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American Journal of Clinical Nutrition, Vol. 82, No. 5, 980-987, November 2005
© 2005 American Society for Clinical Nutrition

ORIGINAL RESEARCH COMMUNICATION

Fetal programming of body composition: relation between birth weight and body composition measured with dual-energy X-ray absorptiometry and anthropometric methods in older Englishmen1,2,3

Osama A Kensara, Steve A Wootton, David I Phillips, Mayke Patel, Alan A Jackson, Marinos Elia and Hertfordshire Study Group

1 From the Institute of Human Nutrition, University of Southampton, Southampton General Hospital, Southampton, United Kingdom (OAK, SAW, AAJ, and ME), and the MRC Epidemiology Resource Centre, University of Southampton, Southampton, United Kingdom (DIP and MP)

2 Supported in part by NIH grant RO1 HD 41107-01 (to DIP), by core funds from the Institute of Human Nutrition and Epidemiology Resource Centre, and a grant from Umm Al Qura University, Saudi Arabia, which supported OAK during his studies at the University of Southampton.

3 Address reprint requests to M Elia, Institute of Human Nutrition, University of Southampton, Southampton General Hospital, Tremona Road, Southampton SO16 6YD, United Kingdom. E-mail: elia{at}soton.ac.uk.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Reduced fetal growth is associated with differences in body composition in adult life that may predispose to cardiovascular disease and diabetes. Most published data are based on simple anthropometric measures, which incompletely describe body composition.

Objective: The objective was to assess body composition and fat distribution by using dual-energy X-ray absorptiometry (DXA).

Design: This was a case-control study of 64–72-y-old white men (n = 32) with a low (: 2.76 kg) or high (: 4.23 kg) birth weight.

Results: Compared with the high-birth-weight group, after adjustment for weight and height, the low-birth-weight group had a higher percentage body fat (29.31% compared with 25.33%; P = 0.029) and fat mass (P = 0.039) but a lower fat-free soft tissue (56.32 compared with 59.22 kg; P = 0.024), muscle mass (27.25 compared with 29.22 kg; P = 0.022), and muscle-to-fat ratio. Low birth weight was also associated with a higher trunk-to-limb fat ratio after control for total fat mass (1.42 compared with 1.16; P = 0.005) or percentage body fat (P = 0.041). The same body mass index predicted a greater percentage body fat (P = 0.019) in the low- than in the high-birth-weight group, and the same ratio of trunk-to-limb skinfold thickness (or waist-to-hip ratio) predicted a higher trunk-to-limb fat ratio (P < 0.01).

Conclusion: Lifelong differences in adult body composition and fat distribution between the low- and high-birth-weight groups are consistent with programming in early life. The use of BMI to predict percentage body fat and the use of the trunk-to-limb skinfold thickness ratio (and waist-to-hip ratio) to predict the trunk-to-limb fat ratio measured by DXA can be misleading when low- and high-birth-weight groups are compared.

Key Words: Body composition • birth weight • fat • lean mass • muscle programming • body mass index • dual-energy X-ray absorptiometry • waist-to-hip ratio • fat distribution • skinfold thickness


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Low birth weight is associated with increased morbidity and mortality in later life from cardiovascular disease, type 2 diabetes, and the metabolic syndrome (1). Because body fat and percentage body fat have been causally linked to the development of insulin resistance and cardiovascular disease, several studies have evaluated the extent to which birth weight is associated with adiposity and body composition in adult life. However, few reference body-composition techniques (hydrodensitometry and scanning techniques) have been used for this purpose (2–5), and the sample sizes have sometimes been small (5). In most studies, simple surrogate measures of body composition have been used, such as skinfold thicknesses and BMI (in kg/m2), which depend on both adipose and lean tissue mass. Although some reports indicate no relation between birth weight and adult BMI (3, 4, 6, 7), many others indicate a positive relation between the 2 (2, 7–12). A recent review concluded that, despite the lack of information on potential confounding factors, the positive association seemed robust (13). However, these observations present a conundrum, because a high BMI is generally associated with increased risk of cardiovascular disease, but a high BMI in subjects with a high birth weight has been shown to be associated with reduced risk (11, 14). One explanation for this conundrum is that the reduced risk in adults with a high birth weight is mediated by factors that can override any adverse effects of gross body composition, such as excess adiposity. A second, and very different, explanation is that—compared with individuals with a low birth weight—persons with a high birth weight have relatively less body fat (and a lower ratio of fat to lean tissue, which includes insulin-sensitive muscular tissue), even after adjustment for BMI or weight plus height. If this were true, it would seriously question the value of BMI for comparing body composition in this situation. At the same time it would shed fresh light on the link between birth weight and outcomes in later life, allowing a reexamination of previous studies that may have used BMI inappropriately. Such a situation is analogous to ethnic differences in body composition. In comparison with whites (adults and children), Asians have been found to have 2–6% more percentage body fat after adjustment for BMI (15–18), and neonates have been found to have a greater subscapular skinfold thickness after adjustment for the ponderal index (19).

