Veile and Kramer AJHB 2016

OR I G I N AL RE S EARCH ART I C L E

Childhood body mass is positively associated with cesarean birthin Yucatec Maya subsistence farmers

Amanda Veile1 | Karen L. Kramer2

1Department of Anthropology,Center on Aging and the Life Course,Purdue University, West Lafayette,Indiana 47907-20502Department of Anthropology,University of Utah, Salt Lake City,Utah 84112

CorrespondenceAmanda Veile, Purdue University,West Lafayette, IN 47907-2050.E-mail: [email protected].

Abstract

Objective: The epidemiologic link between cesarean birth and childhood obesity isunresolved, partly because most studies come from industrialized settings wheremany post-birth factors affect the risk for obesity. We take advantage of an unusualethnographic situation where hospital and cesarean birth modes have recently beenintroduced among Yucatec Maya subsistence farmers, but young children have hadminimal exposure to the nutritional transition. While we expect to find very low ratesof childhood obesity, we predict that cesarean-born children will be larger and heav-ier than vaginally born children.

Methods: Weight and height were collected monthly on 108 children aged 0–5(3576 observations total). Birth mode and birthweight were collected by maternalinterview. Data were analyzed using linear mixed models that compare child growth[Maya population-specific Z-scores for weight-for-age and body mass index-for-age(WAZ and BMIZ)] in cesarean and vaginally born children aged 0–5 years.

Results: The cesarean rate was 20%, no children were obese, and 5% were over-weight. Cesarean birth was a significant predictor of child WAZ and BMIZ afteraccounting for maternal effects, child birthweight, and sex. Children who were bornby cesarean to mothers with high BMI had the highest WAZ of all children by 5years of age, and the highest BMIZ of all children at all ages.

Conclusion: Cesarean-born Maya children had higher BMI than vaginally born chil-dren, even in the absence of many known confounding factors that contribute tochildhood obesity. Child growth was most sensitive to birth mode when mothers hadhigh BMI.

KEYWORD S

growth, infants and children, cesarean birth, medicalization of birth, Maya

1 | INTRODUCTION

Cesarean delivery and childhood obesity are epidemiologi-cally associated in a variety of urbanized settings from highand middle income countries in Europe and North Americaas well as in Brazil, Iran, and China (Bammann et al., 2014;Blustein et al., 2013; Goldani et al., 2013; Huh et al., 2012;Kuhle et al., 2015; Li et al., 2012, 2013; Mueller et al., 2015;Salehi-Abargouei et al., 2014; Steur et al., 2011; Wang et al.,2013; Zhou et al., 2011). Yet, other studies from similar set-

tings reveal no association between cesarean birth and obe-sity after accounting for confounding factors such asmaternal age, body size, and smoking habits, infant feedingpractices and child birthweight and sex (Ajslev et al., 2011;Azad et al., 2014; Barros et al., 2012; Birbilis et al., 2013;Costantine, 2014; Flemming et al., 2013; Lin et al., 2013;Pei et al., 2014; Rathnayake et al., 2013; Rooney et al.,2011; Steur et al., 2011; _Ządzi�nska and Rosset, 2013). Thisinconsistency in the potential downstream impact of surgicalcesarean birth on childhood obesity arises in part because

Am. J. Hum. Biol. 2016; 1-13 wileyonlinelibrary.com/journal/ajhb VC 2016 Wiley Periodicals, Inc. | 1

Received: 6 May 2016 | Revised: 7 August 2016 | Accepted: 20 August 2016

DOI 10.1002/ajhb.22920

American Journal of Human Biology

many post-birth factors, such as bottle feeding and antibioticuse, also affect the risk for obesity (Azad et al., 2014; De Fil-ippo et al., 2010; Kuhle et al., 2015; Ley et al., 2006; Mel-nik, 2014; Neu and Rushing, 2011; Oddy, 2012). Inparticular, the sanitary and resource-rich environments thatcharacterize Western societies may exacerbate the cesarean-obesity link (Neu and Rushing, 2011). This may also be truein non-Western or less-affluent settings where the nutritionaltransition—the displacement of traditional foods by dietshigh in saturated fats, sugar and refined foods and a declinein physical activity levels—is well established or underway(Misra and Khurana 2008; Popkin, 2004; Prentice, 2006;Rivera, et al. 2002).

Here, we attempt to reconcile inconsistent results byusing longitudinal data to evaluate the impact of birth modeon child growth outcomes in a population that has few of theconfounding factors that otherwise affect disproportionalweight gain in well-fed populations. The sample of youngYucatec Maya children are breastfed, have physically activechildhoods, eat traditional diets, have minimal exposure tomarket foods, and grow up in a relatively unsanitized epide-miological environment. Although young Maya children arerelatively unexposed to many of the modernizing factors thatraise the risk of obesity, cesarean births are increasinglyprevalent in their community (Veile and Kramer, 2015). Thecomparative lack of preexisting obesity-related confoundersallows us to directly observe the impact that birth mode hason early childhood growth, particularly with respect toweight gain. This link is critical to resolve given the signifi-cant global increase in cesarean births and the growing con-cern of childhood obesity as a public health issue.

In this article, we first outline the expected mechanismslinking cesarean birth and child growth outcomes. We thenassess the rates of early childhood obesity and overweight inour study community of Maya subsistence farmers, and uselinear mixed models (LMM) to compare child weight-for ageand body mass index (BMI) trajectories in children born vag-inally versus by cesarean. Our goal is to demonstrate thatcesarean birth will disrupt crucial maturational processes andincrease child growth, even when few obesity-related con-founders exist. We conclude by discussing the public healthrelevance of studying the cesarean-obesity connection inremote but modernizing subsistence communities.

2 | BACKGROUND

Cesarean birth is associated not just with obesity but withother negative health outcomes such as allergies (Eggesbøet al., 2005; Laubereau et al., 2004), asthma (Debley et al.,2005), and celiac disease (Decker et al., 2010). The biologi-cal mechanisms underlying these associations are driven inpart by compromised maturation of the gut microbiome (the

microbe population that lives in the intestine) (Gr€onlundet al., 2000; Huda et al., 2014; Koenig et al., 2011; Molinaroet al., 2012; Musso et al., 2010; Neu and Rushing, 2011).The human gut microbiome is dominated by two bacterialphyla (Firmicutes and Bacteroides), with smaller commun-ities of Proteobacteria, Verrumicrobia, Actinobacteria,Fusobacteria, and Cyanobacteria that together assist theirhost with digestion, nutrient synthesis, priming of immunecells, and protection against intestinal pathogenic coloniza-tion (Cho and Blaser, 2012; Sommer and Backhed, 2013).

Microbial colonization of the infant gut begins at birthfollowing exposure to maternal fecal and vaginal microbiota(Bäckhed et al., 2015; Dominguez-Bello et al., 2010; Muelleret al., 2015). Because cesarean-born infants do not experi-ence this initial exposure, their gut microbiome communitiesresemble those found on the mother’s skin surface, whichhave low Bacteroides populations and higher levels ofStaphylococcus spp. (Costello et al., 2012; Dominguez-Belloet al., 2010). Cesarean-born children exhibit ongoing differ-ences in their gut microbial profiles (including lower micro-bial diversity relative to vaginally born children) until at leastseven years of age (Biasucci et al., 2008; Gr€olund et al.,1999; Kabeerdoss et al., 2013; Salminen et al., 2004). Thematurational processes of the burgeoning gut microbiomeare therefore intimately intertwined with the process ofimmunological development in infancy and early childhood(Gr€onlund et al., 2000; Huda et al., 2014; Koenig et al.,2011; Molinaro et al., 2012; Musso et al., 2010; Neu andRushing, 2011).

