linearity test was not statistically significant (P for nonlinear = 0.075) (Figure 2A). After adjusting for confounding factors, the overall association remained statistically significant (P for overall = 0.008), whilst the non-linearity test remained non-significant (P for non-linearity = 0.387) (Figure 2B).
A stratified analysis was conducted by sex, smoking history, history of HT, age, duration of diabetes and BMI, with eGDR included in the model as a continuous variable. Across all subgroups, higher eGDR levels were associated with a reduced risk of MASLD. Specifically, the analysis by gender demonstrated that among male patients, a 1-unit increase in eGDR was associated with a 22% reduction in the risk of MASLD (OR = 0.78, 95%CI: 0.71-0.86, P < 0.001), whilst among female patients, the risk was reduced by 10% (OR = 0.90, 95%CI: 0.81-1.00, P = 0.049). There was a difference in the strength of the association between the two groups, and the interaction test was statistically significant (P for interaction = 0.042). However, the interaction tests for smoking, age, duration of diabetes and BMI were not statistically significant (all P > 0.05) (Table 4).
| Variables | n (%) | OR (95%CI) | P value | P for interaction |
| All patients | 648 (100.00) | 0.83 (0.77-0.89) | < 0.001 | - |
| Gender | - | - | - | 0.042 |
| Male | 375 (57.87) | 0.78 (0.71-0.86) | < 0.001 | - |
| Female | 273 (42.13) | 0.90 (0.81-1.00) | 0.049 | - |
| Smoking | - | - | - | 0.837 |
| No | 200 (30.86) | 0.82 (0.72-0.93) | 0.003 | - |
| Yes | 448 (69.14) | 0.83 (0.77-0.90) | < 0.001 | - |
| HT | - | - | - | 0.126 |
| No | 289 (44.60) | 0.78 (0.67-0.91) | 0.002 | - |
| Yes | 359 (55.40) | 0.66 (0.56-0.77) | < 0.001 | - |
| Age | - | - | - | 0.721 |
| < 45 | 119 (18.36) | 0.82 (0.68-0.99) | 0.038 | - |
| 45-60 | 224 (34.57) | 0.86 (0.77-0.96) | 0.010 | - |
| ≥ 60 | 305 (47.07) | 0.89 (0.80-0.99) | 0.040 | - |
| Duration | - | - | - | 0.892 |
| < 5 | 289 (44.60) | 0.82 (0.74-0.90) | < 0.001 | - |
| 5-10 | 122 (18.83) | 0.83 (0.71-0.97) | 0.018 | - |
| ≥ 10 | 237 (36.57) | 0.85 (0.75-0.97) | 0.013 | - |
| BMI | - | - | - | 0.682 |
| < 24 | 255 (39.41) | 0.87 (0.77-0.99) | 0.032 | - |
| 24-28 | 189 (29.21) | 0.89 (0.77-1.02) | 0.092 | - |
| ≥ 28 | 203 (31.38) | 0.94 (0.83-1.06) | 0.328 | - |
Male patients with MASLD were found to be younger, had a shorter duration of diabetes, and exhibited higher levels of TC, LDL-C, Hb, WBC and PLT, as well as a higher prevalence of HT; in contrast, female patients with MASLD were characterised primarily by lower HDL-C levels. It is important to note that all of the aforementioned differences were statistically significant (P < 0.05) (Table 5).
