• 3D ultrasonography;
  • air displacement plethysmography;
  • fetal growth;
  • fractional thigh volume;
  • infant body composition;
  • soft tissue


  1. Top of page
  2. Abstract
  7. Acknowledgements


The main goal was to investigate the relationship between prenatal sonographic parameters and birth weight in predicting neonatal body composition.


Standard fetal biometry and soft tissue parameters were assessed prospectively in third-trimester pregnancies using three-dimensional ultrasonography. Growth parameters included biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), mid-thigh circumference and femoral diaphysis length (FDL). Soft tissue parameters included fractional arm volume (AVol) and fractional thigh volume (TVol) that were derived from 50% of the humeral or femoral diaphysis lengths, respectively. Percentage of neonatal body fat (%BF) was determined within 48 h of delivery using a pediatric air displacement plethysmography system based on principles of whole-body densitometry. Correlation and stepwise multiple linear regression analyses were performed with potential prenatal predictors and %BF as the outcome variable.


Eighty-seven neonates were studied with a mean ± SD %BF of 10.6 ± 4.6%. TVol had the greatest correlation with newborn %BF of all single-parameter models. This parameter alone explained 46.1% of the variability in %BF and the best stepwise multiple linear regression model was: %BF = 0.129 (TVol) − 1.03933 (P < 0.001). Birth weight similarly explained 44.7% of the variation in %BF. AC and estimated fetal weight (EFW) accounted for only 24.8% and 30.4% of the variance in %BF, respectively. Skeletal growth parameters, such as FDL (14.2%), HC (7.9%) and BPD (4.0%), contributed the least towards explaining the variance in %BF.


During the late third trimester of pregnancy %BF is most highly correlated with TVol. Similar to actual birth weight, this soft tissue parameter accounts for a significant improvement in explaining the variation in neonatal %BF compared with fetal AC or EFW alone. Copyright © 2009 ISUOG. Published by John Wiley & Sons, Ltd.


  1. Top of page
  2. Abstract
  7. Acknowledgements

Fetal growth is a result of complex interactions between several maternal, fetal and placental mechanisms. A final classification of neonatal growth outcome depends on how this development is defined. Most obstetricians rely on uterine fundal height, fetal abdominal circumference (AC) measurement and/or a sonographic estimate of fetal weight for the detection of intrauterine growth restriction (IUGR). Over four decades ago, Battaglia and Lubchenco1 developed a landmark classification for neonatal growth outcome and this statistical approach has been applied to fetal weight assessment as well. Fetal size is usually categorized on the basis of estimated fetal weight (EFW) being small (< 10th percentile, SGA), appropriate (10–90th percentile, AGA), or large (> 90th percentile, LGA) for gestational age. Unfortunately, this approach does not distinguish between fetuses that are small or large, but otherwise normal, from others that are truly malnourished.

Soft tissue assessment may provide additional information about generalized fetal nutritional status. Several related parameters, such as mid-arm fat and lean mass, mid-thigh fat and lean mass, abdominal fat mass, subscapular fat mass, cheek-to-cheek diameter and buttocks have been used to assess fetal body composition2–13. We previously reported that fractional limb volume can be used for fetal growth assessment and weight estimation using three-dimensional ultrasonography (3DUS)14. These measurements are reproducible and rapidly obtained because only five transverse slices need to be traced around the mid-limb. Furthermore, this technique is more likely to permit clear visualization of soft tissue borders and more confident tracing around each volume slice because only the mid-limb is analyzed.

After birth, neonatal infant body composition is traditionally evaluated using birth weight and anthropometric measurements, including the ponderal index and skinfold thickness15, 16. Hydrostatic weighing is not suitable for newborn body composition studies because complete water submersion is necessary to calculate total body volume. Fortunately, more sophisticated infant body composition methods are now feasible without a need for water submersion. Air displacement plethysmography is a non-invasive technique that uses total body volume and mass to derive a two-compartment model of body composition that includes percentage body fat (%BF) and lean body mass (%LBM)17. This technology has recently been applied to neonatal and infant body composition studies as well18–20.

Because a major goal of prenatal assessment is to identify fetuses with abnormal intrauterine growth, air displacement plethysmography may offer important insight into which fetal growth parameters most closely reflect the generalized nutritional state of neonates. This study examines the value of prenatal growth parameters and birth weight in predicting neonatal body composition.


