Metabolic-associated fatty liver disease (MAFLD), formerly named nonalcoholic fatty liver disease (NAFLD), greatly increases the risk of various complications (1–3), among which cardiovascular disease (CVD) has been identified as the leading cause of deaths, accounting for approximately 48% of the total mortalities (4). Previous studies have also reported that NAFLD is closely related to the early atherosclerotic events of CVD, such as carotid intima-media thickness (CIMT) thickening, coronary artery calcification, decreased vasodilation response, and increased arterial stiffness (5,6). Therefore, for patients with MAFLD, it is important to clarify the risk factors and their early screening value related to CVD.
Hepatic fibrosis severity has been acknowledged as one of the most important factors in predicting arteriosclerotic cardiovascular disease. A recent cohort study with a median follow-up time of 5 years suggested that in NAFLD patients with liver biopsy, advanced fibrosis (liver fibrosis stage 3–4) is an independent risk factor for the development of CVD with an HR of 2.86 (95% confidence interval [CI] 1.36–6.04) (7). In addition, another cohort study of 14,819 patients who suffered from acute coronary syndrome inferred that the risk of recurrence of CVD in patients with hepatic fibrosis assessed by the noninvasive liver fibrosis score (NAFLD fibrosis score [NFS]) is 1.55 times greater than in patients without hepatic fibrosis (8).
NASH-related and NAFLD-related hepatic fibrosis are caused by systemic insulin resistance (IR), leading to intrahepatic fat accumulation and mitochondrial peroxidation (9). Decreased levels of intrahepatic lipid synthesis are due to impaired liver function after the development of advanced hepatic fibrosis (10). Lipoprotein (a) (Lp(a)), a low-density lipoprotein (LDL)-like particle, is also mainly synthesized in the liver and has been identified through the serum level of LPA genes and genetic factors as another independent causal contributor to CVD in different populations (11–14). Furthermore, compared with other serum lipids, such as LDL and triglycerides, the Lp(a) level is a highly heritable biomarker associated with CVD with 75%–95% variability levels due to LPA gene polymorphisms, especially its copy number variants in the kringle IV type 2 domain (15). A cross-sectional study from South Korea reported that the Lp(a) level of patients with NAFLD is lower than that of non-NAFLD patients (16), but the study did not further analyze how hepatic fibrosis affected the Lp(a) levels of patients with NAFLD. A recent study including biopsy-defined NAFLD patients indicated that progressive nonalcoholic steatohepatitis (NASH) reduces Lp(a) levels and that the predictive value of Lp(a) for CVD risk is reduced in patients with NAFLD (17). However, whether the predictive value of Lp(a) differs between MAFLD with and without advanced hepatic fibrosis remains unclear. Therefore, the predictive and clinical values of Lp(a) in MAFLD with different status remain poorly understood.
This study aimed to estimate the relationship between serum Lp(a) and carotid atherosclerosis in non-MAFLD and MAFLD patients with or without advanced fibrosis and to compare the accuracy of Lp(a) in predicting the risk of carotid atherosclerosis in MAFLD patients with different degrees of hepatic steatosis and fibrosis.
Study population and design
This was a cross-sectional study including consecutive patients who were older than 18 years, naïve to treatment of metabolic diseases, and underwent abdominal and carotid B-mode ultrasound at the First Affiliated Hospital of Sun Yat-sen University from January 1, 2015, to June 30, 2021. The protocol and the written informed consent provided by all cases were approved by the Clinical Research Ethics Committee of The First Affiliated Hospital of Sun Yat-sen University. This study complied with the Declaration of Helsinki (Approve No. 2015).
