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Obstetrics & Gynecology 2004;104:286-292
© 2004 by The American College of Obstetricians and Gynecologists
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ORIGINAL RESEARCH

Body Mass Index Change Between Pregnancies and Small for Gestational Age Births

Christine J. Cheng, MD*, Kerry Bommarito, MPH*, Akihiko Noguchi, MD, MPH*{dagger}, William Holcomb, MD{ddagger} and Terry Leet, PhD*{ddagger}

From the *Department of Community Health, Saint Louis University School of Public Health; and Departments of {dagger}Pediatrics and {ddagger}Obstetrics, Gynecology, and Women's Health, Saint Louis University School of Medicine, St. Louis, Missouri.


   ABSTRACT
TOP
ABSTRACT
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
OBJECTIVE: To estimate whether maternal weight changes between pregnancies influence the risk for small for gestational age (SGA) births.

METHODS: SGA cases (n = 8,062) below the tenth percentile birth weight for gestational age were selected from liveborn singletons born of Missouri residents during 1989–1997. Normal weight controls (n = 8,062) were selected according to birth year. The risk of SGA from interpregnancy body mass index (BMI) change and other maternal factors was estimated using logistic regression analysis.

RESULTS: An increase in BMI between pregnancies decreased SGA risk (adjusted odds ratio = 0.8; 95% confidence interval 0.7, 1.0). Other risk factors were prior SGA (4.4; 4.0, 4.8), preeclampsia/eclampsia (2.6; 2.1, 3.2), maternal cardiac disease (1.8; 1.1, 2.9), inadequate weight gain (1.9; 1.8, 2.2), and cigarette smoking (1.9; 1.7, 2.3 for 1–9 cigarettes per day; 2.5; 2.2, 2.8 for 10–19/d; and 2.8; 2.5, 3.3 for 20/d or more).

CONCLUSION: Increase in interpregnancy BMI lowers SGA risk, but adequate weight gain during pregnancy is more effective.

LEVEL OF EVIDENCE: II-2

Low birth weight (less than 2,500 g) infants have a higher risk of morbidity and mortality. Small for gestational age (SGA) infants, defined as below tenth percentile birth weight for gestational age, have higher mortality rates in the perinatal period1,2 and are at risk for later problems such as continued poor physical growth,3–5 poor school performance,6–8 and insulin resistance.9 The causes for SGA births seem to be multifactorial and include biologic, social, economic, and behavioral factors,10,11 which are difficult to change.

Many studies have identified low maternal prepregnancy weight12–16 and body mass index (BMI)16,17 as risk factors for SGA births. In a large Swedish study, Cnattingius et al17 found that the risk of delivering an SGA infant was highest among lean women regardless of parity. Previously reported risk levels in underweight women have ranged from 1.5 to 2.4 times greater than normal.13,14 Maternal factors such as having a previous SGA birth12 and smoking13 can further increase the likelihood of SGA up to 6 to 8 times that of normal-weight women. Because of these previous findings, we chose to explore the relationship between appropriate weight gain between pregnancies and the risk for SGA, using birth certificate data from Missouri.


   MATERIALS AND METHODS
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ABSTRACT
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
A population-based unmatched case-control study of second-born SGA infants born in Missouri during 1989–1997 was conducted. Cases and controls were selected from the Missouri maternally linked cohort, which links birth certificate data for siblings using maternal identifiers. The methods used to link birth certificate data into sibships and to determine the accuracy of the data have been described elsewhere (Schramm W. Data quality: new certificates. Presented at the AVRHS/VSCP Project Directors Meeting, San Francisco, May 1991).18 The year 1989 was chosen as the start of the study interval because it was the first year that clinical estimate of gestation age became a required field on the Missouri birth certificate. At the initiation of this study, 1997 was the last year of available data. Using an estimated 10% incidence of SGA in the study population, a sample size of 7,536 (3,768 cases and 3,768 controls) would produce an unmatched case-control study with 80% power to detect a statistically significant (P < .05) odds ratio of 1.24 or greater. The Saint Louis University Institutional Review Board classified this study under exemption 45 CFR 46.101(b), which permits epidemiologic research using publicly available data without personal identifiers.

