Skip to main content

Advertisement

SpringerLink
Log in

A new risk stratification strategy for fatty liver disease by incorporating MAFLD and fibrosis score in a large US population

  • Original Article
  • Published:
Hepatology International Aims and scope Submit manuscript

Abstract

Background

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly proposed definition of fatty liver disease (FLD) independent of excessive alcohol consumption (EAC) and hepatitis viral infection. Evidence on the mortality risk in different types of FLD [nonalcoholic FLD (NAFLD), alcoholic FLD (AFLD), and MAFLD] is sparse, hindering the identification of high-risk populations for preferential clinical surveillance.

Methods

A total of 11,000 participants in the Third National Health and Nutrition Examination Survey were enrolled. Participants were categorized into three groups [FLD( − ), MAFLD( − ), and MAFLD( +)] according to FLD and MAFLD criteria, and further categorized into six groups by EAC. Multivariate Cox proportional hazard model was used to estimate the risk of all-cause, cardiovascular-related, and cancer-related mortality.

Results

During a median follow-up of 23.2 years, a total of 3240 deaths were identified. Compared with FLD( − )/EAC( − ) participants, MAFLD( +) individuals had higher all-cause mortality risk [hazard ratio (HR) = 1.28, 95% confidence interval (CI) = 1.18–1.39] regardless of EAC status [MAFLD( +)/NAFLD: HR = 1.22, 95%CI = 1.11–1.34; MAFLD( +)/AFLD: HR = 1.83, 95%CI = 1.46–2.28], while not for MAFLD( − ) individuals. Furthermore, diabetes-driven-MAFLD had higher mortality risk (HR = 2.00, 95%CI = 1.77–2.27) followed by metabolic dysregulation-driven-MAFLD (HR = 1.30, 95%CI = 1.06–1.60) and overweight/obesity-driven-MAFLD (HR = 1.11, 95%CI = 1.00–1.22). Additionally, MAFLD( − ) participants with elevated fibrosis score were also associated with statistically significantly higher mortality risk (HR = 3.23, 95%CI = 1.63–6.40).

Conclusions

Utilizing a representative sample of the US population, we proved the validity of MAFLD subtype and fibrosis score, rather than the traditional definition (NAFLD and AFLD), in the risk stratification of FLD patients. These findings may be applied to guide the determination of surveillance options for FLD patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The Third National Health and Nutrition Examination Survey (NHANES III) dataset are publicly available at National Center for Health Statistics of the Center for Disease Control and Prevention and the link to the database is https://wwwn.cdc.gov/nchs/nhanes/Nhanes3/Default.aspx.

Abbreviations

MAFLD:

Metabolic dysfunction-associated fatty liver disease

FLD:

Fatty liver disease

NAFLD:

Nonalcoholic fatty liver disease

AFLD:

Alcoholic fatty liver disease

T2DM:

Type 2 diabetes mellitus

NHANES:

National health and nutrition examination survey

NCHS:

National center for health statistics

HSUE:

Hepatic steatosis ultrasound examination

EAC:

Excessive alcohol consumption

APRI:

Aspartate aminotransferase to platelet ratio index

FIB-4:

Fibrosis-4

NFS:

NAFLD fibrosis score

NDI:

National death index

ICD-9:

International classification disease-ninth

HR:

Hazard ratio

CI:

Confidence interval

BMI:

Body mass index

HDL-C:

High-density lipoprotein cholesterol

CRP:

C-reactive protein

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

HOMA-IR:

Homeostasis model assessment of insulin resistance

References

  1. Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, et al. A new definition for metabolic dysfunction-associated fatty liver disease: an international expert consensus statement. J Hepatol. 2020;73(1):202–209

    Article  Google Scholar 

  2. Prasoppokakorn T, Pitisuttithum P, Treeprasertsuk S. Pharmacological therapeutics: current trends for metabolic dysfunction-associated fatty liver disease (MAFLD). J Clin Transl Hepatol. 2021;9(6):939–946

