Introduction
Cardiovascular diseases remain the main cause of morbidity and mortality worldwide.Reference Nguyen, Wallace, Pepe, Menheniott, Moss and Burgner1 While adult lifestyles are important factors for its development, previous studies have focused on the investigation of the existence of critical windows in early life, when environmental exposures would have long-term effect on the development of disease.Reference Sun, Burgner and Ponsonby2
In particular, childhood excess weight (clinically defined as a body mass index (BMI) >85th and ≤97th percentile) and obesity (BMI > 97th percentile) are rising concerns. Their incidence increased significantly worldwide during the last decades. In North America, approximately one-third of children are overweight or obese.Reference Gurnani, Birken and Hamilton3 In Portugal, the prevalence of childhood obesity and excess weight has been decreasing in the last few years, but it is still high.4 Childhood obesity is known to correlate with disturbed endothelial function,Reference Urbina, Kimball, Khoury, Daniels and Dolan5 increased arterial stiffness,Reference Urbina, Kimball, Khoury, Daniels and Dolan5 increased arterial wall thicknessReference Meyer, Kundt, Steiner, Schuff-Werner and Kienast6, and increased left ventricular mass, all concurring for a higher cardiovascular risk profile.Reference Mehta7 In fact, despite the clustering of childhood obesity with other known cardiovascular risk factors (such as hypercholesterolemia, high blood pressure, and diabetes), the former appears to be the strongest correlator of accumulated risk in later life.Reference Goodman, Dolan, Morrison and Daniels8
Childhood obesity is a complex syndrome. Several factors appear to predispose for this condition: genetic determinants, level of physical activity, food intake, and environmental factors. Among the latter, the influence of the intrauterine and early postnatal environment seems to play a role.Reference Gurnani, Birken and Hamilton3 In fact, childhood obesity was found to be associated with primiparity, maternal obesity or diabetes, maternal smoking during pregnancy, and delivery through cesarian section, even after controlling for confounders.Reference Gurnani, Birken and Hamilton3,Reference Reynolds, Jacobsen and Drake9–Reference Chrestani, Santos, Horta, Dumith and Dode13 Small for gestational age (SGA) or large for gestational age infants at birth were also found to have higher rates of childhood obesity.Reference Ong, Ahmed, Emmett, Preece and Dunger14–Reference Whincup, Kaye and Owen17 On the other hand, breastfeeding seems to be protective against this syndrome.Reference Kajantie, Eriksson, Osmond, Thornburg and Barker32 This explains why children of lower socioeconomic strata, with decreased rates of breastfeeding, have higher rates of obesity.Reference Gibbs and Forste18 Lastly, the association of prematurity with childhood obesity is controversial.Reference Simmons19
In the early 1990s, the “Developmental Origins of Health and Disease Hypothesis” (DOHaD) was postulated, proposing that early life exposure to some triggers, such as undernourishment, could lead to epigenetic-mediated metabolic, endocrine, and cardiovascular adaptations that permanently change the function and structure of the body and can be responsible for future burden of disease. The paradigmatic example of this theory are the SGA and fetal growth-restricted infants, who are known to suffer from increased rates of obesity, cardiovascular disease, and type II diabetes in adult life.Reference Barker20 Furthermore, in utero and early postnatal exposure to some stressors, even in the absence of identifiable physical markers, could be enough to cause these long-term deleterious adaptations. Some authors argue that there is probably a direct effect of these environmental stressors in the fetal development.Reference Riedel, Schonberger and Yang21
Such stressors are known to generate pro-inflammatory environments. In fact, several in vitro and in vivo studies suggest that inflammation could be the causal pathway between in utero pro-inflammatory exposures (such as maternal obesity, diabetes, and tobacco use) and childhood phenotype.Reference Nguyen, Wallace, Pepe, Menheniott, Moss and Burgner1
