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. 2017 Mar 23;7:45100. doi: 10.1038/srep45100

Retinol Binding Protein-4 Levels and Non-alcoholic Fatty Liver Disease: A community-based cross-sectional study

Xuechen Chen 1,2,*, Tianran Shen 1,2,*, Qing Li 3, Xu Chen 1,2, Yanping Li 1,2, Dan Li 1,2, Gengdong Chen 2, Wenhua Ling 1,2,a, Yu-ming Chen 1,2,b
PMCID: PMC5362806  PMID: 28332619

Abstract

Previous reports on the association between retinol binding protein 4 (RBP4) and nonalcoholic fatty liver disease (NAFLD) were controversial. This study aimed to investigate the association between the serum RBP4 levels and occurrence of NAFLD in Chinese population. In total, 2938 participants aged 40–75 years were involved in this community-based cross-sectional study. General information, lifestyle factors, serum levels of RBP4 and the presence of NAFLD were determined. Patients with NAFLD had significantly higher concentrations of RBP4 (37.9 ± 6.8 μg/ml) than did non-NAFLD controls (35.0 ± 6.7 μg/ml) (P < 0.001). The odds ratios (ORs) of NAFLD for the highest (vs. lowest) quartile of RBP4 were 1.884 (95% CI: 1.391, 2.551) for females (P < 0.001), and 2.107 (95% CI: 1.357, 3.273) for male participants (P < 0.01) after adjusting for related factors. The serum RBP4 levels were positively associated with the prevalence of NAFLD in middle-aged and elderly Chinese people, and Homeostatic model assessment-insulin resistance (HOMA-IR), trunk fat, the waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting insulin, high density lipoprotein cholesterol (HDL-C) and triglycerides (TG) might be implicated in the pathogenesis of RBP4 in NAFLD.

Non-alcoholic fatty liver disease (NAFLD) is defined as a condition in which more than 5% of the hepatocytes exhibit macroscopic steatosis with no cause of secondary hepatic fat accumulation such as increased alcohol consumption, the use of steatogenic medications or hereditary disorders1. The estimated worldwide prevalence of NAFLD ranges approximately from 6% to 35%, with a median of 20% in the general population, based on various assessment methods2. NAFLD encompasses simple steatosis and non-alcoholic steatohepatitis (NASH), which can progress to fibrosis and, finally, cirrhosis, even with hepatic cellular cancer (HCC)3. Furthermore, in most patients, NAFLD is often associated with an increased risk of obesity, insulin resistance (IR), and hyperlipidemia, all of which are components of the metabolic syndrome4. NAFLD, with its high prevalence and severe complications, has, accordingly, become the most common liver disease in adults and an alarming public health problem globally.

The contribution of retinol binding protein 4 (RBP4) in obesity and IR was first discovered in adipose-Glut4−/− mice5, after which the results of a human study showed that elevated serum RBP4 levels were correlated with the magnitude of IR and metabolic risk factors in lean, obese, and diabetic subjects6. The main function of RBP4, which is highly expressed in the liver, is to transport retinol from the liver stores to extra-pancreatic tissues7. Adipose tissue (AT) has the second highest rate of expression8. A few studies have demonstrated that elevated RBP4 is a risk factor associated with NAFLD9,10. Several human studies have shown that the serum RBP4 levels have a positive association with the degree of liver fat accumulation and liver enzyme levels, including serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) and γ-glutamyltranspeptidase (GGT)11,12.

However, the data in the literature concerning the role of RBP4 in IR and NAFLD are inconsistent. The serum RBP4 levels were not associated with whole-body or hepatic IR in Mexican Americans13. In addition, several studies have shown that the serum RBP4 levels were not different between the steatosis group and controls14,15. Furthermore, serum RPB4 was found to be significantly lower in NAFLD patients than in controls16. Conflicting results from these studies mentioned above might be mainly because of the different populations as well as the limited samples of these studies. To date, the study with the largest sample to investigate the relationship between RBP4 and NAFLD was conducted in 748 school children, and only 219 of them volunteered to accept ultrasound examinations17. Therefore, a large-size human population is required to observe the association of serum RBP4 and the occurrence of NAFLD. Accordingly, we conducted a large community-based cross-sectional study to explore the association between RBP4 and NAFLD. Elucidating these questions can help find whether RBP4 predicts the development of NAFLD and aid in the identification of potential biomarkers to prevent NAFLD progression.

Results

Characteristics of participants

Our data showed that patients with NAFLD had higher levels of body mass index (BMI), waist-to-hip ratio (WHR), trunk fat percentage and serum ALT, fasting glucose, homeostatic model assessment-insulin resistance (HOMA-IR), triglycerides (TG) and uric acid (UA) as well as a high prevalence of high blood pressure (HBP) and diabetes mellitus (DM), but they had lower physical activities and serum high density lipoprotein cholesterol (HDL-C) than did those with non-NAFLD in both the female and male groups. NAFLD patients had higher low density lipoprotein cholesterol (LDL-C) than did non-NAFLD subjects only in the female group. There were no differences in household income and serum AST and total cholesterol (TC) between NAFLD and non-NAFLD participants divided by sex (Table 1).