Compromised gut microbiota development in turn islinked to child and adult obesity because the microbiomeplays important roles in metabolism, fat storage, andinflammatory processes (Balamurugana et al., 2010; Ber-voets et al., 2013; Goldani et al., 2013; Kalliomäki et al.,2008; Neu and Rushing, 2011; Reinhardt et al., 2009;Sweeney and Morton, 2013; Vael et al., 2011). Several ani-mal and some human studies demonstrate differences inthe microbiome composition of obese and lean individuals;for example, the microbiota of obese and high-fat fed miceare characterized by greater Firmicutes and fewer Bacter-oides bacteria than are found in lean individuals (Caniet al., 2007; Ley et al., 2005; Murphy et al., 2010; Turn-baugh et al., 2008, 2009). While much remains unknownregarding the connection between human microbiomes andobesity (Harley and Karp, 2012; Walters et al., 2014), cer-tain alterations in human gut microbiome profiles are asso-ciated with increased energy harvest capacity (Jumpertzet al., 2011; Rosenbaum et al., 2015). Gut microbiota alsohelp to regulate inflammatory signaling pathways; systemiclow-grade inflammation is commonly associated with obe-sity and its related pathologies in mice and humans (Caniet al., 2007; Thompson et al., 2014).

2 | American Journal of Human Biology VEILE AND KRAMER

Despite the potential negative health consequences asso-ciated with cesarean births, their rates are rising in most partsof the world; in Mexico, for example, the national cesareanbirthrate is 47.5% (Vogel et al., 2015). Cesarean birth rateshave been rising in Mexico’s rural indigenous communitiessince the inception of new government health programs inthe 1990s (Barber, 2009). Rural communities may evenexperience cesarean birth rates that exceed the World HealthOrganization (WHO) recommended maximum cesarean rateof 15%, and even the 19% optimum rate recently publishedin the Journal of the American Medical Association (Molinaet al., 2015; Veile and Kramer, 2015; WHO, 1985). Further,in many rural indigenous Mexican populations the transitionfrom traditional homebirths to hospital births and risingcesareans has been abrupt (Jordan, 1992; Veile and Kramer,2015). This rapid and dramatic shift will inevitably influencechild health and growth patterns.

Many studies that support a cesarean birth-childhood obe-sity link have been conducted in Westernized, affluent andindustrialized nations, where obesity is prevalent and arisesfor a variety of reasons besides birth mode (Neu and Rushing,2011, Kuhle et al., 2015). For example, high-sugar and high-fat diets contribute to the obesity pandemic (Popkin et al.,2012) in part due to their effect on gut microbiome composi-tion (De Filippo et al., 2010). Bottle feeding and frequentantibiotic consumption during infancy also modulate gutmicrobiome development and are associated with obesity inchildhood and later in life (Ajslev et al., 2011; Bailey et al.,2014; Koletzko et al., 2013). Low physical activity levels fur-ther contribute to obesity and characterize many modernized,urbanized and otherwise obesogenic environments (Cordainet al., 1998; Lieberman, 2006; Popkin, 2002).

A recent meta-analysis by Kuhle et al. (2015) addressedseveral of these issues. The authors analyzed results of 28epidemiologic studies of cesarean birth and childhood obe-sity in high and middle-income countries. They found thatcesarean birth was associated with a 34% elevated relativerisk of childhood obesity (with no difference between highand middle-income countries), which decreased when addi-tional early life obesity-related confounders were accountedfor. However, the study populations (including those frommiddle income countries) were largely drawn from urban set-tings (Barros et al., 2012; Goldani et al., 2013; Li, 2008, Liet al., 2012; Lin et al. 2013; Rathnayake et al., 2013; Salehi-Abargouei et al., 2014; Zhou et al., 2011), where the nutri-tional transition is underway. In these settings, early lifedietary influences and activity patterns may obscure the rela-tionship between cesareans and childhood obesity.

To unravel the effects that cesarean birth has in theabsence of many obesity-related confounders, we takeadvantage of a large longitudinal database of monthly meas-urements from birth to age five from a population of tradi-

tional Maya where young children have had minimalexposure to the nutritional transition. We expect to find verylow rates of childhood obesity in this population. However,if cesarean birth is an important factor in the risk of obesity,we predict that differences in children’s weight and BMI willvary with birth mode and emerge postnatally, even whenchildhood obesity rates are low.

3 | METHODS

3.1 | The study setting

Mexico provides an ideal setting to evaluate this predictionbecause cesarean birth rates are rising (from 24.2% in 1994to 47.5% in 2011), even among rural farmers (Barber, 2009;Vogel et al., 2015). The Yucatec Maya in the study commu-nity are subsistence maize agriculturalists whose economicstructure, demographic characteristics, and subsistence andhousehold organization have been recorded since 1992 anddescribed extensively elsewhere (Kramer, 2005, 2009;Kramer and McMillan, 1998). Although substantial eco-nomic and technological changes have occurred over thepast 20 years, including the introduction of electricity, run-ning water, a paved road, motorized vehicles, mechanizedfarming and a rudimentary health clinic, young children’sdiet and activity patterns remain relatively unchanged.

The majority of consumed calories come from maize andgarden fruits, vegetables, and beans. Market foods such asrice and pasta provide occasional dietary supplementation.Children spend most of their day outside, are given great lati-tude to independently explore their environment, and arephysically active from a young age. By age three or four,children run errands, perform simple domestic tasks and takecare of their younger siblings. Although schools have beenbuilt in recent years, few children under the age of five attendthem.

Since 2007, a local health promotor has monitored childhealth and growth in a community clinic as part of a govern-ment poverty-alleviation program. Most young women givebirth in hospital, where the cesarean birthrate is �30% (Veileand Kramer, 2015). Elective cesarean is not practiced in thecommunity and cesarean births are viewed by women as anunfortunate medical necessity (Veile and Kramer, 2015).Although hospital births are becoming common, somewomen prefer to give birth at home assisted by a local mid-wife. Prolonged and intensive breastfeeding is the norm;although some mothers practice limited formula feeding, for-mula augments but never replaces breastmilk (Veile andKramer, 2015). Children are generally weaned by two and ahalf years of age (median age5 2.58, n5 74, 95%CI5 2.46–2.69). Though extremely short in stature, Mayachildren and adults are generally adequately nourished, and

VEILE AND KRAMER American Journal of Human Biology | 3

child mortality and morbidity are low in the community(Kramer et al., 2016).

The community is located in a remote tropical setting,and the environment has many more pathogenic risks com-pared to most Western settings. For example, children arefrequently exposed to domestic animals and their feces andto standing water during the rainy season. All households atminimum have a designated area outside used as a latrine,though a handful of families have a covered facility with aseptic reservoir excavated into the bedrock. Shallow latrinescan render living areas susceptible to fecal contamination,especially in the rainy season.

Until a few years ago, pigs freely roamed through thecommunity. While a nuisance, they disposed of organictrash. The introduction of plastic and cardboard containersnow poses a challenge to villagers in terms of trash disposal.Some burn or bury their trash, while others maintain above-ground trash pits some distance from the house. Birth modeis not statistically associated with household sanitary condi-tions (neither for human waste disposal: septic tank versuslatrine, Pearson Chi-Square 50.07, P5 .800, n5 108, norfor trash disposal method: burn versus bury, Pearson Chi-Square5 0.64, P5 .420, n5 108), and therefore thesehousehold conditions should not confound its effect on childgrowth.

3.2 | Data collection

Mothers were recruited during household visits and selectedaccording to the following criteria: (1) they had childrenaged 0–5 in the period between 2007 and 2014 when childgrowth measurements were taken, and (2) the birthweight oftheir children was known. The final sample consisted of 57mothers (66% of the 87 married women in Xculoc who wereaged 17–50 as of 2014) and 108 children (with 3756monthly anthropometric observations). The height andweight of the Maya children were collected in the commu-nity clinic each month by a community-based, physician-trained health promotor using standard weigh scales and sta-diometers. All community mothers participated in the pro-gram with few missed monthly measurements. Childrenentered the program at birth and censused out on their fifthbirthday. Maternal height and weight were also collectedannually and maintained in a database by KK.