One highly pro-inflammatory fetal exposure is preeclampsia. Preeclampsia is defined as a new-onset hypertension (blood pressure >140/90 mmHg) after 20 weeks of gestation in association with proteinuria (300 mg or more of protein in a 24-h urine collection) or hypertension without proteinuria but associated with organ dysfunction: thrombocytopenia, liver damage, new development of renal failure, pulmonary edema, or new-onset cerebral or visual complaints. It can be further classified in early (onset before 34 weeks of gestation) or late, being the former usually associated with worse outcomes.22 Although the pathophysiology for this syndrome is not fully understood, the delivery of the placenta is the only known treatment at this time, which suggests a placental origin for the disease. There appears to be an inadequate invasion of the muscular layer of the uterine spiral arteries by the trophoblast, resulting in an impaired, high vascular resistance placentation and placental ischemia in the early stages of pregnancy.Reference Khong, De Wolf, Robertson and Brosens23,Reference Rankin and McLaughlin24
In preeclamptic pregnancies, placental ischemia seems to prompt the release of antiangiogenic and pro-inflammatory soluble factors, like sFLt1 and soluble endoglin, that lead to systemic endothelial dysfunction and eventually microangiopathy in the third trimester. According to the target organ affected, it manifests as seizures, eclampsia, HELLP syndrome (hypertension, elevated liver enzymes, and low platelet count), glomerular endotheliosis, and proteinuria.Reference Naljayan and Karumanchi25 Moreover, the increased vascular resistance in the uteroplacental circulation and the impaired blood flow to the fetus may explain the increased frequency of fetal growth restriction, preterm delivery, and placental abruption in these pregnancies.Reference Fuke, Aono, Imai, Suehara, Fujita and Nakayama26
There are some known risk factors for preeclampsia: nulliparity, age > 40 years old, lower socioeconomic status, higher prepregnancy BMI, pregestational diabetes, multiple gestation, molar pregnancy, chronic kidney disease, chronic hypertension, prior history of preeclampsia or SGA infant, and a partner who fathered a preeclamptic pregnancy in the past.Reference Naljayan and Karumanchi25 The pathway that mediates the relation between these risk factors and the faulted placentation is unclear. Genetic factors, oxidative stress, dysregulation of the immune system, and enzymatic deficiencies have been suggested as having a potential role.Reference Naljayan and Karumanchi25 On the other hand, tobacco use seems to be protective against the development of preeclampsia, possibly due to its association with preterm birth (thus shortening pregnancy and decreasing the overall risk of preeclampsia).Reference Stone, Diallo, Shyken and Leet27
This syndrome complicates 4%–8% of pregnancies worldwide. In Portugal, the incidence of preeclampsia seems to be lower than in most countries, with some studies reporting incidences as low as 1.4%.Reference Povoa, Costa, Rodrigues, Patricio and Cardoso28 It is a very important cause of maternal and perinatal morbidity and mortality, with an estimated 60,000 related maternal deaths per year worldwide.22 It is estimated that preeclampsia accounts for 12%–25% of all fetal growth restriction and SGA infants, 15%–20% of preterm deliveries, and 25% of stillborn, encompassing significant current and future health burden.Reference Duley29 Additionally, women with pregnancies complicated by preeclampsia appear to be at risk for long-term cardiovascular and metabolic disease.Reference Ahmed, Dunford, Mehran, Robson and Kunadian30 The advances in healthcare and social changes allowed the increase in the mean maternal age and burden of comorbidities, and consequently the incidence of preeclampsia is rising.Reference Shih, Peneva, Xu, Sutton, Triche and Ehrenkranz31
Following the DOHaD theory, it was suggested that in utero exposure to a pro-inflammatory environment such as what is experienced in preeclampsia could lead to permanent changes in function and structure of the body of the offspring. In some studies, children born from preeclamptic mothers were found to have higher systolic and diastolic blood pressures at 9 and 17 years of age and an increased risk of stroke in adult life, independently of BMI, although other papers show conflicting results.Reference Kajantie, Eriksson, Osmond, Thornburg and Barker32–Reference Pinheiro, Brunetto, Ramos, Bernardi and Goldani34