Fat distribution also predicts risk of cardiovascular disease and type 2 diabetes, independently of fat mass (FM) and percentage body fat. It is possible that early life programming of body composition affects fat distribution between body segments, but reference scanning body-composition techniques do not appear to have been used to address this issue. In contrast, the waist-to-hip circumference ratio or ratio of trunk to limb skinfold thickness (7) have frequently been used for this purpose, but the results have been variable, at least partly because of the limitations associated with the use of these techniques. For example, the waist-to-hip ratio (20) is influenced by skeletal shape and muscular development, and, in some individuals, by intestinal gas and fecal mass. In addition, in subjects with a low BMI, it has been suggested that waist circumference reflects the variation in abdominal lean tissue as much as it reflects variation in abdominal adiposity (8). Skinfold thicknesses reflect only subcutaneous fat at the measurement sites, rather than total fat (internal and subcutaneous fat) over entire body segments. Furthermore, if birth weight is related to percentage fat, which in turn is related to fat distribution, then the use of simple unadjusted (or incorrectly adjusted) anthropometric indexes of fat distribution may produce biased results and potentially incorrect conclusions.

The aims of this study were 2-fold: 1) to assess whether birth weight is related to the mass and proportion of tissues (lean fat, muscle fat), percentage body fat, and its distribution across body segments in older adults, as measured by dual-energy X-ray absorptiometry (DXA), and 2) to test the hypothesis that anthropometric markers of health risk, such as BMI, skinfold thicknesses, and waist-to-hip ratio can be misleading in comparisons of body composition and fat distribution between low- and high-birth-weight groups.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
Thirty-two healthy adult white men aged 64–72 y were recruited and invited to the Wellcome Trust Clinical Research Facility, Southampton General Hospital (Table 1Go), to undergo assessments of anthropometric and body-composition characteristics. All subjects were born in Hertfordshire, where detailed birth records were routinely kept. The subjects were chosen randomly from among those below the 25th centile of birth weight [<3.18 kg (7 lb); n = 16] or above the 75th centile [>3.86 kg (8.5 lb); n = 16] of birth weight. Only 55% of those who were contacted by letter (after permission was granted by their general practitioners) agreed to have a home visit and participate in the body-composition studies. Written informed consent was obtained from each subject, and ethical approval for the study was obtained from local research committees. This study was part of a larger study that included other measurements of energy metabolism and fuel selection (data not reported in this article), which for practical reasons limited the sample size. All the required measurements were obtained from each subject participating in the study. Weight was measured to the nearest 0.1 kg with a Seca 708 electronic weighing scale, and height was measured to the nearest 0.1 cm with a Seca electronic stadiometer (Seca Ltd, Medical Scales and Measurement Systems, Birmingham, United Kingdom). Information on socioeconomic status (both currently and at the time of birth), smoking status, alcohol consumption, and physical activity (Table 2Go) was obtained by using questionnaires that were administered to the subjects on the study day. The study was conducted according to the South-West Hampshire Local Ethics Committee.


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  		TABLE 1 Characteristics of the 32 older men with low or high birth weight (BW)1

 

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  		TABLE 2 Socioeconomic status and physical activity in 32 older men with either low or high birth weight (BW)

 
Methods
Body composition was measured by using DXA Hologic Delphi (Vertec Scientific Ltd, Reading, United Kingdom), which processed data using software version 12.2 to obtain information on fat-free soft tissue (FFST) mass, bone mineral content (BMC), and the percentage of these components in the whole body and in specific body segments. It was also assessed by using the BOD POD S/T air-displacement plethysmography unit (Life Measurement Inc, Cranlea & Co, Birmingham, United Kingdom).

Whole body weight (in kg), obtained from the sum of the weights of body segments reported by DXA, was highly correlated (r = 0.997) with measured weight. Waist and hip circumferences were measured according to standard procedures (21). Skinfold thicknesses (biceps, triceps, subscapular, and suprailiac) were measured according to the method of Durnin and Womersley (22) and were used to calculate percentage body fat. Midupper arm circumference was measured midway between the acromion and olecranon, and, together with triceps-skinfold thickness, was used to calculate arm muscle area (23):

(1)
All anthropometric measurements were made by a single observer.