| Variables | Male | Female | ||||
| Non-MASLD | MASLD (n = 169) | P value | Non-MASLD | MASLD (n = 99) | P value | |
| Age (year) | 58.59 ± 12.31 | 49.11 ± 12.97 | < 0.001 | 63.15 ± 10.65 | 57.63 ± 11.93 | < 0.001 |
| Duration (year) | 6.00 (2.25, 15.00) | 3.00 (1.00, 8.00) | < 0.001 | 10.00 (3.00, 17.00) | 4.00 (2.00, 10.00) | < 0.001 |
| SBP (mmHg) | 137.60 ± 18.35 | 139.93 ± 14.87 | 0.174 | 141.48 ± 20.46 | 140.39 ± 20.16 | 0.673 |
| DBP (mmHg) | 83.88 ± 12.12 | 87.84 ± 11.20 | 0.001 | 77.10 ± 10.70 | 82.54 ± 13.42 | < 0.001 |
| BMI (kg/m2) | 25.34 ± 3.86 | 28.67 ± 4.97 | < 0.001 | 24.85 ± 3.63 | 27.55 ± 4.55 | < 0.001 |
| WC (cm) | 95.80 ± 9.45 | 103.93 ± 11.79 | < 0.001 | 90.43 ± 12.28 | 95.59 ± 10.96 | < 0.001 |
| HC (cm) | 100.95 ± 8.14 | 107.25 ± 10.77 | < 0.001 | 98.63 ± 9.70 | 102.79 ± 11.10 | 0.001 |
| LDL-C (mmol/L) | 2.96 ± 0.90 | 3.16 ± 0.93 | 0.038 | 3.11 ± 0.95 | 3.23 ± 1.01 | 0.299 |
| HDL-C (mmol/L) | 1.19 ± 0.28 | 1.15 ± 0.31 | 0.200 | 1.33 ± 0.29 | 1.24 ± 0.29 | 0.016 |
| TG (mmol/L) | 2.03 ± 2.68 | 3.12 ± 3.60 | 0.001 | 1.56 ± 1.66 | 2.09 ± 1.43 | 0.008 |
| TC (mmol/L) | 4.72 ± 1.28 | 5.18 ± 1.53 | 0.002 | 4.90 ± 1.31 | 5.15 ± 1.40 | 0.143 |
| ALT (U/L) | 20.00 (15.00, 27.00) | 29.00 (21.00, 46.00) | < 0.001 | 18.00 (13.00, 29.00) | 28.00 (18.50, 42.00) | < 0.001 |
| AST (U/L) | 17.00 (14.00, 21.00) | 22.00 (17.00, 32.00) | < 0.001 | 18.00 (14.00, 23.00) | 22.00 (16.00, 36.50) | < 0.001 |
| FBG (mmol/L) | 9.15 ± 3.33 | 9.92 ± 3.29 | 0.025 | 9.14 ± 3.73 | 9.28 ± 2.73 | 0.737 |
| HbA1c (%) | 8.60 ± 2.29 | 8.75 ± 1.86 | 0.488 | 8.55 ± 2.02 | 8.68 ± 1.95 | 0.605 |
| BUN (mmol/L) | 6.17 ± 2.17 | 5.69 ± 3.41 | 0.104 | 5.31 ± 1.87 | 5.33 ± 1.56 | 0.912 |
| UA (mmol/L) | 350.02 ± 97.94 | 386.92 ± 104.17 | < 0.001 | 296.78 ± 84.93 | 325.13 ± 97.32 | 0.013 |
| Scr (mmol/L) | 71.85 ± 25.51 | 67.20 ± 13.64 | 0.033 | 58.05 ± 22.45 | 58.41 ± 32.85 | 0.913 |
| Hcy (μmol/L) | 14.96 ± 6.32 | 16.03 ± 8.10 | 0.153 | 12.30 ± 5.82 | 12.61 ± 9.22 | 0.737 |
| RBC (× 1012/L) | 4.80 ± 0.56 | 4.98 ± 0.51 | 0.001 | 4.28 ± 0.50 | 4.51 ± 0.40 | < 0.001 |
| Hb (g/L) | 147.66 ± 16.79 | 154.50 ± 16.42 | < 0.001 | 128.18 ± 16.78 | 134.32 ± 13.88 | 0.002 |
| WBC (× 109/L) | 6.05 ± 1.63 | 6.57 ± 2.11 | 0.007 | 5.91 ± 1.77 | 6.39 ± 1.93 | 0.037 |
| PLT (× 109/L) | 207.93 ± 54.88 | 229.49 ± 59.17 | < 0.001 | 230.16 ± 60.66 | 240.97 ± 63.53 | 0.165 |
| Smoking | - | - | 0.782 | - | - | 1.000 |
| No | 109 (52.91) | 87 (51.48) | - | 171 (98.28) | 98 (98.99) | - |
| Yes | 97 (47.09) | 82 (48.52) | - | 3 (1.72) | 1 (1.01) | - |
| HT | - | - | 0.015 | - | - | 0.721 |
| No | 100 (48.54) | 61 (36.09) | - | 83 (47.70) | 45 (45.45) | - |
| Yes | 106 (51.46) | 108 (63.91) | - | 91 (52.30) | 54 (54.55) | - |
| eGDR quantile | - | - | < 0.001 | - | - | 0.412 |
| Q1 | 31 (15.05) | 63 (37.28) | - | 38 (21.84) | 30 (30.30) | - |
