Skip to main content
Download PDF
Download ePub

Association of serum uric acid with hepatic steatosis detected by controlled attenuation parameter in the United States population

Lipids in Health and Disease volume 22, Article number: 76 (2023) Cite this article

Abstract

Background

The relationship between serum uric acid (SUA) and nonalcoholic fatty liver disease (NAFLD) has been previously reported. Controlled attenuation parameter (CAP) has better diagnostic performance than ultrasonography for assessing hepatic steatosis. The association of SUA with hepatic steatosis detected by CAP is worth further study.

Methods

The US population aged 20 years or older from the National Health and Nutrition Examination Survey (NHANES) was assessed. Hepatic steatosis was evaluated by the controlled attenuation parameter (CAP). NAFLD status was defined as CAP values of 268 dB/m without hepatitis B or C virus infection or considerable alcohol consumption. Multiple imputations were performed to fill in the missing covariate values. Linear regression, logistic regression, and smooth curve fitting were used to examine the association.

Results

In total, 3919 individuals participated in this study. There was a positive association between SUA (µmol/L) and CAP (β = 0.14, 95% CI: 0.12-0.17, P < 0.01). After stratification by sex, a significant relationship between SUA and CAP existed in both males (β = 0.12, 95% CI: 0.09-0.16, P < 0.01) and females (β = 0.17, 95% CI: 0.14-0.20, P < 0.01) after multiple imputation. The inflection points of the threshold effect of SUA on CAP were 487.7 µmol/L in males and 386.6 µmol/L in females. There was a positive association between SUA (mg/dL) and NAFLD (OR = 1.30, 95% CI: 1.23-1.37, P < 0.01). After stratification by race, positive relationships were also observed. Meanwhile, a positive relationship existed between hyperuricemia and NAFLD (OR = 1.94, 95% CI: 1.64-2.30, P < 0.01). The positive relationship was more significant in females than in males (P for interaction < 0.01).

Conclusions

There was a positive association between SUA and CAP, as well as between SUA and NAFLD. Subgroup studies stratified by sex and ethnicity demonstrated that the effects were consistent.

Introduction

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide [1]. NAFLD is a disease spectrum defined by hepatic steatosis (HS), with epidemiological statistics indicating a global prevalence of 25% [2,3,4]. NAFLD is characterized by lipid accumulation, insulin resistance, and metabolic stress-induced liver injury [5, 6]. Obesity, type 2 diabetes, hyperlipidemia, hypertension, and metabolic syndrome are all frequently related to NAFLD [4, 7]. Patients with NAFLD can be assessed in a variety of ways, including ultrasonography, magnetic resonance imaging (MRI), vibration controlled transient elastography (VCTE) and hepatic biopsy [8, 9]. Hepatic biopsy is regarded as the gold standard for determining the severity of HS [10]. However, hepatic biopsy is an invasive procedure not available for large-scale screenings. Noninvasive approaches for assessing HS have been developed. Among imaging approaches, the controlled attenuation parameter (CAP) function of the FibroScan device appears to be the most promising noninvasive test for quantifying HS [11].

Serum uric acid (SUA) is the last product of purine metabolism, acting as a natural antioxidant in humans. A rise or fall in SUA levels can cause or disclose a variety of disorders [12]. Hyperuricemia, similar to NAFLD, is intimately associated with metabolic dysregulation, such as obesity and insulin resistance (IR) [13, 14]. Several pieces of evidence suggest that increased SUA plays a crucial role in the pathophysiology of NAFLD [15, 16]. However, in different sexes, the link between SUA and NAFLD has proven to be disputed. Recent research on 541 women found that SUA was not independently related to the risk of NAFLD [17]. A beneficial relationship between SUA and NAFLD was found in females but not in males [18]. Moreover, previous studies rarely evaluated ethnic subgroups.