According to the Asia-Pacific MAFLD Guidelines 2020 (18), the MAFLD in our study was established based on the radiologically defining fatty liver plus the presence of any one of the following 3 conditions including overweight/obesity (body mass index [BMI] ≥ 23 kg/m2), type 2 diabetes mellitus, or lean/normal weight (BMI < 23 kg/m2) with metabolic dysregulation. Metabolic dysregulation was defined as the presence of at least 2 of the following conditions: (i) waist circumference ≥90 in men and 80 cm in women, (ii) blood pressure ≥130/85 mm Hg or specific drug treatment, (iii) TG ≥ 1.70 mmol/L or specific drug treatment, (iv) HDL-C <1.0 mmol/L for male patients and <1.3 mmol/L for female patients, (v) impaired fasting glucose, (vi) homeostasis model assessment-insulin resistance (HOMA-IR) score ≥2.5, or (vii) C-reactive protein level >2 mg/L. The non-MAFLD cases referred to participants who do not meet the criteria mentioned above.
The exclusion criteria included (i) concomitant hepatocellular carcinoma or decompensated cirrhosis or liver failure; (ii) a prior use of steroids, amiodarone, tamoxifen, or methotrexate; (iii) pregnancy or breastfeeding; (iv) self-reported excessive alcohol consumption (>140 g per week for male patients or >70 g per week for female patients); (v) other concomitant liver diseases, including hepatocellular carcinoma, hepatitis B or C viral infection, or autoimmune liver disease; and (vi) current infection, surgery within the past year, inflammatory disease, or cancer.
Patient information, including age, sex, preexisting disorders, smoking status, and alcohol consumption, was collected with a structured questionnaire completed during a face-to-face interview in the fatty liver center. Anthropometric measurements including body weight and height, waist and hip circumference, and blood pressure were performed in all cases. The BMI was calculated as weight (kg) divided by height (m) squared. Sitting blood pressure was measured twice by using portable sphygmomanometers (Omron electronic monitor J710, Japan) applied to the right upper arm after a 30-minute rest. Serum samples were collected after at least 8 hours of fasting for the following measurements as previously described (19). In brief, Lp(a) was measured using an immunoturbidimetry method using a biochemical analyzer from the Beckman Coulter Au 5800 System. The other apolipoproteins, creatinine, blood urea nitrogen, uric acid, liver enzymes (alanine aminotransferase [ALT] and aspartate aminotransferase), and gamma glutamyl transpeptidase were also measured using the biochemical analyzer from the Beckman Coulter Au 5800 System. FBG and serum insulin (FINS) were measured using an Abbott c8000 Automatic Biochemistry Analyzer (Abbott). Lipids including total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were determined directly by Beckman Coulter reagent test kits using the enzymatic colorimetric method.
Hypertriglyceridemia was defined as triglyceride>1.7 mmol/L. Obesity was defined as BMI ≥ 25 kg/m2 (20). Impaired fasting glucose was defined as fasting glucose ranging from 5.6 to 6.9 mmol/L. Hyper-LDL-emia was defined as LDL-C > 3.2 mmol/L. Metabolic syndrome was diagnosed according to the modified criteria for an Asian population (21). HOMA-IR = FINS (µU/mL) * FBG (mmol/L)/22.5.
The NFS was calculated with baseline serological examination data according to the previous study (22). Advanced hepatic fibrosis was defined as NFS > −1.836 according to the optimal cutoff value in Chinese patients (22).
Radiology assessments for liver and carotid atherosclerosis (outcomes)
High-resolution B-mode ultrasonography was used to evaluate fatty changes in liver, liver stiffness, and carotid atherosclerosis by 2 fixed sonologists with at least 5 years of experience in ultrasound-based imaging assessment. Fatty liver was evaluated in all cases with the criteria of parenchymal brightness, increased echo contrast of liver to kidney, presence of posterior attenuation of ultrasound beam, vessel blurring, and difficult visualization of the gallbladder wall or the diaphragm. Liver stiffness measurement (LSM) was also assessed using two-dimensional shear wave elastography (SWE) by abdominal ultrasounds. The cutoff value for LSM for different grades of hepatic fibrosis was defined as follows: F0–F1, <6.3 kPa; F2, 6.3–8.9 kPa; F3, 9.0–10.9 kPa; and F4, >11.0 kPa (23). Carotid atherosclerosis was defined as the presence of carotid intima-media thickening or plaques. The CIMT was measured 3 times, and finally, the average value of measurement was adopted for analysis, and carotid intima-media thickening was diagnosed as CIMT ≥ 1.0 mm. The presence of carotid plaques was defined as focal thickening ≥1.5 mm of the carotid artery (24,25).
