Comparison of the Diagnostic Value Between Triglyceride-Glucose Index and Triglyceride-to-High Density Lipoprotein Cholesterol Ratio in MAFLD Patients: A Retrospective Cross-Sectional Study

doi:10.21203/rs.3.rs-1224490/v1

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Research Article

Comparison of the Diagnostic Value Between Triglyceride-Glucose Index and Triglyceride-to-High Density Lipoprotein Cholesterol Ratio in MAFLD Patients: A Retrospective Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-1224490/v1

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Background The triglyceride and glucose index (TyG) and triglyceride-to-high density lipoprotein cholesterol ratio (TG/HDL-C) were found to be substitute markers of insulin resistance (IR). We aimed to compare the efficacy of the two indicators in the diagnosis of Metabolic-Associated Fatty Liver Disease (MAFLD), which was rarely covered in the literature, and to construct a novel disease diagnosis model.

Methods A retrospective cross-sectional study was carried out in West China Hospital of Sichuan University and 229 people (97 MAFLD and 132 Non-MAFLD) were included. Biochemical indexes were collected and analyzed by logistic regression to screen out indicators that expressed differently in MAFLD patients and healthy controls and incorporate them into a diagnostic model. MAFLD was diagnosed by Ultrasound.

Results After adjusting for age, gender and BMI, Serum ALT, AST, AST/ALT (A/A), FPG, Cys-C, URIC, TG, HDL-C, ALP, GGT, nonHDL-C, LDL-C/HDL-C, nonHDL-C/HDL-C, TG/HDL-C, TC/HDL-C, TyG and TyG-BMI were risk factors of MAFLD through binary logistics regression analysis. The odds ratio of TG/HDL-C and TyG were 5.387 (95%CI: 2.986-9,718) and 107.945 (95% CI: 25.824-451.222). In identifying MAFLD, TyG, TG/HDL-C and TG were found to be the most vital indexes by the random forest method and the area under the curve (AUC) of them are all greater than 0.9 respectively. In addition, the combination of gender, BMI, ALT, TG, HDL-C, TyG and TyG-BMI had a great diagnostic efficiency for MAFLD.

Conclusions TyG and TG/HDL-C were potential risk factors for MAFLD and the former made a better performance in diagnosing MAFLD. The combination of gender, BMI, ALT, TG, HDL-C, TyG and TyG-BMI improved the diagnostic capability of MAFLD.

General Biochemistry

Molecular Genetics

Molecular Biology

Endocrinology & Metabolism

MAFLD

TyG

TG/HDL-C

Renaming “Nonalcoholic Fatty Liver Disease (NAFLD)” to “MAFLD” takes into account the current understanding of the pathogenesis of MAFLD and its rising prevalence(1). Affecting more than one-quarter of adults in the world, MAFLD is becoming one of the most vital causes of liver diseases(2). The incidence of MAFLD will continue to increase in developed countries such as the US and the European countries (Germany, France, Italy, and United Kingdom), causing a tremendous economic burden(3). However, the occurrence rate of MAFLD in China will be the fastest, which is expected to rise from 243.66 million in 2016 to 314.58 million in 2030⁴. The challenges of MAFLD in China are as follows: a large number of cases, high genetic susceptibility, the appearance of patients young and with a low body mass index (BMI), absence of attention and recognition, lack of adequate diagnostic methods and treatments(5).

Although the pathogenesis theory of MAFLD has changed from two-stroke theory to the multi-stroke theory, IR still plays a significant role in the development of MAFLD(6). Regarded as a gold standard method to measure IR, hyperinsulinemic-euglycemic clamp (HIEC) is complicated, time-consuming and expensive. TG/HDL-C and TyG are found to be useful biomarkers in identifying IR individuals from a large group of Chinese(7,8). Early in 2005, TG/HDL-C was considered to be an important factor in predicting IR and increased risk of cardiovascular diseases in patients(9). As a result, TG/HDL-C is correlated with the occurrence of diabetes mellitus with related vascular diseases and fatty liver(10–12). The TyG, calculated by FPG and TG, has a close connection with IR which is related to glucolipid metabolism(13). Being used to detect IR, TyG seems to be cheaper and more convenient than the homeostasis model assessment of insulin resistance (HOMA-IR) index(14). TyG is also associated with cardiometabolic diseasesand is a risk prognostic factor for stroke, diabetes mellitus, acute myocardial infarction and acute coronary syndrome(15–17). Besides, TyG may be a useful indicator of MAFLD not only in adults but also in children and the elderly(18–20).