Hence, we conducted a cross-sectional study to investigate the association between SUA and hepatic steatosis detected by CAP in the US population from the 2017–2018 National Health and Nutrition Examination Survey (NHANES). A further detailed evaluation by sex and ethnicity was also performed.

Methods

Study population

The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional research project managed by the Centers for Disease Control and Prevention (CDC) that examines the health status of the nationwide population. The National Center for Health Statistics (NCHS) ethics review board approved the study, and all individuals signed an informed consent form.

These data are available on the website of NHANES 2017–2018 (available online at: http://www.cdc.gov/NCHS/nhanes.htm). After excluding age < 20 years (N = 3685), missing transient elastography (N = 1059), missing SUA (N = 244), individuals with hepatitis B (N = 15), hepatitis C (N = 22), and considerable alcohol consumption (≥ 30 g for males and ≥ 20 g for females) (N = 310), 3919 individuals took part in this study in total.

Variables

The exposure variable was serum uric acid, which was measured by the Roche Cobas 6000 Chemistry Analyzer. Uric acid is oxidized by uricase in this approach. The peroxide formed by this reaction is then acted on by peroxidase in the presence of 4 aminophenazone to yield a detectable colored product. It is a two-point, endpoint reaction, with the measurement taking place at 546 nm. SUA values were displayed in µmol/L, which were translated to mg/dL by dividing by 59.5. Hyperuricemia was defined as SUA concentrations ≥ 420 µmol/L in males and ≥ 360 µmol/L in females [19].

The outcome variable was hepatic steatosis detected by CAP using FibroScan performed on eligible participants. NAFLD status was defined as CAP values of 268 dB/m without hepatitis B or C virus infection or considerable alcohol consumption. Based on the AUROC values (AUROC 0.86) and commonly used cutoff point, 268 dB/m was defined as the cutoff point in our study. Examinations were considered reliable if they had a fasting time of at least 3 h and 10 or more complete measures.

Covariates

The continuous covariates were age, body mass index (BMI), waist circumference, ratio of family income, alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine, gamma glutamyl transferase (GGT), serum cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glycohemoglobin, HOMA-IR, dietary fat intake, and physical activity. The category covariates were gender, race/ethnicity, education levels, smoking habits, diabetes, and hypertension.

The demographic questionnaires for the family and sample members were administered in the house by trained interviewers utilizing the Computer Assisted Personal Interview (CAPI) system, including age, gender, ethnicity, education levels, and ratio of family income. The blood samples were forwarded to the NHANES laboratory for analysis, including liver and kidney function, blood lipids, and glycohemoglobin. The formula HOMA-IR = fasting insulin (mU/L) * fasting glucose (mmol/L)/22.5 was used to calculate HOMA-IR. Physical activity was determined by multiplying the weekly amount of time spent in each activity by the metabolic equivalent of the task (MET). Data on dietary fat intake were obtained from the 24-hour dietary survey. Ethnicity was classified as non-Hispanic white, non-Hispanic black, Mexican American, other Hispanic and other race. The levels of education were less than 12th grade, high school graduate or college degree, and college graduate or above. Smoking habits were classified as current, former, or never. Hypertension and diabetes were assessed by self-report.

Statistical analyses

The continuity variables were recorded as the mean ± standard deviation (SD), and the categorical variables were recorded as percentages. The missing values of covariates were treated through the multiple imputation procedure. Multiple imputations were used in our analysis, based on five replications and a chained equation approach method in the R MI procedure. Because parts of the covariates were not normally distributed, log e transformations were conducted for analysis. Multivariate linear regression models were applied to assess the association between SUA and CAP. Multivariate logistic regression models were applied to assess the association between SUA and NAFLD. Age, gender, race, BMI, waist circumference, education levels, ratio of family income, ALT, AST, GGT, serum creatinine, triglyceride, serum cholesterol, HDL-C, LDL-C, glycohemoglobin, HOMA-IR, dietary fat intake, smoking habits, MET, hypertension, and diabetes were considered potential factors. To address the nonlinear relationship, smooth curve fitting and a generalized additive model were used. The threshold effect of the nonlinear relationship between SUA and CAP was investigated using a segmented regression model. A statistically significant difference was shown by a two-sided P < 0.05. All statistical data were analyzed using R (http://www.R-project.org, The R Foundation, Boston, MA, USA) and EmpowerStats software (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA, USA).

