Noninvasive Liver Fibrosis Indices as Indicators of Microvascular and Macrovascular Complications in Type 2 Diabetes

Abstract

Objective:

Nonalcoholic fatty liver disease (NAFLD) is more prevalent in patients with obesity, diabetes, and metabolic syndrome, which are risk factors for nonalcoholic steatohepatitis and liver fibrosis. NAFLD is related to cardiovascular outcomes in diabetes. We aimed to investigate the relationship between diabetic complications and NAFLD fibrosis score (NFS) and Fibrosis-4 score (FIB-4).

Methods:

Three hundred patients with type 2 diabetes mellitus (T2DM) were retrospectively evaluated according to NAFLD diagnosis on ultrasound in outpatient clinic. Risk of advanced fibrosis was estimated using FIB-4 and NFS. Diabetic complications of the patients were noted.

Results:

Presence of diabetic retinopathy is related to FIB-4 (P = 0.001) and NFS (P < 0.001) scores. NFS score (P = 0.037), not FIB-4 (P = 0.517), is related to diabetic nephropathy. Among macrovascular complications, only coronary artery disease is related to NFS and FIB-4 scores (P = 0.037 and P = 0.004, respectively). Although we cannot establish any association between fasting blood glucose, glycosylated hemoglobin (HbA1c) values and noninvasive liver fibrosis scores (P > 0.05), diabetes duration, and age positively correlated with the FIB-4 score (P = 0.033, P = 0.001). In logistic regression analysis, NFS > 0.676 values are associated with increased rates of diabetic retinopathy, independent of age, sex, HbA1c, and duration diabetes (odds ratio: 1.155, P = 0.030). FIB-4 has no relation with microvascular complications according to logistic regression analysis (P > 0.05 for all). Neither FIB-4 nor NFS has an effect on the presence of macrovascular complications (P > 0.05 for all).

Conclusion:

Our findings suggest that increase in NFS score is associated with the presence of diabetic retinopathy, independent of confounding factors. Further studies are needed on the applicability of noninvasive fibrosis scores in monitoring the presence of diabetic microvascular and macrovascular complications.

Introduction

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease, and the estimated worldwide prevalence is currently 25%, ranging from 23% to 48% in general population.¹ Obesity, diabetes, and metabolic syndrome are known risk factors for nonalcoholic steatohepatitis and liver fibrosis.² It has been recently reported that 50%–70% of patients with diabetes have NAFLD.³ Therefore, the management of NAFLD in higher-risk groups has major clinical importance.

NAFLD represents a range of conditions from steatosis to nonalcoholic steatohepatitis, which may progress to fibrosis and liver cirrhosis.⁴ Populational studies are generally based on ultrasound diagnosis of NAFLD. Liver biopsy is the gold standard method for the diagnosis of fibrosis in NAFLD. However, in clinical practice, it is difficult to interpret because of health costs and invasive technique.⁵ Noninvasive scoring systems give opportunity to screen high-risk patients for hepatosteatosis and liver fibrosis. NAFLD fibrosis score (NFS) and Fibrosis-4 score (FIB-4) are the most extensively studied and validated tests for diagnostic accuracy in fibrosis. The European Association for the Study of the Liver (EASL) and the American Association for the Study of Liver Diseases (AASLD) guidelines suggest using scores for the diagnosis, prognosis, and progression of steatosis, whatever imaging tools are not available or feasible.^5,6 Besides prediction of hepatic outcomes, FIB-4 and NFS have been validated in general population to predict metabolic and cardiovascular outcomes and mortality.⁵

Evidence indicates that the presence and severity of NAFLD are associated with an increased prevalence of type 2 diabetes mellitus (T2DM), and NAFLD is a contributing factor to the development of diabetes-related complications.⁴ There are limited studies regarding the possible link between noninvasive liver fibrosis scores and diabetic complications in patients with T2DM.^7,8 In this study, we aimed to investigate the relationship between macrovascular and microvascular complication of diabetes and NFS and FIB-4 scores.

Methods

Study participants

This is a retrospective observational study of patients with T2DM, who followed in the outpatient clinic between 2019 and 2022. The inclusion criteria were age ≥30 and the diagnosis of type 2 diabetes. A total of 456 patients with T2DM were screened, and those who had laboratory analysis and abdominal ultrasonography in the last 3 months before admission were included to study. NAFLD disease was established according to the ultrasound findings. Patients with acute and chronic viral hepatitis, autoimmune liver disease, and daily alcohol use (more than 20 g for women and 30 g for men), who were pregnant, and with malignancy were excluded. Finally, of the cases, 300 patients with T2DM were included in the study. Body mass index (BMI) was calculated as kilogram per square meter. The presence of diabetic macrovascular (cerebrovascular disease, coronary artery disease, and peripheral artery disease) and microvascular complications (diabetic retinopathy and diabetic neuropathy) were recorded manually from the electronic system.

Assessment of fatty liver: NFS and FIB-4 calculations

A total of 300 patients with T2DM were stratified into two groups based on the presence of fatty liver on ultrasound examination. Diagnosis of fatty liver was based on the presence of hepatorenal contrast, liver brightness, vascular blurring, and deep attenuation. The risk of advanced fibrosis was estimated using FIB-4 and NFS for all patients. Patients having T2DM with NAFLD and without NAFLD on ultrasound were compared for demographic characteristics, laboratory data, diabetic complications, and FIB-4 and NFS scores.

The NFS score was calculated using the following formula: 1.625 + (0.037 × age [years]) + (0.094 × BMI [kg/m²]) + (1.13 × DM [yes = 1, no = 0]) + (0.99 × aspartate aminotransferase [AST]/alanine aminotransferase [ALT] ratio) − (0.013 × platelets [×109/L]) − (0.66 × albumin [g/dL]). Cutoff values for NFS score were categorized as recommended; the NFS at <−1.455 indicates absence of advanced fibrosis and NFS values between−1.455 and +0.676 indicate an intermediate risk for liver fibrosis. Finally, NFS values higher than +0.676 represent a high risk for liver fibrosis.⁹

FIB-4 score was calculated using the following formula: (Age × AST[U/L])/(Platelet count [109/L] × √ALT[U/L]). FIB-4 values were categorized according to recommended cutoff values; FIB-4 < 1.30 defines low risk; FIB-4 = 1.30–2.67 defines intermediate risk; and FIB-4 ≥ 2.67 defines high risk for liver fibrosis.¹⁰

Statistical analysis

Categorical variables were showed as frequency and percentage values. Continuous variables were reported as mean, standard deviation, median, and minimum and maximum values. The Kolmogorov–Smirnov test was used to analyze the normal distribution of continuous variables. Chi-square analysis was used to compare categorical variables. Where appropriate, categorical variables were evaluated with Fisher–Freeman–Halton. The Mann–Whitney U test was used to compare two independent groups for the variables that did not fulfill the assumption of normal distribution. Kruskal–Wallis H test was used in comparison for more than two groups. Dunn’s multiple comparison test with Bonferroni correction was used to determine the source of the difference in Kruskal–Wallis H test. Logistic regression analysis was carried out to identify the effect of independent variables on complications of diabetes. Pearson correlation analysis was performed to determine relevant factors for FIB-4 and NFS scores. P < 0.05 was considered statistically significant. Analyses were performed with the SPSS 23 (Statistical Package for the Social Sciences) package program that IBM Corp. released in 2015 (IBM SPSS statistics for Windows, Version 23.0. Armonk, NY).

Results

In this study, 300 patients with T2DM were evaluated. Baseline characteristics of patients having T2DM with and without NAFLD according to the ultrasound examination are demonstrated in Table 1. The mean age of patients was 59.39 ± 9.79 years, and the mean duration of diabetes was 14.89 ± 9.7 years in overall group. Both groups had similar glycosylated hemoglobin (HbA1c) values (P = 0.630). On the contrary, patients having T2DM with NAFLD had higher BMI values than non-NAFLD group (32.06 ± 5.11 vs. 27.83 ± 4.55, P < 0.001). Serum triglycerides and non-HDL-c values were higher in patients with NAFLD than without NAFLD (P = 0.001 and P = 0.046, respectively). While serum AST was similar, serum ALT was higher in NAFLD group (P = 0.200 and P = 0.001). Platelet count, which is included in both NFS and FIB-4 score calculation, was higher in patients with NAFLD (278.70 ± 76.24 × 109/L, P = 0.014). Serum albumin was 4.37 ± 0.28 gr/dL in patients with NAFLD and 4.28 ± 0.26 gr/dL in patients without NAFLD (P = 0.018). Estimated glomerular filtration rate was lower in NAFLD group (P = 0.006) (Table 1).