Table 1. Demographic characteristics, physical and metabolic measurements by sex and NAFLD status.

  Female Male
non-NAFLD NAFLD P non-NAFLD NAFLD P
N 1022 997   436 483  
Age (years) 59.5 ± 5.6 60.2 ± 5.3 0.003 63.0 ± 6.5 62.1 ± 6.0 0.033
Postmenopausal 1010 (98.8%) 973 (97.6%) 0.036 — — —
Household income, yuan/month/person     0.703     0.357
<4000 879 (87.1%) 852 (87.1%)   346 (79.5%) 363 (75.6%)  
4000–6000 84 (8.3%) 75 (7.7%)   61 (14.0%) 82 (17.1%)  
>6000 46 (4.6%) 51 (5.2%)   28 (6.4%) 35 (7.3%)  
Current smoking 3 (0.3%) 5 (0.5%) 0.502 65 (14.9%) 108 (22.4%) 0.004
Current drinking 5 (0.5%) 5 (0.5%) 1 153 (35.1%) 173 (35.8%) 0.836
Physical activities, MET/d 34.4 ± 5.5 33.8 ± 5.2 0.007 34.4 ± 6.1 33.3 ± 6.1 0.007
BMI (kg/m2) 22.0 ± 2.6 24.8 ± 3.1 <0.001 22.4 ± 2.5 25.3 ± 2.9 <0.001
WHR 0.90 ± 0.07 0.93 ± 0.07 <0.001 0.91 ± 0.06 0.95 ± 0.05 <0.001
Trunk fat percentage (%) 34.3 ± 5.5 38.5 ± 4.1 <0.001 24.8 ± 5.4 29.9 ± 4.3 <0.001
HBP 201 (19.7%) 358 (35.9%) <0.001 124 (28.4%) 174 (36.0%) 0.006
DM 58 (5.7%) 86 (8.6%) 0.008 35 (8.0%) 54 (11.2%) 0.205
ALT (U/L) 16 [11,18] 19 [12,22] <0.001 15 [12,20] 19 [14,25] <0.001
AST (U/L) 20 [16,22] 20 [15,22] 0.195 19 [16,22] 19 [16,22] 0.648
Fasting glucose (mmol/L) 4.85 [4.31,5.20] 5.11 [4.40,5.39] <0.001 4.82 [4.40,5.38] 4.92 [4.50,5.49] 0.014
HOMA-IR 1.55 [0.94,1.89] 2.65 [1.50,3.24] <0.001 1.16 [0.85,1.76] 2.11 [1.45,3.05] <0.001
TG (mmol/L) 1.28 [0.81,1.47] 1.73 [1.05,2.06] <0.001 1.15 [0.80,1.55] 1.44 [1.04,2.10] <0.001
TC (mmol/L) 5.73 ± 1.05 5.70 ± 1.03 0.42 5.37 ± 1.03 5.24 ± 0.98 0.065
HDL-C (mmol/L) 1.63 ± 0.42 1.38 ± 0.35 <0.001 1.38 ± 0.36 1.18 ± 0.31 <0.001
LDL-C (mmol/L) 3.62 ± 0.91 3.71 ± 0.88 0.029 3.42 ± 0.87 3.40 ± 0.92 0.763
UA (μmol/L) 314.9 ± 70.9 346.4 ± 77.3 <0.001 387.4 ± 87.3 403.5 ± 86.8 0.005
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Association of serum RBP4 levels with metabolic risk factors

We next assessed the correlation between the serum RBP4 levels and a battery of known metabolic risk factors (Table 2). Our analysis revealed a significantly positive association between serum RBP4 and age, BMI, WHR, systolic blood pressure (SBP), diastolic blood pressure (DBP), trunk fat and several biochemical parameters, including fasting glucose, fasting insulin, and serum TG, ALT and UA in all participants (P < 0.001 for all above parameters). In addition, a negative association between RBP4 and the HDL-C levels was observed (P < 0.001). However, there was no significant association between RBP4 and SBP and fasting glucose in the male group among the variables mentioned above. A positive association between RBP4 and LDL-C was found only in female participants.

Table 2. Correlation between RBP4 and metabolic risk factors.

