4.6 Article

Associations between Ionomic Profile and Metabolic Abnormalities in Human Population

期刊

PLOS ONE
卷 7, 期 6, 页码 -

出版社

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0038845

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资金

  1. Ministry of Science and Technology of China (973 Program) [2009CB941400, 2011CB966200, 2011CB504002, 2008DFA31960, 2011CB910200]
  2. National High Technology Research and Development Program (863 Program) [2009AA022704]
  3. National Natural Science Foundation of China [10979071, 30970665, 31030039, 30930081, 81021002, 90913009, 30901193]
  4. Knowledge Innovation Program Project of the Chinese Academy of Sciences [KSCX2-EW-R-10, KSCX2-EW-R-04]
  5. Chinese Academy of Sciences Hundred Talents Program [KSCX2-YW-R-141]
  6. Science & Technology Commission of Shanghai Municipality grant [10JC1416800]
  7. Chief Scientist Program of Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences [SIBS2008006]
  8. Clinical Center of Shanghai Xuhui District Central Hospital grant [CRC2010012]
  9. CAS/SAFEA International Partnership Program for Creative Research Teams
  10. China Postdoctoral Science Foundation [20110490751, 2012M510902]
  11. SIBS-CAS Outstanding Youth fellowship [2010KIP309]

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Background: Few studies assessed effects of individual and multiple ions simultaneously on metabolic outcomes, due to methodological limitation. Methodology/Principal Findings: By combining advanced ionomics and mutual information, a quantifying measurement for mutual dependence between two random variables, we investigated associations of ion modules/networks with overweight/obesity, metabolic syndrome (MetS) and type 2 diabetes (T2DM) in 976 middle-aged Chinese men and women. Fasting plasma ions were measured by inductively coupled plasma mass spectroscopy. Significant ion modules were selected by mutual information to construct disease related ion networks. Plasma copper and phosphorus always ranked the first two among three specific ion networks associated with overweight/obesity, MetS and T2DM. Comparing the ranking of ion individually and in networks, three patterns were observed (1) Individual ion, such as potassium and chrome, which tends to work alone; (2) Module ion, such as iron in T2DM, which tends to act in modules/network; and (3) odule-individual ion, such as copper in overweight/obesity, which seems to work equivalently in either way. Conclusions: In conclusion, by using the novel approach of the ionomics strategy and the information theory, we observed potential associations of ions individually or as modules/networks with metabolic disorders. Certainly, these findings need to be confirmed in future biological studies.

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