Project description:Sacrocolpopexy has been dubbed the âgold standardâ repair for apical pelvic organ prolapse (POP). This study sought to determine a genetic cause for sacrocolpopexy failure by comparing genotypes from 10 women who suffered from early POP reoccurance after sacrocolpopexy surgery, versus 40 randomly selected women with long term success after the same procedure. We objectively defined early overt failure after robotic-assisted laparoscopic sacrocolpopexy as having a pelvic organ prolapse quantification system examination (POP-Q) of stage III or IV occurring in more than one compartment within six months after surgery. All medical records identified during this process were then reviewed by a panel of urogynecology attendings and fellows to select patients who were truly clinical outliers. By this method we identified 10 patients (cases) who experienced early overt surgical failure. We also randomly selected 40 controls from our research database which includes greater than 500 patients who underwent robotic-assisted laparoscopic sacrocolpopexy during the same time period and had been objectively and subjectively assessed for â¥ 12 months with surgical success at â¥ 12 months that did not undergo prolapse re-operation or re-treatment. Demographics and peri-operative details were compared between cases and controls. Exclusion criteria for controls included use of other graft material besides polypropylene mesh, prior surgery for prolapse involving graft material, and conversion to laparotomy. DNA from the 10 cases and 40 controls was isolated from buccal swabs and genotyped on a single nucleotide polymorphism (SNP) array that contains 250,000 markers (NspI 250K SNP array, Affymetrix, Santa Clara, CA). All women in this study identified as Caucasian. All subjects provided written informed consent to study participation and data release. This was a case-control study approved by the Institutional Review Board at the Atlantic Health System in Morristown New Jersey (R11-10-004). This case-control study compared single genotypes of 10 cases to 40 controls. All subjects were identified as Caucasian. Cases were women who experienced early overt POP recurrence after robotic sacrocolpopexy, and controls were randomly selected women with long term success after the same procedure.

Project description:Sacrocolpopexy has been dubbed the “gold standard” repair for apical pelvic organ prolapse (POP). This study sought to determine a genetic cause for sacrocolpopexy failure by comparing genotypes from 10 women who suffered from early POP reoccurance after sacrocolpopexy surgery, versus 40 randomly selected women with long term success after the same procedure. We objectively defined early overt failure after robotic-assisted laparoscopic sacrocolpopexy as having a pelvic organ prolapse quantification system examination (POP-Q) of stage III or IV occurring in more than one compartment within six months after surgery. All medical records identified during this process were then reviewed by a panel of urogynecology attendings and fellows to select patients who were truly clinical outliers. By this method we identified 10 patients (cases) who experienced early overt surgical failure. We also randomly selected 40 controls from our research database which includes greater than 500 patients who underwent robotic-assisted laparoscopic sacrocolpopexy during the same time period and had been objectively and subjectively assessed for ≥ 12 months with surgical success at ≥ 12 months that did not undergo prolapse re-operation or re-treatment. Demographics and peri-operative details were compared between cases and controls. Exclusion criteria for controls included use of other graft material besides polypropylene mesh, prior surgery for prolapse involving graft material, and conversion to laparotomy. DNA from the 10 cases and 40 controls was isolated from buccal swabs and genotyped on a single nucleotide polymorphism (SNP) array that contains 250,000 markers (NspI 250K SNP array, Affymetrix, Santa Clara, CA). All women in this study identified as Caucasian. All subjects provided written informed consent to study participation and data release. This was a case-control study approved by the Institutional Review Board at the Atlantic Health System in Morristown New Jersey (R11-10-004).

Project description:All subjects were recruited at Centennial Women?s Hospital and the Perinatal Research Center in Nashville, TN beginning in 2003. Pregnant women were enrolled during their first clinical visit after obtaining informed consent as described previously. Demographic and clinical data specific to the fetus was collected from clinical records. Gestational age of the neonate was determined by maternal reporting of the last menstrual period and corroboration by ultrasound dating. Accurate knowledge of gestational age (GA) is essential for proper monitoring and care of neonates. However, accurate GA measures are often not available. DNA methylation has previously been shown to associate with GA, and has been used to accurately predict chronological age in adults. In the current study, we examine whether DNA methylation in cord blood can be used to predict gestational age at birth. Results: We found that GA can be accurately predicted from DNA methylation of neonatal cord blood and blood spot samples (DNAm GA), using 148 CpG sites selected through elastic net regression in six training datasets (N=207). We evaluated predictive accuracy in six testing datasets (N=1,202), and found that the accuracy of DNAm GA meets or exceeds accuracy of gestational age estimates based on established methods. We also found an increased DNAm GA, relative to clinical GA, was associated with increased birthweight percentile (p=.00057), adjusting for GA, sex, and ancestry, suggesting that DNAm GA could represent developmental age more accurately than clinical estimates of GA. Conclusions: Further development of this predictor could provide a method of accurate neonatal estimation of GA for use in resource-limited populations, or in cases where GA cannot be estimated clinically. When clinical estimates are available, the predictor can be used to test hypotheses related to developmental age and other early life circumstances, and may provide increased accuracy beyond clinical estimates. 36 Umbilical cord blood samples were collected in EDTA tubes soon after placental delivery. Blood samples were centrifuged at 3,000 RPM to separate plasma, and buffy coats were aliquoted and stored at -80oC. DNA was extracted using the DNeasy Kit (Qiagen). DNA methylation was interrogated for each sample using the HumanMethylation450 BeadChip (Illumina).

Project description:All subjects were recruited at Centennial Women?s Hospital and the Perinatal Research Center in Nashville, TN beginning in 2003. Pregnant women were enrolled during their first clinical visit after obtaining informed consent as described previously. Demographic and clinical data specific to the fetus was collected from clinical records. Gestational age of the neonate was determined by maternal reporting of the last menstrual period and corroboration by ultrasound dating. Accurate knowledge of gestational age (GA) is essential for proper monitoring and care of neonates. However, accurate GA measures are often not available. DNA methylation has previously been shown to associate with GA, and has been used to accurately predict chronological age in adults. In the current study, we examine whether DNA methylation in cord blood can be used to predict gestational age at birth. Results: We found that GA can be accurately predicted from DNA methylation of neonatal cord blood and blood spot samples (DNAm GA), using 148 CpG sites selected through elastic net regression in six training datasets (N=207). We evaluated predictive accuracy in six testing datasets (N=1,202), and found that the accuracy of DNAm GA meets or exceeds accuracy of gestational age estimates based on established methods. We also found an increased DNAm GA, relative to clinical GA, was associated with increased birthweight percentile (p=.00057), adjusting for GA, sex, and ancestry, suggesting that DNAm GA could represent developmental age more accurately than clinical estimates of GA. Conclusions: Further development of this predictor could provide a method of accurate neonatal estimation of GA for use in resource-limited populations, or in cases where GA cannot be estimated clinically. When clinical estimates are available, the predictor can be used to test hypotheses related to developmental age and other early life circumstances, and may provide increased accuracy beyond clinical estimates.

Dataset Information

Point of Care 3D Ultrasound for Various Applications: A Pilot Study

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets