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Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease
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Available online at
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Original article
Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease Wang a,b, X. Chen a,b, H. Zhang a,b, J. Pang a,b, J. Lin b,c, X. Xu d, L. Yang a,b,d, J. Ma a,b, W. Ling a,b,d,*, Y. Chen b,c,d
Q1 X.
a
Department of Nutrition, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, Guangdong Province, PR China Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, Guangdong Province, PR China Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, Guangdong Province, PR China d Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, Guangdong Province, PR China b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 March 2019 Received in revised form 28 April 2019 Accepted 28 April 2019 Available online xxx
Aim. – Retinol-binding protein 4 (RBP4), primarily secreted by liver and adipose tissue, has been linked with non-alcoholic fatty liver disease (NAFLD). However, investigations on the relationships between RBP4 and NAFLD have produced inconsistent results. Therefore, the association between serum RBP4 levels and the development or regression of NAFLD was prospectively investigated. Methods. – A total of 3389 Chinese adults, aged 40–75 years and followed-up for 3.09 years, were included and analyzed in the study. NAFLD was diagnosed by abdominal ultrasonography. Serum RBP4 levels were measured, and their relationship to NAFLD development and regression assessed. Results. – Of the 1318 participants without NAFLD at baseline, 410 developed NAFLD after follow-up. Baseline RBP4 was positively associated with incident NAFLD: the fully adjusted odds ratio (OR) was 2.01 with a 95% confidence interval (CI) of 1.33–3.04 (P = 0.003 for trend). After follow-up, a significant increase in RBP4 levels was observed in participants who developed NAFLD. On the other hand, in 1382 subjects diagnosed with NAFLD at baseline, 339 experienced NAFLD regression after follow-up. Thus, baseline RBP4 was inversely associated with NAFLD regression: the fully adjusted OR was 0.52, 95% CI: 0.34–0.80 (P < 0.001 for trend). A significant decrease in RBP4 after follow-up was also noted in participants with NAFLD regression. Conclusion. – Serum RBP4 concentrations are associated with the development and regression of NAFLD, making them a potential novel preventative and therapeutic target in NAFLD management.
C 2019 Published by Elsevier Masson SAS.
Keywords: Adipokine Fatty liver disease Longitudinal study
Abbreviations NAFLD Non-alcoholic fatty liver disease Retinol-binding protein 4 RBP4 Body mass index BMI Waist circumference WC Hip circumference HC Blood pressure BP
* Corresponding author at: Department of Nutrition, School of Public Health, Sun Yat-Sen University, 74 Zhongshan road 2, Guangzhou, Guangdong Province, 510080, PR China. E-mail addresses: [email protected] (W. Ling), [email protected] (Y. Chen).
TG TC HDL-C LDL-C AST ALT UA ALP HOMA IR WHR OR CI
Triglycerides Total cholesterol High-density lipoprotein cholesterol Low-density lipoprotein cholesterol Aspartate aminotransferase Alanine transaminase Uric acid Alkaline phosphatase Homoeostasis model assessment Insulin resistance Waist-to-hip ratio Odds ratio Confidence interval
https://doi.org/10.1016/j.diabet.2019.04.009 C 2019 Published by Elsevier Masson SAS. 1262-3636/
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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Introduction
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Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease the world over [1], with a global prevalence of 25.24% [2]. It comprises a wide spectrum of histopathological features, ranging from isolated hepatic steatosis to non-alcoholic steatohepatitis (NASH), with evidence of hepatocellular injury and fibrosis leading to cirrhosis [3]. The early stage of NAFLD (simple steatosis) is reversible and benign; however, once it progresses to the NASH stage, the risk associated with liver cancer and death caused by NAFLD is conspicuously increased [4]. Therefore, identifying those at high risk and susceptible to NAFLD in the general population, and the NAFLD patients who are less likely to regress to normal, is of major importance in the prevention and treatment of NAFLD. Retinol-binding protein 4 (RBP4) was initially known as a specific transport protein for delivering retinol (vitamin A) from the liver to peripheral targets [5]. Recently, researchers have also found that RBP4 functions as an adipokine playing a central role in glucose and lipid metabolism [6]. As for NAFLD, elevated RBP4 levels can exacerbate hepatic de novo lipogenesis and induce hepatic mitochondrial dysfunction to promote hepatic steatosis [7,8]. Another study found that overexpression of human RBP4 in adipose tissue aggravated hepatic steatosis [9]. In addition, a growing number of animal experiments have indicated that elevated RBP4 levels directly contribute to NAFLD progression. Nevertheless, there is a lack of longitudinal data as to whether increased RBP4 levels can lead to the development of NAFLD. In epidemiological studies, previous reports on the association between RBP4 and NAFLD have been inconsistent due to unexamined confounders, different diagnostic standards and participant heterogeneity [10–12]. In addition, almost all of these studies were cross-sectional in nature and, as such, not able to address the cause-and-effect relationship between RBP4 and NAFLD. In our previous study [13], serum RBP4 levels were positively associated with prevalence of NAFLD in middle-aged and elderly Chinese after adjusting for age and physical and metabolic parameters. In that same population, it was also possible to observe changes of NAFLD status over a mean duration of 3.09 years and to measure serum RBP4 levels again after the follow-up. This was followed by an examination of the association between RBP4 and NAFLD prospectively to determine whether increased RBP4 levels can lead to the development of NAFLD in the Chinese population, and whether serum RBP4 concentration is an independent predictor of NAFLD.
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Participants and methods
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Participant recruitment and follow-up
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Our study was based on the Guangzhou Nutrition and Health Study, a community-based prospective cohort study of middleaged and elderly people living in Southern China (ClinicalTrials.gov NCT03179657). The study was endorsed by the ethics committee of the School of Public Health at Sun Yat-Sen University (approval number ZDGWYL2009-3) and performed in accordance with the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants at the time of initial enrolment. All participants were instructed to maintain their current (pre-study) lifestyles. Before the initial enrolment, all participants underwent a comprehensive questionnaire survey and body assessment, including physical examination, routine biochemical blood analyses, and hepatitis virus and human immunodeficiency virus (HIV) tests. Subjects with the following conditions were excluded:
excessive alcohol consumption ( 140 g/week for men, 70 g/ week for women); viral or autoimmune hepatitis; drug- or toxininduced liver disease; genetic liver disease; HIV infection; chronic kidney disease or renal failure; biliary obstructive disease; any kind of cancer; current treatment with systemic corticosteroids or anti-inflammatory therapy; or pregnancy. Between September 2008 and February 2010, a total of 3169 eligible Chinese adults, aged 40–75 years, were recruited, completed a questionnaire survey and had their anthropometric measurements taken. Of these, 2510 participants were included in the first follow-up survey between April 2011 and March 2013; all again completed a questionnaire survey and had their anthropometric measurements taken, their blood samples collected and abdominal ultrasonography performed. An additional 879 participants were recruited between March 2013 and November 2013 to account for participant attrition, resulting in 2945 participants included in the second follow-up survey at a (mean) 3.09 0.41 years between April 2014 and May 2017. Yet again, all underwent a questionnaire survey, anthropometric measurements and blood sampling, and repeat abdominal ultrasonography. The process and timeline applied in this prospective cohort study are shown in Fig. 1. Abdominal ultrasonography in this cohort was conducted from the time of the first follow-up. Thus, in the present study, our ‘baseline data’ were derived from the first follow-up survey, and the ‘follow-up data’ were based on the second follow-up survey.
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Questionnaire data collection and anthropometric measurements
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Eligible participants were invited to attend the follow-up centre at the School of Public Health, Sun Yat-Sen University, through phone calls or short digital messaging. Medical staff then conducted face-to-face interviews using validated and structured questionnaires to collect information on the participants’ sociodemographic data, dietary intakes, lifestyle and habits, history of chronic diseases and menopausal status (women). Current smoking was defined as regularly smoking 1 cigarette/day for at least the last 6 months. Current alcohol consumption was defined as drinking any type of alcoholic beverages at least once a week for at least the last 6 months (excessive alcohol consumption was excluded). Total metabolic equivalents (METs) were calculated to estimate daily physical activity levels using a 24-h self-reported questionnaire (updated version of the Compendium of Physical Activities) [14]. Body weight and height were measured with participants wearing light clothing and no shoes. Waist circumference (WC) was measured at the midline between the costal margin and iliac crest, and hip circumference (HC) was measured at the point of maximum girth around the buttocks. Blood pressure was measured on the left arm in a sitting position with a digital sphygmomanometer (HEM-711, OMRON Corp., Kyoto, Japan) after participants had rested for at least 10 min. Each measurement was taken twice after an interval of at least 3 min between measurements. Diagnosis of hypertension was based on the 2017 American College of Cardiology (ACC)/American Heart Association (AHA) Guideline [15]. Dual-energy X-ray absorptiometry scans (Discovery W, Hologic, Inc., Bedford, MA, USA) were used to quantify fat mass in the trunk region, defined as that part of the trunk between the upper horizontal boundary just below the chin and a lower boundary above the oblique lines passing through each hip joint [16].
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Blood collection and laboratory measurements
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Venous blood samples were collected after 8–10 h of overnight fasting during both the first and second follow-up surveys. Abdominal ultrasonography was done on the same day as the blood collection. Samples were centrifuged on site for separation of
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Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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Fig. 1. Flow chart of study participant recruitment and follow-up process. Of the 3169 eligible participants initially recruited, 2510 were included in the first follow-up survey, with an additional 879 recruited to account for participant attrition, and ‘Baseline Data’ were derived from the surveys of these participant numbers combined. After a mean duration of 3.09 0.41 years, 2945 participants completed the second follow-up survey, from which the ‘Follow-up Data’ were derived. NAFLD: non-alcoholic fatty liver disease.
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serum and plasma within 2 h of collection, then subpackaged into several aliquots and stored in 80 8C in ultra-low-temperature freezers. Serum full-length RBP4 levels were measured via sandwich enzyme-linked immunosorbent assay (ELISA) kits (catalogue No. AG-45A-0035YEK-KI01, AdipoGen Life Sciences, Inc., San Diego, CA, USA), using a threshold detection level of 380 pg/mL (0.39–25 ng/mL). Intra-assay coefficients of variation were 2.02–3.59%. Absorbance was determined using a Spark 10 M multimode microplate reader (Tecan Trading AG, Ma¨nnedorf, Switzerland). Serum fasting glucose, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST), alanine transaminase (ALT) and uric acid (UA) were measured, using a Hitachi 7600-010 automated analyzer (Hitachi, Ltd, Tokyo, Japan), at baseline. Homoeostasis model assessment (HOMA) for insulin resistance (IR) was calculated as fasting glucose (mmol/L) fasting insulin (mU/mL)/22.5. The 2018 American Diabetes Association Standards of Medical Care in Diabetes was used as a guideline for diabetes diagnosis [17].