During reproductive history interviews in 2015, motherswere asked if their children were born vaginally or by cesar-ean delivery, and how much their children weighed at birth(with documentation provided when available). Reportedmaternal and child ages were cross-checked with demo-graphic data and reproductive histories collected at regularintervals since 1992. Permits for research were secured fromthe local government and the health promoter at the commu-nity clinic. Research protocols were approved by the Com-

mittee for the Protection of Human Subjects at DartmouthCollege, Hanover, New Hampshire, USA, and at the Univer-sity of Utah, Salt Lake City.

3.3 | Data analysis

Both WHO and Maya population-specific Z-scores were cal-culated. The justification for using population-specific Z-scores, and methods used to compute them, are described indetail in Kramer et al. (2016). Here, we use Mayapopulation-specific Z-scores for intrapopulation comparisonsand WHO Z-scores for cross-cultural reference purposesonly.

Child underweight, overweight and obesity were calcu-lated as per the WHO criteria for children (�22,� 2,and� 3 WHO BMI Z-scores, respectively). Maternal under-weight, overweight and obesity were calculated as per theWHO criteria for adults (BMI� 18.5,� 25 and� 30, respec-tively). Because we had repeated measures on children, theywere classified as “underweight,” “overweight,” or “obese”at each observation. To calculate a community-wide preva-lence, children were classified as “underweight,” “over-weight,” or “obese” only when the respective criteria wasmet on >50% of observations.

Population-specific Z-scores were derived from a refer-ence population of 104 Maya children (aged 0–5) for whomlongitudinal growth data are available. This reference popu-lation was used to compute Z-scores in another growthpaper, and is used here to maintain consistency (Krameret al., 2016). Population-specific Z-scores are defined asstandard deviation (z5 (x2 l)/r) of log-normalized, age-specific (monthly), and gender-specific raw weight or BMIvalues [weight (kg)/height (m2)]. Maya population-specificWAZ and BMIZ were then the outcome variables of a seriesof LMM fit by REML (Restricted Maximum Likelihood), astatistical method of estimating unbiased variance compo-nents (Corbeil and Shayle 1976).

Models were constructed using SPSS Statistics 22 (IBMCorp., 2013). We accounted for the nonindependent erroracross several factors by treating these models as a nestedrepeated-measure LMM. Because some children had thesame mother, child’s ID was nested within mother’s id andtreated as a random effect. The initial models accounted forthe following covariates: maternal age, maternal BMI, childbirthweight, child age, child sex, and enrollment in Oportu-nidades (now Prospera), a Mexican government poverty-alleviation program which can influence child growth out-comes (Fernald et al., 2009). We also tested for significanttwo-and three-way interactions between birth mode andbirthweight, birth mode and maternal age, birth mode andmaternal BMI, birth mode and child age. Best-fit models foreach outcome variable (WAZ and BMIZ) were selected bycomparing AIC values when variables were dropped from


the full model using backward selection. If the change inAIC between two models differed by� 2, the model withfewer predictor variables was selected.

3.4 | Sample considerations

Anthropometric data were missing for two mothers. Heightdata for one child, and Oportunidades data for three childrenwere missing. Because the Centers for Disease Control andWHO do not recommend clinical use of BMI in infants <2years of age (CDC, 2015), BMI was only computed for the93 children who were over age two at the time of measure-ment. One child with a low birthweight (1800 g) wasexcluded from the final growth models as a statistical outlier.When this outlier was retained in the models, the strength ofthe interactions between birth mode and birthweight substan-tially increased; removing the outlier dampened the interac-tion effect.

Due to the longitudinal nature of the growth data (withchildren of different ages phasing in and out of the sample atdifferent times), measurements did not exist for all childrenat all ages. We therefore displayed the data using the meanof the predicted values derived from the best fit model andfor children who were sampled at a given time [one monthof age and 60 months of age for WAZ (Figure 1), and 24months of age and 60 months of age for BMIZ (Figure 2)].We then calculated a 95% confidence interval around thebest-fit models predicted mean values using standard errorsfor the predicted mean values. In Figures 1 and 2, children’sbirthweights and maternal BMI were grouped as “high” ver-sus “low” if their values fell above or below the samplemean, respectively.

4 | RESULTS

4.1 | Descriptive statistics

Descriptive statistics for the study sample are provided inTable 1. The child sample was 61% male and 39% female,and 70% (n5 105) of children lived in families who werebeneficiaries of Oportunidades. All children in the samplewere born between 2002 and 2015, of which 80% werehospital-born and 20% born in the community (n5 108).Cesarean-born children comprised 20% of the samplewhereas 80% were vaginally born (n5 108). Among hospitalbirths, the cesarean rate was 26% (n5 86). Only seven chil-dren (6%) were low birthweight (LBW< 2500 g), which issimilar to Mexico’s national LBW rate (Buekens et al.,2013).

Vaginally born children did not significantly differ fromcesarean-born children in mean birthweight or in maternalBMI (f5 0.07, P5 .79, n5 108; Table 1). However, vagi-nally born children had significantly lower WHO Z-scores

than cesarean-born children (f 5 89.17, P 5 <.01, n5 108for WAZ, f 5 19.30, P 5 <.01, n5 108 for HAZ, f 5111.75, P 5 <.01, n 5 93 for BMIZ, Table 1). Of children

FIGURE 1 Predicted mean population-specificWAZ forMaya children ages 1–60 months (males and females com-bined), stratified bymaternal BMI, showing 95% confidenceintervals at 1 and 60 months

FIGURE 2 Predicted mean population-specific BMIZ forMaya children at 24 months (males and females combined),showing 95% confidence intervals. Cesarean births are denotedby the yellow bars and vaginal births by the white bars. To dis-play the significance of the interaction effects, child BMIZ-scores are further stratified bymaternal BMI (MBMI: high/low) and child birthweight (high/low)


ages 2–5 (n5 93), according to the WHO criteria no chil-dren were obese or underweight and 5 children were over-weight (5%). Using the WHO criteria for adults, 21% oftheir mothers were obese, 55% were overweight, and 24%were normal weight (n5 56).

4.2 | Weight-for-age

Cesarean birth was a significant predictor of child WAZ; thispersisted when accounting for a number of confounders andafter including significant interactions. In the best-fit predic-tion model (Table 2), birthweight, child age, female sex, andmaternal BMI were positively associated with child WAZ,while maternal age was negatively associated with childWAZ. The model also accounts for significant interactionsbetween birth mode and maternal BMI, and between birthmode and child age (Table 2).

Figure 1 reveals the nature of the interactions. Notably,children whose mothers had high BMI had greater WAZ-scores in early infancy than children whose mothers had lowBMI, regardless of their mode of birth. Among children whosemothers had high BMI, cesarean and vaginally born childrenhad identical WAZ-scores at one month (WAZ5 0.38), butthen their WAZ trajectories diverged due to the interactioneffects of birth mode and child age. WAZ increased incesarean-born children with age, and decreased in vaginallyborn children with age. By 60 months, the cesarean-born chil-dren had substantially higher WAZ-scores than did the vagi-nally born children (WAZ5 0.91 vs.20.07, respectively).

Among children whose mothers had low BMI, cesarean-born children had higher WAZ-scores than vaginally bornchildren at one month (WAZ520.05 vs. 20.26, respec-tively). Their WAZ trajectories differ across childhood. Incesarean-born children, the WAZ trajectory remains virtuallyunchanged from 1 to 60 months. In contrast, vaginally bornchildren experienced a linear increase in WAZ-score from 1to 60 months (at 60 months, WAZ520.05 vs. 20.01,respectively). By 60 months, their WAZ-scores nearly con-verge, and do not differ substantially from that of vaginallyborn children whose mothers had high BMI.