| Q2 | 55 (26.70) | 38 (22.49) | - | 43 (24.71) | 25 (25.25) | - |
| Q3 | 54 (26.21) | 40 (23.67) | - | 45 (25.86) | 22 (22.22) | - |
| Q4 | 66 (32.04) | 28 (16.57) | - | 48 (27.59) | 22 (22.22) | - |
Subsequent analysis of the correlation between eGDR quartiles and MASLD was conducted by stratifying the data by sex. In the male patient population, the incidence of MASLD exhibited a marked downward trend as eGDR quartiles increased. In the fully adjusted model, the OR for the development of MASLD in the male Q4 group compared with the Q1 group was 0.27 (95%CI: 0.14-0.51, P < 0.001), indicating a stronger association than in the overall population. Furthermore, when eGDR was treated as a continuous variable, for every 1-unit increase in eGDR among men, the risk of MASLD was reduced by 22% (OR = 0.78, 95%CI: 0.71-0.86, P < 0.001). Among female patients, although higher eGDR levels were associated with a trend towards a reduced risk of MASLD, the differences between the various quartiles and the Q1 group were not statistically significant (all P > 0.05) (Table 6).
| Variables | Model 1 | Model 2 | Model 3 | Model 4 | |||||
| OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | OR (95%CI) | P value | ||
| Male | eGDR | ||||||||
| Q1 | 1.00 (reference) | - | 1.00 (reference) | - | 1.00 (reference) | - | 1.00 (reference) | - | |
| Q2 | 0.34 (0.19-0.62) | < 0.001 | 0.34 (0.19-0.62) | < 0.001 | 0.37 (0.20-0.70) | 0.002 | 0.42 (0.22-0.81) | 0.009 | |
| Q3 | 0.36 (0.20-0.66) | < 0.001 | 0.36 (0.20-0.66) | < 0.001 | 0.41 (0.22-0.77) | 0.006 | 0.44 (0.23-0.84) | 0.013 | |
| Q4 | 0.21 (0.11-0.39) | < 0.001 | 0.21 (0.11-0.39) | < 0.001 | 0.24 (0.12-0.45) | < 0.001 | 0.27 (0.14-0.51) | < 0.001 | |
| Female | eGDR | ||||||||
| Q1 | 1.00 (reference) | - | 1.00 (reference) | - | 1.00 (reference) | - | 1.00 (reference) | - | |
| Q2 | 0.74 (0.37-1.46) | 0.383 | 0.68 (0.33-1.42) | 0.307 | 0.76 (0.36-1.60) | 0.471 | 0.81 (0.37-1.74) | 0.583 | |
| Q3 | 0.62 (0.31-1.25) | 0.179 | 0.47 (0.23-0.99) | 0.050 | 0.51 (0.24-1.09) | 0.084 | 0.56 (0.25-1.23) | 0.147 | |
| Q4 | 0.58 (0.29-1.16) | 0.125 | 0.50 (0.24-1.04) | 0.064 | 0.60 (0.28-1.29) | 0.191 | 0.71 (0.32-1.58) | 0.401 | |
In the male cohort, both the unadjusted model (Figure 3A) and the adjusted model (Figure 3B) demonstrated a significant negative linear correlation between eGDR and the risk of MASLD. The overall associations were statistically significant (all P for overall < 0.05), whilst the non-linearity tests were not statistically significant. In the female cohort, the overall associations in both the unadjusted model (Figure 3C) and the adjusted model (Figure 3D) were not statistically significant (P for overall: 0.199 and 0.589, respectively), and the non-linearity tests were also not statistically significant.