Clinical diagnosis of NAFLD
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Abdominal ultrasonography to diagnose NAFLD was performed with a digital Doppler ultrasound machine (SSI-5500, SonoScape Medical Corp., Shenzhen, China), using a 3.5-MHz probe according to the latest standard criteria issued by the Chinese Liver Disease Association [18]. Participants with secondary hepatic fat accumulation due to significant alcohol consumption, hereditary disorders or the use of steatogenic medications were excluded. Scanning were conducted by a group of experienced sonographers who were blinded to the participants’ data. Images were captured using a standardized protocol, with participants lying in a supine position with their right arm raised above their head. Validity was confirmed for 34 participants using further computed tomography (CT) evaluations by radiologists who were blinded to ultrasound findings, and good agreement was observed among these validity assessment samples (Spearman’s r = 0.905, kappa = 0.691, total agreement = 85%; P < 0.001). Between-operator reliability for ultrasound NAFLD evaluations was determined in 100 participants
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Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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and showed excellent precision (Spearman’s r = 0.911, kappa = 0.875, total agreement = 93%; P < 0.001).
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Statistical analyses
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These were performed using SPSS version 22.0 software (IBM Corp., Armonk, NY, USA). A two-tailed P-value < 0.05 was considered statistically significant. Normally distributed data were expressed as means standard deviation (SD) and compared using
Student’s t tests. Non-normally distributed data were logarithmically transformed to approximate normality before analysis or were reported as medians [25th, 75th percentiles], and compared using Mann–Whitney U-tests. Categorical variables were expressed as frequencies and percentages, and compared using chi-squared tests. Pearson or Spearman correlation analyses were conducted to assess the relationships between RBP4 levels and metabolic risk factors. Multivariable logistic regression was used to examine the association between RBP4 and the development or regression of NAFLD. Moreover,
Table 1 Baseline clinical parameters of 1318 participants who did and did not develop non-alcoholic fatty liver disease (NAFLD) during follow-up. Variables
Gender (males/females) Age (years) Height (cm) Body weight (kg) Body weight change (kg)b BMI (kg/m2) BMI change (kg/m2)b WC (cm) WC change (cm)b HC (cm) HC change (cm)b WHR WHR changeb Trunk fat mass (kg) Albumin (g/L) Insulin (mU/mL)a Fasting glucose (mmol/L)a HOMA-IRa AST/ALT ratioa TC (mmol/L) TG (mmol/L)a LDL-C (mmol/L) HDL-C (mmol/L) LDL-C/HDL-C ratio UA (mmol/L) ALP (U/L)a hs-CRP (mg/L) RBP4 (mg/mL) Physical activity (METs/day) Current smokingc Current drinkingc,d Hypertensionc Diabetesc Dyslipidaemiac Use of antihypertensive agentsc Always Usually Often Sometimes Seldom Use of hypoglycaemic agentsc Always Usually Often Sometimes Seldom Use of lipid-lowering agentsc Always Usually Often Sometimes Seldom
NAFLD status during follow-up Non-NAFLD (n = 908)
NAFLD (n = 410)
P (non-NAFLD vs. NAFLD)
304/604 60.47 5.81 158.42 7.67 54.43 8.45 0.12 2.21 21.62 2.48 0.03 0.90 79.58 7.70 2.25 4.95 89.01 5.22 0.20 13.01 0.89 0.07 0.03 0.06 7.74 2.34 45.23 3.92 5.70 [4.13, 7.70] 4.71 [4.30, 5.15] 1.18 [0.84, 1.67] 1.31 [1.11, 1.58] 5.58 1.03 1.04 [0.76, 1.40] 3.54 0.87 1.59 0.43 2.39 0.90 332.18 81.86 67.07 [57.45, 80.07] 1.58 4.08 34.73 6.67 24.59 6.37 101/908 (11.12%) 67/908 (7.38%) 194/908 (21.37%) 55/908 (6.06%) 288/908 (31.72%)
97/313 60.17 5.75 157.48 7.14 57.79 8.01 0.62 2.18 23.24 2.32 0.34 0.93 84.95 7.25 2.31 4.71 91.62 5.20 0.30 2.80 0.93 0.06 0.02 0.05 9.58 2.15 45.07 5.69 7.45 [5.64, 9.95] 4.77 [4.39, 5.30] 1.65 [1.17, 2.23] 1.25 [1.05, 1.47] 5.64 1.10 1.25 [0.90, 1.76] 3.59 0.98 1.45 0.36 2.62 0.95 350.80 85.38 69.27 [56.73, 82.12] 1.76 2.80 36.50 5.96 24.42 6.61 42/410 (10.24%) 36/410 (8.78%) 99/410 (24.15%) 31/410 (7.56%) 179/410 (43.66%)
< 0.001 0.387 0.035 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.822 < 0.001 0.867 < 0.001 0.143 < 0.001 0.542 < 0.001 0.009 < 0.001 0.002 0.301 < 0.001 0.336 < 0.001 < 0.001 < 0.001 0.057 0.437 < 0.001 0.657 0.635 0.380 0.261 0.306 < 0.001 0.663
155/194 (79.90%) 2/194 (1.03%) 4/194 (2.06%) 4/194 (2.06%) 29/194 (14.95%)
75/99 (75.76%) 8/99 (8.08%) 4/99 (4.04%) 2/99 (2.02%) 10/99 (10.10%)
33/55 (60.00%) 5/55 (9.09%) 1/55 (1.82%) 2/55 (3.63%) 14/55 (25.45%)
13/31 (41.94%) 1/31 (3.23%) 0/31 (0.00%) 2/31 (6.45%) 15/31 (48.39%)
55/288 (19.10%) 3/288 (1.04%) 8/288 (2.78%) 14/288 (4.86%) 208/288 (72.22%)
35/179 (19.55%) 2/179 (1.12%) 7/179 (3.91%) 10/179 (5.59%) 125/179 (69.83%)
0.048
0.786
ALP: alkaline phosphatase; ALT: alanine transaminase; AST: aspartate aminotransferase; BMI: body mass index; HC: hip circumference; HDL-C: high-density lipoprotein cholesterol; HOMA-IR: homoeostasis model assessment of insulin resistance; hs-CRP: high-sensitivity C-reactive protein; LDL-C: low-density lipoprotein cholesterol; METs: metabolic equivalents (of energy); RBP4: retinol-binding protein 4; TC: total cholesterol; TG: triglycerides; UA: uric acid; WC: waist circumference; WHR: waist-to-hip ratio. a Statistical significance estimated after log transformation and expressed as medians [25th, 75th percentiles] (data not normally distributed). b Follow-up data minus baseline data. c Expressed as frequency and percentage (categorical variables). d Excessive alcohol consumption ( 140 g/week for men, 70 g/week for women) initially excluded.
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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multiple stepwise logistic regression analyses were performed to determine independent predictors of NAFLD, while potential confounders were determined based on univariate results and reports in the literature. The RBP4 level was treated as either a categorical variable (by quartiles) or a continuous variable after standardization (per SD). Subgroup analyses with gender (male vs female), age (< 65 vs. 65, years), body mass index (BMI; < 24 vs. 24 kg/m2) and TG (< 1.7 vs. 1.7 mmol/L) were further explored to test whether results were consistent among different subgroups.
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Results
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NAFLD incidence and regression rates
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As outlined in Fig. 1, of the initial 3389 participants at baseline, a total of 2945 participants were included in the follow-up, with 444 participants lost to follow-up due to either study withdrawal, refusal to participate in the follow-up, serious disease or death. Mean follow-up duration was 3.09 0.41 years, while the attrition
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Table 2 Baseline clinical parameters of 1382 participants who did and did not experience non-alcoholic fatty liver disease (NAFLD) regression during follow-up. Variables
Gender (males/females) Age (years) Height (cm) Body weight (kg) Body weight change (kg)b BMI (kg/m2) BMI change (kg/m2)b WC (cm) WC change (cm)b HC (cm) HC change (cm)b WHR WHR changeb Trunk fat mass (kg) Albumin (g/L) Insulin (mU/mL)a Fasting glucose (mmol/L)a HOMA-IRa AST/ALT ratioa TC (mmol/L) TG (mmol/L)a LDL-C (mmol/L) HDL-C (mmol/L) LDL-C/HDL-C ratio UA (mmol/L) ALP (U/L)a hs-CRP (mg/L) RBP4 (mg/mL) Physical activity (METs/day) Current smokingc Current drinkingc,d Hypertensionc Diabetesc Dyslipidaemiac Use of antihypertensive agentsc Always Usually Often Sometimes Seldom Use of hypoglycaemic agentsc Always Usually Often Sometimes Seldom Use of lipid-lowering agentsc Always Usually Often Sometimes Seldom
NAFLD status during follow-up Non-NAFLD (n = 339)
NAFLD (n = 1043)
P (non-NAFLD vs. NAFLD)
111/228 60.86 5.81 158.75 7.80 60.26 9.00 1.00 3.17 23.86 2.68 0.27 1.31 84.75 7.56 2.86 5.81 93.41 5.81 1.19 3.56 0.91 0.07 0.04 0.07 10.10 2.51 44.30 4.70 7.85 [5.76, 10.16] 4.68 [4.22, 5.12] 1.62 [1.13, 2.13] 1.22 [1.05, 1.44] 5.45 1.02 1.14 [0.87, 1.56] 3.59 0.88 1.44 0.38 2.64 0.87 343.74 80.38 71.20 [58.66, 81.64] 2.29 7.01 35.58 6.47 23.91 6.16 24 (7.08%) 22 (6.49%) 105 (30.97%) 33 (9.73%) 128 (37.76%)
355/688 60.64 5.48 159.14 7.58 64.21 9.74 0.01 2.44 25.30 2.98 0.08 0.96 89.23 8.05 2.51 4.42 94.79 6.14 0.44 3.08 0.94 0.06 0.03 0.05 11.33 2.60 44.92 4.17 10.32 [7.53, 14.58] 4.92 [4.50, 5.50] 2.31 [1.61, 3.31] 1.06 [0.85, 1.27] 5.57 1.03 1.55 [1.14, 2.17] 3.63 0.91 1.28 0.34 2.97 0.92 369.94 84.14 71.24 [59.75, 85.35] 2.28 4.02 38.33 6.45 23.69 6.10 59 (5.66%) 95 (9.11%) 374 (35.86%) 99 (9.49%) 495 (47.46%)
0.662 0.537 0.425 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.318 < 0.001 < 0.001 < 0.001 0.007 < 0.001 0.019 < 0.001 < 0.001 < 0.001 < 0.001 0.053 < 0.001 0.477 < 0.001 < 0.001 < 0.001 0.137 0.979 < 0.001 0.563 0.331 0.132 0.101 0.895 0.002 0.657
83/105 (79.05%) 7/105 (6.67%) 2/105 (1.90%) 5/105 (4.76%) 8/105 (7.62%)
284/374 (75.94%) 19/374 (5.08%) 11/374 (2.94%) 9/374 (2.41%) 51/374 (13.64%)
25/33 (75.76%) 0/33 (0.00%) 0/33 (0.00%) 0/33 (0.00%) 8/33 (24.24%)
71/99 (71.72%) 1/99 (1.01%) 1/99 (1.01%) 1/99 (1.01%) 25/99 (25.25%)
33/128 (25.78%) 0/128 (0.00%) 4/128 (3.13%) 5/128 (3.91%) 86/128 (67.19%)
110/495 (22.22%) 14/495 (2.83%) 16/495 (3.23%) 28/495 (5.66%) 327/495 (66.06%)
0.886
0.944
ALP: alkaline phosphatase; ALT: alanine transaminase; AST: aspartate aminotransferase; BMI: body mass index; HC: hip circumference; HDL-C: high-density lipoprotein cholesterol; HOMA-IR: homoeostasis model assessment of insulin resistance; hs-CRP: high-sensitivity C-reactive protein; LDL-C: low-density lipoprotein cholesterol; METs: metabolic equivalents (of energy); RBP4: retinol-binding protein 4; TC: total cholesterol; TG: triglycerides; UA: uric acid; WC: waist circumference; WHR: waist-to-hip ratio. a Statistical significance estimated after log transformation and expressed as medians [25th, 75th percentiles] (data not normally distributed). b Follow-up data minus baseline data. c Expressed as frequency and percentage (categorical variables). d Excessive alcohol consumption ( 140 g/week for men, 70 g/week for women) initially excluded.