To estimate the risk of SGA among second births, cases were identified as singleton second-born infants who were born SGA, defined as being less than the tenth percentile of birth weight, based on a U.S. national fetal growth reference curve.19 An equal number of second-born controls was randomly selected by year of birth from the remaining cohort of mothers with 2 live births during the study period. Subjects with missing demographic or obstetric data that prevented computation of the primary variables (BMI, SGA status) were excluded.

Factors that previously have been associated with SGA births13,16,20,21–24 were evaluated as covariates, provided the information was available on the birth certificate. The covariates included maternal demographic and lifestyle variables (age, race, height, prepregnancy weight, cigarette smoking status, marital status, Medicaid, and education). Maternal age was divided into three categories (younger than 20 years, 20–34 years, and 35 years or older). Maternal race was categorized as non-Hispanic White, non-Hispanic African American, or other. The "other" races were excluded from the final model, because they made up less than 3% of all Missouri residents. Maternal prepregnancy weight and height values were based on self-report and used to calculate maternal BMI (kg/m2) for the first birth, which was categorized as underweight (less than 19.8), normal (19.8–26.0), overweight (26.1–29.0), or obese (more than 29.0).25 The average number of cigarettes smoked daily during pregnancy, as reported on the birth certificate, was grouped into four categories (0, 1–9, 10–19, and 20 cigarettes per day or more). Marital status, Medicaid enrollment, and level of maternal education (12 or fewer years, more than 12 years) were included as dichotomous variables.

The medical and obstetric covariates were clinically estimated gestational age of the second infant, interval between births, low weight gain during second pregnancy, SGA status of the prior sibling, Kotelchuck index of prenatal care use,26,27 paternity, preeclampsia or eclampsia, and maternal disease (anemia, cardiac disease, type 2 diabetes, renal disease, and chronic hypertension). Interpregnancy birth interval, calculated as months between the first and second births minus the clinically estimated gestational age of the second sibling,28,29 has been shown to affect perinatal outcomes. Birth intervals of 18 to 23 months have been reported to carry the lowest risk of SGA,28–30 so we categorized time between sibling births as less than 18 months, 18–23 months, and more than 23 months. Weight gain during pregnancy was divided by the completed week of gestation, and categorized as low (less than 0.2 kg/wk) or not low (0.2 kg/wk or more).13 The Kotelchuck index was used to estimate the adequacy of prenatal care use. The algorithm compares the observed number of prenatal visits to the expected number based on the gestational age at initiation of care. The four categories were labeled as inadequate (less than 50% of expected visits), intermediate (50–79%), adequate (80–109%), and adequate plus (more than 109%). Paternity for each of the 2 siblings was classified as "same" or "different/unknown." SGA in the prior infant, preeclampsia or eclampsia, and maternal chronic diseases were included as dichotomous variables.

The effect of BMI change between births on the risk of SGA among second-born infants was quantified using the odds ratio. Logistic regression analysis was used to calculate both crude odds ratios (cOR) and adjusted odds ratios (aOR) with 95% confidence intervals (CI). Two-way interactions between BMI change and potential confounders were assessed to test for effect modification, but no statistically significant interactions were seen. Regression diagnostics isolated no influential outlying observations and additional modeling detected no significant interaction effects. All analyses were performed with SPSS 10.0 (SPSS, Inc., Chicago, IL).


