Hongyan Zhao1,2†
Bo Ban
Guanqi Gao
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- 1School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, China
- 2Department of Endocrinology, Linyi People’s Hospital Affiliated to Shandong Second Medical University, Linyi, Shandong, China
- 3Department of Endocrinology, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
Aims: Non-alcoholic fatty liver disease (NAFLD) is closely related to type 2 diabetes (T2D), with reduced insulin sensitivity being a key factor in their disrupted metabolic processes. The single point insulin sensitivity estimator (SPISE) is a novel index. This study aims to explore the association between SPISE and NAFLD in T2D population.
Methods: This study included a total of 2,459 patients with T2D. SPISE was calculated based on high density lipoprotein-cholesterol (HDL-c), triglycerides (TG), and body mass index (BMI). Participants were categorized into NAFLD and non-NAFLD groups based on the results of ultrasonographic diagnosis. The relationship between SPISE and NAFLD was analyzed separately for each gender.
Results: The overall prevalence of NAFLD is 38.5%. In females and males, the SPISE was significantly reduced in the NAFLD group compared to the non-NAFLD group (both P < 0.05). The prevalence of NAFLD showed a significant reduction across quartiles of the SPISE in both genders (both P < 0.05).
Additionally, univariate correlation analysis showed a negative correlation between SPISE and NAFLD (both P < 0.05). In multivariate regression analysis, a reduced SPISE was identified as an independent risk factor for NAFLD (odds ratios of 0.572 and 0.737, 95% CI of 0.477–0.687 and 0.587–0.926, respectively).
Moreover, the area under the receiver operating characteristic (ROC) curve for SPISE was 0.209 in females and 0.268 in males (95% CI of 0.175–0.244 and 0.216–0.320, respectively). These results are more meaningful than those of other variables.
Conclusion: SPISE is significantly reduced in NAFLD patients with T2D. Compared to other indicators, SPISE demonstrates superior predictive value in diagnosing NAFLD, and it is independent of gender.
1 Introduction
Non-alcoholic fatty liver disease (NAFLD) is a widespread health issue, with a worldwide prevalence of 25% (1). It has become the primary cause of chronic liver disease under the influence of type 2 diabetes (T2D) and obesity (2). Reports indicate that NAFLD has become the fastest-growing cause of liver-related deaths globally (3). Moreover, it is closely associated with the progression of chronic kidney disease (CKD) and cardiovascular disease (CVD) (4, 5). Many metabolic disorders not only affect the incidence of NAFLD but also increase the risk of its progression to non-alcoholic steatohepatitis (NASH), cirrhosis, hepatocellular carcinoma, and even death (6). And the link between T2D and NAFLD is thoroughly documented (7). Research indicates that T2D is associated with more than double the risk of advanced hepatopathy (8). Furthermore, a meta-analysis reported that the prevalence of NASH with T2D patients was approximately 37.3%, significantly higher than the prevalence of progressive NAFLD in the general population (9). Therefore, in clinical practice, it would be valuable to have a simple and inexpensive index that could screen for NAFLD among T2D patients.
Numerous studies indicate that reduced insulin sensitivity (Si) or insulin resistance (IR) is one of the key pathophysiological factors in NAFLD (10–12). The gold standard for measuring insulin sensitivity is the hyperinsulinemic-euglycemic clamp (13); however, due to its cost, time consumption, and invasiveness, it is not widely used in clinical practice. The single point insulin sensitivity estimator (SPISE) is an alternative index of IR calculated from high density lipoprotein-cholesterol (HDL-c), triglycerides (TG), and body mass index (BMI) (14). Research indicates a strong correlation between SPISE and the hyperinsulinemic-euglycemic clamp (15). Additionally, the SPISE index is closely related to metabolic syndrome (MetS), cardiovascular metabolic risk in adolescents, and the cardiovascular prognosis of patients with T2D (16–18). It is also worth mentioning that SPISE is not only considered an effective indicator for predicting diabetes development in obese children (19), but SPISE-5.4 has also been proven to be a good predictor of diabetes development (20). Recent studies have reported a significant reduction in the SPISE among adolescents with obesity-related NAFLD (21). Additionally, research from Japan indicates that a reduction in SPISE is associated with an increased risk of NAFLD (22). Research also suggested an association between SPISE and pediatric NAFLD; however, after adjusting for confounding factors, this association is no longer significant (23). Currently, there is scarce research on the relationship between SPISE and NAFLD among T2D patients. This study aims to clarify the link between SPISE and NAFLD in T2D patients and assess SPISE’s predictive potential for NAFLD in this population.
