Metabolic associated fatty liver disease

Metabolic associated fatty liver disease DEFAULT

Metabolic Associated Fatty Liver Disease Is Associated With an Increased Risk of Severe COVID-19: A Systematic Review With Meta-Analysis

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents a global health challenge. Coronavirus disease 2019 (COVID-19) is mostly a self-limiting disease; however, in some cases, mortality can reach 3–7% (1). The high mortality has been mainly linked to the excessive production of pro-inflammatory cytokines that lead to organ failure, most importantly, acute respiratory distress syndrome (ARDS) (2).

Advanced age and comorbidities, such as hypertension, chronic obstructive pulmonary disease, or cardiovascular diseases are proved risk factors in COVID-19 (1, 3). Patients with elements of metabolic syndrome (MS), such as diabetes, obesity, or hyperlipidemia are more susceptible to infection and also have worse outcomes in COVID-19 (4, 5). MS was found to be associated with chronic low-grade inflammation that compromises the immune system and causes microvascular endothelial dysfunction, which may contribute to poor outcomes in COVID-19 (6, 7).

Metabolic-associated fatty liver disease (MAFLD) and non-alcoholic fatty liver disease (NAFLD) are the most common chronic liver diseases (CLD), which affect about a quarter of the world's adult population (8). Pre-existing liver diseases such as NAFLD or the recently defined MAFLD, as the hepatic manifestations of MS (8), might also be significant risk factors of hospitalization and severity in COVID-19 (9, 10). The MAFLD criteria are based on evidence of hepatic steatosis in addition to one of the following three criteria: overweight/obesity, presence of type 2 diabetes mellitus, and proof of metabolic dysregulation (8).

According to recent publications, the presence of MAFLD and NAFLD may exacerbate the virus-induced inflammatory “storm” possibly through the hepatic release of pro-inflammatory cytokines and by increased reactive oxygen production in COVID-19 patients (11–13).

There are still limited reports on how MAFLD and NAFLD influence clinical outcomes in patients with COVID-19, and there are no meta-analytical reports of the available evidence. This meta-analysis aimed to evaluate if MAFLD or NAFLD are associated with a more severe disease course of COVID-19.

Methods and Materials

We report our systematic review and meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 Statement (Supplementary Table 1) (14). We registered the protocol of this study onto the International Prospective Register of Systematic Reviews (CRD42020210923) and adhered to it during the course, except for including mortality in our outcomes (see https://www.crd.york.ac.uk/prospero).

Search and Selection

A systematic search was performed in five databases, namely Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Embase, and MEDLINE (via PubMed) without any search restrictions from inception to 15th Sept, 2020. The following search key was used: (NASH OR steatohepatitis OR “metabolic associated fatty liver disease” or “Non-alcoholic Fatty Liver Disease” OR “Nonalcoholic Fatty Liver Disease” OR MAFLD OR NAFLD) AND (“COVID 19” OR “Wuhan virus” OR “coronavirus” OR “2019 nCoV” OR “SARS-Cov-2”).

After the removal of duplicates with a reference manager software (EndNote X9, Clarivate Analytics, Philadelphia, PA, USA), papers for title, abstract, and full-text were screened by two independent authors separately according to a predetermined set of rules. In the case of any disagreement, a consensus was reached after discussion with a third author.

Eligible studies reported on (P) patients with confirmed SARS-CoV-2 infection and compared the outcomes of patients (I and C) with and without MAFLD or NAFLD to each other. The outcomes (O) were severe COVID-19, ICU admission, and in-hospital mortality. Studies with cohort or case-control design (>5 participants) were considered eligible. The severity of COVID-19 was classified according to the guidelines on the Diagnosis and Treatment of COVID-19 issued by the National Health Commission of China (Supplementary Table 2) (15). When there were multiple publications using data with overlapping study populations, we included the one with greater sample size.

Data Extraction

Two independent review authors performed data extraction from eligible studies into a standardized data collection form. A third independent author resolved disagreements.

The following information was extracted from each study: first author, year of publication, digital object identifier, study design, study period, the number of centers, study site (country), demographic characteristics of the study population, the number of patients, the number of participants with and without MAFLD or NAFLD separately, the number of patients with event (severe COVID-19, ICU admission, mortality) with and without MAFLD or NAFLD separately, and, if available, odds ratios for COVID-19 severity, ICU admission, and mortality regarding MAFLD or NAFLD, and parameters included in multivariate adjustments.

Statistical Analysis

All calculations were performed by Stata 15 data analysis and statistical software (Stata Corp LLC, College Station, TX, USA). All outcomes were handled as dichotomous variables, and odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated (reference groups: patients without NAFLD or MAFLD). Random effects model was used to calculate the pooled estimates using the DerSimonian-Laird method (16). A p-value of < 0.05 was considered statistically significant. Forest plots were used to present the results of the meta-analyses.

Heterogeneity was tested with I2 and χ2 tests. As suggested by the Cochrane Handbook (17), I2 values were interpreted as “might not be important” (0–40%), “moderate” (30–60%), “substantial” (50–90%), and “considerable” (75–100%) heterogeneity, with a p < 0.1 considered significant (18).

We were unable to assess the presence of publication bias because of the low number of studies included in each analysis.

Assessment of Risk of Bias

Two independent review authors carried out the assessment. Discrepancies were resolved by third-party arbitration. We used the modified version of the Quality in Prognostic Studies (QUIPS) tool (19) as per the recommendations of the Cochrane Prognosis Methods Group (20). Methodological details of the assessment are summarized in Supplementary Appendix 1.

Results

Search and Selection

The selection process is detailed in Figure 1. We identified 319 records in five databases for evaluation. After the removal of duplicates and careful selection, 25 articles were eligible for full-text assessment. Altogether, 10 papers were eligible for qualitative and quantitative synthesis (9, 10, 21–27), however, we excluded one due to overlapping study population (12).

www.frontiersin.org

Figure 1. Preferred Reporting in Systematic Reviews and Meta-analyses (PRISMA) flowchart showing the selection process.

Characteristics of the studies included

The main characteristics of the studies are summarized in Table 1. Two articles recruited subjects from the USA, one from Israel, and another six from China. Except for two prospective study, all were retrospective cohort studies. MAFLD was defined in all studies based on the consensus by Eslam et al. (8); NAFLD was defined by the presence of hepatic steatosis on imaging. The proportion of patients with MAFLD and NAFLD ranged from 28 to 50%, and from 6 to 38%, respectively, across studies. Eligibility criteria of the studies included are presented in Supplementary Table 3.

www.frontiersin.org

Table 1. Basic characteristics of included studies in the systematic review and meta-analysis.

Quantitative Syntheses

In our meta-analysis, we included a total of six studies with 7,284 patients evaluating the severity of COVID-19, the proportion of severe COVID-19 ranged from 10 to 19%. Three articles with 7,433 patients reported on the need for ICU admission, the proportion of ICU admission ranged from 6 to 38%.

MAFLD was associated with an increased risk of severe COVID-19 compared to the non-MAFLD group [28 vs. 13%, respectively; OR = 2.61, CI: 1.75–3.91 in a homogenous dataset (I2 = 0.0% with p = 0.483)] (Figure 2A). Similarly, in the NAFLD vs. non-NAFLD comparison, NAFLD proved to be a risk factor as well [36 vs. 12%, respectively; OR = 5.22, CI: 1.94–14.03 in a heterogenous dataset (I2 = 85.1% with p = 0.001)] (Figure 2B).

www.frontiersin.org

Figure 2. Odds ratio for COVID-19 severity in patients (A) with MAFLD vs. non-MAFLD, (B) with NAFLD vs. non-NAFLD, and odds ratio for ICU admission in patients (C) with NAFLD vs. non-NAFLD.

Although patients with NAFLD were more likely to be admitted to ICU compared to those without NAFLD, the difference did not reach the level of statistical significance [24 vs. 7%, respectively; OR = 2.29, CI: 0.79–6.63 in a heterogenous dataset (I2 = 85.1% with p = 0.001)] (Figure 2C).

Qualitative Syntheses

We were not able to make a meta-analytical analysis for the MAFLD vs. non-MAFLD comparison on the rate of ICU admission, however, two studies (10, 27) reported on ICU admission. Gao et al. (27) in non-diabetic MAFLD patients found an increased risk of intensive care requirement in those with critical illness compared to non-MAFLD patients (p = 0.003, 4.6 vs. 0.0%, respectively). Zhou et al. (10), in a matched cohort of MAFLD and non-MAFLD patients, found a significantly increased risk of the composite outcome of severe and critical COVID-19 in MAFLD patients compared to the non-MAFLD group (OR = 3.65, CI: 1.31–10.16).

Regarding in-hospital mortality, Hashemi et al. (23) found similar rates in COVID-19 patients with NAFLD compared to those without NAFLD (p = 0.54, 16.4 vs. 13.2%).

A summary of multivariate logistic regression analyses from each study included can be found in Supplementary Table 4. Most of the studies adjusted for age, sex, and underlying conditions in multivariate analysis. In the study of Ji et al. (24), NAFLD was associated with COVID-19 progression (adjusted OR = 6.4, CI: 1.5–31.2). Bramante et al. (9) found an increased odds of hospital admission in COVID-19 patients with NAFLD (adjusted OR = 2.04, CI: 1.55–2.69). Based on two studies, ICU admission (adjusted OR = 1.70, CI: 1.20–2.40; adjusted OR = 2.3, CI: 1.27–4.17, respectively) and need for mechanical ventilation (adjusted OR = 1.98, CI: 1.28–3.06; adjusted OR = 2.15, CI: 1.18–3.91, respectively) were also increased with NAFLD (9, 23). Finally, NAFLD was not found to increase in-hospital mortality in COVID-19 (adjusted OR = 0.99, CI: 0.54–1.77) (9).

On the other hand in COVID-19 patients with MAFLD, Mahamid et al. (25) found that MAFLD was associated with severe COVID-19 in both sexes (adjusted OR = 3.29, CI: 3.28–3.58 for men, adjusted OR = 3.25, CI: 3.09–3.47 for women), independently of MS. In the study of Zhou et al. (21), an association between the presence of MAFLD and COVID-19 severity was observed in patients younger than 60 years (adjusted OR = 2.67, CI: 1.13–6.34), but not in those above 60 years (adjusted OR = 0.61, CI: 0.18–2.03). In non-diabetic patients, Gao et al. (27) found an increased risk of severe COVID-19 only in MAFLD patients with both obesity and metabolic dysregulation (adjusted OR = 5.25, CI: 1.23–22.33), but the difference was non-significant if only one of the criteria was present (OR = 2.60, CI: 0.47–14.42).

Risk of Bias Assessment

Among the included studies, three were of moderate overall risk of bias. All the other studies were rated to carry high overall risk of bias. The summary of risk of bias assessment is shown in Supplementary Figures 1–5.

Discussion

In our meta-analysis, we aimed to analyse the association between MAFLD or NAFLD and COVID-19 outcomes. Based on our results, we identified that MAFLD is associated with 2.6 times higher risk of severe COVID-19 compared to the non-MAFLD group. In the NAFLD vs. non-NAFLD comparison, we found a five-times increased risk of severe COVID-19. The rate of the ICU admission was higher in NAFLD patients compared to those without NAFLD; however, the difference was statistically non-significant. Finally, we did not find any difference regarding in-hospital mortality in COVID-19 patients with MAFLD or NAFLD in qualitative synthesis.

Previous reviews have assessed the effect of MAFLD or NAFLD in COVID-19 patients, however, to our knowledge, this is the first systematic review and meta-analysis in this topic (6, 11, 28).

Six of the included articles reported on covariate adjusted results (9, 21, 23–25, 27), most of them supporting our conclusion on the impact of MAFLD and NAFLD in COVID-19. We could not perform a meta-analytical evaluation of these results, as there were different outcomes assessed and covariates adjusted for. Based on these results, MAFLD and NAFLD are associated with a higher risk of severe COVID-19 and ICU admission both in uni- and multi-variate analyses.

Previously several comorbidities such as hypertension, diabetes, extreme obesity, and cardiovascular disease were reported to be associated with worse prognosis in COVID-19 patients (3, 5). Several meta-analyses reported on the role of CLD in COVID-19 (29, 30). Based on our previous paper (31), pre-existing liver diseases and on-admission liver-related laboratory results predicted a more severe outcome in SARS-CoV-2 infection. However, none of the articles performed sub-group analysis based on the underlying liver condition.