Therefore, the purpose of this study was to compare the ability of TG/HDL-C and TyG to distinguish MAFLD from healthy people and establish a better prediction model for MAFLD.

Patients

A total of 229 participants were enrolled from West China Hospital between October 2018 and March 2021, including 97 patients with MAFLD as the experimental group and 132 individuals who underwent a physical examination as the control group. MAFLD was diagnosed by experienced clinicians based on the abdominal ultrasound diagnosis of hepatic steatosis and evidence of any of three conditions, namely overweight/obesity, type 2 diabetes mellitus (T2DM), or metabolic disorders(21).

Laboratory measurements

Fasting blood samples from the elbow vein were used to quantifyalanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), fasting plasma glucose (FPG), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid (URIC) and cystatin C (Cys-C) by Roche’s automatic biochemical analyzer and the corresponding kit (Roche, Mannheim, Germany). In this study, theTyG was calculated as established formulas, TyG = Ln [TG (mg/dl) × FPG (mg/dl)/2](14). The formula for calculating mg/dL from mmol/ L was as follows: for FPG, 1 mmol/ L = 18 mg/dL; for TG, 1 mmol/ L = 88.5 mg/dL.

Statistical analysis

SPSS 22.0, R software 4.1.1and GraphPad Prism 9.0.0 were used for statistical analysis and graph drawing. Values were expressed as mean ± standard deviation (SD) or median (25-75 percentiles) for continuous variables and frequencies for categorical variables. Student’s t-test and Mann-Whitney U test were used to compare two groups of normal or non-normal distributed continuous variables respectively. Pearson’s chi-square test for categorical variables was performed. Logistic regression analysis was used to find independent risk factors for MAFLD, and the OR value was expressed with a 95% confidence interval (CI). Receivers operating characteristic curve (ROC curve) was used for diagnostic value analysis, and the maximum value of the Youden index (sensitivity+specificity-1) was taken as the optimal cut-off value. The difference was statistically significant when P<0.05.

Baseline characteristics of the study participants

229 patients were enrolled in the present study. Table 1 listed the baseline characteristics of the subjects with and without MAFLD. Compared to non-MAFLD, individuals in MAFLD had higher levels ofBMI, ALT, AST, FPG, UREA, CREA, Cys-C, URIC, TG, TC, ALP, GGT, LDH, HBDH, nonHDL-C, TyG, TyG-BMI and lowerA/A, TP, ALB, GLB and HDL-C (all P < 0.005).As a result, ratios of the above indicators such as LDL-C/HDL-C, nonHDL-C/HDL-C, TG/HDL-C and TC/HDL-C were greater in MAFLD patients than in the other group.

