Introduction

Nonalcoholic fatty liver disease (NAFLD) is becoming an increasingly serious public health concern globally, particularly among patients with diabetes, where its prevalence has risen significantly.1 A recent epidemiological study shows that the global prevalence of NAFLD among patients with type 2 diabetes (T2D) has reached nearly 69%.2 NAFLD is not limited to the liver, it is a multisystem disease that involves extrahepatic organs and multiple physiological regulatory pathways. Through mechanisms such as chronic inflammation, lipid metabolism abnormalities, insulin resistance (IR) and atherosclerosis, it significantly increases the risk of T2D, cardiovascular disease (CVD), chronic kidney disease (CKD) and other related conditions.3–5 Therefore, early identification and screening of NAFLD in T2D is crucial for mortality associated with NAFLD.

There is substantial evidence indicating that IR is a crucial factor in the pathogenesis of NAFLD, as it diminishes insulin sensitivity (IS) across various tissues, including systemic, hepatic and adipose tissues,1,6 thereby promoting hepatic fat accumulation and metabolic dysfunction.7 The euglycemic hyperinsulinemic clamp technique is the gold standard for measuring IR,8 but its time-consuming and invasiveness limits its use in large scale epidemiological studies. The homeostasis model assessment index (HOMA-IR) has been proposed as a simpler method for assessing IR.9 Although this method facilitates large cohort studies, it relies on fasting plasma insulin (FINS), which are not commonly conducted and can fluctuate significantly. Additionally, researches indicate that fluctuations in insulin levels can significantly depend on an individual’s glucose tolerance and the effects of therapy.10,11

In recent years, several new non-insulin-based surrogate markers of IR have been developed. The natural log transformation of the glucose disposal rate (loge GDR) was recently developed as a novel IS prediction model by Ciardullo et al for patients with T2D, based on routinely available clinical and biomarker data, including triglycerides (TG), urinary albumin-to-creatinine ratio (UACR), γ-glutamyl transferase (GGT) and body mass index (BMI). The components of loge GDR reflect key metabolic processes, including lipid metabolism, liver function, renal function and obesity. These factors are closely associated with the pathogenesis of NAFLD.12,13 Therefore, as a comprehensive surrogate marker of IS, we speculate that the loge GDR may be also closely associated with NAFLD; however, there have been no published studies to support this hypothesis.

The UACR, as an important component of loge GDR, is the diagnostic markers of diabetes nephropathy (DN), which is closely related to IR.14,15 Although studies displayed that the severe IR diabetes might have the highest risk for DN and NAFLD,16 strong evidences have demonstrated that even in populations with normal UACR, the risk of NAFLD, CVD and other diseases may still be elevated.17–19 Therefore, our study aims to explore the correlation between loge GDR and NAFLD in normoalbuminuric patients with T2D.

Meanwhile, a series of commonly used effective IR indicators and related derivative parameters as the covariates are included them in our study as well, including triglyceride glucose index (TyG), triglyceride glucose-body mass index (TyG-BMI), triglyceride/high-density cholesterol–lipoprotein ratio (TG/HDL-c) and triglyceride glucose-γ-glutamyl transferase (TyG-GGT), which have been confirmed to strongly associated with NAFLD;20–22 other indicators similar to the components of loge GDR, such as the uric acid (UA) index and the combination of fasting blood glucose (FBG) and BMI (ByG), have recently been proposed.23,24 These indicators have been shown to be closely associated with diabetes and CVD, and we included in our study as well.

Materials and Methods Patients

Our study retrospectively analyzed the inpatients with T2D aged 18 to 87 years from the Department of Endocrinology of Linyi People’s Hospital, from January 2020 to March 2023. The exclusion criteria were (1) patients with other types of diabetes; (2) patients with other liver disease, including viral hepatitis, autoimmune, drug-induced liver diseases and acute liver injury; (3) patients with a history of excessive alcohol intake (>70 g/week for women or 140 g/week for men);25 (4) missing the measurement of NAFLD. In the end, a total of 1227 normoalbuminuric patients with T2D were included in this study.

General Conditions and Clinical Data

The patients’ general conditions, including age, sex, duration of diabetes, height and weight, were recorded.