Liver fat content (LFC) was evaluated with fat signal fraction by 2-point DIXON fat-water separation MRI (SIEMENS 3.0T MAGNETOM Verio) (26). The criteria of the hepatic steatosis degree were classified by LFC as follows: without fatty liver (<5%), mild steatosis (5–15%), moderate steatosis (15–20%), and severe steatosis (>20%) (27). All radiology assessments were conducted by experienced radiologists who were blinded to the characteristics of all cases and the aim of this study.
Differences between groups were determined using the Student t test, ANOVA, and Pearson's χ2 test. Nonlinear fitting was used to evaluate the correlation between Lp(a) and NFS, LSM, and LFC. The association between Lp(a) and carotid atherosclerosis in MAFLD patients with various degrees of hepatic fibrosis was analyzed using logistic regression models. We further used a receiver operating characteristic (ROC) curve and the areas under the receiver operator characteristic curve (AUCs) to evaluate the efficiency of Lp(a) in predicting carotid atherosclerosis. The sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio were also calculated (see Figures 1–2, Supplementary Digital Content 1, https://links.lww.com/CTG/A815). Potential confounders highly related to MAFLD risk, including age, sex, BMI, current smoking, ALT, uric acid, and HOMA-IR, were adjusted for the regression models. A 2-tailed P value of less than 0.05 was considered statistically significant. All statistical analyses of this study were performed by SPSS statistical software (version 25.0, SPSS, Chicago, IL).
A total of 4,348 individuals (1346 MAFLD and 3,002 non-MAFLD) with an average age of 46.4 years and 59.4% males were enrolled. A total of 1036 patients with MAFLD and 156 non-MAFLD patients underwent MRI-PDFF estimations. A total of 360 patients with MAFLD and 150 non-MAFLD patients underwent LSM by 2D shear wave ultrasound. Patients with MAFLD were older and had a higher proportion of male patients, hypertension, diabetes, and carotid arteriosclerosis (P < 0.05). Patients with MAFLD also presented higher BMI, waist and hip circumference, waist-to-hip ratio, blood pressure, ALT, aspartate aminotransferase, gamma glutamyl transpeptidase, FBG, uric acid, FINS, HOMA-IR, lipids, and apolipoproteins, but not Lp(a) (all P < 0.05) (Table 1). Characteristics of patients with and without advanced fibrosis, which were assessed by NFS or LSM, are further compared in Table 1, Supplementary Digital Content 1, https://links.lww.com/CTG/A815.
