Abstract
Background
Several genetic and metabolic variables, most notably the variation in the adipokine gene rs1501298, have been linked to metabolic-associated fatty liver disease etiopathogenesis (MAFLD). Liver biopsy, the gold standard for diagnosing MAFLD, is an invasive procedure; therefore, alternative diagnostic methods are required. Consequently, the integration of these metabolic variables with some of the patients’ characteristics may facilitate the development of noninvasive diagnostic methods that aid in the early detection of MAFLD, identification of at-risk individuals and planning of management strategies.
Methods
This study included 224 Egyptians (107 healthy individuals and 117 MAFLD patients). Age, sex, BMI, clinical and laboratory characteristics, and rs1501299 adipokine gene polymorphisms were examined. The rs1501299 variant, insulin resistance, hypertension, obesity, blood pressure, lipid profile, hemoglobin A1C level, and hepatic fibrosis predictors were evaluated for MAFLD risk. The feasibility and effectiveness of developing non-invasive MAFLD diagnostic models will be investigated.
Results
The +276G/T (rs1501299) polymorphism (GG vs GT/TT) was linked with MAFLD (OR: 0.43, CI: 0.26–0.69, P = 0.002). The GG variants had lower MAFLD rates than those of the GT and TT variants. In addition to altered lipid profiles, patients with MAFLD showed increased gamma-glutamyl transferase levels (GGT: 56 IU/L vs. 36 IU/L). Genetic diversity also affects the accuracy of hepatic fibrosis and steatosis prediction. Hepatic fibrosis and steatosis predictors had receiver operating characteristic (ROC) AUCs of 0.529%, 0.846%, and 0.700–0.825%, respectively. We examined a diagnostic model based on these variables and demonstrated its effectiveness.
Conclusion
The Adipokine variant rs1501299 increased the risk of MAFLD. Identifying and genotyping this variation and other metabolic variables allow for a noninvasive diagnostic model for early MAFLD diagnosis and identification of those at risk. This study illuminates the prevention and management of MAFLD. Further research with more participants is needed to verify these models and to prove their MAFLD diagnostic efficacy.
Keywords: metabolic metabolic-associated fatty liver disease, adipokine gene polymorphism, hepatic indices, metabolic syndrome
Metabolic-associated fatty acid disease (MAFLD) is a highly heterogeneous disease and is regarded as one of the most frequent hepatic disorders worldwide. It is one of the main causes of liver cirrhosis, hepatocellular carcinoma (HCC), and liver transplantation.1 Various genetic and environmental factors can influence MAFLD development and progression. MAFLD is associated with several comorbidities that can affect the disease’s progression. One of these comorbidities is metabolic syndrome (MetS), the most distinguishing symptom of MAFLD, affecting 36–67% of patients.2,3 MetS is a significant contributor to disease progression in MAFLD, and there is a bidirectional relationship between MetS and MAFLD.4 A meta-analysis of 81,411 participants found a substantial increase in the incidence of MetS among patients with nonalcoholic fatty liver disease (NAFLD) over a 5-year follow-up period, with a relative risk of 3.22.5 Most MAFLD patients are obese, with up to 75% of patients being overweight, and MAFLD affects 90% of severely obese patients.6 The onset and progression of MAFLD is influenced by both genetic and environmental variables, which are complex, multistep processes.7, 8, 9 Furthermore, host genetic variations have been linked to the vulnerability and pathogenesis of metabolic syndrome. For example, variations in the adiponectin gene, particularly the rs1501299 single nucleotide polymorphism (SNP), are associated with low adiponectin levels, obesity, insulin resistance, and MAFLD.10,11 This SNP has been identified as a potential risk factor for the incidence and progression of NAFLD in several populations.
Previous studies have identified the rs1501299 SNP in the adiponectin gene as a potential risk factor for the incidence and progression of MAFLD in several populations. However, data on Egyptians are scarce. Given the complexities of the influence of various genetic factors on different ethnicities, this study aimed to fill a knowledge gap, investigate the correlation between this gene variant and MAFLD, establish a noninvasive diagnostic model for the same, and investigate its efficacy in the Egyptian population.
Material and Methods
Study Design
This retrospective case–control study was conducted between June 2021 and September 2022 in patients with MAFLD. Both the case and control groups were recruited from the outpatient hepatology clinic of the Minia University, Egypt. Two hundred and twenty-four participants were enrolled in the study and divided into two groups. The first group contained 117 prediabetic obese patients with verified MAFLD and the second group included 107 non-MAFLD individuals as controls. Specific inclusion criteria were defined in our study for the enrolled patients as follows: male or female patients, at least 19 years of age, with a diagnosis of fatty liver based on upper abdominal ultrasonography (US), and a prediabetic diagnosis according to the 2022 criteria from the American Diabetes Association. MAFLD was diagnosed by a radiologist based on the brightness of the liver and the presence of diffuse echogenicity in the liver parenchyma on abdominal ultrasonography. The participants in both the control and patient groups were obese or overweight (BMI > 25). The exclusion criteria were pregnancy and lactation, cirrhosis, autoimmune hepatitis, and other causes of liver disease (viral hepatitis, hemochromatosis, Wilson's disease), chronic enteropathies, chronic kidney disease, and long-term use of steatogenic drugs, such as methotrexate, tamoxifen, steroids, and amiodarone. We collected information on age, sex, smoking, history of alcohol use (>30 g/day for males and >15 g/day for females), and medications from all participants.
