Elevated small dense low‐density lipoprotein cholesterol to high‐density lipoprotein cholesterol ratio is associated with an increased risk of metabolic dysfunction associated fatty liver disease in Chinese patients with type 2 diabetes mellitus

ABSTRACT

Introduction

It is demonstrated that elevated small dense low‐density lipoprotein cholesterol (sdLDL‐C), and reduced high‐density lipoprotein cholesterol (HDL‐C) is associated with Metabolic dysfunction‐associated fatty liver disease (MAFLD). This study aims to explore the relationship between sdLDL‐C to HDL‐C ratio (SHR) and MAFLD in Chinese patients with type 2 diabetes mellitus (T2DM).

Materials and Methods

A cross‐sectional study was performed among 1904 patients with T2DM. Weighted multivariable logistic regression analysis was conducted to explore the relationship between the SHR and the risk of MAFLD. In addition, this study used a two‐part linear regression model to identify threshold effects. Subgroup analysis, interaction tests and receiver operating characteristic (ROC) curve analysis were also carried out.

Results

The overall MAFLD prevalence reached 48.1%. Multiple logistic regression analysis showed that SHR was positively correlated with the risk of MAFLD (OR = 2.37, 95% CI = 1.80–3.12). Subgroup analysis stratified by age, gender, hypertension and BMI showed that there was a consistent positive correlation. A non‐linear relationship and saturation effect between SHR and MAFLD risk were identified, with an inverted L shaped curve and an inflection point at 1.02. The area under the curve (AUC) for SHR in the ROC analysis was significantly greater than sdLDL‐C and HDL‐C, with a sensitivity of 71.2% and a specificity of 62.1%.

Conclusions

Elevated levels of SHR is independently associated with an increased risk of MAFLD in patients with T2DM. SHR may be taken as practical indicators to assess the risk of MAFLD in T2DM patients.

Elevated levels of SHR are independently associated with an increased risk of MAFLD in patients with type 2 diabetes mellitus. SHR had better diagnostic efficacy for MAFLD than sdLDL‐C and HDL‐C alone. SHR may be taken as a practical indicator to assess the risk of MAFLD in patients with type 2 diabetes mellitus.

INTRODUCTION

Metabolic dysfunction‐associated fatty liver disease (MAFLD), previously known as nonalcoholic fatty liver disease (NAFLD), is a hepatic manifestation of metabolic syndrome characterized by fat accumulation in the liver, thus resulting in diseases such as cirrhosis, fibrosis, steatosis, and hepatocellular carcinoma ¹ . MAFLD results from liver fat accumulation, which is complicated with type 2 diabetes mellitus, obesity, or metabolic disorders. The prevalence rate in the general population reaches 25.9–38.0% ² . Moreover, a recent analysis combined with multiple studies found that patients with type 2 diabetes mellitus have a higher risk of developing MAFLD compared with the general population. The estimated prevalence of MAFLD in type 2 patients with diabetes mellitus ranges from 55.5 to 70% ³ , ⁴ . Therefore, it is crucial to carry out the identification and early intervention of risk factors associated with MAFLD, so as to reduce its occurrence in patients with type 2 diabetes mellitus.

Patients with type 2 diabetes mellitus and dyslipidemia often exhibit reduced levels of high‐density lipoprotein (HDL) cholesterol, elevated triglyceride levels, and abundance of sdLDL‐C particles, even if their low‐density lipoprotein cholesterol (LDL‐C) is not elevated ⁵ , ⁶ . Compared with large LDL‐C, sdLDL‐C has a lower affinity for the LDL receptor, longer half‐life in plasma, stronger binding to arterial proteoglycans, easier penetration into arterial subendothelial space, and higher susceptibility to oxidation ⁷ . Previous studies have demonstrated a significant relationship between sdLDL and the occurrence and progression of NAFLD in the general population ⁸ , ⁹ . High‐density lipoprotein cholesterol (HDL‐c) is widely regarded as ‘good cholesterol’ that can bind lipid molecules such as cholesterol and triglyceride (TG), and actively participate in the process of cholesterol clearance, resulting in a reduced risk of NAFLD ¹⁰ . SHR is used to amplify the difference between non‐MAFLD and MAFLD groups and to evaluate its potential as a diagnostic biomarker for MAFLD. To our knowledge, it is the first study to report the effectiveness of SHR in the diagnosis of MAFLD.

