2021
Live Birth with or without Preimplantation Genetic Testing for Aneuploidy
Yan J, Qin Y, Zhao H, Sun Y, Gong F, Li R, Sun X, Ling X, Li H, Hao C, Tan J, Yang J, Zhu Y, Liu F, Chen D, Wei D, Lu J, Ni T, Zhou W, Wu K, Gao Y, Shi Y, Lu Y, Zhang T, Wu W, Ma X, Ma H, Fu J, Zhang J, Meng Q, Zhang H, Legro R, Chen Z. Live Birth with or without Preimplantation Genetic Testing for Aneuploidy. New England Journal Of Medicine 2021, 385: 2047-2058. PMID: 34818479, DOI: 10.1056/nejmoa2103613.Peer-Reviewed Original ResearchConceptsConventional in vitro fertilizationCumulative live birth rateLive birth rateIn vitro fertilizationGood-quality blastocystsPGT-A groupPGT-APreimplantation genetic testingLive birthsGenetic testingClinical pregnancy lossImprove pregnancy outcomesInitial embryo transferEmbryo-transfer proceduresYears of ageNeonatal complicationsPregnancy outcomesSubfertile womenEmbryo transferAdverse eventsPregnancy lossNoninferiority marginEmbryo selectionPrimary outcomeBlastocyst
2020
Endometrial thickness after ovarian stimulation with gonadotropin, clomiphene, or letrozole for unexplained infertility, and association with treatment outcomes
Quaas AM, Gavrizi SZ, Peck JD, Diamond MP, Legro RS, Robinson RD, Casson P, Christman GM, Zhang H, Hansen KR, Network E. Endometrial thickness after ovarian stimulation with gonadotropin, clomiphene, or letrozole for unexplained infertility, and association with treatment outcomes. Fertility And Sterility 2020, 115: 213-220. PMID: 32972733, PMCID: PMC7796980, DOI: 10.1016/j.fertnstert.2020.07.030.Peer-Reviewed Original ResearchConceptsLive birth rateEndometrial thicknessUnexplained infertilityOvarian stimulationRisk ratioLive birthsEMT groupHuman chorionic gonadotropin administrationHigher live birth rateIntrauterine insemination treatmentMean endometrial thicknessMultiple Intrauterine GestationsOvarian stimulation medicationChorionic gonadotropin administrationProspective cohort analysisUnadjusted risk ratioAdjusted risk ratioBirth rateOS-IUIThin endometriumIntrauterine gestationGonadotropin administrationStimulation medicationsClinical trialsTreatment outcomes
2019
Associations Between Anti-Mullerian Hormone and Cardiometabolic Health in Reproductive Age Women Are Explained by Body Mass Index
Rios JS, Greenwood EA, Pavone MEG, Cedars MI, Legro RS, Diamond MP, Santoro N, Sun F, Robinson RD, Christman G, Zhang H, Huddleston HG. Associations Between Anti-Mullerian Hormone and Cardiometabolic Health in Reproductive Age Women Are Explained by Body Mass Index. The Journal Of Clinical Endocrinology & Metabolism 2019, 105: dgz012. PMID: 31586179, PMCID: PMC7024739, DOI: 10.1210/clinem/dgz012.Peer-Reviewed Original ResearchConceptsHomeostasis model assessment-insulin resistancePolycystic ovary syndromeAnti-Mullerian hormoneBody mass indexC-reactive proteinUnexplained infertilityWaist circumferenceOVA groupLipoprotein cholesterolMass indexRelationship of AMHModel assessment-insulin resistanceHigh-density lipoprotein cholesterolLow-density lipoprotein cholesterolSite-adjusted modelsAssessment-insulin resistanceAntral follicle countCardio-metabolic healthReproductive-aged womenMultivariable linear regression modelsReproductive-age womenClinical populationsLow-density lipoproteinCardiometabolic agingCardiometabolic indices
2018
Subtype classification and heterogeneous prognosis model construction in precision medicine
You N, He S, Wang X, Zhu J, Zhang H. Subtype classification and heterogeneous prognosis model construction in precision medicine. Biometrics 2018, 74: 814-822. PMID: 29359319, DOI: 10.1111/biom.12843.Peer-Reviewed Original ResearchConceptsRegularization regressionVariable selectionHigh-dimensional predictorsNecessary statistical methodsVariable selection methodsExpectation-maximization algorithmOracle propertyPenalty parameterSemiparametric modelStatistical methodsParametric modelNumerical calculationsProper choiceModel constructionSelection methodGene expression datasetsModelEstimatorSubtype-specific risk factorsRegularizerSurvival probabilityHigh-throughput technologiesExpression datasetsAlgorithmSimulations