Table 1. -
Characteristics of patients with and without MAFLD
||All cases n = 4,348
||MAFLD n = 1,346
||Non-MAFLD n = 3,002
|Age (yr), mean ± SD
||46.4 ± 13.3
||47.7 ± 13.8
||45.7 ± 12.7
|Male, n (%)
|Hypertension, n (%)
|Diabetes, n (%)
|Current smoker, n (%)
||24.3 ± 3.3
||26.1 ± 3.0
||23.2 ± 2.9
||0.87 ± 0.05
||0.90 ± 0.05
||0.89 ± 0.05
|SBP, mm Hg
||125 ± 18
||133 ± 18
||123 ± 16
|DBP, mm Hg
||78 ± 14
||83 ± 14
||77 ± 12
|Liver and metabolism marker, mean ± SD
| ALT (U/L)
| AST (U/L)
| GGT (U/L)
| ALB (g/L)
||44.3 ± 8.8
||44.4 ± 13.1
||44.3 ± 3.2
| Uric acid (μmol/L)
||375 ± 100
||405 ± 103
||359 ± 93
| FBG (mmol/L)
||5.3 ± 1.9
||5.7 ± 2.2
||5.2 ± 1.6
| FINS (mmol/L)
|Lipid profiles, mean ± SD
| Total cholesterol (mmol/L)
||5.1 ± 1.6
||5.3 ± 2.0
||5.0 ± 1.0
| Triglyceride (mmol/L)
||1.58 ± 1.20
||2.00 ± 1.43
||1.38 ± 1.03
| HDL-C (mmol/L)
||1.27 ± 0.32
||1.16 ± 0.33
||1.33 ± 0.33
| LDL-C (mmol/L)
||3.20 ± 0.82
||3.29 ± 0.84
||3.15 ± 0.82
| ApoA1 (g/L)
||1.30 ± 0.28
||1.28 ± 0.27
||1.36 ± 0.25
| ApoB (g/L)
||0.95 ± 0.24
||1.00 ± 0.30
||0.91 ± 0.24
| ApoE (mg/L)
||45 ± 17
||49 ± 21
||42 ± 14
| Lp(a) (mg/L)
| PLT (109/L)
||239 ± 59
||247 ± 55
||235 ± 60
|FIB-4 > 3.25, n (%)
|NFS > −1.836, n (%)
|CIMT ≥ 1.0 mm, n (%)
ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CIMT, carotid intima-media thickness; GGT, gamma glutamyl transpeptidase; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment-insulin resistance; LDL-C, low-density lipoprotein cholesterol; Lp(a), lipoprotein (a); MAFLD, metabolic-associated fatty liver disease; NFS, NAFLD fibrosis score.
aP value was the comparison of MAFLD and non-MAFLD patients.
bData were presented as median (interquartile range).
Associations between Lp(a) and hepatic fibrosis, LFC, and metabolic disorders
The nonlinear association of Lp(a) levels with hepatic fibrosis and steatosis markers in patients with MAFLD was performed by smooth curve fitting analyses (Figure 1). Lp(a) increased slowly between −5.0 and 0 as NFS increased and became stable when NFS reached 0 (Figure 1a). By contrast, Lp(a) decreased sharply between 3.5 and 6.3 as LSM increased and decreased much more slowly, and the curve became smooth when LSM was higher than 6.3 (Figure 1b). In MAFLD patients with hepatic fibrosis stages F0–F1, the curve fluctuated as LFC accumulated. However, in patients with hepatic fibrosis stage F2, Lp(a) decreased sharply between 5% and 20% as LFC increased and fluctuated when LFC was higher than 20%. In patients with hepatic fibrosis stages F3–4, Lp(a) exhibited a sharply increasing trend between 5% and 12% and then fluctuated between 12% and 20%. After the LFC was higher than 20%, Lp(a) held a stable increasing trend (Figure 1c).
Among the non-MAFLD cases, there were no significant differences of the average Lp(a) levels between those who were healthy and with different metabolic disorders (all P < 0.01, Figure 2). A similar trend was also observed in patients with MAFLD. However, non-MAFLD patients with obesity and hyper-LDL-C-emia or metabolic syndrome both had higher serum Lp(a) than MAFLD patients without metabolic disorders, obesity, and hypertriglyceridemia (all P < 0.05) (Figure 2a). By contrast, non-MAFLD patients had higher levels of Lp(a) than MAFLD patients with NFS ≤ −1.836 (P = 0.040), and they also had higher Lp(a) than patients with MAFLD who underwent SWE (vs. LSM<6.3 kPa, P = 0.011; vs. 6.3 kPa ≤ LSM < 8.9 kPa, P = 0.032; vs. LSM ≥ 9.0 kPa, P = 0.038) (Figure 2b–c). MAFLD patients with milder hepatic steatosis also had higher Lp(a) (pairwise P < 0.05) (Figure 2d).
