DOI: 10.1002/pd.4336
RESEARCH LETTER
Maternal body composition and birth weight Clare O’ Connor1,2*, Amy O’ Higgins2, Ricardo Segurado3, Michael J. Turner2, Bernard Stuart1,2 and Máireád M. Kennelly1,2 1
Ultrasound and Fetal Medicine Centre, Coombe Women and Infants University Hospital, Dublin, Ireland UCD Centre for Human Reproduction, Coombe Women and Infants University Hospital, Dublin, Ireland 3 CSTAR, University College Dublin, Belfield, Dublin, Ireland *Correspondence to: Clare O’Connor. E-mail: [email protected] 2
Funding sources: None Conflicts of interest: None declared
Previous work utilising segmental bioelectrical impedance analysis (BIA) has shown a correlation between maternal lean body mass and birth weight (BW).1 Importantly, in this large prospective observational study, there was no correlation between maternal fat mass and BW. Numerous smaller studies utilising serial BIA have shown second trimester body water to be predictive of BW.2 A smaller study performing BIA near term reported a correlation between lean body mass and BW.3 A postpartum assessment of women who delivered at term highlighted that lean body mass and total body water explained the major proportion of the BW.4 Maternal obesity is usually classified on the basis of a BMI, calculated as weight (kg)/[height (m)]2 > =30. BMI is a surrogate marker of adiposity but gives no information about actual fat mass. Most epidemiologic studies linking maternal BMI and BW are retrospective and based on self-reporting of weight and height, which under reports obesity levels. BIA is a direct measure of both fat and lean body (fat-free) mass. Bioelectrical Impedance Analysis is a simple, non-invasive system of measuring maternal body composition and estimates body composition by measuring the impedance or resistance to a small electrical current passed across body tissues. The body offers two types of resistance to the electrical current: capacitative resistance (reactance) and resistive resistance (resistance). The capacitance arises from the cell membranes and the resistance from the extracellular and intracellular fluid. Impedance is used to describe the combination of the two. The resistance of a tissue increases as the amount of suspended non-conducting material increases. The greater the lean body mass or water content of a person, the faster the current will pass through. The greater the fat mass, the greater the resistance to the current and the slower the current passing through. Measurement of BIA indices is an indirect evaluation of the body compartments. Previous studies5 have shown a strong correlation between the estimations of body composition Prenatal Diagnosis 2014, 34, 605–607
using isotope techniques and BIA indices providing strong validation proofs. It offers a safe, non-invasive, relatively inexpensive and easy to perform method for the measurement of maternal body composition.6 Previous clinical research has shown that in early pregnancy, the use of multi-frequency bioelectric impedance analysis is feasible and reproducible, and that it correlates strongly with clinical and endocrine markers of maternal adiposity.7 This prospective longitudinal study of 254 singleton pregnancies was part of the fetal growth trajectory study, which was carried out over a two-year period from July 2011 to June 2013. A power calculation deemed that 70 high risk women (including those with pre-gestational Diabetes Mellitus or obesity) and 100 controls would be adequate to achieve 80% power to detect a doubling of risk of abnormally high or low birth weight deliveries at a statistically significant level of p < 0.05. The calculations assumed a 20% risk in the control sample and a doubling to 40% in the high risk group. Women were recruited at their convenience after confirmation of an ongoing intrauterine pregnancy at their first-trimester booking scan. Inclusion criteria included age over 18 years, white European ethnicity and a singleton pregnancy. Exclusion criteria included a history of essential hypertension or pre-eclampsia and significant medical problems such as renal disease, cystic fibrosis, congenital cardiac disease with chronic hypoxia and drug abuse that may affect fetal growth. Those with pre-gestational Diabetes or that subsequently developed gestational Diabetes were included in the analysis. Subjects gave written informed consent and were given information leaflets. The study was approved by the Hospital Research Ethics Committee in June 2011. Bioelectric impedance analysis was performed using the Tanita MF 180CA with the woman in her bare feet and wearing light clothing; Segmental bioelectrical impedance analysis of the trunk and individual limbs was performed on each patient. Birth weight was recorded at delivery using an electronic scales and BW centiles were calculated using hospital reference norms. GROW centiles were also calculated, which © 2014 John Wiley & Sons, Ltd.