4.3 | BMI-for-age

In the best-fit model (Table 3), birthweight, female sex,maternal BMI, and Oportunidades status were positivelyassociated with child BMIZ. The model also accounts forsignificant interactions between birth mode and maternalBMI, and between birth mode and birthweight. Cesareanbirth predicts high BMIZ, and effect is most pronounced inchildren with high birthweights, and whose mothers had ahigh BMI.

Cesarean-born children who had high birthweights andwhose mothers had high BMI, had higher BMIZ-scores at 24T

ABLE1

Descriptiv

estatisticsforlin

earpredictorandoutcom

evaria

bles

forMayachild

ren(m

ales

andfemales

combined)

aged

1–60

months

Variable

Vaginal

births

(n5

86)

Cesareanbirths

(n5

22)

F-test

Total

(n5

108)

Mean

SDMin

Max

Mean

SDMin

Max

FSig.

Mean

SD

Birthweight(g)

3048.72

451.45

1800.00

4000.00

3077.27

422.22

2000.00

3800.00

0.07

0.79

3054.54

443.88

Child

Age

(Months)

31.86

17.15

1.00

60.00

31.52

16.64

1.00

60.00

0.25

0.61

31.79

17.04

a Child

WHO

WAZ

20.98

0.91

23.99

2.36

20.65

0.74

22.64

1.09

89.17

<0.01

20.91

0.89

a Child

WHO

HAZ

22.78

1.06

25.79

1.74

22.59

1.07

25.88

0.41

19.30

<0.01

22.74

1.07

a,b C

hild

WHO

BMIZ

0.90

0.69

21.58

3.08

1.27

0.77

21.12

3.36

111.75

<0.01

0.98

0.73

MaternalAge

(Years)

29.09

6.86

17.00

48.00

29.23

7.68

19.00

46.00

0.01

0.94

29.12

7.00

MaternalBMI

27.33

3.38

20.93

35.78

27.55

4.35

20.64

38.14

0.06

0.80

27.38

3.56

MaternalHeight(cm)

144.31

3.96

136.90

151.50

141.69

4.64

133.70

149.60

6.61

0.01

143.81

4.21

a Z-scoresaveraged

across

allobservations,andcombinedforboys

andgirls,F-testperformed

onallobservations

(n5

3756

obs.forWAZ,n53755

obs.forHAZ,and

n5

2391

obs.forBMIZ).

b BMIcalculated

forchild

renaged

24–60monthson

ly.


months than did vaginally-born children who had high birth-weights and whose mothers had high BMI (mean predictedWAZ5 0.59 vs. 20.15, respectively). By 60 months, thedifference had increased such that their mean predicted WAZscores were 1.11 versus 20.01, respectively. Cesarean-bornchildren with high birthweights and low maternal BMI, alsohad significantly higher mean predicted BMIZ-scores thanvaginally born children with high birthweights and lowmaternal BMI (mean predicted WAZ5 0.07 vs. 20.34,respectively). By 60 months, the difference had increasedslightly such that their mean predicted BMIZ scores were0.29 versus 20.17, respectively.

Cesarean-born children with low birthweights and highmaternal BMI, had significantly higher mean predictedBMIZ scores than vaginally born children with low birth-weights and high maternal BMI (mean predicted

WAZ520.05 vs. 20.23, respectively). By 60 months, thedifference had decreased slightly such that their mean pre-dicted BMIZ scores were 0.13 versus 20.12, respectively.Cesarean-born children with low birthweights and lowmaternal BMI had significantly higher mean predictedBMIZ-score than vaginally-born children with low birth-weights and low maternal BMI (mean predictedWAZ520.21 vs. 20.44, respectively). By 60 months, thedifference had decreased slightly such that their mean pre-dicted BMIZ scores were 20.07 versus 20.29, respectively.

5 | DISCUSSION

5.1 | Summary of results

Even in the absence of childhood obesity and severalobesity-related confounders, we find evidence that cesareanbirth is associated with child growth outcomes in the Mayacommunity (summarized in Table 4). WAZ is high incesarean-born children, but in only those whose mothers hadhigh BMI; differences between cesarean and vaginally bornchildren born to mothers with high BMI increased with childage. Among children whose mothers had low BMI, we sawless pronounced differences in WAZ between cesarean andvaginally born children. We saw increased BMIZ incesarean-born children even when accounting for birthweightand maternal BMI. The effect of cesarean birth on childBMIZ was most pronounced in children who had high birth-weights (and whose mothers had high BMI), and least pro-nounced in children who had low birthweights (and whosemothers had low BMI). This pattern was consistent in chil-dren from 24 to 60 months of age.

In other words, cesarean birth was a predictor of child-hood WAZ and BMIZ after accounting for maternal effectsand child birthweight and sex. Children who were born bycesarean to mothers with high BMI had the highest WAZ ofall children by 5 years of age, and the highest BMIZ of allchildren at all ages. Our findings are consistent with those ofBluestein and coworkers (2013). They report that in a cohortof 10,219 British mother-child pairs, the association betweencesarean birth and childhood obesity was strong only amongchildren born to overweight and obese mothers. This is sug-gestive of a cross-culturally robust pattern; however, Mayawomen are very short in contrast to British women and arein the early stages of the nutritional transition. Their dietaryprofiles and body composition will therefore differ substan-tially; indeed different biological mechanisms may underliethis phenomenon in such distinct populations.

5.2 | Obesity in the Maya

The child overweight rate is low (5%) but still was higherthan we expected. It may continue to rise due to very recent

TABLE 3 Best-fit prediction model for Maya population-specific BMI-for-age Z-scores in 93 cesarean and vaginallyborn children (males and females combined) ages 24–60months

Parameter Estimate Std. Error Sig.

Intercept 24.1899 0.4213 <0.0001

Cesarean 22.7683 0.4604 <0.0001

Birthweight 0.0008 0.0001 <0.0001

Child Age 0.0036 0.0020 0.0745

Maternal BMI 0.0756 0.0118 <0.0001

Female 0.1679 0.0419 0.0001

Oportunidades 0.2307 0.0514 <0.0001

Cesarean3Birthweight 0.0006 0.0001 <0.0001

Cesarean3Maternal BMI 0.0512 0.0137 0.0002

TABLE 2 Best-fit model for Maya population-specificweight-for-age Z-score in 107 cesarean and vaginally bornchildren (males and females combined) aged 1–60 months

Parameter Estimate Std. Error Sig.

Intercept 23.2395 0.2731 <0.0001

Cesarean 20.7045 0.2934 0.0164

Birthweight 0.0007 0.0000 <0.0001

Child Age (months) 0.0062 0.0021 0.0038

Female 0.1391 0.0337 <0.0001

Maternal Age (years) 20.0147 0.0028 <0.0001

Maternal BMI 0.0631 0.0093 <0.0001

Cesarean3Maternal BMI 0.0332 0.0105 0.0016

Cesarean3Child Age 0.0065 0.0024 0.0063


modernizing influences such as the introduction of television,western biomedicine, and market foods. Childhood obesity iscurrently absent in the study community, although we foundhigh rates of maternal obesity. We attribute the discrepanciesin maternal and childhood obesity less to differences in diet,than to their disparate physical activity patterns. Kramer hasobserved that women’s energy expenditure has decreasedsubstantially since the introduction of labor-saving techologysuch as mechanized maize processing and water collection(Kramer and McMillan 1998, 1999, 2006). More recentlyadditional labor-saving technology such as mechanized farm-ing and clothes washing machines have been introduced.

In contrast to adult women, young children are physicallyactive and highly mobile. Children do not begin schoolinguntil age five or six, and televisions only exist in a few

homes. Preschool aged children spend much of their timetraveling though the community on foot and are frequentlyobserved engaging in active play, running errands, carryingtheir young siblings, or doing domestic chores.

