How can I interpret abnormal liver function tests in the context of potential liver disease?

Interpreting abnormal liver function tests (LFTs) in the context of potential liver disease requires a systematic approach that considers the pattern of enzyme abnormalities, clinical context, and risk factors. First, differentiate whether the abnormality is hepatocellular (elevated alanine aminotransferase [ALT] and aspartate aminotransferase [AST]) or cholestatic (elevated alkaline phosphatase [ALP] and gamma-glutamyl transferase [GGT]) as this guides the differential diagnosis ¹.

Hepatocellular patterns often indicate liver cell injury from causes such as viral hepatitis, alcoholic liver disease, or non-alcoholic fatty liver disease (NAFLD) ¹. Cholestatic patterns suggest bile duct obstruction or cholestatic liver diseases. Elevated bilirubin alongside these enzymes may indicate more severe or advanced liver dysfunction ¹.

Consider the degree of enzyme elevation: mild elevations (less than 5 times the upper limit of normal) are common in NAFLD or medication effects, whereas very high elevations (greater than 10 times) may suggest acute viral hepatitis or drug-induced liver injury (Agrawal et al., 2016). The ratio of AST to ALT can also provide clues; for example, an AST:ALT ratio greater than 2 is suggestive of alcoholic liver disease (Thapa and Walia, 2007).

It is essential to interpret LFTs in the clinical context, including risk factors such as alcohol use, metabolic syndrome, viral hepatitis exposure, and medication history ¹. Further investigations, including viral serology, imaging, and possibly liver biopsy, may be warranted based on initial findings ¹.

In pregnancy, interpretation requires caution as physiological changes can alter LFTs; thus, pregnancy-specific reference ranges and clinical correlation are necessary (Guarino et al., 2020).

Overall, abnormal LFTs should prompt a structured evaluation to identify the underlying cause, assess severity, and guide management, integrating clinical assessment with biochemical patterns and risk factors ¹; (Agrawal et al., 2016); (Thapa and Walia, 2007).