    PubMed  PubMed Central  Google Scholar 

  3. Le P, Chaitoff A, Rothberg MB, McCullough A, Gupta NM, Alkhouri N. Population-based trends in prevalence of nonalcoholic fatty liver disease in US adults with type 2 diabetes. Clin Gastroenterol Hepatol. 2019;17(11):2377–2378

    Article  Google Scholar 

  4. Lin H, Zhang X, Li G, Wong GL, Wong VW. Epidemiology and clinical outcomes of metabolic (dysfunction)-associated fatty liver disease. J Clin Transl Hepatol. 2021;9(6):972–982

    PubMed  PubMed Central  Google Scholar 

  5. Chang Y, Cho YK, Cho J, Jung HS, Yun KE, Ahn J, et al. Alcoholic and nonalcoholic fatty liver disease and liver-related mortality: a cohort study. Am J Gastroenterol. 2019;114(4):620–629

    Article  Google Scholar 

  6. Wild SH, Walker JJ, Morling JR, McAllister DA, Colhoun HM, Farran B, et al. Cardiovascular disease, cancer, and mortality among people with type 2 diabetes and alcoholic or nonalcoholic fatty liver disease hospital admission. Diabetes Care. 2018;41(2):341–347

    Article  Google Scholar 

  7. Huang Q, Zou X, Wen X, Zhou X, Ji L. NAFLD or MAFLD: which has closer association with all-cause and cause-specific mortality?-Results from NHANES III. Front Med. 2021;8: 693507

    Article  Google Scholar 

  8. Kim D, Konyn P, Sandhu KK, Dennis BB, Cheung AC, Ahmed A. Metabolic dysfunction-associated fatty liver disease is associated with increased all-cause mortality in the United States. J Hepatol. 2021;75(6):1284–1291

    Article  CAS  Google Scholar 

  9. Nguyen VH, Le MH, Cheung RC, Nguyen MH. Differential Clinical characteristics and mortality outcomes in persons with NAFLD and/or MAFLD. Clin Gastroenterol Hepatol. 2021;19(10):2172–81.e6

    Article  Google Scholar 

  10. Chen X, Chen S, Pang J, Tang Y, Ling W. Are the different MAFLD subtypes based on the inclusion criteria correlated with all-cause mortality? J Hepatol. 2021;75(4):987–989

    Article  Google Scholar 

  11. Semmler G, Wernly S, Bachmayer S, Leitner I, Wernly B, Egger M, et al. Metabolic dysfunction-associated fatty liver disease (MAFLD)-rather a bystander than a driver of mortality. J Clin Endocrinol Metab. 2021;106(9):2670–2677

    Article  Google Scholar 

  12. Wang X, Wu S, Yuan X, Chen S, Fu Q, Sun Y, et al. Metabolic dysfunction-associated fatty liver disease and mortality among Chinese adults: a prospective cohort study. J Clin Endocrinol Metab. 2021;107(2):e745–e755

    Article  Google Scholar 

  13. Moon JH, Kim W, Koo BK, Cho NH. Metabolic dysfunction-associated fatty liver disease predicts long-term mortality and cardiovascular disease. Gut Liver. 2022;16(3):433-442. https://doi.org/10.5009/gnl210167

    Article  PubMed  Google Scholar 

  14. Vilar-Gomez E, Chalasani N. Non-invasive assessment of non-alcoholic fatty liver disease: clinical prediction rules and blood-based biomarkers. J Hepatol. 2018;68(2):305–315

    Article  CAS  Google Scholar 

  15. Wang Z, Bertot LC, Jeffrey GP, Joseph J, Garas G, de Boer B, et al. Serum fibrosis tests guide prognosis in metabolic dysfunction-associated fatty liver disease patients referred from primary care. Clin Gastroenterol Hepatol. 2021;S1542–3565(21):01055–01057

    Google Scholar 

  16. Tang LJ, Ma HL, Eslam M, Wong GL, Zhu PW, Chen SD, et al. Among simple non-invasive scores, Pro-C3 and ADAPT best exclude advanced fibrosis in Asian patients with MAFLD. Metabolism. 2021;128: 154958

