brains versus brawn: an empirical test of barker's brain sparing model

Original Research Article

Brains Versus Brawn: An Empirical Test of Barker’s Brain Sparing ModelJACK BAKER,* MEGAN WORKMAN, EDWARD BEDRICK, M. ANDERSON FREY, MAGDALENA HURTADO, AND OSBJORN PEARSONDepartment of Anthropology, Bureau of Business and Economic Research, University of New Mexico, Albuquerque, New Mexico

ABSTRACT The Barker model of the in utero origins of diminished muscle mass in those born small invokes theadaptive ‘‘sparing’’ of brain tissue development at the expense of muscle. Though compelling, to date this model has notbeen directly tested. This article develops an allometric framework for testing the principal prediction of the Barkermodel—that among those born small muscle mass is sacrificed to spare brain growth—then evaluates this hypothesisusing data from the third National Health and Nutrition Examination Survey (NHANES III). The results indicate clearsupport for a negative relationship between the allometric development of the two tissues; however, a further considera-tion of conserved mammalian fetal circulatory patterns suggests the possibility that system-constrained patterns ofdevelopmental damage and ‘‘bet-hedging’’ responses in affected tissues may provide a more adequate explanation of theresults. Far from signaling the end of studies of adaptive developmental programming, this perspective may open apromising new avenue of inquiry within the fields of human biology and the developmental origins of health and dis-ease. Am. J. Hum. Biol. 22:206–215, 2010. ' 2009 Wiley-Liss, Inc.

Previous research clearly indicates that both brain andmuscle mass development are sacrificed in those bornsmall (Baker et al., 2008; Euser et al., 2005; Hediger et al.,1998). These effects appear to be life-long in nature andthe observed reduction in muscle mass development hasbeen associated with a number of chronic disease sequel-lae such as Type 2 diabetes, hypertension, and cardiovas-cular disease (Barker, 2004; Cameron and Demerath,2002; Yajnik, 2000, 2004). It has long been known that fe-tal undernutrition in a variety of animals (includinghumans) produces disproportionate growth as some tis-sues continue to grow rather normally while others arestunted (Widdowson and McCance, 1974, 1975). Inhumans, in utero brain growth tends to be preserved atthe expense of other tissues (Hofman, 1983; Rudolph,1984). In 1993, David Barker proposed that insulin resist-ance and associated reductions in muscle mass (Barker,1993; Yajnik, 2000, 2004), had their origins in an in uterotrade-off between the development of muscle and brainmasses under conditions of energetic limitations that areultimately reflected in reduced birth weight. Since bothtissues undergo concurrent critical periods of develop-ment, during which time the number of cells comprisingeach tissue are ‘‘set’’ and after which the amount of eachrelative to total body mass remain fairly constant acrossthe life-course (Cameron and Demerath, 2002; Davisonand Dobbing, 1968)—this trade-off would be predicted tocontinue postnatally and even into adulthood. The use ofthe term ‘‘trade-off ’’ makes plain what has been implicit tothat argument all along—that an adaptive preference forbrain mass development exists and is ‘‘acted’’ upon whenenergetic limitations are experienced in utero (Lumberset al., 2001). Barker’s model has been seen to enjoy a largeamount conceptual plausibility and the logical entail-ments of his proposition have important implications forthe study of human variation in body composition andemerging theories about the developmental origins ofchronic diseases related to reduced muscle mass. While itwould be fair to state that Barker’s seminal work hasshaped much of the subsequent work within human biol-

ogy, it is also true that much of the discussion has involveda consideration of plausible relationships between the sep-arate observations of brain sparing and reduced musclemass in collections of studies (Baker et al., 2008). To dateno direct empirical test of the principal prediction ofBarker’s model—that the relationship between the devel-opmental trajectories of brain and muscle tissues will dif-fer among those born small—has been made within a sin-gle sample.Studies have associated low birth weight with a variety

of body composition outcomes including not only reducedskeletal muscle mass, but also more centralized fat pat-terning, and—in some studies—increased risk for obesityduring child and adulthood (Barker et al., 1997; Euseret al., 2005; Hediger et al., 1998; Kahn et al., 2000;Kensara et al., 2005; Koziel and Jankowska, 2002; Laite-nen et al., 2004; Larciprete et al., 2005; Law et al., 1992;Malina et al., 1996; Padoan et al., 2004; Ravelli et al.,1976; Sachdev et al., 2005; Schroeder et al., 1999; Stanneret al., 1977; Valdez et al., 1994; Wells et al., 2005). Previ-ous findings have also repeatedly linked reduced neuro-logic/cognitive development to low birthweight (Aminet al., 1997; Georgieff et al., 1985; Gross et al., 1978; Gut-brod et al., 2000; Hack et al., 1984; Hediger et al., 1998;Kitchen et al., 1980; Pena et al., 1988; Stanley and Spei-del, 1985; Vohr and Oh, 1983). No study to date hasdirectly integrated a test of how each of these componentsof body composition changes in those born small relates toone another. This article seeks to extend this literature bytesting the principal prediction of the Barker model ofBrain Sparing through a simultaneous consideration of