The association between MAFLD or NAFLD and COVID-19 severity is certainly multifactorial. MS and elements of it have been already linked to untoward outcomes in COVID-19 (32). In type 2 diabetes, the second most common comorbidity in COVID-19, the poor prognosis is likely the consequence of the whole clinical picture: poor glucose control, advanced age, and diabetes-associated comorbidities (33). Obesity is associated with chronic inflammation compromising the immune response resulting in an increased risk of more severe infections (34, 35), on the other hand, obesity is also a significant risk factor for ICU admission and invasive mechanical ventilation (5). In patients with diabetes, hyperinflammatory response, microvascular endothelial dysfunction, and microthrombi formation may contribute to the poorer outcomes in COVID-19 (6).

Similarly, based on previous reports (26), in patients with MAFLD, a pro-inflammatory state could exacerbate the SARS-CoV-2 induced cytokine storm. Ji et al. (24) found in a retrospective study that COVID-19 patients with MAFLD had a poorer prognosis, two-fold higher prevalence of severe disease course, and also higher viral shedding time, and more liver failure during hospitalization.

In the included studies several differences between study populations were highlighted. Increased liver fat content was associated with a higher risk of symptomatic COVID-19 in univariate analysis (OR = 1.85, 95% OR: 1.05–3.25) (36). Moreover, the authors found that obesity and concomitant >10% liver fat content exposed an increased risk of severe COVID-19 (OR = 2.96, 95% CI: 1.12–7.78); those obese patients with normal liver fat content (<5%) showed no elevation of risk (OR = 0.36, 95% CI: 0.1–1.26). The importance of the liver fat content has been pointed out in the study by Bramante et al. (9) as well.

On the other hand, the presence of fibrosis in MAFLD patients is another risk factor for severity of COVID-19, independently of metabolic comorbidities. Based on Targher et al. (12), the severity of COVID-19 significantly increased with the extent of liver fibrosis; those with a FIB-4 score higher than 2.67 had the highest risk of developing severe COVID-19 (OR = 5.73, 95% CI: 1.84–17.9). After adjustment for sex, obesity, and diabetes, this considerable association persisted (adjusted OR = 2.91, 95% CI: 1.20–7.06).

The same authors demonstrated that the presence of MAFLD together with a neutrophil-to-lymphocyte ratio (NLR) higher than 2.8 is associated with a higher risk of severe COVID-19 compared to patients without MAFLD and with normal NLR (26). NLR was previously highlighted to be a useful, widely available prognostic factor in the early phase of SARS-CoV-2 infection (37).

Another interesting point was reported by Zhou et al. (21). In COVID-19 patients with MAFLD under 60 years, a more than 4-fold risk of severe COVID-19 was observed compared to those without MAFLD (OR = 3.97, 95% CI: 1.89–8.35); after adjusting for covariates (adjusted OR = 2.67, 95% CI: 1.13–6.34) the risk remained significantly higher. In contrast, in multivariate analysis in elderly patients, MAFLD was not associated with severity of COVID-19. These results need to be supported by further cohort analysis.

None of the studies reported on long-term outcomes in COVID-19.

Strengths and Limitations

Considering the strengths of our meta-analysis, a rigorous methodology was followed, and we did not deviate from the pre-study protocol, except for including mortality in our investigated outcomes. Several limitations must be considered when interpreting our results. First of all, we could not analyse in-hospital mortality in our meta-analysis. Secondly, our study involved data from only nine articles. It must be noted that, we detected significant differences despite the limited study populations, however, with considerable statistical heterogeneity in some of our results. Most of the studies included a low number of patients. The number of studies prevented us from analyzing publication bias (<10 articles). Most of the articles were published from Asian countries; therefore, it is difficult to generalize these results. Also the rate of MAFLD and NAFLD in the study populations differed from the rate reported in the general population. The definition of MAFLD was homogenous, however, NAFLD was diagnosed using different methods across studies. Finally, data came mostly from retrospective studies, with most of them carrying high risk of bias.

Conclusion

Implication for Practice

In conclusion, the presence of MAFLD or NAFLD is associated with a more severe COVID-19. The presence of further metabolic dysfunction may have additional negative impact on the course of COVID-19. Based on this, health-care providers should follow MAFLD patients cautiously and preventive measures should be taken in these high-risk populations. Therefore, weight loss and regular physical activity should be encouraged in MAFLD patients.

Implication for Research

The underlying mechanisms behind our results are still poorly understood. Further research is needed to understand the effect of the pro-inflammatory state associated with MAFLD on the cytokine storm caused by SARS-CoV-2 infection. The severity of COVID-19 should be further stratified based on the severity of MAFLD to explore further high-risk patient groups. Further research is needed to support our results as well as other outcomes, such as mortality, should be analyzed.

Data Availability Statement

The original contributions generated for the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

Author Contributions

GP and PJH designed the research and the study concept. ZS and SV performed the data extraction. NF analyzed and interpreted the data. FD and KO performed the quality and risk assessment, PJH, BE, SV, SK, PH, and GP wrote the article. BE, PH, and GP conducted a critical revision of the manuscript for important intellectual content. All of the co-authors granted final approval of the version of the article to be published.

Funding

Study costs are covered by an Economic Development and Innovation Operative Program Grant (GINOP 2.3.2-15-2016-00048) and by a Human Resources Development Operational Program Grant (EFOP-3.6.2-16-2017-00006), both co-financed by the European Union (European Regional Development Fund) within the framework of the Széchenyi 2020 Program. Sponsors had no role in the design, data collection, analysis, interpretation, and preparations of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The analysis was conducted on behalf of the Translational Action and Research Group against Coronavirus (KETLAK) Study Group. Future study costs will be covered by Economic Development and Innovation Operative Program Grant (GINOP-2.3.4-15-2020-00010).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2021.626425/full#supplementary-material

References

3. Zádori N, Váncsa S, Farkas N, Hegyi P, Eross B, Szakó L, et al. The negative impact of comorbidities on the disease course of COVID-19. Intensive Care Med. (2020) 46:1784–6. doi: 10.1007/s00134-020-06161-9

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Garg S, Kim L, Whitaker M, O'Halloran A, Cummings C, Holstein H, et al. Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019-COVID-NET, 14 States, March 1-30, 2020. Mortal Wkly Rep. (2020) 69:458–464. doi: 10.15585/mmwr.mm6915e3

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Földi M, Farkas N, Kiss S, Zádori N, Váncsa S, Szakó L, et al. Obesity is a risk factor for developing critical condition in COVID-19 patients: a systematic review and meta-analysis. Obes Rev. (2020) 21:e13095. doi: 10.1111/obr.13095

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Dongiovanni P, Meroni M, Longo M, Fracanzani AL. MAFLD in COVID-19 patients: an insidious enemy. Expert Rev Gastroenterol Hepatol. (2020) 14:867–72. doi: 10.1080/17474124.2020.1801417

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Serné EH, de Jongh RT, Eringa EC, IJzerman RG, Stehouwer CD. Microvascular dysfunction. Hypertension. (2007) 50:204–11. doi: 10.1161/HYPERTENSIONAHA.107.089680

CrossRef Full Text | Google Scholar

8. Eslam M, Sanyal AJ, George J. MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology. (2020) 158:1999–2014.e1. doi: 10.1053/j.gastro.2019.11.312

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Bramante C, Tignanelli CJ, Dutta N, Jones E, Tamariz L, Clark JM, et al. Non-alcoholic fatty liver disease (NAFLD) and risk of hospitalization for Covid-19. medRxiv [Preprint]. (2020). doi: 10.1101/2020.09.01.20185850

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Zhou Y-J, Zheng KI, Wang X-B, Sun Q-F, Pan K-H, Wang T-Y, et al. Metabolic-associated fatty liver disease is associated with severity of COVID-19. Liver Int. (2020) 40:2160–3. doi: 10.1111/liv.14575

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Sharma P, Kumar A. Metabolic dysfunction associated fatty liver disease increases risk of severe Covid-19. Diabetes Metab Syndr. (2020) 14:825–7. doi: 10.1016/j.dsx.2020.06.013

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Targher G, Mantovani A, Byrne CD, Wang X-B, Yan H-D, Sun Q-F, et al. Risk of severe illness from COVID-19 in patients with metabolic dysfunction-associated fatty liver disease and increased fibrosis scores. Gut. (2020) 69:1545. doi: 10.1136/gutjnl-2020-321611

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Assante G, Williams R, Youngson NA. Is the increased risk for MAFLD patients to develop severe COVID-19 linked to perturbation of the gut-liver axis? J Hepatol. (2020) doi: 10.1016/j.jhep.2020.05.051

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Moher D, Liberati A, Tetzlaff J, Altman DJB. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. (2009) 339:b2535. doi: 10.1136/bmj.b2535

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, et al. Coronavirus disease 2019 (COVID-19): a perspective from China. Radiology. (2020) 296:E15–E25. doi: 10.1148/radiol.2020200490

PubMed Abstract | CrossRef Full Text | Google Scholar

16. DerSimonian R, Laird N. Meta-analysis in clinical trials. Controll Clin Trials. (1986) 7:177–88. doi: 10.1016/0197-2456(86)90046-2

CrossRef Full Text | Google Scholar

17. Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane Handbook for Systematic Reviews of Interventions. Oxford, UK: John Wiley & Sons (2019).

PubMed Abstract | Google Scholar

18. Higgins JP, Thompson SG, Deeks JJ, Altman DGJB. Measuring inconsistency in meta-analyses. BMJ. (2003) 327:557–60. doi: 10.1136/bmj.327.7414.557

CrossRef Full Text | Google Scholar

19. Hayden JA, van der Windt DA, Cartwright JL, Côté P, Bombardier CJAoim. Assessing bias in studies of prognostic factors. Ann Intern Med. (2013) 158:280–6. doi: 10.7326/0003-4819-158-4-201302190-00009

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Riley RD, Moons KGM, Snell KIE, Ensor J, Hooft L, Altman DG, et al. A guide to systematic review and meta-analysis of prognostic factor studies. BMJ. (2019) 364:k4597. doi: 10.1136/bmj.k4597

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Zhou Y-J, Zheng KI, Wang X-B, Yan H-D, Sun Q-F, Pan K-H, et al. Younger patients with MAFLD are at increased risk of severe COVID-19 illness: a multicenter preliminary analysis. J Hepatol. (2020) 73:719–21. doi: 10.1016/j.jhep.2020.04.027

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Huang R, Zhu L, Wang J, Xue L, Liu L, Yan X, et al. Clinical features of patients with COVID-19 with nonalcoholic fatty liver disease. Hepatol Commun. (2020) 64:1758–68. doi: 10.1002/hep4.1592

CrossRef Full Text | Google Scholar

23. Hashemi N, Viveiros K, Redd WD, Zhou JC, McCarty TR, Bazarbashi AN, et al. Impact of chronic liver disease on outcomes of hospitalized patients with COVID-19: A multicentre United States experience. Liver Int. (2020) 40:2515–21. doi: 10.1111/liv.14583

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Ji D, Qin E, Xu J, Zhang D, Cheng G, Wang Y, et al. Non-alcoholic fatty liver diseases in patients with COVID-19: retrospective study. J Hepatol. (2020) 73:451–3. doi: 10.1016/j.jhep.2020.03.044

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Mahamid M, Nseir W, Khoury T, Mahamid B, Nubania A, Sub-Laban K, et al. Nonalcoholic fatty liver disease is associated with COVID-19 severity independently of metabolic syndrome: a retrospective case-control study. Eur J Gastroenterol Hepatol. (2020). doi: 10.1097/MEG.0000000000001902. [Epub ahead of print].