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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rate was 13.10%. Participants included in the follow-up were compared with those who were not: no differences were observed in their demographic, anthropometric and blood biochemical indices (data not shown). A total of 1318 participants without NAFLD at baseline completed all of the study examinations after the follow-up, and 410 developed NAFLD (97 men, 313 women). NAFLD incidence rate during the 3-year follow-up was 31.11% (annual rate: 10.12%). On the other hand, 1382 participants who had NAFLD at baseline completed all of the study examinations after the follow-up, and 339 participants showed NAFLD regression (111 men, 228 women). The NAFLD regression rate during the 3-year follow-up was 24.53% (annual rate: 8.02%).
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Participants’ baseline clinical parameters
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Table 1 shows that the participants who developed NAFLD (n = 1318) had significantly higher levels of body weight, BMI, WC, HC, waist-to-hip ratio (WHR), trunk fat mass, fasting glucose and insulin, HOMA-IR, TG, LDL-C/HDL-C ratio, UA and RBP4, but lower HDL-C levels and AST/ALT ratios than participants without NAFLD at follow-up (all P < 0.01). On further comparisons with follow-up data, these NAFLD participants also had greater body weight and BMI changes than those who were non-NAFLD (all P < 0.001). On the other hand, the baseline characteristics of 1382 participants with NAFLD at baseline (Table 2) revealed inverse findings.
Participants who regressed to non-NAFLD had significantly lower levels of body weight, BMI, WC, HC, WHR, trunk fat mass, albumin, fasting glucose and insulin, HOMA-IR, ALT, TG, LDL-C/HDL-C ratio, UA and RBP4, but higher HDL-C levels and AST/ALT ratios than those with NAFLD at follow-up (all P < 0.05). Compared with the follow-up data, participants who regressed to normal liver function showed greater body weight, BMI and HC changes than those who sustained their non-NAFLD status (all P < 0.001).
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Baseline RBP4 levels in non-NAFLD participants with or without NAFLD at follow-up
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Serum RBP4 concentrations at baseline were significantly higher in participants who progressed to NAFLD (36.50 5.96 mg/mL) during follow-up than in those without progression (34.73 6.67 mg/mL; P < 0.001). Comparisons of RBP4 between baseline and follow-up levels revealed no significant differences in participants not developing NAFLD. In contrast, a significant increase in RBP4 levels was observed during follow-up in participants who did develop NAFLD (P < 0.01; Fig. 2A). When stratified by quartiles of baseline RBP4 levels, participants with RBP4 in quartile 1 (Q1) had an incidence rate of 18.79%. Notably, this rate rose to 27.36%, 34.94% and 37.31% in those with Q2, Q3 and Q4 levels, respectively, of RBP4 at baseline (P < 0.001 for trend; Fig. 2B).
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Fig. 2. Serum retinol-binding protein 4 (RBP4) levels and rates of incidence or regression of non-alcoholic fatty liver disease (NAFLD) are expressed as means SD (mg/mL): (A) baseline concentration of serum RBP4 in non-NAFLD participants with or without NAFLD development after the 3.09-year follow-up; (B) incidence rate of NAFLD by quartiles of RBP4 levels at baseline (P < 0.001 for trend); (C) baseline level of serum RBP4 in NAFLD participants with or without regression of NAFLD after follow-up; and (D) regression rate of NAFLD by quartiles of RBP4 levels at baseline (P < 0.001 for trend). ** P < 0.001; *** P < 0.001; n.s. not significant.
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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Table 3 Adjusted odds ratios (95% CIs) for risk of non-alcoholic fatty liver disease (NAFLD) development and probability of NAFLD regression in participants after 3.09 years of followup by quartile or per 1-SD (standard deviation) increase in baseline serum retinol-binding protein 4 (RBP4) levels. Serum RBP4 quartile (mg/mL)
NAFLD development in those without NAFLD at baseline Model 1 P-values Model 2 P-values Model 3 P-values NAFLD regression in those with NAFLD at baseline Model 1 P-values Model 2 P-values Model 3 P-values
Quartile 1
Quartile 2
Quartile 3
Quartile 4
< 31.20 (n = 330) 1.000
31.20–35.27 (n = 329) 2.335 (1.626–3.354) < 0.001 2.050 (1.396–3.011) < 0.001 1.981 (1.340–2.929) 0.001 33.88–37.46 (n = 346) 0.847 (0.617–1.165) 0.307 0.963 (0.686–1.352) 0.829 1.111 (0.781–1.582) 0.558
35.28–38.90 (n = 332) 2.655 (1.847–3.818) < 0.001 2.319 (1.579–3.406) < 0.001 2.053 (1.385–3.044) < 0.001 37.47–41.59 (n = 346) 0.401 (0.281–0.572) < 0.001 0.443 (0.305–0.644) < 0.001 0.551 (0.373–0.815) 0.003
> 38.90 (n = 327) 3.238 (2.237–4.689) < 0.001 2.442 (1.645–3.624) < 0.001 2.014 (1.333–3.044) 0.001 > 41.59 (n = 345) 0.284 (0.192–0.418) < 0.001 0.334 (0.223–0.501) < 0.001 0.518 (0.336–0.800) 0.003
1.000 1.000 < 33.88 (n = 345) 1.000 1.000 1.000
P for trend
Per 1-SD increase
< 0.001
1.429 (1.262–1.619) < 0.001 1.302 (1.139–1.490) < 0.001 1.207 (1.048–1.389) 0.009
< 0.001 0.003
< 0.001 < 0.001 < 0.001
0.631 (0.552–0.722) < 0.001 0.665 (0.577–0.767) < 0.001 0.783 (0.672–0.913) 0.002
Model 1: adjusted for age and gender. Model 2: adjusted as for model 1 plus body mass index, waist-to-hip ratio, physical activity (METs/day), current smoking/drinking, history of hypertension/diabetes. Model 3: adjusted as for model 2 plus homoeostasis model assessment of insulin resistance, aspartate aminotransferase/alanine transaminase ratio, total cholesterol, triglycerides, high-density/low-density lipoprotein cholesterol ratio, uric acid, albumin, alkaline phosphatase.
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Baseline RBP4 in NAFLD participants with or without NAFLD regression at follow-up
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On the other hand, serum RBP4 concentrations at baseline were significantly lower in participants with NAFLD regression (35.58 6.47 mg/mL) during follow-up than in those with NAFLD (38.33 6.45 mg/mL; P < 0.001). However, no significant difference was noted in serum RBP4 levels at both baseline and follow-up in participants without NAFLD regression, although a significant decrease in RBP4 levels at follow-up was observed in those who did experience NAFLD regression (P < 0.01; Fig. 2C). When stratified by quartiles of baseline RBP4, participants in Q1 had a regression rate of 34.78%. As expected, this rate was reduced to 31.21%, 18.21% and 13.91% in those with Q2, Q3 and Q4, respectively, levels of RBP4 at baseline (P < 0.001 for trend; Fig. 2D).
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Longitudinal analyses of the association between baseline RBP4 and development or regression of NAFLD at follow-up
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In participants without NAFLD at baseline, the risk of developing NAFLD significantly increased the higher the quartile of RBP4 in both the minimally (model 1) and fully adjusted (model 3) models. Odds ratios (ORs) and 95% confidence intervals (CIs) of NAFLD development for Q4 (vs. Q1) were 3.24 (95% CI: 2.24–4.69, P = 0.001 for trend) in model 1 and 2.01 (95% CI: 1.33–3.04, P = 0.003 for trend) in model 3 (Table 3). When treated as a continuous variable, each 1-SD increase in RBP4 level was associated with a 20.7% increase in risk of NAFLD development (OR: 1.21, 95% CI: 1.05–1.39; P = 0.009). In contrast, RBP4 levels were inversely associated with NAFLD regression in participants diagnosed with NAFLD at baseline (all P < 0.01 for trend). The minimally and fully adjusted ORs (95% CI) were 0.28 (0.19–0.42) and 0.52 (0.34–0.80), respectively, when comparing the extreme quartiles. Furthermore, each 1-SD increase in RBP4 level was associated with a 21.70% decrease in probability of NAFLD regression (OR: 0.78, 95% CI: 0.67–0.91; P = 0.002; Table 3).