*Correspondence to: Jack Baker, Department of Anthropology, Bureau ofBusiness and Economic Research, University of New Mexico, Albuquerque,NM 87131-0001, USA. E-mail: [email protected]

Received 20 March 2009; Revision received 19 June 2009; Accepted 26June 2009

DOI 10.1002/ajhb.20979

Published online 21 August 2009 in Wiley InterScience (www.interscience.wiley.com).

AMERICAN JOURNAL OF HUMAN BIOLOGY 22:206–215 (2010)

VVC 2009 Wiley-Liss, Inc.

how muscle and head cirumference development isaffected by experience of low birth weight in the samesample of individuals. To achieve this aim, this articleextends methods of developmental allometry (Cane, 1993;Huxley, 1932; Pleasants et al., 1997) that permit such asimultaneous assessment of the developmental trajecto-ries of multiple tissues. A finding relating the develop-ment of the two tissues in the direction proposed byBarker would further support the model, suggesting thepossibility that adaptive brain preservation occurs at theexpense of muscle tissue through a direct trade-offbetween the development of the two tissues (Barker,1994). It accomplishes this while simultaneously consid-ering postnatal exposures that might confound thisrelationship.

Physiological trade-offs such as the one suggested byBarker’s model are generally conceptualized within physi-ological ecology as negative correlations between compet-ing energetic investments (Reznick, 1985; Stearns, 1992;Zera and Harshman, 2001). However, when applied tobody composition components that are known to covarypositively with body size increases, such a model frame-work is inadequate. Instead, the conceptual framework ofallometry—in which increases in component tissues orinvestments are considered as they relate to overall bodysize increases (Huxley, 1932)—allows Barker’s model to beneatly reconceptualized as a consideration of how theallometry of brain mass and muscle mass relative to post-natal body size increases (growth) is affected by environ-mental exposures. This approach—known as developmen-tal allometry—has been used previously in studies of traitcovariation within physiological and evolutionary ecology(Cane, 1993; Schlichting and Pigliucci, 1999) as well as inhuman biology and exercise science (Armstrong et al.,

1999; Beunen et al., 2002). An allometric reconceptualiza-tion of Barker’s model makes specific, testable predictionsabout the relationship between the postnatal developmen-tal trajectories of muscle and brain mass. Figure 1illustrates these predictions graphically. Here, the pro-posed relationship between brain mass, muscle mass, andbody size is reviewed: the line describing muscle massincreases as a function of body weight is significantlylower for those born small. The brain mass is equalbetween the two groups and indistinguishable as a single,solid line.Barker’s model contends that preferences for brain-cen-

tric somatic investments (usefully termed somatic priorityrules by Zera and Harshman, 2001) are shaped by in uteroundernutrition reflected in low birth weight. The pattern-ing of such somatic investments has been proposed to besubject to natural selection (Schlichting and Pigliucci,1999; Zera and Harshman, 2001). Previous researcherswithin human biology have suggested the possibility thatdevelopmental programming of these rules is adaptive(fitness-enhancing) in nature (Ellison, 2005; Gluckmanand Hanson, 2004; Kuzawa and Sweet, 2009—but seeBaker et al., 2008; Holland-Jones, 2005) and body compo-sition has been specifically discussed in this light as well(Baker et al., 2008, 2009; Kuzawa et al., 2007; Ravelliet al., 1976; Stanner et al., 1997). Barker’s model invokesonly a preference for brain tissue over muscle tissue andthe suggestion that such sparing is adaptive in general(Barker, 1993). This article tests the principal predictionof Barker’s model: that preserved brain mass developmentpredicts reduced muscle mass development among thoseborn small after controls for potential confounding post-natal exposures related to ethnicity, race, and socio-eco-nomic status.

Fig. 1. The Barker model conceptualized in an allometric framework. The model predicts that brain development is buffered, implying equalpostnatal growth. Muscle mass between those born normal size (– � – � – � – � –) and those low birth weight (- - - - - - -) differ significantly, withthe muscle mass growth rates of those born small being significantly lower than those born normal size.