Ethical Approval
The research ethics committee at the Faculty of Medicine, Minia University, approved the study (IRB Approval No. 19:1/2021), which was conducted in accordance with the principles of the Helsinki Declaration. Before participation, all patients and healthy controls were briefed about the study methodology and asked to sign a written informed consent form.
Sample Size
A simplified formula was used: (P1 = (OR × Po)/(1−Po + OR × Po)−n = 2pq (Za + Zß)2/(P1−Po)2). In this study (P0 = 0.2, OR = 2, a = 0.05, b = 0.1, Za = 1.96, Zb = 1.282, and N = 224), the sample size of 224 participants met the research standards and was sufficient to test the authors' hypothesis.
Laboratory Investigations
Blood Samples
For this study, 10 mL of blood was collected from all patients (5 ml was collected in dry tubes and 5 ml was collected in EDTA tubes). The serum tubes were then centrifuged at 3000 rpm for 10 min at 4 °C for routine investigation. The following laboratory tests were performed on the samples using an automated chemistry analyzer (HITACHI Co. 311): fasting blood glucose (mg/dL), glycated hemoglobin (HbA1C in %), fasting insulin (mU/L), albumin (g/dL), alanine transaminase (ALT, U/L), aspartate transaminase (AST in U/L), gamma-glutamyl transferase (GGT in U/L), alkaline phosphatase (ALP in U/L), LDL-C (in mg/dL), and HDL-C (in mg/dL), triglycerides (in mg/dL), and total cholesterol (in mg/dL). Markers of inflammatory and hepatic function, alpha-fetoprotein (in ng/mL), and high-sensitivity of C-reactive protein (hsCRP, in mg/dL) were detected. An enzyme-linked immunosorbent assay (ELISA) was used to analyze serum 25(OH) D, fasting insulin, hsCRP, and AFP (EIA-5396; DRG International Inc., Springfield, New Jersey, USA), according to the manufacturer's instructions. Definitions of hepatic fibrosis predictor indices are provided in the supplementary material.
DNA Extraction and Genotyping of Adipokine rs1501299
DNA was extracted using a QIAamp ® DNA Blood Mini Kit (QIAGEN GmbH, Hilden, Germany) according to the manufacturer's instructions. The concentration of the extracted DNA was measured using a NanoDrop ® (ND-1000) spectrophotometer (NanoDrop Technologies Inc., Washington, USA). The rs1501299 G/T nucleotide polymorphisms in the adipokine gene were analyzed using the TaqMan® SNP genotypic test (SNPs). Predesigned primer/probe sets were used to genotype SNP primers for the adiponectin gene. To amplify the DNA, the Rotor-Gene Q technique (Qiagen) was employed, which involved running at 95 °C for 10 min, followed by 45 cycles at 95 °C for 15 s, and then at 60 °C for 1 min.
Statistical Analysis
All statistical analyses were performed using the IBM SPSS Statistics software (version 25.0; SPSS, Inc.). The statistical significance was set at P < 0.05. The clinical features of the patients were summarized using the mean and standard deviation (SD) or median and interquartile range (IQR). The Kolmogorov–Smirnov test was used to assess the normal distribution of continuous variables. Continuous variables with a normal distribution were compared using the independent-sample t-test, whereas non-normally distributed continuous variables were compared using the Mann–Whitney U-test. For categorical variables, the chi-squared test was used. SNP analysis was performed using SNP stats (Institut Català d'Oncologia, Barcelona, Spain; https://www.snpstats.net/start.htm). The predominant homozygous genotype or allele in the healthy control population was used as the reference. Unconditional multivariate logistic regression was used to examine the probability and clinical data while controlling for age, BMI, and sex. A P-value of less than 0.05 and a 95% confidence interval (CI) were considered statistically significant.
The diagnostic value of each variable was determined by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). The optimum cut-off value for NAFLD diagnosis was identified (maximum value for the sum of sensitivity and specificity). The diagnostic indices are expressed as percentages. Stepwise logistic regression was applied to variables with a P-value of 0.05 in multivariate studies and a high area under the ROC. Insulin resistance (IR) index (HOMA-IR), metabolic syndrome score (MetS), hepatic fibrosis predictor indices (API, APRI, AAR, BAAT, BRAD, FIB-4, Fibro index, FIBROQ, Forns index, FPI, King score, LOK index, NAFLD fibrosis), and hepatic steatosis predictor indices (HIS, NAFLD LFS, TyG Index) were calculated as previously described (Supplementary 1).