Results

An overview of the general characteristics of the study subjects is shown in Table 1, including age, gender, race/ethnicity, education levels, ratio of family income, BMI, waist circumference, ALT, AST, GGT, serum creatinine, triglyceride, serum cholesterol, HDL-C, LDL-C, glycohemoglobin, HOMA-IR, dietary fat intake, smoking habits, MET, smoking habits, hypertension, and diabetes. Our study enrolled 3919 individuals, including 1971 males and 1948 females. The mean SUA was 325.98 ± 88.35 µmol/L. The prevalence of hyperuricemia was 19.32% in total, 21.87% in males, and 16.74% in females. The mean median CAP was 266.32 ± 62.23 dB/m.

Table 1 Characteristics of the study population
Full size table

Table 2 displays the outcomes of the multivariate linear regression. There was a significant association between SUA and median CAP in all models, with or without multiple imputations. Stratified by sex, a positive relationship between SUA and CAP existed in both males (β = 0.12, 95% CI: 0.09-0.16, P < 0.01) and females (β = 0.17, 95% CI: 0.14-0.20, P < 0.01) after multiple imputation. The correlation between SUA and CAP in males in model 2 (β = 0.08, 95% CI: −0.01-0.16, P = 0.06) was negligible before imputations. After stratification by race, as shown in Supplementary Table S1, positive relationships were also observed. Similarly, the outcomes for hyperuricemia and CAP were the same.

Table 2 Association between SUA (µmol/L) and CAP among US adults aged ≥ 20 years
Full size table

Smooth curve fitting revealed a nonlinear correlation between SUA and CAP (Fig. 1). The threshold effect of SUA on CAP is observed in Table 3. A significant association between SUA and CAP existed inside the inflection point (SUA = 475.8 µmol/L) (β = 0.18, 95% CI: 0.16-0.21, P < 0.01). There was no connection after the inflection (β = −0.01, 95% CI: −0.06-0.04, P = 0.64). Similar relationships were obtained for males and females; the inflection points were 487.7 µmol/L and 386.6 µmol/L, respectively. The relationship between SUA and CAP existed as an inflection point for both males and females.

Fig. 1
figure 1

Association between SUA and CAP among US adults. The solid red line represents the relationship in males, and the blue bars represent the relationship in females. Adjust for age, race, BMI, waist circumference, education levels, ratio of family income, ALT, AST, GGT, serum creatinine, triglyceride, serum cholesterol, HDL-C, LDL-C, glycohemoglobin, HOMA-IR, dietary fat intake, smoking habits, MET, hypertension and diabetes

Full size image
Table 3 Threshold effect analysis of SUA (µmol/L) on CAP using the segmented regression model
Full size table

There was a positive association between SUA (mg/dL) and NAFLD, according to Table 4. Regardless of the optimum cutoff values for NAFLD (248 dB/m, 268 dB/m, or 280 dB/m), the positive associations were consistent in both males and females after multiple imputations. The correlations between SUA and NAFLD in males in Model 2 were insignificant before imputation. Stratified by race, as shown in Supplementary Table S2, positive relationships were also observed.

Hyperuricemia and NAFLD were found to be positively related (OR = 1.94, 95% CI: 1.64-2.30, P < 0.01), as shown in Table 5. Stratified by gender, the P for interaction was less than 0.01 in model 2, and the positive correlation was more significant in females than in males.