Variables Total (n = 2938) Female (n = 2019) Male (n = 919)
r P r P r P
Age (years) 0.065 <0.001 0.055 0.014 −0.067 0.043
BMI (kg/m2) 0.186 <0.001 0.189 <0.001 0.141 <0.001
WHR 0.183 <0.001 0.167 <0.001 0.142 <0.001
SBP (mmHg) 0.123 <0.001 0.110 <0.001 0.053 0.109
DBP (mmHg) 0.121 <0.001 0.090 <0.001 0.068 0.040
Fasting glucose (mmol/l) 0.104 <0.001 0.130 <0.001 0.056 0.091
Fasting insulin (μU/ml) 0.211 <0.001 0.268 <0.001 0.171 <0.001
HOMA-IR 0.213 <0.001 0.268 <0.001 0.160 <0.001
TG (mmol/l) 0.357 <0.001 0.368 <0.001 0.352 <0.001
TC (mmol/l) −0.008 0.672 0.036 0.105 0.054 0.104
LDL-C (mmol/l) −0.002 0.923 0.047 0.035 0.008 0.809
HDL-C (mmol/l) −0.270 <0.001 0.248 <0.001 −0.143 <0.001
ALT (U/L) 0.161 <0.001 0.128 <0.001 0.160 <0.001
AST (U/L) 0.008 0.646 −0.014 0.534 0.054 0.103
Trunk Fat (kg) 0.131 <0.001 0.212 <0.001 0.129 <0.001
UA (μmol/L) 0.312 <0.001 0.268 <0.001 0.200 <0.001
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Relationship between serum RBP4 and IR and other correlated metabolic risk factors

To investigate the relationship between serum RBP4 and IR as well as other correlated metabolic risk factors, multiple linear regression was performed using RBP4 as a dependent variable. As presented in Table 3, HOMA-IR, trunk fat, WHR, SBP, fasting insulin, HDL-C and TG were all selected as independent variables in all subjects before and after adjusting for age, sex, BMI (except for BMI, trunk fat and WHR), current smoking and drinking, physical activities, history of diabetes and hypertension, LDL-C, Lg (ALT), and UA (P < 0.001 for all of the above parameters). However, DBP and fasting glucose were just significant variables associated with the serum RBP4 levels before adjustment.

Table 3. Association between serum RBP4 and IR as well as other correlated metabolic risk factors, using multiple linear regression analysis.

  Unadjusted Adjusted
β ± SE P β ± SE P
HOMA-IR 4.74 ± 0.46 <0.001 2.86 ± 0.54 <0.001
Trunk fat (kg) 0.31 ± 0.044 <0.001 0.29 ± 0.044 <0.001
WHR 19.15 ± 1.90 <0.001 7.55 ± 2.08 <0.001
SBP (mmHg) 0.046 ± 0.007 <0.001 0.018 ± 0.007 0.009
DBP (mmHg) 0.081 ± 0.012 <0.001 0.018 ± 0.012 0.152
Fasting glucose (mmol/L) 6.81 ± 1.55 <0.001 1.49 ± 1.50 0.323
Fasting Insulin (μU/ml) 5.18 ± 0.52 <0.001 3.28 ± 0.60 <0.001
HDL-C (mmol/L) −4.58 ± 0.30 <0.001 −2.77 ± 0.32 <0.001
TG (mmol/L) 10.42 ± 0.52 <0.001 8.69 ± 0.53 <0.001
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Fasting glucose, fasting insulin, HOMA-IR and TG were put into linear regression after log transformation. All variables were adjusted for age, sex, BMI (except for BMI, trunk fat and WHR), Current smoking and drinking, Physical activities, history of diabetes and hypertension, LDL-C, lg (ALT), UA.

Serum RBP4 levels in non-NAFLD subjects and NAFLD patients

The serum RBP4 levels ranged from 13.3 to 67.9 μg/ml among the participants. The results demonstrated that patients with NAFLD had significantly higher RBP4 concentrations (37.9 ± 6.8 μg/ml) than did those with non-NAFLD (35.0 ± 6.7 μg/ml) (P < 0.001). After the adjustment for variables (included in model 3 of Table 4), the serum RBP4 levels in patients with NAFLD (36.0 ± 6.5 μg/ml) were significantly higher than those in control subjects (34.6 ± 6.5 μg/ml) of females and in males (39.7 ± 7.3 μg/ml vs. 38.4 ± 7.4 μg/ml) (both P < 0.001) (Fig. 1).

Table 4. Adjusted odds ratios (95% confidence interval) of NAFLD according to the sex and quartiles of serum RBP4 in total participants using Adjusted Logistic Regression.

  RBP4 (μg/ml) P for trend
Quartile 1 Quartile 2 Quartile 3 Quartile 4
Female <31.05 31.06–35.19 35.20–39.35 >39.35  
Model 1 1 1.464 (1.130–1.897) 2.852 (2.200–3.697) 3.646 (2.801–4.746) <0.001
Model 2 1 1.399 (1.073–1.825) 2.579 (1.978–3.363) 3.188 (2.434–4.175) <0.001
Model 3 1 1.140 (0.855–1.520) 1.930 (1.444–2.580) 1.884 (1.391–2.551) <0.001
Male <34.37 34.38–38.91 38.92–43.43 >43.43  
Model 1 1 1.650 (1.135–2.399) 1.674 (1.151–2.435) 3.119 (2.113–4.602) <0.001
Model 2 1 1.370 (0.919–2.043) 1.354 (0.910–2.015) 2.500 (1.654–3.779) <0.001
Model 3 1 1.294 (0.856–1.955) 1.240 (0.817–1.881) 2.107 (1.357–3.273) 0.008
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Model 1: Adjusted for age, postmenopausal (for female) and household income.