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Serum RBP4 level an independent predictor of NAFLD incidence and regression
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Independent predictors of NAFLD incidence and regression were identified using multiple stepwise logistic regression
Table 4 Multiple stepwise logistic regression analyses of factors predicting progression (development and regression) of non-alcoholic fatty liver disease (NAFLD) during follow-up. NAFLD status
Variables
OR (95% CI)#
P
Development (n = 1318)
Gender BMI HOMA-IR Triglycerides RBP4 Quartile 1 Quartile 2 Quartile 3 Quartile 4 BMI HOMA-IR Triglycerides AST/ALT ratio RBP4 Quartile 1 Quartile 2 Quartile 3 Quartile 4
0.455 1.260 1.298 1.448
(0.336–0.615) (1.191–1.333) (1.123–1.500) (1.198–1.751)
< < < <
1.000 2.012 2.130 2.146 0.870 0.810 0.576 1.397
(1.372–2.951) (1.446–3.138) (1.435–3.210) (0.825–0.917) (0.717–0.915) (0.474–0.702) (1.021–1.913)
< 0.001 < 0.001 < 0.001 < 0.001 0.001 < 0.001 0.037
Regression (n = 1382)
1.000 1.142 (0.811–1.608) 0.563 (0.388–0.816) 0.528 (0.352–0.792)
0.001 0.001 0.001 0.001
0.446 0.002 0.002
NB: Variables in original model were age, gender, body mass index (BMI), physical activity [metabolic equivalents (METs)/day], homoeostasis model assessment of insulin resistance (HOMA-IR), aspartate aminotransferase (AST)/alanine transaminase (ALT) ratio, total cholesterol, low-density/high-density lipoprotein cholesterol ratio, uric acid, albumin, alkaline phosphatase, retinol-binding protein 4 (RBP4) levels in quartiles. OR: odds ratio; CI: confidence interval.
analyses including age, gender, BMI, WHR, physical activity (METs/day), HOMA-IR, AST/ALT ratio, TC, TG, LDL-C/HDL-C ratio, UA, albumin, alkaline phosphatase (ALP) and RBP4. Serum RBP4 level at baseline (OR for Q4: 2.15, 95% CI: 1.44–3.21; P < 0.001) together with gender, BMI, HOMA-IR and TG (all P < 0.01) were independent predictors of NAFLD incidence at follow-up (Table 4). Indeed, BMI, HOMA-IR, TG and RBP4 correlated positively with incident NAFLD, whereas gender was inversely correlated with incident NAFLD. In addition, RBP4 level (OR for Q4: 0.53, 95% CI: 0.35–0.79; P = 0.002) together with BMI, HOMA-IR, TG and AST/ALT ratio (all P < 0.01) were all independent predictors of NAFLD regression at follow-up. BMI, HOMA-IR, TG and RBP4 were inversely correlated with NAFLD regression, whereas AST/ALT ratio was positively
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correlated with NAFLD regression (Table 4). Further adjustments for current smoking and drinking status, and history of hypertension and diabetes, did not alter any of the above results.
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Subgroup analyses
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Effects of the interaction between baseline RBP4 levels and gender, age, BMI and TG were also examined. Positive associations between RBP4 and risk of developing NAFLD were more often found in women and in participants with younger age, higher BMI or lower TG and HOMA-IR values (Table S1; see supplementary materials associated with this article online). Moreover, the inverse associations between RBP4 and the possibility of NAFLD regression appeared to be more substantial in female participants and in those with older age, lower BMI and HOMA-IR scores, or higher TG levels (Table S2; see supplementary materials associated with this article online).
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Discussion
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RBP4 has been identified as an adipokine [19] with an important role in metabolic disorder diseases, including NAFLD [20,21]. However, evidence supporting the potential role of RBP4 in NAFLD development is limited in humans. Indeed, the present study constitutes the first clinical evidence elucidating the association between serum RBP4 levels and the development or regression of NAFLD in a community-based middle-aged and elderly Chinese population after 3.09 years of follow-up. The results of this longitudinal study suggest that RBP4 levels are associated with the development and regression of NAFLD, and are also an independent predictor of NAFLD progression. However, the prevalence of NAFLD in our cohort was notably higher than the global prevalence rate [2], with 50.73% of our cases having ultrasound evidence of steatosis. The main reasons attributed to this high prevalence are, first and foremost, the older age of our participants, as ageing is a significant independent risk factor of NAFLD [22,23]. Second, the modernization of Guangzhou has resulted in substantial lifestyle changes, such as the popularity of high-fat, high-calorie diets coupled with more sedentary lifestyles, that are related to the substantially increased prevalence of NAFLD [24]. Indeed, the relatively high prevalence of NAFLD in urbanized areas such as Beijing and Shanghai (35.10% and 43.50%, respectively) [25,26] supports our hypothesis that higher levels of urbanization may be contributing to the greater prevalence of NAFLD. Third, the protective effect of oestrogens in women is attenuated or lost after menopause, and longer durations of oestrogen deficiency increase the risk of liver disease in postmenopausal women [27,28]. Therefore, the high prevalence of NAFLD in our present study might also be partly due to the large proportion of female participants (67.83%) and their menopausal status (96.59%). NAFLD is a recognized hepatic phenotype of metabolic impairment [29], and our present findings are consistent with a previous report by Kim et al. [30], which revealed that participants who developed NAFLD at follow-up had abnormal metabolic profiles. In our study, the non-NAFLD participants at baseline who developed NAFLD after the follow-up had significantly higher body weight, BMI, WHR, trunk fat mass, fasting glucose and insulin, HOMA-IR, TG, LDL-C/HDL-C ratio and UA levels, but lower HDL-C and AST/ALT ratios than those not developing NAFLD, whereas an inverse relationship was observed among those who initially had NAFLD, but who regressed to normal after the follow-up. Moreover, RBP4 has been reported to cause IR and dyslipidaemia by stimulating the production of pro-inflammatory cytokines and/ or disturbing normal insulin signalling [31]. Our study results showed that baseline RBP4 levels were positively correlated with
BMI, HOMA-IR, TG and LDL-C/HDL-C ratio, but negatively correlated with HDL-C after adjusting for age and gender (Table S3; see supplementary materials associated with this article online). These results indicate that RBP4 levels are positively correlated with metabolic disorders, and suggest that elevated RBP4 levels might be contributing to the development of NAFLD in part by worsening IR and lipid metabolism disorders. Currently, an increasing number of studies have indicated that RBP4 may be a direct cause of NAFLD progression. Our previous study found that treatment with RBP4 can significantly increase expression of peroxisome proliferator-activated receptor-g coactivator (PGC)-1b and sterol regulatory element-binding protein (SREBP)-1c, which ultimately promote de novo lipogenesis [7]. In addition, human-RBP4-expressing mice have exhibited mitochondrial liver dysfunction, which resulted in more likely progression to NASH when challenged by a high-fat diet [8]. Furthermore, another study demonstrated that, in mice with adipocyte-specific overexpression of RBP4, inflammation was induced in adipose tissue that, in turn, triggered increased lipolysis within adipocytes, leading to increased free fatty acid flux into the liver [9]. Therefore, to verify whether RBP4 can directly lead to NAFLD development, our cohort study also adjusted for related factors of IR and dyslipidaemia, and found that baseline RBP4 levels were positively associated with incident NAFLD, but inversely related to NAFLD regression. Thus, these results provide evidence that RBP4 could be playing a vital role in the pathophysiological course of NAFLD that might directly lead to progression at the population level. Previous reports of the relationship between RBP4 and NAFLD in population studies were rather inconsistent. Recently, a metaanalysis to examine all published data on RBP4 and NAFLD [32], with subgroup analyses stratified by NAFLD diagnostic methods, indicated that RBP4 levels in NAFLD participants diagnosed by ultrasonography were significantly higher than in the controls (five studies, n = 3524). However, no significant difference was found among those diagnosed by liver biopsy (five studies, n = 647) [32]. This finding might be attributed to the smaller number of patients diagnosed by liver biopsy, which cannot detect differences in RBP4 levels between those with NAFLD and the healthy (non-NAFLD) population, in whom substantial between-study heterogeneity was found. However, none of the relevant currently available epidemiological studies addressed the cause-and-effect relationship, given their cross-sectional design. For this reason, our investigation was initially of the association between circulating RBP4 levels and NAFLD in a prospective cohort study. Participants were followedup for 3.09 years to identify any changes of NAFLD status, and serum RBP4 levels were again measured after the follow-up. Results showed a significant increase of RBP4 in participants who developed NAFLD. In contrast, a marked decrease of RBP4 levels was observed in participants whose NAFLD regressed. Multiple stepwise logistic regression analyses showed that baseline RBP4 levels were an independent predictor of NAFLD incidence and regression. Thus, these results also suggest that RBP4 may be a causal factor of NAFLD. Nevertheless, several limitations of our study should be mentioned. First, similar to most epidemiological studies, a selection bias may have been present: our participants were volunteers recruited from a local community and, as such, they may have been more health-conscious, and the inclusion of a monoethnic (Chinese) cohort of adult individuals also limits generalization of any conclusions of this study to other ethnic groups. Second, as with other longitudinal studies, residual confounding is unavoidable. Our participants’ lifestyle modifications, and changes in health status and medical treatments (due to, for example, new cases of diabetes or dyslipidaemia), genetic factors [such as rs738409 non-synonymous single nucleotide polymorphisms (SNPs) in patatin-like phospholipase domain-containing protein 3 (PNPLA3) genotypes]
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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and other adipocytokines (such as adiponectin and leptin) may have potentially influenced the development or regression of NAFLD. However, these mediators of different pathways involved in the pathogenesis of NAFLD were not factored into our analyses, thereby preventing any analysis of whether these factors had moderating effects on the outcome. Third, the follow-up period may have been insufficient for tracking the development of advanced NAFLD and its comorbidities. Finally, ultrasound scanning for an NAFLD diagnosis is somewhat dependent on the operator and not sensitive enough for minor steatosis. Although a standardized protocol was used and the scanning sonographers were selected because of their experience, the lack of histological confirmation of liver status may yet constitute another weakness of the present study. In summary, this prospective cohort study performed in a Chinese population has revealed an association between serum RBP4 levels and NAFLD progression. Our results suggest that circulating RBP4 levels are associated with NAFLD progression, and that RBP4 may constitute a potential novel preventative and therapeutic target in NAFLD management. However, given the limitations of our study, future prospective studies involving larger sample sizes of multiethnic populations with NAFLD diagnosed and assessed by transient elastography, magnetic resonance spectroscopy or biopsy are now needed to further clarify the issue.
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Funding
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This work was supported by the Major Projects of Guangzhou Health Collaborative Innovation [grant number 201604020002], National Natural Science Foundation of China [grant numbers 81730090, 81573142 and 81372977] and 5010 Program for Clinical Researches of Sun Yat-Sen University [grant number 2007032].
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Author contributions
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X.W. and W.L. developed the overall research plan and had study oversight; X.X., L.Y. and J.M. provided research guidance; X.W., J.L., J.P. and H.Z. participated in collecting data and biological samples; X.W. and X.C. performed measurements of serum RBP4 levels and analyzed data; and X.W. and W.L. wrote the manuscript and had primary responsibility for the final content. All authors read and approved the final manuscript.
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Disclosure of interest
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The authors declare that they have no competing interest.
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Acknowledgments
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The authors are grateful to all the volunteers for their participation.
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Appendix A. Supplementary data
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Supplementary material related to this article can be found, in the online version, at https://doi.org/10.1016/j.diabet.2019.04.009.
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Original article
Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease Wang a,b, X. Chen a,b, H. Zhang a,b, J. Pang a,b, J. Lin b,c, X. Xu d, L. Yang a,b,d, J. Ma a,b, W. Ling a,b,d,*, Y. Chen b,c,d
Q1 X.
a
Department of Nutrition, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, Guangdong Province, PR China Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, Guangdong Province, PR China Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou 510080, Guangdong Province, PR China d Guangdong Engineering Technology Center of Nutrition Transformation, Guangzhou 510080, Guangdong Province, PR China b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 12 March 2019 Received in revised form 28 April 2019 Accepted 28 April 2019 Available online xxx
Aim. – Retinol-binding protein 4 (RBP4), primarily secreted by liver and adipose tissue, has been linked with non-alcoholic fatty liver disease (NAFLD). However, investigations on the relationships between RBP4 and NAFLD have produced inconsistent results. Therefore, the association between serum RBP4 levels and the development or regression of NAFLD was prospectively investigated. Methods. – A total of 3389 Chinese adults, aged 40–75 years and followed-up for 3.09 years, were included and analyzed in the study. NAFLD was diagnosed by abdominal ultrasonography. Serum RBP4 levels were measured, and their relationship to NAFLD development and regression assessed. Results. – Of the 1318 participants without NAFLD at baseline, 410 developed NAFLD after follow-up. Baseline RBP4 was positively associated with incident NAFLD: the fully adjusted odds ratio (OR) was 2.01 with a 95% confidence interval (CI) of 1.33–3.04 (P = 0.003 for trend). After follow-up, a significant increase in RBP4 levels was observed in participants who developed NAFLD. On the other hand, in 1382 subjects diagnosed with NAFLD at baseline, 339 experienced NAFLD regression after follow-up. Thus, baseline RBP4 was inversely associated with NAFLD regression: the fully adjusted OR was 0.52, 95% CI: 0.34–0.80 (P < 0.001 for trend). A significant decrease in RBP4 after follow-up was also noted in participants with NAFLD regression. Conclusion. – Serum RBP4 concentrations are associated with the development and regression of NAFLD, making them a potential novel preventative and therapeutic target in NAFLD management.
C 2019 Published by Elsevier Masson SAS.
Keywords: Adipokine Fatty liver disease Longitudinal study
Abbreviations NAFLD Non-alcoholic fatty liver disease Retinol-binding protein 4 RBP4 Body mass index BMI Waist circumference WC Hip circumference HC Blood pressure BP
* Corresponding author at: Department of Nutrition, School of Public Health, Sun Yat-Sen University, 74 Zhongshan road 2, Guangzhou, Guangdong Province, 510080, PR China. E-mail addresses: [email protected] (W. Ling), [email protected] (Y. Chen).
TG TC HDL-C LDL-C AST ALT UA ALP HOMA IR WHR OR CI
Triglycerides Total cholesterol High-density lipoprotein cholesterol Low-density lipoprotein cholesterol Aspartate aminotransferase Alanine transaminase Uric acid Alkaline phosphatase Homoeostasis model assessment Insulin resistance Waist-to-hip ratio Odds ratio Confidence interval
https://doi.org/10.1016/j.diabet.2019.04.009 C 2019 Published by Elsevier Masson SAS. 1262-3636/
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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Introduction
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Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease the world over [1], with a global prevalence of 25.24% [2]. It comprises a wide spectrum of histopathological features, ranging from isolated hepatic steatosis to non-alcoholic steatohepatitis (NASH), with evidence of hepatocellular injury and fibrosis leading to cirrhosis [3]. The early stage of NAFLD (simple steatosis) is reversible and benign; however, once it progresses to the NASH stage, the risk associated with liver cancer and death caused by NAFLD is conspicuously increased [4]. Therefore, identifying those at high risk and susceptible to NAFLD in the general population, and the NAFLD patients who are less likely to regress to normal, is of major importance in the prevention and treatment of NAFLD. Retinol-binding protein 4 (RBP4) was initially known as a specific transport protein for delivering retinol (vitamin A) from the liver to peripheral targets [5]. Recently, researchers have also found that RBP4 functions as an adipokine playing a central role in glucose and lipid metabolism [6]. As for NAFLD, elevated RBP4 levels can exacerbate hepatic de novo lipogenesis and induce hepatic mitochondrial dysfunction to promote hepatic steatosis [7,8]. Another study found that overexpression of human RBP4 in adipose tissue aggravated hepatic steatosis [9]. In addition, a growing number of animal experiments have indicated that elevated RBP4 levels directly contribute to NAFLD progression. Nevertheless, there is a lack of longitudinal data as to whether increased RBP4 levels can lead to the development of NAFLD. In epidemiological studies, previous reports on the association between RBP4 and NAFLD have been inconsistent due to unexamined confounders, different diagnostic standards and participant heterogeneity [10–12]. In addition, almost all of these studies were cross-sectional in nature and, as such, not able to address the cause-and-effect relationship between RBP4 and NAFLD. In our previous study [13], serum RBP4 levels were positively associated with prevalence of NAFLD in middle-aged and elderly Chinese after adjusting for age and physical and metabolic parameters. In that same population, it was also possible to observe changes of NAFLD status over a mean duration of 3.09 years and to measure serum RBP4 levels again after the follow-up. This was followed by an examination of the association between RBP4 and NAFLD prospectively to determine whether increased RBP4 levels can lead to the development of NAFLD in the Chinese population, and whether serum RBP4 concentration is an independent predictor of NAFLD.
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Participants and methods
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Participant recruitment and follow-up
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Our study was based on the Guangzhou Nutrition and Health Study, a community-based prospective cohort study of middleaged and elderly people living in Southern China (ClinicalTrials.gov NCT03179657). The study was endorsed by the ethics committee of the School of Public Health at Sun Yat-Sen University (approval number ZDGWYL2009-3) and performed in accordance with the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants at the time of initial enrolment. All participants were instructed to maintain their current (pre-study) lifestyles. Before the initial enrolment, all participants underwent a comprehensive questionnaire survey and body assessment, including physical examination, routine biochemical blood analyses, and hepatitis virus and human immunodeficiency virus (HIV) tests. Subjects with the following conditions were excluded:
excessive alcohol consumption ( 140 g/week for men, 70 g/ week for women); viral or autoimmune hepatitis; drug- or toxininduced liver disease; genetic liver disease; HIV infection; chronic kidney disease or renal failure; biliary obstructive disease; any kind of cancer; current treatment with systemic corticosteroids or anti-inflammatory therapy; or pregnancy. Between September 2008 and February 2010, a total of 3169 eligible Chinese adults, aged 40–75 years, were recruited, completed a questionnaire survey and had their anthropometric measurements taken. Of these, 2510 participants were included in the first follow-up survey between April 2011 and March 2013; all again completed a questionnaire survey and had their anthropometric measurements taken, their blood samples collected and abdominal ultrasonography performed. An additional 879 participants were recruited between March 2013 and November 2013 to account for participant attrition, resulting in 2945 participants included in the second follow-up survey at a (mean) 3.09 0.41 years between April 2014 and May 2017. Yet again, all underwent a questionnaire survey, anthropometric measurements and blood sampling, and repeat abdominal ultrasonography. The process and timeline applied in this prospective cohort study are shown in Fig. 1. Abdominal ultrasonography in this cohort was conducted from the time of the first follow-up. Thus, in the present study, our ‘baseline data’ were derived from the first follow-up survey, and the ‘follow-up data’ were based on the second follow-up survey.
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Questionnaire data collection and anthropometric measurements
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Eligible participants were invited to attend the follow-up centre at the School of Public Health, Sun Yat-Sen University, through phone calls or short digital messaging. Medical staff then conducted face-to-face interviews using validated and structured questionnaires to collect information on the participants’ sociodemographic data, dietary intakes, lifestyle and habits, history of chronic diseases and menopausal status (women). Current smoking was defined as regularly smoking 1 cigarette/day for at least the last 6 months. Current alcohol consumption was defined as drinking any type of alcoholic beverages at least once a week for at least the last 6 months (excessive alcohol consumption was excluded). Total metabolic equivalents (METs) were calculated to estimate daily physical activity levels using a 24-h self-reported questionnaire (updated version of the Compendium of Physical Activities) [14]. Body weight and height were measured with participants wearing light clothing and no shoes. Waist circumference (WC) was measured at the midline between the costal margin and iliac crest, and hip circumference (HC) was measured at the point of maximum girth around the buttocks. Blood pressure was measured on the left arm in a sitting position with a digital sphygmomanometer (HEM-711, OMRON Corp., Kyoto, Japan) after participants had rested for at least 10 min. Each measurement was taken twice after an interval of at least 3 min between measurements. Diagnosis of hypertension was based on the 2017 American College of Cardiology (ACC)/American Heart Association (AHA) Guideline [15]. Dual-energy X-ray absorptiometry scans (Discovery W, Hologic, Inc., Bedford, MA, USA) were used to quantify fat mass in the trunk region, defined as that part of the trunk between the upper horizontal boundary just below the chin and a lower boundary above the oblique lines passing through each hip joint [16].
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Blood collection and laboratory measurements
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Venous blood samples were collected after 8–10 h of overnight fasting during both the first and second follow-up surveys. Abdominal ultrasonography was done on the same day as the blood collection. Samples were centrifuged on site for separation of
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Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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Fig. 1. Flow chart of study participant recruitment and follow-up process. Of the 3169 eligible participants initially recruited, 2510 were included in the first follow-up survey, with an additional 879 recruited to account for participant attrition, and ‘Baseline Data’ were derived from the surveys of these participant numbers combined. After a mean duration of 3.09 0.41 years, 2945 participants completed the second follow-up survey, from which the ‘Follow-up Data’ were derived. NAFLD: non-alcoholic fatty liver disease.
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serum and plasma within 2 h of collection, then subpackaged into several aliquots and stored in 80 8C in ultra-low-temperature freezers. Serum full-length RBP4 levels were measured via sandwich enzyme-linked immunosorbent assay (ELISA) kits (catalogue No. AG-45A-0035YEK-KI01, AdipoGen Life Sciences, Inc., San Diego, CA, USA), using a threshold detection level of 380 pg/mL (0.39–25 ng/mL). Intra-assay coefficients of variation were 2.02–3.59%. Absorbance was determined using a Spark 10 M multimode microplate reader (Tecan Trading AG, Ma¨nnedorf, Switzerland). Serum fasting glucose, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST), alanine transaminase (ALT) and uric acid (UA) were measured, using a Hitachi 7600-010 automated analyzer (Hitachi, Ltd, Tokyo, Japan), at baseline. Homoeostasis model assessment (HOMA) for insulin resistance (IR) was calculated as fasting glucose (mmol/L) fasting insulin (mU/mL)/22.5. The 2018 American Diabetes Association Standards of Medical Care in Diabetes was used as a guideline for diabetes diagnosis [17].