Table 1.

Demographic Characteristics of Patients with Type 2 Diabetes According to Nonalcoholic Fatty Liver Disease Presence in Ultrasound

	Overall n = 300Mean ± SD	NAFLD n = 260Mean ± SD	Non-NAFLD n = 40Mean ± SD	P value
Age (years)	59.39 ± 9.79	60.09 ± 9.23	54.83 ± 12.00	0.011
Male, n (%)	178 (59.3)	105 (40.4)	17 (42.5)	0.936
Diabetes duration (years)	14.89 ± 9.7	15.00 ± 6.86	14.23 ± 5.90	0.572
BMI (kg/m²)	31.50 ± 5.24	32.06 ± 5.11	27.83 ± 4.55	<0.001
FIB-4 index	1.10 ± 0.67	0.71 ± 0.04	0.33 ± 0.05	0.460
NFS	−0.82 ± 1.24	−0.81 ± 1.28	−0.86 ± 0.96	0.813
FBG (mg/dL)	158.34 ± 50.34	159.43 ± 50.00	151.30 ± 52.63	0.242
HbA1c (%)	7.96 ± 1.24	7.97 ± 1.22	7.91 ± 1.39	0.630
TSH (mU/L)	2.66 ± 1.24	2.68 ± 4.55	2.39 ± 0.85	0.419
Total cholesterol (mg/dL)	199.03 ± 47.82	200.85 ± 48.19	187.20 ± 44.07	0.098
LDL-c (mg/dL)	116.34 ± 38.99	117.18 ± 39.48	111.05 ± 35.72	0.408
TG (mg/dL)	177.24 ± 117.13	183.65 ± 119.89	135.63 ± 87.53	0.001
Non-HDL-c (mg/dL)	151.20 ± 46.32	153.26 ± 46.76	138.25 ± 41.68	0.046
AST (IU/L)	23.13 ± 10.07	27.38 ± 16.79	20.23 ± 4.59	0.200
ALT (IU/L)	26.30 ± 16.11	27.38 ± 16.79	19.30 ± 7.78	0.001
PLT (×10⁹/L)	274.88 ± 74.05	278.70 ± 76.24	250.03 ± 52.03	0.014
Albumin (gr/dL)	4.36 ± 0.28	4.37 ± 0.28	4.28 ± 0.26	0.018
eGFR (mL/min/1.73m²)	87.76 ± 22.76	86.35 ± 22.37	96.90 ± 23.41	0.006

Statistical significance at P < 0.05.

ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; LDL-c, low-density lipoprotein cholesterol; TG, triglyceride; Non-HDL-c, non-high density lipoprotein cholesterol; NAFLD, nonalcoholic fatty liver disease; NFS, NAFLD fibrosis score; PLT, platelets; TSH, thyroid-stimulating hormone.

The co-morbidities and diabetes-related complications are analyzed in Table 2. Among microvascular complications, the only difference is in diabetic nephropathy, which is higher in percentage in patients with NAFLD. Around 40% of the patients with NAFLD had diabetic nephropathy (P = 0.015). Macrovascular complications were in similar percentage in T2DM patients with NAFLD and without NAFLD, namely, coronary artery disease (P = 0.659), cerebrovascular disease (P = 0.089), and peripheral vascular disease (P = 1.000). When we analyzed medications, metformin (P = 0.016), dipeptidyl peptidase-4 inhibitors (P = 0.003), and sodium glucose co-transporter (SGLT2) inhibitors (P = 0.013) were more frequently used by patients having T2DM with NAFLD (Table 2).

Table 2.