Results
Clinical and Demographic Characteristics
In total, 117 patients with MAFLD and 107 non-MAFLD controls were enrolled in our study. No statistically significant differences were detected between the case and control groups in terms of the demographic characteristics. However, when BMI and various biochemical indicators such as CRP, FBG, INR, AFP, ALT, AST, GGT, total bilirubin, triglycerides, total cholesterol, and LDL were compared, the MAFLD group showed significantly higher values than the control group (P-value<0.05). In contrast, other indicators, including HOMA-IR, BUN, albumin, hemoglobin, WBCs, and creatinine, showed statistically insignificant differences between the two groups (Table 1).
Table. 1.
The Baseline Demographic and Clinical Characteristics of Both MAFLD and Non-MAFLD Individuals.
| Variable | MAFLD individuals | Non-MAFLD individuals | P value |
|---|---|---|---|
| No. of participants | 117 | 107 | |
| Baseline characteristics | |||
| Age (IQR) | 55 (43, 61) | 48 (43, 53) | P = 0.1540 |
| Gender (% of females) | 55% | 48.5% | P = 0.763 |
| Obesity (IQR) | 34 (30,35) | 27(25,30) | P < 0.06 |
| Active smoking | 32.7% | 32.8% | P = 0.09 |
| Alcohol intake | 2.8% | 1.9% | P = 0.8 |
| BMI(IQR) | 35 (30, 37) | 26 (22, 30) | P < 0.0001∗ |
| Laboratory results | |||
| CRP (IQR) | 11.2 (8, 13) | 4.6 (3.6, 6.1) | P < 0.0001∗ |
| Glucose level (IQR) | 105 (99, 112) | 95 (89, 100) | P < 0.0001∗ |
| HOMA-IR (IQR) | 3 (2, 3.7) | 3 (2, 3.6) | P = 0.5447 |
| MetS(SD) | 34(31.78%) | 77(71.96%) | P < 0.0001∗ |
| INR(IQR) | 1.1 (1.2, 1.3) | 0.98 (0.95, 1) | P < 0.0001∗ |
| BUN (Mean ± SD) | 4.9 ± 1.03 | 4.5 ± 0.9 | P = 0.0137∗ |
| Albumin(IQR) | 3.8 (3.6, 4) | 3.8 (3.6, 4) | P = 0.047∗ |
| AFP(IQR) | 9 (7, 12) | 6 (5, 7) | P < 0.0001∗ |
| ALT(IQR) | 34 (33, 43) | 29 (26, 36) | P < 0.0001∗ |
| AST(IQR) | 40 (34, 44) | 33 (26, 38) | P < 0.0001∗ |
| T. bilirubin(IQR) | 1 (0.8, 1.2) | 0.8 (0.6, 0.9) | P < 0.0001∗ |
| GGT (IQR) | 56 (36, 64) | 36 (26, 41) | P < 0.0001∗ |
| TG(IQR) | 170 (134, 200) | 144 (125, 155) | P < 0.0001∗ |
| T. Chol (IQR) | 189 (180, 190) | 147 (134, 156) | P < 0.0001∗ |
| HDL(IQR) | 32 (30, 44) | 41 (40, 47) | P < 0.0001∗ |
| LDL(IQR) | 120 (102, 152) | 103 (99, 112) | P < 0.0001∗ |
| Hb (IQR) | 10.7 (10, 12.6) | 10.7 (10, 13) | P = 0.96 |
| WBCs (IQR) | 6.8 (3.6, 8.8) | 4.4 (0.7, 6.4) | P = 0.83 |
| Creatinine (IQR) | 0.9 (0.8, 1.1) | 1 (0.9, 1.1) | P = 0.2445 |
| Ultrasound findings | |||
| Fatty liver | 73% | 0% | |
| Bright liver | 27% | 0% | |
| Splenomegaly | 13% | 0% | |
∗P-value < 0.05. MAFLD, Metabolic-Associated Fatty Liver Disease; IQR, Interquartile Range; BMI, Body Mass Index; CRP, C Reactive protein; HOMA-IR, Homeostatic Model Assessment for Insulin Resistance; Mets, Metabolic Syndrome; SD, Standard Deviation; INR, International normalized ratio; BUN, Blood Urea Nitrogen; AFP, Alpha-fetoprotein; ALT, alanine transaminase; AST, Aspartate Aminotransferase; GGT, Gamma-Glutamyl Transferase; TG, Triglycerides; T. Chol, total cholesterol; HDL, High-Density Lipoprotein; LDL, Low-Density Lipoprotein; HB, Hemoglobin; WBCs, white blood cell.