Smoking and drinking status were assessed. The data on drinking were based on self-reported information collected during their hospitalization and were recorded immediately during the medical history intake. These data were obtained by trained medical staff through clinical interviews and were recorded in real time. According to relevant guidelines,25 excessive alcohol consumption is defined as more than 70 grams per week for females and more than 140 grams per week for males. Based on self-reported alcohol intake, we calculated the total weekly alcohol consumption in grams.

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using standardized methods with an automated electronic sphygmomanometer (OMRON HEM-725, Omron Corporation, Dalian, China). Participants were instructed to rest for at least 5 minutes in a quiet environment before measurements. Each participant underwent at least two readings, taken 1–2 minutes apart, with the average recorded as the final result. In cases of significant discrepancies between readings, additional measurements were taken, and the average of the consistent values was used to exclude outliers.

The visceral fat area (VFA) and subcutaneous fat area (SFA) were tested by bioelectrical impedance analysis (HDS-2000, Omron, Kyoto, Japan).

Biochemical Measurements

Blood samples were collected in the morning after an overnight fast and analyzed for TG, total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-c), low-density lipoprotein-cholesterol (LDL-c), aspartate aminotransferase (AST), alanine aminotransferase (ALT), GGT, FBG, glycosylated haemoglobin (HbA1c, high performance liquid chromatography), UA, serum creatinine (Scr) and hemoglobin (Hb) were measured by a biochemical autoanalyzer (Cobas c 702, Roche, Germany). UACR was measured using an autoanalyzer (Beckman Coulter AU5821). FINS was measured using a direct chemiluminescence method with a fully automated sample processing system (Aptio Automation, SIEMENS, USA). Normoalbuminuric was defined as UACR < 30 mg/g.

Definition of NAFLD

NAFLD was diagnosed via liver ultrasonography, which revealed the presence of fatty liver. After excluding other potential causes of hepatic steatosis (eg, history of alcohol consumption, drug use, or viral hepatitis), the ultrasound diagnostic criteria for NAFLD included vascular blurring, deep attenuation, abnormal liver-kidney echo contrast, and increased liver brightness.

Parameter Calculations

1. BMI = weight (kg)/height (m);2

2. TyG index = ln [TG (mg/dL) * FBG (mg/dL)/2];26

3. TyG-BMI = TyG * BMI;27

4. TyG-GGT = TyG * GGT;21

5. ByG index = ln [BMI (kg/m2) * FBG (mg/dL)/2];24

6. TG/HDL-c ratio = TG (mmol/L)/HDL-c (mmol/L);28

7. HOMI-IR = FBG (mmol/L) * FINS (IU/mL)/22.5;9

8.UA index = ln [TG (mg/dL) * UA (mg/dL) * FBG (mg/dL)/2];23

9. eGFR = 175 * Scr (mg/dL)−1.234 * age −0.179 * (0.79, if female);29

10. Loge GDR = 5.3505–0.3697 * loge (GGT, IU/L) - 0.2591 * loge (TG, mg/dL) - 0.1169 * loge (UACR, mg/g) - (0.0279*BMI, kg/m2).30

Statistical Analysis

The data in this study were analyzed using SPSS 26.0 (SPSS Inc, Chicago, IL, USA). Normally distributed variables were expressed as mean ± SD, and were compared using Independent-Samples T test between the two groups. Abnormal distributions were expressed as median and interquartile ranges, and were compared using by Mann–Whitney U-test between the two groups. Categorical variables were described as percentage (%), and were compared using the chi-square test. Between the loge GDR tertiles groups, we performed an Analysis of Variance (ANOVA) and Student-Newman-Keuls tests for multiple and pairwise comparisons of normally distributed data, and Kruskal–Wallis one way ANOVA test for abnormal distributions. Logistic regression analysis was used to analyze the independent correlates of NAFLD. The receiver operating characteristic (ROC) curve and related area under the ROC curve (AUC) were used to assess the loge GDR’s effectiveness in predicting NAFLD, and further performed a differential analysis. Statistical analyses were performed using two-sided tests, with a P-value of less than 0.05 considered statistically significant.