Whole-body muscle mass was estimated by using the equation of Kim et al (24), which was established by relating DXA measurements of appendicular (limb) FFST with whole-body measurements of muscle mass obtained by using magnetic resonance imaging in healthy adults. The equation for men is as follows:

(2)
Fat distribution was estimated as the nonlimb-to-limb FM ratio, trunk-to-limb FM ratio, and abdominal-to-limb FM ratio. The FMs in the trunk and limbs were those reported by the DXA machine, whereas the abdominal FM had to be established. First, the region of interest was identified as the area between the superior border of the iliac crest to the inferior border of the fourth lumbar vertebra (25). The region of interest was identified manually to the nearest pixel (1.3035 cm) and then its composition was established by using the software program version 12.2 of Hologic Delphi. The bitrochanteric breadth was estimated with the use of a photoenlarged paper copy of the scans by using the known diameter of the scanned area as the reference distance.

The inter- and intraobserver CVs for body fat and fat-free mass (FFM) (as a percentage of body weight) for repeated measurements in the same subjects is {approx}1% for DXA and air-displacement plethysmography ({approx}3–4% of actual FM, {approx}1.4% of actual FFM). For BMI, the CV is <1% (interobserver and intraobserver CVs). For DXA the CVs (as a percentage of actual tissue or body component) were as follows: {approx}2–3% for fat in the trunk and in the limbs, {approx}2.0% for the trunk-to-limb fat ratio, {approx}0.9% for bone mineral, {approx}1% for FFST and 1.5% of estimated skeletal muscle mass, and 0.9% for bone mineral content. The intraobserver CVs for measurements with the same scan were 1.35% for the bitrochanteric breadth (manual procedure) and 3.5% for abdominal FM. The intraobserver variation for skinfold thicknesses ({approx}5–10%) (23, 26, 27) yield CVs for skinfold ratios of 3–10% ({approx}3.5–7.0% for the ratio of subscapular + suprailiac/biceps + triceps) when calculated with the use of propagation of errors (28) and correlation coefficients between numerator and denominator obtained in our study (r = 0.40–0.77)). Interobserver variations were generally greater (26, 27, 29). The CVs for midarm circumference and for waist-to-hip ratio were <{approx}2%.

Statistical analysis
One-way analysis of variance was used to compare the mean differences in weight, height, FFST, FM, muscle mass, and the ratios of nonlimb to limb, trunk to limb, and abdomen to limb FM between both the low- and high-birth-weight groups. Analysis of covariance was also used to explore the effect of birth weight (low- and high-birth-weight groups as fixed factors) on body composition and fat distribution, with and without adjustment for covariates such as body mass index (BMI) and weight plus height. Chi-square tests were used to assess categorical data, such as socioeconomic status. Total observed variance was taken to be the sum of biological variance, variance due to observer error, and variance due to instrument error, which was assumed to be negligible (26). The statistical analyses were carried out by using the STATISTICAL PACKAGE FOR THE SOCIAL SCIENCES (version 12.0; SPSS Inc, Chicago, IL). Continuous data are expressed as means ± SEs.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Subject characteristics
The mean birth weights of the low- and high-birth-weight groups were 2.76 ± 0.058 and 4.23 ± 0.077 kg (6.08 ± 0.13 and 9.30 ± 0.17 lb), respectively. There was no significant difference between the groups with respect to gestational age (39 ± 0.5 and 40 ± 0.3 wk), smoking status, alcohol consumption, socioeconomic status at birth and at the time of the study, and physical activity (Table 2Go). Although subjects in the low-birth-weight group were significantly shorter (8.0 cm) and lighter (8.3 kg) as adults than were the subjects in the high-birth-weight group, their BMIs were not significantly different (Table 1Go).

Body composition and fat distribution measured by DXA
Compared with the high-birth-weight group, the low-birth-weight group was found to have significantly less FFM ({approx}8 kg), less FFST ({approx}7 kg) in the whole body (Table 1Go) and in the limbs (limb FFM, {approx}3.8 kg; limb FFST, {approx}3.6 kg), and less muscle in the whole body ({approx}4.4 kg), but not significantly less FM. Because these differences could be due to the smaller adult size (weight and height) of the low-birth-weight group, the data were reexamined after control for weight plus height (Table 3Go) (weight + height2 or weight and 1/height2 gave identical or almost identical results and significance values; data not shown). However, both FFM and FFST remained significantly lower in the low-birth-weight subjects than in the high-birth-weight subjects, by {approx}3 kg (muscle mass by {approx}2 kg), whereas FM was significantly lower by {approx}3 kg, corresponding to {approx}4% of body weight (Table 3Go). The effect of other potential variables, such as socioeconomic status, physical activity, and smoking status (Table 2Go) were also examined, by adding them as covariates (no more than one at a time) in the model, but because they were evenly matched between groups and had no significant effect, they were removed from the model for simplicity. The same applied to the other analyses reported below.