207BRAINS VERSUS BRAWN

American Journal of Human Biology

MATERIALS AND METHODS

This study tests the alternative hypotheses that: (1)brain growth rate among those born small does not differfrom those born normal-sized, (2) muscle growth rateamong those born small is reduced compared to thoseborn normal-sized, (3) increased rates of brain growth pre-dict reduced rates of skeletal muscle growth among bothnormal size and small-size birth groups. To test thesehypotheses, we extracted data from the third NationalHealth and Nutrition Examination Survey (NHANES III),conducted among United States children in 1998. Datawere extracted from the NHANES III for children aged2 months to 8 years for which data on both head and armcircumferences were available. This yielded a total avail-able sample of 7,962 children. Of these, 1,102 childrenwere excluded because data were missing for both armand head circumferences. These 1,102 children were con-sidered separately and did not differ significantly fromthose included in the study in terms of body size by age orconfounding variables included in the analysis. Because ofthe complex survey design involved in the NHANES (seeHediger et al., 1998 for similar thoughts), the analysiswas weighted using standard weights developed by theNHANES survey team to adjust the sampling frame toavailable Census data. Use of unweighted data can signifi-cantly bias parameter estimates and confidence intervalswhen a complex survey design is employed (NCHS, 2006),as with the NHANES III; their inclusion ensures that theparameters developed continue to be unbiased estimators.These weights are a necessary part of the analysisbecause the primary sampling units (geographic) used toassure inclusion of certain minority groups are known todiffer systematically from Census data, thus potentiallybiasing the analysis in terms of universal inference to theUnited States population. For greater detail, the reader isreferred to a more complete description developed by theNHANES team (NCHS, 2006).Size at birth categories were based on clinical diagnosis

of low birth weight: those born small were those born atless than 2,500-g birth weight. Brain mass was operation-alized using head circumference, which relates directly tobrain mass, is associated with nearly every known mea-sure of cognitive development, and has been used repeat-edly in studying postnatal brain growth in a large numberof studies (Frisk et al., 2002; Hansen and Lou, 2000;Hebestreit et al., 2003). This relationship is especially pro-nounced during the first 2 years of life (Frisk et al., 2002),but continues to about 6 years of age at which time headcircumference has typically reached about 95% of theadult value (Frisk et al., 2002). Muscle mass was opera-tionalized using the upper arm muscle area (UAMA)equation suggested by Heymsfield et al. (1982), which hasbeen observed to be highly correlated with urinary creati-nine levels (Boye et al., 2002; Frisancho, 1990; Lukaski,1996) and reasonably highly correlated with alternativemeasures of muscle mass such as DEXA or Slaughter’sequations (Boye et al., 2002; Chomtho et al., 2006). Inspite of some debate about its usefulness, the UAMAequation has been shown repeatedly to be a reasonablemeasure to use in comparing the muscle mass of differentgroups (Boye et al., 2002; Frisancho, 1990; Heymsfieldet al., 1982) and the lowest observed correlations are stillon the order of 60% or greater (Chomtho et al., 2006).Although anthropometric data are subject to interob-

server error in survey-based studies such as theNHANES; however, NHANES personnel were extensivelytrained in standard protocols for data collection (thecomplete manual used for collecting anthropometricdata in the NHANES III is available on line (http://www.cdc.gov/nchs/data/nhanes/nhanes3/cdrom/NCHS/MANUALS/ANTHRO.PDF). Previous research has sug-gested that interobserver error in population-based stud-ies (specifically the NHANES II) may be as high as 25%(Heymsfield et al., 1982) and may generally be greater forskinfolds than for circumference measurements (Chumleaet al., 1990; Marks et al., 1989). Reliability estimates foranthropometric measurements reported in the NHANESII (conducted 1976–1980) suggested quite high reliabilityfor arm circumference (�97%) while skinfold measureswere less reliable (81%) (Marks et al., 1989). With specificreference to the reliability of the UAMA measure, Heyms-field et al.’s (1982) study of the specifically suggested uni-form over-estimation of muscle mass, which would not beexpected to differ significantly between those born smalland those born normal size and if anything would poten-tially bias the results of this study toward not detecting anegative relationship between head circumference andupper-arm muscle area. Given this anticipated relation-ship, if a significant effect is observed it may be an under-estimate of the true relationship. Under these conditions,interobserver error would not be expected to invalidatethe findings of this study.Body size by age was measured as weight in kilograms