Model 2: Adjusted for variables in Model 1 plus waist-to-hip ratio (WHR), trunk fat, current smoking and drinking, physical activity, hypertension and diabetes.

Model 3: Adjusted for variables in Model 2 plus fasting glucose, HOMA-IR, TG, HDL-C, ALT, and UA.

Figure 1. Serum RBP4 concentrations (μg/ml) in non-NAFLD subjects and NAFLD patients.

Figure 1

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*NAFLD vs. non-NAFLD subjects in both female and male groups adjusted by variables (included in model 3) in the general linear model, P < 0.001.

Association of serum RBP4 with NAFLD

To explore the ability of RBP4 to predict the presence of NAFLD, multivariate logistic regression analysis was performed. We found that serum RBP4 levels were positively associated with NAFLD in all subjects [odds ratio (OR) for the highest quartile vs. lowest quartile, 3.481 (95% CI: 2.806–4.320, P < 0.001). As shown in Table 4, after adjusting for age, postmenopausal (females) and household income in both the female and male groups, the serum RBP4 levels were significantly positively associated with the prevalence of NAFLD (all P < 0.01). Furthermore, these associations remained robust after adjusting for the physical and metabolic parameters in models 2 and 3. The ORs of NAFLD for the highest (vs. lowest) quartile in model 3 were 1.884 (95% CI: 1.391, 2.551) for females (P < 0.001) and 2.107 (95% CI: 1.357, 3.273) for males (P < 0.01). The ROC analysis of serum RBP4 levels for the prediction of NAFLD can be found in Supplementary Fig. S1.

Discussion

In this study, we confirmed a positive correlation between the serum RBP4 levels and the prevalence of NAFLD in a large, community-based, middle-aged and elderly Chinese population. It appears that the correlation is independent of IR, trunk fat and other metabolic factors, with no gender difference. Furthermore, our study demonstrated a significant association between the serum RBP4 levels and metabolic risk factors, including IR, dyslipidemia and obesity, in this cohort.

The current study revealed a surprisingly high prevalence of NAFLD, with 50% of the cases presenting ultrasonographical evidence of steatosis. The major reasons for the high prevalence of NAFLD are as follows: First, there was an increasing relative risk of NAFLD with older age groups that was independent of other factors18, and the protective effect of female hormones became lost during the postmenopausal period19. Second, although the ultrasonography used in this study was an established tool for NAFLD screening, inter- and intra-observer variability could not be ignored compared with liver biopsy20. Third, the substantially increased prevalence of fatty liver observed over the last decade was in parallel with regional trends in over-nutrition, diabetes, dyslipidemia and, especially, obesity21. Therefore, the increased prevalence of NAFLD might be due to the high proportion of overweight (73.9%) and obese (7.5%) subjects in our study.

The serum RBP4 levels were positively associated with the occurrence of NAFLD in the current study, consistent with the results of previous studies9,10,12. We noticed that the concentration of serum RBP4 varied by gender and was generally lower in women than in men. However, only the highest quartiles of RBP4 in the male group were significantly associated with a high risk of NAFLD, which indicated that there must be a protective element to neutralize the adverse effect of RBP4 in male patients. Several studies have hypothesized that the sex hormone testosterone might play a role in this situation22,23. The mechanisms underlying the association between testosterone and NAFLD are still largely unclear. One possible explanation is that testosterone is an insulin sensitizer and is thus a metabolic hormone24. In addition, testosterone may act, in part, via an effect on the key regulatory lipogenic enzymes to protect against hepatic steatosis25.

However, there are some controversies among the previously published studies on the relationship between RBP4 and NAFLD14,16. The reasons why we have inconsistent findings can be roughly summarized as follows: (1) Circulating RBP4 is cleared by the kidney glomerular filtration, and kidney dysfunction probably contributes to high levels of serum RBP426. Furthermore, the risk of kidney disease is higher in patients with metabolic syndrome27, which, if it is not strictly controlled in the subjects, will confound the relationship between NAFLD and RBP4. (2) The existence of substantial differences in the characteristics of study populations, as well as different methodologies of RBP4 examination, may lead to inconsistent results28. (3) RBP4 was negatively associated with ALT in all participants and was especially higher in patients with NAFLD9,10,12. Elevated serum ALT is usually used as a cytolytic marker of the liver because it will be released into the serum when the liver is injured29. In parallel, RBP4 may also increase in serum when hepatocytes damage occurs. It is noteworthy that we initially excluded patients with CKD to reduce the bias caused by renal dysfunction. Additionally, our analysis included a nearly 3000-member community-based Chinese population that could reduce the measurement bias and balance the confounding factors. However, considering the feasibility and invasiveness of liver biopsy, we chose abdominal ultrasonography, which could not eliminate the effect of liver damage.