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  		TABLE 3 Anthropometric measures in the low- and high-birth-weight (BW) groups1

 
The gross body-composition differences between the low- and high-birth-weight groups obtained by DXA were confirmed with air-displacement plethysmography. Even greater differences in percentage body fat were noted with this technique after control for BMI (30.67 ± 1.61% and 25.05 ± 1.61%; P = 0.020) or for weight plus height (30.97 ± 1.76% and 24.94 ± 1.76%; P = 0.039). There were also differences between groups in kilograms fat and FFM; however, for simplicity, further consideration was only given to DXA, which provided additional information on segmental fat distribution.

Fat distribution assessed by DXA was found to differ between the low- and high-birth-weight groups, in favor of a more central distribution in the low-birth-weight group (Table 1Go for unadjusted values and Table 4Go for values adjusted for weight plus height and for total-body FM). This finding was due to a combination of a greater trunk (and nonlimb fat) and a lower limb FM in the low-birth-weight group (Table 4Go). The trunk-to-limb fat ratios were also significantly greater in the low-birth-weight group after adjustment for percentage body fat: 1.37 ± 0.055 and 1.21 ± 0.055, respectively (P = 0.041). The ratio of abdominal fat to limb FM was also greater in the low-birth-weight group than in the high-birth-weight group (0.29 ± 0.013 and 0.027 ± 0.013; P = 0.314), but there was substantial variability, and the upper and lower abdominal boundaries could only be defined to the nearest pixel (1.3035 cm), which corresponded to {approx}15% of the distance between these 2 boundaries.


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  		TABLE 4 Fat mass distribution, measured by dual-energy X-ray absorptiometry, in the low- and high-birth-weight (BW) groups1

 
Anthropometric adjustments and predictions of body composition and fat distribution measured by DXA
Regression analysis showed that BMI predicted 35% of the variance in percentage body fat (% fat = –1.606 ± 1.052 BMI; r2 = 0.35), which increased significantly to 47% when birth weight category was added to the model (% fat = –1.838 ± 1.126 BMI – 3.596 B; r2 = 0.47), where B = 0 for the low-birth-weight group and 1 for the high-birth-weight group). Significant differences in percentage body fat were observed between the low- and high-birth-weight groups, both before and after (29.98 ± 1.14% and 24.66 ± 1.14%, respectively; P = 0.003) control for weight, weight + height (29.31% and 25.33%, respectively; Table 3Go), and BMI (29.31% and 25.33%, respectively; Table 3Go).

Percentage body fat, estimated from skinfold thicknesses in all subjects (27.3 ± 0.62%; n = 32), was not significantly different from measurements obtained by DXA (27.3 ± 0.95%; n = 32) or air-displacement plethysmography (27.9 ± 1.38%). Although skinfold thicknesses predicted a lower percentage body fat in the low- than in the high-birth-weight group, the difference was not significant, either before or after adjustment for BMI or weight plus height. More than 90% of the variability in all skinfold-thickness ratios and waist-to-hip circumference was estimated to be due to biological variability and <10% to measurement error. The mean ratios of trunk (subscapular + suprailiac) to limb skinfold thicknesses (biceps + triceps) were not different between the low- and high-birth-weight groups (80–100% of the values obtained in the high-birth-weight group, both before (Table 1Go) and after adjustment for BMI and weight plus height (data not shown). This was also shown by analysis of covariance, in which the skinfold-thickness ratio was used as a covariate. This analysis showed that any given trunk-to-limb skinfold-thickness ratio was associated with a significantly greater ratio of whole trunk to whole limb fat obtained by DXA in the low- than in the high-birth-weight group (Table 5Go), even when further adjustments were made for BMI (Table 5Go). The same was found to apply to the waist-to-hip ratio (Table 5Go).


