DHEAS Patterning across Childhood in Three Sub-Saharan Populations: Associations with Age, Sex, Ethnicity, and Cortisol.

Abstract

Objectives: Hormones have many roles in human ontogeny, including timing of life history ‘switch points’ across development. Limited hormonal data exist from non-Western children, leaving a significant gap in our understanding of the diversity of life history patterning. This cross-sectional study examines dehydroepiandrosterone sulfate (DHEAS) production in relation to age, sex, ethnicity, and cortisol concentrations, as well as average age of adrenarche, among Aka and Ngandu children of the Central African Republic and Sidama children of Ethiopia.
Methods: Hair was collected from 480 children (160 per population) aged 3-18 years old. These samples were analyzed for DHEAS and cortisol concentrations using ELISAs. A generalized additive model was used to examine DHEAS patterning in relation to age, sex, cortisol, and ethnicity. The derivative of DHEAS as a function of age was used to identify average age of adrenarche in each population.
Results: DHEAS patterning in these three populations is distinct from Euro-American patterns of production. In all three groups, the population-level age at adrenarche onset occurs slightly later than Euro-American averages, with both Central African populations experiencing a later onset than the Ethiopian population.
Conclusions: DHEAS patterns and age at adrenarche vary across cultures, perhaps indicating adaptive life history responses in diverse eco-cultural environments. Delayed involution of the fetal zone and DHEAS patterning may offer both cognitive protection and immune defense in high-risk, nutritionally-poor environments. Additional research in the majority world is essential to improving our understanding of the diversity of hormonal development and timing of ‘switch points’ in life history trajectories.

Full text

ORIGINAL RESEARCH ARTICLE
DHEAS patterning across childhood in three sub-Saharan
populations: Associations with age, sex, ethnicity, and cortisol
Courtney Helfrecht
|
Edward H. Hagen
|
David DeAvila
|
Robin M. Bernstein
|
Samuel J. Dira
|
Courtney L. Meehan
Department of Anthropology, Washington
State University, Pullman, Washington
P.O. Box 4910 99164-4910
Correspondence
Courtney Helfrecht, Department of
Anthropology, Washington State
University, P.O. Box 644910,
Pullman, WA.
Email: chelfrecht@wsu.edu
Funding information
Funded by the National Science
Foundation, Grant Numbers: BCS-
1260428 and BCS-0955213
Abstract
Objectives: Hormones have many roles in human ontogeny, including the timing of
life history ‘switch points’across development. Limited hormonal data exist from
non-Western children, leaving a significant gap in our understanding of the diversity
of life history patterning. This cross-sectional study examines dehydroepiandrosterone
sulfate (DHEAS) production in relation to age, sex, ethnicity, and cortisol concentra-
tions, as well as average age of adrenarche, among Aka and Ngandu children of the
Central African Republic and Sidama children of Ethiopia.
Methods: Hair was collected from 480 children (160 per population) aged 3-
18 years old. These samples were analyzed for DHEAS and cortisol concentrations
using ELISAs. A generalized additive model was used to examine DHEAS patterning
in relation to age, sex, cortisol, and ethnicity. The derivative of DHEAS as a function
of age was used to identify average age of adrenarche in each population.
Results: DHEAS patterning in these three populations is distinct from Euro-
American patterns of production. In all three groups, the population-level age at adre-
narche onset occurs slightly later than Euro-American averages, with both Central
African populations experiencing a later onset than the Ethiopian population.
Conclusions: DHEAS patterns and age at adrenarche vary across cultures, perhaps
indicating adaptive life history responses in diverse eco-cultural environments.
Delayed involution of the fetal zone and DHEAS patterning may offer both cognitive
protection and immune defense in high-risk, nutritionally-poor environments. Addi-
tional research in the majority world is essential to improving our understanding of
the diversity of hormonal development and timing of ‘switch points’in life history
trajectories.
KEYWORDS
adrenarche, cortisol, DHEAS, life history theory, sub-Saharan Africa
1
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INTRODUCTION
As mediators between biology, environment, and behavior,
hormones have received significant attention in anthropo-
logical research (e.g., Bartz et al., 2010; Bribiescas, 1996;
Burnham et al., 2003; Ellis & Essex, 2007; Ellison, 2001;
Flinn, 1999, 2006; Flinn & Ward, 2005; Gettler, McDade,
Agustin, Feranil, & Kuzawa, 2013; Gray & Campbell, 2009;
Lee, Macbeth, Pagani, & Young, 2009; Nepomnaschy &
Flinn, 2009; Worthman, 1999; Worthman & Konner, 1987).
Hormones are essential to a wide range of biological proc-
esses—ranging from regulation of fetal development to the
triggering of life history events to immune system mainte-
nance—and are crucial to uncovering our plasticity in
Am J Hum Biol. 2017;e23090.
https://doi.org/10.1002/ajhb.23090
wileyonlinelibrary.com/journal/ajhb V
C2017 Wiley Periodicals, Inc.
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Received: 31 May 2017
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Revised: 5 October 2017
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Accepted: 22 November 2017
DOI: 10.1002/ajhb.23090
American Journal of Human Biology

