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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Design and Methods
  5. Results
  6. Discussion
  7. References

Objective

Excessive weight gain during pregnancy has an important influence on fetal growth and on weight development in future generations.

Design and Methods

A prospective cohort study of 325 obese and nonobese Caucasian women with naturally conceived, singleton pregnancies was performed. They were followed up until delivery for maternal weight gain and for fetal growth with ultrasound-based weight estimations and final birth weight. Using cluster analysis distinct profiles of maternal weight gain during pregnancy were obtained. Longitudinal regression analysis was performed to investigate the relationship of the maternal weight gain profile and BMI on fetal growth and final birth weight.

Results

Cluster analysis revealed four discernable maternal weight gain profiles: 12 cases (3.7%) ended up at their starting weight or decreased in weight (cluster 1), 16 cases (4.9%) who slightly increased in weight (maximum 4 kg) as compared to their initial weight (cluster 2), 114 cases (35.1%) who gained between 4 and 12 kg in weight (cluster 3), and 183 cases (56.3%) who showed the largest weight gain: more than 12 kg (cluster 4). There were statistically significant differences in fetal growth associated with weight gain cluster, which became apparent late in the second trimester and increased toward the end of pregnancy. Maternal BMI and maternal weight gain profile were independent predictors of fetal growth and birth weight.

Conclusions

Therefore, the conclusion is that the cluster analysis permits to discern four gestational weight gain (GWG) patterns in obese and nonobese subjects and that both maternal BMI and maternal weight gain pattern during pregnancy positively influence fetal growth and birth weight.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Design and Methods
  5. Results
  6. Discussion
  7. References

Maternal weight gain shows a wide variation, even in a low-risk homogeneous Caucasian population with normal pregnancy outcome [1]. Despite this, the importance of adequate gestational weight gain (GWG) is well documented [2, 3].

Excessive weight gain, based on the 2009 IOM guidelines, has an incidence of up to 50% in pregnancy and currently represents an important problem in Western populations [4-7]. It is associated with gestational diabetes mellitus (GDM), an abnormal increase in fetal growth, neonatal macrosomia, emergency caesarean section, and postpartum weight retention [8]. This in turn feeds into the pandemic of obese pregnant mothers with its resultant deleterious influence on future generations through the effect of fetal programming [9-14].

Besides GWG, preconception BMI also affects fetal growth and pregnancy outcome: an abnormally high or low maternal BMI prior to gestation has been shown to influence fetal growth in the second and third trimester of pregnancy and is associated with a variety of adverse outcomes [15-17]. However, maternal BMI does not seem to significantly affect growth in embryonic crown rump length (CRL) during the first trimester [18].

BMI groups are defined by the World Health Organization (WHO): BMI ≤ 18.5 kg/m2 underweight; BMI ranging from 18.5 to 24.9 kg/m2 normal weight; BMI ranging from 25.0 to 29.9 kg/m2 overweight and ≥ 30.0 kg/m2 obese. In recognition of the dual influence on fetal growth, current guidelines refer to an appropriate GWG range that is best adhered to for each of the BMI groups, with the lowest gain being advised in the obese group [7, 19].

This is based on epidemiologic data looking at a total maternal weight gain and its relationship to healthy pregnancy outcome [8]. Few studies, however, have looked longitudinally at maternal weight during pregnancy [20-23]. Women's individual weight gain pattern, influenced by nutrition and life-style during pregnancy, can be expected to have a greater impact on embryonic or fetal growth than preconception BMI [24, 25]. Because maternal weight gain pattern is being monitored at sparse set of time points, cluster analysis is particularly useful by using a discriminant function to determine the greatest separation between the clusters [26].

The aim of this study was to investigate whether the maternal weight gain profile in pregnancy and preconception BMI independently influence fetal growth.

Design and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Design and Methods
  5. Results
  6. Discussion
  7. References

This prospective study was performed at the Department of Obstetrics and Gynecology of the University Hospitals Leuven (Belgium), with approval of the Ethics Committee for Human Experimentation of the Faculty of Medicine, Katholieke Universiteit Leuven. All women signed informed written consent to take part in the study.

Between April 2007 and April 2008, the nutritional intake and GWG in 605 pregnancies were prospectively recorded by a single clinician (IG). From this cohort there was a well-documented subgroup of 373 healthy Caucasian women who underwent ultrasound examinations to measure fetal size in the University Hospital Leuven and delivered in this center. They were recruited before 15 weeks gestation at the time of their first antenatal clinic visit and pregestational BMI was documented at this time. All the pregnancies were conceived naturally and showed normal evolution at the time of inclusion.