C. O’ Connor et al.
606
are customised centiles that include maternal demographics such as BMI and gestational age at delivery. An appropriately grown fetus was one where the estimated fetal weight plotted between the 10th and 90th centiles. A small for gestational age fetus was one where the estimated fetal weight (Hadlock 4) was less than the 10th centile for gestational age. Intrauterine growth restriction was defined as a BW less than the 10th centile. The definition of large for gestational age was based on centile thresholds > 90th centile. Approximate normality of all measures was verified by visual inspection. To estimate the relationship between maternal body composition, BW and BW centile, univariable and multivariable linear regression analyses were performed. Diagnostic plots were inspected to ensure validity of the regression models, and the influence of outliers was explored Demographic variables and clinical parameters were summarised as mean (SD). Possible confounding variables were examined using independent sample t-tests. For all tests, p-values < 0.05 were considered significant. All data were analysed using SPSS version 20 (IBM
Table 1 Multivariate linear regression analysis of predictors of birth weight Maternal predictors of birth weight Fat mass Fat-free mass
Regression coefficient 0.8 13.7
95% confidence interval 7.1, 8.7 0.4, 27.1
Smoking
-155.5
273.3,
Gestational age (weeks)
182.7
152.3, 213.1
Maternal age (years) Parity
1.3 52.8
37.6
p-value 0.8 0.04 0.01 37 weeks. The mean BW was 3484 g (standard deviation 570 g). BW and BMI were normally distributed. At delivery, BW was plotted against gestational age and 15.0% (37/246) plotted greater than the 90th centile according to hospital reference norms8 and 5.6% (14/246) infants plotted less that the 10th centile. Using the GROW customised growth centiles, 10.3% (25/246) infants plotted > 90th centile and 13.2% (32/246) infants plotted < 10th centile. Smokers had a higher percentage of babies that were < 10th centile using BW centiles and GROW centiles. Non-smokers had a higher percentage of babies born > 90th centile by BW centiles and GROW centiles. Univariate analyses (using BW as a continuous variable) were performed examining the relationships between fat mass, visceral fat and lean body mass, and their association with BW. The variable that was associated with BW was lean body mass (p = 0.03). Fat mass did not reach significance (p = 0.06). Visceral fat level did not associate with BW. Of the maternal demographics, maternal BMI was not associated with BW. Maternal waist circumference showed no association with BW. A multivariable analysis was then performed using a multiple linear regressional model examining the relationship between lean body mass, fat mass, BMI, gestational age and smoking to determine the most powerful predictors of BW. In this analysis, BMI and fat mass were not significant and lean body mass remained the most significant predictor of BW (p = 0.04), (Table 1). The relationship between BW and lean body mass is illustrated in Figure 1.
Figure 1 Relationship between birth weight and lean body/fat-free mass using multivariate linear regression analysis.
Prenatal Diagnosis 2014, 34, 605–607
© 2014 John Wiley & Sons, Ltd.