  1. Top of page
  2. Abstract
  7. Acknowledgements

This was a prospective, cross-sectional study of gravid women who were within 4 days of delivery. Women in their third trimester of pregnancy were included. Subjects with congenital anomalies and poorly visualized fetal limbs owing to technical factors were excluded. Menstrual age was calculated from the first day of the last normal menstrual period; this information was confirmed by either a first-trimester or early second-trimester dating scan as described previously21–25. Maternal age, gravidity, menstrual age at time of scan, gender, ethnicity and presence of obstetric complications were also documented. All participants gave written informed consent and were enrolled under protocols approved by the Institutional Review Boards at William Beaumont Hospital, Wayne State University, and the Eunice Shriver Kennedy National Institute of Child Health and Human Development.

All ultrasound studies were performed by a single American Registry of Diagnostic Medical Sonographers-certified sonographer who was also experienced with 3DUS. Two-dimensional ultrasonography was used to scan each fetus once. Standard fetal biometric parameters (biparietal diameter (BPD), head circumference (HC), AC, humeral diaphysis length (HDL) and femoral diaphysis length (FDL)) were measured prospectively22–25. Fetal weights were estimated using two-parameter (AC and FDL) and three-parameter (BPD, AC and FDL) models as described by Hadlock and coworkers26.

3DUS was also used to acquire fractional arm (AVol) and fractional thigh (TVol) volumes, using hybrid mechanical and curved-array abdominal ultrasonic transducers (RAB 4-8P, RAB 2-5P; Voluson 730 Expert, GE Healthcare, Milwaukee, WI, USA). Fractional limb volumes were measured offline using commercially available software (4D View 5.0, GE Healthcare). These soft tissue parameters were derived from 50% of diaphyseal bone length as described previously14, 27, 28. Each partial volume was subdivided into five equidistant slices that were centered along the fetal arm or thigh. The volume of interest was magnified to fill at least one-half of the display. Soft tissue borders were enhanced by use of a color filter (sepia) with additional gamma curve adjustments for brightness and contrast. Fractional limb volumes were calculated after each of the five slices had been manually traced in a transverse view of the extremity.

A pediatric air displacement plethysmography system (PEA POD®, Life Measurement, Inc., Concord, CA, USA), applying principles of whole-body densitometry, was used to determine %BF and % fat-free mass (%FFM) in all neonates within 48 h of delivery18–20. The within-day and between-day reliability and precision of this system has been well validated in neonates. Furthermore, quality control measures and the automated nature of this equipment minimize operator bias29, 30. After standardized system calibration and weight determination, the neonate was placed in a chamber to measure body volume based on air displacement. These results were used to calculate density for a two-compartment model of body composition. For purposes of this study, the %BF results were emphasized because %LBM was simply the mathematical difference between the former value and 100%.

Statistical analysis

Standard numerical techniques included computing descriptive statistics such as mean ± SD, minimum, median and maximum to assess the symmetry of data distribution and the validity of the assumption of normality. Histograms were inspected for all continuous variables (including %BF) to assess normality of the data distribution because our regression analysis was based on the principal underlying assumption that the errors were normally distributed. The Anderson–Darling, Kolmogorov–Smirnov and Shapiro–Wilks tests were used to formally test the assumption of normality of data distribution. A scatterplot matrix of all continuous variables, including the pairwise correlations, was computed to assess the presence of significant interrelations among the different parameters, including %BF. This assessment helped determine whether transformations would be needed to linearize data before regression analysis was applied.

Correlations between each of these parameters and %BF were evaluated using coefficient of determination (R2). Stepwise multiple regression analysis was also used to identify the smallest subset of prenatal growth parameters that could explain the greatest variance in %BF. A stepwise selection procedure was used and a level of P < 0.05 was applied for entry into the model. The maximum R2 improvement selection method was also used to define the best single-parameter contribution of each predictor by ranking them in ascending order of R2 values31. P-values less than an α of 0.05 (probability of Type I error) were considered statistically significant. Statistical analysis was performed using the SAS System for Windows version 9.1.3, Service Pak 2 (SAS, Cary, NC, USA).


  1. Top of page
  2. Abstract
  7. Acknowledgements

The mean ± SD maternal age was 30.5 ± 5.2 years. No subjects were excluded on the basis of poorly visualized fetal limbs. Eighty-seven infants were delivered at 38.9 ± 1.4 weeks' menstrual age, during a study period that extended from August 2004 to November 2006. Birth weights were normally distributed with a mean value of 3462 ± 579 g. The racial distribution was 72 White, nine Black and six Asian. Most pregnancies were uncomplicated, but some cases had comorbidities such as pre-eclampsia (n = 2), diabetes (n = 8) and tobacco exposure (n = 5). The mean ± SD %BF was 10.6 ± 4.6%. Conversely, the mean% LBM was 89.5 ± 4.7% for this two-compartment model of infant body composition. Mean ± SD values for growth parameters BPD, HC, AC, HDL, FDL and EFW are summarized in Table 1. The median values for fractional limb volumes were 36.4 (interquartile range (IQR), 32.7–43.1) mL for AVol and 86.9 (IQR, 71.6–107.6) mL for TVol.