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  		TABLE 5 Trunk-to-limb, trunk-to-arm, and trunk-to-leg fat mass ratios obtained by dual-energy X-ray absorptiometry (DXA) in low- and high-birth-weight (BW) groups

 
The midupper arm cross-sectional areas tended to be smaller in the low- than in the high-birth-weight group both before (47.1 ± 2.18 and 53.8 ± 2.59 cm2, respectively; P = 0.057) and after (42.2 ± 2.63 and 53.8 ± 2.63 cm2, respectively; P = 0.109) adjustment for height or for height plus weight (47.7 ± 2.65 and 53.0 ± 2.65 cm2, respectively; P = 0.175), but the differences were not statistically significant. The bitrochanteric breadth, which was close to the level of the hip circumference measurement, was found to be significantly lower in the low- than in the high-birth-weight group, both before (30.49 ± 0.37 and 32.60 ± 0.39 cm, respectively; P = 0.001) and after (31.01 ± 0.34 and 32.09 ± 0.34 cm, respectively; P = 0.044) adjustment for height or for weight plus height (31.00 ± 0.35 and 32.09 ± 0.35 cm, respectively; P = 0.05). Whole-body bone mineral content did not differ significantly between the low- and the high-birth-weight groups before or after adjustment for BMI, weight, height, or weight plus height (Table 3Go).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
This study raises 3 issues about gross body composition in older men, in relation to their birth weight, which are relevant to mortality and morbidity from cardiovascular disease and diabetes in later life. First, this study not only confirmed that low birth weight is associated with lower adult weight, height, and FFM, but it also found that the low birth weight is associated with less appendicular and whole-body FFST and less muscle. This observation is in keeping with programming during early life of adult weight and height and mass of various lean tissues. Second, it suggests that differences in tissue proportions and fat distribution measured by DXA persist even after adjustments for body size (weight + height) or adiposity (FM and percentage body fat), which implies subtle effects on body composition Third, it suggests that biased and potentially misleading information can be obtained when traditional anthropometric measures are used to compare body composition and fat distribution (over body segments) between low- and high-birth-weight groups. These last 2 issues are considered separately in more detail below.

Body composition and fat distribution measured by DXA
The low-birth-weight group was found to have a greater amount ({approx}3 kg) and proportion ({approx}4%, after control for weight plus height) of fat, a more central fat distribution (after control for the amount of or percentage of body fat), and a lower muscle mass and muscle to fat ratio (after control for weight, or weight plus height, or BMI), all of which contribute to an increased risk of cardiovascular disease and diabetes. Although the ratio of abdominal to limb FM was greater in the low than in the high-birth-weight group, the differences were not significant, possibly because of anatomical variation and the inability to define upper and lower boundaries of the region of interest by <1.3035 cm (1 pixel, or {approx}15% of the distance between the boundaries). It is unlikely that significant differences in fat and lean tissue masses between the groups were due to methodologic problems associated with DXA because of the concordance of results with those obtained by air-displacement plethysmography, which is based on very different principles for measuring body composition.

The observed association of birth weight with body composition (lean and FM) in older adults is consistent with the results of another DXA study in England, which suggested that body composition is programmed (2). Although this study did not report on muscle mass, fat distribution, or muscle-fat ratios, it reported a weak but significant association between whole-body bone mineral content and birth weight category (after adjustment for height and sex), which was not observed in our study. Two other studies have suggested that muscle mass is reduced in low-birth-weight individuals. One of these studies, which was based on a timed overnight urine collection for creatinine excretion (30), considered weight and not height in the comparison; the other study, which was based on anthropometric measures of the thigh adjusted for weight but not height (8). The results of the present study, which are based on DXA estimates of muscle mass, suggest differences between the low- and high-birth-weight groups after adjustment for both weight and height. This could help explain another observation (31), which is the reported positive relation between muscular strength and birth weight, after adjustment for weight and height. Unfortunately, no estimates of muscle mass were made in this last study.

Anthropometric adjustments and predictions of body composition and fat distribution measured by DXA
Body fat
This study suggests that the use of BMI (or weight plus height) to compare body composition between the high- and low-birth-weight groups can be misleading. At the same BMI, or same weight and height, percentage body fat was greater in the low-birth-weight group ({approx}4% of body weight) than in the high-birth-weight group. Therefore, previous studies that have attempted to control for differences in body composition using BMI may have done so inadequately. This could help explain (together with differences in fat distribution, in the muscle-to-fat ratio, or in both) the apparent paradox of a lower risk of cardiovascular disease and diabetes in high-birth-weight individuals with the same and even greater BMI than in low-birth-weight individuals (11, 14). An analogous situation concerns ethnic differences in body composition. At the same adult BMI, Asians often have 2–6% more body weight as fat than do whites (15–18), who generally have a lower risk of cardiovascular disease and diabetes than do Asians. A study that compared anthropometric estimates of body composition in neonates born in England (mean birth weight: 3.49 kg) and India (mean birth weight: 2.70 kg; 2.76 kg in the low-birth-weight group in the present study) suggests that ethnic differences are present early in life (19). The extent to which these ethnic differences are due to genetic or environmental factors operating in early life is uncertain. The failure of skinfold thicknesses to demonstrate significant differences in percentage body fat between birth-weight groups in our study may have been due to greater technical error in skinfold-thickness measurements relative to DXA, which showed significant differences between birth weight groups.