Maternal lean body mass influences birth weight
Our study shows that maternal body composition influences BW. A multivariable linear regression analysis revealed that first-trimester fat-free mass was the strongest predictor of BW. Lean body mass including muscle mass, water and bone was a stronger predictor of BW than either fat mass or BMI. This echoes recent studies, which also found fat-free mass to be the strongest predictor of birth weight.3,1 Other studies have shown body water in the second trimester was predictive of BW.2 Similarly, a postpartum analysis highlighted lean body mass and total body water as predictors of BW.4 The findings in this study may in part be explained by maternal hemodynamic adaptations occurring in the first and second trimester. It has been speculated that the correlation between lean body mass and fetal growth may be mediated by fluid retention, including plasma volume, which may in turn influence the cardiac output and ultimately the uterine blood flow.2 In addition, the development of a low resistance and high capacity circulation, with the concomitant increase in total body water and extracellular water in the first and second trimesters are known to influence fetal growth. It has been reported in humans and animal studies that plasma volume, the major contributor to the total body water, correlates with BW.9 A major strength of our study is that we have used the novel but accurate technique of BIA to directly measure maternal body composition, which means fat and lean body mass have been measured and also their distributions in early pregnancy have been measured. Strengths of this study also include the accurate dating of all pregnancies by first-trimester ultrasonography. Although gestational age at delivery is a strong predictor of BW, few studies confirm the dates with an early ultrasound scan. Also, all patients had BMI calculated after digital measurement of height and weight. This contrasts with other studies in which BMI is often calculated using selfreporting of weight and height. A potential weakness in our study is that recruitment was by convenience and was not consecutive. The analysis is also based on proprietary formulae, which were calculated for American and European women. The use of a homogenous ethnic group removes ethnicity as a potential confounding variable; however, in view of ethnic differences in adiposity,
607
the findings from this study may not be applicable to other ethnic groups. BIA was not calculated in the infants, which is a weakness of our study, given that there can be variation in infant body composition. A salient learning point from this study and others is that BW does not appear to be influenced by maternal fat mass as strongly as was previously hypothesised. Our findings are based on measurements obtained in the first trimester. Further studies are required using BIA longitudinally to accurately assess gestational weight gain in order to improve our understanding of the impact that maternal body composition can have on the offspring.
Ethical approval The study was approved by the Hospital Research Ethics Committee in June 2011. WHAT’S ALREADY KNOWN ABOUT THIS TOPIC? • Birth weight (BW) prediction is an important part of obstetric management. • It is a key determinant of perinatal outcome with poor outcomes resulting from delivering macrosomic neonates or growth-restricted neonates. • Maternal obesity, assessed by BMI, has been associated with an increased risk of fetal macrosomia, neonatal adiposity and metabolic syndrome. • There is a paucity of data in the literature on maternal body composition and its relationship to BW.
WHAT DOES THIS STUDY ADD? • First trimester maternal body composition was assessed accurately and objectively by bioelectrical impedance analysis and correlated to BW. • We accurately identify which maternal body composition parameters including visceral fat and waist circumference independently influence BW. • We also explore customised centiles and population based centiles at birth. • Potentially, this study may help to identify a modifiable risk factor to reduce the incidence of macrosomia and neonatal adiposity.
REFERENCES 1. Kent E, O’Dwyer V, Fattah C, et al. Correlation between birth weight and maternal body composition. Obstet Gynecol 2013;121(1):46–50. 2. Ghezzi F, Franchi M, Balesteri D, et al. Bioelectrical impedance analysis during pregnancy and neonatal birth weight. Eur J Obstet Gynecol Reprod Biol 2001;98(2):171–6. 3. Larciprete G, Valensise H, Vasapollo B, et al. Maternal body composition at term gestation and birth weight: is there a link? Acta Diabetol 2003;40:222–4. 4. Sanin Aguirre LH, Reza-López S, Levario-Carrillo M. Relation between maternal body composition and birth weight. Biol Neonate 2004;86(1):55–62. Epub 2004 Mar 30. 5. Lukaski HC, Siders WA, Nielsen EJ, et al. Total body water in pregnancy: assessment by using bioelectrical impedance. Am J Clin Nutr 1994;59(3):578–85.
Prenatal Diagnosis 2014, 34, 605–607
6. Chumlea WC, Guo SS, Kuczmarski RJ, et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes Relat Metab Disord 2002;26:1596–609. 7. Fattah C, Barry S, O’connor N, et al. Maternal leptin and body composition in the first trimester of pregnancy. Gynecol Endocrinol 2011;27(4):263–6. 8. Stratton JF, Scanaill SN, Stuart B, et al. Are babies of normal birth weight who fail to reach their growth potential as diagnosed by ultrasound at increased risk? Ultrasound Obstet Gynecol 1995;5:114–8. 9. Mardones-Santander F, Salazar G, Rosso P, et al. Maternal body composition near term and birth weight. Obstet Gynecol 1998;91(6):873–7
© 2014 John Wiley & Sons, Ltd.
