Low levels of total and high-molecular-weight adiponectin may predict non-alcoholic fatty liver in Korean adults

Journal Pre-proof Low levels of total and high-molecular-weight adiponectin may predict non-alcoholic fatty liver in Korean adults

Young-Sang Kim, Soo-Hyun Lee, Seung Geon Park, Bo Youn Won, Hyejin Chun, Doo-Yeoun Cho, Moon-Jong Kim, Ji Eun Lee, Ji-Hee Haam, Kunhee Han PII:

S0026-0495(19)30241-0

DOI:

https://doi.org/10.1016/j.metabol.2019.154026

Reference:

YMETA 154026

To appear in:

Metabolism

Received date:

13 June 2019

Accepted date:

21 November 2019

Please cite this article as: Y.-S. Kim, S.-H. Lee, S.G. Park, et al., Low levels of total and high-molecular-weight adiponectin may predict non-alcoholic fatty liver in Korean adults, Metabolism(2019), https://doi.org/10.1016/j.metabol.2019.154026

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© 2019 Published by Elsevier.

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Low levels of total and high-molecular-weight adiponectin may predict nonalcoholic fatty liver in Korean adults

Authors:

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Young-Sang Kima, Soo-Hyun Leea, Seung Geon Parkb, Bo Youn Wonc, Hyejin Chuna, Doo-

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Yeoun Chod, Moon-Jong Kima, c, Ji Eun Leea, Ji-Hee Haame, Kunhee Hanf

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a Department of Family medicine, CHA Bundang Medical Center, CHA University, Seongnam, Korea, 13496 b Department of Family Medicine, Incheon Veterans Hospital, Incheon, Korea, 22182

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c Department of Family Medicine, Chaum Medical Checkup Center Samseongdong Branch, CHA University, Seoul, Korea, 06169 d Department of Clinical Pharmacology, CHA Bundang Medical Center, CHA University, Seongnam, Korea, 13496

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e Department of Family Medicine, Chaum Life Center, CHA University, Seoul, Korea, 06062

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f Department of Family Medicine, Seonam Hospital, Seoul, Korea, 08049

Correspondence to: Young-Sang Kim, MD, PhD Department of Family Medicine, CHA Bundang Medical Center, CHA University, 59 Yatap-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea, 13496. Phone: +82-31-780-5360, Fax: +82-31-780-5944, Email: [email protected]

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Abstract Objectives: While weight gain is known as a predictor of non-alcoholic fatty liver disease (NAFLD) incidence, it remains controversial whether adipokine levels predict the development of NAFLD. We aimed to investigate the relationship of total adiponectin, high-molecular-weight (HMW) adiponectin, and leptin with the development and improvement of non-alcoholic fatty

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liver (NAFL) independent of sex and weight change over a maximum of 8.5 years.

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Methods: This prospective study enrolled 2,735 participants in a hospital health check-up setting.

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Adipokine levels were measured at baseline. NAFL was assessed with liver ultrasonography, and

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the development or improvement of NAFL was determined by repeated ultrasonography at

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follow-ups.

Results: Cross-sectional analyses revealed that total and HMW adiponectin levels were inversely

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associated with NAFL prevalence. In longitudinal analyses, the incidence of NAFL was 5.6 per

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100-person-years during the observation period. The hazard ratios (HRs) per 1 µg/mL increase in the levels of total and HMW adiponectin were 0.900 (0.836–0.969) and 0.846 (0.754–0.948),

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respectively. Sex-stratified analyses showed that total and HMW adiponectin levels were significantly related to NAFL incidence only in women. In the subgroup of minimal weight change, only HMW adiponectin was a significant predictor for NAFL. Leptin predicted NAFL in the subgroup with weight gain. The improvement of NAFL was influenced by weight change, but not by adipokine levels. Conclusions: Low levels of total and HMW adiponectin may predict the development of NAFL independent of pathophysiological factors including obesity and insulin resistance. This predictability was evident in women. Leptin was a significant predictor for NAFL in the subjects

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with weight gain.

Key words: adipokine; adiponectin; high-molecular-weight adiponectin; leptin; non-alcoholic

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fatty liver disease

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1. Introduction Non-alcoholic fatty liver disease (NAFLD) is characterized by hepatic steatosis without causes for secondary hepatic fat accumulation such as significant alcohol consumption, use of steatogenic medication, or hereditary disorders [1]. Insulin resistance (IR) is the key risk factor in the pathogenesis of NAFLD [2]. Linked to IR, NAFLD is recognized as an independent risk

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factor of metabolic disorders such as obesity, dyslipidemia, type 2 diabetes, and cardiovascular

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disease [3-7].

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Longitudinal studies have attempted to find a precipitating factor of NAFLD other than IR.

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Several studies have suggested that weight reduction is associated with lower progression or remission of NAFLD [8, 9]. Other longitudinal studies have suggested novel precipitating factors

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of NAFLD such as childhood overweight, low skeletal muscle mass, low urine pH, and reduced

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lung function [10-13].