This study aims to evaluate the relationship between SHR and MAFLD among type 2 diabetes mellitus patients. Furthermore, we conducted interaction and stratified analyses based on factors such as gender, age, BMI, and hypertension.

METHODS

Subjects and study design

In this cross‐sectional study, 1904 type 2 diabetes mellitus patients who were admitted to the Department of Endocrinology of the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University between January 2020 and August 2022 were included. The study was approved by the hospital's ethical review committee (approval No.: LCKY2020‐01), and written consent was obtained from all type 2 diabetes mellitus patients. The inclusion criteria were as follows: type 2 diabetes mellitus diagnosis according to WHO criteria, age ≥20 years old, complete biochemical parameters and clinical information, and available abdominal ultrasound findings ¹¹ . Patients with viral hepatitis, liver diseases caused by drugs, severe kidney dysfunction, and other infectious or systemic diseases were excluded.

Biochemical and anthropometric measurements

The following data were collected at admission: history of hypertension, lipid‐lowering drugs (LLDs), smoking habits, alcohol intake, and physical measurements including height, blood pressure, waist circumference, and weight. Specifically, the definitions of alcohol status, hypertension, BMI, and smoking were described in our previous study ¹² . Overweight and obesity was defined as BMI ≥24 kg/m².

Blood samples were collected in the morning after fasting for at least 12 h. LDL‐C, serum uric acid, alanine aminotransferase (ALT), total cholesterol (TC), glycosylated hemoglobin (HbA1c), triglycerides (TG), 2 h postprandial C‐peptide (2 h PCP), HDL‐C, fasting C‐peptide (FCP), aspartate aminotransaminase (AST), albumin, creatinine, 2 h postprandial plasma glucose (2 h PPG), and fasting plasma glucose (FPG) were determined as described previously ¹² .

Serum sdLDL‐C measurement

Serum sdLDL‐C levels were measured using a Denka Seiken homogeneous assay adaptable to autoanalyzers. The sdLDL‐C Seiken kit (Denka Seiken, Tokyo, Japan) was used for quantitative determination of sdLDL‐C in plasma samples on a Hitachi 7600‐020 automated analyzer (Hitachi), according to the manufacturer's instructions. The two end point technique was used to measure serum sdLDL‐C levels as described previously ¹³ , ¹⁴ . The temperature was maintained at 37°C. The primary wavelength was 600 nm and the secondary wavelength was 700 nm.

Abdominal ultrasonography and diagnostic criteria

Abdominal ultrasound examinations were performed to diagnose hepatic steatosis ¹² . Since the target population of this study was patients with type 2 diabetes mellitus, MAFLD was diagnosed according to the definition proposed by an international expert panel from 22 countries when hepatic steatosis and type 2 diabetes mellitus were confirmed by ultrasonography ¹⁵ .

Statistical analysis

In this analysis, continuous data were expressed as the weighted mean ± standard deviation (SD), while categorical variables were expressed as weighted proportions. The study participants were divided into four groups or quartiles based on their SHR levels. In order to evaluate the differences between each group, the weighted χ² test was used for categorical variables. In order to investigate the relationship between SHR and NAFLD status, a weighted multivariate logistic regression model was adopted. Subgroup analyses were carried out to examine the potential impacts of gender, BMI, hypertension as well as age on the relationship between SHR and NAFLD. In order to identify any potential nonlinear relationship between SHR and MAFLD probabilities, smooth curve fitting and generalized additive model were adopted. Finally, the diagnostic effectiveness of SHR in detecting MAFLD was evaluated using ROC curve studies. The statistical analysis was conducted using EmpowerStats software (http://www.empowerstats.com) and R (http://www.R‐project.org), with significance determined at P < 0.05.

RESULTS

Baseline characteristics of participants

A total of 1904 participants aged 28–88 years old were included in this study, the prevalence of MAFLD was 48.1%. The weighted population characteristics of participants by serum SHR quartiles (Q1: <0.476; Q2: 0.476–0.803; Q3: 0.803–1.278; Q4: >1.278) are shown in Table 1. Compared with the bottom quartile, those in the top SHR quartile were more likely to be younger and male, with a higher prevalence of MAFLD as well as increased levels of height, weight, BMI, waist circumference, ALT, GGT, creatinine, uric acid, hemoglobin A1c, 2 h PPG, FCP, 2 h PCP, albumin, TC, TG, LDL‐C, and sdLDL‐C. In contrast, their HDL‐C levels were lower (P < 0.0001; Table 1).