RBP4 is synthesized primarily in the liver, with its secretion being dependent on the retinol concentrations. RBP4 forms a 1:1:1 complex with retinol (holo-RBP4) and transthyretin in circulation. Its most well-defined function is to transport retinol to peripheral tissues in circulation7. After releasing retinol into the target cells, the remaining apo-RBP4 is rapidly filtered through the glomeruli and reabsorbed in the proximal tubular cells and catabolized30. Studies on whether RBP4 is a cause or just relevant to NAFLD have not achieved consistent results. The role of RBP4 in IR and lipid mechanism may explain the interaction between RBP4 and NAFLD. First, RBP4 may influence the insulin signaling pathway through the inhibition of IRS1 (insulin receptor substrate 1) phosphorylation in primary human adipocytes and skeletal muscle in mice5,31. Second, RBP4 could cause IR by stimulating the production of proinflammatory cytokines, which finally leads to intracellular fat accumulation32,33. Third, the impact of RBP4 on stimulating lipogenesis in hepatocytes has been investigated. It might initially up-regulate peroxisome proliferator activated receptor-γ coactivator 1-β (PGC1β) and finally promote the transcription of downstream target lipogenic genes, thereby stimulating de novo lipogenesis in vitro and in vivo34.

Some limitations merit consideration. The cross-sectional observational design was one of the major limitations in our investigation, which precluded causal inferences. In addition, we might still ignore many other mediators in different biological pathways involved in the pathogenesis of NAFLD. For example, it is well recognized that a nonsynonymous single nucleotide polymorphism rs738409 (I148M) in patatin-like phospholipase domain-containing protein 3 (PNPLA3) predisposes a patient to susceptibility to chronic liver disease35,36,37. PNPLA3 promotes the extracellular release of retinol from lipid droplets in hepatic stellate cells, and I148M mutation results in a loss of this function38. Moreover, the circulating concentrations of RBP4 were declined in overweight/obese individuals with NAFLD carrying the PNPLA3 148 M mutant38, which could be a confounding factor in the current study. Abdominal ultrasonography was used as a noninvasive modality to screen the general population to detect NAFLD because liver biopsy is invasive, difficult to perform in large populations and carries some risk of complications39. Therefore, the absence of histological confirmation of the liver status could be another major weakness of this study. Except for these factors, the method used to measure the RBP4 concentration (enzyme immunoassay) could underestimate its level28. Finally, most of our participants were middle-aged and elderly people in a Chinese population, which limited the generality of the results.

In conclusion, our study provides clinical evidence revealing that the serum concentrations of RBP4 were elevated in NAFLD patients in a Chinese population. These findings indicated that RBP4 might be a noninvasive molecular biomarker that detects the presence of NAFLD in middle-aged and elderly population. Additional large-scale studies are needed to explore these associations in other populations. However, further work is needed to determine the physiological role of RBP4 in the liver, which is crucial for clarifying the causal relationship between RBP4 and NAFLD and seeking efficacious therapies to improve NAFLD.

Methods

Study population

Our study was performed on a community-based, prospective cohort, namely the Guangzhou Nutrition and Health Study (GNHS). This cohort was established to investigate the potential association between nutrition, diet, and genetic factors, along with their interactions, with non-communicable chronic diseases. Between June 2008 and June 2010, 3169 participants aged 40–75 years who had at least a 5-year continuous residence in Guangzhou were initially recruited into this study. Of these participants, 2465 were followed up between April 2011 and March 2013. An additional 871 participants were recruited between March 2013 and August 2013.

All of the participants underwent a comprehensive physical examination, routine biochemical analysis of blood, hepatitis virus test and B-scan ultrasonography. Subjects with the following conditions were excluded from the study: excessive alcohol consumption (≥140 g/wk for males or ≥70 g/wk for females40); viral hepatitis [with positive hepatitis B surface antigen (HbsAg) or positive anti-HCV antibody40]; biliary obstructive diseases; drug- or toxin-induced liver diseases; autoimmune hepatitis; chronic inflammatory disease; the presence of severe medical diseases such as cancer, stroke and heart failure; current treatment with systemic corticosteroids, anti-inflammatory and lipid-lowering therapy; and pregnancy. In total, 2938 participants, followed up from the original cohort (n = 2245) and newly recruited participants (n = 693), were finally analyzed in this study (the study flow-chart is presented in Fig. 2).

Figure 2. Flow-chart of the recruited participants.