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Adipokines are endocrine factors produced by the adipose tissue [14]. Leptin and adiponectin are known to play diverse roles in insulin sensitivity, immunity, and inflammation [15]. Considering

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this reason, studies have investigated the association between adipokines and NAFLD. Metaanalyses of cross-sectional studies and several randomized controlled trials have shown that higher circulating leptin and lower circulating adiponectin levels were observed in patients with simple steatosis (SS) than in controls, in patients with non-alcoholic steatohepatitis (NASH) than in controls, and in patients with NASH than in patients with SS [16, 17]. However, limited studies were conducted longitudinally, and the results were not consistent. It has been suggested that baseline adiponectin levels may predict the NAFLD incidence [18]; however, other studies have shown that the baseline leptin and adiponectin levels were not proven as an independent

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predictive factor of NAFLD incidence [8, 9, 19]. Circulating adiponectin exists in three forms—trimers, hexamers, and high-molecular-weight (HMW) oligomers, of which HMW adiponectin is the most active and relatively important isoform for improving insulin sensitivity [20]. Although limited studies have investigated the association between HMW adiponectin and NAFLD [21, 22], no longitudinal study has explored

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the influence of HMW adiponectin on the development of NAFLD. So far, only a single

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longitudinal study was conducted in an Asian ethnic group [18], and it is controversial whether

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adipokine levels independently predict NAFLD incidence. Hence, the current study investigated the relationship of adipokines (total adiponectin, HMW adiponectin, and leptin) with the

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development and improvement of non-alcoholic fatty liver (NAFL) in Korean adults over a

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maximum of 8.5 years. In addition, we also investigated whether the relationship is independent

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of the influence of sex and weight change.

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2. Subjects and Methods

2.1. Study design and study population Among all adults who attended the health check-up program in CHA Bundang Medical Center from May 2006 to May 2007, 2,735 participants had undergone adipokine and insulin tests and agreed to participate in this study. All the subjects participated in our study voluntarily without incentives and submitted informed consent forms. We excluded 17 subjects who had not undergone liver ultrasonography, 114 who had positive serologic markers for hepatitis B or C, and 547 who consumed excessive amount of alcohol (30 g for men and 20 g for women). Then, we excluded the subjects who had a history of any malignancy (n = 28), severe cardiovascular

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diseases (unstable angina, myocardial infarction, and stroke, n = 26), thyroid disease (n = 15), and hormone therapy (n = 15). In addition, we excluded the subjects with established liver disease such as chronic hepatitis or cirrhosis (n = 13), AST or ALT serum levels three times higher than normal upper limits (n = 10), and estimated glomerular filtration rate less than 60 mL/min/1.73 m2 (n = 32). Finally, 1,026 men and 940 women were included in this study (Figure

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1). Subsequently, we retrospectively explored the participants’ clinical visit records until December 2014. A total of 980 subjects participated in the health examination program again and

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had undergone liver ultrasonography once or more. The current study was approved by the

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Institutional Review Board of the CHA Bundang Medical Center in Korea (2017-01-023-003).

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2.2. Medical history, lifestyle habits and anthropometric measurements

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We obtained data regarding medical histories and lifestyle habits using a self-reported

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questionnaire. The subjects were classified into three categories (non-, ex-, and current smoker) according to the smoking status. Exercise habit was divided into routine and nonroutine groups

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according to the intensity and frequency. Height, weight, waist circumference (WC), and blood

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pressure were measured in a standardized manner. Body mass index (BMI) was calculated using the values of height and weight. 2.3. Laboratory tests. The serum concentrations of glucose, creatinine, liver enzymes, and lipid profiles were analyzed using automatic analyzers (Roche/Hitachi Modular Analytics D2400 & P800 module; Roche, Tokyo, Japan). Erythrocyte sedimentation rate (ESR) was measured using an automated analyzer with photometric capillary stopped flow kinetic analysis (Alifax SpA, Polverara, Italy). Insulin and adipokines were assayed in the manner described in a previous study [23]. IR was

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approximated using the Homeostasis Model Assessment (HOMA2) calculator, v2.2.3 (Oxford Centre for Diabetes, Endocrinology and Metabolism, UK, available from http://www.dtu.ox.ac.uk). To estimate the fibrosis, FIB-4 scores were calculated for each subject [24]. The subjects whose FIB-4 score was over 2.67 were determined to have liver fibrosis [25]. 2.4. Liver steatosis on ultrasonography.

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Ultrasonography was conducted at baseline and follow-ups. The diagnosis severity of liver

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steatosis was made based on the findings of abdominal ultrasonography using a 3.5-MHz

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transducer (General Electric Logiq S6; General Electric Medical Systems, Milwaukee, WI, USA).

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Experienced radiologists who were unaware of the aims of this study performed the ultrasonography examinations. Images were captured in a standard manner, with the patient in a

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supine position with the right arm raised above the head. NAFL was defined according to the

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criteria described by Saadeh et al. [26]. The subjects having the findings with normal echogenicity were considered as those without NAFL. In the subjects without NAFL at baseline,

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the time of NAFL development was defined as the time of the first emergence of liver steatosis.

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Once diagnosed with NAFL, the following ultrasonography findings were ignored. In the same manner, when a subject with NAFL at baseline did not manifest liver steatosis for the first time, the subject was interpreted to be improved. Once improvement was determined, the following ultrasonography findings were ignored. 2.5. Statistical analysis. For descriptive analysis, the results were expressed as mean ± SD, median (interquartile range), or number (proportion). The variables between the groups with and without NAFL were

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compared using independent t-test, Mann–Whitney U-test, and chi-square test. To reduce the skewness, some variables like triglyceride and ESR were used in the subsequent parametric analyses after logarithmical transformation. The association between each traditional metabolic parameter and the prevalence of NAFL was assessed using logistic regression analyses. To assess the association between adipokine (total

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adiponectin, HMW adiponectin, and leptin) levels and baseline NAFL, logistic regression

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models were formulated. In the logistic models, essential confounders such as age, sex, and WC

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were included first. Then, metabolic factors (triglyceride, HOMA2-IR, and systolic blood pressure), inflammatory marker (ESR), medical histories (hypertension, diabetes, and

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menopause), and lifestyle habits (smoking and exercise) were sequentially considered as

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confounders. To assess the interaction between adipokines and sex, the interaction terms were

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also included in the logistic models. To assess the mutual influence between adiponectin and leptin, logistic regression models including total or HMW adiponectin and leptin were