Table 1.

Weighted characteristics of the study population based on sdLDL‐C/HDL‐C quartiles

Characteristic	Q1	Q2	Q3	Q4	P value
Number	477	475	476	476
Age, year	61.7 ± 15.1	59.4 ± 14.2	57.4 ± 13.8	51.6 ± 14.4	<0.001
Male, %	53.0	58.5	60.3	71.8	<0.001
NAFLD, %	26.4	43.2	57.8	65.1	<0.001
Height, cm	163.0 ± 8.8	164.0 ± 10.2	164.3 ± 8.4	166.7 ± 8.4	<0.001
Weight, cm	61.7 ± 11.8	63.7 ± 15.9	65.7 ± 13.4	70.9 ± 13.8	<0.001
Body mass index, kg/m2	23.2 ± 3.8	24.2 ± 17.0	24.2 ± 4.2	25.4 ± 4.0	0.359
Waist circumference, cm	84.4 ± 10.8	86.6 ± 10.7	87.5 ± 10.0	89.8 ± 10.5	<0.001
Systolic blood pressure, mmHg	139.6 ± 28.4	139.4 ± 28.7	140.6 ± 26.1	140.9 ± 24.5	0.440
Diastolic blood pressure, mmHg	83.1 ± 39.3	81.8 ± 8.9	83.4 ± 8.3	85.3 ± 8.7	0.241
Hypertension, %	52.2	45.7	50.2	44.5	0.055
Current smoking, %	34.1	26.6	30.6	27.3	0.336
Current drinking, %	25.4	21.9	22.6	21.1	0.559
Lipid‐lowering drugs, %	26.5	22.9	27.7	28.1	0.644
Hemoglobin A1c, mmol/L	8.7 ± 2.2	9.2 ± 2.3	9.3 ± 2.1	9.8 ± 2.1	<0.001
FPG, mmol/L	6.80 ± 2.39	6.70 ± 2.07	6.94 ± 1.99	6.89 ± 1.79	0.531
2 h PPG, mmol/L	17.06 ± 3.85	16.63 ± 4.07	16.52 ± 3.78	16.25 ± 3.49	0.001
FCP, ng/mL	0.75 ± 0.74	0.78 ± 0.61	0.90 ± 0.61	0.99 ± 0.69	<0.001
2 h PCP, ng/mL	2.29 ± 1.78	2.58 ± 1.89	2.96 ± 2.07	3.08 ± 2.06	<0.001
ALT, U/L	25.5 ± 36.4	26.4 ± 38.1	25.6 ± 20.1	35.9 ± 34.9	<0.001
AST, U/L	25.5 ± 28.8	24.6 ± 26.3	23.5 ± 14.9	28.1 ± 22.9	0.122
GGT, U/L	41.8 ± 97.5	38.9 ± 82.9	42.4 ± 77.9	77.6 ± 176.2	<0.001
Albumin, g/dL	39.5 ± 3.9	40.2 ± 4.3	40.7 ± 4.4	41.3 ± 5.2	<0.001
Creatinine, μmol/L	72.2 ± 39.4	71.3 ± 34.6	69.9 ± 28.9	69.9 ± 42.2	0.399
Uric acid, μmol/L	320.2 ± 96.7	331.0 ± 102.6	360.9 ± 107.1	373.2 ± 106.3	<0.001
Total cholesterol, mmol/L	3.74 ± 1.02	4.22 ± 1.02	4.77 ± 1.13	5.49 ± 1.24	<0.001
Triglycerides, mmol/L	1.05 ± 0.51	1.50 ± 1.81	2.28 ± 2.14	3.33 ± 2.21	<0.001
HDL‐cholesterol, mmol/L	1.24 ± 0.39	1.05 ± 0.28	0.96 ± 0.24	0.87 ± 0.20	<0.001
LDL‐cholesterol, mmol/L	2.02 ± 0.78	2.63 ± 0.84	3.02 ± 0.94	3.42 ± 1.08	<0.001
sdLDL‐c, mmol/L	0.41 ± 0.16	0.65 ± 0.19	0.99 ± 0.26	1.49 ± 0.42	<0.001
sdLDL‐c/HDL‐c	0.34 ± 0.09	0.62 ± 0.09	1.03 ± 0.14	1.73 ± 0.44	<0.001