2 Materials and methods
2.1 Study participants
During the period from February 2020 to March 2023, we collected clinical data from patients with T2D who were treated at the Department of Endocrinology of the Linyi People’s Hospital, Shandong Province, China. Exclusion criteria included: (1) patients under the age of 18; (2) patients with liver or kidney dysfunction; (3) evidence of autoimmune hepatitis, viral hepatitis, drug-induced fatty liver, or other chronic liver diseases; (4) habitual drinkers who consume alcohol more than 5 days per week, equivalent to an average daily intake of 38 grams for males and 26 grams for females (24); (5) patients with incomplete clinical data. Ultimately, 2,459 eligible patients were included in the study.
2.2 Anthropometric and Biochemical measurements
Participant demographic information and clinical baseline data were collected, such as age, gender, duration of diabetes, and smoking and drinking habits. Height, weight, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured and recorded. Morning fasting venous blood samples were collected to determine levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), serum creatinine (Scr), uric acid (UA), fasting plasma glucose (FBG); glycated hemoglobin (HbA1c, high performance liquid chromatography) and hemoglobin (Hb), were measured using a biochemical analyzer (Cobas c 702, Roche, Germany). Urinary albumin to creatinine ratio (UACR) was tested by an autoanalyzer (Beckman Coulter AU5821). Fasting insulin (FINS, direct chemiluminescence method) was measured by the fully automated sample processing system (Aptio Automation, SIEMENS, USA).
Bioelectrical impedance analysis (Omron DUALSCAN HDS-2000, Kyoto, Japan) was employed to assess visceral fat area (VFA) and subcutaneous fat area (SFA).
2.3 Definition of NAFLD
Fatty liver diagnosis begins with ultrasound imaging and is supplemented by clinical evaluation, including medical history and physical examination, with specific attention to alcohol intake. Additional factors such as viral hepatitis and medication use are assessed. Laboratory tests, particularly liver function tests, help rule out other fatty liver conditions, culminating in a definitive diagnosis of NAFLD.
Parameter calculations
1. Body mass index (BMI) = weight (kg) / height (m)2
2. TG/HDL-c = TG (mmol/l) / HDL-c (mmol/l)
3. SPISE index = (600 × HDL-c [mg/dL]0.185) / (TG [mg/dL]0.2 × BMI [kg/m2]1.338) (14)
4. HOMA-IR = FPG (mmol/L) × FINS (IU/mL)/22.5 (25).
2.4 Statistical analysis
Statistical analyses were performed using SPSS 22.0 (SPSS Inc, Chicago, IL, USA). Normally distributed variables were described using mean ± SD and analyzed with independent samples T-tests. Non-normally distributed variables were described using medians (interquartile ranges) and analyzed with Mann-Whitney U tests. Analysis of variance (ANOVA) and Student–Newman–Keuls tests were performed for multiple and pairwise comparisons of normally distributed data, and Kruskal-Wallis 1-way ANOVA test for abnormal distributions. Categorical variables were presented as percentage (%) and assessed using chi-square tests. Independent factors influencing NAFLD were identified using Spearman’s correlation and logistic regression analyses. Significance was set at P < 0.05 (two-tailed). The SPISE index’s ability to predict NAFLD was evaluated via the receiver operating characteristic (ROC) curve analysis.