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formulated. Also, in the final logistic model, variables were included by the forward selection

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method among all available variables. The longitudinal relationship between each traditional metabolic parameter and the development of NAFL was assessed using Cox regression analyses. The incidence of NAFL was assessed according to the adipokine levels of the subjects without NAFL. The subjects were categorized into tertiles according to their levels of total adiponectin, HMW adiponectin, and leptin. The Kaplan–Meier curves were drawn for the tertiles of total adiponectin, HMW adiponectin, and leptin, and the curves were compared using the log-rank test. To calculate the hazard ratio (HR) of the tertiles for the NAFL incidence compared with the third tertile, Cox regression models

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were formulated with the confounders used in the logistic regression models. To minimize the effects of weight change, the rate of the weight difference between two time points (weight change percentage) were also included in the Cox models. The HRs per 1-unit change in adipokine levels were also calculated using the Cox model. In the final models, the interaction effects between adipokines and sex were additionally considered. The mutual influence between

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adiponectin and leptin was assessed using Cox regression models including total or HMW adiponectin and leptin. To assess the difference between men and women, sex-stratified Cox

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analyses were performed. To reduce the influence of body weight changes on the development

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and regression of NAFL, the subjects were stratified into tertiles according to the weight change

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percentage. The risk of adipokine levels for NAFL incidence was assessed using Cox regression

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analyses in each tertile group of weight change percentage, respectively.

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The improvement rate of NAFL was also assessed according to the adipokine levels of the subjects with NAFL at baseline. The HRs per 1–unit change in adipokine levels were calculated

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using Cox regression analyses including the same adjusting factors as previous Cox models.

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We considered P < 0.05 to be statistically significant. All statistical analyses were performed using SPSS 25.0 (IBM, Armonk, NY, USA).

3. Results The baseline characteristics of 1,966 subjects are presented in Table 1. The mean age of the groups with and without NAFL was 44.1 and 41.6 years, respectively. The proportion of men was significantly higher in the NAFL group than in the group without NAFL. The metabolic factors, such as blood pressure, obesity index, and lipid profiles, were significantly different

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between the two groups. The mean levels of the total and HMW adiponectin were significantly higher in the group without NAFL than in the NAFL group; contrarily, leptin levels were significantly higher in the NAFL group than in the group without NAFL. The FIB-4 score and the proportion of liver fibrosis (FIB-4 > 2.67) were not different between two groups. The association between each metabolic parameter and the prevalence of NAFL is shown in

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Supplemental Table 1. All the parameters were significantly correlated with the NAFL

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prevalence without any adjustment. The relationship between the serum levels of adipokines and

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NAFL was assessed at baseline (Table 2). Total and HMW adiponectin significantly lowered the odds for NAFL in all logistic models. The odds ratios of 1 µg/mL increase in total and HMW

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adiponectin for NAFL prevalence were 0.751 and 0.610 in full-adjusted model, respectively.

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Leptin was significantly related to the NAFL prevalence after being adjusted for age, sex, and

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WC. However, logistic models adjusted for additional variables including triglyceride and HOMA2-IR revealed no significant relationship between leptin and NAFL. The interaction terms

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between adipokines and sex were additionally included in the logistic models (Supplemental

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Table 2). Sex, triglyceride, and HOMA2-IR were significantly related to NAFL in these models; any of the interaction terms were not significant. In the logistic models mutually including total or HMW adiponectin and leptin, the prevalence of NAFL was negatively related to total or HMW adiponectin, and positively related to leptin (Supplemental Table 3). In Model 3 formulated using the forward selection method, the variables of HMW adiponectin, leptin, sex, waist circumference, triglyceride, HOMA2-IR, and history of hypertension, diabetes, and menopause were included among all available variables (Supplemental Table 3). Among the 980 subjects who completed follow-ups, seven subjects were diagnosed or suspected

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with liver cirrhosis (Figure 1). After excluding them, 718 subjects without NAFL and 480 with NAFL at baseline were included for longitudinal analyses to assess the development and improvement of NAFL. The mean and maximum period of follow-up was 3.8 and 8.5 years, respectively. Among the 718 subjects without NAFL, 153 developed NAFL. The incidence (cumulative method) and incidence density of NAFL were 21.3 per 100 persons and 5.6 per 100-

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person-years, respectively. Among the 255 subjects with NAFL, 65 were improved. The

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improvement rate and improvement density were 25.5 per 100 persons and 6.7 per 100-person-

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years, respectively.

The association between each metabolic parameter and the development of NAFL was assessed

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(Supplemental Table 1). Like the results of logistic models, all the parameters were significantly

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correlated with the NAFL incidence without any adjustment. To observe the cumulative

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incidence of NAFL, the subjects without NAFL were categorized into tertiles according to the levels of total adiponectin, HMW adiponectin, and leptin, respectively (Figure 3 and Table 3).