RESEARCH LETTER
Maternal body composition and birth weight Clare O’ Connor1,2*, Amy O’ Higgins2, Ricardo Segurado3, Michael J. Turner2, Bernard Stuart1,2 and Máireád M. Kennelly1,2 1
Ultrasound and Fetal Medicine Centre, Coombe Women and Infants University Hospital, Dublin, Ireland UCD Centre for Human Reproduction, Coombe Women and Infants University Hospital, Dublin, Ireland 3 CSTAR, University College Dublin, Belfield, Dublin, Ireland *Correspondence to: Clare O’Connor. E-mail: [email protected] 2
Funding sources: None Conflicts of interest: None declared
Previous work utilising segmental bioelectrical impedance analysis (BIA) has shown a correlation between maternal lean body mass and birth weight (BW).1 Importantly, in this large prospective observational study, there was no correlation between maternal fat mass and BW. Numerous smaller studies utilising serial BIA have shown second trimester body water to be predictive of BW.2 A smaller study performing BIA near term reported a correlation between lean body mass and BW.3 A postpartum assessment of women who delivered at term highlighted that lean body mass and total body water explained the major proportion of the BW.4 Maternal obesity is usually classified on the basis of a BMI, calculated as weight (kg)/[height (m)]2 > =30. BMI is a surrogate marker of adiposity but gives no information about actual fat mass. Most epidemiologic studies linking maternal BMI and BW are retrospective and based on self-reporting of weight and height, which under reports obesity levels. BIA is a direct measure of both fat and lean body (fat-free) mass. Bioelectrical Impedance Analysis is a simple, non-invasive system of measuring maternal body composition and estimates body composition by measuring the impedance or resistance to a small electrical current passed across body tissues. The body offers two types of resistance to the electrical current: capacitative resistance (reactance) and resistive resistance (resistance). The capacitance arises from the cell membranes and the resistance from the extracellular and intracellular fluid. Impedance is used to describe the combination of the two. The resistance of a tissue increases as the amount of suspended non-conducting material increases. The greater the lean body mass or water content of a person, the faster the current will pass through. The greater the fat mass, the greater the resistance to the current and the slower the current passing through. Measurement of BIA indices is an indirect evaluation of the body compartments. Previous studies5 have shown a strong correlation between the estimations of body composition Prenatal Diagnosis 2014, 34, 605–607
using isotope techniques and BIA indices providing strong validation proofs. It offers a safe, non-invasive, relatively inexpensive and easy to perform method for the measurement of maternal body composition.6 Previous clinical research has shown that in early pregnancy, the use of multi-frequency bioelectric impedance analysis is feasible and reproducible, and that it correlates strongly with clinical and endocrine markers of maternal adiposity.7 This prospective longitudinal study of 254 singleton pregnancies was part of the fetal growth trajectory study, which was carried out over a two-year period from July 2011 to June 2013. A power calculation deemed that 70 high risk women (including those with pre-gestational Diabetes Mellitus or obesity) and 100 controls would be adequate to achieve 80% power to detect a doubling of risk of abnormally high or low birth weight deliveries at a statistically significant level of p < 0.05. The calculations assumed a 20% risk in the control sample and a doubling to 40% in the high risk group. Women were recruited at their convenience after confirmation of an ongoing intrauterine pregnancy at their first-trimester booking scan. Inclusion criteria included age over 18 years, white European ethnicity and a singleton pregnancy. Exclusion criteria included a history of essential hypertension or pre-eclampsia and significant medical problems such as renal disease, cystic fibrosis, congenital cardiac disease with chronic hypoxia and drug abuse that may affect fetal growth. Those with pre-gestational Diabetes or that subsequently developed gestational Diabetes were included in the analysis. Subjects gave written informed consent and were given information leaflets. The study was approved by the Hospital Research Ethics Committee in June 2011. Bioelectric impedance analysis was performed using the Tanita MF 180CA with the woman in her bare feet and wearing light clothing; Segmental bioelectrical impedance analysis of the trunk and individual limbs was performed on each patient. Birth weight was recorded at delivery using an electronic scales and BW centiles were calculated using hospital reference norms. GROW centiles were also calculated, which © 2014 John Wiley & Sons, Ltd.