Table 4 Association between SUA (mg/dL) and NAFLD among US adults aged ≥ 20 years
Full size table
Table 5 Association between hyperuricemia and NAFLD among US adults aged ≥ 20 years
Full size table

Discussion

In our study, we explored the association of serum uric acid with hepatic steatosis detected by a controlled attenuation parameter in the US population. There was a positive relationship between SUA and CAP, as well as between SUA and NAFLD, in both males and females. After stratification by race, positive relationships were also observed. The relationship between SUA and CAP existed as an inflection point for both males and females. Within the inflection point, a positive relationship existed; beyond the infection point, there was no relationship.

The primary results of our investigation are broadly similar to previous findings. However, gender differences in the relationship between SUA and NAFLD remain debatable. Previous meta-analyses showed that young women were at higher risk than age-matched men and older women [20]. Wang et al. found that the correlation between SUA and NAFLD existed in women but not in men [18]. Their results could be attributed to missing values when compared to the findings of our investigation. Meanwhile, missing data are a prevalent issue with publicly available datasets. In our study, there were missing variables, which we addressed with multiple imputations. Multiple imputations are a technique for reducing missing values and providing a trustworthy dataset for study [21]. We found that the associations were consistent among different sexes after the application of multiple imputations. Furthermore, we observed associations between hyperuricemia and NAFLD. The positive correlation was more significant in females than in males in the interaction test. The findings of a study of 166 individuals with biopsy-proven NAFLD were identical to ours, and females were at higher risk. [16]. Based on the study population, these association studies were most regularly reported in East Asian populations, particularly in China [22, 23]. The multiracial and multicultural nation’s NHANES database shows a representatively larger population. The previous studies did not evaluate ethnic subgroups. Therefore, we conducted an ethnicity-based subgroup analysis, and the findings in all racial subgroups were consistent. Gender and ethnicity had no effect on the observed relationships in this study.

The controlled attenuation parameter (CAP) assessed by the FibroScan device, which is a marker of hepatic steatosis, can be derived by simultaneously monitoring the attenuation of the ultrasound signal via the liver. In noninvasive examinations, B-mode ultrasonography, the most common method for detecting steatosis, is unreliable in assessing steatosis severity, especially in mild cases [24]. The magnetic resonance imaging (MRI) technique is extremely precise in detecting and quantifying HS [25], but it is insufficiently available to analyze a large sample of patients [11]. The FibroScan device is the most acceptable for the patient, but it has a variety of computed cutoff values [26]. HS, according to histological findings, is classified as absent-S0 (normal liver), mild-S1, 5–33%, moderate-S2, 36–66%, and severe-S3, more than 66%. In a meta-analysis [27], the overall performance of CAP compared to hepatic biopsy was as follows: S1 had a cutoff value of 248 dB/m (AUROC 0.82); S2, 268 dB/m (AUROC 0.86); and S3, 280 dB/m (AUROC 0.88). Cutoff values of 248 dB/m, 268 dB/m, and 280 dB/m were used in our study as sensitivity analyses. The sensitivity analyses revealed no significant changes in the results, indicating that the results were stable.

In this study, an inflection point is first reported between SUA and CAP in both males and females. Liver biopsy evaluation is impractical in a large sample population. Because CAP correlates well with histological steatosis [28], there may be a nonlinear relationship between SUA and HS. The inflection point was 487.7 µmol/L in males and 386.6 µmol/L in females. The significance of the inflection point is unknown, and we have no answer for this problem; additional research is needed. However, we can hypothesize that when the urate concentration exceeds 405 µmol/L, the limit of solubility under physiological conditions, urate crystals may form as monosodium urate (MSU). Because urate has both anti-inflammatory and proinflammatory effects, uric acid has the most impact on NAFLD at the inflection point [29].