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The cumulative incidence of NAFL was significantly different among the tertiles of total and

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HMW adiponectin (P < 0.001 by log-rank tests; Figure 3). In contrast, leptin levels did not influence the incidence of NAFL (P = 0.061 by log-rank test). In all Cox regression models adjusted for potential confounders, including weight change percentage, the subjects at the first and second tertiles for total and HMW adiponectin had significantly higher HR than those at the third tertile. In the full-adjusted models, the HRs per 1 µg/mL increase in total and HMW adiponectin levels were 0.900 (0.836–0.969) and 0.846 (0.754–0.948), respectively (Figure 2). In contrast, although leptin was significant in the model adjusted for age, sex, and WC (Model 2), the relationship between leptin and NAFL incidence was not significant in the Cox regression model including additional covariates (Model 3–5 and Supplemental Table 4). Unlike the results

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of logistic regression analyses, in the Cox models mutually including total or HMW adiponectin and leptin, leptin was not a significant predictor of NAFL incidence (Supplemental Table 5). In the models considering the interaction between sex and total or HMW adiponectin, the interaction terms were significant for NAFL incidence (Supplemental Table 4). The interaction between sex and leptin was not significant in this model. Besides total and HMW adiponectin,

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WC and HOMA2-IR at baseline and rate of weight change were strongly related to the NAFL incidence. Since the interaction between adiponectin and sex was significant, sex-stratified

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subgroup analyses were performed (Table 4). The relationship between total or HMW

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adiponectin and NAFL incidence was only significant in women. The HR per 1 µg/mL increase

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in total and HMW adiponectin levels in women were 0.757 and 0.677, respectively. To reduce

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the influence of body weight changes on NAFL incidence, subgroup analyses were performed using stratification by tertiles of weight change percentage. In the subgroup of minimal weight

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change (T2), HMW adiponectin was still a significant factor of NAFL incidence (HR per 1

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µg/mL = 0.794, P = 0.034) while total adiponectin showed a tendency (P = 0.059). In the subgroup of weight gain (T3), leptin independently increased the risk of NAFL (HR per 1 µg/mL

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= 1.172, P = 0.013).

The improvement of NAFL was assessed in the subjects with NAFL at baseline (Figure 2 and Supplemental Table 6). Total adiponectin, HMW adiponectin, and leptin did not influence the ultrasonographic improvement in any Cox model; weight change percentage was the single significant factor influencing the improvement.

4. Discussion

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In the current study, the longitudinal relationship between adipokines and incidence of NAFL was investigated in Korean adults over a maximum of 8.5 years. Low levels of total and HMW adiponectin were significantly related to the prevalence and incidence of NAFL. The relationship between adiponectin and NAFL incidence was prominent in women. Even in the subjects with minimal weight change, the baseline levels of HMW adiponectin predicted NAFL development.

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High levels of leptin significantly predicted the development of NAFL in the subjects with

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weight gain. However, total adiponectin, HMW adiponectin, and leptin did not influence the

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improvement of NAFL.

The relationship between adipokines and the prevalence of NAFL was assessed with consecutive

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adjustment for potential confounders. Initially, the prevalence of NAFL was crudely evaluated

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according to the levels of adipokines. Then, the models were adjusted for the basic personal

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characteristics such as age, sex, and obesity. Obesity is well known to be one of the most important risk factor for NAFLD [27]. As expected, lower levels of total and HMW adiponectin

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and higher levels of leptin were associated with higher prevalence of NAFL. The results were in

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concordance with the previous meta-analyses [16, 17]. Free fatty acid and hepatic triglyceride accumulation is a cardinal feature of NAFLD, and commonly occurs in the setting of insulin resistance and obesity [28]. Hence, the next models were adjusted for triglyceride and IR. Unlike adiponectin, leptin did not have significance in the models including the variables of triglyceride and IR. Our results suggest that leptin may not be related to NAFL independent of metabolic manifestations. Similarly, results obtained from leptin NAFLD studies are controversial and heterogeneous [29]. In the full-adjusted model, we adjusted for additional metabolic syndrome (MS)-related variables; the inclusion of the additional factors did not change the significance of the models.

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The incidence of NAFL was assessed according to the baseline levels of adipokines. In contrast to the models assessing the association between adipokines and the prevalence of NAFL, leptin was not an independent factor for the development of NAFL even in the crude model. The models to assess the incidence of NAFL were adjusted for the potential confounders used in the logistic models in the same orders. Our results showed that baseline levels of total and HMW

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adiponectin may predict the development of NAFL. Since weight gain is an important risk factor for the development of NAFLD [30, 31], weight change needed to be considered as a potential

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risk factor in Cox models. As expected, we showed that weight change percentage is a significant

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risk factor for the development of NAFL and improvement. Besides, the baseline levels of total

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and HMW adiponectin are risk factors for the development of NAFL independent of

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pathophysiological factors including weight change. Even in the subgroup with minimal weight change, HMW adiponectin is a significant predictor of NAFLD. Unexpectedly, in the subgroup

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with weight gain (T3), higher leptin levels significantly predict NAFLD. In animal models, leptin

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may have a dual action on NAFLD [32]. Leptin may protect from hepatic steatosis, at least at the initial stages of the disease, but it may act as an inflammatory and fibrogenic factor when the

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NAFLD persists or progresses. Hence, it is hypothesized that leptin plays a role in accelerating the progression of NAFLD when hepatic steatosis is induced by weight gain. Meanwhile, weight loss was the only significant factor to predict the improvement in ultrasonography. It is in line with previous studies that showed an impact of weight loss on the ultrasonographic improvement [30, 33]. Several longitudinal studies do not support the relationship between baseline adiponectin levels and the incidence of NAFLD. In a 7-year prospective study [9], individuals who developed NAFLD had lower baseline adiponectin levels than those who remained NAFLD-free. However,

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adiponectin levels did not independently predict NAFLD incidence or remission. In another study, individuals who developed NAFLD had similar baseline adiponectin levels to those who did not develop NAFLD [19]. A prospective study with paired liver biopsies reported that disease progression was not influenced by baseline adiponectin level or its changes [8]. On the other hand, our results are consistent with several studies. Recombinant adiponectin exerts a

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hepatoprotective effect in mice with NASH [34, 35]. In a human genomic study, three single nucleotide polymorphisms of adiponectin gene were proposed to increase NAFLD progression

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[36]. A study has shown that low serum adiponectin levels predict NAFLD incidence in 3 years

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[18]. Further studies may be helpful to confirm the longitudinal relationship between adiponectin

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and NAFLD incidence.

