Preeclampsia and gestational weight gain in the Norwegian Fit for Delivery trial

Hillesund, Elisabet Rudjord; Seland, S; Bere, Elling; Sagedal, Linda Reme; Torstveit, Monica Klungland; Lohne-Seiler, Hilde; Vistad, Ingvild; Øverby, Nina Cecilie

doi:10.1186/s13104-018-3396-4

Cited by 20 publications

(25 citation statements)

References 19 publications

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“…Speci cally, excessive GWG in the pre-pregnancy overweight and obese mothers was signi cantly associated with increased risk of PE. This is in keeping with the ndings reported by previous works (31,32). This nding was also appreciated for MPE and LOPE (6,33).…”

Section: Discussionsupporting

confidence: 92%

The Effects of Pre-Pregnancy Body Mass Index and Gestational Weight Gain on the Risk of Preeclampsia at a Tertiary Referral Hospital, Northern Tanzania

Amagulu¹,

Alloyce²,

Tobi³

et al. 2020

Preprint

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Background: Pre/eclampsia and other hypertensive disorders of pregnancy contributed to 18% of the maternal mortality reported in Northern Tanzanian. There is increasing prevalence of obesity in Tanzania which is related to excessive weight gain in pregnancy. Both high BMI and excessive gestation weight gain are identified to increase risk of PE and subtypes, however this is still inconclusive and little is known about the joint effect of pre-pregnancy BMI and GWG on risk of PE and its subtypes in Africa. We evaluated the independent and joint effects of pre-pregnancy BMI and GWG on the risk of pre-eclampsia and its subtypes among women who delivered at Kilimanjaro Christian Medical Center (KCMC) from October 2018 to May 2019, Northern Tanzania.Methods: We performed a retrospective birth cohort study from October 2018 to May 2019 at KCMC, Tanzania. Pre-pregnancy BMI was categorized using WHO categories into Underweight (˂ 18.5kg/m²), Normal weight (18.5-24.9kg/m²), Overweight (25-29.9kg/m²) and Obese (≥ 30kg/m²). Gestational Weight Gain (GWG) was categorized using the 2009 Institute of Medicine (IOM) guidelines into Inadequate, Adequate and Excessive weight gain in pregnancy. Multinomial logistic regression analysis was used to adjust for confounders using relative risk, 95% confidence interval for the risk ratios (RR) that did not cross 1 and p<0.05 were regarded statistically significant.Results: Among the 1309 women analysed, 5.3% were Underweight, 51.1% Normal weight, 26.9% Overweight and 16.7% were Obese. About 43.5% had excessive GWG. Women with PE were 9.5%. Both obesity and excessive GWG independently increased risk of PE with adjusted RR=2.42, 95%CI: 1.48-3.96 and RR=1.77, 95%CI: 1.16-2.69 when compared to normal BMI and adequate GWG respectively. Jointly, Obesity and Excessive GWG had the highest risk of PE (ARR=4.95, 95%CI: 2.21-11.10). The increased risk was similar for Mild PE (MPE), Severe PE or eclampsia (SP/E) and Late Onset PE (LOPE). No association was found for Early Onset PE (EOPE).Conclusion: Pre-pregnancy Obesity and Excessive GWG independently and jointly increases risk for PE and the risk varies by PE subtype.

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Section: Discussionsupporting

confidence: 92%

The Effects of Pre-Pregnancy Body Mass Index and Gestational Weight Gain on the Risk of Preeclampsia at a Tertiary Referral Hospital, Northern Tanzania

Amagulu¹,

Alloyce²,

Tobi³

et al. 2020

Preprint

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“…Our results also showed that gestational weight gain (GWG) was important in predicting GH compared to pre-pregnancy BMI, but GWG may be associated with fluid retention, and this clinical factor had limited predictive value [ 7 , 31 , 32 ]. In our cohort, the percentage of women with GWG above the IOM recommendation range was 36.8%, but for the women with excessive pre-pregnancy BMI, it was 59.0%.…”

Section: Discussionmentioning

confidence: 99%

Pre-Pregnancy Obesity vs. Other Risk Factors in Probability Models of Preeclampsia and Gestational Hypertension

Lewandowska

Więckowska

Sajdak³

et al. 2020

Nutrients

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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 ≥ 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 2015–2016). 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 < 0.001) to the base model. The highest IDI index was obtained for prior GH/PE (IDI = 0.068, p < 0.001). The addition of BMI as a continuous variable or BMI ≥ 25 kg/m2 improved the classification for healthy and sick women the most (NRI = 0.571, p < 0.001). In the PE prediction, AUC increased most strongly when we added BMI categories (AUC = 0.726, p < 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 ≥ 25 kg/m2 for the GH prediction, and BMI ≥ 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.

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“…BIA has been used in various clinical situations and pre-pregnancy and pregnancy conditions, including oedema [60], GDM [48,50,51,61,62], excessive gestational weight gain (EGWG) [2,3,48,51,52,55,57,[62][63][64][65], preeclampsia (PE) [57,60,[66][67][68][69][70][71][72][73][74], gestational hypertension (GH) [73,75] or hyperemesis gravidarum [76]. Hashimoto et al compared the impedance analysis in healthy women and in women undergoing haemodialysis whose management of bodily fluids during pregnancy is known to be complicated [35].…”

Section: Pregnancymentioning

confidence: 99%

The Possibility of Using Bioelectrical Impedance Analysis in Pregnant and Postpartum Women

et al. 2021

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Pregnancy is a time of significant changes occurring in the composition of a woman’s body in order to provide support for the growth and development of the foetus. Bioelectrical impedance analysis (BIA) is used to assess the body composition and hydration status. This technique represents a non-invasive, reliable, and fast clinical approach, which is well tolerated by patients. A segmental impedance measurement might be advantageous in pregnant women, particularly in late pregnancy. The purpose of this paper is to provide a review of different applications of BIA in pregnant and postpartum women. It seems that BIA has a better prognostic potential for gestational and post-partum outcomes than body mass index. The BIA method can be successfully used to study the effect of excessive gestational weight gain in pregnancy on the development of obstetric complications. Studying the mother’s body composition and correlating it with her energy balance could facilitate the development of dietary recommendations for women. Evaluation of the body composition can provide important clues for diagnosis of gestational diabetes mellitus in pregnant women with a low risk of this disease. BIA is also used as one of the additional tests in assessing the risk of developing gestational hypertension and preeclampsia.

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Preeclampsia and gestational weight gain in the Norwegian Fit for Delivery trial

Cited by 20 publications

References 19 publications

The Effects of Pre-Pregnancy Body Mass Index and Gestational Weight Gain on the Risk of Preeclampsia at a Tertiary Referral Hospital, Northern Tanzania

The Effects of Pre-Pregnancy Body Mass Index and Gestational Weight Gain on the Risk of Preeclampsia at a Tertiary Referral Hospital, Northern Tanzania

Pre-Pregnancy Obesity vs. Other Risk Factors in Probability Models of Preeclampsia and Gestational Hypertension

The Possibility of Using Bioelectrical Impedance Analysis in Pregnant and Postpartum Women

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