Despite numerous studies, the underlying mechanisms between SUA and NAFLD remain unclear. Although SUA is positively associated with related metabolic disorders, it does not appear to be a causative factor. In the Mendelian randomized study, we still lacked evidence of pathogenicity between SUA and NAFLD [30]. In animal research, studies have shown that inhibition of xanthine oxidase may reduce uric acid production and decrease liver fat accumulation [31, 32]. However, there was research that supported the opposite viewpoint: inhibition of xanthine oxidase reduces uric acid but does not influence metabolic homeostasis [33].

Study strengths and limitations

Notably, the major strengths of this study should be considered. First, this is a large-sample study using a large population (3919 participants) drawn from the NHANES database and using high-precision VCTE compared with ultrasonography. Second, in our research, multiple imputation, the most accurate method of imputation, was used to solve missing values. Sensitivity analysis and subgroup analysis were performed to show consistent results. Nonetheless, there were constraints. First, owing to the cross-sectional nature of the investigation, a causal link cannot be established. Second, we controlled as many covariates as possible; additional covariates such as drug use and period status might have caused bias. Third, we are aware of the missing hepatic biopsy. However, in large-sample investigations, biopsies are both impractical and unethical.

Conclusion

There was a positive association between SUA and CAP, as well as between SUA and NAFLD. Subgroup studies stratified by sex and ethnicity demonstrated that the effects were consistent. However, further mechanistic study is still needed.

Data availability

The database used for this investigation can be found in online repositories. For more information, visit https://www.cdc.gov/nchs/nhanes/index.htm.

References

  1. Powell EE, Wong VW, Rinella M. Non-alcoholic fatty liver disease. Lancet (London England). 2021;397(10290):2212–24.

    Article  CAS  PubMed  Google Scholar 

  2. Younossi Z, Tacke F, Arrese M, Chander Sharma B, Mostafa I, Bugianesi E, Wai-Sun Wong V, Yilmaz Y, George J, Fan J, et al. Global perspectives on nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology (Baltimore MD). 2019;69(6):2672–82.

    Article  PubMed  Google Scholar 

  3. Younossi Z, Anstee QM, Marietti M, Hardy T, Henry L, Eslam M, George J, Bugianesi E. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2018;15(1):11–20.

    Article  PubMed  Google Scholar 

  4. Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology (Baltimore MD). 2016;64(1):73–84.

    Article  PubMed  Google Scholar 

  5. Friedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ. Mechanisms of NAFLD development and therapeutic strategies. Nat Med. 2018;24(7):908–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Pouwels S, Sakran N, Graham Y, Leal A, Pintar T, Yang W, Kassir R, Singhal R, Mahawar K, Ramnarain D. Non-alcoholic fatty liver disease (NAFLD): a review of pathophysiology, clinical management and effects of weight loss. BMC Endocr disorders. 2022;22(1):63.

    Article  CAS  Google Scholar 

  7. Cotter TG, Rinella M. Nonalcoholic fatty liver Disease 2020: the state of the Disease. Gastroenterology. 2020;158(7):1851–64.

    Article  CAS  PubMed  Google Scholar 

  8. Agbim U, Asrani SK. Non-invasive assessment of liver fibrosis and prognosis: an update on serum and elastography markers. Expert Rev Gastroenterol Hepatol. 2019;13(4):361–74.

    Article  CAS  PubMed  Google Scholar 

  9. Piazzolla VA, Mangia A. Noninvasive diagnosis of NAFLD and NASH. Cells 2020, 9(4).

  10. Takahashi Y, Fukusato T. Histopathology of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. World J Gastroenterol. 2014;20(42):15539–48.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Sheka AC, Adeyi O, Thompson J, Hameed B, Crawford PA, Ikramuddin S. Nonalcoholic steatohepatitis: a review. JAMA. 2020;323(12):1175–83.

    Article  CAS  PubMed  Google Scholar 

  12. Copur S, Demiray A, Kanbay M. Uric acid in metabolic syndrome: does uric acid have a definitive role? Eur J Intern Med. 2022;103:4–12.