Association of Adipokine rs1501299 With the Susceptibility to Metabolic-Associated Fatty Liver Disease Etiopathogenesis
The minor allele frequency (MAF) for SNPs in the control group was greater than 0.1, which was close to the global MAF for adipokine rs1501299 (minor T = 0.3) (T)| population MAF:0.44. The distribution of 1501299 in non-MAFLD and MAFLD patients was not substantially different from that in HWE (P > 0.05). The genotype and allele frequencies of the 1501299 MAFLD patients are shown in Table 2. The frequencies of the 1501299 major G and minor T alleles were significantly different between non-MAFLD and MAFLD patients (P = 0.0001), with the minor T allele linked to an increased risk of NAFLD [adjusted OR (95% CI) = 3.17 (1.49–6.74), P = 0.0016]. In the codominant models, the rs1501299 genotype frequencies were notably different between patients with MS and stable controls GG, GT, TT (18.7%, 65.4%, and 15.9%, respectively) in non-MAFLD individuals versus (37.4%, 57%, and 5.6%) in NAFLD patients, respectively; P = 0.0001). The GT + TT genotype was associated with a 4.65% higher incidence of MAFLD than the GG genotype (GT + TT vs. GG, P = 0.0013). The dominant homozygous GG genotype (4.32, 7.33) increased MAFLD susceptibility (GG versus GT and GT, P = 0.0001) in the dominant model. The homozygous GG genotype was associated with a 2.54-fold increased incidence of MAFLD (GG vs. GG + GT, P0.0001). Age, sex, and BMI affected the data (Table 1), and there was no difference in age and sex between MAFLD and non-MAFLD groups; however, there was a significant difference between the two groups and BMI (Table 1).
Table 2.
Adipokine Association With Response Patient.
| Model | Genotype | Non-MAFLD | MAFLD | OR (95% CI) | P-value |
|---|---|---|---|---|---|
| Codominant | G/G | 40 (37.4%) | 20 (18.7%) | 1 | 0.0044∗ |
| G/T | 61 (57%) | 70 (65.4%) | 4.32 (1.59–11.78) | ||
| T/T | 6 (5.6%) | 17 (15.9%) | 7.33 (1.44–37.28) | ||
| Dominant | G/G | 40 (37.4%) | 20 (18.7%) | 1 | 0.0013∗ |
| G/T–T/T | 67 (62.6%) | 87 (81.3%) | 4.65 (1.73–12.45) | ||
| Recessive | G/G–G/T | 101 (94.4%) | 90 (84.1%) | 1 | 0.17 |
| T/T | 6 (5.6%) | 17 (15.9%) | 2.64 (0.64–10.87) | ||
| Over dominant | G/G-T/T | 46 (43%) | 37 (34.6%) | 1 | 0.032∗ |
| G/G | 61 (57%) | 70 (65.4%) | 2.54 (1.06–6.08) | ||
| Allelic | G | 141(63%) | 110(49%) | 1 | 0.0016∗ |
| T | 73(32%) | 114(51%) | 3.17 (1.49–6.74) |
MAFLD, Metabolic-Associated Fatty Liver Disease; OR, Odds Rat. Data are presented as Mean ± SD, ∗P-value < 0.05. (n = 214, adjusted by age + sex + BMI).
There were no differences (P < 0.05) in the distribution of genes according to BMI groups (Table 5) showed no significant differences (P > 0.05) in participants’ BMI and SNP.
Table 5.
Clinical Features Based on Genotypes and Haplotypes.
| GG Non MAFLD | GT Non-MAFLD | TT Non-MAFLD | P-value | GG MAFLD | GT MAFLD | GG MAFLD | P-value | GG | GT |
TT |
P-value | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | SD. | Mean | SD. | Mean | SD. | Mean | SD. | Mean | SD. | Mean | SD. | Mean | SD. | Mean | SD. | Mean | SD. | ||||
| ALT | 30.00 | 7.60 | 31.00 | 5.20 | 25.00 | 4.80 | 0.02∗ | 36.00 | 5.30 | 39.00 | 7.70 | 40.0 | 8.70 | 0.10 | 36.00 | 7.40 | 35.40 | 7.71 | 32.40 | 7.44 | <0.0001∗ |