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The relationship between isoforms of adiponectin and NAFLD was not well studied yet. Several

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human studies have shown the relationship between HMW adiponectin and NAFLD. An intervention study described the changes of 15 severely obese women before and after bariatric

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surgery [21]. Liver steatosis was negatively related to HMW adiponectin, and the changes in the

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degree of hepatic steatosis were also correlated with the changes in HMW adiponectin. In another study, the levels of total and all isoforms of adiponectin were significantly lower in the NAFLD group than in controls [22]. However, no population-based longitudinal studies have shown the practicality of HMW adiponectin as a predictor of NAFLD incidence. Here we showed that HMW adiponectin predicts NAFLD incidence even in the subgroup with a minimal change of body weight. Our study provides longitudinal evidence that supports the significant relationship between HMW adiponectin and the development of NAFLD. While our cross-sectional analyses revealed no significant interaction between sex and

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adipokines, the influence of total and HMW adiponectin on NAFL incidence was different between men and women. Previous longitudinal studies of the relationship between adipokines and NAFLD incidence did not provide the results on the difference between men and women [8, 9, 18, 19]. A few studies of the relationship between adiponectin and MS showed a difference between sexes. The association between low HMW adiponectin levels and the presence of MS

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was stronger in women than in men [37]. Similarly, after being adjusted for BMI, the association between total adiponectin and the presence of MS was significant in women but not in men [38].

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Consistent with our study, these studies showed that the levels of total and HMW adiponectin

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were higher in women than in men. Similarly, it was confirmed that Korean women had higher

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adiponectin levels than men [39]. The reasons underlying the sex-specific difference in the

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relationship between adiponectin and NAFLD incidence are yet to be elucidated. However, hormonal factors might influence the levels of adipokines and liver changes. Testosterone has

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inhibitory effects on adiponectin production and its secretion from adipocytes [40]. In contrast, in

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Korean cohort studies, the levels of adiponectin significantly predicted type 2 diabetes and MS in both men and women [39, 41]. Further studies are required to clarify the mechanism underlying

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the difference between men and women. The current study has some limitations. First, the levels of adipokines were not repeatedly measured. Weight loss may lead to improvements in the levels of leptin and adiponectin [42, 43]. As a previous study reported that the change of hepatic steatosis was correlated with the changes in HMW adiponectin [21], the changes in adipokine levels may play a role in NAFLD pathogenesis. Second, the diagnosis of NAFL was based on liver ultrasonography. This method cannot determine fatty infiltration below 30% or early cirrhosis and cannot differentiate between SS and NASH. In addition, liver fibrosis cannot be determined by this method. Instead, we have

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intended to describe liver fibrosis using a calculation method [25]. Since our population is relatively healthy, the subjects with fibrosis defined using FIB-4 score were very rare. Despite several limitations, ultrasonography is a noninvasive and easily accessible technique to diagnose NAFLD. Third, about half of the participants were not followed up. Since this study was based on a health examination program, we could not help but depend on the subjects’ voluntary return

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visit after the first enrollment. Hence, we could not be aware of the changes in the subjects who did not visit again. High proportion of follow-up loss might hinder the presence of cirrhotic

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changes and result in small number of ultrasonographic change into cirrhosis. However, since the

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study started with a large sample, we could determine a significant relationship between baseline

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adipokine levels and NAFLD incidence using a population follow-up. Last, the self-reported

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questionnaire did not include enough information on medication use and surgical intervention. Although we excluded the subjects who had a medical history influencing liver function or

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weight change, the medication history including antibiotics, pain killers, anti-obesity medications,

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and herbs was not collected. Similarly, although those who underwent cancer surgery were

questionnaire.

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excluded from our study, other surgical histories were not adequately included in the

In summary, low levels of total and HMW adiponectin may independently predict the development of NAFL in Korean adults at a maximum of 8.5 years. The influence of total and HMW adiponectin on NAFL incidence was more evident in women than in men. HMW adiponectin may also be a significant predictor for NAFL even in subjects with minimal weight change. Leptin may predict NAFL incidence in the subjects with weight gain. However, total adiponectin, HMW adiponectin, and leptin may not be a predictor for the improvement of NAFL, indicating that the assays for these adipokines may not be useful for monitoring NAFL

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progression.

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Funding This study was supported by Yuhan Co, Ltd. (2017-01-023) and the Cooperative Research Program for Agricultural Science & Technology Development (Project No. PJ011253062018) of

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Rural Development Administration, Republic of Korea.

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Author contributions

Study concept and design: YS Kim, SH Lee, SG Park, H Chun; acquisition of data: SH Lee, SG

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Park, H Chun, MJ Kim, JH Haam; analysis and interpretation of data: YS Kim, SH Lee, SG Park,

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BY Won, H Chun, DY Cho; drafting of the manuscript: YS Kim, SH Lee, H Chun, DY Cho, JE

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Lee, JH Haam; critical revision of the manuscript for important intellectual content: DY Cho, K Han, MJ Kim, BY Won; statistical analysis: YS Kim, DY Cho; obtained funding: YS Kim, DY

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Cho, H Chun; administrative, technical, or material support: H Chun, K Han; study supervision:

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JH Haam, K Han.

Acknowledgements

The authors thank to all staffs to help recruiting the subjects, conducting the measurements, and documenting the data in CHA Bundang Medical Center. The authors thank to KW Hong for the statistical advice. The authors also thank to the BY Chun for the advice on the epidemiologic view. The authors would like to thank Enago for the English language review.

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Conflict of interest The authors declare no conflicts of interest.

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management of non-alcoholic fatty liver disease: practice Guideline by the American Association

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Gastroenterological Association. Hepatology. 2012;55:2005-23.