The present study, based on a cohort of hospitalised patients with T2DM, systematically analysed the association and dose-response relationship between eGDR and MASLD, and further investigated the moderating effect of gender on this association. The findings indicated that lower eGDR levels exhibited an independent association with an elevated risk of MASLD, characterised by a linear dose-dependent relationship. This association manifested significant gender heterogeneity, with the strength of the association being considerably higher in males than in females. This provides novel epidemiological evidence to facilitate a more profound understanding of gender disparities in the onset of MASLD.
IR, as a precipitating factor for MASLD, has been confirmed by numerous basic and clinical studies to have a causal association with hepatic steatosis. Previous epidemiological studies have predominantly used the Homeostatic Model Assessment of IR (HOMA-IR) as an assessment indicator, and have consistently found a positive correlation between IR and the risk of developing MASLD[14]. A meta-analysis incorporating 18 cross-sectional studies demonstrated that HOMA-IR levels in patients with MASLD were significantly higher than in healthy controls, and that the degree of IR was positively correlated with the grade of hepatic steatosis, with a combined OR of 2.13[15]. However, as HOMA-IR primarily reflects hepatic IR and relies on fasting insulin measurements, its application in routine clinical practice is limited.
The eGDR is a straightforward instrument for evaluating insulin sensitivity, predicated on BMI, HT status and HbA1c. It furnishes a comprehensive reflection of systemic insulin sensitivity, obviating the necessity for insulin level testing, rendering it especially well-suited for screening large populations and routine clinical utilisation. At present, the number of studies which have examined the direct association between eGDR and MASLD is relatively small. The present study found that, in patients with T2DM, eGDR was negatively correlated with the risk of MASLD, consistent with the findings of previous studies in different populations[16-18]. The present study utilised a RCS model to further elucidate the linear dose-response relationship between the two variables. Non-linearity tests for both the overall population and the male subgroup were not statistically significant, and no obvious threshold effect was observed. The findings of this study indicate that, in individuals diagnosed with T2DM, a progressive worsening of IR is associated with an elevated risk of developing MASLD. Furthermore, the elevated risk of hepatic steatosis is present even at the stage of mild IR. IR has been demonstrated to accelerate de novo lipogenesis in the liver by inhibiting the adenylate-activated protein kinase pathway and upregulating the expression of sterol regulatory element-binding protein-1c in the liver. Concurrently, impaired insulin signalling activates Kupffer cells and hepatic stellate cells, inducing the release of pro-inflammatory factors such as tumor necrosis factor-αand interleukin-6, thereby driving the progression from MASLD to non-alcoholic steatohepatitis and liver fibrosis. Furthermore, it has been demonstrated that IR can induce mitochondrial dysfunction and disrupt quality control, thereby exacerbating oxidative stress and energy metabolism disorders in hepatocytes. In addition, microRNAs have been shown to contribute to the onset and progression of diabetes and metabolic disorders through their immunoregulatory effects, jointly driving the progression of liver disease[19-21].
From a pathophysiological perspective, IR promotes hepatic steatosis primarily via the dual pathways of lipotoxicity and glucotoxicity. On the one hand, IR in peripheral adipose tissue leads to uncontrolled lipolysis, resulting in large amounts of free fatty acids entering the liver via the portal vein. This exceeds the capacity of hepatocytes for oxidation and excretion via very low-density lipoproteins, leading to the deposition of TG within hepatocytes; on the other hand, hepatic IR can further exacerbate the hepatic lipid burden by promoting de novo fatty acid synthesis in the liver[22-24]. Furthermore, IR can induce a state of chronic low-grade inflammation, driving the progression from simple steatosis to steatohepatitis and fibrosis. In this study, after adjusting for markers of liver damage and inflammation such as ALT, AST and WBC, the association between eGDR and MASLD remained statistically significant, suggesting that the impact of IR on MASLD may be independent of inflammatory liver damage.