3 Results
3.1 Clinical and biochemical characteristics
As shown in Table 1, this study included 2459 patients with T2D, with a mean age of 57.10 ± 13.4 years. The overall incidence of NAFLD was 38.5%, with rates of 36.1% in females and 41.8% in males. Compared to males, females had higher levels of age, SBP, TC, LDL-c, HDL-c and SPISE, but lower proportion of smokers, BMI, VFA, SFA, DBP, TG/HDL-c ratio, ALT, AST, GGT, UA, Scr, and Hb (all P < 0.05). There were no significant differences in diabetes duration, TG, HbA1c, FPG, UACR, HOMA-IR and FINS between the two groups (all P > 0.05).
TABLE 1
Table 1. Clinical and biochemical characteristics stratified by gender.
As shown in Table 2, for each gender, subjects were divided into two groups, including non-NAFLD and NAFLD groups, and the levels of each variable were compared. For females, compared to the non-NAFLD group, the NAFLD group showed significantly increased BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, TG/HDL-c ratio, FPG, ALT, AST, GGT, UA, Hb, FINS and HOMA-IR (all P < 0.05), while age, diabetes duration, HDL-c, Scr, UACR and SPISE were significantly decreased (all P < 0.05). For males, the proportion of smokers and the levels of BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, TG/HDL-c ratio, FBG, ALT, AST, GGT, UA, Hb and HOMA-IR were higher in the NAFLD group compared to the non-NAFLD group (all P < 0.05), while age, diabetes duration, HDL-c, Scr, UACR and SPISE were lower (all P < 0.05).
TABLE 2
Table 2. Comparison of clinical and biochemical characteristics between non-NAFLD and NAFLD groups.
As shown in Table 3, male and female patients were separately divided into four groups according to the quartiles of the SPISE: Q1 group (female: 2.58–5.22; male: 2.25–4.81), Q2 group (female: 5.22–6.25; male: 4.81–5.87), Q3 group (female: 6.25–7.57; male: 5.87–7.08), and Q4 group (female: 7.57–14.52; male: 7.08–15.05). For the females, as the quartiles of SPISE increased, the duration of diabetes, HDL-c showed a gradual increased, while the age, BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, TG/HDL-c, FPG, ALT, AST, GGT, UA, Hb, FINS, HOMA-IR and the incidence of NAFLD exhibited a gradual decreased (all P < 0.05). There was no statistically significant difference in the proportion of smokers, HbA1c, Scr and UACR among the four groups (all P > 0.05). For the males, as the quartiles of SPISE increased, the age, duration of diabetes, HDL-c showed a gradual increased, while the proportion of smokers, BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, TG/HDL-c, HbA1c, FPG, ALT, AST, GGT, UA, UACR, Hb, HOMA-IR and the incidence of NAFLD exhibited a gradual decreased (all P < 0.05). There was no statistically significant difference in Scr and FINS among the four groups (all P > 0.05).
TABLE 3
Table 3. Comparison of variables according to the categories of the SPISE.
3.2 Univariate analysis
As shown in Table 4, the relationship between NAFLD and each variable was analyzed using Spearman’s correlation analysis. In females, the results indicated that NAFLD was positively correlated with BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, TG/HDL-c, HbA1c, FBG, ALT, AST, GGT, UA, Hb, FINS and HOMA-IR (all P < 0.05), and negatively correlated with age, duration of diabetes, HDL-c, Scr, UACR, and SPISE (all P < 0.05). In males, the proportion of smokers, BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, TG/HDL-c, FBG, ALT, AST, GGT, UA, Hb and HOMA-IR were positively correlated with NAFLD, while age, duration of diabetes, HDL-c, UACR and SPISE were negatively correlated (all P < 0.05). In females, there was no significant relationship between NAFLD and the proportion of smokers (all P > 0.05), and in males, there was no apparent relationship between NAFLD and HbA1c, Scr and FINS (all P > 0.05).
TABLE 4
Table 4. The correlation between NAFLD and different variables by univariate analysis.