Figure 2

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The study protocol was approved by the Ethics Committee of the School of Public Health at Sun Yat-sen University. Written informed consent was obtained from all participants at the initial enrollment and at each of the follow-ups. Therefore, the study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Data collection

A structured questionnaire was designed to collect the participants’ socio-demographic characteristics (e.g., age, sex and household income), health-related lifestyle factors (e.g., smoking, alcohol drinking and physical activity) and history of chronic disease by trained staff through face-to-face interviews. Participants wore lightweight clothes and no shoes for the measurement of weight and height. BMI was calculated as the weight (kg)/height2 (m2) (BMI ≥ 24 for overweight and ≥28 for obesity according to Chinese classification41). Daily physical activity was estimated using a 24-h physical activity questionnaire, and the metabolic equivalent (MET) intensity was also calculated42. The fat mass (FM) and %FM of the trunk region were quantified by Dual-energy X-ray absorptiometry (Discovery W; Hologic Inc., Waltham, MA, USA). The trunk region was defined as the area between an upper horizontal border below the chin, and a lower border was formed by oblique lines passing through the hip joints.

The data on current alcohol consumption was obtained by a self-administered questionnaire. Individuals were first questioned for whether they consume alcohol currently. Participants who answered ‘no’ were considered non-drinkers. Participants who responded ‘yes’ were further asked about the average drinking frequency per week and the average volume of wine or beer consumed per drinking day. The average amount of alcohol (g/week) = the average drinking frequency* average amount of alcohol consumed per drinking day [%ABV (alcohol by volume)*0.79336 (g/ml)*Volume (ml)].

Abdominal ultrasonography

All ultrasound examinations, using a Doppler sonography machine (Sonoscape SSI-5500, Shenzhen, China) with a 3.5-MHz probe, were performed to diagnose NAFLD by a single experienced radiologist who was blinded to the laboratory and clinical data. The diagnosis of NAFLD was based on standard criteria issued by the Fatty Liver Disease Study Group of the Chinese Liver Disease Association43.

Biochemical measurements

Venous blood samples were collected from the participants after overnight fasting. The serum was separated into several aliquots and stored at −80 °C within 2 hours. Colorimetric methods were used to measure fasting glucose, TG, TC, HDL-C, LDL-C, AST, ALT and UA in a Hitachi 7600-010 automated analyzer (Hitachi, Tokyo, Japan). IR was evaluated using the homeostasis model assessment (HOMA) in which HOMA = fasting glucose (mmol/L) × fasting insulin (mU/L)/22.5. The diagnosis of virus infection is established through serological testing using chemiluminescence immunoassay. All patients with positive HbsAg or positive anti-HCV antibody were also under the detection of HBV DNA and HCV RNA in serum using polymerase chain reaction (PCR).

Serum RBP4 was measured using enzyme-linked immunosorbent assay (ELISA) kit (Adipogen, San Diego, California, USA), and the absorbance was determined using a microplate spectrophotometer (BIO-TEK, Winooski, Vermont, USA). The lowest level of RBP4 that can be detected by this assay is 380 pg/ml, and the intra-assay coefficients of variation for RBP4 were 3.59%.

Statistical analysis

Descriptive statistics were computed for all variables. The normality test of Kolmogorov-Smirnov was performed to assess whether the data were normally distributed. The means ± SDs or medians [25th, 75th percentiles] were used to describe continuous factors. For categorical variables, the frequencies and percentages were estimated. Categorical data were compared using Chi-squared test. Student’s t-test was used for parametric data comparison, and the Mann–Whitney U-test was used for non-parametric data.

The association between the serum RBP4 levels and metabolic risk factors was estimated using Pearson’s or Spearman’s correlation coefficient. Logistic regression analyses were used to estimate the ORs and 95% confidence intervals (CIs) in three models for the risk of NAFLD with increasing quartiles of serum RBP4 levels, using the lowest quartile as the reference group. Analysis of covariance (ANCOVA) assessed the serum RBP4 levels of NAFLD patients compared with non-NAFLD subjects stratified by sex. A two-tailed P-value < 0.05 was considered statistically significant. All statistical procedures were performed using SPSS Statistics (version 22.0, SPSS Inc, Chicago, IL).

Additional Information

How to cite this article: Chen, X. et al. Retinol Binding Protein-4 Levels and Non-alcoholic Fatty Liver Disease: A community-based cross-sectional study. Sci. Rep. 7, 45100; doi: 10.1038/srep45100 (2017).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material

Supplementary Information
srep45100-s1.pdf (232.7KB, pdf)

Acknowledgments

We thank the staff and students involved in the data collection. This research was supported by grants from the National Basic Research Program of China (973 Program, 2012CB517506), Guangzhou Science and Technology Project (42050027) and National Natural Science Foundation (81370528).

Footnotes

The authors declare no competing financial interests.