Comparison of Complications, Co-morbidities, and Medications of Patients with Type 2 Diabetes Mellitus According to the Presence of NAFLD

	NAFLD n = 260 n (%)	Non-NAFLD n = 40 n (%)	P value
Microvascular complications
Diabetic neuropathy	133 (56.1)	22 (57.9)	0.977
Diabetic retinopathy	92 (38.0)	12 (36.4)	1.000
Diabetic nephropathy	104 (40)	8 (20)	0.015
Macrovascular co-morbidities
Coronary artery disease	91 (35.0)	12 (30.0)	0.659
Cerebrovascular disease	42 (16.2)	2 (5.0)	0.089
Peripheral vascular disease	18 (6.9)	2 (5.0)	1.000
Medications
Insulin	168 (64.6)	27 (67.5)	0.859
Metformin	229 (88.1)	29 (72.5)	0.016
Sulfonylurea	44 (16.9)	6 (15)	0.939
Pioglitazone	3 (1.2)	1 (2.5)	0.438
DPP4 inhibitors	159 (61.2)	14 (35.0)	0.003
SGLT2 inhibitors	42 (16.2)	0	0.013
Acarbose	7 (2.7)	0	0.600
GLP-1 analogs	10 (3.8)	0	0.369
Meglitinides	6 (2.3)	0	1.000
Statins	151 (58.1)	26 (65.0)	0.512
Fenofibrate	14 (5.4)	2 (5.0)	1.000

DPP4, dipeptidyl peptidase-4; GLP-1, glucagon-like peptide-1; SGLT2, sodium glucose co-transporter.

Although FIB-4 and NFS scores were similar in both NAFLD and non-NAFLD groups (Table 2), correlation analysis revealed that the presence of diabetic retinopathy is related to FIB-4 (P = 0.001) and NFS (P < 0.001) scores (Table 3). NFS score (P = 0.037), not FIB-4 (P = 0.517), is related to diabetic nephropathy. Among macrovascular complications, only coronary artery disease is related to NFS and FIB-4 scores (P = 0.037 and P = 0.004, respectively). We cannot establish any association between fasting blood glucose, HbA1c values, and noninvasive liver fibrosis scores (P > 0.05). However, diabetes duration and age positively correlated with the FIB-4 score (P = 0.033, P = 0.001) (Table 3).

Table 3.

Correlation Analysis Between Microvascular and Macrovascular Complications and Glycemic Status of T2DM Patients with NAFLD and NFS and FIB-4 Scores

	FIB-4	NFS
Neuropathy
cc	0.022	0.052
P	0.718	0.392
Retinopathy
cc	0.197	0.262
P	0.001	<0.001
Nephropathy
cc	0.038	0.120
P	0.517	0.037
Coronary artery disease
cc	0.164	0.121
P	0.004	0.037
Cerebrovascular disease
cc	0.053	0.045
P	0.359	0.442
Peripheral artery disease
cc	−0.054	0.031
P	0.351	0.590
Age
cc	0.307	0.98
P	0.001	0.090
Fasting blood glucose
cc	0.003	−0.005
P	0.602	0.391
HbA1c
cc	−0.002	0.101
P	0.727	0.080
Diabetes duration
cc	0.123	0.018
P	0.033	0.753

cc, correlation coefficient; FIB-4, fibrosis-4 score; HbA1c, glycosylated hemoglobin, T2DM, type 2 diabetes mellitus.

Logistic regression analysis showed that in model 1 that is adjusted for age and gender, NFS values higher than +0.676 have an impact on diabetic retinopathy (odds ratio [OR]:1.155, P = 0.030). After the adjustment for HbA1c and diabetes duration in model 2, NFS (values of >0.676) still affects the presence of diabetic retinopathy (OR: 1.166, P = 0.037). In contrast, NFS values did not affect the presence of neuropathy and nephropathy (P > 0.05 for all). Furthermore, FIB-4 has no relation with microvascular complications according to logistic regression analysis (P > 0.05 for all) (Table 4).

Table 4.

