During the past decades, many epidemiological, toxicological and biological studies have been performed to assess the role of environmental chemicals as potential toxicants for diverse human disorders. However, the relationships between diseases based on chemical exposure have been rarely studied by computational biology. We developed a human environmental disease network (EDN) to explore and suggest novel disease-disease and chemical-disease relationships. The presented scored EDN model is built upon the integration on systems biology and chemical toxicology using chemical contaminants information and their disease relationships from the reported TDDB database. The resulting human EDN takes into consideration the level of evidence of the toxicant-disease relationships allowing including some degrees of significance in the disease-disease associations. Such network can be used to identify uncharacterized connections between diseases. Examples are discussed with type 2 diabetes (T2D). Additionally, this computational model allows to confirm already know chemical-disease links (e.g. bisphenol A and behavioral disorders) and also to reveal unexpected associations between chemicals and diseases (e.g. chlordane and olfactory alteration), thus predicting which chemicals may be risk factors to human health. With the proposed human EDN model, it is possible to explore common biological mechanism between two diseases through chemical exposure helping us to gain insight into disease etiology and comorbidity. Such computational approach is an alternative to animal testing supporting the 3R concept.