Author Contributions X.C.C., W.L. and Y.C. developed the overall research plan and had study oversight; Q.L. and D.L. provided the research guidance; X.C.C., X.C. and Y.L. conducted the whole research; X.W., Z.Y. and G.C. participated in the collection of the data and samples of population study; X.C.C., X.C. and Q.L. analyzed the data; T.R.S. made a great contribution to paper revision; and X.C.C. wrote the manuscript and had the primary responsibility for the final content. All of the authors read and approved the final manuscript.

References

  1. Al N. C. E. The Diagnosis and Management of Non-Alcoholic Fatty Liver Disease: Practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology 55, 2005 (2012). [DOI] [PubMed] [Google Scholar]
  2. Vernon G., Baranova A. & Younossi Z. M. Systematic review: the epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults. Aliment Pharmacol Ther 34, 274 (2011). [DOI] [PubMed] [Google Scholar]
  3. Dyson J. et al. Hepatocellular cancer: the impact of obesity, type 2 diabetes and a multidisciplinary team. J Hepatol 60, 110 (2014). [DOI] [PubMed] [Google Scholar]
  4. Marchesini G. et al. Nonalcoholic fatty liver disease: a feature of the metabolic syndrome. Diabetes 50, 1844 (2001). [DOI] [PubMed] [Google Scholar]
  5. Yang Q. et al. Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes. Nature 436, 356 (2005). [DOI] [PubMed] [Google Scholar]
  6. Graham T. E. et al. Retinol-binding protein 4 and insulin resistance in lean, obese, and diabetic subjects. N Engl J Med 354, 2552 (2006). [DOI] [PubMed] [Google Scholar]
  7. Newcomer M. E. & Ong D. E. Plasma retinol binding protein: structure and function of the prototypic lipocalin. Biochim Biophys Acta 1482, 57 (2000). [DOI] [PubMed] [Google Scholar]
  8. Tsutsumi C. et al. Retinoids and retinoid-binding protein expression in rat adipocytes. J Biol chem 267, 1805 (1992). [PubMed] [Google Scholar]
  9. Wu H. et al. Serum retinol binding protein 4 and nonalcoholic fatty liver disease in patients with type 2 diabetes mellitus. Diabetes Res Clin Pract 79, 185 (2008). [DOI] [PubMed] [Google Scholar]
  10. Terra X. et al. Retinol binding protein-4 circulating levels were higher in nonalcoholic fatty liver disease vs. histologically normal liver from morbidly obese women. Obesity (Silver Spring) 21, 170 (2013). [DOI] [PubMed] [Google Scholar]
  11. Stefan N. et al. High circulating retinol-binding protein 4 is associated with elevated liver fat but not with total, subcutaneous, visceral, or intramyocellular fat in humans. Diabetes Care 30, 1173 (2007). [DOI] [PubMed] [Google Scholar]
  12. Seo J. A. et al. Serum retinol-binding protein 4 levels are elevated in non-alcoholic fatty liver disease. Clin Endocrinol (Oxf) 68, 555 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Chavez A. O. et al. Retinol-binding protein 4 is associated with impaired glucose tolerance but not with whole body or hepatic insulin resistance in Mexican Americans. Am J Physiol Endocrinol Metab 296, E758 (2009). [DOI] [PubMed] [Google Scholar]
  14. Cengiz C., Ardicoglu Y., Bulut S. & Boyacioglu S., Serum retinol-binding protein 4 in patients with nonalcoholic fatty liver disease: does it have a significant impact on pathogenesis? Eur J Gastroenterol Hepatol 22, 813 (2010). [DOI] [PubMed] [Google Scholar]
  15. Milner K. L. et al. Adipocyte fatty acid binding protein levels relate to inflammation and fibrosis in nonalcoholic fatty liver disease. Hepatology 49, 1926 (2009). [DOI] [PubMed] [Google Scholar]
  16. Schina M. et al. Circulating and liver tissue levels of retinol-binding protein-4 in non-alcoholic fatty liver disease. Hepatol res 39, 972 (2009). [DOI] [PubMed] [Google Scholar]
  17. Huang S. C. & Yang Y. J., Serum retinol-binding protein 4 is independently associated with pediatric NAFLD and fasting triglyceride level. J Pediatr Gastroenterol Nutr 56, 145 (2013). [DOI] [PubMed] [Google Scholar]
  18. Wang Z. et al. Prevalence and associated metabolic factors of fatty liver disease in the elderly. Exp Gerontol 48, 705 (2013). [DOI] [PubMed] [Google Scholar]
  19. Codes L. et al. Liver fibrosis in women with chronic hepatitis C: evidence for the negative role of the menopause and steatosis and the potential benefit of hormone replacement therapy. Gut 56, 390 (2007). [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Strauss S., Gavish E., Gottlieb P. & Katsnelson L., Interobserver and intraobserver variability in the sonographic assessment of fatty liver. AJR Am J Roentgenol 189, W320 (2007). [DOI] [PubMed] [Google Scholar]