C. O’ Connor et al.
606
are customised centiles that include maternal demographics such as BMI and gestational age at delivery. An appropriately grown fetus was one where the estimated fetal weight plotted between the 10th and 90th centiles. A small for gestational age fetus was one where the estimated fetal weight (Hadlock 4) was less than the 10th centile for gestational age. Intrauterine growth restriction was defined as a BW less than the 10th centile. The definition of large for gestational age was based on centile thresholds > 90th centile. Approximate normality of all measures was verified by visual inspection. To estimate the relationship between maternal body composition, BW and BW centile, univariable and multivariable linear regression analyses were performed. Diagnostic plots were inspected to ensure validity of the regression models, and the influence of outliers was explored Demographic variables and clinical parameters were summarised as mean (SD). Possible confounding variables were examined using independent sample t-tests. For all tests, p-values < 0.05 were considered significant. All data were analysed using SPSS version 20 (IBM
Table 1 Multivariate linear regression analysis of predictors of birth weight Maternal predictors of birth weight Fat mass Fat-free mass
Regression coefficient 0.8 13.7
95% confidence interval 7.1, 8.7 0.4, 27.1
Smoking
-155.5
273.3,
Gestational age (weeks)
182.7
152.3, 213.1
Maternal age (years) Parity
1.3 52.8
37.6
p-value 0.8 0.04 0.01 37 weeks. The mean BW was 3484 g (standard deviation 570 g). BW and BMI were normally distributed. At delivery, BW was plotted against gestational age and 15.0% (37/246) plotted greater than the 90th centile according to hospital reference norms8 and 5.6% (14/246) infants plotted less that the 10th centile. Using the GROW customised growth centiles, 10.3% (25/246) infants plotted > 90th centile and 13.2% (32/246) infants plotted < 10th centile. Smokers had a higher percentage of babies that were < 10th centile using BW centiles and GROW centiles. Non-smokers had a higher percentage of babies born > 90th centile by BW centiles and GROW centiles. Univariate analyses (using BW as a continuous variable) were performed examining the relationships between fat mass, visceral fat and lean body mass, and their association with BW. The variable that was associated with BW was lean body mass (p = 0.03). Fat mass did not reach significance (p = 0.06). Visceral fat level did not associate with BW. Of the maternal demographics, maternal BMI was not associated with BW. Maternal waist circumference showed no association with BW. A multivariable analysis was then performed using a multiple linear regressional model examining the relationship between lean body mass, fat mass, BMI, gestational age and smoking to determine the most powerful predictors of BW. In this analysis, BMI and fat mass were not significant and lean body mass remained the most significant predictor of BW (p = 0.04), (Table 1). The relationship between BW and lean body mass is illustrated in Figure 1.
Figure 1 Relationship between birth weight and lean body/fat-free mass using multivariate linear regression analysis.
Prenatal Diagnosis 2014, 34, 605–607
© 2014 John Wiley & Sons, Ltd.
