{"id":"15796056048f","type":"article","url":"https://hartvaat.nl/2026/02/11/dna-methyleringsmarkers-voor-zwangerschapshypertensie-via-machine-learning/","title":"DNA-methyleringsmarkers voor zwangerschapshypertensie via machine learning","title_en":"DNA Methylation Markers for Pregnancy Hypertension via Machine Learning Methods","category":"hypertensie","category_label":"Hypertensie","professions":["huisarts","internist"],"tags":["gepersonaliseerde-geneeskunde","vrouwen","zwangerschap-hart"],"journal":"Hypertension","doi":"https://www.ahajournals.org/doi/abs/10.1161/HYPERTENSIONAHA.125.25388","source_url":"https://doi.org/https://www.ahajournals.org/doi/abs/10.1161/HYPERTENSIONAHA.125.25388","authors":["Jueming Lei"],"significance":5,"published":"2026-02-11","source_date":"2026-02-11","image":"","kennis":["https://hartvaat.nl/kennis/hypertensie/hypertensie-en-zwangerschap/","https://hartvaat.nl/kennis/hypertensie/eindorgaanschade-hypertensie/"],"congress":"","summary_en":"This study developed a prediction model for hypertensive disorders of pregnancy by integrating DNA methylation biomarkers and clinical factors using machine learning. First-trimester epigenetic screening may enable early identification of at-risk pregnancies.","created":"2026-07-03T10:25:23Z","updated":"2026-07-03T13:24:48Z","licence":"Citeer vrij, met bronvermelding en een link naar hartvaat.nl (de url van het record). Samenvattingen zijn redactioneel werk van HartVaat; de oorspronkelijke publicaties blijven van hun uitgevers (doi). Geen medisch advies.","body_markdown":"Deze studie ontwikkelde een voorspelmodel voor hypertensieve aandoeningen bij zwangerschap, waaronder gestationele hypertensie en pre-eclampsie, door epigenetische biomarkers en klinische factoren te integreren met behulp van machine learning.","abstract_original":"Hypertension, Volume 83, Issue 4, Page e25388, April 1, 2026. BACKGROUND:This study aims to develop a prediction model to identify individuals at risk of hypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, by integrating epigenetic biomarkers and clinical factors in the first trimester of pregnancy.METHODS:A 2-stage nested case-control study, matched by age and body mass index, was conducted with 618 pregnant women in China, with peripheral blood samples collected in the first trimester to evaluate the average methylation levels of differentially methylated regions (DMRs) between controls and HDP cases. In stage 1 (discovery set), 24 controls and 27 cases were used to identify the differential DMRs. In stage 2, 294 controls and 273 cases were used to validate the previously identified DMRs. DMRs selected from the intersectional results of lasso regression, XGBoost, random forest, and Shapley Additive Explanations models were further combined"}