and used as a proxy for growth. While these data are notlongitudinal, they capture average trends in growth thatare ultimately reflected in the distribution of body sizes byage. By design, the data are cross-sectional in nature(CDC, 2009; WHO, 2003). Although complications arise inthe measurement of longitudinal trends (such as collec-tions of individual growth trajectories) using cross-sec-tional data, such data are universally used to assessgrowth at the population level. Cross-sectional data allowprecise measurement of statistical properties of thegrowth that characterize a population and although thereare as many as 30 different methods for statistically mod-eling growth, (WHO, 2003), virtually all depend uponsuch cross-sectional data to compute percentile distribu-tions, confidence-intervals, and other associated statistics.Cross-sectional data are, of course, subject to bias intro-duced by cohort effects (WHO, 2003), but over a short pe-riod such as captured in the current study (participantsaged 2 months to 8 years at the time of data collection)these are expected to be somewhat minimal (Prahl-Ander-son and Kowalski, 1997). Cross-sectional data are usedwidely to characterize group-level trends in growth andare universally used in field studies designed to character-ize important differences in growth that provide opportu-nities for clinical screening that require adequate sensitiv-ity for detecting important deficits (Cacciari et al., 2002;CDC, 2009; Chumlea, 2005; Khadilkar et al., 2007; WHO,2003). Last, since the principal question involves post-natal trajectories of tissue growth after exposure to lowbirth weight, it is expected that such cross-sectional datashould be adequate to capture these coarse-grainedeffects, especially in a large sample characterized by agreat amount of statistical power to detect effects of thisexposure (Samuels and Witmer, 1998).A number of potential confounders were included in the

analysis. It is known that both sex and ethnic background

208 J. BAKER ET AL.


predict differences in body composition across develop-ment (Malina, 1996). In the current analysis, both sex anddifferences between African-American, White, andMexican-American children were statistically controlledwithin the regression analysis. As socio-economic statusand variables such as parental education and measures ofpoverty are known to relate to body composition differ-ences as well (Wilmore and Costill, 2004), we includedmeasures of SES such as the Poverty Index Ratio (whichmeasures relative wealth in terms of the establishedpoverty level), the educational level of the head of house-hold, the sex of the head of household (to capture dual-wage households), and household size (reflecting level ofsharing of resources) as control variables while estimatingthe relationships between body size and tissue develop-ment trajectories.

An allometric framework based on regression

Physiological trade-offs between traits that simultane-ously covary with body size increases require specialmethodologies to detect. Allometry considers the relation-ship between body mass and other somatic variables thatmay be proportionally scaled to it on a logarithmic scaleand allows description of relationships between body sizeincreases during growth and the corresponding growth ofcomponent tissues such as muscle, fat, brains, organweights, etc. (Pleasants et al., 1997). Developmentalallometry considers these changes across development,and has been used previously in studies of physiologicaland evolutionary ecology (Cane, 1993; Schlichting andPigliucci, 1999; Shipley and Peters, 1990); as well as totrack changes in postnatal physiological development inhumans (Armstrong et al., 1999; Beunen et al., 2002).This approach differs from evolutionary allometry in thatpopulations (and the individuals that comprise them,obviously), not species, are the unit of analysis. Funda-mentally, allometry posits that relationships betweenbody size and other variables of interest (brain and musclemasses in the current study) may be modeled within thelanguage of power functions of the general form (Huxley,1932; Pleasants et al., 1997; Shlichting and Pigliucci,1999):

Y ¼ AWx

This equation describes the size of the tissue of interest(Y) in terms of how it changes as a function of body size (W)observed across growth (Beunen et al., 2002; Shipley andPeters, 1990)—encapsulated in the allometric coefficient(x). This relationship may be linearized for convenience asthe Napierian logarithms of the original variables:

lnðYÞ ¼ lnðAÞ þ x � lnðWÞ

and its parameters estimated using standard methods oflinear regression (Pleasants et al., 1997; Sprent, 1972) ofthe form:

lnðYiÞ ¼ b0 þ b1Wi þ e1

The regression constant b0 then reflects an estimate A,while b1 is an estimate of the allometric exponent x. In an

allometric regression, estimation of the allometric coeffi-cient is the important consideration rather than the r2

value derived from such a model. A model with a tightconfidence interval for its estimate of the allometric coeffi-cient is the focus of such a model; rather than attemptingto capture all of the factors associated with variation inthe tissue of interest (here head circumference and musclemass), the model seeks to accurately describe the relation-ship of each to body size increases across growth. Thismeans that the emphasis in interpretation of the resultsof such a model should rest upon the coefficients ratherthan the amount of total variance captured (as with the r2

measure).It is standard practice to build regression models to esti-

mate allometric relationships after log-transforming bodysize and the characters of interest (here upper arm musclearea and head circumference) to linearize the relationship(Armstrong et al., 1999; Beunen et al., 2002; Pleasantset al., 1997; Sprent, 1972). This method allows adequatecontrol for any number of postnatal confoundingfactors known to also be associated with development ofmuscle and brain mass, potentially strengtheninginferences about the effect of low birthweight on theexamined relationships. A simple statistical method basedon a standard F test exists for assessing whether theslopes (the rate of growth of brain or muscle mass)differ between those born small and those born normalsize (Neter et al., 1999) (equivalent to testing Barker’shypothesis).Use of an allometric regression method allows two use-