A relationship between maternal preeclampsia and childhood obesity is recognized. However, most published studies fail to control for maternal prepregnancy BMI, smoking status, and parity, which are strong confounder of this association. In fact, a higher maternal BMI before conception is a known risk factor for both childhood obesityReference Washburn, Nixon, Russell, Snively and O’Shea35 and hypertensive complications of pregnancy like preeclampsia.Reference Povoa, Costa, Rodrigues, Patricio and Cardoso28
One small study, limited to children born before 37 weeks of gestation, showed a significant increase in obesity at 14 years old in male but not female infants born from preeclamptic mothers, without controlling for maternal prepregnancy BMI. A different study corroborated these findings, reporting that BMI z-score was higher in the offspring of preeclamptic mothers, independently from weight or gestational age at birth.Reference Gu, Lu and Li36 Other authors report no association between preeclampsia and childhood obesity, although failing to control for maternal prepregnancy BMI.Reference Miettola, Hartikainen and Vaarasmaki37 A published meta-analysis reported a significant increase in the BMI of children born from preeclamptic mothers, but it again fails to control for strong known confounders such as maternal prepregnancy BMI, smoking status, and partity.Reference Davis, Lazdam and Lewandowski38
The association between preeclampsia and childhood excess weight/obesity was not studied taking into account these potentially strong confounders. Taking these confounders into consideration could contribute to deepen our knowledge on the true association between preeclampsia and childhood obesity.
Methods
Participants
This study was conducted using Generation XXI Birth Cohort. From April 2005 to August 2006, during the postdelivery hospital admission (between 24 and 72 h after delivery) maternal consent was asked from all eligible mothers of singleton live births, in the five units delivering maternal and neonatal medical care in metropolitan area of Porto, Portugal. In total, 8351 children and their mothers were initially included (91.4% of those invited), of whom 5133 (61.4%) remained in the study 10 years after delivery (when the primary outcome was recorded). The women were inquired by trained professionals by face-to-face interview using a questionnaire on demographics, socioeconomic status, habits, family history, gynecological and obstetrical history, health conditions, prenatal care, and exposures during pregnancy. Hospital and pregnancy records were reviewed for associated medical conditions, pregnancy, and delivery data and complications. Data regarding newborn birth weight, Apgar score, and postpartum complications were collected from clinical records. Anthropometries were measured by trained health professionals.
Evaluations
All participants in the study were asked to attend further evaluations at 4, 7, and 10 years. All the evaluations, including anthropometrics and other data collection, were performed by trained health professionals.
Maternal data were also collected through inquiry (Fig. 1).
Flow chart of the participants in the study.
Assessment methods
Our main outcome is directly measured children’s BMI z-score at 10 years of age. Our exposure of interest is preeclampsia. Potential confounders were considered variables that cumulatively were an independent risk factor for childhood obesity, were associated with preeclampsia, and were not in the causal pathway between preeclampsia and childhood obesity. Parity, maternal prepregnancy BMI, gestational diabetes, maternal tobacco use during pregnancy, and maternal socioeconomic status were considered as potential confounders.
Children’s weight was assessed to the nearest gram using digital (SECA®) scales, at birth and at 10 years of age. Birth weight was assessed at birth, without clothes or diapers. Offspring’s height at 10 years of age was measured in centimeters and rounded to the next decimal case with a stadiometer (SECA®). Children BMI was calculated dividing weight in kilograms by height in squared meters. Children’s BMI was converted into BMI z-scores (sex and age-specific standard deviation scores) according to the recommendations of the World Health Organization (WHO, 2006). Both maternal prepregnancy BMI and adjusted children BMI scores were used as continuous variables. Categorization of maternal and children BMI followed WHO criteria: underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), obesity class I (30–34.9 kg/m2), obesity class II (35–39.9 kg/m2), and obesity class III (>40 kg/m2).