Results Baseline Clinical and Biochemical Characteristics

The baseline clinical and biochemical characteristics of the subjects are presented in Table 1. For normally distributed variables, data are expressed as mean ± standard deviation, and differences between the two groups are analyzed using an independent samples t-test. Abnormally distributed variables are presented as median with interquartile ranges (25th percentile ~ 75th percentile), and comparisons are made using the Mann–Whitney U-test. Compared with the non-NAFLD group, the BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, FBG, FINS, AST, ALT, GGT, UA, Hb, TyG index, TyG-BMI, TyG-GGT, ByG index, UA index, TG/HDL-c ratio, HOMA-IR and the percentage of smoking and males were higher in NAFLD group (all P < 0.05). The age, duration of diabetes, HDL-c and the loge GDR were lower in the NAFLD group (all P < 0.001). The HbA1c, Scr, eGFR and UACR were no different between the two group (all P > 0.05).

Table 1 Clinical and Biochemical Characteristics by Presence of NAFLD

According to the loge GDR tertiles, the subjects were divided into three groups (Table 2). For normally distributed variables, data are expressed as mean ± standard deviation, and comparisons across the three groups are performed using a one-way analysis of variance (ANOVA). For abnormally distributed variables, data are reported as median with interquartile ranges (25th percentile ~ 75th percentile), and group differences are assessed using the Kruskal–Wallis test. As the tertiles of loge GDR increased, the age, duration of diabetes and HDL-c were elevated, while the BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, FBG, FINS, HbA1c, ALT, AST, GGT, UA, Scr, UACR, Hb, TyG index, TyG-BMI, TyG-GGT, ByG index, UA index, TG/HDL-c ratio, HOMA-IR, the percentage of smoking, males and NAFLD were decreased (all P < 0.001). The eGFR was no difference between the three groups (P = 0.483).

Table 2 Comparison of Variables According to the Categories of Loge GDR

Univariate Analysis

A univariate regression analysis was conducted to identify the factors associated with NAFLD (Table 3). The sex, smoking, BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, FBG, FINS, AST, ALT, GGT, UA, Hb, TyG index, TyG-BMI, TyG-GGT, ByG index, UA index, TG/HDL-c ratio and HOMA-IR were positively corrected with NAFLD, and the age, duration of diabetes, HDL-c and the loge GDR were negatively related to NAFLD (all P < 0.05). The HbA1c, Scr, eGFR and UACR were not correlated with NAFLD (all P > 0.05).

Table 3 Univariate Analysis for NAFLD

Multivariate Analysis

The NAFLD was utilized as the dependent variable, and adjusting for the sex, smoking, BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, FBG, FINS, AST, ALT, GGT, UA, Hb, TyG index, TyG-BMI, TyG-GGT, ByG index, UA, TG/HDL-c ratio, HOMA-IR, age, duration of diabetes and HDL-c, the logistic regression analysis was conducted to examine the independent correlates of NAFLD (Table 4). The results showed that the loge GDR (OR: 0.084; 95% CI: 0.040–0.177), BMI (OR: 1.196; 95% CI: 1.110–1.228), UA (OR: 1.004; 95% CI: 1.001–1.007) and DBP (OR: 1.026; 95% CI: 1.005–1.026) were independently related to NAFLD.

Table 4 The Independent Variables for NAFLD

Areas Under the ROC Curve Analysis

We compared the AUC for loge GDR with those of its individual components (BMI, GGT, UACR and TG), traditional NAFLD-related markers (AST, ALT and GGT), other commonly used indicators linked to IR or metabolism (TG/HDL-c ratio, TyG index, TyG-GGT, TyG-BMI, ByG index, UA index and HOMA-IR), and the variables included in the regression model (DBP, UA and BMI) as shown in Table 5. We found that the AUC of loge GDR was 0.797, which was higher than other variables. Further, we conducted a differential analysis of ROC, and the results showed that loge GDR was higher than TG/HDL-c ratio, TyG index, TyG-GGT, ByG index, UA index, HOMA-IR, BMI, GGT, UACR, TG, VFA, SFA, DBP, AST, ALT and GGT (all P < 0.05), while the difference between loge GDR and TyG-BMI was not statistically significant (P = 0.245).

Table 5 Analysis of the Areas Under the ROC Curves for Predicting NAFLD

Discussion

In this cross-sectional study, we observed a strong correlation between loge GDR and NAFLD, with the incidence of NAFLD increasing progressively as loge GDR tertiles decrease. Additionally, multivariate analysis indicated that the loge GDR was independently associated with NAFLD in normoalbuminuric patients with T2D.