| AST | 33.00 | 10.00 | 34.00 | 7.30 | 29.00 | 11.00 | 0.03∗ | 41.00 | 7.20 | 40.00 | 7.10 | 38.0 | 9.00 | 0.45 | 37.07 | 7.92 | 35.95 | 10.05 | 35.90 | 10.20 | 0.05∗ |
| BIL | 0.74 | 0.22 | 0.75 | 0.18 | 0.57 | 0.27 | 0.44 | 0.89 | 0.32 | 0.99 | 0.28 | 0.95 | 0.27 | 0.70 | 0.79 | 0.26 | 0.88 | 0.27 | 0.86 | 0.32 | 0.52 |
| INR | 1.00 | 0.10 | 1.00 | 0.10 | 1.00 | 0.10 | 0.44 | 1.30 | 0.20 | 1.20 | 0.20 | 1.30 | 0.20 | 0.20 | 1.07 | 0.18 | 0.21 | 0.21 | 1.21 | 0.22 | 0.02∗ |
| Albumin | 3.90 | 0.20 | 3.80 | 0.20 | 3.80 | 0.20 | 0.33 | 3.80 | 0.20 | 3.80 | 0.30 | 3.70 | 0.20 | 0.57 | 3.83 | 0.24 | 3.81 | 0.23 | 3.77 | 0.22 | 0.93 |
| Glucose | 92.00 | 11.00 | 94.00 | 11.00 | 108.00 | 13.00 | 0.01∗ | 109.0 | 13.00 | 107.00 | 13.00 | 106. | 12.0 | 0.80 | 107 | 11.8 | 100.70 | 13.96 | 97.77 | 14.28 | 0.03∗ |
| Insulin | 12.00 | 4.20 | 13.00 | 4.80 | 11.00 | 4.50 | 0.62 | 10.00 | 3.70 | 12.00 | 4.70 | 11.0 | 5.00 | 0.42 | 11.29 | 4.10 | 12.05 | 4.744 | 11 | 4.78 | 0.42 |
| AFP | 6.70 | 4.30 | 6.10 | 2.50 | 6.10 | 1.90 | 0.07 | 13.00 | 7.80 | 11.00 | 5.70 | 9.10 | 4.30 | 0.04 | 8.64 | 6.31 | 8.47 | 5.028 | 8.33 | 4.02 | 0.03∗ |
| BMI | 27.00 | 4.90 | 26.00 | 4.90 | 27.00 | 3.00 | 0.44 | 34.00 | 3.80 | 34.00 | 5.00 | 32.0 | 4.30 | 0.38 | 29.36 | 5.71 | 30.07 | 6.161 | 31 | 4.55 | 0.21 |
| TG | 142.0 | 19.00 | 147.00 | 22.00 | 146.00 | 30.00 | 0.34 | 156.0 | 43.00 | 176.00 | 39.00 | 169. | 40.0 | 0.84 | 146.90 | 29.65 | 162.50 | 35.2 | 163 | 38 | 0.01∗ |
| CHOL | 149.0 | 21.00 | 149.00 | 18.00 | 141.00 | 14.00 | 0.40 | 185.0 | 14.00 | 193.00 | 25.00 | 185. | 28.0 | 0.00∗ | 161.10 | 25.01 | 172.50 | 31.34 | 173 | 31.9 | 0.04∗ |
| HDL | 43.00 | 10.00 | 45.00 | 7.90 | 46.00 | 9.90 | 0.17 | 34.00 | 9.00 | 35.00 | 9.20 | 39.0 | 11.0 | 0.50 | 40.17 | 10.74 | 39.51 | 9.816 | 41.1 | 11.2 | 0.58 |
| LDL | 106.0 | 11.00 | 109.00 | 10.00 | 110.00 | 14.00 | 0.63 | 123.0 | 24.00 | 129.00 | 29.00 | 122. | 26.0 | 0.59 | 111.50 | 18.43 | 119.30 | 24.5 | 119 | 23.6 | 0.05∗ |
| BUN | 4.38 | 0.71 | 4.53 | 0.91 | 5.35 | 0.96 | 0.03 | 4.70 | 1.01 | 4.80 | 1.05 | 5.10 | 0.97 | 0.92 | 4.51 | 0.84 | 5.18 | 0.957 | 5.18 | 0.957 | 0.02∗ |
| MetS | 0.30 | 0.50 | 0.30 | 0.50 | 0.30 | 0.50 | <0.0001∗ | 0.80 | 0.40 | 0.70 | 0.50 | 0.80 | 0.40 | 0.96 | 0.47 | 0.50 | 0.52 | 0.502 | 0.65 | 0.487 | 0.98 |
| CRP | 5.10 | 2.60 | 5.30 | 3.80 | 4.40 | 0.70 | 0.72 | 9.80 | 4.70 | 10.00 | 4.20 | 10.0 | 4.80 | 0.72 | 6.65 | 4.06 | 7.83 | 4.672 | 8.63 | 4.83 | 0.42 |
| GGT | 35.00 | 8.10 | 35.00 | 9.60 | 39.00 | 16.00 | 0.0511 | 57.00 | 21.00 | 49.00 | 18.00 | 52.0 | 20.0 | 0.69 | 42.30 | 17.15 | 42.31 | 16.2 | 49 | 19.5 | 0.48 |
| PLT | 251.4 | 72.41 | 248.90 | 70.55 | 211.70 | 50.37 | 0.62 | 282.0 | 87.00 | 276.00 | 84.00 | 292. | 68.0 | 0.56 | 261.50 | 78.11 | 263.50 | 79.04 | 271 | 72.69 | 0.88 |
Data are presented as the mean ± SD, ∗P-value < 0.05. ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; BIL, Bilirubin; INR, International Normalized Ratio; AFP, Alpha-Fetoprotein; BMI, Body Mass Index; TG, Triglycerides; CHOL, Total Cholesterol; HDL, High-Density Lipoprotein; LDL, Low-Density Lipoprotein; BUN, Blood Urea Nitrogen; Mets, Metabolic Syndrome; CRP, C-Reactive Protein; GGT, Gamma-Glutamyl Transferase; PLT, Platelets Level.