Maternal age, parity, prepregnancy BMI, gestational age at birth, type of delivery, history of gestational diabetes, gestational hypertension, and exposure to preeclampsia were obtained through medical clinical records. Gestational diabetes was diagnosed with a first trimester fasting glucose ≥92 mg/dl or a positive glucose tolerance test between 24 and 28 weeks of gestation. Gestational hypertension was diagnosed as a new-onset hypertension (blood pressure > 140/90 mmHg) after 20 weeks of gestation without significant proteinuria (<300 mg or less of protein in a 24-h urine collection) or organ dysfunction. Preeclampsia was diagnosed according to previously stated ACOG criteria.22
Familial socioeconomic status was ascertained through direct inquiry: educational attainment was measured in years of schooling and household monthly income was collected as a categorical variable (<500, 500–1000, 1001–1500, 1501–2000, 2001–2500, 2501–3000, >3000 euros). Maternal tobacco use during pregnancy was self-reported as a binary variable (yes, if any; or no, if none).
The Generation XXI study followed the Ethical Principles for Medical Research Involving Human Subjects stated in the Declaration of Helsinki and had the approval of Centro Hospitalar São João/University of Porto Medical School ethics committee.
Statistics
Continuous data are reported as means and standard deviations. Categorical data are reported as numbers and proportions. Proportions were compared using the Chi-square test, and means were compared using student’s t-test. Multiple linear regression models were used to analyze the primary outcome (offspring BMI z-score at 10 years of age), controlling for confounders. Multiple logistic regression models were used to analyze the binary outcome (excess weight/obesity), controlling for confounders. Confounders were tested and remained in the model based on statistical significance to the model. Since there is a strong association between parity and maternal prepregnancy BMI (with higher parity being associated with higher mean BMI), an interaction term of parity and prepregnancy BMI was tested, to assess if there was a differential effect of maternal BMI on childhood obesity according to parity. In addition, since some authors postulate that there is an association between fetal gender and the incidence of preeclampsia (depending on the studies, some reporting an increased incidence of preeclampsia in male fetuses and others in female fetuses), an interaction term of fetal female gender (binary) and preeclampsia was also tested.
Two-sided P-values of <0.05 were used to determine statistical significance, and two-sided confidence intervals of 95% are reported.
Role of the funding source
The funding source did not have any role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.
Results
Our study sample is described in Table 1. The mean maternal age at delivery was 29.8 years and mean maternal prepregnancy BMI was 23.9 kg/m2. Most women (57.4%) were primiparas, 19.8% reported smoking during pregnancy, and 7.1% had the diagnosis of diabetes previously to pregnancy. The incidence of gestational diabetes was 6.6% and 2.3% had gestational hypertension. Preeclampsia complicated 1.3% of the pregnancies.
Comparison between eligible participants included in the study and lost to follow-up regarding maternal, pregnancy, and newborn characteristics
Participants lost to follow-up are younger and less educated. They also tend to have a lower proportion of primiparas, lower rate of delivery through cesarean section, and higher proportion of tobacco smoke during pregnancy than their counterparts. Interestingly, patients who did not continue in the study were more likely to have had preeclampsia.
Preeclamptic mothers, as described in Table 2, were more frequently primipara (71.6% vs 57.2%), had a higher BMI (25.8 vs 23.9 kg/m2), and a higher incidence of gestational diabetes (13.4% vs 6.6%) and hypertension (29.9% vs 1.9%). Their children were born earlier, more frequently delivered through cesarian section, and had lower birth weights (2406 g vs 3203 g). In the same Table 2, at 10 years of age, children born from preeclamptic mothers had higher mean BMI (19.2 vs 18.8 kg/m2), BMI z-score (0.89 vs 0.72), and were more likely classified as overweight or obese (52.2% vs 42.4%) but these differences were not statistically significant.
Maternal, pregnancy, and child characteristics according to preeclampsia status
A linear regression with BMI z-score confirmed this trend (Table 3). Maternal prepregnancy BMI, maternal smoking during pregnancy, and primiparity were identified as potential confounders and added to the model. This lead to a significant decrease in the beta coefficient for preeclampsia from 0.175 to −0.014.