Table 1. Prenatal parameters obtained by two-dimensional ultrasonography in 87 subjects
Growth parameterMean ± SD
  1. AC, abdominal circumference; BPD, biparietal diameter; EFW, estimated fetal weight; FDL, femoral diaphysis length.

Biparietal diameter (cm)9.5 ± 0.4
Head circumference (cm)34.6 ± 1.6
Abdominal circumference (cm)36.1 ± 2.8
Humeral diaphysis length (cm)6.5 ± 0.3
Femoral diaphysis length (cm)7.5 ± 0.4
Mid-thigh circumference (cm)18.4 ± 1.9
EFW –AC, FDL (g)3851 ± 665
EFW –BPD, AC, FDL (g)3863 ± 647

TVol was the most highly correlated prenatal parameter of %BF of all the single-parameter models on the basis of both the maximum R2 and stepwise multiple regresson analyses. This parameter alone explained 46.1% of the variability in %BF (Figure 1). None of the other variables, including birth weight, had the level of statistical significance (P < 0.05) to be entered in the stepwise multiple regression model. The best stepwise linear regression model was: %BF = 0.129(TVol)− 1.03933 (P < 0.001). By comparison, birth weight similarly accounted for 44.7% of the variation in %BF. The AC and two-parameter Hadlock model for EFW explained only 24.8% and 30.4% of the variance in %BF, respectively. Skeletal growth parameters, such as FDL (14.2%), HC (7.9%) and BPD (4.0%), contributed the least towards explaining the variance in %BF.

thumbnail image

Figure 1. Contribution of parameters explaining variance in percentage neonatal body fat. AC, abdominal circumference; BPD, biparietal diameter; EFW, estimated fetal weight; FDL, femoral diaphysis length.

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  1. Top of page
  2. Abstract
  7. Acknowledgements

Despite significant advances in imaging technology, the definition of IUGR remains imprecise owing to the use of unclear terminology and lack of consistent diagnostic criteria. In order to better understand this problem, a literature search of peer-reviewed articles was undertaken in Ultrasound in Obstetrics and Gynecology over the past 12 years (1995–2007). We identified 108 Original Articles and 11 Case Reports that were electronically catalogued using the MeSH term ‘IUGR’. Although articles were submitted from 25 countries, the majority originated from the UK (19.3%), USA (14.3%), Germany (13.4%), Italy (13.4%) and Sweden (9.0%). The terms ‘IUGR’ or ‘SGA’ were variably defined using EFW and AC with or without Doppler sonography. Diagnostic criteria included EFW < 10th percentile in 41 papers (34.5%), EFW < 5th percentile in 18 papers (15.1%) and EFW < 3rd percentile in four papers (3.4%). Other investigators used fetal AC for the diagnosis of IUGR: AC < 10th percentile in two papers (1.7%) and AC < 5th percentile in 21 papers (17.6%); 15 investigators (12.6%) who used AC measurements for this purpose also added Doppler ultrasound criteria to make the diagnosis of IUGR. This lack of uniformity underscores the need for more precise evaluation of fetal nutritional status, beyond the use of EFW alone.

Fetal growth parameters, such AC and FDL, are typically used to calculate EFW and this result is subsequently compared with neonatal population standards to establish the risk of adverse pregnancy outcome. Fetal nutritional status is generally considered to be adequate when estimated weight is within the normal range. Nonetheless, EFW does not carry the same prognostic significance as actual birth weight because the former cannot be measured directly before birth. Fetal weight estimation models also demonstrate reduced precision as a result of cumulative measurement errors. One systematic review of 11 different fetal weight estimation methods concluded that large random errors are an important obstacle to confident use of EFW in clinical practice because 95% CIs exceeded 14% of actual birth weight in reported studies32.