Clinical diagnosis of NAFLD
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Abdominal ultrasonography to diagnose NAFLD was performed with a digital Doppler ultrasound machine (SSI-5500, SonoScape Medical Corp., Shenzhen, China), using a 3.5-MHz probe according to the latest standard criteria issued by the Chinese Liver Disease Association [18]. Participants with secondary hepatic fat accumulation due to significant alcohol consumption, hereditary disorders or the use of steatogenic medications were excluded. Scanning were conducted by a group of experienced sonographers who were blinded to the participants’ data. Images were captured using a standardized protocol, with participants lying in a supine position with their right arm raised above their head. Validity was confirmed for 34 participants using further computed tomography (CT) evaluations by radiologists who were blinded to ultrasound findings, and good agreement was observed among these validity assessment samples (Spearman’s r = 0.905, kappa = 0.691, total agreement = 85%; P < 0.001). Between-operator reliability for ultrasound NAFLD evaluations was determined in 100 participants
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and showed excellent precision (Spearman’s r = 0.911, kappa = 0.875, total agreement = 93%; P < 0.001).
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Statistical analyses
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These were performed using SPSS version 22.0 software (IBM Corp., Armonk, NY, USA). A two-tailed P-value < 0.05 was considered statistically significant. Normally distributed data were expressed as means standard deviation (SD) and compared using
Student’s t tests. Non-normally distributed data were logarithmically transformed to approximate normality before analysis or were reported as medians [25th, 75th percentiles], and compared using Mann–Whitney U-tests. Categorical variables were expressed as frequencies and percentages, and compared using chi-squared tests. Pearson or Spearman correlation analyses were conducted to assess the relationships between RBP4 levels and metabolic risk factors. Multivariable logistic regression was used to examine the association between RBP4 and the development or regression of NAFLD. Moreover,
Table 1 Baseline clinical parameters of 1318 participants who did and did not develop non-alcoholic fatty liver disease (NAFLD) during follow-up. Variables
Gender (males/females) Age (years) Height (cm) Body weight (kg) Body weight change (kg)b BMI (kg/m2) BMI change (kg/m2)b WC (cm) WC change (cm)b HC (cm) HC change (cm)b WHR WHR changeb Trunk fat mass (kg) Albumin (g/L) Insulin (mU/mL)a Fasting glucose (mmol/L)a HOMA-IRa AST/ALT ratioa TC (mmol/L) TG (mmol/L)a LDL-C (mmol/L) HDL-C (mmol/L) LDL-C/HDL-C ratio UA (mmol/L) ALP (U/L)a hs-CRP (mg/L) RBP4 (mg/mL) Physical activity (METs/day) Current smokingc Current drinkingc,d Hypertensionc Diabetesc Dyslipidaemiac Use of antihypertensive agentsc Always Usually Often Sometimes Seldom Use of hypoglycaemic agentsc Always Usually Often Sometimes Seldom Use of lipid-lowering agentsc Always Usually Often Sometimes Seldom
NAFLD status during follow-up Non-NAFLD (n = 908)
NAFLD (n = 410)
P (non-NAFLD vs. NAFLD)
304/604 60.47 5.81 158.42 7.67 54.43 8.45 0.12 2.21 21.62 2.48 0.03 0.90 79.58 7.70 2.25 4.95 89.01 5.22 0.20 13.01 0.89 0.07 0.03 0.06 7.74 2.34 45.23 3.92 5.70 [4.13, 7.70] 4.71 [4.30, 5.15] 1.18 [0.84, 1.67] 1.31 [1.11, 1.58] 5.58 1.03 1.04 [0.76, 1.40] 3.54 0.87 1.59 0.43 2.39 0.90 332.18 81.86 67.07 [57.45, 80.07] 1.58 4.08 34.73 6.67 24.59 6.37 101/908 (11.12%) 67/908 (7.38%) 194/908 (21.37%) 55/908 (6.06%) 288/908 (31.72%)
97/313 60.17 5.75 157.48 7.14 57.79 8.01 0.62 2.18 23.24 2.32 0.34 0.93 84.95 7.25 2.31 4.71 91.62 5.20 0.30 2.80 0.93 0.06 0.02 0.05 9.58 2.15 45.07 5.69 7.45 [5.64, 9.95] 4.77 [4.39, 5.30] 1.65 [1.17, 2.23] 1.25 [1.05, 1.47] 5.64 1.10 1.25 [0.90, 1.76] 3.59 0.98 1.45 0.36 2.62 0.95 350.80 85.38 69.27 [56.73, 82.12] 1.76 2.80 36.50 5.96 24.42 6.61 42/410 (10.24%) 36/410 (8.78%) 99/410 (24.15%) 31/410 (7.56%) 179/410 (43.66%)
< 0.001 0.387 0.035 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.822 < 0.001 0.867 < 0.001 0.143 < 0.001 0.542 < 0.001 0.009 < 0.001 0.002 0.301 < 0.001 0.336 < 0.001 < 0.001 < 0.001 0.057 0.437 < 0.001 0.657 0.635 0.380 0.261 0.306 < 0.001 0.663
155/194 (79.90%) 2/194 (1.03%) 4/194 (2.06%) 4/194 (2.06%) 29/194 (14.95%)
75/99 (75.76%) 8/99 (8.08%) 4/99 (4.04%) 2/99 (2.02%) 10/99 (10.10%)
33/55 (60.00%) 5/55 (9.09%) 1/55 (1.82%) 2/55 (3.63%) 14/55 (25.45%)
13/31 (41.94%) 1/31 (3.23%) 0/31 (0.00%) 2/31 (6.45%) 15/31 (48.39%)
55/288 (19.10%) 3/288 (1.04%) 8/288 (2.78%) 14/288 (4.86%) 208/288 (72.22%)
35/179 (19.55%) 2/179 (1.12%) 7/179 (3.91%) 10/179 (5.59%) 125/179 (69.83%)
0.048
0.786
ALP: alkaline phosphatase; ALT: alanine transaminase; AST: aspartate aminotransferase; BMI: body mass index; HC: hip circumference; HDL-C: high-density lipoprotein cholesterol; HOMA-IR: homoeostasis model assessment of insulin resistance; hs-CRP: high-sensitivity C-reactive protein; LDL-C: low-density lipoprotein cholesterol; METs: metabolic equivalents (of energy); RBP4: retinol-binding protein 4; TC: total cholesterol; TG: triglycerides; UA: uric acid; WC: waist circumference; WHR: waist-to-hip ratio. a Statistical significance estimated after log transformation and expressed as medians [25th, 75th percentiles] (data not normally distributed). b Follow-up data minus baseline data. c Expressed as frequency and percentage (categorical variables). d Excessive alcohol consumption ( 140 g/week for men, 70 g/week for women) initially excluded.
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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multiple stepwise logistic regression analyses were performed to determine independent predictors of NAFLD, while potential confounders were determined based on univariate results and reports in the literature. The RBP4 level was treated as either a categorical variable (by quartiles) or a continuous variable after standardization (per SD). Subgroup analyses with gender (male vs female), age (< 65 vs. 65, years), body mass index (BMI; < 24 vs. 24 kg/m2) and TG (< 1.7 vs. 1.7 mmol/L) were further explored to test whether results were consistent among different subgroups.
5
Results
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NAFLD incidence and regression rates
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As outlined in Fig. 1, of the initial 3389 participants at baseline, a total of 2945 participants were included in the follow-up, with 444 participants lost to follow-up due to either study withdrawal, refusal to participate in the follow-up, serious disease or death. Mean follow-up duration was 3.09 0.41 years, while the attrition
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Table 2 Baseline clinical parameters of 1382 participants who did and did not experience non-alcoholic fatty liver disease (NAFLD) regression during follow-up. Variables
Gender (males/females) Age (years) Height (cm) Body weight (kg) Body weight change (kg)b BMI (kg/m2) BMI change (kg/m2)b WC (cm) WC change (cm)b HC (cm) HC change (cm)b WHR WHR changeb Trunk fat mass (kg) Albumin (g/L) Insulin (mU/mL)a Fasting glucose (mmol/L)a HOMA-IRa AST/ALT ratioa TC (mmol/L) TG (mmol/L)a LDL-C (mmol/L) HDL-C (mmol/L) LDL-C/HDL-C ratio UA (mmol/L) ALP (U/L)a hs-CRP (mg/L) RBP4 (mg/mL) Physical activity (METs/day) Current smokingc Current drinkingc,d Hypertensionc Diabetesc Dyslipidaemiac Use of antihypertensive agentsc Always Usually Often Sometimes Seldom Use of hypoglycaemic agentsc Always Usually Often Sometimes Seldom Use of lipid-lowering agentsc Always Usually Often Sometimes Seldom
NAFLD status during follow-up Non-NAFLD (n = 339)
NAFLD (n = 1043)
P (non-NAFLD vs. NAFLD)
111/228 60.86 5.81 158.75 7.80 60.26 9.00 1.00 3.17 23.86 2.68 0.27 1.31 84.75 7.56 2.86 5.81 93.41 5.81 1.19 3.56 0.91 0.07 0.04 0.07 10.10 2.51 44.30 4.70 7.85 [5.76, 10.16] 4.68 [4.22, 5.12] 1.62 [1.13, 2.13] 1.22 [1.05, 1.44] 5.45 1.02 1.14 [0.87, 1.56] 3.59 0.88 1.44 0.38 2.64 0.87 343.74 80.38 71.20 [58.66, 81.64] 2.29 7.01 35.58 6.47 23.91 6.16 24 (7.08%) 22 (6.49%) 105 (30.97%) 33 (9.73%) 128 (37.76%)
355/688 60.64 5.48 159.14 7.58 64.21 9.74 0.01 2.44 25.30 2.98 0.08 0.96 89.23 8.05 2.51 4.42 94.79 6.14 0.44 3.08 0.94 0.06 0.03 0.05 11.33 2.60 44.92 4.17 10.32 [7.53, 14.58] 4.92 [4.50, 5.50] 2.31 [1.61, 3.31] 1.06 [0.85, 1.27] 5.57 1.03 1.55 [1.14, 2.17] 3.63 0.91 1.28 0.34 2.97 0.92 369.94 84.14 71.24 [59.75, 85.35] 2.28 4.02 38.33 6.45 23.69 6.10 59 (5.66%) 95 (9.11%) 374 (35.86%) 99 (9.49%) 495 (47.46%)
0.662 0.537 0.425 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.318 < 0.001 < 0.001 < 0.001 0.007 < 0.001 0.019 < 0.001 < 0.001 < 0.001 < 0.001 0.053 < 0.001 0.477 < 0.001 < 0.001 < 0.001 0.137 0.979 < 0.001 0.563 0.331 0.132 0.101 0.895 0.002 0.657
83/105 (79.05%) 7/105 (6.67%) 2/105 (1.90%) 5/105 (4.76%) 8/105 (7.62%)
284/374 (75.94%) 19/374 (5.08%) 11/374 (2.94%) 9/374 (2.41%) 51/374 (13.64%)
25/33 (75.76%) 0/33 (0.00%) 0/33 (0.00%) 0/33 (0.00%) 8/33 (24.24%)
71/99 (71.72%) 1/99 (1.01%) 1/99 (1.01%) 1/99 (1.01%) 25/99 (25.25%)
33/128 (25.78%) 0/128 (0.00%) 4/128 (3.13%) 5/128 (3.91%) 86/128 (67.19%)
110/495 (22.22%) 14/495 (2.83%) 16/495 (3.23%) 28/495 (5.66%) 327/495 (66.06%)
0.886
0.944
ALP: alkaline phosphatase; ALT: alanine transaminase; AST: aspartate aminotransferase; BMI: body mass index; HC: hip circumference; HDL-C: high-density lipoprotein cholesterol; HOMA-IR: homoeostasis model assessment of insulin resistance; hs-CRP: high-sensitivity C-reactive protein; LDL-C: low-density lipoprotein cholesterol; METs: metabolic equivalents (of energy); RBP4: retinol-binding protein 4; TC: total cholesterol; TG: triglycerides; UA: uric acid; WC: waist circumference; WHR: waist-to-hip ratio. a Statistical significance estimated after log transformation and expressed as medians [25th, 75th percentiles] (data not normally distributed). b Follow-up data minus baseline data. c Expressed as frequency and percentage (categorical variables). d Excessive alcohol consumption ( 140 g/week for men, 70 g/week for women) initially excluded.
