Systems signatures reveal unique remission-path of Type 2 diabetes following Roux-en-Y gastric bypass surgery
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- Systems Signatures Reveal Unique Remission-path of Type 2 Diabetes Following Roux-en-Y Gastric Bypass Surgery
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Roux-en-Y Gastric bypass surgery (RYGB) is emerging as a powerful tool for treatment of obesity and may also cause remission of type 2 diabetes. However, the molecular mechanism of RYGB leading to diabetes remission independent of weight loss remains elusive. In this study, we profiled plasma metabolites and proteins of 10 normal glucose-tolerant obese (NO) and 9 diabetic obese (DO) patients before and 1-week, 3-months, 1-year after RYGB. 146 proteins and 128 metabolites from both NO and DO groups at all four stages were selected for further analysis. By analyzing a set of bi-molecular associations among the corresponding network of the subjects with our newly developed computational method, we defined the represented physiological states (called the edge-states that reflect the interactions among the bio-molecules), and the related molecular networks of NO and DO patients, respectively. The principal component analyses (PCA) revealed that the edge states of the post-RYGB NO subjects were significantly different from those of the post-RYGB DO patients. Particularly, the time-dependent changes of the molecular hub-networks differed between DO and NO groups after RYGB. In conclusion, by developing molecular network-based systems signatures, we for the first time reveal that RYGB generates a unique path for diabetes remission independent of weight loss.
Original language | English |
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Journal | EBioMedicine |
Volume | 28 |
Pages (from-to) | 234-240 |
Number of pages | 7 |
ISSN | 2352-3964 |
DOIs | |
Publication status | Published - 2018 |
- Journal Article, Obesity/genetics, Diabetes Mellitus, Type 2/blood, Gastric Bypass, Weight Loss, Humans, Metabolome, Gene Regulatory Networks, Principal Component Analysis, Systems Biology, Blood Proteins/metabolism, Gastric bypass surgery, Network biomarker, Systems biology, Network, Diabetes
Research areas
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ID: 189764538