Logistic Regression Analyses of Microvascular Complication in Patients Having T2DM with NAFLD

	Retinopathy		Neuropathy		Nephropathy
	OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value
NFS
Model 1
Low	Ref (1)		Ref (1)		Ref (1)
Intermediate-high	0.820 (0.126–1.514)	0.021	0.192 (−0.410 to 0.745)	0.569	−0.358 (−0.759 to 1.101)	0.719
High	1.155 (0.109–2.200)	0.030	0.380 (−0.746 to 1.177)	0.661	0.595 (−1.031 to 1.137)	0.137
Model 2
Low	Ref (1)		Ref (1)		Ref (1)
Intermediate-high	0.770 (0.045–1.494)	0.037	0.090 (−0.494 to 0.703)	0.765	−0.439 (−1.037 to 0.159)	0.150
High	1.166 (−0.073 to 2.259)	0.037	0.239 (−0.498 to 0.678)	0.633	0.072 (−0.876 to 1.019)	0.882
FIB-4
Model 1
Low	Ref (1)		Ref (1)		Ref (1)
Intermediate-high	0.314 (−0.314 to 0.942)	0.327	−0.048 (−0.683 to 0.586)	0.586	−0.291 (−0.938 to 0.281)	0.291
Model 2
Low	Ref (1)		Ref (1)		Ref (1)
Intermediate-high	0.425 (−0.234 to 1.084)	0.206	−0.008 (−0.639 to 0.654)	0.982	−0.391 (−1.014 to 0.232)	0.218

Data are presented as odds ratios (95% confidence intervals).

Model 1. Adjusted for age and gender.

Model 2. Adjusted for age, gender, HbA1c, and diabetes duration.

CI, confidence interval; OR, odds ratio; T2DM, type 2 diabetes mellitus.

When we examine the relationship between noninvasive fibrosis indices and macrovascular complication, neither FIB-4 nor NFS influences the presence of peripheral artery disease, cerebrovascular disease, and coronary artery disease (P > 0.05 for all) (Table 5).

Table 5.

Logistic Regression Analyses of Macrovascular Complication in Patients Having T2DM with NAFLD

	Peripheral arterial disease		Cerebrovascular disease		Coronary artery disease
	OR (95% CI)	P value	OR (95% CI)	P value	OR (95% CI)	P value
NFS
Model 1
Low	Ref (1)		Ref (1)		Ref (1)
Intermediate	0.433 (−0.772 to 1.638)	0.481	−0.616 (−1.440 to 0.208)	0.143	−0.032 (−0.659 to 0.594)	0.920
High	0.413 (−1.435 to 2.262)	0.661	−0.228 (−1.397 to 0.940)	0.702	−0.975 (−2.048 to 0.099)	0.075
Model 2
Low	Ref (1)		Ref (1)		Ref (1)
Intermediate	0.260 (−0.974 to 1.494)	0.680	−0.147 (−1.468 to 0.219)	0.147	−0.138 (−0.788 to 0.513)	0.678
High	0.416 (−1.454 to 2.286)	0.663	−0.585 (−1.528 to 0.862)	0.585	−1.016 (−2.105 to 0.073)	0.067
FIB-4
Model 1
Low	Ref (1)		Ref (1)		Ref (1)
Intermediate-high	−1.455 (−3.002 to 0.091)	0.065	0.308 (−0.430 to 1.046)	0.413	0.245 (−0.369 to 0.859)	0.245
Model 2
Low	Ref (1)		Ref (1)		Ref (1)
Intermediate-high	−1.612 (−3.252 to 0.027)	0.054	0.300 (−0.447 to 1.047)	0.431	0.341 (−0.288 to 0.970)	0.288

Data are presented as odds ratios (95% confidence intervals).

Model 1: Adjusted for age and gender.

Model 2: Adjusted for age, gender, HbA1c, and diabetes duration.

Discussion

NAFLD is related to cardiovascular outcomes in diabetes.¹¹ However, the association of the degree of fibrosis and diabetic complications remains unknown. In this study, we demonstrated that FIB-4 and NFS scores, which are noninvasive markers of NAFLD, are closely associated to macrovascular and microvascular complications of diabetes. Although diabetic retinopathy is related to both FIB-4 and NFS, diabetic nephropathy is related only to NFS. In addition, NFS and FIB-4 scores are related only to the presence of coronary artery disease among macrovascular complications after adjusting for confounding factors, including age, sex, diabetes duration, and HbA1c.