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Targher G, Mantovani A, Byrne CD, Wang XB, Yan HD, Sun QF, et al. Detrimental effects of metabolic dysfunction-associated fatty liver disease and increased neutrophil-to-lymphocyte ratio on severity of COVID-19. Diabetes Metab Syndr. (2020) 46:505–7. doi: 10.1016/j.diabet.2020.06.001

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Gao F, Zheng KI, Wang X-B, Yan H-D, Sun Q-F, Pan K-H, et al. Metabolic associated fatty liver disease increases coronavirus disease 2019 disease severity in nondiabetic patients. J Gastroenterol Hepatol. (2020) 36:204–7. doi: 10.1111/jgh.15112

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Portincasa P, Krawczyk M, Smyk W, Lammert F, Di Ciaula A. COVID-19 and non-alcoholic fatty liver disease: two intersecting pandemics. Eur J Clin Invest. (2020) 50:e13338. doi: 10.1111/eci.13338

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Lippi G, de Oliveira MHS, Henry BM. Chronic liver disease is not associated with severity or mortality in Coronavirus disease 2019 (COVID-19): a pooled analysis. Eur J Gastroenterol Hepatol. (2021) 33:114–5. doi: 10.1097/MEG.0000000000001742

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Kovalic AJ, Satapathy SK, Thuluvath PJ. Prevalence of chronic liver disease in patients with COVID-19 and their clinical outcomes: a systematic review and meta-analysis. Hepatol Int. (2020) 14:612–20. doi: 10.1007/s12072-020-10078-2

CrossRef Full Text | Google Scholar

31. Váncsa S, Hegyi PJ, Zádori N, Szakó L, Vörhendi N, Ocskay K, et al. Pre-existing liver diseases and on-admission liver-related laboratory tests in COVID-19: a prognostic accuracy meta-analysis with systematic review. Front Med. (2020) 7:743. doi: 10.3389/fmed.2020.572115

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Williamson E, Walker AJ, Bhaskaran KJ, Bacon S, Bates C, Morton CE, et al. OpenSAFELY: factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients. medRxiv [Preprint]. (2020). doi: 10.1101/2020.05.06.20092999

CrossRef Full Text | Google Scholar

33. Apicella M, Campopiano MC, Mantuano M, Mazoni L, Coppelli A, Del Prato S. COVID-19 in people with diabetes: understanding the reasons for worse outcomes. Lancet Diabetes Endocrinol. (2020) 8:782–92. doi: 10.1016/S2213-8587(20)30238-2

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Lighter J, Phillips M, Hochman S, Sterling S, Johnson D, Francois F, et al. Obesity in patients younger than 60 years is a risk factor for COVID-19 hospital admission. Clin Infect Dis. (2020) 71:896–7. doi: 10.1093/cid/ciaa415

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Poulain M, Doucet M, Major GC, Drapeau V, Sériès F, Boulet L-P, et al. The effect of obesity on chronic respiratory diseases: pathophysiology and therapeutic strategies. Can Med Assoc J. (2006) 174:1293. doi: 10.1503/cmaj.051299

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Roca-Fernandez A, Dennis A, Nicolls R, McGonigle J, Kelly M, Banerjee R. High liver fat associates with higher risk of developing symptomatic covid-19 infection - initial uk biobank observations. medRxiv [Preprint]. (2020). doi: 10.1101/2020.06.04.20122457

CrossRef Full Text | Google Scholar

37. Ciccullo A, Borghetti A, Zileri Dal Verme L, Tosoni A, Lombardi F, Garcovich M, et al. Neutrophil-to-lymphocyte ratio and clinical outcome in COVID-19: a report from the Italian front line. Int J Antimicrob Agents. (2020) 56:106017. doi: 10.1016/j.ijantimicag.2020.106017

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: SARS-CoV-2, COVID-19, pandemic, prognosis, non-alcoholic fatty liver disease, metabolic associated fatty liver disease

Citation: Hegyi PJ, Váncsa S, Ocskay K, Dembrovszky F, Kiss S, Farkas N, Erőss B, Szakács Z, Hegyi P and Pár G (2021) Metabolic Associated Fatty Liver Disease Is Associated With an Increased Risk of Severe COVID-19: A Systematic Review With Meta-Analysis. Front. Med. 8:626425. doi: 10.3389/fmed.2021.626425

Received: 05 November 2020; Accepted: 22 February 2021;
Published: 12 March 2021.

Edited by:

Hu Zhang, Sichuan University, China

Copyright © 2021 Hegyi, Váncsa, Ocskay, Dembrovszky, Kiss, Farkas, Erőss, Szakács, Hegyi and Pár. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gabriella Pár, pargabriella@gmail.com

These authors have contributed equally to this work

Sours: https://www.frontiersin.org/articles/10.3389/fmed.2021.626425/full

Prevalence of and risk factors for metabolic associated fatty liver disease in an urban population in China: a cross-sectional comparative study

  • Research article
  • Open Access
  • Published:
  • Yu-ling Chen1,
  • Hao Li1,
  • Shu Li1,
  • Zhou Xu2,
  • Shen Tian1,
  • Juan Wu1,
  • Xin-yu Liang1,
  • Xin Li1,
  • Zi-li Liu1,
  • Jun Xiao1,
  • Jia-ying Wei1,
  • Chen-yu Ma1,
  • Kai-nan Wu1,
  • Liang Ran3 &
  • Ling-quan KongORCID: orcid.org/0000-0001-5705-90011

BMC Gastroenterologyvolume 21, Article number: 212 (2021) Cite this article

  • 1064 Accesses

  • 2 Citations

  • Metrics details

Abstract

Background

Metabolic associated fatty liver disease (MAFLD) is a new definition for liver disease associated with known metabolic dysfunction. Based on new diagnostic criteria, we aimed to investigate its prevalence and risk factors in Chinese population.

Methods

We conducted this study in a health examination population who underwent abdominal ultrasonography in China. The diagnosis of MAFLD was based on the new diagnostic criteria. The characteristics of the MAFLD population, as well as the associations between MAFLD and metabolic abnormalities, were explored. Mann–Whitney U test and chi-square test were performed to compare different variables. Binary logistic regression was used to determine the risk factors for MAFLD.

Results

Among 139,170 subjects, the prevalence of MAFLD was 26.1% (males: 35.4%; females: 14.1%). The prevalence based on female menopausal status, that is, premenopausal, perimenopausal, and postmenopausal, was 6.1%, 16.8%, and 30.2%, respectively. In different BMI groups (underweight, normal, overweight and obese), the prevalence was 0.1%, 4.0%, 27.4% and 59.8%, respectively. The proportions of abnormal metabolic features in the MAFLD group were significantly higher than those in the non-MAFLD group, as was the proportion of elevated alanine aminotransferase (ALT) (42.5% vs. 11%, P < 0.001). In nonobese individuals with MAFLD, the proportions of abnormal metabolic features were also all significantly higher than those in nonobese individuals without MAFLD. The prevalence of metabolic syndrome (MS), dyslipidaemia, and hyperuricaemia, respectively, in the MAFLD group (53.2%, 80.0%, and 45.0%) was significantly higher than that in the non-MAFLD group (10.1%, 41.7%, and 16.8%). Logistic regression revealed that age, BMI, waist circumference, ALT, triglycerides, fasting glucose, uric acid and platelet count were associated with MAFLD.

Conclusions

MAFLD is prevalent in China and varies considerably among different age, sex, BMI, and female menopausal status groups. MAFLD is related to metabolic disorders, especially obesity, while metabolic disorders also play important roles in the occurrence of MAFLD in nonobese individuals. MAFLD patients exhibit a high prevalence of MS, dyslipidaemia, hyperuricaemia, and elevated liver enzymes. MAFLD tends to coexist with systemic metabolic disorders, and a deep inner relationship may exist between MAFLD and MS. Metabolic disorders should be considered to improve the management of MAFLD.

Peer Review reports

Background

Metabolic associated fatty liver disease (MAFLD), formerly known as nonalcoholic fatty liver disease (NAFLD), is a new definition of liver disease associated with known metabolic dysfunction and is the most common chronic liver disease worldwide. NAFLD affects 24% of the adult population worldwide and poses a threat to human health [1]. NAFLD is generally considered to be closely related to obesity and multiple metabolic disorders, and can vary from hepatic steatosis to steatohepatitis, fibrosis or cirrhosis [2]. It is regarded as the hepatic manifestation of multisystem metabolic dysfunction [3]. Previously, the diagnosis of NAFLD was an exclusion diagnosis [3]; however, since research has progressed, NAFLD has been found to be derived from the potential state of multiple metabolic dysfunctions with complex pathophysiological characteristics, and its high prevalence in the general population makes it common to coexist with other liver diseases, which indicates that the exclusion criteria can no longer meet the current requirements for the diagnosis of the disease. Hence, in a recent international expert consensus, “MAFLD” was considered to be a better descriptor of liver disease associated with known metabolic dysfunction [4], and a set of positive diagnostic criteria were quickly released [5] so that MAFLD could be accurately, comprehensively and easily diagnosed. With the patient population being somewhat different from that of NAFLD, disease characteristics can be better manifested through the patient population diagnosed by new diagnostic criteria.

Therefore, this cross-sectional comparative study aims to investigate the prevalence and risk factors for MAFLD based on the new diagnostic criteria to better elucidate the association between MAFLD and multiple metabolic disorders, and provide a more accurate reference for the management and prevention of MAFLD.

Methods

Study population

This cross-sectional study used data from an urban population in Southwest China who participated in the health examination at the Quality Control Center of Health Examination in Chongqing, Southwest China, which is also known as the Health Management Center of the First Affiliated Hospital of Chongqing Medical University, from January 2015 to September 2018. In China, many organizations and companies may organize health check-ups for their employees and some individuals would also voluntarily go to medical institutions for regular health examinations to get known about their health condition. Therefore, the population of this study is a sample of an urban population in Southwest China. Our study included 139,170 participants, all of them had undergone comprehensive anthropometric measurements and clinical examinations, which included abdominal ultrasonography and the collection of fasting blood and urine samples. Repeat examinations of the same person were recognized by their unique health examination ID, and only one data set was randomly involved in the study. The exclusion criteria were incomplete data; age younger than 18 years; history of malignancy; history of oophorectomy or hysterectomy; and history of liver surgery or nephrectomy. The study was approved and supervised by The Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (approval number: 2019-141) and was conducted in accordance with the Principles of the Helsinki Declaration. Requirement for informed consent was waived because all information was anonymous and retrospective.

Anthropometric measurements and clinical examination

Blood pressure and anthropometric parameters, including height, weight and waist circumference, were measured using standardized procedures by trained examiners. Body mass index (BMI) was calculated as follows: BMI (kg/m2) = weight (kg)/height squared (m2). Venous blood samples of all participants were collected after at least 8 h of fasting and were analysed by standard laboratory procedures in the laboratory of The First Affiliated Hospital of Chongqing Medical University, which is certified by the College of American Pathologists (CAP No. 7215494). Abdominal ultrasound was performed using ultrasound scanners (Aplio500, Toshiba Medical Systems, Japan or HD11XE, Philips Medical Systems, USA). All abdominal ultrasonographies were performed and evaluated by experienced ultrasonographers at the Quality Control Center of Health Examination. Because the diagnosis of MAFLD does not involve the assessment of alcohol consumption and hepatitis virus, we did not include the two examinations in our study. Disease histories were checked in the health examination results of each participant. All data were recorded in the electronic medical record system of the Quality Control Center of Health Examination in Chongqing.

Diagnosis of MAFLD

In our study, the diagnosis of MAFLD was based on the ultrasonically diagnosed hepatic steatosis and the presence of one of the following three criteria: overweight or obesity (defined as BMI ≥ 23 kg/m2 in Asians), type 2 diabetes mellitus, or metabolic dysregulation. Metabolic dysregulation was defined by the presence of at least two of the following metabolic risk abnormalities: 1) waist circumference ≥ 90/80 cm in Asian men and women; 2) blood pressure ≥ 130/85 mmHg or specific drug treatment; 3) plasma triglycerides ≥ 1.70 mmol/L or specific drug treatment; 4) plasma HDL-cholesterol < 1.0 mmol/L for men and < 1.3 mmol/L for women or specific drug treatment; 5) prediabetes (i.e., fasting glucose levels 5.6 to 6.9 mmol/L, or 2-h post-load glucose levels 7.8 to 11.0 mmol or HbA1c 5.7% to 6.4%; 6) plasma high-sensitivity C-reactive protein (hs-CRP) level > 2 mg/L; and 7) homeostasis model assessment (HOMA)-insulin resistance score ≥ 2.5 [5]. The diagnosis of hepatic steatosis on ultrasound was based on the presence of hepatorenal echo contrast, liver parenchymal brightness, deep attenuation, and vascular blurring [6, 7].