   RESULTS
TOP
ABSTRACT
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Initially the study population included 8,062 cases and 8,062 controls. After excluding study subjects with missing data, 6,973 cases (86%) and 7,141 controls (89%) were available for analysis. Subjects with missing data did not differ significantly from those who were included in the analysis (data not shown). Table 1 shows the risk of each demographic and lifestyle factor for SGA. Stratified analysis demonstrated that any BMI increase between pregnancies decreased the risk of SGA birth (cOR = 0.7, CI = 0.7–0.8), whereas any BMI decrease between pregnancies increased the risk of SGA (cOR = 1.4, CI = 1.3–1.5). Younger mothers (less than 20 years old) were more likely to have an SGA child. Unmarried mothers (cOR = 1.6, CI = 1.5–1.8), those with 12 years of education or less (cOR = 1.9, CI = 1.8–2.0), and those enrolled in Medicaid (cOR = 1.9, CI = 1.8–2.0) were more likely to have an SGA infant. Different fathers for the two siblings or unknown paternity of the second child, was associated with SGA (cOR = 1.5, CI = 1.4– 1.6). Mothers who were underweight were more likely to give birth to an SGA infant (cOR = 2.0, CI = 1.9–2.2), whereas being overweight or obese seemed protective (cORoverweight = 0.8, CI = 0.8–0.9; cORobese = 0.8, CI = 0.7–0.8). The overweight and obese groups had similar odds ratio estimates and therefore were combined into a single group for all subsequent analysis. Cigarette smoking during pregnancy demonstrated a dose-response relationship.


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  		Table 1. Independent Risk Factors Associated With Small for Gestational Age Birth in the Second Pregnancy Among 16,124 Infants Born in Missouri, 1989–1997; Maternal Demographic and Lifestyle Factors

 

Of the medical and obstetric factors examined, previous SGA birth had the highest unadjusted risk for subsequent SGA birth (cOR = 5.1, CI = 4.7–5.6) (Table 2). Low weight gain during the second pregnancy (cOR = 1.8, CI = 1.6–2.0) was more likely to lead to an SGA infant. Preterm delivery, defined as less than 37 completed weeks of gestation, produced an odds ratio of 1.7 (CI = 1.5–1.9) for SGA. Mothers with "inadequate" prenatal care were at higher risk for SGA (OR = 1.6, CI = 1.4–1.7) than those with "intermediate" (cOR = 1.1, CI = 1.0–1.1) or "adequate plus" (OR = 1.3, CI = 1.2–1.3) prenatal care. The maternal medical conditions that increased the risk for SGA birth were preeclampsia (OR = 2.2, CI = 1.8–2.6) and cardiac disease (OR = 1.6, CI = 1.1–2.5). Maternal diabetes (gestational or other) offered protection against SGA (OR = 0.7, CI = 0.6–0.9). The birth interval between the first and second children did not affect SGA risk.


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  		Table 2. Independent Risk Factors Associated With Small for Gestational Age Birth in the Second Pregnancy Among 16,124 Infants Born in Missouri, 1989–1997; Obstetrical and Medical Factors

 

The aOR and 95% CI for covariates included in the multivariable logistic regression model are presented in Table 3. Unchanged or increased BMI between the pregnancies reduced the risk for SGA (aOR = 0.8, CI = 0.7–1.0), whereas a decrease in BMI between pregnancies increased the risk (aOR = 1.2, CI = 1.0–1.4). Smoking increased SGA again with a dose-response pattern. Preeclampsia or eclampsia increased SGA risk, as did low weight gain during pregnancy and maternal cardiac disease. Inadequate prenatal care, low maternal education, and Medicaid enrollment had lower risk for SGA. Marital status, paternity, preterm delivery and the other maternal medical conditions did not contribute significantly to the model. Maternal age, race, and year of birth of the second child did not affect SGA risk.


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  		Table 3. Logistic Regression Model for Risk of Small for Gestational Age Birth in the Second Pregnancy Among 14,114 Infants Born in Missouri, 1989–1997

 

To examine whether the risks varied according to maternal prepregnancy weight, separate logistic regression models were created for the study population stratified by BMI category. In this analysis, BMI decrease between pregnancies remained a risk factor for SGA in all BMI categories except for women who were overweight. The reason for this finding is unclear. None of the other risk factors differed markedly between the BMI categories, except maternal cardiac disease, which was associated with a very high risk for SGA in obese women (Fig. 1).



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  		Fig. 1. Multivariate logistic regression model of small for gestational age (SGA) risk by maternal body mass index (BMI) category. Risk is calculated as adjusted odds ratio, with 95% confidence intervals listed. The BMI categories are in kg/m2. Medicaid status, Kotelchuck index, and preeclampsia or eclampsia have been omitted. Vertical line signifies odds ratio = 1.0.

