Villa PM, Marttinen P, Gillberg J, Lokki AI, Majander K, Ordén M-R, et al. (2017) Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study. PLoS ONE 12(3): e0174399. https://doi.org/10.1371/journal.pone.0174399
Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study
|Author:||Villa, Pia M.1; Marttinen, Pekka2; Gillberg, Jussi2;|
1Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
2Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland
3Immunobiology, Research Programs Unit, University of Helsinki, Helsinki, Finland, Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
4Bacteriology and Immunology, University of Helsinki, Helsinki, Finland
5Institute for Archaeological Sciences, University of Tübingen, Tübingen, Germany
6Obstetrics and Gynecology, Kuopio University Hospital, Kuopio, Finland
7Suomen Terveystalo Oy, Kuopio, Finland
8Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
9HUSLAB, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
10National Institute for Health and Welfare, Helsinki and Oulu, Finland
11PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
12Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
13Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
|Online Access:||PDF Full Text (PDF, 1.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201706027009
Public Library of Science,
|Publish Date:|| 2017-06-02
Objectives: 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.
Conclusion: 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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3123 Gynaecology and paediatrics
This work was supported by Academy of Finland (grants no. 259272, 286607 to PM, 121196, 134957, 278941 to HL, 284859 to KR and to AKP and EK), EVO (a special state subsidy for health science research) to EH, the Finnish Medical Foundation to HL, the Jane and Aatos Erkko Foundation to HL, the Novo Nordisk Foundation to EK, the Päivikki and Sakari Sohlberg Foundation to HL and PV, the Clinical Graduate School in Paediatrics and Obstetrics/Gynaecology in University of Helsinki to PV, the Signe and Ane Gylleberg Foundation to KR and EK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
|Academy of Finland Grant Number:||
259272 (Academy of Finland Funding decision)
286607 (Academy of Finland Funding decision)
121196 (Academy of Finland Funding decision)
134957 (Academy of Finland Funding decision)
278941 (Academy of Finland Funding decision)
284859 (Academy of Finland Funding decision)
All relevant data are within the paper and its Supporting Information files.
© 2017 Villa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.