Definitions

BMI groups of underweight (< 18.5 kg/m2), normal (≥ 18.5 kg/m2, < 23.0 kg/m2), overweight (≥ 23.0 kg/m2, < 25.0 kg/m2) and obese (≥ 25.0 kg/m2) were categorized based on the BMI criteria for Asians made by the WHO [8]. Metabolic syndrome (MS) was defined in accordance with the criteria by Joint Statement [9], which was based on the presence of at least 3 of the following components: (1) elevated waist circumference (≥ 90 cm for men and ≥ 80 cm for women); (2) elevated triglycerides (≥ 1.70 mmol/L) or drug treatment for elevated triglycerides; (3) reduced HDL-C (< 1.0 mmol/L for men and < 1.3 mmol/L for women) or drug treatment for reduced HDL-C; (4) elevated blood pressure (≥ 130/85 mm Hg) or drug treatment for hypertension; and (5) elevated fasting glucose (≥ 5.6 mmol/L) or drug treatment for elevated glucose. Dyslipidaemia was defined according to the guidelines for the prevention and treatment of dyslipidaemia in Chinese adults [10] as follows: a total cholesterol level of ≥ 5.2 mmol/L; LDL-C level of ≥ 3.4 mmol/L; HDL-C level of < 1 mmol/L; and triglycerides level of ≥ 1.7 mmol/L. Hyperuricaemia was defined as a uric acid level of ≥ 416 μmol/L for men or ≥ 357 μmol/L for women [11]. Menopausal status was defined as premenopausal period (≤ 45 years old), perimenopausal period (45–54 years old) and postmenopausal period (≥ 55 years old) according to the mean menopausal period for the Chinese female population [12, 13]. Elevated liver enzymes were defined as ALT > 35 IU/L and AST > 40 IU/L [14].

Statistical analysis

All continuous variables were tested for normality and are described by medians (interquartile range) and proportions. The Mann–Whitney U test was performed to compare continuous variables due to their nonnormal distribution. For categorical variables, the chi-square test was performed to compare different variables. The specific prevalence of different age, BMI and female menopausal status groups and their 95% confidence intervals (CIs) were calculated. Binary logistic regression analysis was performed to explore the related risk factors for MAFLD. Odds ratios (ORs) and their 95% CIs were finally calculated. Binary logistic regression was performed using RStudio version 4.0.1, and other analyses were performed using SPSS 25. A two-tailed p value < 0.05 was considered statistically significant.

Results

General data for the participants

Of the 139,170 Chinese adults enrolled in the study, 78,176 subjects (56.2%) were males and 60,994 (43.8%) were females. The baseline characteristics of the study subjects are shown in Table 1. Compared with individuals without MAFLD, those with MAFLD were older, predominantly male and had higher values of body mass index (BMI), waist circumference, blood pressure, fasting glucose, total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), albumin, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), creatinine, uric acid, white blood cell count (WBC), red blood cell count (RBC), haemoglobin, platelet count, and hematocrit (HCT), and lower value of high-density lipoprotein cholesterol (HDL-C) (P < 0.05). Compared with males with MAFLD, females with MAFLD tended to be older and had higher values of systolic pressure, fasting glucose, total cholesterol, HDL-C, LDL-C, and platelet count (P < 0.05).

Full size table

Prevalence of MAFLD and stratification by age, sex, menopausal status, and BMI

Of the 139,170 participants, 36,306 (26.1%) were diagnosed with MAFLD, and a significant difference was found between males and females in the prevalence of MAFLD (35.4% vs. 14.1%, P < 0.001). After adjusting for age and sex, the overall prevalence of MAFLD was 23.8% (males: 32.3%, females: 13.4%). The age-specific prevalence of MAFLD is shown in Fig. 1. For the total population, the prevalence tended to rise with increasing age and then decrease, with a peak prevalence of 34.5% in the 55–59 age range. For females, the prevalence of MAFLD rose slowly in the 18–49 age range; however, it rose steeply after the age of 50, which was consistent with the appearance of the perimenopausal period and peaked at 35.2% in the 65–69 age range. For males, the prevalence rose rapidly between the ages of 18–39, rose slowly after 40 years and peaked at 42.5% in the 50–54 age range. The prevalence of MAFLD before the age of 65 was significantly higher in males than in females (36.2% vs. 12.2%, χ2 value = 9378.514, P < 0.001), whereas after the age of 65, the prevalence was significantly lower in males than in females (28.2% vs. 33.0%, χ2 value = 35.532, P < 0.001). In females, the prevalence of MAFLD was 6.1% [CI 5.9–6.4%] for the premenopausal group, 16.8% [CI 16.2–17.4%] for the perimenopausal group and 30.2% [CI 29.4–30.9%] for the postmenopausal group, and there was a significant difference in prevalence among the different groups (χ2 value = 4764.496, P < 0.001) (Fig. 2). In different BMI groups, the prevalence of MAFLD was 0.1% [CI 0.1–0.2%] for the underweight group, 4.0% [CI 3.8–4.1%] for the normal group, 27.4% [CI 26.9–27.9%] for the overweight group and 59.8% [CI 59.3–60.3%] for the obese group, and there was a significant difference among different BMI groups (χ2 value = 41,904.598, P < 0.001) (Fig. 3).

The age-specific prevalence of MAFLD. The age-specific prevalence of MAFLD and its 95% CIs in males, females and total population were calculated

Full size image

The prevalence of MAFLD and dyslipidaemia in females based on menopausal status. The prevalence of MAFLD and dyslipidaemia and its 95% CIs in females with different menopausal status were calculated

Full size image

The prevalence of MAFLD based on BMI groups. The BMI-stratified MAFLD prevalence and its CIs in males, females and total population were calculated

Full size image

Related risk factors for MAFLD and the association between MAFLD and metabolic disorders

The proportions of abnormal metabolic features in the MAFLD group were all significantly higher than those in the non-MAFLD group (P < 0.001, Table 2). The proportions of elevated liver enzymes, particularly elevated ALT, was also significantly higher in individuals with MAFLD than in individuals without MAFLD. Individuals with MAFLD were more likely to have multiple metabolic disorders, and the prevalence of MS, dyslipidaemia and hyperuricaemia in individuals with MAFLD was all significantly higher than that in individuals without MAFLD (Fig. 4) (for MS: 53.2% [CI 52.7–53.7%] vs. 10.1% [CI 9.9–10.2%], χ2 value = 29,779.866, P < 0.001; for dyslipidaemia: 80.0% [CI 79.5–80.4%] vs. 41.7% [CI 41.4–42.0%], χ2 value = 15,754.446, P < 0.001; for hyperuricaemia: 45.0% [CI 44.5–45.5%] vs. 16.8% [CI 16.6–17.1%], χ2 value = 11,587.748, P < 0.001). Notably, for males with MAFLD, the prevalence of dyslipidaemia and hyperuricaemia was significantly higher than that in females with MAFLD (for dyslipidaemia: 81.6% [CI 81.2–82.1%] vs. 74.5% [CI 73.6–75.5%], χ2 value = 206.391, P < 0.001; for hyperuricaemia: 49.1% [CI 48.6–49.7%] vs. 31.6% [CI 30.6–32.5%], χ2 value = 822.636, P < 0.001), whereas the prevalence of MS in males with MAFLD was significantly lower than that in females with MAFLD (50.6% [CI 50.0–51.2%] vs. 61.4% [CI 60.4–62.5%], χ2 value = 309.026, P < 0.001). Moreover, the prevalence of MAFLD increased with increasing numbers of MS components individuals had (Table 3). For individuals with and without MS, there was also a significant difference in the prevalence of MAFLD (65.1% [CI 64.6–65.7%] vs. 15.5% [CI 15.3–15.7%], χ2 value = 29,779.866, P < 0.001). In the binary logistic regression, our results revealed that eight variables were closely correlated with MAFLD, including age, BMI, waist circumference, ALT, triglycerides, fasting glucose, uric acid and platelet count (Table 4). Among these variables, triglycerides, BMI and fasting glucose had the most significant associations with MAFLD, exhibiting the highest OR values of 1.776, 1.476 and 1.403, respectively.

Full size table

Prevalence of MS, dyslipidaemia and hyperuricaemia in individuals with and without MAFLD. The prevalence of MS, dyslipidaemia and hyperuricaemia and its CIs in individuals (total population, males and females) with and without MAFLD were calculated

Full size image

Full size table

Full size table

Association between nonobese individuals and MAFLD

Among the nonobese population, the prevalence of MAFLD was 11.5% (males: 16.4%, females: 6.9%). The proportions of abnormal metabolic features and elevated liver enzymes in nonobese individuals with MAFLD were all significantly higher than those in nonobese individuals without MAFLD (P < 0.001) (Table 2). Compared with obese individuals with MAFLD, the proportions of patients with elevated waist circumference, elevated systolic and diastolic pressure and elevated liver enzymes were significantly lower in the nonobese MAFLD group, whereas no significant differences were found for elevated triglycerides, elevated LDL-C, reduced HDL-C and elevated fasting glucose between the two groups.

Discussion

In the present study, the prevalence and risk factors for MAFLD were explored, and significant differences in the prevalence of MAFLD among groups based on sex, age, BMI and female menopausal status were revealed. To our knowledge, this study is the first to focus on the prevalence and associated metabolic characteristics of MAFLD in an urban Chinese population since the new definition of MAFLD was established [4, 5].

The age-specific prevalence shown in Fig. 1 revealed that males still predominated in the population with MAFLD. Moreover, for males, they were at an increased risk of MALFD at a younger age as evidenced by the rapid increase in the prevalence of MAFLD in the 18–39 age group, which should be given special attention. The peak prevalence in the 50–54 age group also indicated that MAFLD is more prevalent in their middle ages. We observed that older men had a lower prevalence of MAFLD than middle-aged men. Possible reasons for this result might include the following: some individuals may die of other diseases at older ages as fatty liver can significantly increase overall mortality [15], and thus these individuals are not counted as part of the MAFLD population; compared with older men who typically retire, middle-aged men who are at the peak of their careers may experience more pressure and engage in social behaviour that may lead to unhealthy lifestyles, which can increase their risk of having metabolic disorders. In females, the trend of prevalence differed from males. We observed that the prevalence of males rose rapidly during younger ages, rose slowly in their middle ages and then declined. While for females, the prevalence rose slowly during younger ages and then rose rapidly after the age of 45, which was consistent with the emergence of the perimenopausal period. Moreover, between the ages of 45 and 69, the prevalence in males showed a downward trend, whereas the prevalence in women still rose rapidly. These trends differences between sexes suggested that there might be a certain correlation between MAFLD and female menopausal status. Previous studies have found that a decrease in oestrogen in perimenopausal and postmenopausal women can lead to fat redistribution and thus cause metabolic disorders, including dyslipidaemia and glucose intolerance [16]. Our study also found that the prevalence of dyslipidaemia in females increased from the premenopausal period to the perimenopausal period and then to the postmenopausal period, which paralleled the rising prevalence of MAFLD in females in the three menopausal status groups (Fig. 2). This result indicates that the increase in MAFLD prevalence in women may be related to dyslipidaemia and metabolic disorders caused by a decline in oestrogen levels. Studies have also found that oestrogen might have favourable effects on lipid metabolism in the liver [17], which might be a protective factor against fatty liver in females [18]. Therefore, in combination with our findings and previous conclusions, oestrogen may also be a protective factor for females with MAFLD, and low oestrogen levels during the perimenopausal and postmenopausal periods may be an important risk factor for MAFLD in females.

Previous studies have found that the presence of NAFLD is closely correlated with components of MS, such as obesity, insulin resistance, hypertension and dyslipidaemia, and is considered to be the liver manifestation of MS [19]. Our study also found that after stratification by BMI, the prevalence of MAFLD increased sharply with increasing BMI, reaching 59.8% in obese individuals (Fig. 3). In the binary logistic regression analysis (Table 4), BMI and waist circumference were also significantly associated with MAFLD, indicating that obesity is closely associated with MAFLD and that obesity management should be emphasized, as weight loss has been proven to reduce steatosis [20].

In individuals with MAFLD, the proportions of abnormal metabolic features were all significantly higher than those in individuals without MAFLD (Table 2), confirming that MAFLD is closely associated with MS components, including abdominal obesity, hypertension, dyslipidaemia, and dysglycaemia. Among them, in addition to elevated waist circumference, the most significant difference was found in elevated triglycerides, and triglycerides were also shown to be significantly associated with MAFLD in the logistic regression (Table 4), with the highest OR value of 1.776, which suggests that elevated triglycerides may be an important risk factor for MAFLD. Moreover, the difference in the proportion of subjects with elevated fasting glucose was also highly significant, and fasting glucose was also significantly associated with MAFLD in the logistic regression with an OR value of 1.403, which is consistent with a previous study that showed a correlation between fatty liver and dyslipidaemia and dysglycaemia [21], indicating that elevated fasting glucose may also be an important risk factor for MAFLD. Previous studies have shown that NAFLD is not only closely correlated with cardiovascular and renal diseases associated with MS but also precedes the presentation of metabolic derangements [22], while a recent article found that compared with NAFLD, MAFLD can better identify patients with more metabolic disorders and a higher risk of disease progression [23]. In our study, we found a high prevalence of metabolic abnormalities (Table 2) and MS (Fig. 4) in patients with MAFLD. Meanwhile, individuals with more MS risk factors had a higher prevalence of MAFLD (Table 3), and patients with MS also had a higher prevalence of MAFLD than those without MS. These findings suggested that MAFLD is prone to coexist with systemic metabolic disorders, and a deep inner relationship between MAFLD and MS may exist in which the two diseases have a great influence and interaction on each other. Notably, we noticed that the prevalence of MS in females with MAFLD was significantly higher than that in males with MAFLD (Fig. 4), indicating that among patients with MAFLD, females may be more susceptible to MS than males, which warrants further investigation.