2016
Baseline AMH Level Associated With Ovulation Following Ovulation Induction in Women With Polycystic Ovary Syndrome
Mumford SL, Legro RS, Diamond MP, Coutifaris C, Steiner AZ, Schlaff WD, Alvero R, Christman GM, Casson PR, Huang H, Santoro N, Eisenberg E, Zhang H, Cedars MI. Baseline AMH Level Associated With Ovulation Following Ovulation Induction in Women With Polycystic Ovary Syndrome. The Journal Of Clinical Endocrinology & Metabolism 2016, 101: 3288-3296. PMID: 27228369, PMCID: PMC5010565, DOI: 10.1210/jc.2016-1340.Peer-Reviewed Original ResearchConceptsPolycystic ovary syndromeAnti-Müllerian hormoneSerum anti-Müllerian hormoneAMH levelsOvulation inductionOvary syndromeHigh serum anti-Müllerian hormoneMean AMHSensitivity of folliclesHigher AMH levelsAntral follicle countBody mass indexRandomized clinical trialsOvulation induction medicationsWomen ages 18II trialFollicle countMass indexInduction medicationsInsulin levelsClinical trialsOvulatory responseHealth centersAromatase activityFSH stimulationVitamin D Status Relates to Reproductive Outcome in Women With Polycystic Ovary Syndrome: Secondary Analysis of a Multicenter Randomized Controlled Trial
Pal L, Zhang H, Williams J, Santoro NF, Diamond MP, Schlaff WD, Coutifaris C, Carson SA, Steinkampf MP, Carr BR, McGovern PG, Cataldo NA, Gosman GG, Nestler JE, Myers E, Legro RS, Network F. Vitamin D Status Relates to Reproductive Outcome in Women With Polycystic Ovary Syndrome: Secondary Analysis of a Multicenter Randomized Controlled Trial. The Journal Of Clinical Endocrinology & Metabolism 2016, 101: 3027-3035. PMID: 27186859, PMCID: PMC4971341, DOI: 10.1210/jc.2015-4352.Peer-Reviewed Original ResearchConceptsPolycystic ovary syndromeLive birthsVitD statusOvary syndromeSerum 25OHDIndependent predictorsSecondary analysisOvulation induction outcomesSerum 25OHD levelsVitamin D statusHealth diagnostic criteriaD statusNonpregnant populationRetrospective cohortControlled TrialsVitamin DPregnancy lossReproductive outcomesDiagnostic criteriaTrial dataProgressive improvementWomenInduction outcomeNational InstituteSyndrome
2015
Identification and replication of prediction models for ovulation, pregnancy and live birth in infertile women with polycystic ovary syndrome
Kuang H, Jin S, Hansen KR, Diamond MP, Coutifaris C, Casson P, Christman G, Alvero R, Huang H, Bates GW, Usadi R, Lucidi S, Baker V, Santoro N, Eisenberg E, Legro RS, Zhang H, Network F, Bartlebaugh C, Dodson W, Estes S, Gnatuk C, Ladda R, Ober J, Easton C, Hernandez A, Leija M, Pierce D, Bryzski R, Awonuga A, Cedo L, Cline A, Collins K, Krawetz S, Puscheck E, Singh M, Yoscovits M, Barnhart K, Lecks K, Martino L, Marunich R, Snyder P, Schlaff W, Comfort A, Crow M, Hohmann A, Mallette S, Ringbloom M, Tang J, Mason S, DiMaria N, Rhea M, Turner K, Haisenleder D, Trussell J, DelBasso D, Li Y, Makuch R, Patrizio P, Sakai L, Scahill L, Taylor H, Thomas T, Tsang S, Zhang M, Lamar C, DePaolo L, Guzick D, Herring A, Redmond J, Thomas M, Turek P, Wactawski-Wende J, Rebar R, Cato P, Dukic V, Lewis V, Schlegel P, Witter F. Identification and replication of prediction models for ovulation, pregnancy and live birth in infertile women with polycystic ovary syndrome. Human Reproduction 2015, 30: 2222-2233. PMID: 26202922, PMCID: PMC4542721, DOI: 10.1093/humrep/dev182.Peer-Reviewed Original ResearchConceptsPolycystic ovary syndromePregnancy outcomesInfertile womenClomiphene citrateOvary syndromeChi-square testLogistic regression modelsLive birthsBaseline sex hormone-binding globulinPolycystic Ovary Syndrome ISecondary analysisEunice Kennedy Shriver National InstituteSex hormone-binding globulinSevere PCOS phenotypeFree androgen indexRole of smokingKey baseline characteristicsHormone-binding globulinSignificant risk factorsPARTICIPANTS/MATERIALSSignificant predictorsROLE OF CHANCEPredictors of ovulationGrant supportNIH grant support