The endpoint of the pregnancies was the date of delivery, which ranged from 177 to 291 days gestation, on which the last maternal weight and the actual neonatal weight was measured. Exclusion criteria were inadequate knowledge of the Dutch language, multiple pregnancies, and underlying or development of significant maternal or fetal pathology. The incidence of premature delivery was 21/325 (6.4%), in accordance to earlier studies in the general obstetrical population [27].

From the 373 pregnancies initially recruited, 48 pregnancies were excluded because of missing complete longitudinal data from either weight measurements of the mother or from estimated weight measurements of the fetus. This was because of either drop outs (lost to follow-up or migration) or miscarriage, termination of pregnancy, fetal death and twins, leaving a total of 325 viable, singleton pregnancies included in the final analysis (Figure 1).

image

Figure 1. Inclusion flowchart based on preconception BMI groups. Missing longitudinal data (maternal weight measurements or fetal biometry measurements) were excluded.

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All women underwent first trimester sonographic estimation of gestational age (GA) based on CRL obtained between 7 and 14 weeks of gestation.

Maternal weight measurements were performed using a calibrated balance (Seca Alpha Model 770, Teleflex medical, Belgium), accurate to 0.1 kg in a standardized way (without shoes, with indoor clothing). Height was measured with a microtoise to the nearest 0.5 cm, all as described earlier in Guelinckx et al. [1]. Preconception BMI was calculated using height measurement at the first visit and the self-reported weight prior to conception (kg/m2) [1, 7]. The total GWG was computed using the final weight measured on the day of delivery minus the self-reported weight prior to conception.

Each subject followed routine antenatal care including a minimum of three ultrasound examinations (first, second, and third trimester) and additional examinations if necessary. All scans were performed by experienced ultrasonographers on a high-end ultrasound machine (Voluson 730-E or Voluson E-8, GE Medical systems, kretztechnik, Zipf, Austria) and they measured the bilateral parietal diameter (BPD), the abdominal circumference (AC), and the femur length (FL). The estimated fetal weight (EFW) was calculated using Hadlock IV curves [log(10) EFW = 1.335 − 0.0034(AC)(FL) + 0.0316(BPD) + 0.0457(AC) + 0.1623(FL)] [28]. These curves are the same for both male and female fetuses.

Birth weight was measured 1 hour after delivery according to the recommendations of the WHO's rules on the Baby Friendly Hospital Initiative. The birth weight was measured on a standard calibrated neonatal scale (Seca, model 376, 0.1-7.5 kg, Teleflex medical).

Statistical analysis

Firstly, a cluster analysis was performed on the weight gain patterns of all women (Figure 2) [26]. Subsequently, the measurements were retrospectively assigned to four different clustered groups based on their GWG. Next, the maternal weight gain cluster assignment was used as an explanatory variable in a mixed effects longitudinal regression model to assess the evolution of the EFW (Hadlock IV curves) over time [29]. In this analysis, the actual birth weight of the neonate was added to the fetal weight estimates as an extra observation per case (Figure 3). Other explanatory variables in the analysis were BMI (in kg/m2), (G)DM (yes/no), GA (in days), parity (0-5), maternal age (in years), vitamin use (yes/no), and complications (yes/no). This model was further reduced by tests for the presence of random effects (GA, GA2, and GA3) and their structure and for the main and interaction effects of the explanatory variables. However, maternal weight gain profile and BMI were forced in the model. Finally, F-tests were performed for comparing the evolution of the EFW over time for each pair of weight gain profile with correction for multiple testing being applied using the approach by Hochberg [30]. Statistical analyses were performed using SAS 9.2 (SAS Institute Inc., Cary, NC, USA) and Matlab 7.4 (The MathWorks Inc., Natick, MA, USA).

image

Figure 2. Maternal weight gain profile during gestational age (GA) reduced to four clusters.

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image

Figure 3. Evolution of estimated fetal weight (EFW) during pregnancy according to GWG cluster. Birth weight measurements are represented by gray dots.

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Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Design and Methods
  5. Results
  6. Discussion
  7. References

Study population

A total of 325 out of 373 prospectively included pregnant women from different preconception BMI subgroups were included in the final analysis: 24 (7.4%) were underweight, 71 (21.8%) showed normal weight, 92 (28.3%) were overweight, and 138 (42.5%) were obese (Figure 1). Baseline characteristics of the study population are summarized in Table 1.

Table 1. Subjects baseline characteristics after exclusion (N = 325)
 BMI category (kg/m2)a
Underweight <18.5 N = 24 (7.4%)Normal weight 18.5-24.9 N = 71 (21.8%)Overweight 25.0-29.9 N = 92 (28.3%)Obese ≥30.0 N = 138 (42.5%)
  1. a

    BMI group (WHO criteria).