Girls are slightly (but significantly) heavier and havegreater BMI than boys in our models. We offer two tenativeexplanations. First, it may reflect the disparate activity pat-terns that emerge between the sexes from an early age.Though boys and girls are both free to wander the village,boys tend to wander more widely and engage more in com-petitive sports and rough and tumble play. Second, it may beindicative of shifting parental investment patterns. ThroughOportunidades (Prospera), families receive larger stipendsfor female children as an incentive to keep them in school.The stipends do not differentiate until secondary school

TABLE 4 Predicted mean Maya population-specific Z-scores and predicted weight and BMI value by child age and birthmode, birthweight, and maternal BMI (n denotes sample size per category at a given age)

Birth mode BW BMI OutcomeChild Age(months) n

Predicted MeanMaya Z-score Predicted value

Cesarean NA High WAZ 1 5 0.38 4.42 kg

Vaginal NA High WAZ 1 15 0.38 4.42 kg

Cesarean NA Low WAZ 1 5 20.05 4.09 kg

Vaginal NA Low WAZ 1 17 20.26 3.95 kg

Cesarean NA High WAZ 60 5 0.91 17.06 kg

Vaginal NA High WAZ 60 28 20.07 15.48 kg

Cesarean NA Low WAZ 60 7 20.05 15.51 kg

Vaginal NA Low WAZ 60 25 20.01 15.58 kg

Cesarean High High BMIZ 24 4 0.59 18.48

Vaginal High High BMIZ 24 14 20.15 17.56

Cesarean High Low BMIZ 24 2 0.07 17.83

Vaginal High Low BMIZ 24 6 20.34 17.34

Cesarean Low High BMIZ 24 1 0.05 17.81

Vaginal Low High BMIZ 24 8 20.23 17.47

Cesarean Low Low BMIZ 24 6 20.21 17.49

Vaginal Low Low BMIZ 24 17 20.44 17.22

Cesarean High High BMIZ 60 4 1.11 17.50

Vaginal High High BMIZ 60 15 20.01 16.36

Cesarean High Low BMIZ 60 1 0.29 16.66

Vaginal High Low BMIZ 60 9 20.17 16.21

Cesarean Low High BMIZ 60 1 0.13 16.50

Vaginal Low High BMIZ 60 16 20.12 16.26

Cesarean Low Low BMIZ 60 6 20.07 16.31

Vaginal Low Low BMIZ 60 15 20.29 16.09


(starting at �11 years of age), but parents be may faculta-tively increasing investment even in young female children.If so, this may be viewed as a behavioral response to novelsocioeconomic conditions, as daughters now potentiallyyield a greater economic payoff (at least in the short term).

5.3 | Birth mode, maternal size, and child growth

We find that childhood WAZ and BMIZ increases are pro-nounced in children who are both cesarean-born and whosemothers have high BMI. This is consistent with our sugges-tion that cesarean-linked disruptions in the development ofinfant microbiota have an influence on later fat storage pat-terns. In a number of studies, maternal BMI is also a predic-tor of offspring obesity (Bammann et al., 2014; Catalanoet al., 2009; Oken, 2009; Poston, 2012). Indeed, levels ofglucose, triglycerides, and fatty acids are high in obese preg-nant mothers, which contributes to increased fetal adiposityand can have long-lasting effects on the child’s metabolicfunction (Drake and Reynolds, 2010; HAPO 2009).

Previous work among Yucatec Maya (in the urban centerof Merida) reveals similar correlations between maternal andchild body sizes, although they were strongest for lineargrowth measures (Azcorra, 2014; Varela-Silva et al., 2009).The researchers conclude that the biological effects of a poordiet on growth are transmitted intergenerationally throughmatrilineal intrauterine environments, and that poor healthoutcomes in Maya children today (obesity and stunting)reflect a history of deprivation. Compared with the urbanMaya sample, our study community is rural and much lessintegrated into Mexico’s market economy, and baseline ratesof obesity and overweight are much lower.

We find that the interaction between cesarean birth andhigh maternal BMI (not cesarean birth per se) predicts nota-bly increased WAZ and BMIZ-scores at most stages ofchildhood. This may be due in part to compromised mother-infant transmission of microbial communities. For example,overweight Spanish pregnant women exhibit higher concen-trations of Bacteroides, Clostridium, and Staphylococcus andtheir infants have elevated fecal Bacteroides, Clostridium,and Staphylococcus concentrations and lower Bifidobacte-rium concentrations during the first six months of life (Col-lado et al., 2008, 2010). If similar alterations are present inthe gut microbiome of obese Maya women, then ourobserved associations between maternal BMI and offspringBMIZ and WAZ may also be driven in part by mother-to-infant transmission of gut microbiota.

However, as we have described, in a cesarean birth thistransfer is disrupted. We suspect that this disruption amplifliesthe effects of maternal obesity on child growth, although anadditional (nonmutually exclusive) mechanism has been pro-posed (Gur et al., 2015). Cesarean birth is considered to bemore stressful than a vaginal birth and is associated with

increased glucocorticoids and catecholamine hormones(Loughran et al., 1986; Ryding et al., 1998; Scheinin et al.,1990). These can negatively impact infant adaptation to extra-uterine life and compromise longer-term health outcomes(Hillman et al., 2012). Future research efforts should attemptto untangle the interactions of maternal condition and birthmode and their effects on growth, the development of stressneurobiology, and microbiome assemble in infancy and earlychildhood.

6 | CONCLUSION

Cesarean birth rates are rising world wide, and the maternal-child health costs of unnecessary cesareans are potentially quitehigh. The costs have mainly been documented in industrializedsettings; yet it is plausible that they are manifested differentlyin poor and rural settings. As we have demonstrated, the poten-tial interactions and outcomes are complex and require furtherexamination, especially in communities that are in the earlystages of the nutritional transition. Some of the negative out-comes associated with unnecessary cesareans may be exacer-bated; for example, poor women who have had cesareans maylack the resources to pay for prolonged postnatal hospital care.Furthermore, they may live in settings with limited sanitaryinfrastructure, which increases the risk of postnatal mother-infant morbidity. Finally, cesarean births are associated immu-nopathologies and obesity; this may exacerbate chronic diseaseepidemics that are underway or burgeoning due to the nutri-tional transition.

ACKNOWLEDGMENTS

We thank the Maya for their ongoing willingness andpatience to participate in this study. We are particularlygrateful to Maximiliano Moo and Dra. Ada Fuentes for dil-igently facilitating the local anthropometry program. Wethank Russell Greaves, Rogelia Moo Che and Mirna MooChe for their assistance in data collection and Kristen D.Chalmers for assistance with the literature review. Thisresearch was supported by NSF award #0964031 and by agrant from the Claire Garber Goodman Fund for theAnthropological Study of Human Culture, DartmouthCollege.

REFERENCES

Ajslev, T., Andersen, C., Gamborg, M., Sørensen, T., & Jess,T. (2011). Childhood overweight after establishment of thegut microbiota: The role of delivery mode, pre-pregnancyweight and early administration of antibiotics. InternationalJournal of Obesity, 35, 522–529. 4

Azad, M., Bridgman, S., Becker, A., & Kozyrskyj, A. (2014).Infant antibiotic exposure and the development of


childhood overweight and central adiposity. InternationalJournal of Obesity, 38, 1290–1298.

Azcorra, H. (2014). Intergenerational factors that shape thenutritional status of urban Maya households in Merida,Mexico. A 3-generations study. Ph.D. thesis. Loughbor-ough University, Leicestershire, United Kingdom.

Bäckhed, F., Roswall, J., Peng, Y., Feng, Q., Jia, H.,Kovatcheva-Datchary, P., & Zhong, H. (2015). Dynamicsand Stabilization of the Human Gut Microbiome duringthe First Year of Life. Cell Host & Microbe, 17, 690–703.