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[2] European Association for the Study of the L, European Association for the Study of D, European Association for the Study of O. EASL-EASD-EASO Clinical Practice Guidelines for

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the management of non-alcoholic fatty liver disease. J Hepatol. 2016;64:1388-402.

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element-binding factor-1c polymorphism on incidence of nonalcoholic fatty liver disease and on the severity of liver disease and of glucose and lipid dysmetabolism. Am J Clin Nutr. 2013;98:895-906. https://doi.org/10.3945/ajcn.113.063792 [20] Liu M, Liu F. Regulation of adiponectin multimerization, signaling and function. Best Pract Res Clin Endocrinol Metab. 2014;28:25-31. https://doi.org/10.1016/j.beem.2013.06.003 [21] Engl J, Sturm W, Sandhofer A, Kaser S, Tschoner A, Tatarczyk T, et al. Effect of pronounced weight loss on visceral fat, liver steatosis and adiponectin isoforms. Eur J Clin Invest. 2008;38:238-44. https://doi.org/10.1111/j.1365-2362.2008.01929.x

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[22] Bianchi G, Bugianesi E, Frystyk J, Tarnow L, Flyvbjerg A, Marchesini G. Adiponectin isoforms, insulin resistance and liver histology in nonalcoholic fatty liver disease. Dig Liver Dis. 2011;43:73-7. https://doi.org/10.1016/j.dld.2010.05.011 [23] Haam JH, Kim YS, Kim MJ, Koo HS, Kim HY, Kim HJ, et al. A cross-sectional study of the association between adipokine levels and bone mineral density according to obesity and

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Gastroenterol Hepatol. 2009;7:1104-12. https://doi.org/10.1016/j.cgh.2009.05.033

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[26] Saadeh S, Younossi ZM, Remer EM, Gramlich T, Ong JP, Hurley M, et al. The utility of radiological imaging in nonalcoholic fatty liver disease. Gastroenterology. 2002;123:745-50.

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[27] Stefan N, Haring HU, Cusi K. Non-alcoholic fatty liver disease: causes, diagnosis, cardiometabolic consequences, and treatment strategies. Lancet Diabetes Endocrinol. 2019;7:313-24. https://doi.org/10.1016/S2213-8587(18)30154-2 [28] Townsend SA, Newsome PN. Non-alcoholic fatty liver disease in 2016. Br Med Bull. 2016;119:143-56. https://doi.org/10.1093/bmb/ldw031 [29] Adolph TE, Grander C, Grabherr F, Tilg H. Adipokines and Non-Alcoholic Fatty Liver Disease: Multiple Interactions. Int J Mol Sci. 2017;18. https://doi.org/10.3390/ijms18081649 [30] Kim HK, Park JY, Lee KU, Lee GE, Jeon SH, Kim JH, et al. Effect of body weight and

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lifestyle changes on long-term course of nonalcoholic fatty liver disease in Koreans. Am J Med Sci. 2009;337:98-102. https://doi.org/10.1097/MAJ.0b013e3181812879 [31] Chang Y, Ryu S, Sung E, Woo HY, Cho SI, Yoo SH, et al. Weight gain within the normal weight range predicts ultrasonographically detected fatty liver in healthy Korean men. Gut. 2009;58:1419-25. https://doi.org/10.1136/gut.2008.161885

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[32] Polyzos SA, Kountouras J, Mantzoros CS. Leptin in nonalcoholic fatty liver disease: a narrative review. Metabolism. 2015;64:60-78. https://doi.org/10.1016/j.metabol.2014.10.012

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[33] Hamaguchi M, Kojima T, Takeda N, Nakagawa T, Taniguchi H, Fujii K, et al. The metabolic

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syndrome as a predictor of nonalcoholic fatty liver disease. Ann Intern Med. 2005;143:722-8.

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[34] Xu A, Wang Y, Keshaw H, Xu LY, Lam KS, Cooper GJ. The fat-derived hormone

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adiponectin alleviates alcoholic and nonalcoholic fatty liver diseases in mice. J Clin Invest. 2003;112:91-100. https://doi.org/10.1172/JCI17797

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[35] Fukushima J, Kamada Y, Matsumoto H, Yoshida Y, Ezaki H, Takemura T, et al. Adiponectin

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prevents progression of steatohepatitis in mice by regulating oxidative stress and Kupffer cell phenotype polarization. Hepatol Res. 2009;39:724-38. https://doi.org/10.1111/j.1872-

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[36] Zhou YJ, Zhang ZS, Nie YQ, Cao J, Cao CY, Li YY. Association of adiponectin gene variation with progression of nonalcoholic fatty liver disease: A 4-year follow-up survey. J Dig Dis. 2015;16:601-9. https://doi.org/10.1111/1751-2980.12288 [37] Eglit T, Lember M, Ringmets I, Rajasalu T. Gender differences in serum high-molecularweight adiponectin levels in metabolic syndrome. Eur J Endocrinol. 2013;168:385-91. https://doi.org/10.1530/EJE-12-0688 [38] Ahonen T, Saltevo J, Laakso M, Kautiainen H, Kumpusalo E, Vanhala M. Gender

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differences relating to metabolic syndrome and proinflammation in Finnish subjects with elevated blood pressure. Mediators Inflamm. 2009;2009:959281. https://doi.org/10.1155/2009/959281 [39] Jee SH, Ahn CW, Park JS, Park CG, Kim HS, Lee SH, et al. Serum adiponectin and type 2 diabetes: a 6-year follow-up cohort study. Diabetes Metab J. 2013;37:252-61.

