In a metabolic syndrome model, TSOD mice, animals developed overt symptoms at 8-10 weeks old that mirrored the human disorder, which is related to excess abdominal fat and can lead to various health problems including heart disease. In a mathematical theory developed previously by the Aihara laboratory called dynamical network biomarkers (DNB) the researchers expected that a pre-disease state should precede a transition to overt disease.<\/p>\n
The team measured large scale gene expression using DNA microarrays to capture the genetic signature of possible pre-disease state at 5 weeks of age before the emergence of symptoms. They found that 147 genes comprised a set that could describe a 5 week old animal\u2019s pre-disease state within the DNB theory. Although the results are promising, future studies will need to replicate them and deal with possible confounding conditions such as parallel changes in the microbiome of the adipose tissue where the samples were collected. <\/p>\n
The findings add metabolic syndrome to a growing list of major diseases and conditions such as Alzheimer\u2019s, diabetes, influenza, and cancer where the DNB theory has been shown to be useful in disease prediction. Future work will focus on psychiatric and other brain disorders. The hope is that a mathematical understanding of how human disease can be characterized by critical states and their transitions will allow earlier interventions in pervasive illnesses. More broadly, the DNB theory may be applied to any dynamic process to predict \u201ctipping points\u201d. <\/p>\n
Correspondent: Charles Yokoyama<\/a>, Ph.D., IRCN Science Writing Core<\/p>\n
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