Fatty liver prediction: a multivariable linear regression model based on transient elastography using routine biochemical indicators and waist circumference

Jingwen Chen

doi:10.1117/12.2692637

7 September 2023 Fatty liver prediction: a multivariable linear regression model based on transient elastography using routine biochemical indicators and waist circumference

Jingwen Chen

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Proceedings Volume 12789, International Conference on Modern Medicine and Global Health (ICMMGH 2023); 1278918 (2023) https://doi.org/10.1117/12.2692637
Event: International Conference on Modern Medicine and Global Health (ICMMGH 2023), 2023, Oxford, United Kingdom

Abstract

Fatty liver disease is now the second most common liver disease after viral hepatitis, and its increasing prevalence and younger age of onset are posing a serious threat to human health worldwide. However, as early fatty liver is a reversible disease, early diagnosis has an important impact on timely treatment measures. The majority of current liver fat prediction methods have a high threshold of understanding, and some indicators of influencing factors are difficult to obtain, which does not meet the public's need for daily self-monitoring of liver fat content and does not facilitate the detection of asymptomatic early fatty liver. This study, therefore, investigated the association between liver fat accumulation and routine biochemical indicators and waist circumference, based on the reference indicators of the FLI. This study obtained a sample size of 8451 independent individuals by drawing on publicly available data from NHANES, extracted and analyzed from NHANES 2017 to March 2020 (pre-epidemic). The type of data used included examination and laboratory data, non-invasive measurement of liver fat content using transient elastography (TE). The analysis of the correlation between Albumin, Aspartate Aminotransferase, Lactate Dehydrogenase (LDH), Triglycerides, waist circumference and CAP respectively were forming by R (version 4.2.1). Through analysis, this paper found that all the above indicators are highly significantly correlated with CAP, however, the first three have a weaker correlation. Triglycerides was generally correlated with CAP. Waist circumference was highly significant and strongly correlated with CAP. Finally, an adjusted R-squared=0.4492 multivariable linear regression model with the above indicators was developed.

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Jingwen Chen "Fatty liver prediction: a multivariable linear regression model based on transient elastography using routine biochemical indicators and waist circumference", Proc. SPIE 12789, International Conference on Modern Medicine and Global Health (ICMMGH 2023), 1278918 (7 September 2023); https://doi.org/10.1117/12.2692637

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KEYWORDS

Liver

Diseases and disorders

Elastography

Linear regression

Data modeling

Statistical analysis

Biopsy

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