There is a paucity of information regarding the general effects of intrauterine growth restriction on adipose tissue distribution at birth, and changes in total and regional content during the first few months of postnatal growth. Previous studies of body composition in AGA and GR infants have largely relied on indirect methodology to estimate adipose tissue content and are unable to provide information about specific adipose tissue depots, particularly intra-abdominal adipose tissue.
We, and others, have shown that MRI can be used in the direct assessment of adipose tissue content and distribution in neonates 2 , 3. The technique has been validated in animals and cadavers and can be readily used in longitudinal measurements 4 — 7. In the present study, we have compared the content and distribution of adipose tissue at birth in infants born at or near term, with an appropriate birth weight for gestational age, with that of infants with intrauterine growth restriction.
Mothers undergoing investigation for fetal growth restriction were initially approached during the antenatal period to explain the study. Consent to recruit their infant into the study was formally obtained after delivery. Details of antenatal growth assessments were documented.
Infants were classified as either AGA or GR if they had evidence of deceleration in growth in utero , together with clinical signs at birth suggestive of fetal malnutrition loose thin skin with prominent ribs, a scaphoid abdomen, and muscle wasting over the cheeks, arms, buttocks, and thighs and had a birth weight at or below the 9 th centile 8.
Mothers were asked whether they had any dietary restrictions and data were obtained on racial group Caucasian, African, Asian, mixed and infant gender. Written informed parental consent was obtained. Subjects were imaged in a 1. No sedation was used and infants were positioned supine during natural sleep. Images were analyzed as previously described 3 , 7 using an image segmentation program that employs a threshold range and a contour-following algorithm with an interactive image-editing facility.
The initial analysis of MRI data results in the expression of adipose tissue content as volume of adipose tissue. This value may be converted to adipose tissue mass on the assumption that the density of adipose tissue is 0. Adipose tissue mass may be converted to lipid fat mass, assuming that 1 g of adipose tissue contains approximately 0. Data in this article are expressed as adipose tissue mass, because the adipose tissue lipid content may not be the same in GR and AGA infants, nor the same in infants as in adults.
We have expressed our data as lipid fat mass only where necessary for comparison with other published data,. Total adipose tissue was separated into total subcutaneous adipose tissue and total internal adipose tissue. Total subcutaneous adipose tissue was subdivided into abdominal subcutaneous and nonabdominal subcutaneous adipose tissue. Total internal adipose tissue was subdivided into intra-abdominal internal and nonabdominal internal adipose tissue.
Abdominal adipose tissue content was obtained by quantifying adipose tissue in the slices from the top of the sacrum to the slice containing the top of the liver or base of the lungs 3. Subcutaneous adipose tissue in this region of the body was termed abdominal subcutaneous adipose tissue.
Internal adipose tissue in the region was termed intra-abdominal internal adipose tissue. All other internal adipose tissue was termed nonabdominal internal. This includes, for example, adipose tissue between muscle planes and in bone marrow. Weight g , length cm , and head circumferences cm were recorded at each examination by a single trained observer TAMH.
Crown—heel length was measured on a recumbent infant board with a sliding footboard Rollametre: Raven Equipment Ltd. Head circumference was measured using a plastic tape measure, taking the mean of three measurements Child Growth Foundation tape measure. Results are presented as mean and SD. Multiple regression analysis was used to compare the AGA and GR groups after adjusting for gender, postmenstrual age at the time of imaging, racial group, and whether the mother had any dietary restrictions.
In view of the relatively small number of infants, these variables were selected for inclusion in the multiple regression model based on knowledge of the relevant literature and not on internal study evidence. In addition, residuals from the multiple regression model were tested for normality. Data were analyzed using Stata Thirty-five singleton infants were recruited into this study.
Infants were imaged as soon as possible after birth. The mean SD age at imaging was 1. Four mothers excluded eggs, fish, and meat, but not dairy products, from their diet. Although the infants were all born at or near term, the gestational age at birth of the GR infants was slightly but significantly less than that of the AGA infants [GR, All subsequent multiple regression analyses were therefore performed allowing for postmenstrual age at time of imaging.
As anticipated, the length, weight, head circumference, and ponderal index of the GR infants were significantly smaller than the AGA infants Table 1. Absolute total adipose tissue mass and all subcutaneous compartments were significantly smaller in GR infants Table 2 having allowed for postmenstrual age, gender, maternal dietary restriction, and race. There was no significant difference between the groups in total internal and intra-abdominal internal adipose tissue content.