Linear regression models showing the association between eclampsia/preeclampsia and BMI z-score at 10 years of age – crude and adjusted for prepregnancy BMI, primipara status, and tobacco smoke during pregnancy
* P < 0.005.
Type of delivery and maternal gestational diabetes were not significantly associated with the outcome in our cohort and thus were not included in our final model. An interaction term of parity and maternal prepregnancy BMI was tested and the results were not significantly different, so it was not included in our model. Our study had no power to stratify the cohort in male and female offspring, although the addition of an interaction term to the model (fetal gender × preeclampsia) suggested that there could be a differential effect of gender on the outcome (P = 0.008). However, due to our limited ability to accurately study interactions given the small number of events, we opted for the noninclusion of this term in our model.
Furthermore, categorization of maternal BMI as defined by WHO showed an approximately linear association between maternal prepregnancy BMI and offspring BMI z-score (Table 4).
Linear regression models showing the association between eclampsia/preeclampsia and BMI z-score at 10 years of age – crude and adjusted for prepregnancy BMI, primipara status, and tobacco smoke during pregnancy
* P < 0.005.
A multiple logistic regression for the binary outcome childhood obesity/excess weight, with the same terms in the model, was conducted (Table 5), attributing a nonsignificant influence of preeclampsia on the outcome, OR = 1.23 (95% CI 0.74–2.03). However, a significant association between childhood obesity and maternal excess weight was found, OR 1.88 (95% CI 1.62–2.17), as well as with maternal obesity class I, II, and III, OR 4.77 (95% CI 3.67–6.20), OR 4.69 (95% CI 2.93–7.51), and OR 4.19 (95% CI 1.71–10.26), respectively. A positive association was also found with primipara status, OR = 1.21 (95% CI 1.07–1.36) and tobacco use during pregnancy OR = 1.45 (95% CI 1.25–1.67).
Logistic regression models showing the association between eclampsia/preeclampsia and overweight/obesity status – crude and adjusted for prepregnancy BMI and multipara status
* P < 0.005.
Discussion
Childhood obesity is a complex syndrome. Preeclampsia, a severe inflammatory exposure, is postulated by some authors to increase the risk of offspring childhood obesity and cardiovascular disease in the future.Reference Kajantie, Eriksson, Osmond, Thornburg and Barker32–Reference Pinheiro, Brunetto, Ramos, Bernardi and Goldani34
However, most studies did not account for strong confounders of this association such as maternal prepregnancy BMI, diabetes, and tobacco use. Our study aimed to analyze the association between preeclampsia and childhood excess weight/obesity, accounting for these confounders.
As seen in Table 1, in our cohort, the incidence of preeclampsia was low (1.3%) when compared to the incidence reported by other European countries (4%–8%).22 However, it is consistent with previous studies conducted in Portugal that report national incidences of 1.4% and question the possible role for the Mediterranean climate or diet in decreasing the risk for this condition.Reference Povoa, Costa, Rodrigues, Patricio and Cardoso28 Additionally, mean maternal BMI in our cohort (23.9 kg/m2) is slightly lower than that reported for Portuguese females over 18 years old by WHO.39
Individuals lost to follow-up are younger and less educated, which is consistent with reported higher rates of loss to follow-up. They also had a higher incidence of preeclampsia (1.9% vs 1.3%, P = 0.026) that might be explained by their younger age. This differential loss to follow-up might have unfortunately decreased the power of our study.
Preeclamptic mothers, as summarized in Table 2, present the traditional risk factors described in the literature.Reference Naljayan and Karumanchi25 They were more frequently primipara, had a higher BMI, and a higher incidence of gestational diabetes and hypertension when compared with their controls. Their children were born earlier, more frequently delivered through cesarian section, and had lower birth weights, outcomes consistent with previous knowledge.Reference Naljayan and Karumanchi25 There seems to be an increased prevalence of female offspring amongst women with preeclampsia (61.2% vs 38.8%) although not significant. The literature is conflicting regarding the influence of fetal gender in preeclampsia risk.Reference Jaskolka, Retnakaran, Zinman and Kramer40,Reference Schalekamp-Timmermans, Arends and Alsaker41
According to some authors, the pro-inflammatory and pro-oxidative environment associated with preeclampsia could trigger fetal genetic programming, favoring childhood obesity.