It was shown in our study that the prevalence of dyslipidaemia and hyperuricaemia was significantly higher in individuals with MAFLD than in those without MAFLD (Fig. 4). Dyslipidaemia is a well-known risk factor for NAFLD [3], and this can also be reflected in the sharp rise in the prevalence of MAFLD in perimenopausal and postmenopausal women in our study, which might be related to dyslipidaemia due to oestrogen deficiency (Fig. 2), indicating that dyslipidaemia may also be a risk factor for MAFLD. In the binary logistic regression analysis, uric acid was shown to be significantly correlated with MAFLD. Previous cross-sectional and prospective studies have found that elevated serum uric acid could independently predict an increased risk of NAFLD, even serum uric acid levels within the normal range were closely correlated with the presence of NAFLD independently [24,25,26]. Hence, combining the findings in our study with previous studies, serum uric acid might be considered an independent risk factor for MAFLD.

The present study revealed that individuals with MAFLD are more likely to have elevated liver enzymes, particularly elevated ALT, than those without MAFLD (Table 2), which indicates a higher proportion of abnormal liver function in individuals with MAFLD. Moreover, Table 4 shows that ALT was significantly correlated with MAFLD, and previous studies have shown that elevated ALT is associated with the progression of NAFLD into steatohepatitis and even liver fibrosis [27], indicating that elevated ALT might also have important clinical significance for MAFLD. Platelets are elevated during inflammation, and previous studies have found a linear correlation between platelet count and the severity of liver fibrosis in individuals with NAFLD [28]. In our study, we also found that platelet count was significantly correlated with MAFLD (Table 4), indicating that platelet count and ALT levels may be used as a reference indicator of MAFLD development and the resulting liver fibrosis. As some blood biomarkers, such as the NAFLD fibrosis score (NFS), have been used to assess the degree of liver fibrosis in NAFLD patients [29], more studies are also needed to build mathematical models on fibrosis biomarkers and explore a noninvasive fibrosis scoring system for MAFLD patients.

Although the occurrence of NAFLD is closely correlated with obesity, nonobese individuals may also suffer from NAFLD, particularly in the Asia–Pacific region [30]. In our study, the proportions of abnormal metabolic features in nonobese individuals with MAFLD were all significantly higher than those in nonobese individuals without MAFLD (Table 2), suggesting that metabolic disorders also play an important role in the occurrence of MAFLD in nonobese individuals. Meanwhile, between obese and nonobese MAFLD patients, the proportions of patients with elevated blood pressure and elevated liver enzymes were significantly higher in obese MAFLD patients, while the relationship between obesity and elevated liver enzymes, potential liver function impairment, and elevated blood pressure has also been described in previous articles [31, 32], suggesting that obese patients may have an increased risk of cardiovascular events and liver function impairment; however, no significant difference was found in the proportions of patients with abnormal blood lipids and elevated fasting glucose between the obese and nonobese MAFLD groups. These results indicate that even in nonobese MAFLD patients, there were already metabolic abnormalities in blood lipids and blood glucose levels that were comparable to those in obese MAFLD patients, which needs to be given sufficient attention.

Our study also has certain limitations. First, being a cross-sectional study, the natural course of MAFLD and causal relationships cannot be determined. Our study included a large sample size with a wide range of clinical data, making it possible to adjust for underlying confounding factors. Second, the diagnosis of MAFLD was based on ultrasonography, which might be partially insensitive to mild hepatic steatosis; however, ultrasonography has been widely used in epidemiological investigations of fatty liver because it is safe, noninvasive, and widely available; has acceptable sensitivity and specificity in the detection of hepatic steatosis [33]; and is also recommended as the first-line imaging method by the Association for the Study of the Liver (APASL) in the clinical guidelines for MAFLD [34]. Using ultrasound to screen for MAFLD might underestimate the prevalence of MAFLD; however, the possible underestimated value in our study has already shown the heavy burden of MAFLD in China, indicating that MAFLD in China should be given more attention. Third, certain selection bias may exist because the population who participated in the health examinations included in our study tended to be more concerned about their health. Fourth, some information was not available from the current health examination data, such as detailed medication history of participants and data on hepatitis virus and alcohol consumption. Further studies with subgroup analyses on viral hepatitis and alcohol consumption are needed. Last, because menopausal history was difficult to obtain in the population undergoing health examination, we artificially categorized menopausal status by age based on relevant research and the mean menopausal age of women, which might harbour information bias due to misclassification.

Conclusions

Our study revealed a high prevalence of MAFLD within an urban Chinese population. The prevalence of MAFLD varies considerably between different groups based on sex, age, BMI and female menopausal status. An increased prevalence was found to be associated with obesity and multiple metabolic disorders, and individuals with MAFLD had a high prevalence of MS, dyslipidaemia, and hyperuricaemia. MAFLD tends to coexist with systemic metabolic disorders, the presence of MAFLD and MS interact with each other, and they may have a deep influence on each other. Moreover, nonobese individuals also suffer from MAFLD, which was also found to be closely correlated with metabolic disorders. We also confirmed the high proportion of elevated ALT in individuals with MAFLD. Multiple metabolic disorders, especially obesity, should be given more attention to prevent and better manage MAFLD. More research is needed to determine the potential mechanisms underlying the occurrence of MAFLD, and to better understand the relationship and causality between MAFLD and multiple metabolic disorders, which would provide crucial implications for the prevention and treatment of MAFLD.

Availability of data and materials

The datasets analysed during the current study are not publicly available because all data were recorded in the electronic medical record system of the Quality Control Center of Health Examination in Chongqing. However, they are available from the corresponding author on reasonable request.

Abbreviations

Metabolic associated fatty liver disease

Nonalcoholic fatty liver disease

Metabolic syndrome

Body mass index

High-density lipoprotein cholesterol

Low-density lipoprotein cholesterol

Alanine aminotransferase

Aspartate aminotransferase

Blood urea nitrogen

White blood cell count

Red blood cell count

Hematokrit

Odds ratio

Confidence interval

NAFLD fibrosis score

References

  1. 1.

    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.

    PubMedArticle Google Scholar

  2. 2.

    Matteoni CA, Younossi ZM, Gramlich T, Boparai N, Liu YC, McCullough AJ. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity. Gastroenterology. 1999;116(6):1413–9.

    CASPubMedArticle Google Scholar

  3. 3.

    Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M, Harrison SA, Brunt EM, Sanyal AJ. The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American Association for the Study of Liver Diseases. Hepatology. 2018;67(1):328–57.

    Article Google Scholar

  4. 4.

    Eslam M, Sanyal AJ, George J. International consensus P: MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology. 2020;158(7):1999e1991-2014e1991.

    Article Google Scholar

  5. 5.

    Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, Zelber-Sagi S, Wai-Sun Wong V, Dufour JF, Schattenberg JM, et al. A new definition for metabolic dysfunction-associated fatty liver disease: an international expert consensus statement. J Hepatol. 2020;73(1):202–9.

    Article Google Scholar

  6. 6.

    Needleman L, Kurtz AB, Rifkin MD, Cooper HS, Pasto ME, Goldberg BB. Sonography of diffuse benign liver disease: accuracy of pattern recognition and grading. AJR Am J Roentgenol. 1986;146(5):1011–5.

    CASPubMedArticle Google Scholar

  7. 7.

    Kojima S, Watanabe N, Numata M, Ogawa T, Matsuzaki S. Increase in the prevalence of fatty liver in Japan over the past 12 years: analysis of clinical background. J Gastroenterol. 2003;38(10):954–61.

    PubMedArticle Google Scholar

  8. 8.

    World Health Organization. Regional Office for the Western, Pacific. The Asia-Pacific perspective : redefining obesity and its treatment. https://apps.who.int/iris/handle/10665/206936. Accessed 20 May 2020. Sydney : Health Communications Australia; 2000.

  9. 9.

    Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5.

    CASPubMedArticle Google Scholar

  10. 10.

    The Joint Committee on The Revision of Guidelines for the Prevention and Treatment of Dyslipidemia in Chines Adults. The guidelines for the prevention and treatment of dyslipidemia in Chinese Adults (2016 Revision). http://www.chinacirculation.org/Magazine/Show/76944. Chin Circ J. 2016;31(10):937–50; (in Chinese).

  11. 11.

    Fang J, Alderman MH. Serum uric acid and cardiovascular mortality the NHANES I epidemiologic follow-up study, 1971–1992. National Health and Nutrition Examination Survey. JAMA. 2000;283(18):2404–10.

    CASPubMedArticle Google Scholar

  12. 12.

    Collaborative Group on Hormonal Factors in Breast C: Menarche, menopause, and breast cancer risk: individual participant meta-analysis, including 118964 women with breast cancer from 117 epidemiological studies. Lancet Oncol. 2012;13(11):1141–51.

  13. 13.

    Yang L, Li L, Millwood IY, Lewington S, Guo Y, Sherliker P, Peters SA, Bian Z, Wu X, Yu M, et al. Adiposity in relation to age at menarche and other reproductive factors among 300 000 Chinese women: findings from China Kadoorie Biobank study. Int J Epidemiol. 2017;46(2):502–12.

    PubMed Google Scholar

  14. 14.

    Chalasani N, Aljadhey H, Kesterson J, Murray MD, Hall SD. Patients with elevated liver enzymes are not at higher risk for statin hepatotoxicity. Gastroenterology. 2004;126(5):1287–92.

    CASPubMedArticle Google Scholar

  15. 15.

    Simon TG, Roelstraete B, Khalili H, Hagstrom H, Ludvigsson JF. Mortality in biopsy-confirmed nonalcoholic fatty liver disease: results from a nationwide cohort. Gut. 2020;6:66.

    Google Scholar

  16. 16.

    Suzuki A, Abdelmalek MF. Nonalcoholic fatty liver disease in women. Womens Health. 2009;5(2):191–203.

    Google Scholar

  17. 17.

    Shen L, Fan JG, Shao Y, Zeng MD, Wang JR, Luo GH, Li JQ, Chen SY. Prevalence of nonalcoholic fatty liver among administrative officers in Shanghai: an epidemiological survey. World J Gastroenterol. 2003;9(5):1106–10.

    PubMedPubMed CentralArticle Google Scholar

  18. 18.

    Gutierrez-Grobe Y, Ponciano-Rodriguez G, Ramos MH, Uribe M, Mendez-Sanchez N. Prevalence of non alcoholic fatty liver disease in premenopausal, posmenopausal and polycystic ovary syndrome women. The role of estrogens. Ann Hepatol. 2010;9(4):402–9.

    PubMedArticle Google Scholar

  19. 19.

    Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, Smith SC Jr, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement: executive summary. Crit Pathw Cardiol. 2005;4(4):198–203.

    PubMedArticle Google Scholar

  20. 20.

    Ueno T, Sugawara H, Sujaku K, Hashimoto O, Tsuji R, Tamaki S, Torimura T, Inuzuka S, Sata M, Tanikawa K. Therapeutic effects of restricted diet and exercise in obese patients with fatty liver. J Hepatol. 1997;27(1):103–7.

    CASPubMedArticle Google Scholar

  21. 21.

    Speliotes EK, Massaro JM, Hoffmann U, Vasan RS, Meigs JB, Sahani DV, Hirschhorn JN, O’Donnell CJ, Fox CS. Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology. 2010;51(6):1979–87.

    CASPubMedPubMed CentralArticle Google Scholar

  22. 22.

    Ghouri N, Preiss D, Sattar N. Liver enzymes, nonalcoholic fatty liver disease, and incident cardiovascular disease: a narrative review and clinical perspective of prospective data. Hepatology. 2010;52(3):1156–61.

    PubMedArticle Google Scholar

  23. 23.

    Lin S, Huang J, Wang M, Kumar R, Liu Y, Liu S, Wu Y, Wang X, Zhu Y. Comparison of MAFLD and NAFLD diagnostic criteria in real world. Liver Int. 2020;40(9):2082–9.

    PubMedArticle Google Scholar

  24. 24.