Birth weight is one of the most significant postnatal predictors of pregnancy outcome and is the main growth parameter that is routinely evaluated in newborns. Body composition analysis further considers individual components that contribute to total body weight. These components include water, fat, protein and minerals. Fat is the most variable component of the neonate and is the most important energy store of the body33, 34. Dual energy X-ray absorptiometry studies of neonates also suggest that body weight is the dominant predictor of fat mass35. Humans are unique among mammalian species because their fetuses deposit significant quantities of fat and they have one of the greatest percentages of body fat at birth36. Sparks and coworkers37 used published data for human fetal weight, water and fat composition to estimate that fat deposition represents over half the caloric accretion from the 27th week of gestation until term, and 90% of the caloric accretion at term. Fetal fat deposition appeared to increase linearly until approximately 30 weeks. After this pivotal point, fetal fat deposition increased exponentially until delivery. Such changes reflect longitudinal studies of fractional limb volumes in fetuses with normal growth outcomes27, 28. Lapillonne and coworkers38 have also used dual-energy X-ray absorptiometry to evaluate body composition of SGA infants within 48 h of delivery. Significant decreases in total body fat, lean mass and bone mineral content occur in such infants compared with AGA fetuses of the same birth weight.

Soft tissue can also be evaluated using prenatal ultrasonography. As one example, multivariate statistics have been used to demonstrate that decreased soft tissue mass, as indicated by fetal thigh circumference, is one of the earliest manifestations of IUGR39. Catalano and colleagues40 used birth weight and other anthropometric parameters to describe the relationship between newborn body composition and fetal growth outcome within 24 h of delivery. Percentage body fat was estimated from skinfold thickness and ponderal indices. Seventeen neonates (9%) were SGA (< 10th percentile), 147 (78.2%) AGA and 24 (12.8%) LGA. The correlation between ponderal index and %BF was poor (R2 = 0.15). Despite the fact that neonatal fat mass constituted only 14% of total birth weight, it explained 46% of its variance. Other investigators compared the subcutaneous fat and lean mass area of the fetal thigh in 17 growth-restricted fetuses and 20 normal control subjects12. This study established a diagnosis of IUGR that was based only on a small AC (< 2 SD). Growth-restricted fetuses had reduced subcutaneous fat and lean mass compared with normal fetuses. There was a disproportionate reduction in fat mass compared with lean mass when the results were normalized for body size.

Volume is a fundamental physical property that can be used to develop new fetal body composition models. In this investigation, stepwise multiple regression demonstrated that TVol made the greatest contribution to the observed variance in %BF (46.1%) for term neonates. This result was similar to actual BW, which explained 44.7% of %BF. TVol accounted for an additional 21.3% of the total variation in %BF when compared with AC alone. Our observations have important implications for the diagnosis and management of fetuses with growth disorders. First, because fetal body composition cannot be quantified directly before delivery, fractional limb volume may improve our ability to identify and monitor intrauterine growth abnormalities. Second, many investigators have defined IUGR with AC measurements or in combination with Doppler ultrasonography41–47. If the goal is to detect malnourished fetuses, the rationale for using single parameters such as AC, or even EFW, to detect and monitor fetal growth abnormalities may require critical re-examination. In this study, EFW accounted for only 28–30% of the variance in %BF. Third, the Barker hypothesis links low birth weight to increased risk of heart disease, diabetes and hypertension in adults as a result of fetal programming48–51. Sayer and Cooper52 have reviewed potential mechanisms for fetal programming of body composition and musculoskeletal development. The association of low birth weight with altered fat distribution, reduced muscle mass and low bone mineral content in adults may be caused by a direct effect on cell number, altered stem cell function, and resetting of regulatory hormonal axes.

This study prospectively examined fetuses during the late third trimester of pregnancy. The correlation of prenatal sonographic parameters, such as EFW, to birth weight and neonatal body composition will require critical examination at earlier gestational ages. Despite the contemporary obstetric practice for comparing EFW with actual birth weight, it has been proposed that the use of birth weight curves for this purpose may not optimally detect malnourished fetuses because IUGR is over-represented in premature newborns53. However, because fractional limb volume includes both fat and lean mass, a subanalysis of these separate components may offer a new opportunity to assess fetal body composition. This task could be improved if automated image segmentation of relevant soft tissue boundaries is developed successfully for this purpose. As a final note, TVol still accounts for no more than half of the variance in neonatal %BF. Other novel fetal soft tissue parameters must be identified and validated to improve our understanding of soft tissue development and, possibly, the subsequent risk for disease in later life.


  1. Top of page
  2. Abstract
  7. Acknowledgements

The authors wish to acknowledge the technical assistance of Melissa Powell, RDMS, and Beverley McNie, BS, CCRP. This research was supported partially by the Perinatology Research Branch, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Department of Health and Human Services.


  1. Top of page
  2. Abstract
  7. Acknowledgements
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