OBJECTIVE: Examination of the amniotic fluid proteome has been used to identify biomarkers for intra-amniotic inflammation as well as those that may be useful in predicting the outcome of preterm labor. The purpose of this study was to combine a novel computational method of pattern discovery with mass spectrometric proteomic profiling of amniotic fluid to discover biomarkers of intra-amniotic infection/inflammation (IAI). METHODS: This cross-sectional study included patients with spontaneous preterm labor and intact membranes who delivered at term (n = 59) and those who delivered preterm with IAI (n = 60). Proteomic profiling was performed using surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. A proteomic profile was acquired through multiple simultaneous SELDI conditions, which were combined in a single proteomic 'fingerprint' using a novel computational approach. Classification of patients based on their associated surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF) mass spectra as belonging to either the class of individuals with preterm delivery with IAI or term delivery was accomplished by constructing an empirical model. The first phase in the construction of this empirical model involved the selection of adjustable parameters utilizing a training/testing subset of data. The second phase tested the generalization of the model by utilizing a blinded validation set of patients who were not employed in parameter selection. RESULTS: Gestational age at amniocentesis was not significantly different between the groups. Thirty-nine unique mass spectrometric peaks discriminated patients with preterm labor/delivery with IAI from those with preterm labor and term delivery. In the testing/training dataset, the classification accuracies (averaged over 100 random draws) were: 91.4% (40.2/44) for patients with preterm delivery with IAI, and 91.2% (40.1/44) for term delivery. The overall accuracy of the classification of patients in the validation dataset was 90.3% (28/31). CONCLUSIONS: Proteomic analysis of amniotic fluid allowed the identification of mass spectrometry features, which can distinguish patients with preterm labor with IAI from those with preterm labor without inflammation or infection who subsequently delivered at term.
OBJECTIVE: Examination of the amniotic fluid proteome has been used to identify biomarkers for intra-amniotic inflammation as well as those that may be useful in predicting the outcome of preterm labor. The purpose of this study was to combine a novel computational method of pattern discovery with mass spectrometric proteomic profiling of amniotic fluid to discover biomarkers of intra-amniotic infection/inflammation (IAI). METHODS: This cross-sectional study included patients with spontaneous preterm labor and intact membranes who delivered at term (n = 59) and those who delivered preterm with IAI (n = 60). Proteomic profiling was performed using surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. A proteomic profile was acquired through multiple simultaneous SELDI conditions, which were combined in a single proteomic 'fingerprint' using a novel computational approach. Classification of patients based on their associated surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF) mass spectra as belonging to either the class of individuals with preterm delivery with IAI or term delivery was accomplished by constructing an empirical model. The first phase in the construction of this empirical model involved the selection of adjustable parameters utilizing a training/testing subset of data. The second phase tested the generalization of the model by utilizing a blinded validation set of patients who were not employed in parameter selection. RESULTS: Gestational age at amniocentesis was not significantly different between the groups. Thirty-nine unique mass spectrometric peaks discriminated patients with preterm labor/delivery with IAI from those with preterm labor and term delivery. In the testing/training dataset, the classification accuracies (averaged over 100 random draws) were: 91.4% (40.2/44) for patients with preterm delivery with IAI, and 91.2% (40.1/44) for term delivery. The overall accuracy of the classification of patients in the validation dataset was 90.3% (28/31). CONCLUSIONS: Proteomic analysis of amniotic fluid allowed the identification of mass spectrometry features, which can distinguish patients with preterm labor with IAI from those with preterm labor without inflammation or infection who subsequently delivered at term.
Authors: Ramsi Haddad; Barbara R Gould; Roberto Romero; Gerard Tromp; Riaz Farookhi; Sam S Edwin; Mi Ran Kim; Hans H Zingg Journal: Am J Obstet Gynecol Date: 2006-09 Impact factor: 8.661
Authors: Clare A Berry; Ilias Nitsos; Noah H Hillman; J Jane Pillow; Graeme R Polglase; Boris W Kramer; Matthew W Kemp; John P Newnham; Alan H Jobe; Suhas G Kallapur Journal: Reprod Sci Date: 2011-04-14 Impact factor: 3.060
Authors: Roberto Romero; Juan Pedro Kusanovic; Hernan Muñoz; Ricardo Gomez; Ronald F Lamont; Lami Yeo Journal: J Matern Fetal Neonatal Med Date: 2010-04
Authors: Roberto Romero; Juan Pedro Kusanovic; Ricardo Gomez; Ronald Lamont; Egle Bytautiene; Robert E Garfield; Pooja Mittal; Sonia S Hassan; Lami Yeo Journal: J Matern Fetal Neonatal Med Date: 2010-04
Authors: Pooja Mittal; Roberto Romero; Adi L Tarca; Juan Gonzalez; Sorin Draghici; Yi Xu; Zhong Dong; Chia-Ling Nhan-Chang; Tinnakorn Chaiworapongsa; Stephen Lye; Juan Pedro Kusanovic; Leonard Lipovich; Shali Mazaki-Tovi; Sonia S Hassan; Sam Mesiano; Chong Jai Kim Journal: J Perinat Med Date: 2010-07-14 Impact factor: 1.901
Authors: Ramkumar Menon; Anne L Dunlop; Michael R Kramer; Stephen J Fortunato; Carol J Hogue Journal: Acta Obstet Gynecol Scand Date: 2011-05-26 Impact factor: 3.636