Association between Lp(a) and carotid atherosclerosis
Lp(a) was categorized by the cutoff values for the 25th, 50th, and 75th percentiles, represented by Q1, Q2, Q3, and Q4; Q1 of all samples was set as a reference. In all cases or non-MAFLD subjects, there was a dose-dependent relationship between Lp(a) and risk of carotid atherosclerosis in both the crude model and the model adjusted for age, sex, BMI, smoking history, ALT, uric acid, HOMA-IR, total cholesterol, triglycerides, HDL-C, and ApoB/ApoA ratio (P < 0.001). In the multivariate adjusted model of all cases, the risk of carotid atherosclerosis in the Q4 group was 2.02 times higher than that in the Q1 group (95% CI 1.59–2.55). In the non-MAFLD subjects, the adjusted odds ratio (OR) of the Q4 group was 3.37 (95% CI 2.45–4.63), suggesting that Lp(a) is an independent predicting risk factor of carotid atherosclerosis in non-MAFLD patients. However, in all the patients with MAFLD, a positive relationship was only observed in the crude model (P < 0.001), and such a relationship did not exist in MAFLD patients with advanced fibrosis assessed by LSM (see Table 2, Supplementary Digital Content 1, https://links.lww.com/CTG/A815).
The relationship between Lp(a) and carotid atherosclerosis in non-MAFLD patients and MAFLD patients with various conditions of metabolic disorders was also analyzed. Figure 3 shows that there was a dose-response association between Lp(a) and carotid atherosclerosis, and patients with MAFLD had higher risk ratios than the non-MAFLD cases in each quartile of Lp(a). Compared with the Q1 group in non-MAFLD cases, the Q4 group of Lp(a) in patients with MAFLD had the highest risk of carotid atherosclerosis (adjusted OR: 15.01, 95% CI: 7.01–28.99). In addition, only the Q2 group of MAFLD patients with LSM < 6.3 kPa was associated with a higher risk of carotid atherosclerosis (adjusted OR 3.64, 95% CI: 1.29–9.27).
Predictive value of Lp(a) for atherosclerosis in non-MAFLD subjects and MAFLD patients with different stages of fibrosis
We used ROC curves with adjusting confounding factors including age, sex, BMI, smoking history, ALT, uric acid, HOMA-IR, total cholesterol, triglycerides, HDL-C, and ApoB/ApoA ratio to evaluate the efficacy of Lp(a) in predicting carotid atherosclerosis (see Table 3, Supplementary Digital Content 1, https://links.lww.com/CTG/A815). For non-MAFLD patients, MAFLD without and with advanced fibrosis defined as NFS > −1.836, and the areas under the curve (AUCs) for Lp(a) decreased from 0.819, 0.781, to 0.692 (P < 0.001) (Figure 4a). On the other hand, AUC for Lp(a) in predicting the risk of carotid atherosclerosis in non-MAFLD subjects had no significant difference with those in MAFLD with LSM < 8.9 kPa, but was higher than that in MAFLD patients with LSM ≥ 9 kPa (AUC 0.828 for MAFLD with LSM < 6.3 kPa; AUC 0.891 for MAFLD with LSM from 6.3 to 8.9 kPa) (P < 0.001) (Figure 4b). The cutoff value, sensitivities, specificities, positive predictive values, and negative predictive values for models mentioned above are also summarized in Tables 4–6, Supplementary Digital Content 1, https://links.lww.com/CTG/A815.
We conducted a cross-sectional study on the relationship between Lp(a) and degree of hepatic steatosis and fibrosis as well as the prediction of carotid atherosclerosis of Lp(a) in non-MAFLD and MAFLD subjects with or without advanced fibrosis. We found that the association of Lp(a), LFC, and the risk of carotid atherosclerosis varied with different degrees of hepatic fibrosis. We also found that the predictive value of Lp(a) for carotid atherosclerosis was reduced as hepatic fibrosis aggregated.