ful extensions that permit direct assessment of whetherthe two tissues ‘‘trade-off ’’ in terms of their postnatal de-velopment. Though it is often unconsidered, the propertiesof linear regression include an ability to assess the direc-tional effects of variables not included in a regressionmodel (such as muscle mass in an allometric regression ofbrain mass on body size) through a consideration of‘‘partial residual vectors.’’ Larsen and McLeary (1972)define the partial residual vector for the response as the‘‘dependent variable vector (simply the set of values of thedependent variable predicted from in the regression) cor-rected for all independent variables except the ‘‘ith’’ vari-able’’ (p 781) (see Cook, 1994 for a more sophisticated ex-planation more directly founded on a discussion of mar-ginal probabilities). The partial residual vector for theleft-out independent or ‘‘ith’’ variable is defined analo-gously, as the vector of residuals from the regression ofthis variable on the other predictors in the model. The im-portance of the ‘‘ith’’ variable as a predictor of theresponse is related to the strength of the associationbetween these two sets of partial residuals. In particular,a plot of the two sets of partial residuals, called an ‘‘addedvariable plot’’ (Cook, 1993, 1994; Larsen and McLeary,1972), provides a visual assessment of the direction andstrength of the effect of the ‘‘ith’’ variable on the depend-ent variable, adjusting for the remaining predictors in themodel. This left-out variable may then be inserted into theregression and its coefficient assessed in terms of directionand statistical significance, but in the case an allometricregression this would seem inappropriate. The ‘‘ith’’ variablewould be correlated with body size as well as the outcomevariable, introducing the same problem as originallyinspires the use of an allometric approach: a spurious corre-lation between muscle and brain masses could occur basedon their mutual relationship with body size.



In the case of an allometric model designed to detect atrade-off between two tissues after controlling for themutual dependence on body size, the relationship betweenthe partial residual vectors from one regression model (saybrains regressed on body size) to the partial residuals forthe other tissue (say muscle mass after regressing on bodysize) permits a useful extension of methods allowing detec-tion of physiological trade-offs using a simple correlationbetween the residuals. The residuals of each model describethe variation of the predicted values for each case from theoverall average of the sample at that body size level (Neteret al., 1999) as a general property. Positive residuals indi-cate higher than expected values while negative residualsindicate lower than expected values. This general relation-ship holds regardless of which of the tissues of interest(brains or muscle) is considered, in the context of an addedvariable plot, as the dependent variable, and which isemployed as the ‘‘ith’’ variable. In terms of the Barkermodel, these properties will allow tests of a correlationbetween higher than expected values of brain mass andlower than expected values of muscle mass (as visualizedin Fig. 2). The inference to be drawn would be that higherthan expected values of one variable predict lower thanexpected values of the other variable, after consideration oftheir mutual dependence on body size.This study employed backward selection to build an

appropriate allometric regression model (Neter et al.,1999). All variables were included in the original analysis,and then eliminated one at a time based on the highestP-value associated with the variables. At each iteration,the regression model was refit and in the final model, allincluded variables were significant at the a 5 0.05 level.Moreover, at each iteration we examined diagnostic plotsfor regression model fit including residual by fit plots,histograms of the residuals, and scatterplots of includedpredictor variables (which are ideally expected to beunrelated to one another within a regression model). In

addition to meeting the criteriae of: (1) no systematic rela-tionship between residuals and fitted values, (2) randomerror in the residuals, and (3) no significant statisticalrelationship between predictors, the final model exhibiteda Mallow’s C-p criteria score that equaled the number ofpredictors included in the model, suggesting no left outvariable error (Mauro, 1990) in the model. Together, thesemeasures suggest that the model results reported in thisstudy are systematic, real, and unbiased. Tests were con-ducted to assess whether low birth weight individuals hadidentical slopes to those born normal size for each regres-sion (head circumference and upper-arm muscle area),graphical partial residual plots were assessed to examinedirectional relationships between increases in head cir-cumference and upper-arm muscle area, and Pearsoncorrelations were computed to assess whether theserelationships were statistically significant.

Fig. 3. In log–log space, a roughly linear relationship existsbetween body weight increases and increases in both head circumfer-ence (black) and upper arm muscle area (red). [Color figure can beviewed in the online issue, which is available at www.interscience.wiley.com.]

Fig. 2. The expected relationship if the Barker model holds is thatthe residuals of a regression of brain mass on body size (with appro-priate controls for intervening variables) will be negatively correlatedwith those of a model of muscle mass by body size. Since the averageresidual in a regression model will by definition be 0, strongest sup-port (shaded oval area) for the principal prediction of the Barkermodel are results in which values are found within the lower right-hand quadrant: higher than expected increases in brain mass predictlower than expected muscle mass.

