@article{fdi:010079271,
  title = {{A}dipose gene expression prior to weight loss can differentiate and weakly predict dietary responders},
  author = {{M}utch, {D}.{M}. and {T}emmani, {M}.{R}. and {H}enegar, {C}. and {C}ombes, {F}. and {P}elloux, {V}. and {H}olst, {C}. and {S}ørensen, {T}.{I}.{A}. and {A}strup, {A}. and {M}artinez, {J}.{A}. and {S}aris, {W}.{H}.{M}. and {V}iguerie, {N}. and {L}angin, {D}. and {Z}ucker, {J}ean-{D}aniel and {C}l{\'e}ment, {K}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground.{T}he ability to identify obese individuals who will successfully lose weight in response to dietary intervention willrevolutionize disease management. {T}herefore, we asked whether it is possible to identify subjects who will lose weight duringdietary intervention using only a single gene expression snapshot.{M}ethodology/{P}rincipal {F}indings.{T}he present studyinvolved 54 female subjects from the {N}utrient-{G}ene {I}nteractions in {H}uman {O}besity-{I}mplications for {D}ietary {G}uidelines({NUGENOB}) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss priorto the 10-week consumption of a low-fat hypocaloric diet. {U}sing several statistical tests revealed that the gene expressionprofiles of responders (8–12 kgs weight loss) could always be differentiated from non-responders (,4 kgs weight loss). {W}ealso assessed whether this differentiation was sufficient for prediction. {U}sing a bottom-up (i.e. black-box) approach, standardclass prediction algorithms were able to predict dietary responders with up to 61.1%68.1% accuracy. {U}sing a top-downapproach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%62.2%.{C}onclusion.{A}dipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate respondersfrom non-responders as well as serve as a weak predictor of subjects destined to lose weight. {W}hile the degree of predictionaccuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that thecomprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition.},
  keywords = {},
  booktitle = {},
  journal = {{P}los {O}ne},
  volume = {2},
  numero = {12},
  pages = {art. e1344 [8 ]},
  ISSN = {1932-6203},
  year = {2007},
  DOI = {10.1371/journal.pone.0001344},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079271},
}