title: Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study.
abstract: OBJECTIVE
  Preeclampsia is divided into early-onset (delivery before 34 weeks of gestation) and late-onset (delivery at or after 34 weeks) subtypes, which may rise from different etiopathogenic backgrounds. Early-onset disease is associated with placental dysfunction. Late-onset disease develops predominantly due to metabolic disturbances, obesity, diabetes, lipid dysfunction, and inflammation, which affect endothelial function. Our aim was to use cluster analysis to investigate clinical factors predicting the onset and severity of preeclampsia in a cohort of women with known clinical risk factors.
  
  
  METHODS
  We recruited 903 pregnant women with risk factors for preeclampsia at gestational weeks 12+0-13+6. Each individual outcome diagnosis was independently verified from medical records. We applied a Bayesian clustering algorithm to classify the study participants to clusters based on their particular risk factor combination. For each cluster, we computed the risk ratio of each disease outcome, relative to the risk in the general population.
  
  
  RESULTS
  The risk of preeclampsia increased exponentially with respect to the number of risk factors. Our analysis revealed 25 number of clusters. Preeclampsia in a previous pregnancy (n = 138) increased the risk of preeclampsia 8.1 fold (95% confidence interval (CI) 5.7-11.2) compared to a general population of pregnant women. Having a small for gestational age infant (n = 57) in a previous pregnancy increased the risk of early-onset preeclampsia 17.5 fold (95%CI 2.1-60.5). Cluster of those two risk factors together (n = 21) increased the risk of severe preeclampsia to 23.8-fold (95%CI 5.1-60.6), intermediate onset (delivery between 34+0-36+6 weeks of gestation) to 25.1-fold (95%CI 3.1-79.9) and preterm preeclampsia (delivery before 37+0 weeks of gestation) to 16.4-fold (95%CI 2.0-52.4). Body mass index over 30 kg/m2 (n = 228) as a sole risk factor increased the risk of preeclampsia to 2.1-fold (95%CI 1.1-3.6). Together with preeclampsia in an earlier pregnancy the risk increased to 11.4 (95%CI 4.5-20.9). Chronic hypertension (n = 60) increased the risk of preeclampsia 5.3-fold (95%CI 2.4-9.8), of severe preeclampsia 22.2-fold (95%CI 9.9-41.0), and risk of early-onset preeclampsia 16.7-fold (95%CI 2.0-57.6). If a woman had chronic hypertension combined with obesity, gestational diabetes and earlier preeclampsia, the risk of term preeclampsia increased 4.8-fold (95%CI 0.1-21.7). Women with type 1 diabetes mellitus had a high risk of all subgroups of preeclampsia.
  
  
  CONCLUSIONS
  The risk of preeclampsia increases exponentially with respect to the number of risk factors. Early-onset preeclampsia and severe preeclampsia have different risk profile from term preeclampsia.
authors.affiliation: Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
authors.forename: Pia M
authors.identifier: http://orcid.org/0000-0002-0317-0713
authors.initials: PM
authors.lastname: Villa
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
authors.forename: Pekka
authors.initials: P
authors.lastname: Marttinen
authors.affiliation: Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
authors.forename: Jussi
authors.initials: J
authors.lastname: Gillberg
authors.affiliation: Immunobiology, Research Programs Unit, University of Helsinki, Helsinki, Finland.|Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.|Bacteriology and Immunology, University of Helsinki, Helsinki, Finland.
authors.forename: A Inkeri
authors.initials: AI
authors.lastname: Lokki
authors.affiliation: Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany.
authors.forename: Kerttu
authors.initials: K
authors.lastname: Majander
authors.affiliation: Obstetrics and Gynecology, Kuopio University Hospital, Kuopio, Finland.
authors.forename: Maija-Riitta
authors.initials: MR
authors.lastname: Ordén
authors.affiliation: Suomen Terveystalo Oy, Kuopio, Finland.
authors.forename: Pekka
authors.initials: P
authors.lastname: Taipale
authors.affiliation: Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
authors.forename: Anukatriina
authors.initials: A
authors.lastname: Pesonen
authors.affiliation: Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
authors.forename: Katri
authors.initials: K
authors.lastname: Räikkönen
authors.affiliation: HUSLAB, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
authors.forename: Esa
authors.initials: E
authors.lastname: Hämäläinen
authors.affiliation: National Institute for Health and Welfare, Helsinki and Oulu, Finland.|PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland.|Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
authors.forename: Eero
authors.initials: E
authors.lastname: Kajantie
authors.affiliation: Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.|Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
authors.forename: Hannele
authors.initials: H
authors.lastname: Laivuori
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D016000:Cluster Analysis; D005260:Female; D005317:Fetal Growth Retardation / Q000453:epidemiology* / Q000517:prevention & control; D005865:Gestational Age; D006801:Humans; D016017:Odds Ratio; D011225:Pre-Eclampsia / Q000453:epidemiology* / Q000517:prevention & control; D011247:Pregnancy; D012307:Risk Factors