Relationship between SHR and MAFLD risk

Three weighted multivariate regression models were constructed between MAFLD prevalence and SHR (Table 2). In the unadjusted model, SHR was positively correlated with MAFLD probabilities [OR = 2.79, 95% CI: (2.33, 3.34)]. After adjusting for gender, age (model 2), BMI, waist circumference, SBP, DBP, FPG, FCP, 2 h PPG, 2 h PCP, TG, HbA1c, ALT, AST, GGT, serum creatinine, serum uric, albumin, drinking, smoking, LLDs (model 3), this positive correlation remained in model 2 [OR = 3.13, 95% CI: (2.59, 3.79)] and model 3 [OR = 2.37, 95% CI: (1.80, 3.12)]. Moreover, compared with the lowest level of SHR (Q1) in model 3 (P for trend <0.001), subjects in quartiles 2, 3, and 4 increased by 0.67, 2.04 and 2.51 in MAFLD risks, respectively. This result indicates that type 2 diabetes mellitus patients with elevated SHR are more likely to develop MAFLD than those with reduced SHR.

Table 2.

Association between sdLDL‐C/HDL‐C and NAFLD in logistic regression analysis

	Model 1 OR (95% CI) P value	Model 2 OR (95% CI) P value	Model 3 OR (95% CI) P value
sdLDL‐C/HDL‐C	2.79 (2.33, 3.34), <0.001	3.13 (2.59, 3.79), <0.001	2.37 (1.80, 3.12), <0.001
sdLDL‐C/HDL‐C (Quartile)
Q1	Reference	Reference	Reference
Q2	2.11 (1.61, 2.78), <0.001	2.20 (1.67, 2.89), 0.034	1.67 (1.15, 2.42), 0.007
Q3	3.81 (2.90, 5.01), <0.001	4.07 (3.08, 5.37), <0.001	3.04 (2.08, 4.43), <0.001
Q4	5.20 (3.94, 6.87), <0.001	6.01 (4.49, 8.05), <0.001	3.51 (2.33, 5.30), <0.001
P for trend	<0.001	<0.001	<0.001

Model I: No covariates were adjusted; Model II: gender and age were adjusted; Model III: BMI, waist circumference, SBP, DBP, FPG, FCP, 2 h PPG, 2 h PCP, TG, HbA1c, ALT, AST, GGT, serum creatinine, serum uric, albumin, drinking, smoking, and LLDs were adjusted.

Subgroup analysis

A thorough subgroup analysis was conducted to assess the consistency of the relationship between SHR and MAFLD risk in various demographic settings. As shown in Table 3, the positive correlation between SHR and MAFLD risk was not significantly affected by gender, hypertension, and age (P > 0.05 for all interactions). The relationship between SHR and MAFLD risk was stronger among normal weight participants (OR = 2.55) than in overweight and obese participants (OR = 1.87, P interaction = 0.020).

Table 3.

Association between sdLDL‐C/HDL‐C and NAFLD stratified by gender, age, BMI, and hypertension

	OR (95% CI) P value	P for interaction
Stratified by gender
Male	2.14 (1.55, 2.95), <0.001	0.412
Female	2.80 (1.69, 4.63), <0.001
Stratified by age
Age ≤60 years old	1.93 (1.38, 2.69), <0.001	0.090
Age >60 years old	3.15 (1.96, 5.05), <0.001
Stratified by BMI
BMI <24 kg/m2	2.55 (1.69, 3.85), <0.001	0.020
BMI ≥24 kg/m2	1.87 (1.28, 2.72), 0.001
Stratified by hypertension
No hypertension	2.07 (1.43, 2.98), <0.001	0.662
Hypertension	2.34 (1.54, 3.54), 0.006

Gender, age, BMI, hypertension (not adjusted for in the subgroup analyses), waist circumference, FPG, FCP, 2 h PPG, 2 h PCP, TG, HbA1c, ALT, AST, GGT, serum creatinine, serum uric, albumin, drinking, smoking, and LLDs were adjusted.

Non‐linearity and threshold effect analysis between SHR and MAFLD

A smooth curve fitting technique was used to depict the non‐linear relationship and saturation effect between SHR and MAFLD status, as shown in Figures 1 and 2. Among all participants, the correlation between SHR and MAFLD status exhibited an inverted L‐shaped curve, with inflection points observed at 1.02 (Table 4). When SHR measurements were below 1.02, there was a significant effect value of 5.807. However, when SHR exceeded 1.02, the effect values were not statistically significant.