Fig. 4. As predicted by the Barker model, a negative relationshipbetween the residuals associated with head circumference and thoseassociated with upper-arm muscle area exists. Those individuals withlarger head circumference tend to, on average, exhibit smaller musclemass residuals. At the extreme (lower right-hand quadrant), aboveaverage residuals for head circumference (above 0) predict lower thanaverage (below 0) residuals for Upper Arm Muscle Area. This nega-tive correlation is significant at the a 5 0.05 level. [Color figure can beviewed in the online issue, which is available at www.interscience.wiley.com.]

210 J. BAKER ET AL.


RESULTS

Roughly linear relationships between log body massincreases and increases in both log head circumferenceand log upper-arm muscle area were observed, suggestingthat a simple a regression analysis is appropriate for thesedata (see Fig. 3). The results of the study (Tables 1–2)indicate that rates of accumulation of head circumferenceare no different between those born small and those bornnormal weight (F 5 2.01, P 5 n.s.). Although the allomet-ric coefficient describing this relationship appears some-what larger for those born small (0.134 for normal and

0.152 for low birth weight), the difference is insignificantat the a 5 0.05 or even 0.010 levels. This suggests thatalthough absolute head circumference may be smallerbetween those born small and those born normal-sized,the rate of accumulation of size in head circumference isnot different between the two groups. The difference, how-ever, between the allometric coefficients describing musclemass growth (Table 3) are highly statistically-significantlydifferent between the groups (0.131 for normal weight and0.101 for low birth weight, F 5 52.13, P 5 0.000). More-over, among the total population and across both birthweight groups, a clear negative relationship between mus-

TABLE 1. Final model results by population grouping, head circumference

Group Variable Constant Allometric cofficient S.E. coeff. P-value Model rsquared

Total study population (n = 7,962) Weight 1.52502 0.135173 0.00916 0.000 76.00%Sex 0.0069955 0.0003157 0.000African-American * * *Mexican-American * * *HOH education 0.0018871 0.0003456HOH sex 20.0010453 0.0003721 0.005Household size * * *Poverty index ratio 0.0006409 0.0001194 0.000

Normal birth weight (n = 7,305) Weight 1.52465 0.133668 0.000945 0.000 75.80%Sex 0.007128 0.003259 0.000African-American * * *Mexican-American * * *HOH education 0.0020139 0.00003817 0.000HOH sex * * *Household size * * *Poverty index ratio 0.006079 0.0001223 0.000

Low birth weight (n = 557) Weight 1.50439 0.151674 0.003543 0.000 76.70%Sex 0.006307 0.001143 0.000African-American * * *Mexican-American * * *HOH education * * *HOH sex * * *Household size * * *Poverty index ratio 0.0018239 0.0004427 0.000

* Eliminated from final model due to statistical insignificance.

TABLE 2. Final model results by population grouping, upper arm muscle area

Group Variable Constant Allometric cofficient s.d. P-value Model rsquared

Total study population Weight 0.447598 0.129535 0.003137 0.000 22.30%Sex 20.016049 0.003137 0.000African-American 20.008788 0.002806 0.002Mexican-American * * *HOH education * * *HOH sex 0.006984 0.001334 0.000Household size 20.00202921 0.0003793 0.000Poverty index ratio 0.0008587 0.00418 0.040

Normal birth weight Weight 0.447498 0.131445 0.003275 0.000 22.80%Sex 20.015528 0.003275 0.000African-American 20.006479 0.00293 0.027Mexican-American * * *HOH education 20.0002881 0.0001321 0.029HOH sex 0.006761 0.001409 0.000Household size 20.0021073 0.004053 0.000Poverty index ratio 0.0012324 0.004538 0.007

Low birth weight Weight 0.48349 0.10103 0.0115 0.000 13.30%Sex 20.1915 0.003751 0.000African-American 20.1232 0.004156 0.003Mexican-American * * *HOH education * * *HOH sex * * *Household size * * *Poverty index ratio * * *

* Eliminated from final model due to statistical insignificance.



cle and brain mass accumulations during the first 8 yearsof life is observed through a negative correlation of theresiduals, which is statistically significant at the a 5 0.05level (see Fig. 4). A higher residual for muscle mass sug-gests a lower residual for head circumference after con-trols for body size and confounding variables. These rela-tionships hold after appropriate statistical controls for awide variety of confounding variables related to socioeco-nomic status, as well as race, ethnicity, and sex. Theresults also hold after accounting for the sampling designof the NHANES III survey (see Materials and Methodsabove) through the use of weightings provided by theNHANES team. This is highly-suggestive of a brains vs.brawn trade-off in general and among those born small aneven more reduced overall accumulation of muscle mass issuggested by the regression results.Female sex was a significant predictor of head circum-