Table 1. The basic characteristics of the Non-MAFLD and MAFLD groups

	Non-MAFLD ( n=132)	MAFLD ( n=97 )	P value
age	36.00 (32.00,46.00)	43.00 (33.00,51.00)	0.019
Male/Female	80/52	75/22	0.008
BMI	22.56 (20.90,24.91)	27.38 (25.25,29.24)	<0.001
TBIL	11.85 (9.50,16.05)	12.10 (9.00,15.90)	0.918
DBIL	3.40 (2.70,4.50)	3.40 (2.60,4.40)	0.597
IBIL	8.10 (6.50,11.22)	8.80 (6.20,11.90)	0.606
ALT	17.50 (14.00,25.00)	46.00 (28.00,76.00)	<0.001
AST	19.00 (16.00,23.00)	31.00 (24.00,42.00)	<0.001
A/A	1.06 (0.89,1.29)	0.67 (0.53,0.86)	<0.001
TP	76.10(3.35)	74.74(4.48)	0.010
ALB	48.91(2.40)	48.17(2.78)	0.033
GLB	26.85 (25.48,29.20)	25.40 (23.60,29.00)	0.012
A/G	1.81 (1.67,1.93)	1.90 (1.66,2.04)	0.111
GLU	4.83 (4.63,5.18)	5.86 (4.92,7.33)	<0.001
UREA	4.45 (3.80,5.20)	4.70 (4.20,5.50)	0.015
CREA	70.00 (60.00,80.00)	77.00 (67.00,89.00)	0.001
Cys-C	0.79 (0.73,0.86)	0.86 (0.81,0.93)	<0.001
URIC	329.10(65.94)	393.14(102.24)	<0.001
TG	1.06 (0.78,1.32)	2.47 (1.76,3.32)	<0.001
TC	4.59(0.66)	4.87(1.02)	0.011
HDL-C	1.50 (1.28,1.70)	1.00 (0.90,1.20)	<0.001
LDL-C	2.77(0.61)	2.87(0.84)	0.284
ALP	69.50 (58.75,83.00)	82.00 (68.00,99.00)	<0.001
GGT	20.00 (13.00,28.00)	50.00 (34.00,74.00)	<0.001
CK	96.50 (74.00,137.75)	109.00 (73.00,131.00)	0.586
LDH	169.50 (155.75,188.00)	183.00 (171.00,208.00)	<0.001
HBDH	131.00 (120.00,145.00)	138.00 (126.00,160.00)	0.003
nonHDL-C	3.07(0.71)	3.84(1.00)	<0.001
LDL-C/HDL-C	1.87 (1.52,2.37)	3.00 (2.46,3.27)	<0.001
nonHDL-C/HDL-C	2.05 (1.63,2.63)	3.94 (3.03,4.45)	<0.001
TG/HDL-C	0.70 (0.48,1.05)	2.43 (1.51,3.76)	<0.001
TC/HDL-C	3.05 (2.63,3.63)	4.94 (4.03,5.45)	<0.001
TyG	8.30 (7.99,8.54)	9.36 (9.01,9.66)	<0.001
TyG-BMI	112.58 (87.85, 153.62)	201.52 (150.72, 261.22)	<0.001

Variables selection and model construction

Among these indexes, 29 variables showed significant differences between MAFLD and non-MAFLD groups by Simple Logistic Regression. The differences of 18 variables remained after adjusting a variety of factors including age, sex and BMI (Table 2). Eventually, a predictive model consisted of gender, BMI, ALT, TG, HDL-C, TyG and TyG-BMI by binary logistic regression.As shown in Figure 1, a dynamic nomogram was exerted to describe the probability of MAFLD in the model. For example, a female patient with a BMI of 32.0 kg/m², ALT of 35 IU/L, TG of 2.0 mmol/L, HDL-C of 1.4 mmol/L, TyG of 9.00 and TyG-BMI of 288 had a likelihood of MAFLD of 0.944 in the model.

Table 2. logistic regression analysis of risk factors for MAFLD patients after adjusting age, gender and BMI

	OR	95% CI	P Value
ALT	1.123	(1.078, 1.170)	.000
AST	1.210	(1.130, 1.296)	.000
A/A	.024	(0.006, 0.101)	.000
FPG	7.158	(3.342, 15.334)	.000
Cys-C	100.408	(3.698, 2726.533)	.006
URIC	1.009	(1.005, 1.014)	.000
TG	13.550	(5.747, 31.944)	.000
HDLC	.007	(0.001, 0.040)	.000
ALP	1.020	(1.004, 1.037)	.016
GGT	1.050	(1.029, 1.071)	.000
nonHDL-C	2.261	(1.464, 3.492)	.000
LDL-C/HDL-C	1.905	(1.251, 2.902)	.003
nonHDL-C/HDL-C	2.285	(1.628, 3.207)	.000
TG/HDL-C	5.387	(2.986, 9.718)	.000
TC/HDL-C	2.285	(1.628, 3.207)	.000
TyG	107.945	(25.824, 451.222)	.000
TyG-BMI	1.191	(1.130, 1.256)	.000

Diagnostic performance ofvital indexesand the predictive model in MAFLD

Through the random forest method, patients were randomly divided into a training set and a test set at a ratio of 2:1. When adopted all the variables in the model, a graph provided an overview of the importance score of each variable in predicting MAFLD. As can be seen in Figure2, TyG, TG/HDL-C and TG were the most important indicators for identifying MAFLD.