    Article  CAS  PubMed  Google Scholar 

  13. Yang C, He Q, Chen Z, Qin JJ, Lei F, Liu YM, Liu W, Chen MM, Sun T, Zhu Q, et al. A bidirectional relationship between Hyperuricemia and Metabolic Dysfunction-Associated fatty liver disease. Front Endocrinol. 2022;13:821689.

    Article  Google Scholar 

  14. Yuan H, Yu C, Li X, Sun L, Zhu X, Zhao C, Zhang Z, Yang Z. Serum uric acid levels and risk of metabolic syndrome: a dose-response Meta-analysis of prospective studies. J Clin Endocrinol Metab. 2015;100(11):4198–207.

    Article  CAS  PubMed  Google Scholar 

  15. Xie D, Zhao H, Lu J, He F, Liu W, Yu W, Wang Q, Hisatome I, Yamamoto T, Koyama H, et al. High uric acid induces liver fat accumulation via ROS/JNK/AP-1 signaling. Am J Physiol Endocrinol metabolism. 2021;320(6):E1032–e1043.

    Article  CAS  Google Scholar 

  16. Petta S, Camma C, Cabibi D, Di Marco V, Craxi A. Hyperuricemia is associated with histological liver damage in patients with non-alcoholic fatty liver disease. Aliment Pharmacol Ther. 2011;34(7):757–66.

    Article  CAS  PubMed  Google Scholar 

  17. Fan N, Zhang L, Xia Z, Peng L, Wang Y, Peng Y. Sex-specific association between serum uric acid and nonalcoholic fatty liver disease in type 2 Diabetic patients. J Diabetes Res. 2016;2016:3805372.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wang R, Xue F, Wang L, Shi G, Qian G, Yang N, Chen X. Serum uric acid to creatinine ratio is associated with higher prevalence of NAFLD detected by FibroScan in the United States. J Clin Lab Anal. 2022;36(8):e24590.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Li Q, Li X, Wang J, Liu H, Kwong JS, Chen H, Li L, Chung SC, Shah A, Chen Y, et al. Diagnosis and treatment for hyperuricemia and gout: a systematic review of clinical practice guidelines and consensus statements. BMJ open. 2019;9(8):e026677.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Liu Z, Que S, Zhou L, Zheng S. Dose-response relationship of serum uric acid with metabolic syndrome and non-alcoholic fatty liver Disease incidence: a Meta-analysis of prospective studies. Sci Rep. 2015;5:14325.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Beesley LJ, Bondarenko I, Elliot MR, Kurian AW, Katz SJ, Taylor JM. Multiple imputation with missing data indicators. Stat Methods Med Res. 2021;30(12):2685–700.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wang X, Han Y, Liu Y, Hu H. Association between serum uric acid-to-creatinine ratio and non-alcoholic fatty liver disease: a cross-sectional study in chinese non-obese people with a normal range of low-density lipoprotein cholesterol. BMC Gastroenterol. 2022;22(1):419.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Huang F, Liu A, Fang H, Geng X. Serum uric acid levels in non-alcoholic steatosis patients: a meta-analysis. Asia Pac J Clin Nutr. 2017;26(2):334–42.

    PubMed  Google Scholar 

  24. Bril F, Ortiz-Lopez C, Lomonaco R, Orsak B, Freckleton M, Chintapalli K, Hardies J, Lai S, Solano F, Tio F, et al. Clinical value of liver ultrasound for the diagnosis of nonalcoholic fatty liver disease in overweight and obese patients. Liver international: official journal of the International Association for the Study of the Liver. 2015;35(9):2139–46.

    Article  CAS  PubMed  Google Scholar 

  25. Selvaraj EA, Mozes FE, Jayaswal ANA, Zafarmand MH, Vali Y, Lee JA, Levick CK, Young LAJ, Palaniyappan N, Liu CH, et al. Diagnostic accuracy of elastography and magnetic resonance imaging in patients with NAFLD: a systematic review and meta-analysis. J Hepatol. 2021;75(4):770–85.

