Metabolic syndrome in pregnancy and risk for adverse pregnancy outcomes: A prospective cohort of nulliparous women

Jessica A Grieger, Tina Bianco-Miotto, Luke E Grzeskowiak, Shalem Y Leemaqz, Lucilla Poston, Lesley M McCowan, Louise C Kenny, Jenny E Myers, James J Walker, Gus A Dekker, Claire T Roberts, Jessica A Grieger, Tina Bianco-Miotto, Luke E Grzeskowiak, Shalem Y Leemaqz, Lucilla Poston, Lesley M McCowan, Louise C Kenny, Jenny E Myers, James J Walker, Gus A Dekker, Claire T Roberts

Abstract

Background: Obesity increases the risk for developing gestational diabetes mellitus (GDM) and preeclampsia (PE), which both associate with increased risk for type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) in women in later life. In the general population, metabolic syndrome (MetS) associates with T2DM and CVD. The impact of maternal MetS on pregnancy outcomes, in nulliparous pregnant women, has not been investigated.

Methods and findings: Low-risk, nulliparous women were recruited to the multi-centre, international prospective Screening for Pregnancy Endpoints (SCOPE) cohort between 11 November 2004 and 28 February 2011. Women were assessed for a range of demographic, lifestyle, and metabolic health variables at 15 ± 1 weeks' gestation. MetS was defined according to International Diabetes Federation (IDF) criteria for adults: waist circumference ≥80 cm, along with any 2 of the following: raised trigycerides (≥1.70 mmol/l [≥150 mg/dl]), reduced high-density lipoprotein cholesterol (<1.29 mmol/l [<50 mg/dl]), raised blood pressure (BP) (i.e., systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg), or raised plasma glucose (≥5.6 mmol/l). Log-binomial regression analyses were used to examine the risk for each pregnancy outcome (GDM, PE, large for gestational age [LGA], small for gestational age [SGA], and spontaneous preterm birth [sPTB]) with each of the 5 individual components for MetS and as a composite measure (i.e., MetS, as defined by the IDF). The relative risks, adjusted for maternal BMI, age, study centre, ethnicity, socioeconomic index, physical activity, smoking status, depression status, and fetal sex, are reported. A total of 5,530 women were included, and 12.3% (n = 684) had MetS. Women with MetS were at an increased risk for PE by a factor of 1.63 (95% CI 1.23 to 2.15) and for GDM by 3.71 (95% CI 2.42 to 5.67). In absolute terms, for PE, women with MetS had an adjusted excess risk of 2.52% (95% CI 1.51% to 4.11%) and, for GDM, had an adjusted excess risk of 8.66% (95% CI 5.38% to 13.94%). Diagnosis of MetS was not associated with increased risk for LGA, SGA, or sPTB. Increasing BMI in combination with MetS increased the estimated probability for GDM and decreased the probability of an uncomplicated pregnancy. Limitations of this study are that there are several different definitions for MetS in the adult population, and as there are none for pregnancy, we cannot be sure that the IDF criteria are the most appropriate definition for pregnancy. Furthermore, MetS was assessed in the first trimester and may not reflect pre-pregnancy metabolic health status.

Conclusions: We did not compare the impact of individual metabolic components with that of MetS as a composite, and therefore cannot conclude that MetS is better at identifying women at risk. However, more than half of the women who had MetS in early pregnancy developed a pregnancy complication compared with just over a third of women who did not have MetS. Furthermore, while increasing BMI increases the probability of GDM, the addition of MetS exacerbates this probability. Further studies are required to determine if individual MetS components act synergistically or independently.

Conflict of interest statement

I have read the journal's policy and the authors of this manuscript have the following competing interests: LCK is a minority shareholder in Metabolomic Diagnostics, an SME which has licensed IP pertaining to biomarkers predictive of pre-eclampsia which LCK invented. JEM receives a stipend as a specialty consulting editor for PLOS Medicine and serves on the journal's editorial board.

Figures

Fig 1. Forest plots for each pregnancy…
Fig 1. Forest plots for each pregnancy complication, the relative risk, the number of women with MetS or its individual components (N), and the number of women with the outcome (n).
All models (except BMI on the forest plot, which represents BMI 2 as the reference compared to BMI ≥30 kg/m2) were adjusted for maternal BMI, age, study centre, ethnicity, socioeconomic index, physical activity, smoking status, depression status, and fetal sex. N = number of women with the metabolic abnormality; n = number of cases (i.e., women who had the outcome). HDL-C, high-density lipoprotein cholesterol; MetS, metabolic syndrome; RR, relative risk.
Fig 2. Predicted probability of each pregnancy…
Fig 2. Predicted probability of each pregnancy outcome for women who did and did not have MetS and the interaction with BMI, estimated from the generalised additive model.
Models adjusted for maternal BMI (as a spline), age, study centre, ethnicity, socioeconomic index, physical activity, smoking status, depression status, and fetal sex, with an interaction term between the metabolic groups and BMI. GDM, gestational diabetes mellitus; LGA, large for gestational age; MetS, metabolic syndrome; PE, preeclampsia; SGA, small for gestational age; sPTB, spontaneous preterm birth.

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Source: PubMed

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