Nulliparous (%)8 (33.3)48 (67.6)48 (52.2)47 (34.1)
Mean age in years (range)29.38 (21-35)29.76 (19-41)28.74 (17-41)28.90 (20-41)
Smoking at inclusion (%)1 (4.2)4 (5.6)4 (4.3)20 (14.5)
Bariatric surgery (%)0 (0)0 (0)3 (3.3)2 (1.4)

Cluster analysis

Individual patients were divided into eight different groups according to their GWG. These were reduced to four clusters, as described above, by assigning the lowest GWG group as cluster 1, the second group as cluster 2, the following three groups taken together as cluster 3 and the subsequent three groups as cluster 4 (Figure 2). Therefore four different clusters with specific GWG patterns remained: 12 cases (3.7%) that ended up at their starting weight or decreased in weight (cluster 1), 16 cases (4.9%), that slightly increased in weight (maximum 4 kg) as compared to their initial weight, 114 cases (35.1%) that gained between 4 and 12 kg in weight (cluster 3) and 183 cases (56.3%) that showed the most weight gain, with more than 12 kg (cluster 4). In general the weight of women in cluster 1 decreased during the first trimester of pregnancy while for example the gestational weight for women in cluster 4 showed the largest increase. Table 2 shows the characteristics per cluster.

Table 2. Subjects (n = 325) characteristics per cluster group
 Cluster ICluster IIClusters IIICluster IV
 N = 12N = 16N = 114N = 183
  1. a

    Mean (standard deviation).

  2. b

    Gestational age (GA) at birth: median (first- and third quartile; Q1and Q3).

  3. c

    Birth-weight: median (Q1, Q3).

  4. d

    Preconception body mass index (BMI): median (Q1, Q3).

Age in yearsa28.33 (2.35)26.63 (5.51)29.37 (3.62)29.28 (4.28)
GA at birth in daysb273 (267, 275)270 (266, 278)273 (267, 280)274 (269, 280)
Birth weight in gramc3265 (3060, 3565)3070 (2923, 3434)3380 (3080, 3610)3550 (3159, 3818)
Preconception BMI in kg/m2 d41.27 (36.82, 45.06)33.68 (31.90, 36)30.17 (25.31, 33.43)27.16 (21.47, 30.61)

Effect of BMI and Clusters on EFW and birth weight

The longitudinal regression model showed that the weight gain profile has a statistically positive significant effect on fetal growth (P < 0.0001) (Figure 3). The following P-values were obtained for the pair-wise comparisons: 0.6234 (cluster 1 vs. cluster 2), 0.0895 (cluster 1 vs. cluster 3), 0.0007 (cluster 1 vs. cluster 4), 0.0022 (cluster 2 vs. cluster 3), 0.0004 (cluster 2 vs. cluster 4), and <0.0001 (cluster 3 vs. cluster 4). On the basis of multiple comparison method of Hochberg, we found statistically significant differences between the clusters' EFW growth curves, except for fetal growth in cluster 1 versus clusters 2 and 3. The relatively large P-value for comparing clusters 1 and 2 is likely to be due to the relatively small number of observations in these clusters.

The model also shows that preconception BMI had an effect on EFW and birth weight, with a larger BMI resulting in a correspondingly larger EFW (P < 0.0001).

We subsequently compared BMI prior to conception with the different weight gain clusters to define their relative influence on birth weight at 280 days. Figure 4 and Table 3 show the estimated weight gain at 280 days with 95% confidence intervals for the four maternal weight gain profiles at BMI levels of 17, 22, 28, and 35 (under-, normal-, overweight, and obese).

image

Figure 4. Mean (CI) estimated fetal weight (EFW) at 280 days (term) for the different GWG clusters divided per BMI category (under-, normal-, over-weight, and obese).

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Table 3. Difference of fetal weight in gram at 280 days for different clusters and BMIs
 Cluster ICluster IICluster IIICluster IV BMI 17BMI 22BMI 28BMI 35
Cluster IV    BMI 35    
Cluster III   +173BMI 28   +130
Cluster II  +142+315BMI 22  +111+241
Cluster I +162+304+477BMI 17 +93+204+334

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Design and Methods
  5. Results
  6. Discussion
  7. References

This article describes the first cluster analysis of GWG and demonstrates the positive influence of increased GWG on fetal growth and birth weight in a predominantly obese sample of pregnant women.