    Article  PubMed  Google Scholar 

  26. Sirli R, Sporea I. Controlled Attenuation Parameter for Quantification of Steatosis: Which Cut-Offs to Use? Can J Gastroenterol Hepatol 2021, 2021:6662760.

  27. Karlas T, Petroff D, Sasso M, Fan JG, Mi YQ, de Lédinghen V, Kumar M, Lupsor-Platon M, Han KH, Cardoso AC, et al. Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis. J Hepatol. 2017;66(5):1022–30.

    Article  PubMed  Google Scholar 

  28. Castera L, Friedrich-Rust M, Loomba R. Noninvasive Assessment of Liver Disease in patients with nonalcoholic fatty liver disease. Gastroenterology. 2019;156(5):1264–1281e1264.

    Article  PubMed  Google Scholar 

  29. Keenan RT. The biology of urate. Semin Arthritis Rheum. 2020;50(3s):2–s10.

    Article  Google Scholar 

  30. Tang Y, Xu Y, Liu P, Liu C, Zhong R, Yu X, Xiao L, Du M, Yang L, Yuan J et al. No Evidence for a Causal Link between Serum Uric Acid and Nonalcoholic Fatty Liver Disease from the Dongfeng-Tongji Cohort Study. Oxid Med Cell Longev 2022, 2022:6687626.

  31. Xu C, Wan X, Xu L, Weng H, Yan M, Miao M, Sun Y, Xu G, Dooley S, Li Y, et al. Xanthine oxidase in non-alcoholic fatty liver disease and hyperuricemia: one stone hits two birds. J Hepatol. 2015;62(6):1412–9.

    Article  CAS  PubMed  Google Scholar 

  32. Nakatsu Y, Seno Y, Kushiyama A, Sakoda H, Fujishiro M, Katasako A, Mori K, Matsunaga Y, Fukushima T, Kanaoka R, et al. The xanthine oxidase inhibitor febuxostat suppresses development of nonalcoholic steatohepatitis in a rodent model. Am J Physiol Gastrointest liver Physiol. 2015;309(1):G42–51.

    Article  CAS  PubMed  Google Scholar 

  33. Harmon DB, Mandler WK, Sipula IJ, Dedousis N, Lewis SE, Eckels JT, Du J, Wang Y, Huckestein BR, Pagano PJ, et al. Hepatocyte-specific ablation or whole-body inhibition of Xanthine Oxidoreductase in mice corrects Obesity-Induced systemic hyperuricemia without improving metabolic abnormalities. Diabetes. 2019;68(6):1221–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Thanks to all individuals working on this project as well as participants involved in the NHANES study.

Funding

This study was not supported by any grant funding.

Author information

Authors and Affiliations

  1. Endoscopy Center, The First People’s Hospital of Kunshan, Kunshan, 215300, China

    Yunfu Feng & Sijie Zheng

  2. Department of Gastroenterology, The Third People’s Hospital of Kunshan, Kunshan, 215300, China

    Yanting Yang

  3. Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu, 215500, China

    Luojie Liu

Authors
  1. Yunfu Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sijie Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luojie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanting Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yunfu Feng: conceptualization, statistical analysis, manuscript writing and editing. Sijie Zheng: methodology and reviewing. Luojie Liu: statistical analysis, reviewing and editing. Yanting Yang: conceptualization, methodology, reviewing and editing. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Luojie Liu or Yanting Yang.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the National Centre for Health Statistics Research Ethics Review Board.

Consent for publication

All individuals signed an informed consent form.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, Y., Zheng, S., Liu, L. et al. Association of serum uric acid with hepatic steatosis detected by controlled attenuation parameter in the United States population. Lipids Health Dis 22, 76 (2023). https://doi.org/10.1186/s12944-023-01846-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12944-023-01846-8

Keywords

Lipids in Health and Disease

ISSN: 1476-511X