ference growth relative to body size in the total study pop-ulation, as well as in both the normal and low birth weightgroupings (Table 1). In no case was African-American orMexican-American ethnicity a significant predictor ofhead circumference. The educational level of the Head ofHousehold was a significant predictor of head circumfer-ence growth in the total population as well as those bornnormal-sized; however, it was not a significant predictorwithin the low birth weight group. The poverty index ratio(PIR) was a significant predictor of head circumference inall three groups; however, the size of the coefficient associ-ated with the PIR was much larger among the low birthweight group (0.0018239 vs. 0.006079 among those bornnormal-sized). In all groupings, the allometric regressionmodel predicted �76% of the variation in head circumfer-ence.The allometric regression of upper arm muscle area on

body size (Table 2) suggested that African-American eth-nicity (Mexican-American ethnicity was not related inany of the groupings) and a variety of socioeconomic pre-dictors were related to muscle mass development in theoverall population and among those born normal size;however, among those born small these factors (with theexception of African-American ethnicity) had no effect atall. Among those born normal-sized, head of householdeducational level and sex, household size, and the PIRwere all related to muscle mass development. HOH educa-tion and household size were negatively related to musclemass development while having a female HOH, beingfemale, and increasing PIR (suggesting less observation ofpoverty) were all positively related to UAMA. It is highlynoteworthy that among those born small, none of thesocio-economic measures were related to UAMA develop-ment at all.

DISCUSSION

In this study—as in numerous others—we observedreductions in both brain mass (F 5 6.03, P 5 0. 01) andmuscle mass (F 5 13.91, P 5 0.01) among those born

small. Though preserved in terms of rate of postnatal tis-sue accumulation, brain size was still smaller among thelow birth weight group. Although the notable ‘‘trade-off ’’between brains and brawn suggested by analysis appliedto the entire study population, this study documents twokey findings that are supportive of Barker’s Brain Sparingmodel for the origins of chronic diseases related to dimin-ished muscle mass and associated insulin resistance.First, after controlling for allometric relationships withoverall body size increases and potential confounding var-iables, relative growth of head circumference is identicalbetween low birth weight and normal birth weight groups.Growth of upper-arm muscle area, however, is not. In thesame sample of low birth weight individuals, the rate ofmuscle growth is stunted and the rate of brain growth ispreserved. This preservation is reflected in the hypothesistest indicating different slopes for muscle mass growthbut not for head circumference (Table 3). The finding is ro-bust—it occurs within an extremely diverse sample ofchildren and in spite of issues of potentially inflated inter-observer error in anthropometric measures and bothsampling and recall/response bias in the survey (Heyms-field et al., 1982; Marks et al., 1989; NCHS, 2006). Thesefindings appear to cross-cut ethnic, racial, and socio-eco-nomic groupings: among those born small only African-American ethnicity had an effect on reduced muscle mass.Moreover, these relationships hold throughout the agesincluded in the study with no obvious breaks in the rela-tionship between 2 months and 8 years of age (Figs. 3 and4). This suggests that the origin of these trajectories startsin utero, precisely as the Barker model predicts. Althoughwe cannot assess whether there is a direct in utero trade-off between brain and muscle development in this study,these differences clearly are present by 2 months of ageand in the current study persist even after broad expo-sures to known intervening variables are accounted for.This suggests that the observed relationships betweenbody size growth and the growth of brain and muscle tis-sues are real and worthy of further consideration.Barker’s hypothesis supposes that brains should be

favored over brawn when resources are limited during de-velopment (Barker, 1993; Lumbers et al., 2001) and thepostnatal trajectories observed in this study are support-ive of this hypothesis. The question of whether thesechanges are adaptive in nature (and if so in what specificway) remains open, however, to debate. If they do in factconstitute adaptations—facultative adjustments inresponse to a clear environmental signal that result ingreater reproductive success (Curio, 1973; Williams,1966)—then the question remains whether these changesare adaptive in gestational time, ecological time, evolu-tionary time, or all of the above. In other words, does theindividual gain a particular advantage during the in uteroperiod by preserving brains such that the observed rela-tionship may persist not due to life-long advantages, butsimply due to simple damage of tissues with long-term‘‘side-effects’’ (Cameron and Demerath, 2002; Gluckmanand Hanson, 2004; Holland-Jones, 2005)? Is there anadvantage in the current ecology experienced during asinge life-time by preserving brains at the expense of mus-cle and, if so, how does this offset the costs of reducedmuscle mass in terms of diminished physical perform-ance? Or is it just advantageous to have a general rule bywhich brains take priority in general—is such a strategyadvantageous across all possible environments? The first

TABLE 3. Test for unequal slopes

Comparison F P-value

Upper arm muscle area 52.03 0.000Head circumference 2.01 n.s

212 J. BAKER ET AL.


perspective supposes that there are immediate advan-tages to such preservation during in utero developmentwhile the second form of adaptive response suggests anintra-generational flexibility in deciding how much ofavailable energy to allocate to each tissue and its associ-ated functions (Sibly and Calow, 1986; Zera andHarshman, 2001). The third possibility suggests a moreuniversal advantage to preserving brains that operates inall circumstances and at all times. Rather than adaptivemodulation of the relative amounts of each on a genera-tion-by-generation time-frame, a model based on evolu-tionary time would suggest that it always pays to do soand that a heuristic response exists that potentiallyconstrains other options.