3.3 Logistic regression analysis
Using NAFLD as the dependent variable, based on the results of Spearman’s correlation analysis, the independent variables included age, diabetes duration, HDL-c, Scr, UACR, SPISE, BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, TG/HDL-c, HbA1c, FBG, ALT, AST, GGT, UA, Hb, FINS and HOMA-IR for females, and the proportion of smokers, age, diabetes duration, HDL-c, UACR, SPISE, BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, TG/HDL-c, FBG, ALT, AST, GGT, UA, Hb and HOMA-IR for males. A binary logistic regression analysis was conducted to identify the independent correlates of NAFLD (Table 5). The results indicated that in females, SPISE (OR: 0.572, 95% CI 0.477–0.687), VFA (OR: 1.009, 95% CI 1.001–1.017), FPG (OR: 1.059, 95% CI 1.002–1.120), DBP (OR: 1.026, 95% CI 1.006–1.046), UA (OR: 1.005, 95% CI 1.002–1.008), TC (OR: 1.236, 95% CI 1.036–1.475), and Scr (OR: 0.973, 95% CI 0.958–0.988) were independently associated with NAFLD, while in males, SPISE (OR: 0.737, 95% CI 0.587–0.926), VFA (OR: 1.013, 95% CI 1.005–1.021), diabetes duration (OR: 0.940, 95% CI 0.903–0.978), Hb (OR: 1.030, 95% CI 1.013–1.047), and GGT (OR: 1.009, 95% CI 1.002–1.016) were independently related to NAFLD.
TABLE 5
Table 5. The relative risk for NAFLD by logistic regression analysis.
3.4 Areas under the ROC curve analysis
Finally, based on the variables that entered the model last, the formula used to calculate SPISE and the insulin resistance-related indicators, the predictive capabilities of SPISE, HDL-c, diabetes duration, Scr, VFA, BMI, GGT, ALT, TG, TG/HDL-c ratio, HOMA-IR, Hb, UA, and TC for NAFLD were evaluated separately for different genders (Table 6). The results showed that in females, the area under the ROC curve for SPISE was 0.209 (95% CI 0.175–0.244, P < 0.001), and in males, it was 0.268 (95% CI 0.216–0.320, P < 0.001), both of which were superior to the other variables.
TABLE 6
Table 6. Analysis of areas under the ROC curves for predicting NAFLD.
4 Discussion
This study found that SPISE was independently associated with NAFLD in T2D population, with no gender differences observed. Additionally, SPISE demonstrated a clear advantage in predicting NAFLD within this population.
NAFLD as the most prevalent liver disease, exhibits an increasing trend in incidence (26). Reports indicated a strong correlation between T2D and NAFLD: the incidence of NAFLD and NASH was particularly pronounced in individuals diagnosed with T2D (9); the existence of NAFLD raised the risk of T2D development by five times (27). In this study, the overall incidence of NAFLD was 38.5%, which is higher than the global incidence rate, further validating the aforementioned perspective (1). Therefore, the high prevalence of NAFLD in T2D population warrants attention. Currently, the routine method for diagnosing NAFLD in clinical practice is through ultrasound. However, due to its time-consuming and labor-intensive nature, it is not suitable for large-scale epidemiological studies. SPISE is an insulin sensitivity index based on lipids and BMI, our study found that it is closely related to traditional IR indicators, including HOMA-IR and the TG/HDL-c ratio. As the SPISE quartiles increased, both HOMA-IR and the TG/HDL-c ratio were gradually decreased. Additionally, some studies had found that the SPISE demonstrated higher accuracy in predicting MetS and IR compared to other measures such as the TG/HDL-c ratio and HOMA-IR (14, 28). Extensive research had confirmed that NAFLD was closely associated with insulin resistance and metabolic syndrome (10, 11, 29, 30). Recent studies have reported that SPISE was closely associated with NAFLD related to adolescent obesity and NAFLD in healthy screening participants (21, 22). However, there is currently a lack of evidence for SPISE as a predictor of NAFLD in T2D population.