Fat distribution
Although DXA measurements showed significant differences between the low- and the high-birth-weight groups in the distribution of body fat between central and peripheral body segments, no significant differences were obtained when the ratio of waist-to-hip circumferences (see below) or trunk-to-limb skinfold thickness were used. This last observation may have been due in part to the poorer precision and accuracy of skinfold-thickness measurements obtained at single subcutaneous sites compared with the reference DXA measurements of total fat, both internal and subcutaneous fat, over entire body segments. However, data from this study challenge previous assumptions about the use of skinfold-thickness ratios (or waist-to-hip ratio) for comparing fat distribution between body segments in low- and high-birth-weight groups in an unbiased manner. The same skinfold-thickness ratios (or the same waist-to-hip ratios) and same BMIs were found to be associated with greater trunk and limb fat distribution measured by DXA (ie, anthropometric indexes of fat distribution may have underestimated the central fat distribution in the low-birth-weight group compared with the high-birth-weight group). For skinfold-thickness ratios, this may have been because of differences between the low- and high-birth-weight groups in the distribution of fat between internal and external fat and between different parts of the same body segment. For the waist-to-hip circumference ratio, it is possible that different musculoskeletal to fat proportions produce the same circumferences. The significantly greater bitrochanteric breadth observed in the high- than in the low-birth-weight group, after adjustment for height or weight plus height, is consistent with this proposal.

Previous attempts to assess fat distribution on the basis of the waist-to-hip ratio or the ratio of trunk to limb skinfold thicknesses have produced inconsistent results, with some studies reporting significantly more central fat distribution in individuals with low birth weight (32–36) and others reporting no such relations (37–40). One of the reasons for the lack of consistency is that different skinfold-thickness ratios [subscapular to triceps (32–35, 40), subscapular + suprailiac to triceps + thigh (35), and subscapular + midaxillary to triceps + medial calf (34)] or different circumference ratios [eg, waist to hip (36–40) or waist-to-thigh (8)] have been used for this purpose. Another reason is that some studies have adjusted for weight and not height (36, 40), others have adjusted for height and not weight (8), whereas most studies have adjusted for BMI (32, 33, 35, 38, 39), which controls for neither weight or height (Table 2Go), and a few studies adjusted for none of these (41–43).

Muscle and muscle-to-fat ratio
The use of midupper arm muscle area as a surrogate measure of muscle mass suggested less muscle in the low- than high-birth-weight group before and after anthropometric adjustments. These differences were not statistically significant, in contrast with the measurements obtained with DXA, which were significantly different. These significant differences were possibly due to the larger technical error associated with the anthropometric measurements, which in reality arise from 2 independent measurements (arm circumference and triceps skinfold thickness). However, the trends observed in this study are consistent with the results of another study (8), which found that birth weight was related to the cross-sectional area of thigh + bone (after adjustment for height and race), assessed by anthropometric methods in applicants for military service in the United States (age: 17–22 y; mean BMI: 23.2).

The present study had several limitations. The number of subjects was small, and it is perhaps surprising that significant results were obtained. However, the study included only subjects with high or low birth weights, excluding subjects with intermediate birth weight, which probably increased the chances of identifying any differences in body composition. It can be hypothesized that intermediate values occur in the "middle" group, but this needs to be established. The different birth weight–disease relations that have been reported, including U-shaped birth weight–adult BMI relations (10), suggest that investigations across all birth weight categories, not just low and high birth weight categories, should be carried out. In our investigation, only men within a narrow age range (64–72 y) were studied, in an attempt to reduce the confounding effects of sex and age, but this limits generalizations to other age groups. However, this approach precludes extrapolation to women and individuals in a different age range. Another limitation was that muscle mass was not measured directly but was estimated through measurements of FFST in the limbs. Finally, because all of the subjects involved in this study were white, there is a need to examine these relations in subjects with different ethnic backgrounds.