In the same Table 2, at 10 years of age, children born from preeclamptic mothers had higher mean BMI, BMI z-score, and were more likely classified as overweight or obese, but these differences were not statistically significant, potentially due to the small sample size. A crude linear regression with the offspring BMI z-score supported these results (Table 3).
However, when the association between preeclampsia and offspring BMI z-score was adjusted for maternal prepregnancy BMI, primipara status, and tobacco smoke during pregnancy, there was a high magnitude change in the beta coefficient (from 0.175 to −0.014) which suggests that these variables are important confounders of this association. In fact, as seen in Table 3, there seems to be a strong association between maternal prepregnancy BMI and offspring BMI z-score, as previously reported in the literature.Reference Gurnani, Birken and Hamilton3,Reference Reynolds, Jacobsen and Drake9–Reference Han, Lawlor and Kimm12 These conclusions are further supported by the finding of a consistent trend between increased maternal prepregnancy BMI categories and higher offspring BMI z-score (Table 4).
The addition of the interaction term (fetal gender × preeclampsia) to the multiple linear regression model in Table 4 was significant (P = 0.008), suggesting a differential effect of preeclampsia on children’s BMI according to gender. However, the very limited number of events prevents us from accurately study interactions in our cohort.
A multiple logistic regression for the binary outcome childhood obesity/excess weight confirmed the findings of the linear regression model (Table 5), attributing a significant OR to maternal prepregnancy BMI, primipara status, and tobacco use during pregnancy but a nonsignificant influence of preeclampsia on the outcome. Once again, the beta coefficient for preeclampsia decreased significantly after controlling for these confounders.
These results suggest that, according to existing literature, maternal prepregnancy BMI, primiparity, and tobacco use during pregnancy significantly increase the likelihood of childhood obesity in the offspring. In addition, the reported association between preeclampsia and childhood obesity is heavily confounded by these variables. In utero exposure to preeclampsia seems to have a small impact in childhood obesity when compared to the previously described confounders. However, further studies are needed to test this hypothesis, controlling for such confounders.
Our study has some important limitations. The small sample size, low number of preeclampsia events, and low prevalence of maternal obesity conferred low power to the study to detect significant differences between groups and to investigate interactions between confounders. This was aggravated by differential loss to follow-up. Furthermore, we have insufficient data on breastfeeding habits, which are known to be a potential confounder of the association between preeclampsia and childhood obesity, and no information regarding induction of labor. Nevertheless, our study includes a significant sample, a considerable follow-up period (10 years) when compared to studies already published, and controls for frequently unaddressed confounders.
Acknowledgments
The authors would like to acknowledge the families participating in Generation XXI, the members of the research team, and the participating hospitals and their staff that made Generation XXI birth cohort possible.
Financial support
This work was funded by Programa Operacional de Saúde – Saúde XXI, Quadro Comunitário de Apoio III and Administração Regional de Saúde Norte (Regional Department of Ministry of Health); FEDER through the Operational Programme Competitiveness and Internationalization and national funding from the Foundation for Science and Technology – FCT (Portuguese Ministry of Science, Technology and Higher Education) (POCI-01- 0145-FEDER-016837), under the project “PathMOB.: Risco cardiometabólico na infância: desde o início da vida ao fim da infância” (Ref. FCT PTDC/DTP-EPI/3306/2014); Unidade de Investigação em Epidemiologia - Instituto de Saúde Pública da Universidade do Porto (EPIUnit) (POCI-01-0145-FEDER-006862; Ref. UID/DTP/04750/2013).
Conflicts of interest
None.
Ethical standards
The Generation XXI study followed the Ethical Principles for Medical Research Involving Human Subjects stated in the Declaration of Helsinki and had the approval of Centro Hospitalar São João/University of Porto Medical School ethics committee.