A salient finding of this study is that the correlation between eGDR and MASLD manifests substantial gender dispar
In addition to sex hormones, gender differences in body fat distribution are also a significant factor. Males are more prone to central obesity and have a higher proportion of visceral fat; visceral fat exhibits greater lipolytic activity and the ability to secrete inflammatory factors, and the free fatty acids it releases enter the liver directly via the portal vein, exerting a far greater influence on hepatic lipid metabolism than subcutaneous fat[28]. Research has indicated that, following adjustment for visceral fat area, the gender disparity in the prevalence of MASLD diminishes considerably, thereby suggesting that visceral fat accumulation serves as a pivotal mediating factor in the elevated prevalence of MASLD among male subjects[29]. In this study, male patients exhibited higher BMI and WC levels, with males constituting 65.43% of the lowest eGDR quartile. The synergistic effect of IR in conjunction with central obesity may have collectively amplified the strength of the association between these two factors in the male population. Furthermore, the presence of unhealthy lifestyle factors, such as elevated alcohol consumption and smoking rates among men, has been demonstrated to exert a synergistic effect with IR, thereby exacerbating liver damage and further strengthening the association between the two.
In clinical practice, the eGDR can be calculated using just three routine parameters: HbA1c, WC and HT status, without the need to measure insulin levels. The system is characterised by its ease of use and cost-effectiveness, rendering it well-suited for routine implementation in endocrinology outpatient clinics and among inpatients. The utilisation of eGDR as a tool for the preliminary screening and stratification of MASLD is a viable option for patients diagnosed with T2DM. Individuals with a high risk of developing these conditions, who also have low eGDR levels, should be given priority for abdominal ultrasound and liver fibrosis assessment. This will allow early detection and intervention. Furthermore, eGDR can serve as a follow-up indicator to dynamically assess improvements in IR, guide adjustments to lifestyle and medication regimens, and provide a reference for the personalised prevention and management of MASLD.
This study has several limitations. Firstly, as a single-centre cross-sectional study, it can only reveal the association between eGDR and the risk of developing MASLD in patients with T2DM; it cannot establish the causal sequence or causal relationship between the two. The study’s conclusions therefore require further validation through large-sample, multicentre prospective cohort studies. At the same time, external validation needs to be conducted in populations across different geographical regions and at different levels of healthcare provision to enhance the generalisability and clinical applicability of the findings. Secondly, this study utilised transabdominal ultrasound for the diagnosis of MASLD; it did not employ liver biopsy pathology as the gold standard for diagnosis, nor did it utilise precise quantitative imaging methods such as magnetic resonance proton density fat fraction (MRI-PDFF). Abdominal ultrasound has limited sensitivity for diagnosing mild hepatic steatosis and cannot accurately grade the severity of steatosis; this may introduce a certain degree of misclassification bias or lead to biased estimates of the strength of association in individuals with mild lesions. Thirdly, this study did not systematically collect lifestyle information from participants, such as dietary patterns, exercise habits and alcohol consumption, nor did it comprehensively record the use of medications for lowering blood glucose or regulating lipid levels. Among these, pioglitazone, GLP-1 receptor agonists and SGLT2 inhibitors can simultaneously improve IR, influence the relevant indicators in the eGDR calculation formula, and delay the progression of MASLD; these factors may introduce residual confounding bias, thereby interfering with the estimation of the strength of association. Future prospective studies should systematically collect data on patients’ medication regimens and conduct stratified analyses based on the use of baseline metabolic medications to exclude the interference of treatment factors on the study results. Fourthly, the study population consisted exclusively of T2DM patients admitted to a single centre; their overall condition was relatively severe and the prevalence of complications was high. Consequently, the study results cannot be directly extrapolated to mild diabetic patients in community outpatient settings, the general healthy population, or populations of other ethnicities and geographical regions, indicating certain limitations in generalisability. Finally, the sample size of female participants in this study was relatively small, and no further stratification by menopausal status was performed; consequently, it was not possible to distinguish differences in the association between eGDR and MASLD between premenopausal and postmenopausal women. Future studies should increase the sample size of female participants and refine the stratification design to explore the potential mechanisms underlying gender differences in this association in greater depth.
This study demonstrates that eGDR is independently and negatively associated with the risk of MASLD in patients with T2DM, with this association being more pronounced in male patients. Therefore, eGDR may facilitate a simple clinical assessment of the risk of MASLD in patients with T2DM.
We sincerely thank every patient who participated in this study.
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