Figure 1.

The smooth curve fit for the association between sdLDL‐C to HDL‐C ratio and the prevalence of metabolic dysfunction‐associated fatty liver disease.

Figure 2.

Subgroups analysis for the association between sdLDL‐C to HDL‐C ratio and the prevalence of metabolic dysfunction‐associated fatty liver disease by gender, body mass index, hypertension, and age.

Table 4.

Threshold effect analysis of sdLDL‐C/HDL‐C on MAFLD using the two‐piecewise linear regression model

Arm circumference	Adjusted OR (95% CI) P value
Fitting by the standard linear model	2.283 (1.744, 2.988), <0.001
Fitting by the two‐piecewise linear model
Inflection point	1.02
sdLDL‐C/HDL‐C <1.02	5.807 (3.338, 10.102), <0.001
sdLDL‐C/HDL‐C >1.02	1.090 (0.701, 1.694), 0.703
Log likelihood ratio	<0.001

ROC analysis

Figure 3 shows the receiver operating characteristic (ROC) curve of SHR, sdLDL‐C and HDL‐C ability to identify NAFLD risk. The area under the curve (AUC) for SHR in the ROC analysis was significantly greater than sdLDL‐C and HDL‐C (0.703, 95% CI 0.680, 0.727), with the sensitivity of 71.2%, the specificity of 62.1% and the cutoff of 0.770.

Figure 3.

Receiver operating characteristic curves of sdLDL‐C to HDL‐C ratio, small dense low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol to identify metabolic dysfunction‐associated fatty liver disease.

DISCUSSION

In this large cross‐sectional study, it was found that SHR level was positively associated with MAFLD in type 2 diabetes mellitus patients. Through subgroup analysis and interaction assessment, we have discovered that except BMI, this positive correlation among subgroups of different ages, gender, and hypertension remains consistent. It is worth noting that we have identified an inverted L‐shaped relationship between SHR and MAFLD risk, with a distinct inflection point occurring at an SHR measurement of 1.02. Additionally, SHR had better diagnostic efficacy for MAFLD than sdLDL‐C and HDL‐C alone.

The prevalence of MAFLD in this study was 48.1%. In previous studies, the prevalence of MAFLD combined with type 2 diabetes were 55.5–70% ³ , ⁴ . The prevalence of MAFLD varies according to the difference in the study settings or the patient properties. Compared with previous studies, the prevalence of MAFLD in this study is relatively low, because all patients in our study can go to the hospitals, they will be healthier than in previous studies. We believe that our results can be extended to type 2 diabetes mellitus outpatients in China.

SdLDL‐C is produced during the process of vascular lipoprotein remodeling, which is caused by lipid metabolism disorders, such as activation of hepatic lipase (HL), an enzyme responsible for hydrolyzing lipoprotein triglycerides and phospholipids, so it is related to the degree of hepatic steatosis ¹⁶ . A study found that in the general population, serum sdLDL‐C level is positively correlated with the prevalence of NAFLD ⁸ . Another study by Sonmez et al. ¹⁷ compared NAFLD and NASH patients, showing that sdLDL‐C levels were higher in NASH patients. sdLDL‐C is also correlated with several metabolic disorders, including diabetes mellitus ¹⁸ , obstructive sleep apnea syndrome ¹⁹ , hypertension ²⁰ , and chronic kidney disease ²¹ .

In contrast, HDL‐C can inhibit the preservation, accumulation, and oxidation of LDL, and play a protective role. HDL‐C promotes the efflux of dietary cholesterol through the reverse cholesterol transport pathway and can exert antioxidant and anti‐inflammatory effects ²² . Therefore, due to the reduction of cholesterol efflux and antioxidant activity, the decrease in HDL‐C may contribute to the development of NAFLD ²³ . Researchers have found that low HDL‐C is also closely related to the progression and severity of NAFLD ²⁴ , ²⁵ . HDL‐C combined with other biomarkers has a good predictive value for NAFLD. It has been reported that the monocyte‐to‐HDL‐C ratio have good predictive value for NAFLD ²⁶ . Xie et al. ²⁷ revealed that the elevated uric acid‐to‐HDL‐C ratio is independently associated with an increased risk of NAFLD and the severity of liver steatosis. Previous studies also have suggested that LDL‐C‐to‐HDL‐C ratio is significantly correlated with acute coronary syndromes ²⁸ . Luo et al. ²⁹ suggested that a higher SHR was significantly associated with an increased presence of carotid plaques. To our knowledge, it is the first study to assess the relationship between SHR and MAFLD. Our study finds that a higher SHR was significantly associated with an increased MAFLD risk in patients with type 2 diabetes mellitus, indicating that the SHR might deserve more attention for type 2 diabetes mellitus patients.

