Comprehensive Analysis of Liver Enzyme Profiles: Age-Related Variations, Predictive Modeling, and Phenotypic Classification

Background: Liver enzymes are critical biomarkers reflecting hepatocellular and cholestatic injury. However, their variability across demographic factors and their interrelationships remain incompletely understood. Objective: To evaluate the distribution, determinants, and clustering patterns of liver enzymes (AST, ALT, ALP) using advanced statistical approaches. Methods: A cross-sectional study was conducted on 50 participants. Descriptive statistics, independent t-tests, Pearson correlation, one-way ANOVA, and multiple linear regression were performed. Log transformation was applied to normalize skewed data. Outlier diagnostics and K-means clustering were used for advanced analysis. Results: The mean age was 21.42 ± 11.02 years. No significant gender differences were observed (p > 0.05). ALP showed significant variation across age groups (p = 0.015). Regression analysis identified age (β = -1.12, p = 0.018) and AST (β = 0.82, p = 0.006) as independent predictors of ALP. Log transformation improved model stability. Cluster analysis identified three distinct biochemical phenotypes: normal, hepatocellular, and cholestatic patterns. Conclusion: ALP is the most variable and clinically informative enzyme, significantly influenced by age and AST. Advanced statistical approaches reveal distinct biochemical phenotypes, supporting heterogeneity in liver function dynamics.