{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T14:01:37Z","timestamp":1790690497567,"version":"4.1.0"},"reference-count":52,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T00:00:00Z","timestamp":1599004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>In the face of the obesity epidemic around the world, attention should be focused on the role of maternal obesity in the development of pregnancy. The purpose of this analysis was to evaluate the prediction of preeclampsia (PE) and isolated gestational hypertension (GH) for a number of maternal factors, in order to investigate the importance of pre-pregnancy obesity (body mass index, BMI \u2265 30 kg\/m2), compared to other risk factors (e.g., prior PE, pregnancy weight gain (GWG), infertility treatment, interpregnancy interval, family history, the lack of vitamin supplementation, urogenital infection, and socioeconomic factors). In total, 912 women without chronic diseases were examined in a Polish prospective cohort of women with a single pregnancy (recruited in 2015\u20132016). Separate analyses were performed for the women who developed GH (n = 113) vs. 775 women who remained normotensive, as well as for those who developed PE (n = 24) vs. 775 controls. The probability of each disease was assessed for the base prediction model (age + primiparity) and for the model extended by one (test) variable, using logistic regression. Three measures were used to assess the prediction: area under curve (AUC) of the base and extended model, integrated discrimination improvement (IDI) (the index shows the difference between the value of the mean change in the predicted probability between the group of sick and healthy women when a new factor is added to the model), and net reclassification improvement (NRI) (the index focuses on the reclassification table describing the number of women in whom an upward or downward shift in the disease probability value occurred after a new factor had been added, including results for healthy and sick women). In the GH prediction, AUC increased most strongly when we added BMI (kg\/m2) as a continuous variable (AUC = 0.716, p &lt; 0.001) to the base model. The highest IDI index was obtained for prior GH\/PE (IDI = 0.068, p &lt; 0.001). The addition of BMI as a continuous variable or BMI \u2265 25 kg\/m2 improved the classification for healthy and sick women the most (NRI = 0.571, p &lt; 0.001). In the PE prediction, AUC increased most strongly when we added BMI categories (AUC = 0.726, p &lt; 0.001) to the base model. The highest IDI index was obtained for prior GH\/PE (IDI = 0.050, p = 0.080). The addition of BMI categories improved the classification for healthy and sick women the most (NRI = 0.688; p = 0.001). After summing up the results of three indexes, the probability of hypertension in pregnancy was most strongly improved by BMI, including BMI \u2265 25 kg\/m2 for the GH prediction, and BMI \u2265 30 kg\/m2 for the PE prediction. Main conclusions: Pre-pregnancy BMI was the most likely factor to increase the probability of developing hypertension in pregnancy, compared to other risk factors. Hierarchies of PE and GH risk factors may suggest different (or common) mechanisms of their development.<\/jats:p>","DOI":"10.3390\/nu12092681","type":"journal-article","created":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T09:29:28Z","timestamp":1599038968000},"page":"2681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Pre-Pregnancy Obesity vs. Other Risk Factors in Probability Models of Preeclampsia and Gestational Hypertension"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8613-4955","authenticated-orcid":false,"given":"Ma\u0142gorzata","family":"Lewandowska","sequence":"first","affiliation":[{"name":"Medical Faculty, Lazarski University, 02-662 Warsaw, Poland"},{"name":"Division of Gynecological Surgery, University Hospital, 33 Polna Str., 60-535 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Wi\u0119ckowska","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Statistics, Poznan University of Medical Sciences, 60-806 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Sajdak","sequence":"additional","affiliation":[{"name":"Division of Gynecological Surgery, University Hospital, 33 Polna Str., 60-535 Poznan, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Lubi\u0144ski","sequence":"additional","affiliation":[{"name":"Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, 71-252 Szczecin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/S2213-8587(19)30026-9","article-title":"The obesity transition: Stages of the global epidemic","volume":"7","author":"Jaacks","year":"2019","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preghy.2018.10.009","article-title":"Risk of hypertension and abnormal biomarkers in the first year postpartum associated with hypertensive disorders of pregnancy among overweight and obese women","volume":"15","author":"Hauspurg","year":"2019","journal-title":"Pregnancy Hypertens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.tjog.2017.01.001","article-title":"Fetal programming of the metabolic syndrome","volume":"56","author":"Marciniak","year":"2017","journal-title":"Taiwan J. 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