ROC curve analyses were conducted to identify the diagnostic value of TG, TG/HDL-C, TyG and the predictive model (Figure3). As a result, the area under receivers operating characteristic curve (AUROC) of the predictive model was 0.989 (95% CI 0.980-0.998) with 0.979 sensitivity and 0.947 specificity when the cut-off value was 0.323, showing the best capacity in assessing MAFLD. The ability in detecting MAFLD of TG, TG/HDL-C and TyG were also good, which were concluded in Table 3.

Table 3 Areas under the curve (AUC) of TG, TG/HDL-C, TyG and the predictive model forMAFLD

Variable	AUROC	95%CI	Cut-off value	Sensitivity (%)	Specificity (%)
TG	0.921	0.886 to 0.957	1.745	75.3	97.0
TG/HDL-C	0.925	0.892 to 0.959	1.260	83.5	90.9
TyG	0.943	0.913 to 0.973	8.804	87.6	93.9
Predictive model	0.989	0.980 to 0.998	0.323	97.9	94.7

As for calibration capability, the p-value of the Hosmer-Lemeshowtest of the predictive model was0.3869. The calibration plot and DCA curves of the model were drawn in Figure4 and Figure5.

This retrospective cross-sectional study assessed the ability of TG, TG/HDL-C and TyG to predict MAFLD and finally constructed a novel model in diagnosing MAFLD. TyG performed the best among the three indexes. The sensitivity and specificity of the new diagnostic model made up by gender, BMI, ALT, TG, HDL-C, TyG and TyG-BMI were improved to more than 90%. It was apparent from the Hosmer-Lemeshow test and the pictures of the calibration plot and DCA curve that the model fits well and could improve the net benefit.

MAFLD is not only the leading cause of chronic liver disease but also an increasing reason for HCC. With MAFLD-HCC, patients were older and had shorter survival times and more advanced tumor(22). Therefore, identifying MAFLD is of vital importance. Liver biopsy is still the gold standard for MAFLD although it has a variety of safety risks such as bleeding, pain, death and so on(23,24). In addition, there exists sampling errors and sampling bias(25). Noninvasive biomarkers which are cheap and effective for diagnosing MAFLD have been under study.

Triglycerides accumulate in the liver and blood of MAFLD. Interestingly, both TG/HDL-C and TyG which are surrogate IR markers derive from triglycerides. Independent of age, BMI and waist circumference, it was observed that whether the fatty liver patients with a normal or even higher level of ALT, the expression of TG/HDL-C was higher than that of the control group(26). Subsequently, several studies found that TG/HDL-C can be considered as a risk factor and even a predictor of MAFLD with a lower cut-off value and a greater AUROC in women rather than in men(27,28). What’s more, in a randomized controlled study, decreases of TG and TG/HDL-C were associated with the resolution ofNonalcoholic steatohepatitis (NASH)(29).Another lipid ratio parameter, TC/HDL-C, was exerted to diagnose MAFLD with an AUROC of 0.645 in Ren’s study(30). Both higher levels of TC/HDL-C and TG/HDL-C indicated more severe liver steatosis while the former showed a higher specificity(31). However, in our study, TG/HDL-C exhibited higher diagnostic efficiency (AUC =92.541, 95%CI:81.454-91.211) of MAFLD than TC/HDL-C (AUC =86.332, 95%CI:81.454-91.211). Helda et al. discovered TC/HDL-C, TG/HDL-C, LDL-C/HDL-C and non-HDL-C/HDL-C were positively related to the severity of hepatic steatosis(32). From the results of our research, all of them were risk factors of MAFLD independent of age, gender and BMI.