Similarly, when expressed as a percentage of body weight, total adipose tissue mass and all subcutaneous compartments were significantly smaller in the GR infants but there was no significant difference in internal adipose tissue compartments Table 3. Both Asian infants were GR. However, multiple regression analysis revealed no influence of race on adipose tissue compartments, nor was any influence of maternal dietary restriction identified.
There were no significant differences in adipose tissue mass between male and female GR and AGA infants total adipose tissue mass: GR male, In adults and children, obesity is strongly associated with increased morbidity.
The average birth weight for babies is around 7. In general:. Newborns often lose around 8 oz In the first month, the typical newborn gains about 0. The average length of full-term babies at birth is 20 in. In the first month, babies typically grow 1. Your baby's head will grow at its fastest rate during the first 4 months after birth than at any other time. This increase is due to rapid brain growth. The average head circumference at birth is about By the end of the first month, it increases to about 15 in.
Many babies look a little less than perfect in the first few days or weeks after birth. Gradually they will gain that cute and healthy baby look. Do not be alarmed if your newborn has:.
Author: Healthwise Staff. Medical Review: Susan C. The skewness and kurtosis of birth weight and gestational age distribution were The two distributions indicated high kurtosis and left skewness, and were approximately normally distributed. Because birth weight and gestational age are highly associated, the skew-to-left residual distribution of birth weight might have been induced by low gestational age.
The mortality curve provides no particular justification for g as the criterion for risk. One fundamental aspect of birthweight-specific mortality is the constancy of its shape. However, the relative decline in mortality has been fairly uniform across all birthweights a constant distance on the log scale , with least change at the smallest weights. The general contrast seen between these two mortality curves is typical. The crucial difference in birthweight-specific mortality between any two groups is usually the height of the mortality curves, rather than their shape.
One essential feature of weight-specific mortality is not observable in Figure 2. This feature becomes apparent only when weight-specific mortality is considered in relation to the distribution of birthweights from which the rates are derived Figure 3. Mean birthweight is several hundred grams lower than optimum birthweight i. Population geneticists have discussed this phenomenon, 28, 29 but epidemiologists have been slower to recognize its implications. The fact that optimum weight maintains a fairly constant distance from mean weight suggests that a shift in the birthweight distribution will produce an equivalent shift in the mortality curve.
If all else is held constant, such a shift produces no net effect on infant mortality. The following section pursues this in detail.
Epidemiologists generally assume that since small babies have high risk, there should logically be an increase in infant mortality with a reduction in mean birthweight. This is not necessarily true. Infant mortality rates are similar in the US as a whole and in the state of Colorado.
Most people in Colorado live at high altitudes, and high altitude produces smaller babies. The shift of Colorado birthweights to lower weights is clearly seen in Figure 4 reprinted from ref. This Figure also shows the curves of weight-specific mortality for Colorado and the US. The two curves intersect. Mortality rates are lower in Colorado for small babies, and higher for large babies. There is no obvious biological explanation for why small babies should do better in Colorado and larger babies should do worse.
Another interpretation of the intersecting mortality curves is that, as birthweights have shifted to lower weights in Colorado, so has optimum weight and in fact the whole mortality curve. We can test this interpretation by adjusting the two weight distributions to a standard z-scale with means set to zero and standard deviations to one. Both sets of weight-specific mortality rates are then placed on this z-scale.
With this adjustment, the two weight distributions correspond nearly exactly, as do the two mortality curves Figure 5. The residual distributions are magnified in the inset box for easier inspection. The simplest explanation for the convergence of mortality curves is that altitude affects birthweight but not mortality. The two mortality curves are essentially the same curve, with the one in Colorado carried along with the shift in birthweight.
For babies weighing less than the optimum weight, this shift gives the appearance of lower mortality at any given birthweight.
For babies heavier than the optimum weight, the shift gives the appearance of higher mortality. In fact, the birthweight distribution and its accompanying mortality curve have shifted without any change in the survival of individual babies.
In this example, fetal growth retardation on the population level has no effect on mortality. We can conclude from this example that the moderate reduction of in utero growth does not necessarily increase an individual baby's mortality risk—nor does it increase the number of small babies at higher risk. This might be regarded as a counter-example to Rose's highly-cited thesis that a modest shift in the population mean of a continuous variable such as blood pressure will place more individuals into the high-risk group at the extreme.
Now imagine a more complicated but plausible scenario. What if a factor decreases birthweight and also increases infant mortality? The same analytical approach can be applied. In the process, we can discover the underlying sense behind the LBW paradox. Mothers who smoke have smaller babies. Their babies, as a whole, have higher infant mortality. If we look at the birthweight and mortality curves for smokers and non-smokers Figure 6 , reprinted from ref.