    Article  Google Scholar 

  17. Decraecker M, Dutartre D, Hiriart JB, Irles-Depé M, Chermak F, Foucher J, et al. Long-term prognosis of patients with metabolic (dysfunction)-associated fatty liver disease by non-invasive methods. Aliment Pharmacol Ther. 2022;55(5):580–592

    Article  Google Scholar 

  18. CDC/NCHS. Analytic and reporting guidelines: the third National Health and Nutrition Examination Survey NHANES III (1988–94). Hyattsville: National Center for Health Statistics Centers for Disease Control and Prevention; 1996

    Google Scholar 

  19. Third National Health and Nutrition Examination Survey. Hepatic/Gallbladder Ultrasound and Hepatic Steatosis (HGUHS). Available at: https://wwwn.cdc.gov/nchs/Data/Nhanes3/34A/HGUHS.htm. Accessed October 14, 2020.

  20. National Health; and Nutrition Examination Survey (NHANES) III. Hepatic Steatosis Ultrasound Images Assessment Procedures Manual 2010. Available at: https://www.cdc.gov/nchs/data/nhanes/nhanes3/hepatic_steatosis_ultrasound_procedures_manual.pdf. Accessed October 15, 2020.

  21. Pearson MM, Kim NJ, Berry K, Moon AM, Su F, Vutien P, et al. Associations between alcohol use and liver-related outcomes in a large national cohort of patients with cirrhosis. Hepatol Commun. 2021;5(12):2080–2095

    Article  CAS  Google Scholar 

  22. Lin S, Huang J, Wang M, Kumar R, Liu Y, Liu S, et al. Comparison of MAFLD and NAFLD diagnostic criteria in real world. Liver Int. 2020;40(9):2082–2089

    Article  Google Scholar 

  23. Lim GEH, Tang A, Ng CH, Chin YH, Lim WH, Tan DJH, et al. An observational data meta-analysis on the differences in prevalence and risk factors between MAFLD vs NAFLD. Clin Gastroenterol Hepatol. 2021;S1542–3565(21):01276–01283

    Google Scholar 

  24. Zelber-Sagi S, Salomone F, Mlynarsky L. The Mediterranean dietary pattern as the diet of choice for non-alcoholic fatty liver disease: evidence and plausible mechanisms. Liver Int. 2017;37(7):936–949

    Article  CAS  Google Scholar 

  25. Schwedhelm C, Boeing H, Hoffmann G, Aleksandrova K, Schwingshackl L. Effect of diet on mortality and cancer recurrence among cancer survivors: a systematic review and meta-analysis of cohort studies. Nutr Rev. 2016;74(12):737–748

    Article  Google Scholar 

  26. Anstee QM, Targher G, Day CP. Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nat Rev Gastroenterol Hepatol. 2013;10(6):330–344

    Article  CAS  Google Scholar 

  27. Shahid RK, Ahmed S, Le D, Yadav S. Diabetes and cancer: risk, challenges, management and outcomes. Cancers. 2021;13(22):5735

    Article  CAS  Google Scholar 

  28. Dongiovanni P, Valenti L. A nutrigenomic approach to non-alcoholic fatty liver disease. Int J Mol Sci. 2017;18(7):1534

    Article  Google Scholar 

  29. Di Angelantonio E, Bhupathiraju SN, Wormser D, Gao P, Kaptoge S, Global BMI Mortality Collaboration, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388(10046):776–786

    Article  Google Scholar 

  30. Ampuero J, Aller R, Gallego-Durán R, Banales JM, Crespo J, García-Monzón C, et al. The effects of metabolic status on non-alcoholic fatty liver disease-related outcomes, beyond the presence of obesity. Aliment Pharmacol Ther. 2018;48(11–12):1260–1270

    Article  CAS  Google Scholar 

  31. Stefan N, Schick F, Häring HU. Causes, characteristics, and consequences of metabolically unhealthy normal weight in humans. Cell Metab. 2017;26(2):292–300