Hepatic fibrosis has been identified as one of the hallmarks of high-risk CVD development in MAFLD. A previous study enrolling 176 Japanese NAFLD patients showed that the serum Lp(a) level of NAFLD patients with advanced fibrosis with histology is significantly lower than that of patients without fibrosis, and a low Lp(a) level is related to a higher risk of advanced fibrosis (F3-4) (17). Another study in Italy with 600 adults with biopsy-proven NAFLD also supported that circulating Lp(a) levels were decreased in advanced fibrosis (F4), which could predict a histologic Metavir stage of F1, F2, F3, and F4 with AUCs as 0.69 (95% CI: 0.69–0.71), 0.72 (95% CI: 0.72–0.73), and 0.75 (95% CI, 0.74–0.76) (all P < 0.0001) (28). However, whether the predictive performance of Lp(a) levels in CVD risk is affected in MAFLD patients with fibrosis has not been explored. Our study also used the NFS to identify advanced fibrosis in patients with MAFLD and found that Lp(a) was less valuable in predicting the risk of carotid atherosclerosis in MAFLD patients with advanced fibrosis. Because NFS is a qualitative analysis to assess whether there is advanced liver fibrosis, it does not accurately indicate the degree of liver fibrosis. Therefore, continuous evaluation of the severity of liver fibrosis in patients requires more accurate imaging or biopsy measurements, such as the SWE used in a previous study (29). Past studies have also reported the relationship between Lp(a) levels and different chronic liver diseases, such as cirrhosis and liver cancer of other etiologies (30–32). These studies found that patients with chronic liver disease have lower Lp(a) levels than healthy controls, and Lp(a) levels decrease from mild fibrosis to cirrhosis or malignancy as liver function declines. Moreover, another study compared the Lp(a) levels of 24 patients with chronic active hepatitis C before and after interferon treatment and found that the Lp(a) levels of patients who respond completely to the treatment are significantly increased, but remain unchanged in patients with no response or partial response (33). Because Lp(a) is less affected by other potential factors, including diet and lifestyle, it could be inferred that severe fibrosis is also attributed to lower serum Lp(a) levels because of impaired liver function that responds to the synthesis of Lp(a) in the liver.
Lp(a) consists of ApoB100, which binds to apolipoprotein (a) by noncovalent interactions and one single disulfide bridge (34). Hepatic fibrosis leads to decreased serum Lp(a) levels, which could be explained by a reduction in the synthesis of Lp(a) components in the liver, in which downregulation of hepatic LPA gene expression in the context of hepatic fibrosis or cirrhosis plays a key role (28). On the other hand, another study indicated that worsening hepatic fibrosis in patients with NAFLD increases the incidence of many metabolic disorders, such as hypertension, type 2 diabetes, and dyslipidemia, leading to a massive rise in the risk of atherosclerosis that is independent of Lp(a) (35). It could be inferred that the contribution of Lp(a) to the development of atherosclerosis decreases because of the increased risk of other major factors.
Lp(a) not only efficiently predicts early and long-term CVD risk but also assesses the risk of recurring adverse cardiovascular events in different populations, such as those with type 2 diabetes or metabolic syndrome (11,12,36). However, research on the influence of MAFLD on Lp(a) prediction for CVD risk is still lacking. This study suggested that in patients with MAFLD, the level of serum Lp(a) is affected by a variety of factors, especially the synthetic function of the liver, resulting in a lower predictive value for the risk of carotid atherosclerosis compared with that of non-MAFLD patients. Therefore, when using Lp(a) as a predictive marker for the risk of carotid atherosclerosis in patients with MAFLD, hepatic fibrosis and steatosis should be considered although Lp(a) still performed well in predicting the CVD risk of MAFLD patients without advanced fibrosis. Clinical studies have suggested that plasma Lp(a) concentrations ≥300 mg/L or ≥30 mg/dL are linked to the risk of developing atherosclerosis early in childhood and adolescence (37,38). Despite this prominence, the application and clinical interpretation of these cutoff Lp(a) levels in atherosclerosis should exclude MAFLD patients with advanced fibrosis. Hepatic fibrosis surrogates may be useful in adjusting for the Lp(a) prediction of CVD by incorporating them into features of predictive models.