ontogeny and life history (e.g., West-Eberhard, 2003;
Worthman, 1993, 1999). It can often be a challenge, how-
ever, to collect these data from children or in diverse cultures
and remote locations. Traditional methods (i.e., collection of
saliva, urine, or serum) are not feasible in many geographies
due to storage needs, collection requirements, or cultural res-
trictions. As a result, we largely lack data on the wide range
of human diversity in hormones (Worthman, 1999), particu-
larly among preadolescent children.
This cross-sectional study has two goals. The first is to
examine the developmental trajectory of dehydroepian-
drosterone sulfate (DHEAS) production and its relationship
with age, sex, and cortisol among children aged 3–18 years
in three sub-Saharan populations—Aka and Ngandu of
the Central African Republic, and Sidama of southwestern
Ethiopia. Although DHEAS has known correlations with age
(e.g., de Peretti & Forest 1978; Orentreich, Brind, Rizer, &
Vogelman, 1984; Sulcova, Hill, Hampl, & Starka, 1997) and
cortisol (e.g., Goodyer, Herbert, & Altham, 1998, 2001;
Phillips et al., 2010), we hypothesize that its patterning may
manifest differently across populations, reflecting adaptive
life history strategies within diverse ecocultural contexts. Our
second goal is to estimate the average age of onset for
adrenarche, the biological event underpinning postnatal
DHEAS production. Adrenarche has known variation in tim-
ing of onset (Del Giudice, Ellis, & Shirtcliff, 2011; Pratt,
Manatunga, & Li, 1994; Rotter, Wong, Lifrak, & Parker,
1985), yet little empirical research has been conducted out-
side of Euro-American populations (but see Worthman,
1993). We hypothesize that onset may be later in comparison
to Euro-American populations, as found in other biological
events such as menarche (Parent et al., 2003; Worthman,
1999). To our knowledge, this is the largest sample to date—
both among children and in sub-Saharan populations—to use
hair hormone analysis to these ends.
1.1
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Life history theory
Life history theory (LHT) is an evolutionary theoretical
framework that centers on energy allocation (Charnov, 1993;
Hill & Kaplan, 1999; Kaplan, Lancaster, & Robson, 2003;
Stearns, 1976, 1992). The primary expectation of LHT is
that a species’(or an individual’s) strategy should reflect the
best possible allocation of energy across the lifespan to maxi-
mize reproductive success. The two main categories of ener-
getic expenditure are somatic and reproductive. Somatic
effort broadly refers to growth and survival/maintenance,
while reproductive effort includes mating, parenting, and
inclusive fitness (Hill & Kaplan, 1999; Kaplan et al., 2003).
Energy or effort allocated to one of these categories cannot
be used for another. Thus, there are competing demands that,
along with natural selection, extrinsic risk (e.g., morbidity/
mortality rates), and the limiting factor of time, shape the life
course of a species. This framework allows for predictions of
the timing of important events across the lifespan, including:
length of gestation; age at weaning; age at menarche; age at
first reproduction; number of offspring; age at menopause;
senescence; and the duration of the lifespan.
Developmental trajectories may be set early in life, dur-
ing critical periods when experience with the environment,
particularly harshness or unpredictability, provides cues as to
the optimal reproductive strategy (e.g., Belsky, Steinberg, &
Draper, 1991; Draper & Harpending, 1982, 1988; Quinlan,
2007). Recent research characterizes human extended child-
hood as a phase of assessment, where children use their
experience with risk and unpredictability, in both the social
and physical environments, to shape their life history strat-
egies (Del Giudice, Angeleri, & Manera, 2009; see also
Belsky et al., 1991; Bogin, 2002). This framework suggests