    Lee JW, Cho YK, Ryan M, Kim H, Lee SW, Chang E, Joo KJ, Kim JT, Kim BS, Sung KC. Serum uric Acid as a predictor for the development of nonalcoholic Fatty liver disease in apparently healthy subjects: a 5-year retrospective cohort study. Gut Liver. 2010;4(3):378–83.

    CASPubMedPubMed CentralArticle Google Scholar

  25. 25.

    Xu C, Yu C, Xu L, Miao M, Li Y. High serum uric acid increases the risk for nonalcoholic Fatty liver disease: a prospective observational study. PLoS ONE. 2010;5(7):e11578.

    PubMedPubMed CentralArticle Google Scholar

  26. 26.

    Hwang IC, Suh SY, Suh AR, Ahn HY. The relationship between normal serum uric acid and nonalcoholic fatty liver disease. J Korean Med Sci. 2011;26(3):386–91.

    CASPubMedPubMed CentralArticle Google Scholar

  27. 27.

    Ratziu V, Giral P, Charlotte F, Bruckert E, Thibault V, Theodorou I, Khalil L, Turpin G, Opolon P, Poynard T. Liver fibrosis in overweight patients. Gastroenterology. 2000;118(6):1117–23.

    CASPubMedArticle Google Scholar

  28. 28.

    Yoneda M, Fujii H, Sumida Y, Hyogo H, Itoh Y, Ono M, Eguchi Y, Suzuki Y, Aoki N, Kanemasa K, et al. Platelet count for predicting fibrosis in nonalcoholic fatty liver disease. J Gastroenterol. 2011;46(11):1300–6.

    PubMedArticle Google Scholar

  29. 29.

    Xiao G, Zhu S, Xiao X, Yan L, Yang J, Wu G. Comparison of laboratory tests, ultrasound, or magnetic resonance elastography to detect fibrosis in patients with nonalcoholic fatty liver disease: A meta-analysis. Hepatology. 2017;66(5):1486–501.

    CASPubMedArticle Google Scholar

  30. 30.

    Xu C, Yu C, Ma H, Xu L, Miao M, Li Y. Prevalence and risk factors for the development of nonalcoholic fatty liver disease in a nonobese Chinese population: the Zhejiang Zhenhai Study. Am J Gastroenterol. 2013;108(8):1299–304.

    PubMedArticle Google Scholar

  31. 31.

    Lam GM, Mobarhan S. Central obesity and elevated liver enzymes. Nutr Rev. 2004;62(10):394–9.

    PubMedArticle Google Scholar

  32. 32.

    Narkiewicz K. Diagnosis and management of hypertension in obesity. Obes Rev. 2006;7(2):155–62.

    CASPubMedArticle Google Scholar

  33. 33.

    Sanyal AJ, American Gastroenterological A. AGA technical review on nonalcoholic fatty liver disease. Gastroenterology. 2002;123(5):1705–25.

    PubMedArticle Google Scholar

  34. 34.

    Eslam M, Sarin SK, Wong VW, Fan JG, Kawaguchi T, Ahn SH, Zheng MH, Shiha G, Yilmaz Y, Gani R, et al. The Asian Pacific Association for the Study of the Liver clinical practice guidelines for the diagnosis and management of metabolic associated fatty liver disease. Hepatol Int. 2020;5:66.

    Google Scholar

Download references

Acknowledgements

This study was supported through provision of data by The Quality Control Center of Health Examination in Chongqing, Chongqing Medical University, China. We thank Dr. Li-ping Liu and Dr. Bo Tu for their help in data recognition and classification regarding abdominal ultrasound reports. There is no funding for this research.

Funding

There is no funding for this research.

Author information

Author notes

Affiliations

  1. Department of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Yu-ling Chen, Hao Li, Shu Li, Shen Tian, Juan Wu, Xin-yu Liang, Xin Li, Zi-li Liu, Jun Xiao, Jia-ying Wei, Chen-yu Ma, Kai-nan Wu & Ling-quan Kong

  2. Department of Thyroid and Breast Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, Sichuan, China

    Zhou Xu

  3. The Health Management Center of the First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Liang Ran

Contributions

Study concept and design, LK and YC; methodology, HL, XL1 and XL2; investigation, SL and ZX; data acquisition, ST and JW1; data analysis, ZL, JX and JW2; visualization, CM; drafting of the manuscript, YC; review and editing, LR, LK and KW. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Liang Ran or Ling-quan Kong.

Ethics declarations

Ethics approval and consent to participate

All procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional research committee (The Ethics Committee of the First Affiliated Hospital of Chongqing Medical University, approval number: 2019-141) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The informed consent was waived because the information of all the participants was anonymous and retrospective.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Yu-ling Chen, Hao Li, Shu Li, Zhou Xu, Shen Tian and Juan Wu have contributed equally to this work

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

Sours: https://bmcgastroenterol.biomedcentral.com/articles/10.1186/s12876-021-01782-w
  1. Chevy silverado 2004 mpg
  2. Scratching post rdr2
  3. F150 rims ebay

A panel of experts proposed a new set of criteria for the diagnosis of metabolic-associated fatty liver disease (MAFLD). These guidelines were published in the Journal of Hepatology. Diagnosis of MAFLD, formerly known as non-alcoholic fatty liver disease, currently involves the exclusion of other chronic liver disease sources, such as excessive alcohol intake. However, investigators proposed adding a series of “positive criteria” to the MAFLD definition, establishing a new set of clinical features that would be requisite for diagnosis. These new positive criteria are independent of alcohol consumption and applicable to patients in all clinical settings.

The present definition of MAFLD is based on the presence of steatosis in >5% of hepatocytes and the absence of excessive alcohol consumption or other causes of chronic liver disease. A panel of experts from 22 countries collaborated to produce updated diagnostic criteria, adding several new positive criteria to this list. Based on these new guidelines, a diagnosis of MAFLD would require the presence of hepatic steatosis in combination with 1 of the 3 following criteria: overweight/obesity; presence of type 2 diabetes mellitus; or evidence of metabolic dysregulation. Investigators defined metabolic dysregulation as having at least 2 of the following metabolic risk abnormalities: high waist circumference, high blood pressure, high cholesterol, pre-diabetes, insulin resistance, and high plasma C-reactive protein levels. These new diagnostic criteria take into account the strong pathological link between greater body weight, diabetes, and MAFLD. Diagnostic criteria based on exclusion fail to address the complexity of MAFLD, authors wrote. Additionally, existing benchmarks for “excessive” alcohol consumption may exclude eligible patients from MAFLD diagnosis. As such, investigators also suggested the use of a “dual etiology” category for patients who meet MAFLD criteria and report another source of liver dysfunction such as Hepatitis B or C.

Guideline authors suggested that MAFLD severity be graded on a continuum rather than dichotomized as steatohepatitic vs non-steatohepatitic. Specifically, they wrote that MAFLD severity would be better described by grade of activity and stage of fibrosis. Additionally, the group proposed a new set of criteria to define MAFLD-associated cirrhosis. To meet these new diagnostic criteria, patients with cirrhosis must have past or present evidence of metabolic risk factors with at least 1 of the following factors: documentation of MAFLD on a previous liver biopsy; or historical documentation of steatosis by hepatic imaging. Cirrhosis in patients with MAFLD should be described as MAFLD-related cirrhosis, authors wrote, rather than “cryptogenic cirrhosis.” Growing evidence suggests that cryptogenic cirrhosis is distinct from MAFLD-related cirrhosis, and that the 2 conditions have distinct clinical outcomes.


Continue Reading

Consensus regarding the diagnostic criteria for MAFLD will ultimately improve clinical care by preventing misclassification of fatty liver from other sources. The use of more stringent criteria may also improve clinical trial relevance because fewer patients with fatty liver unrelated to metabolic dysfunction will be enrolled. “[The new] diagnosis is based on recognition of underlying abnormalities in metabolic health with acceptance that MAFLD may commonly co-exist with other conditions,” authors wrote.

Follow @Gastro_Advisor

Reference

Eslam M, Newsome PN, Anstee QM, et al. A new definition for metabolic associated fatty liver disease: an international expert consensus statement [published online April 8, 2020]. J Hepatol. doi: 10.1016/j.jhep.2020.03.039.

Topics:

LiverNonalcoholic Fatty Liver Disease (NAFLD)Sours: https://www.gastroenterologyadvisor.com/liver/updated-diagnostic-criteria-for-metabolic-associated-fatty-liver-disease/
Non-alcoholic fatty liver disease- causes, symptoms, diagnosis, treatment, pathology

Metabolic associated fatty liver disease: Addressing a new era in liver transplantation

1. 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. 2016;64:73–84. [PubMed] [Google Scholar]

2. NCD Risk Factor Collaboration (NCD-RisC) Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet. 2016;387:1377–1396. [PubMed] [Google Scholar]

3. Younossi ZM, Blissett D, Blissett R, Henry L, Stepanova M, Younossi Y, Racila A, Hunt S, Beckerman R. The economic and clinical burden of nonalcoholic fatty liver disease in the United States and Europe. Hepatology. 2016;64:1577–1586. [PubMed] [Google Scholar]

4. Adams LA, Roberts SK, Strasser SI, Mahady SE, Powell E, Estes C, Razavi H, George J. Nonalcoholic fatty liver disease burden: Australia, 2019-2030. J Gastroenterol Hepatol. 2020;35:1628–1635.[PMC free article] [PubMed] [Google Scholar]

5. Frith J, Day CP, Henderson E, Burt AD, Newton JL. Non-alcoholic fatty liver disease in older people. Gerontology. 2009;55:607–613. [PubMed] [Google Scholar]

6. Diehl AM, Day C. Cause, Pathogenesis, and Treatment of Nonalcoholic Steatohepatitis. N Engl J Med. 2017;377:2063–2072. [PubMed] [Google Scholar]

7. Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M, Harrison SA, Brunt EM, Sanyal AJ. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology. 2018;67:328–357. [PubMed] [Google Scholar]

8. Singh S, Allen AM, Wang Z, Prokop LJ, Murad MH, Loomba R. Fibrosis progression in nonalcoholic fatty liver vs nonalcoholic steatohepatitis: a systematic review and meta-analysis of paired-biopsy studies. Clin Gastroenterol Hepatol 2015; 13: 643-54. :quiz e39–40.[PMC free article] [PubMed] [Google Scholar]

9. Hui JM, Kench JG, Chitturi S, Sud A, Farrell GC, Byth K, Hall P, Khan M, George J. Long-term outcomes of cirrhosis in nonalcoholic steatohepatitis compared with hepatitis C. Hepatology. 2003;38:420–427. [PubMed] [Google Scholar]

10. Sanyal AJ, Harrison SA, Ratziu V, Abdelmalek MF, Diehl AM, Caldwell S, Shiffman ML, Aguilar Schall R, Jia C, McColgan B, Djedjos CS, McHutchison JG, Subramanian GM, Myers RP, Younossi Z, Muir AJ, Afdhal NH, Bosch J, Goodman Z. The Natural History of Advanced Fibrosis Due to Nonalcoholic Steatohepatitis: Data From the Simtuzumab Trials. Hepatology. 2019;70:1913–1927. [PubMed] [Google Scholar]

11. Hagström H, Nasr P, Ekstedt M, Hammar U, Stål P, Hultcrantz R, Kechagias S. Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD. J Hepatol. 2017;67:1265–1273. [PubMed] [Google Scholar]

12. Reig M, Gambato M, Man NK, Roberts JP, Victor D, Orci LA, Toso C. Should Patients With NAFLD/NASH Be Surveyed for HCC? Transplantation. 2019;103:39–44. [PubMed] [Google Scholar]

13. ANZLITR Registry Report 2018. Queensland, Australia and New Zealand Liver and Intestinal Transplant Registry, 2018. [Google Scholar]

14. Younossi Z, Stepanova M, Ong JP, Jacobson IM, Bugianesi E, Duseja A, Eguchi Y, Wong VW, Negro F, Yilmaz Y, Romero-Gomez M, George J, Ahmed A, Wong R, Younossi I, Ziayee M, Afendy A Global Nonalcoholic Steatohepatitis Council. Nonalcoholic Steatohepatitis Is the Fastest Growing Cause of Hepatocellular Carcinoma in Liver Transplant Candidates. Clin Gastroenterol Hepatol 2019; 17: 748-755. :e3. [PubMed] [Google Scholar]

15. Noureddin M, Vipani A, Bresee C, Todo T, Kim IK, Alkhouri N, Setiawan VW, Tran T, Ayoub WS, Lu SC, Klein AS, Sundaram V, Nissen NN. NASH Leading Cause of Liver Transplant in Women: Updated Analysis of Indications For Liver Transplant and Ethnic and Gender Variances. Am J Gastroenterol. 2018;113:1649–1659. [PubMed] [Google Scholar]