Cheng. Interpregnancy BMI and Risk of SGA. Obstet Gynecol 2004.

 


   DISCUSSION
TOP
ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Our study suggests that maternal weight gain between pregnancies can decrease the risk of producing an SGA infant, even after adjusting for other known risk factors. The strongest risk factors identified in this study—prior SGA sibling, maternal smoking during pregnancy, preeclampsia or eclampsia, and poor maternal weight gain during pregnancy, are consistent with those identified in previous studies.11,14–16,21,22,31 Although many studies have associated low maternal height and birthweight with recurrent SGA births,12,13,23,32,33 these factors cannot be easily altered. Low maternal prepregnancy weight, which can be modified, has been a significant risk factor for SGA in several hospital-based14,15 and population-based16,19 studies. Longitudinal studies in Sweden34 and the United States35 have found that women of child bearing age gain an average of 2 kg between pregnancies, with heavier women gaining slightly less. For a skeletally mature adult woman of average height (1.64 meters) and weight (63 kilograms), a 2-kg weight increase between pregnancies would amount to a 0.8 unit increase in body mass index, or a predicted 16% reduction in risk for SGA in the subsequent child.

A primary limitation of this study was its reliance on birth certificate data. However, the birth certificate is the only source of data that is readily available for every infant born in the United States, and it can be obtained easily and at a relative low cost.36 The potential problem is missing or incorrect data. Although 10–15% of our study subjects were excluded due to missing data, a study with more than 90% power and 99% confidence was still achieved. Demographic information has been shown to be reliable, but medical information can be less reliable.36,37 In addition to systematic underreporting of maternal risk factors such as smoking and alcohol use, important factors such as prescription and illicit drug use, congenital and chromosomal abnormalities, and genetic tendencies for SGA such as family history and parental birthweight11,12 are not available on the current birth certificate form.

This study relied on self-reported prepregnancy weight to calculate BMI, which was used to determine the primary exposure. Thus, accurate determination of weight was critical. Although self-reported body weight has been frequently used as a surrogate for measured weight,38 bias due to systematic underreported or overreporting is a concern. Previous investigators have shown self-reported weight to be highly correlated with actual measured weight (r = 0.98–0.99) independent of age.38,39 Heavier individuals tend to underreport their weight, whereas lighter individuals tend to overstate their height and weight. Such misclassification would bias results toward the null and underestimate the actual effects.

The primary outcome of small for gestational age relies also on the accurate estimation of gestational age at birth. Clinically estimated gestational age as listed on the birth certificate was used in this study, which was determined by assessment of the physical and neurologic characteristics of the newborn, plus fetal ultrasound, when available. An alternative method for estimating gestational age calculates the weeks between the mother's reported last menstrual period and the infant's date of birth. Acceptable correlation between these two methods has been shown (r = 0.77),40 although SGA was classified slightly less often with the clinical estimation. Systematic misclassification of SGA infants as normal weight would again bias results toward the null, again underestimating the actual effects.

Although our results show that increased maternal weight and BMI between pregnancies is associated with lower risk of SGA in a subsequent pregnancy, the amount of recommended weight increase is not known. Gaining excessive weight can lead to production of a large birth weight infant, especially among obese women, which has its own risks. In our regression model, interpregnancy weight gain only accounted for 23% of the variance in SGA risk and the corresponding risk was small compared to that of inadequate weight gain during pregnancy. This suggests that the covariates examined are still likely proxies for the true causes of SGA.


   Footnotes
 
Poster presentation at the Society for Maternal–Fetal Medicine 23rd Annual Meeting, San Francisco, California, February 2003.

Received January 27, 2004. Received in revised form April 4, 2004. Accepted April 29, 2004.

Reprints are not available. Address correspondence to: Christine J. Cheng, MD, Department of Surgery, Division of Plastic and Reconstructive Surgery, Washington University School of Medicine, One Children's Place, 11W7, St. Louis, MO 63110; e-mail: chengc{at}msnotes.wustl.edu.

10.1097/01.AOG.0000134526.37657.b0


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 DISCUSSION
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