Diagnostic Performance of Metabolic-associated Fatty Liver Disease Etiopathogenesis Indexes and ADIPOQ Single Nucleotide Polymorphism
The AUC of ROC for API, APRI, AAR, BAAT, BRAD, FIB-4, Fibro index, FIBROQ, Forns index, FPI, King score, LOK index, and NAFLD fibrosis were 0.548, 0.555, 0.529, 0.846, 0.610, 0.656, 0.9278, 0.714,0.692, 0.5518, 0.835, 0.763, and 0.647, respectively. The AUC of the HIS, NAFLD, LFS, and TyG index were 0.825, 0.700, and 0.821, respectively (Figure 1, Table 3).
Figure 1.
Evaluation of the diagnostic performance of noninvasive models for MAFLD indexes using area under the ROC curve. (a) AUC of ROC for API, APRI, AAR, BAAT, BRAD, FIB-4; (b) AUC of ROC for Fibro index, FIBROQ, Forns index, FPI, King score, LOK index, NAFLD fibrosis; (c) AUC of ROC The AUC of HIS, NAFLD LFS, and TyG index. MAFLD, metabolic-associated fatty liver disease etiopathogenesis; ROC, receiver operating characteristic.
Table 3.
The AUC for Multiple Biomarkers for MAFLD.
| Statement | AUC | 95% CI | Sensitivity | Specificity | Cutoff | PPV | NPV |
|---|---|---|---|---|---|---|---|
| Hepatic fibrosis predictors indexes | |||||||
| 1. API | 0.554 | 0.484 to 0.622 | 71.03 | 39.29 | >0.24 | 28.05 | 80.25 |
| 2. APRI | 0.549 | 0.480 to 0.617 | 86.92 | 0.93 | >0.76 | 22.56 | 17.23 |
| 3. AST/ALT | 0.531 | 0.462 to 0.600 | 22.43 | 100 | ≤0.85 | 100 | 79.46 |
| 4. BAAT | 0.845 | 0.791–0.892 | 60.75 | 49.53 | >2 | 28.63 | 79.11 |
| 5. BRAD | 0.608 | 0.541–0.675 | 72.90 | 0.00 | >2 | 19.55 | 0 |
| 6. FIB-4 | 0.656 | 0.588–0.719 | 71.03 | 1.87 | >0.96 | 19.44 | 16.22 |
| 7. Fibro index | 0.927 | 0.884–0.958 | 66.36 | 0 | >2.2 | 18.11 | 0 |
| 8. FIBROQ | 0.711 | 0.645–0.771 | 76.64 | 0.93 | >1.76 | 20.50 | 10.67 |
| 9. Forns index | 0.692 | 0.625 to 0.753 | 62.62 | 0.94 | >6.61 | 17.40 | 7.015 |
| 10. FPI | 0.764 | 0.701 to 0.819 | 62.62 | 0.94 | >20.6 | 17.40 | 7.015 |
| 11. King score | 0.833 | 0.776 to 0.881 | 81.31 | 0.93 | >6.67 | 21.48 | 12.99 |
| 12. Lok index | 0.762 | 0.699–0.817 | 65.42 | 0.93 | >10.4 | 18.04 | 7.47 |
| 13. MAFLD fibrosis | 0.644 | 0.576 to 0.709 | 29.91 | 71.96 | >1.36 | 26.23 | 75.49 |
| Hepatic steatosis predictors indexes | |||||||
| HIS | 0.825 | 0.768 to 0.874 | 73.83 | 0.93 | >39.1 | 19.9 | 9.63 |
| MAFLD LFS | 0.700 | 0.634 to 0.760 | 66.36 | 90.57 | >-0.09 | 70.11 | 88.98 |
| TyG Index | 0.821 | 0.763 to 0.870 | 65.42 | 0 | >4.86 | 65.42 | 17.90 |
| ADIPOQ | |||||||
| SNP rs1501299 | 0.62 | 0.552 to 0.686 | 81.31 | 37.38 | |||
Data are presented as mean ± SD, ∗P-value < 0.05.