IR is well known to play a key role in the development of NAFLD. As a traditional surrogate marker of IR, HOMA-IR has been shown to have a strong association with NAFLD.31 In recent years, various non-insulin-based fasting IR indicators, such as the TG/HDL-c ratio, TyG index, TyG-BMI and TyG-GGT, have also been proposed and proven to be closely linked to NAFLD.20–22 As a new model of IS, our study found that loge GDR is closely related to the aforementioned surrogate markers of IR. With increasing loge GDR quartiles, these markers progressively decrease. However, the relationship between loge GDR and NAFLD remains unclear. Our study is the first to confirm that loge GDR is independently associated with NAFLD. The mechanisms underlying the association between loge GDR and NAFLD are still not well understood. NAFLD is strongly associated with metabolic abnormalities, including decreased IS, obesity, elevated TG, reduced HDL-c levels, persistent inflammation and dysregulated fasting glucose or diabetes.32 Loge GDR is calculated based on BMI, TG, UACR and GGT, each of which has a well-established link to NAFLD. BMI, a widely used measure of obesity, is strongly correlated with fat accumulation and IR—both critical mechanisms in the development of NAFLD.33 High TG levels reflect dysregulated lipid metabolism and intrahepatic fat accumulation, contributing to hepatic steatosis and progression to NAFLD.34 GGT, as a marker of oxidative stress and liver dysfunction, is closely associated with the occurrence of NAFLD.35 UACR, typically, an indicator of glomerular endothelial dysfunction, is strongly associated with chronic inflammation,36 which promotes IR and hepatic lipid accumulation, thereby increasing the risk of NAFLD.37 Together, these components effectively represent the key metabolic pathways contributing to NAFLD, supporting loge GDR’s utility in predicting NAFLD occurrence. Additionally, none of the individual components were included in the regression model, suggesting that as a composite indicator, loge GDR has a stronger relationship with NAFLD.

In this study, we also included IR indicators that integrate glucose metabolism, lipids and obesity (TyG index, TyG-BMI, TyG-GGT and TG/HDL-c ratio), as well as metabolically related indices similar to the components of loge GDR (such as the UA index and ByG), and simple markers of NAFLD (AST, ALT and GGT) for comprehensive analysis. Our results showed that all these indicators were closely associated with NAFLD. However, after adjusting for confounding factors, none of them were retained in the regression model, and their areas under the ROC curve were not superior to that of loge GDR. This indicates that loge GDR, as a novel composite marker of IS, reflects a more comprehensive spectrum of metabolic dysfunctions and may serve as a more reliable indicator for identifying NAFLD.

We acknowledge both the strengths and limitations of our study, as well as directions for future research. This study is the first to investigate the association between loge GDR and NAFLD in T2D patients, demonstrating its stronger relationship with NAFLD and superior predictive ability compared to other IR markers. This finding highlights the potential clinical value of loge GDR in predicting NAFLD in individuals with T2D. However, as a cross-sectional design, it does not allow us to infer causality or fully understand the underlying mechanisms of the observed association. Furthermore, although ultrasound is the most commonly used method in clinical practice, the current lack of standardized parameters for quantifying hepatic steatosis via ultrasound, along with the influence of operator-dependent subjectivity; therefore, the diagnosis of NAFLD based on ultrasound in this study cannot provide precise grading data. Future research should include multicenter, large-scale, prospective studies to validate the clinical predictive capability of loge GDR and differences between it and other surrogate markers of IR and provide deeper insights into the pathophysiological mechanisms linking loge GDR and NAFLD. Additionally, using more precise grading methods, such as transient elastography,38 to provide a more detailed diagnosis and analysis of NAFLD will further enhance the reliability of the results.

Conclusion

The loge GDR may serve as a better simple indicator for predicting NAFLD, potentially facilitating the identification of NAFLD patients in clinical settings.

Ethics Approval and Consent to Participate

All patients included in this study provided written informed consent upon admission, which explicitly stated that their medical records might be used for scientific research purposes. During the study period, no patients raised objections to this. Additionally, the study received ethical approval from the Human Ethics Committee of Linyi People’s Hospital.

Funding

This study was supported by grants from the Postdoctoral Program of Affiliated Hospital of Jining Medical University (JYFY322152).

Disclosure

All authors declare that they have no competing interests in this study.

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