2014
A novel staging model to classify oesophageal squamous cell carcinoma patients in China
Tan H, Zhang H, Xie J, Chen B, Wen C, Guo X, Zhao Q, Wu Z, Shen J, Wu J, Xu X, Li E, Xu L, Wang X. A novel staging model to classify oesophageal squamous cell carcinoma patients in China. British Journal Of Cancer 2014, 110: 2109-2115. PMID: 24569468, PMCID: PMC3992487, DOI: 10.1038/bjc.2014.101.Peer-Reviewed Original ResearchConceptsESCC patientsClinical variablesStaging systemOesophageal squamous cell carcinoma patientsSurvival analysisSquamous cell carcinoma patientsOesophageal squamous cell carcinomaKaplan-Meier survival analysisCell carcinoma patientsPotential prognostic factorsTNM staging systemSquamous cell carcinomaMetastasis (TNM) staging systemOesophageal carcinomaPrognostic factorsCarcinoma patientsCell carcinomaSame hospitalESCC prognosisPredominant subtypeRisk scoreIndependent cohortPatientsSurvival rateStaging model
2013
Gene–environment interactions in severe intraventricular hemorrhage of preterm neonates
Ment LR, Ådén U, Lin A, Kwon SH, Choi M, Hallman M, Lifton RP, Zhang H, Bauer CR. Gene–environment interactions in severe intraventricular hemorrhage of preterm neonates. Pediatric Research 2013, 75: 241-250. PMID: 24192699, PMCID: PMC3946468, DOI: 10.1038/pr.2013.195.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsApgar ScoreBlood CoagulationCerebral VentriclesCerebrovascular CirculationCollagen Type IVFactor VGene-Environment InteractionGenetic Predisposition to DiseaseGenetic VariationGestational AgeHumansHypoxia, BrainInfantInfant, PrematureInflammation MediatorsIntracranial HemorrhagesMethylenetetrahydrofolate Reductase (NADPH2)PhenotypePremature BirthPrognosisRisk FactorsConceptsIntraventricular hemorrhageCerebral injuryPreterm neonatesFactor V Leiden geneRisk of IVHEnvironmental triggersSevere intraventricular hemorrhageCerebral blood flowMethylenetetrahydrofolate reductase (MTHFR) variantsUnknown environmental triggersPresence of mutationsPeriventricular infarctionApgar scorePerinatal hypoxiaPreclinical dataFetal environmentGerminal matrixCerebral vasculatureBlood flowT polymorphismGene-environment interactionsMTHFR 677CHemorrhageNeonatesVascular pathways
2010
The Impact of Environmental and Genetic Factors on Neonatal Late-Onset Sepsis
Bizzarro MJ, Jiang Y, Hussain N, Gruen JR, Bhandari V, Zhang H. The Impact of Environmental and Genetic Factors on Neonatal Late-Onset Sepsis. The Journal Of Pediatrics 2010, 158: 234-238.e1. PMID: 20850766, PMCID: PMC3008342, DOI: 10.1016/j.jpeds.2010.07.060.Peer-Reviewed Original ResearchMeSH KeywordsAge of OnsetBirth WeightBlood-Borne PathogensCohort StudiesConfidence IntervalsCross InfectionEnvironmental ExposureFemaleGenetic Predisposition to DiseaseHospital MortalityHumansInfant, NewbornIntensive Care Units, NeonatalLogistic ModelsMalePrognosisRetrospective StudiesSepsisSurvival RateTime FactorsTwinsTwins, DizygoticTwins, MonozygoticConceptsLate-onset sepsisNewborn intensive care unitIntensive care unitCare unitBirth weightIntensive care unit populationNeonatal late-onset sepsisRetrospective cohort analysisTotal parenteral nutritionRespiratory distress syndromeGenetic factorsLogistic regression analysisMixed-effects logistic regressionNongenetic factorsMixed-effects logistic regression analysisSignificant genetic susceptibilityDistress syndromeParenteral nutritionGestational ageCohort analysisSepsisUnit populationConcordance rateGenetic susceptibilityLogistic regression