AUC, Area Under The curve; CI, confidence interval; PPV, Positive Predictive Value; NPV, Negative Predictive Value; API, Age Platelet Index; APRI, AST to Platelet Ratio index; AST/ALT, Aspartate Aminotransferase to Alanine Aminotransferase Ratio; BAAT, bile acid-CoA, amino acid N-acyltransferase; BRAD, body mass index, AST/ALT ratio, and presence of type 2 diabetes mellitus; FIB-4, Fibrosis-4 Index; FIBROQ, Fibrosis Quantification Index; FPI, Fibrosis Probability Index; HIS, Hepatic Steatosis Index; LFS, Liver Fat Score; TyG Index, Triglyceride-glucose Index; ADIPOQ, adiponectin gene; SNP, Single Nucleotide Polymorphism.
Diagnostic Performance of Some Hepatic Predictor Indexes Combined With Adiponectin Single Nucleotide Polymorphism rs1501299
The diagnostic performance of both the Fibro and HIS indices (0.927 and 0.873, respectively) improved when combined with the GG genotype (0.951, 0.927) and decreased in the TT genotype (0.825, 0.667) (Table 4, Figure 2).
Table 4.
Diagnostic Power of Combined Hepatic Predictor Indexes and SNP rs1501299.
| Statement | AUC | 95% CI |
|---|---|---|
| Fibro index | 0.927 | 0.884–0.958 |
| Fibro index + G/G | 0.951 | 0.863–0.990 |
| Fibro index + G/T | 0.926 | 0.867–0.964 |
| Fibro index + T/T | 0.873 | 0.668–0.974 |
| HIS | 0.825 | 0.768–0.874 |
| HIS + G/G | 0.879 | 0.769–0.949 |
| HIS + G/T | 0.839 | 0.765–0.898 |
| HIS + T/T | 0.667 | 0.442–0.847 |
AUC, Area Under The curve; CI, confidence interval; HIS, Hepatic Steatosis Index.
Figure 2.
Diagnostic performance of some hepatic predictor indexes combined with SNP rs1501299. (a) AUC of ROC for diagnostic performance of the Fibro index with SNP rs1501299; (b) AUC of ROC for diagnostic performance of the HIS index with SNP rs1501299. SNP, single nucleotide polymorphism.
Clinical Features Based on Genotypes and Haplotypes
In subgroup analysis, subjects with the G allele +276 G/T (rs1501299) had significantly higher levels of AST, ALT, MetS, TC, AFP, BUN, LDL, INR, and FBS. Among the healthy controls, individuals with the T allele of +276 G/T (rs1501299) had significantly lower AST, ALT, glucose, and MetS concentrations. The T/G genotype (No. = 130) were separated from T/T (no = 23) in MAFLD subjects with a BMI of 25 kg/m2, and patients carrying the G allele of +276 G/T (rs1501299) had significantly higher TC. Focuses with G/G (36.00 ± 7.40) or T/G (35.4 ± 7.71) had higher ALT levels than those with T/T (32.4 ± 7.44) (P < 0.0001). Subjects with G/G (36.00 ± 7.40) or T/G (35.4 ± 7.71) had higher AST (32.4 ± 7.44) and glucose (97 ± 14.28) levels than those with T/T (P < 0.0001 and <0.003, respectively). Subjects with G/G (9.64 ± 6.31) had higher AFP levels than subjects with T/T (8.33 ± 4.02) (P < 0.003). Subjects with G/G (107 ± 11.8) had higher glucose levels than those with T/T (97 ± 14.28) (P < 0.003) (Table 5).
Discussion
Metabolic dysfunction in patients with MAFLD can be caused by several factors, including hepatic insulin resistance, chronic metabolic syndrome, and inflammatory reactions mediated by hepatokines and lipid accumulation. MAFLD has recently been linked to an increased risk of metabolic diseases in individuals with one or more components of the metabolic syndrome. This has led to the question of whether MAFLD can be used as a surrogate marker for metabolic abnormalities in patients without metabolic syndrome. This is an important clinical issue, because MAFLD is a common global health problem associated with cardiovascular consequences unrelated to metabolic dysfunction. If MAFLD can be used as a surrogate for metabolic abnormalities in patients without metabolic syndrome, it could be used to initiate more aggressive interventions, such as stricter and more immediate dietary adjustments.
Histologically, only macrosteatosis is observed in MAFLD, whereas in nonalcoholic steatohepatitis (NASH), macrosteatosis coexists with a variable mix of hepatocyte ballooning, inflammation, and fibrosis. It is crucial to distinguish between distinct types of MAFLD, as this affects disease prognosis and management.12 The findings of this study indicate that the adipokine SNP (rs1501299) is linked to MAFLD through its relationship with insulin levels, hyperglycemia, lipid profile, and gamma-glutamyl transferase levels, particularly in obese patients. This prospective study aimed to investigate the association between the adipokine rs1501299 polymorphism and these measures as well as smoking, obesity, INR, CRP, and hemoglobin levels. In the current study, the G allele was more prevalent in patients with MAFLD than in controls. In general, the association between this SNP and MAFLD is equivocal, with conflicting results from previous studies. One study found that the adipokine SNP was not strongly associated with the occurrence or progression of MAFLD.13 whereas other studies corroborated the current evidence that adipokine polymorphisms are strongly associated with the occurrence and progression of MAFLD.14,15 Several studies have shown that adipokine polymorphisms negatively affect glucose levels.16 However, in the present study, they had no effect on HOMA-IR or insulin resistance. This analysis revealed that MAFLD patients with this gene polymorphisms are more likely to develop type 2 diabetes mellitus (T2DM) owing to higher glucose levels.