16. Majumdar A, Tsochatzis EA. Changing trends of liver transplantation and mortality from non-alcoholic fatty liver disease. Metabolism. 2020;111S:154291. [PubMed] [Google Scholar]

17. Crossan C, Majumdar A, Srivastava A, Thorburn D, Rosenberg W, Pinzani M, Longworth L, Tsochatzis EA. Referral pathways for patients with NAFLD based on non-invasive fibrosis tests: Diagnostic accuracy and cost analysis. Liver Int. 2019;39:2052–2060. [PubMed] [Google Scholar]

18. Ludwig J, Viggiano TR, McGill DB, Oh BJ. Nonalcoholic steatohepatitis: Mayo Clinic experiences with a hitherto unnamed disease. Mayo Clin Proc. 1980;55:434–438. [PubMed] [Google Scholar]

19. Fouad Y, Waked I, Bollipo S, Gomaa A, Ajlouni Y, Attia D. What's in a name? Liver Int. 2020;40:1254–1261. [PubMed] [Google Scholar]

20. Eslam M, Sanyal AJ, George J International Consensus Panel. MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology 2020; 158: 1999-2014. :e1. [PubMed] [Google Scholar]

21. Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, Zelber-Sagi S, Wai-Sun Wong V, Dufour JF, Schattenberg JM, Kawaguchi T, Arrese M, Valenti L, Shiha G, Tiribelli C, Yki-Järvinen H, Fan JG, Grønbæk H, Yilmaz Y, Cortez-Pinto H, Oliveira CP, Bedossa P, Adams LA, Zheng MH, Fouad Y, Chan WK, Mendez-Sanchez N, Ahn SH, Castera L, Bugianesi E, Ratziu V, George J. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol. 2020;73:202–209. [PubMed] [Google Scholar]

22. Younossi ZM, Rinella ME, Sanyal A, Harrison SA, Brunt E, Goodman Z, Cohen DE, Loomba R. From NAFLD to MAFLD: Implications of a premature change in terminology. Hepatology. 2020 [PubMed] [Google Scholar]

23. Adam R, Karam V, Cailliez V, O Grady JG, Mirza D, Cherqui D, Klempnauer J, Salizzoni M, Pratschke J, Jamieson N, Hidalgo E, Paul A, Andujar RL, Lerut J, Fisher L, Boudjema K, Fondevila C, Soubrane O, Bachellier P, Pinna AD, Berlakovich G, Bennet W, Pinzani M, Schemmer P, Zieniewicz K, Romero CJ, De Simone P, Ericzon BG, Schneeberger S, Wigmore SJ, Prous JF, Colledan M, Porte RJ, Yilmaz S, Azoulay D, Pirenne J, Line PD, Trunecka P, Navarro F, Lopez AV, De Carlis L, Pena SR, Kochs E, Duvoux C all the other 126 contributing centers (www. eltr.org) and the European Liver and Intestine Transplant Association (ELITA). 2018 Annual Report of the European Liver Transplant Registry (ELTR) - 50-year evolution of liver transplantation. Transpl Int. 2018;31:1293–1317. [PubMed] [Google Scholar]

24. Charlton MR, Burns JM, Pedersen RA, Watt KD, Heimbach JK, Dierkhising RA. Frequency and outcomes of liver transplantation for nonalcoholic steatohepatitis in the United States. Gastroenterology. 2011;141:1249–1253. [PubMed] [Google Scholar]

25. Wong RJ, Cheung R, Ahmed A. Nonalcoholic steatohepatitis is the most rapidly growing indication for liver transplantation in patients with hepatocellular carcinoma in the U.S. Hepatology. 2014;59:2188–2195. [PubMed] [Google Scholar]

26. Wong RJ, Aguilar M, Cheung R, Perumpail RB, Harrison SA, Younossi ZM, Ahmed A. Nonalcoholic steatohepatitis is the second leading etiology of liver disease among adults awaiting liver transplantation in the United States. Gastroenterology. 2015;148:547–555. [PubMed] [Google Scholar]

27. Haldar D, Kern B, Hodson J, Armstrong MJ, Adam R, Berlakovich G, Fritz J, Feurstein B, Popp W, Karam V, Muiesan P, O'Grady J, Jamieson N, Wigmore SJ, Pirenne J, Malek-Hosseini SA, Hidalgo E, Tokat Y, Paul A, Pratschke J, Bartels M, Trunecka P, Settmacher U, Pinzani M, Duvoux C, Newsome PN, Schneeberger S European Liver and Intestine Transplant Association (ELITA) Outcomes of liver transplantation for non-alcoholic steatohepatitis: A European Liver Transplant Registry study. J Hepatol. 2019;71:313–322.[PMC free article] [PubMed] [Google Scholar]

28. Calzadilla-Bertot L, Jeffrey GP, Jacques B, McCaughan G, Crawford M, Angus P, Jones R, Gane E, Munn S, Macdonald G, Fawcett J, Wigg A, Chen J, Fink M, Adams LA. Increasing Incidence of Nonalcoholic Steatohepatitis as an Indication for Liver Transplantation in Australia and New Zealand. Liver Transpl. 2019;25:25–34. [PubMed] [Google Scholar]

29. Ong J, Younossi ZM, Reddy V, Price LL, Gramlich T, Mayes J, Boparai N. Cryptogenic cirrhosis and posttransplantation nonalcoholic fatty liver disease. Liver Transpl. 2001;7:797–801. [PubMed] [Google Scholar]

30. Golabi P, Bush H, Stepanova M, Locklear CT, Jacobson IM, Mishra A, Trimble G, Erario M, Venkatesan C, Younossi I, Goodman Z, Younossi ZM. Liver Transplantation (LT) for Cryptogenic Cirrhosis (CC) and Nonalcoholic Steatohepatitis (NASH) Cirrhosis: Data from the Scientific Registry of Transplant Recipients (SRTR): 1994 to 2016. Medicine (Baltimore) 2018;97:e11518.[PMC free article] [PubMed] [Google Scholar]

31. Finkenstedt A, Auer C, Glodny B, Posch U, Steitzer H, Lanzer G, Pratschke J, Biebl M, Steurer M, Graziadei I, Vogel W, Zoller H. Patatin-like phospholipase domain-containing protein 3 rs738409-G in recipients of liver transplants is a risk factor for graft steatosis. Clin Gastroenterol Hepatol. 2013;11:1667–1672. [PubMed] [Google Scholar]

32. Míková I, Neřoldová M, Hubáček JA, Dlouhá D, Jirsa M, Honsová E, Sticová E, Lánská V, Špičák J, Trunečka P. Donor PNPLA3 and TM6SF2 Variant Alleles Confer Additive Risks for Graft Steatosis After Liver Transplantation. Transplantation. 2020;104:526–534. [PubMed] [Google Scholar]

33. Lonardo A, Nascimbeni F, Mantovani A, Targher G. Hypertension, diabetes, atherosclerosis and NASH: Cause or consequence? J Hepatol. 2018;68:335–352. [PubMed] [Google Scholar]

34. Targher G, Byrne CD, Lonardo A, Zoppini G, Barbui C. Non-alcoholic fatty liver disease and risk of incident cardiovascular disease: A meta-analysis. J Hepatol. 2016;65:589–600. [PubMed] [Google Scholar]

35. Alexander M, Loomis AK, van der Lei J, Duarte-Salles T, Prieto-Alhambra D, Ansell D, Pasqua A, Lapi F, Rijnbeek P, Mosseveld M, Avillach P, Egger P, Dhalwani NN, Kendrick S, Celis-Morales C, Waterworth DM, Alazawi W, Sattar N. Non-alcoholic fatty liver disease and risk of incident acute myocardial infarction and stroke: findings from matched cohort study of 18 million European adults. BMJ. 2019;367:l5367.[PMC free article] [PubMed] [Google Scholar]

36. Vanwagner LB, Bhave M, Te HS, Feinglass J, Alvarez L, Rinella ME. Patients transplanted for nonalcoholic steatohepatitis are at increased risk for postoperative cardiovascular events. Hepatology. 2012;56:1741–1750. [PubMed] [Google Scholar]

37. Manoushagian S, Meshkov A. Evaluation of solid organ transplant candidates for coronary artery disease. Am J Transplant. 2014;14:2228–2234. [PubMed] [Google Scholar]

38. Tsochatzis E, Coilly A, Nadalin S, Levistky J, Tokat Y, Ghobrial M, Klinck J, Berenguer M. International Liver Transplantation Consensus Statement on End-stage Liver Disease Due to Nonalcoholic Steatohepatitis and Liver Transplantation. Transplantation. 2019;103:45–56. [PubMed] [Google Scholar]

39. Younossi ZM, Stepanova M, Saab S, Kalwaney S, Clement S, Henry L, Frost S, Hunt S. The impact of type 2 diabetes and obesity on the long-term outcomes of more than 85 000 liver transplant recipients in the US. Aliment Pharmacol Ther. 2014;40:686–694. [PubMed] [Google Scholar]

40. Adams LA, Arauz O, Angus PW, Sinclair M, MacDonald GA, Chelvaratnam U, Wigg AJ, Yeap S, Shackel N, Lin L, Raftopoulos S, McCaughan GW, Jeffrey GP Australian New Zealand Liver Transplant Study Group. Additive impact of pre-liver transplant metabolic factors on survival post-liver transplant. J Gastroenterol Hepatol. 2016;31:1016–1024. [PubMed] [Google Scholar]

41. Park C, Hsu C, Neelakanta G, Nourmand H, Braunfeld M, Wray C, Steadman RH, Hu KQ, Cheng RT, Xia VW. Severe intraoperative hyperglycemia is independently associated with surgical site infection after liver transplantation. Transplantation. 2009;87:1031–1036. [PubMed] [Google Scholar]

42. VanWagner LB, Montag S, Zhao L, Allen NB, Lloyd-Jones DM, Das A, Skaro AI, Hohmann S, Friedewald JJ, Levitsky J. Cardiovascular Disease Outcomes Related to Early Stage Renal Impairment After Liver Transplantation. Transplantation. 2018;102:1096–1107.[PMC free article] [PubMed] [Google Scholar]

43. Singal AK, Salameh H, Kuo YF, Wiesner RH. Evolving frequency and outcomes of simultaneous liver kidney transplants based on liver disease etiology. Transplantation. 2014;98:216–221. [PubMed] [Google Scholar]

44. Singal AK, Hasanin M, Kaif M, Wiesner R, Kuo YF. Nonalcoholic Steatohepatitis is the Most Rapidly Growing Indication for Simultaneous Liver Kidney Transplantation in the United States. Transplantation. 2016;100:607–612. [PubMed] [Google Scholar]

45. Spengler EK, O'Leary JG, Te HS, Rogal S, Pillai AA, Al-Osaimi A, Desai A, Fleming JN, Ganger D, Seetharam A, Tsoulfas G, Montenovo M, Lai JC. Liver Transplantation in the Obese Cirrhotic Patient. Transplantation. 2017;101:2288–2296.[PMC free article] [PubMed] [Google Scholar]

46. LaMattina JC, Foley DP, Fernandez LA, Pirsch JD, Musat AI, D'Alessandro AM, Mezrich JD. Complications associated with liver transplantation in the obese recipient. Clin Transplant. 2012;26:910–918.[PMC free article] [PubMed] [Google Scholar]

47. Leonard J, Heimbach JK, Malinchoc M, Watt K, Charlton M. The impact of obesity on long-term outcomes in liver transplant recipients-results of the NIDDK liver transplant database. Am J Transplant. 2008;8:667–672. [PubMed] [Google Scholar]

48. Saab S, Lalezari D, Pruthi P, Alper T, Tong MJ. The impact of obesity on patient survival in liver transplant recipients: a meta-analysis. Liver Int. 2015;35:164–170. [PubMed] [Google Scholar]

49. Nair S, Vanatta JM, Arteh J, Eason JD. Effects of obesity, diabetes, and prior abdominal surgery on resource utilization in liver transplantation: a single-center study. Liver Transpl. 2009;15:1519–1524. [PubMed] [Google Scholar]

50. Bambha KM, Dodge JL, Gralla J, Sprague D, Biggins SW. Low, rather than high, body mass index confers increased risk for post-liver transplant death and graft loss: Risk modulated by model for end-stage liver disease. Liver Transpl. 2015;21:1286–1294. [PubMed] [Google Scholar]