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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rate was 13.10%. Participants included in the follow-up were compared with those who were not: no differences were observed in their demographic, anthropometric and blood biochemical indices (data not shown). A total of 1318 participants without NAFLD at baseline completed all of the study examinations after the follow-up, and 410 developed NAFLD (97 men, 313 women). NAFLD incidence rate during the 3-year follow-up was 31.11% (annual rate: 10.12%). On the other hand, 1382 participants who had NAFLD at baseline completed all of the study examinations after the follow-up, and 339 participants showed NAFLD regression (111 men, 228 women). The NAFLD regression rate during the 3-year follow-up was 24.53% (annual rate: 8.02%).
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Participants’ baseline clinical parameters
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Table 1 shows that the participants who developed NAFLD (n = 1318) had significantly higher levels of body weight, BMI, WC, HC, waist-to-hip ratio (WHR), trunk fat mass, fasting glucose and insulin, HOMA-IR, TG, LDL-C/HDL-C ratio, UA and RBP4, but lower HDL-C levels and AST/ALT ratios than participants without NAFLD at follow-up (all P < 0.01). On further comparisons with follow-up data, these NAFLD participants also had greater body weight and BMI changes than those who were non-NAFLD (all P < 0.001). On the other hand, the baseline characteristics of 1382 participants with NAFLD at baseline (Table 2) revealed inverse findings.
Participants who regressed to non-NAFLD had significantly lower levels of body weight, BMI, WC, HC, WHR, trunk fat mass, albumin, fasting glucose and insulin, HOMA-IR, ALT, TG, LDL-C/HDL-C ratio, UA and RBP4, but higher HDL-C levels and AST/ALT ratios than those with NAFLD at follow-up (all P < 0.05). Compared with the follow-up data, participants who regressed to normal liver function showed greater body weight, BMI and HC changes than those who sustained their non-NAFLD status (all P < 0.001).
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Baseline RBP4 levels in non-NAFLD participants with or without NAFLD at follow-up
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Serum RBP4 concentrations at baseline were significantly higher in participants who progressed to NAFLD (36.50 5.96 mg/mL) during follow-up than in those without progression (34.73 6.67 mg/mL; P < 0.001). Comparisons of RBP4 between baseline and follow-up levels revealed no significant differences in participants not developing NAFLD. In contrast, a significant increase in RBP4 levels was observed during follow-up in participants who did develop NAFLD (P < 0.01; Fig. 2A). When stratified by quartiles of baseline RBP4 levels, participants with RBP4 in quartile 1 (Q1) had an incidence rate of 18.79%. Notably, this rate rose to 27.36%, 34.94% and 37.31% in those with Q2, Q3 and Q4 levels, respectively, of RBP4 at baseline (P < 0.001 for trend; Fig. 2B).
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Fig. 2. Serum retinol-binding protein 4 (RBP4) levels and rates of incidence or regression of non-alcoholic fatty liver disease (NAFLD) are expressed as means SD (mg/mL): (A) baseline concentration of serum RBP4 in non-NAFLD participants with or without NAFLD development after the 3.09-year follow-up; (B) incidence rate of NAFLD by quartiles of RBP4 levels at baseline (P < 0.001 for trend); (C) baseline level of serum RBP4 in NAFLD participants with or without regression of NAFLD after follow-up; and (D) regression rate of NAFLD by quartiles of RBP4 levels at baseline (P < 0.001 for trend). ** P < 0.001; *** P < 0.001; n.s. not significant.
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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Table 3 Adjusted odds ratios (95% CIs) for risk of non-alcoholic fatty liver disease (NAFLD) development and probability of NAFLD regression in participants after 3.09 years of followup by quartile or per 1-SD (standard deviation) increase in baseline serum retinol-binding protein 4 (RBP4) levels. Serum RBP4 quartile (mg/mL)
NAFLD development in those without NAFLD at baseline Model 1 P-values Model 2 P-values Model 3 P-values NAFLD regression in those with NAFLD at baseline Model 1 P-values Model 2 P-values Model 3 P-values
Quartile 1
Quartile 2
Quartile 3
Quartile 4
< 31.20 (n = 330) 1.000
31.20–35.27 (n = 329) 2.335 (1.626–3.354) < 0.001 2.050 (1.396–3.011) < 0.001 1.981 (1.340–2.929) 0.001 33.88–37.46 (n = 346) 0.847 (0.617–1.165) 0.307 0.963 (0.686–1.352) 0.829 1.111 (0.781–1.582) 0.558
35.28–38.90 (n = 332) 2.655 (1.847–3.818) < 0.001 2.319 (1.579–3.406) < 0.001 2.053 (1.385–3.044) < 0.001 37.47–41.59 (n = 346) 0.401 (0.281–0.572) < 0.001 0.443 (0.305–0.644) < 0.001 0.551 (0.373–0.815) 0.003
> 38.90 (n = 327) 3.238 (2.237–4.689) < 0.001 2.442 (1.645–3.624) < 0.001 2.014 (1.333–3.044) 0.001 > 41.59 (n = 345) 0.284 (0.192–0.418) < 0.001 0.334 (0.223–0.501) < 0.001 0.518 (0.336–0.800) 0.003
1.000 1.000 < 33.88 (n = 345) 1.000 1.000 1.000
P for trend
Per 1-SD increase
< 0.001
1.429 (1.262–1.619) < 0.001 1.302 (1.139–1.490) < 0.001 1.207 (1.048–1.389) 0.009
< 0.001 0.003
< 0.001 < 0.001 < 0.001
0.631 (0.552–0.722) < 0.001 0.665 (0.577–0.767) < 0.001 0.783 (0.672–0.913) 0.002
Model 1: adjusted for age and gender. Model 2: adjusted as for model 1 plus body mass index, waist-to-hip ratio, physical activity (METs/day), current smoking/drinking, history of hypertension/diabetes. Model 3: adjusted as for model 2 plus homoeostasis model assessment of insulin resistance, aspartate aminotransferase/alanine transaminase ratio, total cholesterol, triglycerides, high-density/low-density lipoprotein cholesterol ratio, uric acid, albumin, alkaline phosphatase.
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Baseline RBP4 in NAFLD participants with or without NAFLD regression at follow-up
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On the other hand, serum RBP4 concentrations at baseline were significantly lower in participants with NAFLD regression (35.58 6.47 mg/mL) during follow-up than in those with NAFLD (38.33 6.45 mg/mL; P < 0.001). However, no significant difference was noted in serum RBP4 levels at both baseline and follow-up in participants without NAFLD regression, although a significant decrease in RBP4 levels at follow-up was observed in those who did experience NAFLD regression (P < 0.01; Fig. 2C). When stratified by quartiles of baseline RBP4, participants in Q1 had a regression rate of 34.78%. As expected, this rate was reduced to 31.21%, 18.21% and 13.91% in those with Q2, Q3 and Q4, respectively, levels of RBP4 at baseline (P < 0.001 for trend; Fig. 2D).
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Longitudinal analyses of the association between baseline RBP4 and development or regression of NAFLD at follow-up
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In participants without NAFLD at baseline, the risk of developing NAFLD significantly increased the higher the quartile of RBP4 in both the minimally (model 1) and fully adjusted (model 3) models. Odds ratios (ORs) and 95% confidence intervals (CIs) of NAFLD development for Q4 (vs. Q1) were 3.24 (95% CI: 2.24–4.69, P = 0.001 for trend) in model 1 and 2.01 (95% CI: 1.33–3.04, P = 0.003 for trend) in model 3 (Table 3). When treated as a continuous variable, each 1-SD increase in RBP4 level was associated with a 20.7% increase in risk of NAFLD development (OR: 1.21, 95% CI: 1.05–1.39; P = 0.009). In contrast, RBP4 levels were inversely associated with NAFLD regression in participants diagnosed with NAFLD at baseline (all P < 0.01 for trend). The minimally and fully adjusted ORs (95% CI) were 0.28 (0.19–0.42) and 0.52 (0.34–0.80), respectively, when comparing the extreme quartiles. Furthermore, each 1-SD increase in RBP4 level was associated with a 21.70% decrease in probability of NAFLD regression (OR: 0.78, 95% CI: 0.67–0.91; P = 0.002; Table 3).