Maternal lean body mass influences birth weight
Our study shows that maternal body composition influences BW. A multivariable linear regression analysis revealed that first-trimester fat-free mass was the strongest predictor of BW. Lean body mass including muscle mass, water and bone was a stronger predictor of BW than either fat mass or BMI. This echoes recent studies, which also found fat-free mass to be the strongest predictor of birth weight.3,1 Other studies have shown body water in the second trimester was predictive of BW.2 Similarly, a postpartum analysis highlighted lean body mass and total body water as predictors of BW.4 The findings in this study may in part be explained by maternal hemodynamic adaptations occurring in the first and second trimester. It has been speculated that the correlation between lean body mass and fetal growth may be mediated by fluid retention, including plasma volume, which may in turn influence the cardiac output and ultimately the uterine blood flow.2 In addition, the development of a low resistance and high capacity circulation, with the concomitant increase in total body water and extracellular water in the first and second trimesters are known to influence fetal growth. It has been reported in humans and animal studies that plasma volume, the major contributor to the total body water, correlates with BW.9 A major strength of our study is that we have used the novel but accurate technique of BIA to directly measure maternal body composition, which means fat and lean body mass have been measured and also their distributions in early pregnancy have been measured. Strengths of this study also include the accurate dating of all pregnancies by first-trimester ultrasonography. Although gestational age at delivery is a strong predictor of BW, few studies confirm the dates with an early ultrasound scan. Also, all patients had BMI calculated after digital measurement of height and weight. This contrasts with other studies in which BMI is often calculated using selfreporting of weight and height. A potential weakness in our study is that recruitment was by convenience and was not consecutive. The analysis is also based on proprietary formulae, which were calculated for American and European women. The use of a homogenous ethnic group removes ethnicity as a potential confounding variable; however, in view of ethnic differences in adiposity,
607
the findings from this study may not be applicable to other ethnic groups. BIA was not calculated in the infants, which is a weakness of our study, given that there can be variation in infant body composition. A salient learning point from this study and others is that BW does not appear to be influenced by maternal fat mass as strongly as was previously hypothesised. Our findings are based on measurements obtained in the first trimester. Further studies are required using BIA longitudinally to accurately assess gestational weight gain in order to improve our understanding of the impact that maternal body composition can have on the offspring.
Ethical approval The study was approved by the Hospital Research Ethics Committee in June 2011. WHAT’S ALREADY KNOWN ABOUT THIS TOPIC? • Birth weight (BW) prediction is an important part of obstetric management. • It is a key determinant of perinatal outcome with poor outcomes resulting from delivering macrosomic neonates or growth-restricted neonates. • Maternal obesity, assessed by BMI, has been associated with an increased risk of fetal macrosomia, neonatal adiposity and metabolic syndrome. • There is a paucity of data in the literature on maternal body composition and its relationship to BW.
WHAT DOES THIS STUDY ADD? • First trimester maternal body composition was assessed accurately and objectively by bioelectrical impedance analysis and correlated to BW. • We accurately identify which maternal body composition parameters including visceral fat and waist circumference independently influence BW. • We also explore customised centiles and population based centiles at birth. • Potentially, this study may help to identify a modifiable risk factor to reduce the incidence of macrosomia and neonatal adiposity.
REFERENCES 1. Kent E, O’Dwyer V, Fattah C, et al. Correlation between birth weight and maternal body composition. Obstet Gynecol 2013;121(1):46–50. 2. Ghezzi F, Franchi M, Balesteri D, et al. Bioelectrical impedance analysis during pregnancy and neonatal birth weight. Eur J Obstet Gynecol Reprod Biol 2001;98(2):171–6. 3. Larciprete G, Valensise H, Vasapollo B, et al. Maternal body composition at term gestation and birth weight: is there a link? Acta Diabetol 2003;40:222–4. 4. Sanin Aguirre LH, Reza-López S, Levario-Carrillo M. Relation between maternal body composition and birth weight. Biol Neonate 2004;86(1):55–62. Epub 2004 Mar 30. 5. Lukaski HC, Siders WA, Nielsen EJ, et al. Total body water in pregnancy: assessment by using bioelectrical impedance. Am J Clin Nutr 1994;59(3):578–85.
Prenatal Diagnosis 2014, 34, 605–607
6. Chumlea WC, Guo SS, Kuczmarski RJ, et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes Relat Metab Disord 2002;26:1596–609. 7. Fattah C, Barry S, O’connor N, et al. Maternal leptin and body composition in the first trimester of pregnancy. Gynecol Endocrinol 2011;27(4):263–6. 8. Stratton JF, Scanaill SN, Stuart B, et al. Are babies of normal birth weight who fail to reach their growth potential as diagnosed by ultrasound at increased risk? Ultrasound Obstet Gynecol 1995;5:114–8. 9. Mardones-Santander F, Salazar G, Rosso P, et al. Maternal body composition near term and birth weight. Obstet Gynecol 1998;91(6):873–7
© 2014 John Wiley & Sons, Ltd.