    Article  CAS  Google Scholar 

  32. Lee H, Lee YH, Kim SU, Kim HC. Metabolic dysfunction-associated fatty liver disease and incident cardiovascular disease risk: a nationwide cohort study. Clin Gastroenterol Hepatol. 2021;19(10):2138–47.e10

    Article  Google Scholar 

  33. Wong VW, Lazarus JV. Prognosis of MAFLD vs NAFLD and implications for a nomenclature change. J Hepatol. 2021;75(6):1267–1270

    Article  Google Scholar 

  34. Hernaez R, Lazo M, Bonekamp S, Kamel I, Brancati FL, Guallar E, et al. Diagnostic accuracy and reliability of ultrasonography for the detection of fatty liver: a meta-analysis. Hepatology. 2011;54(3):1082–1090

    Article  Google Scholar 

  35. Mózes FE, Lee JA, Selvaraj EA, Jayaswal ANA, Trauner M, Boursier J, et al. Diagnostic accuracy of non-invasive tests for advanced fibrosis in patients with NAFLD: an individual patient data meta-analysis. Gut. 2022;71(5):1006–1019

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the participants and staff of the National Health and Nutrition Examination Survey (NHANES) for their valuable contributions. The authors assume full responsibility for analyses and interpretation of these data.

Funding

This work was supported by the National Key R&D Program of China (Grant No. 2021YFC2500400), the Beijing-Tianjin-Hebei Basic Research Cooperation Special Project (20JCZXJC00090), the Tianjin Municipal Commission of Health and Wellness Project (TJWJ2021MS008), and the Doctoral Fund of Tianjin Medical University Cancer Institute and Hospital (B2013). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.

Author information

Author notes

  1. Ya-Cong Zhang and Zhang-Yan Lyu contributed equally to this study.

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, West Huan-Hu Rd, Ti Yuan Bei, Hexi District, Tianjin, 300060, People’s Republic of China

    Ya-Cong Zhang, Zhang-Yan Lyu, Bing Ma, Li-Min Li, Wei Wang, Chao Sheng, Hong-Ji Dai, Yu-Bei Huang, Fang-Fang Song, Feng-Ju Song & Ke-Xin Chen

Authors
  1. Ya-Cong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang-Yan Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bing Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li-Min Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chao Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hong-Ji Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yu-Bei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Fang-Fang Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Feng-Ju Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ke-Xin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KXC has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript. Study concept and design: YCZ, ZYL, and KXC. Acquisition of data: YCZ, ZYL, BM, LML, WW, and CS. Analysis and interpretation of data: YCZ, ZYL, CS, HJD, YBH, BM, LML, and WW. Drafting of the manuscript: YCZ, ZYL, and KXC. Critical revision of the manuscript for important intellectual content: YCZ, ZYL, WW, HJD, YBH, FFS, FJS, and KXC. Obtained funding: ZYL, YBH, and KXC. Administrative, technical, or material support: HJD, YBH, FFS, FJS, and KXC. Study supervision: HJD, YBH, FFS, FJS, and KXC.

Corresponding author

Correspondence to Ke-Xin Chen.

Ethics declarations

Conflict of interest

The authors (Ya‑Cong Zhang, Zhang‑Yan Lyu, Bing Ma, Li‑Min Li, Wei Wang, Chao Sheng, Hong‑Ji Dai, Yu‑Bei Huang, Fang‑Fang Song, Feng‑Ju Song, Ke‑Xin Chen) declare that they have no conflict of interest.

Animal research

Not applicable.

Consent to participate

The Third National Health and Nutrition Examination Survey (NHANES III) was approved by the institutional reviews board of the National Center for Health Statistics (https://www.cdc.gov/nchs/nhanes/irba98.htm), and written informed consent was obtained from all individual participants in the study.

Consent to publish

All authors of this manuscript have read and approved the final submitted version and are aware that they are listed as an author on this paper.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 784 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, YC., Lyu, ZY., Ma, B. et al. A new risk stratification strategy for fatty liver disease by incorporating MAFLD and fibrosis score in a large US population. Hepatol Int 16, 835–845 (2022). https://doi.org/10.1007/s12072-022-10362-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12072-022-10362-3

Keywords

Search

Navigation