  21. Fan J. G. & Farrell G. C., Epidemiology of non-alcoholic fatty liver disease in China. J HEPATOL 50, 204 (2009). [DOI] [PubMed] [Google Scholar]
  22. Kim S. et al. A low level of serum total testosterone is independently associated with nonalcoholic fatty liver disease. Bmc Gastroenterol 12, 69 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Polyzos S. A. et al. Sex steroids and sex hormone-binding globulin in postmenopausal women with nonalcoholic fatty liver disease. Hormones (Athens) 12, 405 (2013). [DOI] [PubMed] [Google Scholar]
  24. Dhindsa S. et al. Insulin Resistance and Inflammation in Hypogonadotropic Hypogonadism and Their Reduction After Testosterone Replacement in Men With Type 2 Diabetes. Diabetes Care 39, 82 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kelly D. M. et al. Testosterone suppresses the expression of regulatory enzymes of fatty acid synthesis and protects against hepatic steatosis in cholesterol-fed androgen deficient mice. Life Sci 109, 95 (2014). [DOI] [PubMed] [Google Scholar]
  26. Henze A. et al. Evidence that kidney function but not type 2 diabetes determines retinol-binding protein 4 serum levels. Diabetes 57, 3323 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Chen J. et al. The metabolic syndrome and chronic kidney disease in U.S. adults. Ann Intern Med 140, 167 (2004). [DOI] [PubMed] [Google Scholar]
  28. Graham T. E., Wason C. J., Bluher M. & Kahn B. B., Shortcomings in methodology complicate measurements of serum retinol binding protein (RBP4) in insulin-resistant human subjects. Diabetologia 50, 814 (2007). [DOI] [PubMed] [Google Scholar]
  29. Wieckowska A. & Feldstein A. E., Diagnosis of nonalcoholic fatty liver disease: invasive versus noninvasive. Semin Liver Dis 28, 386 (2008). [DOI] [PubMed] [Google Scholar]
  30. Peterson P. A. & Berggard I. Isolation and properties of a human retinol-transporting protein. J Biol Chem 246, 25 (1971). [PubMed] [Google Scholar]
  31. Ost A. et al. Retinol-binding protein-4 attenuates insulin-induced phosphorylation of IRS1 and ERK1/2 in primary human adipocytes. Faseb J 21, 3696 (2007). [DOI] [PubMed] [Google Scholar]
  32. Moraes-Vieira P. M. et al. RBP4 activates antigen-presenting cells, leading to adipose tissue inflammation and systemic insulin resistance. Cell Metab 19, 512 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Norseen J. et al. Retinol-binding protein 4 inhibits insulin signaling in adipocytes by inducing proinflammatory cytokines in macrophages through a c-Jun N-terminal kinase- and toll-like receptor 4-dependent and retinol-independent mechanism. Mol Cell Biol 32, 2010 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Xia M. et al. Retinol binding protein 4 stimulates hepatic sterol regulatory element-binding protein 1 and increases lipogenesis through the peroxisome proliferator-activated receptor-gamma coactivator 1beta-dependent pathway. Hepatology 58, 564 (2013). [DOI] [PubMed] [Google Scholar]
  35. Mondul A. et al. PNPLA3 I148M Variant Influences Circulating Retinol in Adults with Nonalcoholic Fatty Liver Disease or Obesity. J Nutr 145, 1687 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Pingitore P. et al. PNPLA3 overexpression results in reduction of proteins predisposing to fibrosis. Hum Mol Genet(2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kovarova M. et al. The Genetic Variant I148M in PNPLA3 Is Associated With Increased Hepatic Retinyl-Palmitate Storage in Humans. J Clin Endocrinol Metab 100, E1568 (2015). [DOI] [PubMed] [Google Scholar]
  38. Pirazzi C. et al. PNPLA3 has retinyl-palmitate lipase activity in human hepatic stellate cells. Hum Mol Genet 23, 4077 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Gomercic M., Duvnjak M. & Barsic N. [Ultrasonography in the diagnosis of nonalcoholic fatty liver disease]. Acta Med Croatica 63 Suppl 3, 1 (2009). [PubMed] [Google Scholar]
  40. Fan J. G. et al. Guidelines for the diagnosis and management of nonalcoholic fatty liver disease: update 2010: (published in Chinese on Chinese Journal of Hepatology 2010; 18:163–166). J Dig Dis 12, 38 (2011). [DOI] [PubMed] [Google Scholar]
  41. Zhou B. F. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults–study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci 15, 83 (2002). [PubMed] [Google Scholar]
  42. Ainsworth B. E. et al. Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc 43, 1575 (2011). [DOI] [PubMed] [Google Scholar]
  43. Zeng M. D. et al. Guidelines for the diagnosis and treatment of nonalcoholic fatty liver diseases. J Dig Dis 9, 108 (2008). [DOI] [PubMed] [Google Scholar]

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