With the increment of TyG, the incidence of MAFLD significantly increased(33,34). The AUCs of the TyG index demonstrated by some studies in detecting adult MAFLD were 0.714-0.782 and their MAFLD was diagnosed by ultrasound(19,35–39). Two researches among them mentioned that the AUC of TyG was larger than ALT.Fedchuk L et al. discovered that the AUC of TyG was up to 0.9 (95%CI 0.84-0.94) in biopsy-proven MAFLD with 80% sensitivity and 92% specificity when the cut-off value was 8.38(40). Similar to the research, the AUC of TyG in our study in diagnosing MAFLD was 0.943 (95%CI 0.913-0.973) with 87.6% sensitivity and 93.9% specificity when the cut-off value was 8.804. Among 50 asymptomatic women who underwent a liver biopsy, the TyG displayed a high sensitivity in screening simple steatosis and NASH(41). Based on a large amount of MAFLD participants ( n=11424) in a follow-up study, it was found that patients with a high TyG index were more likely to have MAFLD progression(42). TyG-BMI, one of Modified TyG Indices, performed a better ability to identify MAFLD than TyG itself not only in males but also in females. The AUROC of TyG-BMI in diagnosing MAFLD ranged from 0.774-0.9084(43–45). We discovered that the AUROC of TyG-BMI was 0.949 (95% CI 0.922-0.976), which was higher than other researches. As for non-obese subjects, TyG-BMI could identify MAFLD and predict its occurrence effectively(46,47). In addition to liver steatosis, TyG and its related marker TyG-BMI were discovered to be related to liver fibrosis(39,48).

The limitations of our study were as follows. First of all, lack of a gold standard for diagnosis of MAFLD by liver biopsy. As we all know, ultrasound detection of steatosis less than 20% has limited sensitivity. What also worries us are its poor differentiating effect between steatosis and fibrosis and poor consistency of repeated operations. The detection capability mostly depends on the examiners(49). Secondly, be short of reliable markers of IR such as HOMA-IR, which is a commonly used substitute for hyperinsulinemic-euglycemic glucose clamp technique to detect insulin resistance and verify the relationship between TG/HDL-C, TyG and IR. Last of all, to measure the diagnostic significance of the indicators for MAFLD more accurately, the suspected population of MAFLD should be included, otherwise the value of these indicators will be exaggerated.

This study compared the ability of two indices of surrogate for IR, TG/HDL-C and TyG, in predicting MAFLD for the first time. TG/HDL-C and TyG can be easily measured with high accuracy and low cost due to the convenience of detecting TG, FPG and HDL-C. In conclusion, TyG performed better than TG/HDL-C in predicting MAFLD. In addition, the combination ofgender, BMI, ALT, TG, HDL-C, TyG and TyG-BMI could promote the diagnosis of MAFLD.

TyG: triglyceride and glucose index

TG/HDL-C: triglyceride-to-high density lipoprotein cholesterol ratio

IR: insulin resistance

MAFLD: Metabolic-Associated Fatty Liver Disease

ALT: Alanine aminotransferase

AST: Aspartate aminotransferase

A/A: AST/ALT

GGT: gamma-glutamyl transferase

ALP: alkaline phosphatase

FPG: Fasting plasma glucose

TC: Total cholesterol

TG: Triglyceride

HDL-C: high-density lipoprotein cholesterol

LDL-C: low-density lipoprotein cholesterol

URIC: uric acid

Cys-C: cystatin C

NAFLD: Nonalcoholic Fatty Liver Disease

NASH: Nonalcoholic steatohepatitis

T2DM: type 2 diabetes mellitus

AUROC: area under receivers operating characteristic curve

BMI: body mass index

HIEC: hyperinsulinemic-euglycemic clamp

HOMA-IR: Homeostatic model assessment of insulin resistance

Ethics approval and consent to participate

This study was approved by the Ethics Committee of West China Hospital (Approval No.977 in 2019 ).

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Competing Interests

The authors declare that they have no competing interests.

Funding

This study was supported by the National Natural Science Foundation of China

(Grant No. 81902142) in the design of the study and collection, analysis, and interpretation of dataand Clinical Research Incubation Project of West China Hospital of Sichuan University (Grant No. 2019HXFH052)in writing and publishing the manuscript.

Authors' contributions

DYZ and YLD collected the data of MAFLD patients. LSL collected the data of healthy controls. LZ and HH analyzed and interpreted these data and were contributed equally in writing the manuscript. AZM and LSQ designed the study and corrected the manuscript. All authors read and approved the final manuscript.

Acknowledgments

The authors are grateful to all study participants.

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Comparison of the Diagnostic Value Between Triglyceride-Glucose Index and Triglyceride-to-High Density Lipoprotein Cholesterol Ratio in MAFLD Patients: A Retrospective Cross-Sectional Study

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