Bailey, L. C., Forrest, C. B., Zhang, P., Richards, T. M., Liv-shits, A., & DeRusso, P. A. (2014). Association of antibi-otics in infancy with early childhood obesity. Journal ofthe American Medical Association Pediatrics, 168, 1063–1069. 11

Balamurugana, R., Georgea, G., Kabeerdossa, J., Hepsibaa, J.,Chandragunasekarana, A., & Ramakrishnaa, B. S. (2010).Quantitative differences in intestinal Faecalibacteriumprausnitzii in obese Indian children. British Journal ofNutrition, 103, 335–338. 3

Bammann, K., Peplies, J., De Henauw, S., Hunsberger, M.,Molnar, D., Moreno, L. A., & Siani, A. (2014). Early lifecourse risk factors for childhood obesity: The IDEFICScase-control study. PloS One, 9, e86914.

Barber, S. L. (2009). Mexico’s conditional cash transfer pro-gramme increases cesarean section rates among the ruralpoor. European Journal of Public Health, 184

Barros, F. C., Matijasevich, A., Hallal, P. C., Horta, B. L.,Barros, A. J., Menezes, A. B., & Victora, C. G. (2012).Cesarean section and risk of obesity in childhood, adoles-cence, and early adulthood: Evidence from 3 Brazilianbirth cohorts. The American Journal of Clinical Nutrition,95, 465–470.

Bervoets, L., Van Hoorenbeeck, K., Kortleven, I., Van Noten,C., Hens, N., Vael, C., & Vankerckhoven, V. (2013). Dif-ferences in gut microbiota composition between obese andlean children: a cross-sectional study. Gut Pathogology, 5.

Biasucci, G., Benenati, B., Morelli, L., Bessi, E., & Boehm,G. (2008). Cesarean delivery may affect the early biodiver-sity of intestinal bacteria. The Journal of Nutrition, 138,1796S–1800S. 9

Birbilis, M., Moschonis, G., Mougios, V., & Manios, Y.(2013). Obesity in adolescence is associated with perinatalrisk factors, parental BMI and sociodemographic characteris-tics. European Journal of Clinical Nutrition, 67, 115–121.

Blustein, J., Attina, T., Liu, M., Ryan, A., Cox, L., Blaser,M., & Trasande, L. (2013). Association of caesarean deliv-ery with child adiposity from age 6 weeks to 15 years.International Journal of Obesity, 37, 900–906.

Buekens, P., Canfield, C., Padilla, N., Lona, E. L., & Lozano,R. (2013). Low Birthweight in Mexico: A SystematicReview. Maternal and Child Health Journal, 17, 129–135. 1

Cani, P. D., Neyrinck, A. M., Fava, F., Knauf, C., Burcelin,R. G., Tuohy, K. M., Delzenne, N. M. (2007). Selective

increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanismassociated with endotoxaemia. Diabetologia, 50, 2374–2383.

Catalano, P. M., Farrell, K., Thomas, A., Huston-Presley, L.,Mencin, P., de Mouzon, S. H., & Amini, S. B. (2009).Perinatal risk factors for childhood obesity and metabolicdysregulation. The American Journal of Clinical Nutrition,90, 1303–1313. 5

CDC. (2015). Using the WHO Growth Standard Charts In:Prevention CfDCa, editor. Division of Nutrition, PhysicalActivity, and Obesity.

Cho, I., & Blaser, M. J. (2012). The human microbiome: atthe interface of health and disease. Nature Reviews Genet-ics, 13, 260–270.

Collado, M. C., Isolauri, E., Laitinen, K., & Salminen, S.(2008). “Distinct composition of gut microbiota duringpregnancy in overweight and normal-weight women”. TheAmerican Journal of Clinical Nutrition, 88, 894–899.

Collado, M. C., Isolauri, E., Laitinen, K., & Salminen, S.(2010). Effect of mother’s weight on infant’s microbiotaacquisition, composition, and activity during early infancy:A prospective follow-up study initiated in early pregnancy.The American Journal of Clinical Nutrition, 92, 1023–1030.

Corbeil, R. R., & Shayle, R. S. (1976). Restricted maximumlikelihood (REML) estimation of variance components inthe mixed model. Technometrics, 18, 31–38.

Cordain, L., Gotshall, R., Eaton, S. B., & Eaton, S III.(1998). Physical activity, energy expenditure and fitness:an evolutionary perspective. International Journal ofSports Medicine, 19, 328–335.

Costantine, M. (2014). 120: The effect of mode of delivery onchildhood obesity. American Journal of Obstetrics andGynecology, 210, S73–S74. 1):

Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J.M., & Relman, D. A. (2012). The application of ecologicaltheory towards an understanding of the human micro-biome. Science (New York), 336, 1255–1262.

De Filippo, C., Cavalieri, D., Di Paola, M., Ramazzotti, M.,Poullet, J. B., Massart, S., & Lionetti, P. (2010). Impact ofdiet in shaping gut microbiota revealed by a comparativestudy in children from Europe and rural Africa. Proceedingsof the National Academy of Sciences, 107, 14691–14696.

Debley, J. S., Smith, J. M., Redding, G. J., & Critchlow, C.W. (2005). Childhood asthma hospitalization risk aftercesarean delivery in former term and premature infants.Annals of Allergy, Asthma & Immunology, 94, 228–233. 2

Decker, E., Engelmann, G., Findeisen, A., Gerner, P., Laab,M., Ney, D., & Hornef, M. W. (2010). Cesarean Delivery IsAssociated With Celiac Disease but Not InflammatoryBowel Disease in Children. Pediatrics, 125, e1433–e1440. 6

Dominguez-Bello, M. G., Costello, E. K., Contreras, M.,Magris, M., Hidalgo, G., Fierer, N., & Knight, R. (2010).


Delivery mode shapes the acquisition and structure of theinitial microbiota across multiple body habitats in new-borns. Proceedings of the National Academy of Sciences,107, 11971–11975. 26

Drake, A. J., & Reynolds, R. M. (2010). Impact of maternalobesity on offspring obesity and cardiometabolic diseaserisk. Reproduction, 140, 387–398.

Eggesbø, M., Botten, G., Stigum, H., Samuelsen, S. O., Bru-nekreef, B., & Magnus, P. (2005). Cesarean delivery andcow milk allergy/intolerance. Allergy, 60, 1172–1173. 9

Fernald, L. C. H., Gertler, P. J., & Neufeld, L. M. (2009). 10-year effect of Oportunidades, Mexico’s conditional cashtransfer programme, on child growth, cognition, language,and behaviour: A longitudinal follow-up study. The Lancet,374, 1997–2005. 9706

Flemming, K., Woolcott, C. G., Allen, A. C., Veugelers, P. J., &Kuhle, S. (2013). The association between caesarean sectionand childhood obesity revisited: A cohort study. Archives ofDisease in Childhood, 98, 526–532.

Goldani, M. Z., Barbieri, M. A., da Silva, A., Gutierrez, M.,Bettiol, H., & Goldani, H. (2013). Cesarean section andincreased body mass index in school children: Two cohortstudies from distinct socioeconomic background areas inBrazil. Nutrition Journal, 12, 104.

Gr€olund, M.-M., Lehtonen, O.-P., Eerola, E., & Kero, P.(1999). Fecal microflora in healthy infants born by differ-ent methods of delivery: permanent changes in intestinalflora after cesarean delivery. Journal of Pediatric Gastro-enterology and Nutrition, 28, 19–25. 1

Gr€onlund, M., Arvilommi, H., Kero, P., Lehtonen, O., & Iso-lauri, E. (2000). Importance of intestinal colonisation in thematuration of humoral immunity in early infancy: A pro-spective follow up study of healthy infants aged 0–6months. Archives of Disease in Childhood-Fetal and Neo-natal Edition, 83, F186–F192.