The adipokine SNP rs1015299 has been studied in numerous populations, with varying results. Due to these inconsistent findings, a complex relationship between adipokine rs1501299, insulin resistance, and serum glucose levels has been observed. A previous study found that the effect of ADIPOQ+276 variation on HOMA-IR is related to the degree of obesity, defined as a BMI greater than 26 kg/m2 or 40% body fat.17 In contrast, the current study, which included obese patients, found that adipokines only affected serum glucose levels, with no significant increase in insulin resistance. Several studies have linked various components of the lipid profile to SNP rs1501299. Jang et al. discovered a link between adipokine SNP polymorphisms and elevated triglyceride and low-density lipoprotein (LDL) levels in Koreans.18 However, De Luis et al.14 reported no significant differences in any lipid fraction among the different genotypes. In the current study, patients with adipokine polymorphisms (LLGN MAFLD group) had significantly higher blood LDL, TG, and total cholesterol levels, and significantly lower serum HDL levels in the Egyptian population than in the healthy group. Homeostatic insulin sensitivity (HIS) and the triglyceride-glucose index (TyG) are strong predictors of hepatic steatosis. The HIS index had a higher area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity than those of previous studies.19,20 TyG index is a predictor of MAFLD and a marker of insulin resistance.20 It is important to identify patients with severe fibrosis during MAFLD for practical and prognostic reasons. To avoid the highly invasive process of liver biopsy, there is a need for more noninvasive diagnostic approaches for severe liver fibrosis. The Fibro Index and BARD scores were two promising measures in this study. The Fibro index is a rating system for MAFLD, particularly for early fibrosis.21 Egyptian patients had the highest AUROC (0.9278), as well as good sensitivity and specificity (100 and 99.07, respectively). According to our findings, the BAAT score had high sensitivity and specificity and a good AUROC value of (0.846), which is similar to the findings of several previous studies.21,22 Zheng et al. found that the polymorphisms rs266729 and rs3774261 in the adiponectin gene may be risk factors for NAFLD.23
To avoid the invasive process of liver biopsy, new, easy, and noninvasive methods for liver fibrosis are being developed and shown promise. These approaches, which include blood tests, imaging techniques, and other noninvasive methods, aim to provide a way to diagnose advanced liver fibrosis without the need for a liver biopsy. Although these approaches show promise, further research is required to determine their accuracy and usefulness in clinical practice. In addition to these diagnostic approaches, a single nucleotide polymorphism (rs1501299) in adiponectin gene has been identified as a risk factor for MAFLD in an Egyptian population. This case–control study provides useful insights into the diagnostic role of the dipokine (Adiponectin) rs1501299 gene variant; however, the study has a limitation in that it was a single-center study with a relatively small number of patients. A larger multicenter study is needed to expand our knowledge of the significance of the adipokine (adiponectin) rs1501299 gene variation in different ethnicities.
Credit authorship contribution statement
Conceptualization; A.A.M., S.H., A. A.M, W.H.
Data curation: D.Z., R.M., M.B.H., A.A.H., E.E., M.A.S., S.M.A., R.A.A., and N.M.M.
Formal analysis: S.M.A., R.A.A., N.M.M., M.K.D., S.F., K.N., W.H.
Funding acquisition; A.A.M., S.H.,
Investigation: M.B.H., A.A.H., E.E., M.A.S., S.M.A., R.A.A., N.M.M., and M.K.D.
Methodology; A. A.M., D.Z., R.M., M.B.H., Project administration; A.A.M., S.H., A. A.M., D.Z., R.M., Resources; M.B.H., A.A.H., E.E., M.A.S., S.M.A., R.A.A., N.M.M., M.K.D., S.F., K.N., W.H.
Software; M.A.S., S.M.A., R.A.A., N.M.M., Supervision; A.A.M., W.H.
Validation: M.A.S., S.M.A., R.A.A., N.M.M., M.K.D., S.F., K.N.
Visualization: S.H., A. A. M., D.Z., R.M., M.B.H., A.A.H., and E.E.
Roles/Writing - original draft; A.A.M., W.H.
Writing - review & editing: A.A.M., S.H., A. A.M., D.Z., R.M., M.B.H., A.A.H., E.E., M.A.S., S.M.A., R.A.A., N.M.M., M.K.D., S.F., K.N., W.H.
Conflicts of interest
All authors have none to declare.
Acknowledgments
Researchers express their gratitude to everyone who took part in this study.
Funding
None.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jceh.2024.101409.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
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