Our study corroborated the capability of SPISE to predict NAFLD among T2D population. HOMA-IR and the TG/HDL-c ratio were also closely related to NAFLD (31, 32), and therefore we included these IR-related indicators in our study. The results showed that they did not enter the regression model, and compared to SPISE, their area under the ROC curve was significantly smaller. A Japanese study similarly found that a 1.8-fold increased risk of concurrent NAFLD and T2D was associated with SPISE, aligning with our findings (22). However, that study included only 58 patients with both NAFLD and T2D, whereas our study involved 2,459 T2D patients with NAFLD. Additionally, we conducted gender-stratified analyses, which yielded consistent results, further substantiating the predictive power of SPISE in this group. Beyond IR, dyslipidemia and obesity are also significant factors related to NAFLD (33). SPISE, as a comprehensive indicator that includes metrics related to lipids and obesity, is convenient, accessible and low-cost, making it highly suitable for large-scale clinical application.
In addition, the results of this study indicated that NAFLD was closely associated with VFA in both males and females. This is generally consistent with previous research findings (34). GGT, ALT and AST are liver enzymes closely associated with NAFLD and NASH (35–37). In our analysis of female samples using Spearman’s correlation, AST, GGT, and ALT all showed positive correlations with NAFLD, yet these variables were not included in the final binary logistic regression model. In contrast, in male samples, GGT was incorporated into the regression model. However, the predictive power of the liver enzyme included in the final regression model, as indicated by the area under the ROC curve, remained inferior to that of the SPISE index. This gender discrepancy may stem from differences in research methodologies and sample selection criteria.
5 Limitations
This study faces several limitations. Firstly, due to its cross-sectional design, we cannot establish a causal relationship between the SPISE index and NAFLD. Secondly, the diagnosis of NAFLD was not made using the gold standard of liver biopsy, which may lead to diagnostic bias. Lastly, as this study was conducted at a single center, future research should be multi-center in order to further validate our findings and the replication of the study.
6 Conclusion
This study demonstrated that SPISE may have potential advantages over other commonly used biomarkers in identifying NAFLD among T2D patients. As a simple insulin sensitivity index, the specific utility of SPISE in predicting NAFLD among T2D patients remains to be further investigated.
Data availability statement
The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by Ethics Committee of Linyi People’s Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author contributions
HZ: Conceptualization, Data curation, Methodology, Software, Visualization, Writing – original draft, Writing – review and editing. BJ: Methodology, Software, Visualization, Writing – original draft, Writing – review and editing. XW: Investigation, Writing – review and editing. SS: Writing – review and editing. JS: Writing – review and editing. XM: Data curation, Writing – review and editing. GG: Funding acquisition, Visualization, Writing – review and editing. BB: Funding acquisition, Visualization, Writing – review and editing.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Funding for this study was provided by grants from the Postdoctoral Program of the Affiliated Hospital of Jining Medical University (grant no. JYFY322152).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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Keywords: type 2 diabetes, SPISE, NAFLD, insulin sensitivity, insulin resistance
Citation: Zhao H, Ji B, Wang X, Shi S, Sheng J, Ma X, Ban B and Gao G (2025) Association between SPISE and NAFLD in patients with type 2 diabetes. Front. Med. 12:1454938. doi: 10.3389/fmed.2025.1454938
Received: 26 June 2024; Accepted: 06 January 2025;
Published: 22 January 2025.
Edited by:
Aivaras Ratkevicius, Queen Mary University of London, United KingdomReviewed by:
Kulvinder Kochar Kaur, Kulvinder Kaur Centre For Human Reproduction, IndiaResul Yilmaz, Selçuk University, Türkiye
Copyright © 2025 Zhao, Ji, Wang, Shi, Sheng, Ma, Ban and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Guanqi Gao, Z3VhbnFpX2dhb0B5ZWFoLm5ldA==; Bo Ban, YmFuYm8yMDExQDE2My5jb20=
†These authors have contributed equally to this work and share first authorship