Group THSCR; HAPO Study Cooperative Research Group.(2009). Hyperglycemia and Adverse Pregnancy Outcome(HAPO) Study: Associations With Neonatal Anthropomet-rics. Diabetes, 58, 453–459.

Gur, T. L., Worly, B. L., & Bailey, M. T. (2015). Stress andthe Commensal Microbiota: Importance in Parturition andInfant Neurodevelopment. Frontiers in Psychiatry, 6, 5.

Harley, I. T. W., & Karp, C. L. (2012). Obesity and the gutmicrobiome: Striving for causality. Molecular Metabolism,1, 21–31.

Hillman, N. H., Kallapur, S. G., & Jobe, A. H. (2012). Physi-ology of transition from intrauterine to extrauterine life.Clinics in Perinatology, 39, 769–783. 4

Huda, M. N., Lewis, Z., Kalanetra, K. M., Rashid, M.,Ahmad, S. M., Raqib, R., & Stephensen, C. B. (2014).Stool microbiota and vaccine responses of infants. Pedia-trics, 134, e362–e372.

Huh, S. Y., Rifas-Shiman, S. L., Zera, C. A., Edwards, J. W.R., Oken, E., Weiss, S. T., & Gillman, M. W. (2012).

Delivery by caesarean section and risk of obesity in pre-school age children: A prospective cohort study. Archivesof Disease in Childhood, 97, 610–616. 7

IBM Corp. (2013). IBM SPSS Statistics for Windows, Version22.0. Armonk, NY: IBM Corp.

Jordan, B. (1992). Birth in four cultures: A crossculturalinvestigation of childbirth in Yucatan, Holland, Sweden,and the United States: Waveland Press.

Jumpertz, R., Le, D. S., Turnbaugh, P. J., Trinidad, C., Bogar-dus, C., Gordon, J. I., & Krakoff, J. (2011). Energy-bal-ance studies reveal associations between gut microbes,caloric load, and nutrient absorption in humans. The Amer-ican Journal of Clinical Nutrition, 94, 58–65. 1

Kabeerdoss, J., Ferdous, S., Balamurugan, R., Mechenro, J.,Vidya, R., Santhanam, S., & Ramakrishna, B. S. (2013).Development of the gut microbiota in southern Indianinfants from birth to 6 months: A molecular analysis. Jour-nal of Nutritional Science, 2, e18.

Kalliomäki, M., Collado, M. C., Salminen, S., & Isolauri, E.(2008). Early differences in fecal microbiota compositionin children may predict overweight. The American Journalof Clinical Nutrition, 87, 534–538.

Koenig, J. E., Spor, A., Scalfone, N., Fricker, A. D., Stom-baugh, J., Knight, R., & Ley, R. E. (2011). Succession ofmicrobial consortia in the developing infant gut micro-biome. Proceedings of the National Academy of Sciences,108(Supplement 1), 4578–4585.

Koletzko, B., Beyer, J., Brands, B., Demmelmair, H., Grote,V., Haile, G., & Weber, M. (2013). Early influences ofnutrition on postnatal growth. Recent advances in growthresearch: nutritional, molecular and endocrine perspec-tives (Vol. 71, pp. 11–27). Basel, Karger Publishers.

Kramer, K. (2005). Maya children: Helpers at the farm. Cam-bridge, MA: Harvard University Press.

Kramer, K. (2009). Does It Take a Family to Raise a Child?Cooperative Breeding and the Contribution of Maya Sib-lings, PArents and Older Adults in Raising Children. In G.Bentley & R. Mace (Eds.), Substitute parents: biologicaland social perspectives on alloparenting in human soci-eties (pp. 77–99). Oxford: Berghahn Books.

Kramer, K., & McMillan, G. (1998). How Maya women respondto changing technology. The effect of helping behavior on ini-tiating reproduction. Human Nature, 9, 205–223. 2

Kramer, K., & McMillan, G. (1999). Women’s labor, fertility,and the introduction of modern technology in a rural Mayavillage. Journal of Anthropological Research, 499–520.

Kramer, K., & McMillan, G. (2006). The effect of labor sav-ing technology on longitudinal fertility changes. CurrentAnthropology, 47, 165–172.

Kramer, K., Veile, A., & Ot�arola-Castillo, E. (2016). SiblingCompetition & Growth Tradeoffs. Biological vs. StatisticalSignificance. PloS One, 11, e0150126. 3

Kuhle, S., Tong, O., & Woolcott, C. (2015). Associationbetween caesarean section and childhood obesity: A


systematic review and meta-analysis. Obesity Reviews, 16,295–303. 4

Laubereau, B., Filipiak-Pittroff, B., von Berg, A., Gr€ubl, A.,Reinhardt, D., Wichmann, H. E., & Koletzko, S. (2004).Caesarean section and gastrointestinal symptoms, atopicdermatitis, and sensitisation during the first year of life.Archives of Disease in Childhood, 89, 993–997. 11

Ley, R. E., Bäckhed, F., Turnbaugh, P., Lozupone, C. A., Knight,R. D., & Gordon, J. I. (2005). Obesity alters gut microbialecology. Proceedings of the National Academy of Sciences ofthe United States of America, 102, 11070–11075. 31

Ley, R. E., Turnbaugh, P. J., Klein, S., & Gordon, J. I.(2006). Microbial ecology: Human gut microbes associatedwith obesity. Nature, 444, 1022–1023.

Li, H.-T., Zhou, Y.-B., & Liu, J.-M. (2012). Cesarean sectionmight moderately increase offspring obesity risk. TheAmerican Journal of Clinical Nutrition, 96, 215–216. 1

Li, H., Zhou, Y., & Liu, J. (2013). The impact of cesareansection on offspring overweight and obesity: A systematicreview and meta-analysis. International Journal of Obesity,37, 893–899. 7

Li, H. (2008). A national epidemiological survey on obesityof children under 7 years of age in nine cities of China,2006. Zhonghua Er Ke Za Zhi, 46, 174–178.

Lieberman, L. S. (2006). Evolutionary and anthropologicalperspectives on optimal foraging in obesogenic environ-ments. Appetite, 47, 3–9.

Lin, S. L., Leung, G. M., & Schooling, C. M. (2013). Mode ofdelivery and adiposity: Hong Kong’s “Children of 1997”birth cohort. Annals of Epidemiology, 23, 693–699. 11

Loughran, P., Moore, J., & Dundee, J. (1986). Maternal stressresponse associated with caesarean delivery under generaland epidural anaesthesia. BJOG: An International Journalof Obstetrics & Gynaecology, 93, 943–949.

Melnik, B. C. (2014). The potential mechanistic link betweenallergy and obesity development and infant formula feed-ing. Allergy Asthma Clinical Immunology, 10, 37. 1

Misra, A., & Khurana, L. (2008). Obesity and the metabolicsyndrome in developing countries. Journal of ClinicalEndocrinology and Metabolism, 93(S9-S30).

Molina, G., Weiser, T. G., Lipsitz, S. R., Esquivel, M. M.,Uribe-Leitz, T., Azad, T., & Gawande, A. A. (2015). Rela-tionship between cesarean delivery rate and maternal andneonatal mortality. Journal of the American Medical Asso-ciation, 314, 2263–2270. 21

Molinaro, F., Paschetta, E., Gambino, R., Cassader, M., &Musso, G. (2012). Gut Microbiota as a Modulator of Car-diometabolic Risk: Mechanisms and Therapeutic Implica-tions. Current Cardiovascular Risk Reports, 6, 71–79.

Mueller, N., Whyatt, R., Hoepner, L., Oberfield, S., Domi-nguez-Bello, M. G., Widen, E. M., Hassoun, A., Perera,F., Rundle, A. (2015). Prenatal exposure to antibiotics,cesarean section and risk of childhood obesity. Interna-tional Journal of Obesity, 39, 665–670.