NAFLD is characterized by insulin resistance and hepatic fat accumulation, which includes two distinct entities: NAFL and nonalcoholic steatohepatitis. Age (<50 years), T2DM, and metabolic syndrome are known risk factors for NAFLD.¹² Individuals with T2DM have a higher prevalence of NAFLD and advanced fibrosis, which have been suggested to predict mortality.¹³ Although NAFLD screening is recommended in at-risk patient groups, the risks of liver biopsy and testing costs limit the feasibility of screening programs.¹² Noninvasive fibrosis scores are easily applicable clinical–laboratorial scores that help the clinician to rule out advanced fibrosis. Among noninvasive hepatic fibrosis scores, FIB-4 and NFS were recommended by EASL⁵ and AASLD⁶ as noninvasive screening tests for the estimation of advanced liver fibrosis. Among noninvasive hepatic fibrosis scores, NFS and FIB-4 have been validated to evaluate liver fibrosis in patients with T2DM, also suggested for predicting future cardiovascular events.¹⁴ It has been stated that coexistence of T2DM and NAFLD not only accelerates the progression of liver disease but also worsens dyslipidemia and liver insulin resistance, further exacerbating atherosclerosis and finally increasing the risk of cardiovascular events and kidney disease in patients with T2DM.¹⁵

To date, there have been limited studies regarding the association between NAFLD and microvascular complications (retinopathy, neuropathy, and nephropathy) in patients with T2DM.^16
–18 We demonstrated that, although NFS and FIB-4 were similar in both NAFLD and non-NAFLD patients with T2DM, noninvasive fibrosis scores are related with the presence of microvascular and macrovascular complications of diabetes. Takahashi et al. suggested that ultrasound may underestimate the presence of NAFLD; therefore, the scoring system may be useful to extract high-risk cases for systemic complications in those with and without fatty liver in ultrasound.¹⁹ For this reason, we might observe similar NFS and FIB-4 scores in patients with and without fatty liver. Our results are in accordance with previous data linking NAFLD and microvascular complications^20

–23; however, some results are conflicting.^8,21 We showed that, among microvascular complications, the presence of diabetic retinopathy is correlated with both FIB-4 and NFS scores. In regression analysis, an increase in NFS reflects higher rates of the presence of diabetic retinopathy, independent from age, glycemic regulation, and diabetes duration. Targer et al. reported that NAFLD is closely associated with an increased prevalence of diabetic retinopathy.²⁰ In contrast, Zhang M et al. found that the incidence of diabetic retinopathy is lower in T2DM patients with NAFLD, and diabetes duration was the main determinant.²¹ Leite et al. noted decreased prevalence of retinopathy in patients having T2DM with NAFLD.⁸ To our knowledge, hyperglycemia as a result of insulin deficiency or resistance is the key pathogenic mechanism in diabetic retinopathy.²² Therefore, NAFLD and diabetic retinopathy share common pathophysiological pathways, and casualty should be investigated.

Microvascular complications of diabetes other than diabetic retinopathy, namely, diabetic nephropathy and diabetic neuropathy, were not associated with NFS and FIB-4 scores in this study. There are limited number of studies investigating the relationship between diabetic nephropathy and NAFLD evaluated by noninvasive tests, and the results are controversial. Han et al. demonstrated that hepatic fibrosis, according to NFS, associated with urinary protein-to-creatinine ratio in patients with T2DM and hypothesized simultaneous progression of fibrosis in the hepatic parenchyma and in the renal tubules.²³ However, a similar relationship between noninvasive hepatic fibrosis indices and urinary albumin-to-creatinine ratio was not established. Therefore, Han et al. speculated that non-albumin proteinuria may play a pathophysiological role in hepatic fibrosis in T2DM.²³ Leite NC et al. showed that not FIB-4 but NFS associated with composite renal events in patients with T2DM.¹¹ On the contrary, Casoinic et al. suggested a positive correlation between microalbuminuria and NAFLD on ultrasound.²⁴ In contrast, the prevalence of diabetic nephropathy is inversely associated with the presence of NAFLD on ultrasound in Korean patients with T2DM.¹⁷ These heterogeneous results may have been due to different diagnostic tools to identify NAFLD and fibrosis, as well as different ethnicities. In addition, diabetic nephropathy diagnosis in patients with T2DM was based on urinary albumin-to-creatinine ratio, which may affect the data interpretation.