During recent decades the prevalence of obesity (>30 kg/m2) prior to conception has increased to as high as 25% in some developed countries [5, 6]. Both obesity and excessive GWG increase the risk for pregnancy-related maternal and fetal complications, like GDM, hypertension, preeclampsia, emergency Caesarean section, macrosomia, intrauterine growth restriction, and fetal demise [10, 12, 15, 17]. Additionally excessive GWG results in maternal weight retention after pregnancy with increasing risk for obesity and consequently induces a transgenerational effect influencing the metabolic and vascular traits in the offspring [8, 14]. Therefore, it seems essential to focus on maternal weight gain during pregnancy as an important modifiable factor to decrease these risks [31]. In 2009, the Institute of Medicine (IOM) produced recommendations for total weight gain in pregnancy for each BMI category: 12.5-18 kg (underweight women), 11.5-16 kg (normal weight women), 7.0-11.5 kg (overweight women), and 5.0-9.0 kg (obese women) [7]. The aim of these recommendations is to avoid the additional burden of total excessive GWG, especially in the obese. Previous studies used the preconception BMI class and total GWG to examine their influence on pregnancy and pregnancy outcome, showing that particularly the preconception obese group and the excessive total GWG group have an increased risk for fetal overgrowth or macrosomia and early or late onset overweight in the offspring [32-35].

As fetal growth is not linear, but accelerates at mid trimester, it can be hypothesized that besides total GWG and preconception BMI, the pattern of weight gain during pregnancy also influences fetal growth [1, 22-24, 28]. If so, this could lead to an instrument in pregnancy that is easy to monitor and is easier to influence than preconception BMI. Additionally, such a methodology could also be of importance for the nonobese group, as both normal weight and overweight women can gain significant weight during pregnancy and hereby increase the risks to both themselves and their fetus [1, 4, 6].

The strengths of this study are its prospective design and the fact that detailed information from a single center on clinical parameters and ultrasound data were available for analysis in a large cohort of pregnancies including a significant number of well-categorized obese patients (n = 128, 39.4%). It furthermore represents the first study in which the pattern of GWG is taken into account by clustering rather than merely the resultant total GWG. This knowledge could add important information on understanding normal and abnormal weight gain, especially in the overweight and obese.

Our results show that patients, who start to gain weight in the first trimester and continue doing so during the entire pregnancy, show the largest positive effect on fetal (over-)growth (Figure 3). Around 180-200 days gestation (26-28 weeks), the clusters start to separate from each other in relation to their effect on EFW. These findings were independent of the BMI groups found in these clusters.

When comparing the different BMI groups within each cluster by taking representatives from each group (BMI 17, 22, 28, and 35), we found that preconception BMI remains an important determinant of fetal growth and birth weight. Therefore, it seems that besides the preconception BMI to select high-risk pregnancies, it is equally important to discuss the trajectory of the GWG. Because normal BMI, but excessive maternal weight gain and matching higher cluster, will lead to fetal overgrowth and consequently have an effect on future generations through fetal programming by increasing the risk of child obesity. Two different tools to achieve a reduction of this phenomenon is to discuss the risks of overweight and obesity in the preconception period and to acquire customized maternal weight gain curves (Figure 2). The latter can be adjusted if an abnormal pattern is observed.

Ideally, this information could be useful in clinical management and urge the clinician to intervene when rapid first trimester weight gain is detected in this high-risk subset. Naturally, pregnancy poses a specific problem as any treatment (surgical or pharmacological) might have an adverse effect on the fetus and is therefore ethically and medically not feasible. Much effort has been taken to study the effect on GWG by interventional programs such as diet and/or physical activity advice. They are sometimes hard to compare, because of their heterogeneity. The American College of Obstetrics and Gynecology (ACOG) guidelines [36] recommend at least 30 minutes activity per day for 5 days per week. A recent meta-analysis on interventions appears to favor the diet-based advice on physical activity for the effect on maternal weight gain and obstetric outcome [37]. New techniques to promote lifestyle alterations by motivational interviewing are currently under investigation for the obese and high-risk subset pregnancies.

Some limitations of our study also need to be discussed. Our cohort was part of a larger study group in which nutritional intake was monitored during pregnancy. Patients included could therefore be more aware of the problem of excessive GWG. This knowledge could potentially have influenced GWG in this group. Also, weight prior to conception used to calculate BMI was self-reported, which is difficult to avoid as recruitment took place when women were already pregnant. But self-reported weight has been studied in the context of pregnancy and does not lead to significant bias [38, 39]. Further studies on more heterogeneous groups will give more insight into the effect of GWG using clustering on fetal growth and birth weight. Also, it would be of interest to examine the effect on subsequent pregnancies of GWG control in the obese mothers.

We have shown using cluster analysis that GWG in pregnancy has an effect on fetal growth and actual birth weight partly irrespective of the women's preconception BMI. This effect has most impact from 180 to 200 days gestation when there is a separation between the different GWG clusters. By identifying this relationship between GWG, growth, and birth weight, we may now have the time potential to influence weight gain and so prevent both perinatal complications and possibly improve outcomes in future pregnancies and the metabolic and vascular traits in the offspring.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Design and Methods
  5. Results
  6. Discussion
  7. References
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