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https://doi.org/10.4093/dmj.2013.37.4.252 [40] Wang Y, Lam KS, Yau MH, Xu A. Post-translational modifications of adiponectin:

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mechanisms and functional implications. Biochem J. 2008;409:623-33.

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https://doi.org/10.1042/BJ20071492

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[41] Kim JY, Ahn SV, Yoon JH, Koh SB, Yoon J, Yoo BS, et al. Prospective study of serum

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adiponectin and incident metabolic syndrome: the ARIRANG study. Diabetes Care. 2013;36:1547-53. https://doi.org/10.2337/dc12-0223

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[42] Ma W, Huang T, Zheng Y, Wang M, Bray GA, Sacks FM, et al. Weight-Loss Diets,

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Adiponectin, and Changes in Cardiometabolic Risk in the 2-Year POUNDS Lost Trial. J Clin Endocrinol Metab. 2016;101:2415-22. https://doi.org/10.1210/jc.2016-1207

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[43] Rosenbaum M, Nicolson M, Hirsch J, Murphy E, Chu F, Leibel RL. Effects of weight change on plasma leptin concentrations and energy expenditure. J Clin Endocrinol Metab. 1997;82:3647-54. https://doi.org/10.1210/jcem.82.11.4390

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Figure 1. Flow chart of the study subjects. AST, Aspartate aminotransferase; ALT, Alanine aminotransferase; eGFR, estimated glomerular filtration rate; NAFL, non-alcoholic fatty liver; F/U, follow-up.

Figure 2. The hazard ratios for 1-unit difference in adipokine levels in the development of non-

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alcoholic fatty liver from among the subjects without fatty liver finding at baseline and in the

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improvement from among the subjects with non-alcoholic fatty liver at baseline, respectively.

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The hazard ratios were estimated by Cox regression models. Model 1 is a crude model; Model 2 is adjusted for age, sex, and waist circumference; Model 3 is additionally adjusted for

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triglyceride and HOMA2-IR; Model 4 is additionally adjusted for weight change percentage; and

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Model 5 is a full-adjusted model being adjusted for additional variables of systolic blood

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pressure, erythrocyte sedimentation rate, menopause, past medical histories, and lifestyle habits.

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Error bars represent 95% CI for each risk ratio.

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Figure 3. The Kaplan–Meier survival curve demonstrating the association between adipokines and non-alcoholic fatty liver. The graphs of total (A) and HMW adiponectin (B) show significant difference in cumulative incidence of non-alcoholic fatty liver among the tertiles of each adipokine for 8.5 years. The difference among the tertiles of leptin (C) was not significant (P = 0.601).

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Subjects without NAFL

Subjects with NAFL

(N=1,486)

(N=480)

Age (years)

41.6 ± 9.2

44.1 ± 9.5

<0.001

Sex (men)

657 (44.2%)

369 (76.9%)

<0.001

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Table 1. Baseline characteristics of the study subjects

<0.001

P

Life-style habits and medical history Smoking

Ex-smoker

265 (17.8%)

Current smoker

226 (15.2%) 670 (45.1%)

145 (30.2%) 131 (27.3%) 230 (47.9%)

0.304

46 (9.6%)

<0.001

8 (0.5%)

23 (4.8%)

<0.001

143 (17.2%)

60 (54.1%)

<0.001

125.1 ± 14.3

<0.001

75.1 ± 10.8

82.3 ± 11.4

<0.001

22.2 ± 2.6

25.5 ± 2.7

<0.001

76.0 ± 8.2

86.4 ± 7.4

<0.001

5.00 (4.66–5.33)

5.30 (4.88–5.70)

<0.001

4.78 ± 0.83

5.22 ± 0.91

<0.001

0.98 (0.75–1.36)

1.62 (1.17–2.29)

<0.001

HDL cholesterol (mmol/L)

1.37 ± 0.30

1.17 ± 0.24

<0.001

AST (U/L)

19.8 ± 6.0

26.2 ± 9.7

<0.001

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Routine exercise

204 (42.5%)

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995 (67.0%)

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Non-smoker

31 (2.1%)

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Hypertension Diabetes

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Menopause (women only)

Blood pressure and anthropometric measurement 115.7 ± 13.3

Diastolic BP (mmHg)

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Body mass index (kg/m2)

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Systolic BP (mmHg)

Waist circumference (cm) Laboratory tests Glucose (mmol/L)

Total cholesterol (mmol/L) Triglyceride (mmol/L)

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18.1 ± 9.4

35.0 ± 20.2

<0.001

GGT (U/L)

21.0 ± 16.5

40.2 ± 30.7

<0.001

eGFR (mL/min/1.73 m2)

83.0 ± 12.0

80.1 ± 11.5

<0.001

5 (2–9)

4 (2–9)

0.008

Insulin (µIU/mL)

3.54 ± 1.64

5.63 ± 2.66

<0.001

HOMA 2-IR

0.47 ± 0.22

0.75 ± 0.35

<0.001

0.80 (0.63–1.00)

0.77 (0.63–1.01)

0.452

2 (0.0%)

1 (0.0%)

ESR (mm/h)

FIB-4 FIB-4 > 2.67

HMW adiponectin (µg/ml)

3.20 ± 2.22

Leptin (µg/ml)

4.09 ± 3.48

3.68 ± 1.84

<0.001

1.69 ± 1.09

<0.001

4.86 ± 4.59

0.001

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5.77 ± 3.03

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Adiponectin (µg/ml)

1.000

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Adipokines

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ALT (U/L)

Data are expressed as mean ± SD, median (interquartile range), or number (proportion).

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BP, blood pressure; HDL, high-density lipoprotein; AST, Aspartate aminotransferase; ALT, Alanine aminotransferase; GGT, gamma-glutamyltransferase; eGFR, estimated glomerular filtration rate; ESR, erythrocyte sedimentation rate;

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HOMA2-IR, homeostatic model assessment for insulin resistance; HMW, high-molecular-weight.

