Metabolic alterations are associated with many especially age related diseases and they are caused by changes in the expression of the corresponding enzymes. Although analysis of gene expression data has become a standard approach, predicting large scale metabolic alterations is still challenging. Here, we present a mathematical framework GEMCAT, Gene Expression-based Metabolite Centrality Analysis Tool, that is based on the centrality of nodes in a directed graph. Through integration of differential expression data from either transcriptomics or proteomics we can predict metabolic alterations for a large set of metabolites but avoid artificial biomass or energy constraints required for other genome-scale modelling approaches. We demonstrate the predictive efficacy of GEMCAT using two distinct datasets: one involving RNA sequencing data and metabolomics from a human cell line featuring a deleted mitochondrial NAD-transporter, and another comprising proteomics and metabolomics data from patients afflicted with inflammatory bowel disease. We furthermore extended our approach to trace the experimentally confirmed metabolic alterations back to the expression changes enabling network based multi-omics integration.