Alexandros G .Sfakianakis,ENT,Anapafeos 5 Agios Nikolaos Crete 72100 Greece,00302841026182

Παρασκευή 20 Ιανουαρίου 2017

Exploring the Missing Links between Dietary Habits and Diseases.

Exploring the Missing Links between Dietary Habits and Diseases.

IEEE Trans Nanobioscience. 2017 Jan 16;:

Authors: Bhattacharyya M, Maity S, Bandyopadhyay S

Abstract
Disease dietomics is an emerging area of systems biology that attempts to explore the connections between the dietary habits and diseases. Some of the topical studies highlight that foods might have different impacts over an organism either in progressing a disease (negative association) or in fighting against it (positive association). The association of foods with different diseases can be put together to build a network that might provide a global view of the entire system. Again, such disease-food networks might emerge in a more complex form while considering the disease subtypes individually. Some foods might have positive association with a particular subtype of a disease, whereas it might have no association or negative association with another subtype of the same disease. Therefore, the subtypes might have completely different network patterns. On the other hand, the same food may be helpful for a disease and harmful for another disease or even for a subtype. Analyzing such disease-food networks in different forms might give us important information about the relations between different diseases. In this study, we have analyzed a large-scale disease-food network comprising 162 different diseases and 455 types of foods for gaining knowledge about the connection between these diseases and their subtypes. We have measured the similarity between diseases based on their patterns of association with foods. In addition to observing a high similarity between several disease sub types, particularly for cancer, we have found strong relations between constipation-dysphagia and cancer-cardiovascular disease, which are rarely known. Tendency of occurrence of different diseases can be predicted based on such information.

PMID: 28103559 [PubMed - as supplied by publisher]



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