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Table 2. The association between serum levels of adipokines and non-alcoholic fatty liver at baseline

HMW adiponectin (µg/ml) Odds ratio P 0.495 (0.446 - 0.548) <0.001 0.582 (0.518 - 0.653) <0.001 0.644 (0.572 - 0.724) <0.001 0.610 (0.539 - 0.691) <0.001

Leptin (µg/ml) Odds ratio 1.051 (1.024 - 1.078) 1.117 (1.067 - 1.169) 1.018 (0.968 - 1.071) 1.024 (0.973 - 1.078)

P <0.001 <0.001 0.486 0.360

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Model 1 Model 2 Model 3 Model 4

Total adiponectin (µg/ml) Odds ratio P 0.663 (0.624 - 0.705) <0.001 0.729 (0.680 - 0.781) <0.001 0.770 (0.717 - 0.828) <0.001 0.751 (0.696 - 0.810) <0.001

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Model 1 is a crude model; Model 2 is adjusted for age, sex, and waist circumference; Model 3 is additionally adjusted for triglyceride and HOMA2-IR; Model 4 is a full-adjusted model being adjusted for additional variables of

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systolic blood pressure, erythrocyte sedimentation rate, menopause, past medical histories, and lifestyle habits.

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na

lP

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HMW, high-molecular-weight.

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Table 3. The hazard ratios for tertiles in adipokines in the development of non-alcoholic fatty liver

Range

Model 1

Model 2

Model 3

Model 4

Model 5

0.44–

3.866 (2.446–

2.327 (1.444–

2.237 (1.388–

2.150 (1.333–

2.333 (1.411–

4.18

6.113)

3.750)

3.605)

3.466)

3.857)

4.19–

2.283 (1.405–

1.942 (1.191–

1.975 (1.212–

1.770 (1.082–

2.054 (1.234–

6.15

3.710)

3.166)

3.220)

2.895)

3.418)

Ref

Ref

Ref

Ref

Ref

0.20–

4.425 (2.732–

2.603 (1.572–

2.336 (1.413–

2.147 (1.295–

2.280 (1.358–

1.88

7.169)

4.311)

3.863)

3.557)

3.830)

1.89–

2.903 (1.753–

2.427 (1.456–

2.437 (1.463–

2.102 (1.256–

2.253 (1.338–

3.36

4.808)

4.044)

4.061)

3.519)

3.793)

Ref

Ref

Ref

Ref

Adiponectin

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T2

6.19– T3

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24.73

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HMW

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adiponectin

3.37–

Leptin

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Ref 19.64

ur

T2

na

T1

T3

of

T1

0.26–

1.222 (0.828–

0.454 (0.266–

0.680 (0.381–

0.651 (0.362–

0.597 (0.323–

1.86

1.803)

0.776)

1.213)

1.172)

1.102)

1.89–

1.115 (0.750–

0.516 (0.319–

0.649 (0.388–

0.660 (0.392–

0.628 (0.365–

4.23

1.657)

0.834)

1.085)

1.110)

1.078)

Ref

Ref

Ref

Ref

Ref

T1

T2

4.24– T3 21.26

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Model 1 is a crude model; Model 2 adjusted for age, sex, and waist circumference; Model 3 additionally adjusted for triglyceride and HOMA2-IR; Model 4 additionally adjusted for weight change percentage; Model 5 is a full-adjusted model being adjusted for additional variables of systolic blood pressure, erythrocyte sedimentation rate, menopause, past medical histories, and lifestyle habits.

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ur

na

lP

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HMW, high-molecular-weight; T, tertile.

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Table 4. The hazard ratios for 1–unit change in adipokines in the development of non-alcoholic fatty liver in subgroups stratified by sex and weight change percentage

Adiponectin (per 1

HMW adiponectin (per 1

µg/mL)

µg/mL)

Leptin (per 1 µg/mL)

Hazard ratio

P

Hazard ratio

0.692

0.952 (0.827–1.095)

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0.982 (0.898– Men (n = 345) 1.074) <0.00

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0.757 (0.650– Women (n=3 73)

0.974 (0.856–

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percentage

239)

1.184)

4

<0.00

1.042 (0.953–

0.36

1

1.139)

5

0.982 (0.817–1.180)

0.960 (0.803–

0.65

1.148)

6

0.974 (0.847–

0.70

1.119)

9

1.172 (1.033–

0.01

1.328)

3

0.845

0.886 (0.781–

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T3 (3.0–22.4%, n =

0.98

1.108)

0.059

0.794 (0.641–0.983)

0.034

1.005)

0.944 (0.822–

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240)

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0.692

T2 (-0.2–2.9 %, n =

0.998 (0.841–

0.490

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1

Weight change

239)

P

0.677 (0.539–0.851) 0.881)

T1 (-15.5–-0.2%, n =

Hazard ratio

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Sex

P

0.412

1.084)

0.910 (0.757–1.094)

0.315

Models adjusted for age, sex (not included in the models stratified by sex), waist circumference, triglyceride, HOMA2-IR, systolic blood pressure, ESR, hypertension history, diabetes history, menopause, lifestyle habits of smoking and exercise, and weight change percentage. HMW, high-molecular-weight; T, tertile.

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na

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Highlights

 Low levels of total and HMW adiponectin may predict the development of NAFL.  Sex difference exists in the relationship between adiponectin and NAFL incidence.

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 Leptin may be a significant predictor for NAFL in the subjects with weight gain.

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 Weight reduction, but not adipokines, is associated with the improvement of NAFL.

Figure 1

Figure 2

Figure 3