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Serum RBP4 level an independent predictor of NAFLD incidence and regression
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Independent predictors of NAFLD incidence and regression were identified using multiple stepwise logistic regression
Table 4 Multiple stepwise logistic regression analyses of factors predicting progression (development and regression) of non-alcoholic fatty liver disease (NAFLD) during follow-up. NAFLD status
Variables
OR (95% CI)#
P
Development (n = 1318)
Gender BMI HOMA-IR Triglycerides RBP4 Quartile 1 Quartile 2 Quartile 3 Quartile 4 BMI HOMA-IR Triglycerides AST/ALT ratio RBP4 Quartile 1 Quartile 2 Quartile 3 Quartile 4
0.455 1.260 1.298 1.448
(0.336–0.615) (1.191–1.333) (1.123–1.500) (1.198–1.751)
< < < <
1.000 2.012 2.130 2.146 0.870 0.810 0.576 1.397
(1.372–2.951) (1.446–3.138) (1.435–3.210) (0.825–0.917) (0.717–0.915) (0.474–0.702) (1.021–1.913)
< 0.001 < 0.001 < 0.001 < 0.001 0.001 < 0.001 0.037
Regression (n = 1382)
1.000 1.142 (0.811–1.608) 0.563 (0.388–0.816) 0.528 (0.352–0.792)
0.001 0.001 0.001 0.001
0.446 0.002 0.002
NB: Variables in original model were age, gender, body mass index (BMI), physical activity [metabolic equivalents (METs)/day], homoeostasis model assessment of insulin resistance (HOMA-IR), aspartate aminotransferase (AST)/alanine transaminase (ALT) ratio, total cholesterol, low-density/high-density lipoprotein cholesterol ratio, uric acid, albumin, alkaline phosphatase, retinol-binding protein 4 (RBP4) levels in quartiles. OR: odds ratio; CI: confidence interval.
analyses including age, gender, BMI, WHR, physical activity (METs/day), HOMA-IR, AST/ALT ratio, TC, TG, LDL-C/HDL-C ratio, UA, albumin, alkaline phosphatase (ALP) and RBP4. Serum RBP4 level at baseline (OR for Q4: 2.15, 95% CI: 1.44–3.21; P < 0.001) together with gender, BMI, HOMA-IR and TG (all P < 0.01) were independent predictors of NAFLD incidence at follow-up (Table 4). Indeed, BMI, HOMA-IR, TG and RBP4 correlated positively with incident NAFLD, whereas gender was inversely correlated with incident NAFLD. In addition, RBP4 level (OR for Q4: 0.53, 95% CI: 0.35–0.79; P = 0.002) together with BMI, HOMA-IR, TG and AST/ALT ratio (all P < 0.01) were all independent predictors of NAFLD regression at follow-up. BMI, HOMA-IR, TG and RBP4 were inversely correlated with NAFLD regression, whereas AST/ALT ratio was positively
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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correlated with NAFLD regression (Table 4). Further adjustments for current smoking and drinking status, and history of hypertension and diabetes, did not alter any of the above results.
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Subgroup analyses
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Effects of the interaction between baseline RBP4 levels and gender, age, BMI and TG were also examined. Positive associations between RBP4 and risk of developing NAFLD were more often found in women and in participants with younger age, higher BMI or lower TG and HOMA-IR values (Table S1; see supplementary materials associated with this article online). Moreover, the inverse associations between RBP4 and the possibility of NAFLD regression appeared to be more substantial in female participants and in those with older age, lower BMI and HOMA-IR scores, or higher TG levels (Table S2; see supplementary materials associated with this article online).
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Discussion
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RBP4 has been identified as an adipokine [19] with an important role in metabolic disorder diseases, including NAFLD [20,21]. However, evidence supporting the potential role of RBP4 in NAFLD development is limited in humans. Indeed, the present study constitutes the first clinical evidence elucidating the association between serum RBP4 levels and the development or regression of NAFLD in a community-based middle-aged and elderly Chinese population after 3.09 years of follow-up. The results of this longitudinal study suggest that RBP4 levels are associated with the development and regression of NAFLD, and are also an independent predictor of NAFLD progression. However, the prevalence of NAFLD in our cohort was notably higher than the global prevalence rate [2], with 50.73% of our cases having ultrasound evidence of steatosis. The main reasons attributed to this high prevalence are, first and foremost, the older age of our participants, as ageing is a significant independent risk factor of NAFLD [22,23]. Second, the modernization of Guangzhou has resulted in substantial lifestyle changes, such as the popularity of high-fat, high-calorie diets coupled with more sedentary lifestyles, that are related to the substantially increased prevalence of NAFLD [24]. Indeed, the relatively high prevalence of NAFLD in urbanized areas such as Beijing and Shanghai (35.10% and 43.50%, respectively) [25,26] supports our hypothesis that higher levels of urbanization may be contributing to the greater prevalence of NAFLD. Third, the protective effect of oestrogens in women is attenuated or lost after menopause, and longer durations of oestrogen deficiency increase the risk of liver disease in postmenopausal women [27,28]. Therefore, the high prevalence of NAFLD in our present study might also be partly due to the large proportion of female participants (67.83%) and their menopausal status (96.59%). NAFLD is a recognized hepatic phenotype of metabolic impairment [29], and our present findings are consistent with a previous report by Kim et al. [30], which revealed that participants who developed NAFLD at follow-up had abnormal metabolic profiles. In our study, the non-NAFLD participants at baseline who developed NAFLD after the follow-up had significantly higher body weight, BMI, WHR, trunk fat mass, fasting glucose and insulin, HOMA-IR, TG, LDL-C/HDL-C ratio and UA levels, but lower HDL-C and AST/ALT ratios than those not developing NAFLD, whereas an inverse relationship was observed among those who initially had NAFLD, but who regressed to normal after the follow-up. Moreover, RBP4 has been reported to cause IR and dyslipidaemia by stimulating the production of pro-inflammatory cytokines and/ or disturbing normal insulin signalling [31]. Our study results showed that baseline RBP4 levels were positively correlated with
BMI, HOMA-IR, TG and LDL-C/HDL-C ratio, but negatively correlated with HDL-C after adjusting for age and gender (Table S3; see supplementary materials associated with this article online). These results indicate that RBP4 levels are positively correlated with metabolic disorders, and suggest that elevated RBP4 levels might be contributing to the development of NAFLD in part by worsening IR and lipid metabolism disorders. Currently, an increasing number of studies have indicated that RBP4 may be a direct cause of NAFLD progression. Our previous study found that treatment with RBP4 can significantly increase expression of peroxisome proliferator-activated receptor-g coactivator (PGC)-1b and sterol regulatory element-binding protein (SREBP)-1c, which ultimately promote de novo lipogenesis [7]. In addition, human-RBP4-expressing mice have exhibited mitochondrial liver dysfunction, which resulted in more likely progression to NASH when challenged by a high-fat diet [8]. Furthermore, another study demonstrated that, in mice with adipocyte-specific overexpression of RBP4, inflammation was induced in adipose tissue that, in turn, triggered increased lipolysis within adipocytes, leading to increased free fatty acid flux into the liver [9]. Therefore, to verify whether RBP4 can directly lead to NAFLD development, our cohort study also adjusted for related factors of IR and dyslipidaemia, and found that baseline RBP4 levels were positively associated with incident NAFLD, but inversely related to NAFLD regression. Thus, these results provide evidence that RBP4 could be playing a vital role in the pathophysiological course of NAFLD that might directly lead to progression at the population level. Previous reports of the relationship between RBP4 and NAFLD in population studies were rather inconsistent. Recently, a metaanalysis to examine all published data on RBP4 and NAFLD [32], with subgroup analyses stratified by NAFLD diagnostic methods, indicated that RBP4 levels in NAFLD participants diagnosed by ultrasonography were significantly higher than in the controls (five studies, n = 3524). However, no significant difference was found among those diagnosed by liver biopsy (five studies, n = 647) [32]. This finding might be attributed to the smaller number of patients diagnosed by liver biopsy, which cannot detect differences in RBP4 levels between those with NAFLD and the healthy (non-NAFLD) population, in whom substantial between-study heterogeneity was found. However, none of the relevant currently available epidemiological studies addressed the cause-and-effect relationship, given their cross-sectional design. For this reason, our investigation was initially of the association between circulating RBP4 levels and NAFLD in a prospective cohort study. Participants were followedup for 3.09 years to identify any changes of NAFLD status, and serum RBP4 levels were again measured after the follow-up. Results showed a significant increase of RBP4 in participants who developed NAFLD. In contrast, a marked decrease of RBP4 levels was observed in participants whose NAFLD regressed. Multiple stepwise logistic regression analyses showed that baseline RBP4 levels were an independent predictor of NAFLD incidence and regression. Thus, these results also suggest that RBP4 may be a causal factor of NAFLD. Nevertheless, several limitations of our study should be mentioned. First, similar to most epidemiological studies, a selection bias may have been present: our participants were volunteers recruited from a local community and, as such, they may have been more health-conscious, and the inclusion of a monoethnic (Chinese) cohort of adult individuals also limits generalization of any conclusions of this study to other ethnic groups. Second, as with other longitudinal studies, residual confounding is unavoidable. Our participants’ lifestyle modifications, and changes in health status and medical treatments (due to, for example, new cases of diabetes or dyslipidaemia), genetic factors [such as rs738409 non-synonymous single nucleotide polymorphisms (SNPs) in patatin-like phospholipase domain-containing protein 3 (PNPLA3) genotypes]
Please cite this article in press as: Wang X, et al. Circulating retinol-binding protein 4 is associated with the development and regression of non-alcoholic fatty liver disease. Diabetes Metab (2019), https://doi.org/10.1016/j.diabet.2019.04.009
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and other adipocytokines (such as adiponectin and leptin) may have potentially influenced the development or regression of NAFLD. However, these mediators of different pathways involved in the pathogenesis of NAFLD were not factored into our analyses, thereby preventing any analysis of whether these factors had moderating effects on the outcome. Third, the follow-up period may have been insufficient for tracking the development of advanced NAFLD and its comorbidities. Finally, ultrasound scanning for an NAFLD diagnosis is somewhat dependent on the operator and not sensitive enough for minor steatosis. Although a standardized protocol was used and the scanning sonographers were selected because of their experience, the lack of histological confirmation of liver status may yet constitute another weakness of the present study. In summary, this prospective cohort study performed in a Chinese population has revealed an association between serum RBP4 levels and NAFLD progression. Our results suggest that circulating RBP4 levels are associated with NAFLD progression, and that RBP4 may constitute a potential novel preventative and therapeutic target in NAFLD management. However, given the limitations of our study, future prospective studies involving larger sample sizes of multiethnic populations with NAFLD diagnosed and assessed by transient elastography, magnetic resonance spectroscopy or biopsy are now needed to further clarify the issue.
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Funding
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This work was supported by the Major Projects of Guangzhou Health Collaborative Innovation [grant number 201604020002], National Natural Science Foundation of China [grant numbers 81730090, 81573142 and 81372977] and 5010 Program for Clinical Researches of Sun Yat-Sen University [grant number 2007032].
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Author contributions
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X.W. and W.L. developed the overall research plan and had study oversight; X.X., L.Y. and J.M. provided research guidance; X.W., J.L., J.P. and H.Z. participated in collecting data and biological samples; X.W. and X.C. performed measurements of serum RBP4 levels and analyzed data; and X.W. and W.L. wrote the manuscript and had primary responsibility for the final content. All authors read and approved the final manuscript.
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Disclosure of interest
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The authors declare that they have no competing interest.
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Acknowledgments
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The authors are grateful to all the volunteers for their participation.
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Appendix A. Supplementary data
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Supplementary material related to this article can be found, in the online version, at https://doi.org/10.1016/j.diabet.2019.04.009.
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References
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