In addition to hepatic fibrosis, Lp(a) levels decrease stepwise across the severity of steatosis according to previous research. A South Korean study involving 2,242 ultrasound-diagnosed NAFLD patients reported that the presence of NAFLD is a risk factor leading to a decrease in the level of Lp(a) with median Lp(a) values (mg/dL) of 14.2, 12.0, 10.7, and 8.7 in non-NAFLD patients and NAFLD patients with mild, moderate, and severe steatosis, respectively (39). However, this study lacked stratification analysis of the degree of fibrosis. Our study also found that the relationships between LFC and Lp(a) levels were different in MAFLD patients with various degrees of fibrosis as assessed by LSMs; in particular, there were inverse associations in patients with fibrosis stages F3-4 compared with those with fibrosis stage F0 or F1-2. However, the sample size was relatively small in MAFLD patients with fibrosis stages F3-4. Although we excluded Lp(a) variability related to acute processes, including a recent active infection, inflammation, or surgery, the uncertain relationships between Lp(a) levels with differing steatosis at different LSMs necessitate further prospective studies enrolling a larger population to confirm and explain these conclusions.
This study had several limitations. Past studies have already shown that with the increase in the nonalcoholic fatty liver disease active score and ballooning degree and with the presence of lobular inflammation, the serum Lp(a) level of patients with NAFLD decreases, indicating that the effect of necroinflammation should not be overlooked (17). However, because some of the liver biopsy data were lacking, such as scores or descriptions of NASH and other pathological lesions, we could not provide a more accurate assessment of hepatic fibrosis, although this study included a large sample size. The MAFLD definition is still uncertain even in the APASL guidelines from 2020. Because the serum Lp(a) level is genetically determined, patients of multiple races or with multiple gene polymorphisms of Lp(a) are needed for future study. Our study had a cross-sectional design, which prevented further tracing of the dynamic changes in Lp(a) during the follow-up of hepatic fibrosis progression. Moreover, there were a significant number of non-MAFLD patients with obesity/dyslipidemia/metabolic syndrome in the non-MAFLD control groups, and the risks of misclassified non-MAFLD patients due to the poor sensitivity of abdominal ultrasound remain.
In conclusion, the relationships between serum Lp(a) levels and the severity of steatosis and carotid atherosclerosis in patients with MAFLD were affected by hepatic fibrosis severity. The predictive value of Lp(a) on carotid atherosclerosis risk in patients with MAFLD was lower than that in non-MAFLD patients, and the value continued to decline as hepatic fibrosis worsened.
CONFLICTS OF INTEREST
Guarantor of the article: Bihui Zhong, MD, PhD.
Specific author contributions: B.Z.: project administration and funding acquisition. T.W.: writing—original draft, data analysis. J.Y.: writing—original draft, review and editing. C.S. and Y.L.: data collection. Q.M.: validation. S.F. and W.W.: imaging measurements. T.W. and J.Y. contribute equally to this article.
Financial support: This study is funded by National Natural Science Foundation of China (81870404, 81670518) and China Postdoctoral Science Foundation (2020M683128).
Potential competing interests: None to report.
We are grateful to Professor Aihua Lin from the Department of Medical Statistics, School of Public Health, Sun Yat-sen University, for providing assistance in statistical analysis to this study. This study is funded by National Natural Science Foundation of China (81870404 and 81670518) and China Postdoctoral Science Foundation (2020M683128).
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