Murphy, E., Cotter, P., Healy, S., Marques, T., O’sullivan, O.,Fouhy, F., & Stanton, C. (2010). Composition and energyharvesting capacity of the gut microbiota: Relationship todiet, obesity and time in mouse models. Gut, 59, 1635–1642.

Musso, G., Gambino, R., & Cassader, M. (2010). Obesity,diabetes, and gut microbiota the hygiene hypothesisexpanded? Diabetes Care, 33, 2277–2284. 10

Neu, J., & Rushing, J. (2011). Cesarean versus vaginal deliv-ery: long-term infant outcomes and the hygiene hypothesis.Clinics in Perinatology, 38, 321–331. 2

Oddy, W. H. (2012). Infant feeding and obesity risk in thechild. Breastfeeding Review, 20, 7. 2

Oken, E. (2009). Maternal and child obesity: the causal link.Obstetrics and Gynecology Clinics of North America, 36,361–377.

Pei, Z., Heinrich, J., Fuertes, E., Flexeder, C., Hoffmann, B.,Lehmann, I., & Koletzko, S. (2014). Cesarean delivery andrisk of childhood obesity. The Journal of Pediatrics, 164,1068–1073. 5e1062.

Popkin, B. (2002). The shift in stages of the nutrition transi-tion in the developing world differs from past experiences!Public Health Nutrition, 5, 205–214.

Popkin, B. (2004). The Nutrition Transition and Obesity inthe Developing World. The Journal of Nutrition, 131,871S–873S.

Popkin, B. M., Adair, L. S., & Ng, S. W. (2012). Globalnutrition transition and the pandemic of obesity in develop-ing countries. Nutrition Reviews, 70, 3–21.

Poston, L. (2012). Maternal obesity, gestational weight gainand diet as determinants of offspring long term health. BestPractice & Research Clinical Endocrinology & Metabo-lism, 26, 627–639.

Prentice, A. (2006). The emerging epidemic of obesity indeveloping countries. International Journal of Epidemiol-ogy, 35, 93–99.

Rathnayake, K. M., Satchithananthan, A., Mahamithawa, S.,& Jayawardena, R. (2013). Early life predictors of pre-school overweight and obesity: A case–control study in SriLanka. BMC Public Health, 13, 994.

Reinhardt, C., Reigstad, C. S., & Bäckhed, F. (2009). Intesti-nal microbiota during infancy and its implications for obe-sity. Journal of Pediatric Gastroenterology and Nutrition,48, 249–256.

Rivera, J., Barquera, S., Campirano, F., Campos, I., Safdie, M.,& Tovar, V. (2002). Epidemiological and Nutritional Transi-tion in Mexico; rapid increase of non-communicable dis-eases and obesity. Public Health Nutrition, 5, 113–122. 1(a

Rooney, B. L., Mathiason, M. A., & Schauberger, C. W.(2011). Predictors of obesity in childhood, adolescence,and adulthood in a birth cohort. Maternal and ChildHealth Journal, 15, 1166–1175.

Rosenbaum, M., Knight, R., & Leibel, R. L. (2015). The gutmicrobiota in human energy homeostasis and obesity.Trends in Endocrinology & Metabolism, 26, 493–501.


Ryding, E. L., Wijma, K., & Wijma, B. (1998). Psychologicalimpact of emergency cesarean section in comparison withelective cesarean section, instrumental and normal vaginaldelivery. Journal of Psychosomatic Obstetrics & Gynecol-ogy, 19, 135–144. 3

Salehi-Abargouei, A., Shiranian, A., Ehsani, S., Surkan, P. J.,& Esmaillzadeh, A. (2014). Caesarean delivery is associ-ated with childhood general obesity but not abdominalobesity in Iranian elementary school children. Acta Pae-diatrica, 103, e383–e387.

Salminen, S., Gibson, G., McCartney, A., & Isolauri, E. (2004).Influence of mode of delivery on gut microbiota compositionin seven year old children. Gut, 53, 1388–1389.

Scheinin, M., Scheinin, H., Ekblad, U., & Kanto, J. (1990).Biological correlates of mental stress related to anticipatedcaesarean section. Acta Anaesthesiologica Scandinavica,34, 640–644.

Sommer, F., & Backhed, F. (2013). The gut microbiota-masters of host development and physiology. NatureReviews Microbiology, 11, 227–238.

Steur, M., Smit, H., Schipper, C., Maarten, A., Scholtens, S.,Kerkhof, M., Wijga, A. (2011). Predicting the risk of new-born children to become overweight later in childhood: thePIAMA birth cohort study. International Journal of Pedi-atric Obesity, 6(2Part2), e170–e178.

Sweeney, T. E., & Morton, J. M. (2013). The human gutmicrobiome: A review of the effect of obesity and surgi-cally induced weight loss. Journal of the American MedicalAssociation. Surgery, 148, 563–569.

Thompson, A. L., Houck, K. M., Adair, L., Gordon, -Larsen,P., Du, S., Popkin, B. (2014). Pathogenic and obesogenicfactors associated with inflammation in Chinese children,adolescents and adults. American Journal of Human Biol-ogy, 26, 18–28.

Turnbaugh, P. J., Bäckhed, F., Fulton, L., & Gordon, J. I.(2008). Diet-Induced Obesity Is Linked to Marked butReversible Alterations in the Mouse Distal Gut Micro-biome. Cell Host & Microbe, 3, 213–223.

Turnbaugh, P. J., Hamady, M., Yatsunenko, T., Cantarel, B.L., Duncan, A., Ley, R. E., & Affourtit, J. P. (2009). A

core gut microbiome in obese and lean twins. Nature, 457,480–484.

Vael, C., Verhulst, S. L., Nelen, V., Goossens, H., & Desager,K. N. (2011). Intestinal microflora and body mass indexduring the first three years of life: an observational study.Gut Pathogens, 3, 1–7.

Varela-Silva, M., Azcorra, H., Bogin, B., & Frisancho, A.(2009). Influence of maternal stature, pregnancy age, andinfant birthweight on growth during childhood in Yucatan,Mexico: A test of the intergenerational effects hypothesis.American Journal of Human Biology, 21, 657–663.

Veile, A., & Kramer, K. (2015). Birth and breastfeedingdynamics in a modernizing indigenous community. Journalof Human Lactation, 31, 145–155.

Vogel, J. P., Betr�an, A. P., Vindevoghel, N., Souza, J. P.,Torloni, M. R., Zhang, J., & Ortiz-Panozo, E. (2015).Use of the Robson classification to assess caesarean sec-tion trends in 21 countries: A secondary analysis of twoWHO multicountry surveys. The Lancet Global Health, 3,e260–e270.

Walters, W. A., Xu, Z., & Knight, R. (2014). Meta-analysesof human gut microbes associated with obesity and IBD.FEBS Letters, 588, 4223–4233.

Wang, L., Alamian, A., Southerland, J., Wang, K., Anderson,J., & Stevens, M. (2013). Cesarean section and the risk ofoverweight in grade 6 children. European Journal ofPediatrics, 172, 1341–1347.

WHO (1985). Appropriate Technology for Birth. The Lancet,326, 436–437. 8452

Yuan, C., Gaskins, A. J., Blaine, A. I., et al. (2016). Associa-tion between cesarean birth and risk of obesity in offspringin childhood, adolescence, and early adulthood. Journal ofthe American Medical Association Pediatrics, e162385.

_Ządzi�nska, E., & Rosset, I. (2013). Pre-natal and perinatal fac-tors affecting body mass index in pre-pubertal Polish chil-dren. Annals of Human Biology, 40, 477–484.

Zhou, L., He, G., Zhang, J., Xie, R., Walker, M., & Wen, S.W. (2011). Risk factors of obesity in preschool children inan urban area in China. European Journal of Pediatrics,170, 1401–1406.


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Veile and Kramer AJHB 2016