Analysis based on the ROC curve shows that the SHR ratio has a better predictive value for MAFLD. The SHR ratio might be a better indicator in the clinical auxiliary diagnosis of MAFLD than a single biomarker, including sdLDL‐C and HDL‐C. This better diagnostic ability results from opposite trends of sdLDL‐C and HDL‐C. Further study is needed to determine whether SHR is related to other metabolic diseases, such as insulin resistance, hypertension, and cardiovascular risk.

The results indicate that the correlation between SHR and MAFLD risk is considerably stronger in normal weight individuals than that in overweight and obesity individuals. Previous studies have shown that lean individuals with unhealthy metabolism may have a greater visceral fat accumulation ³⁰ , while nonobese MAFLD patients with unhealthy metabolism typically have higher cardiovascular risks and liver damage ³¹ . Therefore, as an indicator of visceral fat accumulation ³⁰ , SHR exhibits a stronger correlation with nonobese MAFLD patients, which can be explained.

Furthermore, we found that the correlation between SHR and MAFLD status exhibited an inverted L‐shaped curve, with inflection points observed at 1.02. The reason for the difference in the relationship between SHR and MAFLD on both sides of the inflection point might be that other variables also influenced MAFLD. It could be seen from Table S1 that compared with SHR <1.02, patients with SHR ≥1.02 generally have a higher level or proportion of males, BMI, waist circumference, HbA1c, FCP, 2 h PCP, ALT, GGT, and uric acid. However, the abnormality of the above indicators was closely related to the MAFLD ³² , ³³ , ³⁴ . When the SHR was above 1.02, SHR had a relatively weak effect on MAFLD due to the presence of these MAFLD risk factors. On the contrary, when the SHR was <1.02, the level of the risk factors for MAFLD, such as BMI, waist circumference, HbA1c, FCP, 2 h PCP, ALT, GGT, and uric acid was lower. The impact on MAFLD was weakened, at this time, the effect of SHR was relatively enhanced. Our findings provide an essential rationale for preventing MAFLD by intervening in the SHR level in the clinic. In particular, the SHR level should be controlled below 1.02. Because when the SHR level is lower than 1.02, the risk of MAFLD might decrease significantly. The inflection point provides evidence for SHR management for the first time.

The advantage of this study is that the subjects were well characterized on the basis of a large population and different indicators in the model were corrected, thus improving the reliability of the results. However, this study also has some limitations. First of all, the causal relationship between SHR and MAFLD cannot be determined through cross‐sectional studies. Secondly, the diagnosis of MAFLD depends on ultrasonography, which may miss some patients with <30% steatosis ³⁵ . However, it is worth noting that ultrasonography remains the recommended first‐line, reliable, and non‐invasive diagnostic method for screening liver steatosis, especially in large‐scale population studies ³⁶ . Thirdly, the study population is limited to Chinese patients with type 2 diabetes mellitus. Therefore, a further prospective cohort study is necessary to confirm and promote the current findings in a larger population, including those without diabetes mellitus.

CONCLUSION

In conclusion, the combined use of two biomarkers by calculating SHR is closely related to the occurrence of MAFLD, which can improve the diagnostic ability of a single biomarker. This relationship was more closely related to normal weight type 2 diabetes mellitus patients. The measurement of SHR can potentially serve as a practical indicator for diagnosing MAFLD in type 2 diabetes mellitus patients.

DISCLOSURE

The authors declare that they have no conflict of interest.

Approval of the research protocol: This study has been approved by the Ethics Committee of the Second Affiliated Hospital of Wenzhou Medical University.

Informed consent: The written informed consent of all subjects was obtained following the Declaration of Helsinki.

Registry and the registration no. of the study/trial: LCKY2020‐01, date: Jan 2020.

Animal studies: N/A.

Supporting information

Table S1 | The baseline characteristics of participants on both sides of the inflection point according to MAFLD