Study of Hepatic Steatosis Algorithms as A Potential Marker of Metabolic Dysfunction Associated Steatotic Liver Disease

Authors

  • Kalasha Bolakhe Department of Lab Medicine, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal
  • Pabitra Bista Department of Lab Medicine, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal
  • Tika Bahadur Thapa Department of Lab Medicine, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal
  • Sushant Pokhrel Department of Lab Medicine, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal
  • Govardhan Joshi Department of Lab Medicine, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal
  • Suraj Aryal Department of Lab Medicine, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal
  • Dhan Bahadur Gurung Department of Lab Medicine, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal
  • Sanju Maharjan Department of Lab Medicine, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal
  • Sujan Gautam Department of Public Health, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal; Institute of Fundamental Research and Studies, Baniyatar, Kathmandu, Nepal https://orcid.org/0000-0001-8397-1467
  • Sudip Khanal Department of Public Health, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal https://orcid.org/0000-0003-0530-7610
  • Anit Lamichhane Department of Lab Medicine, Manmohan Memorial Institute of Health Sciences, Soalteemode, Kathmandu, Nepal https://orcid.org/0000-0003-1707-8069

DOI:

https://doi.org/10.3126/jmmihs.v9i2.71793

Keywords:

Metabolic dysfunction associated steatotic liver disease (MASLD), Chronic liver disease, Framingham steatosis index (FSI), Triglyceride and glucose index (TyG)

Abstract

Background: Metabolic dysfunction associated steatotic liver disease (MASLD) is the most common chronic liver disease worldwide, with increased liver-related morbidity and mortality. Various non-invasive algorithms have been developed for predicting the presence of MASLD using anthropometric and biochemical parameters. Hence, this study aims to determine hepatic steatosis algorithms as a potential marker of MASLD.

Methods: A total of 200 participants were included in the study, of which 100 were MASLD cases, and 100 were healthy control. Serum ALT, AST, TG, and Glucose were estimated, and Hepatic steatosis algorithms (LAP, FSI, TyG, and HSI) were calculated. The ROC curve was estimated to validate algorithms in patients with MASLD.

Results: Hepatic steatosis algorithms like FSI, LAP, TyG, and HSI were significantly higher (p<0.05) in patients with MASLD compared to healthy control. The AUROC of LAP, FSI, TyG, and HSI was 0.789 (95% CI,0.727-0.851), 0.776 (95% CI, 0.711-0.841), 0.765 (95% CI, 0.697-0.833) and 0.693 (95% CI, 0.620-0.766) respectively. The optimal cut-off value of LAP, FSI, TyG, and HSI for the prediction of MASLD were 31 (71% sensitivity and 70% specificity), 23 (74% sensitivity and 72% specificity), 8.9 (73% sensitivity and 70% specificity) and 34.5 (67% sensitivity and 62% specificity) respectively.

Conclusion: The non-invasive and cost-effective algorithms like LAP, FSI, TyG, and HSI can be potential screening tools for predicting MASLD.

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Published

2024-11-22

How to Cite

Bolakhe, K., Bista, P., Thapa, T. B., Pokhrel, S., Joshi, G., Aryal, S., … Lamichhane, A. (2024). Study of Hepatic Steatosis Algorithms as A Potential Marker of Metabolic Dysfunction Associated Steatotic Liver Disease. Journal of Manmohan Memorial Institute of Health Sciences, 9(2), 8–12. https://doi.org/10.3126/jmmihs.v9i2.71793

Issue

Vol. 9 No. 2 (2024)

Section

Articles

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