There are two distinct distributions of birthweight, and the two mortality curves intersect. Small babies have lower mortality if their mothers smoke. This is the paradox by which Yerushalmy defended smoking. When the picture is adjusted to relative weight the z-scale , there emerges a new relation between the mortality curves Figure 7. Mortality with mother's smoking is higher across the whole range of weights.
Thus, smoking has two discrete effects. It retards fetal growth, shifting the birthweight distribution and, as always, the mortality curve. In addition, smoking also shifts the mortality curve upwards, to higher rates. The increased mortality occurs at every adjusted birthweight. In other words, this effect of smoking on weight-specific mortality is independent of birthweight. The increase of mortality across all weights— crucial evidence of the harmful effect of smoking on infants —is initially hidden by the leftward shift of the mortality curve as it follows the birthweight distribution.
Small babies of mothers who smoke seem to be at lower risk, when in fact they are at higher risk. This is apparent on the relative weight scale the z-scale but not on the absolute scale. MacMahon anticipated this conclusion when he proposed that the LBW paradox was an artefact due to comparison of absolute weights. Relative weights are needed to uncover the essential relation between smoking and infant mortality.
To the extent that smoking increases weight-specific mortality proportionately across all relative weights, smoking acts on infant mortality independently of birthweight.
As discussed earlier, the intersection of weight-specific mortality curves is not uncommon. It can be found in nearly any setting where populations have different mean birthweights. In each case, the underlying difference in weight-specific mortality can be revealed by adjustment to a relative scale of birthweight.
These conclusions extend to any health endpoint associated with birthweight. For example, Liu and colleagues recently published an analysis of cerebral palsy and its association with birthweight in twins and singletons. Twins had higher rates of cerebral palsy overall, but LBW twins had lower rates of cerebral palsy than LBW singletons. The authors resolved this paradox by adjusting birthweight to the Normal distribution of weight in singletons and twins. After adjustment, the increased risk among twins for cerebral palsy was apparent in every stratum of birthweight.
The characteristics of the birthweight distribution and its relation to weight-specific mortality provide a foundation for assessing the earlier assumptions about LBW. Is per cent LBW a good surrogate indictor of a population's infant risk? No, because LBW is easily affected by changes in the predominant distribution which are not reliable indicators of risk. Altitude produces more LBW babies, but this does not lead to an increase in infant deaths.
Babies born of Mexican mothers in the US have a predominant distribution of birthweights shifted to lower weights than non-Hispanic whites. However, Mexican-Americans have lower infant mortality. In this example, the difference in per cent of LBW merely reflects harmless differences in the predominant distribution. Are LBW births really preventable? Preterm delivery is preventable in principle, and preterm births comprise a major portion of LBW. But what about the lower end of the Normal distribution of births?
One option might be to increase the mean or reduce the SD until little of the distribution falls below g. But if the mortality curve automatically shifts with the birthweight distribution, this strategy is of dubious value.
Another alternative would be to change the fundamental Normal distribution of birthweight for example, by truncating its lower tail. This seems infeasible. Elimination of LBW is neither practical nor necessary in order to achieve the lowest possible rates of infant mortality. The arguments above suggest that LBW is muddled as an endpoint, and unreliable as a predictor of population risk. The fact that these uses of LBW are time-honoured is hardly a defence.
What alternatives are available? The answer depends on the purpose of the investigator. If the aim is to assess perinatal health through some convenient surrogate, there are several options depending on the type of data available. If birthweight is the only type of data at hand, the residual distribution should be estimated.
The per cent of births in the residual distribution is preferable to LBW as an indicator of perinatal health. The residual provides an estimate of the number of small preterm births—the babies at highest risk. The proportion of preterm births in the population should be examined directly whenever possible.
The residual distribution of birthweight is informative, but it is not as good as actual information on preterm delivery. This of course assumes that the gestational data are of good quality, which is not always the case. Once the per cent of preterm births is known, the analysis of birthweight can be simplified by restricting the sample to term births. Among term births, the influence of gestational age is minor and can be ignored. The mean and SD of birthweights among term births provide a way to compare fetal growth across groups.
The comparison of fetal growth patterns may be interesting in its own right for example, in understanding the biological effect of a specific exposure , but fetal growth on the population level should not be regarded as a marker of perinatal health. At a given gestational age, births are not a random sample of all intrauterine fetuses. This is especially true of births delivered preterm. When an external factor for example, altitude acts to retard fetal growth, it acts on all babies, not just the small ones.
If an investigator wishes to summarize intrauterine growth in a population, there is no simpler or more direct endpoint than the mean weight of term births. The analysis of birthweight becomes even more complicated when birthweight is not the endpoint in itself, but is treated as an intermediate variable.
0コメント