   ACKNOWLEDGMENTS
 
All authors helped plan the study or analyzed and discussed the results. OAK undertook most of the practical work. None of the authors had a conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. O' Brien PMS, Wheeler T, Barker DPJ. Fetal programming. Influences on development and disease in later life. London, United Kingdom: Royal College of Obstetrics and Gynaecology, 1999.
  2. Gale CR, Martyn CN, Kellingray S, Eastell R, Cooper C. Intrauterine programming and adult body composition. J Clin Endocrinol Metab 2001;86:267–72.[Abstract/Free Full Text]
  3. Singhal A, Wells J, Cole TJ, Fewtrell M, Lucas A. Programming of lean body mass: a link between birth weight, obesity, and cardiovascular disease? Am J Clin Nutr 2003;77:726–30.[Abstract/Free Full Text]
  4. Weyer C, Pratley RE, Lindsay RS, Tataranni PA. Relationship between birth weight and body composition, energy metabolism, and sympathetic nervous system activity later in life. Obes Res 2000;8:559–65.[Medline]
  5. Choi CS, Kim C, Lee WJ, et al. Association between birth weight and insulin sensitivity in healthy young men in Korea: role of visceral adiposity. Diabetes Res Clin Pract 2000;49:53–9.[Medline]
  6. Te Velde SJ, Twisk JW, Van Mechelen W, Kemper HC. Birth weight, adult body composition, and subcutaneous fat distribution. Obes Res 2003;11:202–8.[Medline]
  7. Rogers I, EURO-BLCS Study Group. The influence of birthweight and intrauterine environment on adiposity and fat distribution in later life. Int J Obes Relat Metab Disord 2003;27:755–77.[Medline]
  8. Kahn HS, Narayan KM, Williamson DF, Valdez R. Relation of birth weight to lean and fat thigh tissue in young men. Int J Obes Relat Metab Disord 2000;24:667–72.[Medline]
  9. Gallaher MM, Hauck FR, Yang-Oshida M, Serdula M. Obesity among Mescalero preschool children. Association with maternal obesity and birth weight. Am J Dis Child 1991;145:1262–5.[Abstract]
  10. Curhan GC, Willett WC, Rimm EB, Spiegelman D, Ascherio AL, Stampfer MJ. Birth weight and adult hypertension, diabetes mellitus, and obesity in US men. Circulation 1996;94:3246–50.[Abstract/Free Full Text]
  11. Rich-Edwards JW, Stampfer MJ, Manson JE, et al. Birth weight and risk of cardiovascular disease in a cohort of women followed up since 1976. BMJ 1997;16:396–400.
  12. Sayer AA, Syddall HE, Dennison EM, et al. Birth weight, weight at 1 y of age, and body composition in older men: findings from the Hertfordshire Cohort Study. Am J Clin Nutr 2004;80:199–203.[Abstract/Free Full Text]
  13. Oken E, Gillman MW. Fetal origins of obesity. Obes Res 2003;11:496–506.[Medline]
  14. Frankel S, Elwood P, Sweetnam P, Yarnell J, Smith GD. Birthweight, body-mass index in middle age, and incident coronary heart disease. Lancet 1996;30:1478–80.
  15. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev 2002;3:141–6.[Medline]
  16. Deurenberg-Yap M, Deurenberg P. Is a re-evaluation of WHO body mass index cut-off values needed? The case of Asians in Singapore. Nutr Rev 2003;61:S80–7.[Medline]
  17. Wang J, Thornton JC, Russell M, Burastero S, Heymsfield SB, Pierson RN. Asians have lower body mass index (BMI) but higher percent body fat than do whites: comparisons of anthropometric measurements. Am J Clin Nutr 1994;60:23–8.[Abstract/Free Full Text]
  18. Banerji MA, Faridi N, Atluri R, Chaiken RL, Lebovitz H. Body composition, visceral fat, leptin, and insulin resistance in Asian Indian men. J Clin Endocrinol Metab 1999;84:137–44.[Abstract/Free Full Text]
  19. Yajnik CS, Fall CH, Coyaji KJ, et al. Neonatal anthropometry: the thin-fat Indian baby. The Pune Maternal Nutrition Study. Int J Obes Relat Metab Disord 2003;27:173–80.[Medline]
  20. Seidell JC, Perusse L, Despres JP, Bouchard C. Waist and hip circumferences have independent and opposite effects on cardiovascular disease risk factors: the Quebec Family Study. Am J Clin Nutr 2001;74:315–21.[Abstract/Free Full Text]
  21. WHO. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser 1995;854:1–452.[Medline]