51. Berzigotti A, Albillos A, Villanueva C, Genescá J, Ardevol A, Augustín S, Calleja JL, Bañares R, García-Pagán JC, Mesonero F, Bosch J Ciberehd SportDiet Collaborative Group. Effects of an intensive lifestyle intervention program on portal hypertension in patients with cirrhosis and obesity: The SportDiet study. Hepatology. 2017;65:1293–1305. [PubMed] [Google Scholar]

52. García-Pagàn JC, Santos C, Barberá JA, Luca A, Roca J, Rodriguez-Roisin R, Bosch J, Rodés J. Physical exercise increases portal pressure in patients with cirrhosis and portal hypertension. Gastroenterology. 1996;111:1300–1306. [PubMed] [Google Scholar]

53. Idriss R, Hasse J, Wu T, Khan F, Saracino G, McKenna G, Testa G, Trotter J, Klintmalm G, Asrani SK. Impact of Prior Bariatric Surgery on Perioperative Liver Transplant Outcomes. Liver Transpl. 2019;25:217–227. [PubMed] [Google Scholar]

54. Takata MC, Campos GM, Ciovica R, Rabl C, Rogers SJ, Cello JP, Ascher NL, Posselt AM. Laparoscopic bariatric surgery improves candidacy in morbidly obese patients awaiting transplantation. Surg Obes Relat Dis. 2008;4:159–64; discussion 164. [PubMed] [Google Scholar]

55. Diwan TS, Rice TC, Heimbach JK, Schauer DP. Liver Transplantation and Bariatric Surgery: Timing and Outcomes. Liver Transpl. 2018;24:1280–1287. [PubMed] [Google Scholar]

56. Lin MY, Tavakol MM, Sarin A, Amirkiai SM, Rogers SJ, Carter JT, Posselt AM. Laparoscopic sleeve gastrectomy is safe and efficacious for pretransplant candidates. Surg Obes Relat Dis. 2013;9:653–658. [PubMed] [Google Scholar]

57. Shimizu H, Phuong V, Maia M, Kroh M, Chand B, Schauer PR, Brethauer SA. Bariatric surgery in patients with liver cirrhosis. Surg Obes Relat Dis. 2013;9:1–6. [PubMed] [Google Scholar]

58. Zamora-Valdes D, Watt KD, Kellogg TA, Poterucha JJ, Di Cecco SR, Francisco-Ziller NM, Taner T, Rosen CB, Heimbach JK. Long-term outcomes of patients undergoing simultaneous liver transplantation and sleeve gastrectomy. Hepatology. 2018;68:485–495. [PubMed] [Google Scholar]

59. McCullough AJ, Bugianesi E. Protein-calorie malnutrition and the etiology of cirrhosis. Am J Gastroenterol. 1997;92:734–738. [PubMed] [Google Scholar]

60. Cheung K, Lee SS, Raman M. Prevalence and mechanisms of malnutrition in patients with advanced liver disease, and nutrition management strategies. Clin Gastroenterol Hepatol. 2012;10:117–125. [PubMed] [Google Scholar]

61. Berzigotti A, Saran U, Dufour JF. Physical activity and liver diseases. Hepatology. 2016;63:1026–1040. [PubMed] [Google Scholar]

62. Leibovitz E, Giryes S, Makhline R, Zikri Ditch M, Berlovitz Y, Boaz M. Malnutrition risk in newly hospitalized overweight and obese individuals: Mr NOI. Eur J Clin Nutr. 2013;67:620–624. [PubMed] [Google Scholar]

63. Tandon P, Ney M, Irwin I, Ma MM, Gramlich L, Bain VG, Esfandiari N, Baracos V, Montano-Loza AJ, Myers RP. Severe muscle depletion in patients on the liver transplant wait list: its prevalence and independent prognostic value. Liver Transpl. 2012;18:1209–1216. [PubMed] [Google Scholar]

64. Sinclair M, Chapman B, Hoermann R, Angus PW, Testro A, Scodellaro T, Gow PJ. Handgrip Strength Adds More Prognostic Value to the Model for End-Stage Liver Disease Score Than Imaging-Based Measures of Muscle Mass in Men With Cirrhosis. Liver Transpl. 2019;25:1480–1487. [PubMed] [Google Scholar]

65. Ebadi M, Tandon P, Moctezuma-Velazquez C, Ghosh S, Baracos VE, Mazurak VC, Montano-Loza AJ. Low subcutaneous adiposity associates with higher mortality in female patients with cirrhosis. J Hepatol. 2018;69:608–616. [PubMed] [Google Scholar]

66. Montano-Loza AJ, Meza-Junco J, Prado CM, Lieffers JR, Baracos VE, Bain VG, Sawyer MB. Muscle wasting is associated with mortality in patients with cirrhosis. Clin Gastroenterol Hepatol 2012; 10: 166-173, 173. :e1. [PubMed] [Google Scholar]

67. Eslamparast T, Montano-Loza AJ, Raman M, Tandon P. Sarcopenic obesity in cirrhosis-The confluence of 2 prognostic titans. Liver Int. 2018;38:1706–1717. [PubMed] [Google Scholar]

68. Montano-Loza AJ, Angulo P, Meza-Junco J, Prado CM, Sawyer MB, Beaumont C, Esfandiari N, Ma M, Baracos VE. Sarcopenic obesity and myosteatosis are associated with higher mortality in patients with cirrhosis. J Cachexia Sarcopenia Muscle. 2016;7:126–135.[PMC free article] [PubMed] [Google Scholar]

69. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K International Agency for Research on Cancer Handbook Working Group. Body Fatness and Cancer--Viewpoint of the IARC Working Group. N Engl J Med. 2016;375:794–798.[PMC free article] [PubMed] [Google Scholar]

70. Jinjuvadia R, Lohia P, Jinjuvadia C, Montoya S, Liangpunsakul S. The association between metabolic syndrome and colorectal neoplasm: systemic review and meta-analysis. J Clin Gastroenterol. 2013;47:33–44.[PMC free article] [PubMed] [Google Scholar]

71. VanWagner LB, Rinella ME. Extrahepatic Manifestations of Nonalcoholic Fatty Liver Disease. Curr Hepatol Rep. 2016;15:75–85.[PMC free article] [PubMed] [Google Scholar]

72. Shen H, Lipka S, Kumar A, Mustacchia P. Association between nonalcoholic fatty liver disease and colorectal adenoma: a systemic review and meta-analysis. J Gastrointest Oncol. 2014;5:440–446.[PMC free article] [PubMed] [Google Scholar]

73. Afzali A, Berry K, Ioannou GN. Excellent posttransplant survival for patients with nonalcoholic steatohepatitis in the United States. Liver Transpl. 2012;18:29–37. [PubMed] [Google Scholar]

74. Cholankeril G, Wong RJ, Hu M, Perumpail RB, Yoo ER, Puri P, Younossi ZM, Harrison SA, Ahmed A. Liver Transplantation for Nonalcoholic Steatohepatitis in the US: Temporal Trends and Outcomes. Dig Dis Sci. 2017;62:2915–2922. [PubMed] [Google Scholar]

75. Malik SM, deVera ME, Fontes P, Shaikh O, Ahmad J. Outcome after liver transplantation for NASH cirrhosis. Am J Transplant. 2009;9:782–793. [PubMed] [Google Scholar]

76. Yalamanchili K, Saadeh S, Klintmalm GB, Jennings LW, Davis GL. Nonalcoholic fatty liver disease after liver transplantation for cryptogenic cirrhosis or nonalcoholic fatty liver disease. Liver Transpl. 2010;16:431–439. [PubMed] [Google Scholar]

77. Fatourou EM, Tsochatzis EA. Management of metabolic syndrome and cardiovascular risk after liver transplantation. Lancet Gastroenterol Hepatol. 2019;4:731–741. [PubMed] [Google Scholar]

78. Dureja P, Mellinger J, Agni R, Chang F, Avey G, Lucey M, Said A. NAFLD recurrence in liver transplant recipients. Transplantation. 2011;91:684–689. [PubMed] [Google Scholar]

79. Saeed N, Glass L, Sharma P, Shannon C, Sonnenday CJ, Tincopa MA. Incidence and Risks for Nonalcoholic Fatty Liver Disease and Steatohepatitis Post-liver Transplant: Systematic Review and Meta-analysis. Transplantation. 2019;103:e345–e354. [PubMed] [Google Scholar]

80. Watt KD, Charlton MR. Metabolic syndrome and liver transplantation: a review and guide to management. J Hepatol. 2010;53:199–206. [PubMed] [Google Scholar]

81. Narayanan P, Mara K, Izzy M, Dierkhising R, Heimbach J, Allen AM, Watt KD. Recurrent or De Novo Allograft Steatosis and Long-term Outcomes After Liver Transplantation. Transplantation. 2019;103:e14–e21. [PubMed] [Google Scholar]

82. Germani G, Laryea M, Rubbia-Brandt L, Egawa H, Burra P, OʼGrady J, Watt KD. Management of Recurrent and De Novo NAFLD/NASH After Liver Transplantation. Transplantation. 2019;103:57–67. [PubMed] [Google Scholar]

83. Sprinzl MF, Weinmann A, Lohse N, Tönissen H, Koch S, Schattenberg J, Hoppe-Lotichius M, Zimmermann T, Galle PR, Hansen T, Otto G, Schuchmann M. Metabolic syndrome and its association with fatty liver disease after orthotopic liver transplantation. Transpl Int. 2013;26:67–74. [PubMed] [Google Scholar]

84. Segev DL, Sozio SM, Shin EJ, Nazarian SM, Nathan H, Thuluvath PJ, Montgomery RA, Cameron AM, Maley WR. Steroid avoidance in liver transplantation: meta-analysis and meta-regression of randomized trials. Liver Transpl. 2008;14:512–525. [PubMed] [Google Scholar]

85. Galvin Z, Rajakumar R, Chen E, Adeyi O, Selzner M, Grant D, Sapisochin G, Greig P, Cattral M, McGilvray I, Ghanekar A, Selzner N, Lilly L, Patel K, Bhat M. Predictors of De Novo Nonalcoholic Fatty Liver Disease After Liver Transplantation and Associated Fibrosis. Liver Transpl. 2019;25:56–67. [PubMed] [Google Scholar]

86. D'Alessandro AM, Kalayoglu M, Sollinger HW, Hoffmann RM, Reed A, Knechtle SJ, Pirsch JD, Hafez GR, Lorentzen D, Belzer FO. The predictive value of donor liver biopsies for the development of primary nonfunction after orthotopic liver transplantation. Transplantation. 1991;51:157–163. [PubMed] [Google Scholar]

87. Ploeg RJ, D'Alessandro AM, Knechtle SJ, Stegall MD, Pirsch JD, Hoffmann RM, Sasaki T, Sollinger HW, Belzer FO, Kalayoglu M. Risk factors for primary dysfunction after liver transplantation--a multivariate analysis. Transplantation. 1993;55:807–813. [PubMed] [Google Scholar]

88. Linares I, Hamar M, Selzner N, Selzner M. Steatosis in Liver Transplantation: Current Limitations and Future Strategies. Transplantation. 2019;103:78–90. [PubMed] [Google Scholar]

89. Chu MJ, Dare AJ, Phillips AR, Bartlett AS. Donor Hepatic Steatosis and Outcome After Liver Transplantation: a Systematic Review. J Gastrointest Surg. 2015;19:1713–1724. [PubMed] [Google Scholar]

90. Cesaretti M, Addeo P, Schiavo L, Anty R, Iannelli A. Assessment of Liver Graft Steatosis: Where Do We Stand? Liver Transpl. 2019;25:500–509. [PubMed] [Google Scholar]

91. Jackson KR, Bowring MG, Holscher C, Haugen CE, Long JJ, Liyanage L, Massie AB, Ottmann S, Philosophe B, Cameron AM, Segev DL, Garonzik-Wang J. Outcomes after declining a steatotic donor liver for liver transplant candidates in the United States. Transplantation. :2019. [PubMed] [Google Scholar]

92. Boteon YL, Boteon APCS, Attard J, Mergental H, Mirza DF, Bhogal RH, Afford SC. Ex situ machine perfusion as a tool to recondition steatotic donor livers: Troublesome features of fatty livers and the role of defatting therapies. A systematic review. Am J Transplant. 2018;18:2384–2399. [PubMed] [Google Scholar]

Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772736/

Fatty disease liver associated metabolic

.

Non-alcoholic fatty liver disease: are anxiety and metabolic disorders related?

.

Now discussing:

.



422 423 424 425 426