The utility of this latter perspective appears to beendorsed by the universal negative relationship betweenbrains and brawn seen in Figure 4, but such a modelwould seem to ignore the costs to reducing musclemass. Muscle mass is the single greatest predictor ofvariation in physical performance (Riendeau et al., 1958;Wilmore and Costill, 2004) and cutting muscle mass doesnot just mean preserving brains, it means diminishingthe ability to physically capture energy from the environ-ment (Baker et al., 2008, 2009). It would seem plausiblethat the amount of reduction in relative allocation tomuscle mass should depend upon the immediate environ-mental context and that a model based on constraintsis useful.

It is possible that two responses are occurring simulta-neously: brains are being preferred and preserved whilemuscle mass accumulation is being adjusted to match thecurrent ecology within the limits of this constraint. If so,then a different sort of adaptive model, perhaps based on‘‘making the best of a bad start’’ or bet-hedging is at work(Dawkins, 1980; Holland-Jones, 2005). Such a model couldmake sense of the continuous distribution of muscle masseven among those born small that is observed in theNHANES III dataset. Information on patterns of fetalblood circulation suggest that a model of ‘‘system-con-strained’’ damage such as this might suffice to explain theobserved relationship, since this pattern is apparentlyuniversal within mammals. Fetal circulation involvesreception of blood from the placenta and preferential cir-culation of blood to key areas of the developing organism(Belotti et al., 2000). From the placenta, blood flows firstto the liver of the fetus (where about 1/2 of the volume isreceived), then is preferentially shunted to the brain viathe ductus venosus (Belotti et al., 2000). This shunting isknown to occur during fetal life in other primates(Behrman et al., 1970)—which could reflect a commontrend toward encephalization—but also in species withmuch smaller brain/body mass ratios such as sheep (Edel-stone and Rudolph, 1979; Edelstone et al., 1978, 1980).This shunting is known to account for about 53% of theentire umbilical blood flow (Edelstone and Rudolph, 1979)and is known to increase in lambs to as much as 70%when nutrient flow is impeded (Edelstone et al., 1980).These observations suggest that brain prioritizaion maybe a primitive, rather than derived and characteristic inhumans. If so, then the ‘‘adaptive’’ response in questionmay have little to do with human evolution and couldinstead form a constraint as would be envisioned within abet-hedging type model. These observations may suggestjust such a layered set of adaptations, a long-standing andprimitive heuristic of preserving brain development over

which adjustments in muscle mass accumulation aremade in ecological time.Thus, while the results of this article are clear, their

interpretation remains open to discussion. While thoseborn small were found to simultaneously maintain therate of brain growth while experiencing stunted muscledevelopment, our consideration suggests that theseresults may be interpreted in multiple ways. It is hard toquestion the idea that brain preservation is fitness-enhancing in any organism, but to what extent is this thetarget of current selection in ecological time? To whatextent are individuals ‘‘trading off ’’ the development ofbrains versus brawn based on current environments? Ifthe results do indeed stem from direct, facultative modu-lation of the relative amounts of each tissue developed fora given amount of energy, to what extent is this driven bybrain preservation as a constraint? The constrained mod-ulation perspective may be extremely useful in generatingfurther research as it may open up questions that bynecessity consider constraints in examining physiologicaltrade-offs across development (Reznick, 1985; Schlichtingand Pigliucci, 1999; Stearns, 1992). Though the results ofthis article seem to settle the question of whether brainand muscle tissue development are related in those whohave experienced low birth weight, the findings alsoappear to open new questions for consideration in physio-logical ecology and human biology. Future research mayusefully focus on testing models in which brain preserva-tion is treated as a constraint while the relative allocationof resources to muscle development or modulation of mus-cle-specific energetic costs are considered within a pro-spective study design in which exposures may be morecarefully measured.

ACKNOWLEDGMENTS

This draft was greatly improved with comments fromtwo anonymous reviewers and Keith Hunley. While wesincerely appreciate their input, any shortcomings of thismanuscript remain our responsibility.

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brains versus brawn: an empirical test of barker's brain sparing model

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