It has been reported that systemic inflammation, hepatic insulin resistance, endothelial dysfunction, oxidative distress, and adipose tissue dysfunction are the common pathophysiological pathways of NAFLD and cardiovascular disease.^25,26 In addition, plaque vulnerability is supposed to be a severe issue both for carotid artery inflammation and NAFLD.²⁷ Although T2DM and NAFLD are risk factors for cardiovascular diseases, NAFLD and macrovascular complication relationship was not extensively defined in patients with diabetes.²⁸ Beyond the knowledge of cardiovascular disease and liver fibrosis relationship, we evaluated the macrovascular complications and noninvasive liver fibrosis scores in cohorts of patients with T2DM. We could not establish a relationship between FIB-4, NFS score, and macrovascular complications of diabetes. One of the previous studies demonstrated that liver fibrosis, as detected by Fibroscan, is independently associated with the presence of macrovascular complications.²⁹

Although numerous pathophysiological pathways get involved in diabetes microvascular and macrovascular complications, it has been well established that previous hyperglycemia and HbA1c values are strongly associated.³⁰ As far we know, there are limited data on the association of HbA1c and noninvasive fibrosis indices.³¹ In a study with patients with T2DM, it was revealed that patients with NAFLD on ultrasound and non-NAFLD have similar HbA1c values.³² On the contrary, Yoo JH et al. suggested an association between the coefficient of variation of HbA1c and incident NAFLD in the diabetes group only in those with an increasing trend of postbaseline HbA1c.³³ We cannot establish any association between both fasting blood glucose and HbA1c values with the noninvasive liver fibrosis indices. However, we found that age and diabetes duration are correlated with FIB-4 but not NFS score. Luo Y et al. reported that FIB-4 is correlated with age and diabetes duration.³⁴ They insisted on the effect of age on liver fibrosis rather than diabetes duration. However, we consider that factors effective on various noninvasive liver fibrosis indices in patients with T2DM still need to be clarified.

Our study has few limitations. First, we cannot establish a causal relationship and pathogenesis between NFS value and diabetic retinopathy because of the observational design of the study. In addition, the lack of regular follow-up period limits us from evaluating fibrosis changes in relation to chronic diabetes complications. Second is the lack of liver biopsy to confirm the degree of fibrosis. Third, this is a pilot study from a single tertiary center, so it cannot be applied to general population. Both SGLT2 inhibitors and glucagon-like peptide-1 (GLP-1) agonists have been suggested to have beneficial effects on liver fibrosis and steatosis, as well as glycemic regulation.^35,36 Therefore, a reduced usage of SGLT2 inhibitors and GLP-1 receptor agonists in our study population may interfere with the results. Finally, mean BMI of patents with NAFLD was >30kg/m², which may be related to increased risk factor of NAFLD in patients with T2DM, obesity, and metabolic syndrome.¹² On the contrary, the major strength of our study is to demonstrate the relationship between NFS values, as a noninvasive and simple test for liver fibrosis severity in NAFLD, and diabetes retinopathy patients with T2DM.

Conclusion

As of now, limited studies investigate the relationship between noninvasive liver fibrosis scores and complications in T2DM. In this real-life study, only the values of NFS > 0.676 were found to be associated with diabetic retinopathy in patients having T2DM with NAFLD. However, FIB-4 score was not related to both microvascular and macrovascular complications of diabetes. Nevertheless, further populational studies will clarify not only the relationship of NFS and FIB-4 scores with diabetic complications but also the usability of noninvasive fibrosis scores in monitoring diabetic complications.

Footnotes

Authors’ Contributions

Concept: B.B. and S. Akin. Design: B.B. Supervision: Ö.K. Data collection and processing: H.E. Analysis and interpretation: S. Arslan. Literature search: H.E. Writing: H.E. Critical review: B.B., H.E., and S. Akin.

Ethical Approval

Approval of the ethics committee was obtained on July 27, 2022 (number: 2022/514/230/22) at Istanbul Kartal Dr. Lutfi Kirdar City Hospital. The requirement for written informed consent was waived owing to the retrospective design of the study.

Data Availability Statement

Data are available from the corresponding author upon request.

Author Disclosure Statement

Authors declare there is no conflict of interests in this study.

Funding Information

This research did not receive any specific grant.

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