  22. Durnin JVGA, Womersley J. Body fat assessed from total skinfold thickness: measurements on 481 men and women aged 16 to 72 years. Br J Nutr 1974;32:77–97.[Medline]
  23. Frisancho AR. Anthropometric standards for the assessment of growth and nutritional status. Ann Arbor, MI: The University of Michigan Press, 1993.
  24. Kim J, Wang Z, Heymsfield SB, Baumgartner RN, Gallagher D. Total-body skeletal muscle mass: estimation by a new dual-energy X-ray absorptiometry method. Am J Clin Nutr 2002;76:378–83.[Abstract/Free Full Text]
  25. Park YW, Heymsfield SB, Gallagher D. Are dual-energy X-ray absorptiometry regional estimates associated with visceral adipose tissue mass? Int J Obes Relat Metab Disord 2002;26:978–83.[Medline]
  26. Ulijaszek SJ, Kerr DA. Anthropometric measurement error in the assessment of nutritional status. Br J Nutr 1999;82:165–72.[Medline]
  27. Harries AD, Jones LA, Heatley RV, Newcombe RJ, Rhodes J. Precision of anthropometric measurements: the value of mid-arm circumference. Clin Nutr 1984;2:193–6.
  28. Kendall M, Stewart A. The advanced theory of statistics. 4th ed. London, United Kingdom: Griffin & Co Ltd, 1977.
  29. Fuller NJ, Jebb SA, Goldberg GR, et al. Inter-observer variability in the measurement of body composition. Eur J Clin Nutr 1991;45:43–9.
  30. Phillips DI. Relation of fetal growth to adult muscle mass and glucose tolerance. Diabet Med 1995;12:686–90.[Medline]
  31. Kuh D, Bassey J, Hardy R, Aihie Sayer A, Wadsworth M, Cooper C. Birth weight, childhood size, and muscle strength in adult life: evidence from a birth cohort study. Am J Epidemiol 2002;156:627–33.[Abstract/Free Full Text]
  32. Byberg L, McKeigue PM, Zethelius B, Lithell HO. Birth weight and the insulin resistance syndrome: association of low birth weight with truncal obesity and raised plasminogen activator inhibitor-1 but not with abdominal obesity or plasma lipid disturbances. Diabetologia 2000;43:54–60.[Medline]
  33. Barker M, Robinson S, Osmond C, Barker DJ. Birth weight and body fat distribution in adolescent girls. Arch Dis Child 1997;77:381–3.[Abstract/Free Full Text]
  34. Malina RM, Katzmarzyk PT, Beunen G. Birth weight and its relationship to size attained and relative fat distribution at 7 to 12 years of age. Obes Res 1996;4:385–90.[Medline]
  35. Okosun IS, Liao Y, Rotimi CN, Dever GE, Cooper RS. Impact of birth weight on ethnic variations in subcutaneous and central adiposity in American children aged 5–11 years. A study from the Third National Health and Nutrition Examination Survey. Int J Obes Relat Metab Disord 2000;24:479–84.[Medline]
  36. Loos RJ, Beunen G, Fagard R, Derom C, Vlietinck R. Birth weight and body composition in young adult men—a prospective twin study. Int J Obes Relat Metab Disord 2001;25:1537–45.[Medline]
  37. Esposito-Del Puente A, Scalfi L, De Filippo E, et al. Familial and environmental influences on body composition and body fat distribution in childhood in southern Italy. Int J Obes Relat Metab Disord 1994;18:596–601.[Medline]
  38. Law CM, Barker DJ, Osmond C, Fall CH, Simmonds SJ. Early growth and abdominal fatness in adult life. J Epidemiol Community Health 1992;46:184–6.[Abstract]
  39. Fall CH, Osmond C, Barker DJ, et al. Fetal and infant growth and cardiovascular risk factors in women. BMJ 1995;310:428–32.[Abstract/Free Full Text]
  40. Bavdekar A, Yajnik CS, Fall CH, et al. Insulin resistance syndrome in 8-year-old Indian children: small at birth, big at 8 years, or both? Diabetes 1999;48:2422–9.[Abstract]
  41. Vestbo E, Damsgaard EM, Froland A, Mogensen CE. Birth weight and cardiovascular risk factors in an epidemiological study. Diabetologia 1996;12:1598–602.
  42. Stern MP, Bartley M, Duggirala R, Bradshaw B. Birth weight and the metabolic syndrome: thrifty phenotype or thrifty genotype? Diabetes Metab Res Rev 2000;16:88–93.[Medline]
  43. Martyn CN, Hales CN, Barker DJ, Jespersen S. Fetal growth and hyperinsulinaemia in adult life. Diabet Med 1998;15:688–94.[Medline]
Received for publication March 23, 2005. Accepted for publication July 8, 2005.




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