that life history stages allow an individual to evaluate local
conditions and vary timing of transitions in order to generate
optimal developmental and reproductive strategies. These
transitions are coordinated by hormones, which trigger gene
expression. As parents alone may not be capable of pro-
viding an accurate prediction of future conditions, adaptive
plasticity is invaluable to human development (e.g., West-
Eberhard, 2003). Thus, onset of adrenarche, the developmen-
tal event underpinning human juvenility, can also be thought
of as a ‘switch point’following the evaluative phase of early
childhood—a transition point in human life history where
hormones (in response to environmental cues) “turn on”the
genes that lead to phenotypic variation (Del Giudice et al.,
2009).
1.2
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DHEAS production and adrenarche
The adrenarche event is marked by rising post-natal pro-
duction of dehydroepiandrosterone (DHEA) and its sulfate
(DHEAS; hereafter DHEA/S when referring to both), typi-
cally co-occurring with onset of middle childhood (generally
between 6 and 8 years old; Campbell, 2006, 2011; Del
Giudice et al., 2009; Parker, 1991). DHEAS, which is the
most abundant steroid hormone in circulation, has several
hypothesized functions relating to neurocognitive function,
social and physical development, and protection against
excessive stress (discussed below). Based on these functions,
it appears that our adrenarcheal patterning, in comparison to
other primates, reflects an adaptive extension of juvenility,
one that allows for additional cognitive and behavioral devel-
opment (Bernstein, 2016).
Onset of DHEAS production during childhood, an event
known as adrenarche, is due to the development of the zona
reticularis (ZR) in the adrenal cortex (Dohm, 1973; Parker,
1991). The fetal zone produces DHEA after birth but its sub-
sequent postnatal involution is typically rapid (within
6 months); it is not until around 3–4 years of age that islands
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of ZR start to become evident in children and low levels of
DHEA can sometimes be detected in circulation (Dohm,
1973; Hui et al., 2009). As these islands mature into the ZR
layer, DHEA/S becomes more evident in circulation, and is
almost always detectable by age 8 (Dohm, 1973; Parker,
1991). The layer continues to increase in thickness during
development, reflected by increasing levels of DHEA/S
(Dohm, 1973; Parker, 1991), reaching its terminal thickness
around age 13 (Nakamura, Rainey, Kurotaki, Hui, & Sasano,
2010). Because it typically precedes puberty, adrenarche was
formerly believed to be linked with gonadarche. It is now
clear that these are not directly connected (although there is
some evidence that the same genes control expression, with
estimated heritability between 57% and 65%; see Parker,
1995), as one can proceed in the absence of the other
(Campbell, 2006; Parker, 1991). DHEA/S levels appear to be
associated with age, and continue to rise through the mid-20s
(Worthman, 1999) and in some places as late as the mid-30s
(e.g., Turkana men of northern Kenya; Campbell, Leslie, and
Campbell, 2007), before starting to decline.
The adrenarche event encompasses physical, cognitive,
and social changes that mark a visible transition to middle
childhood (Campbell, 2011; Del Giudice et al., 2009). Physi-
cally, onset of adrenarche triggers: increasing production of
axillary hair, emergence of pubic hair, and rising oil produc-
tion from sebaceous glands (Parker, 1991); growth (Parker,
1991; Zemel & Katz, 1986; but see Campbell, 2011); and
development of the immune system, particularly as an antago-
nist against cortisol (Hechter, Grossman, & Chatterton, 1997).
In addition, DHEAS can act not only as a DHEA reservoir, as
it can be back-converted into DHEA [which in turn can be
converted to testosterone and dihydrotestosterone or estradiol
via steroidogenic enzymes (White & Porterfield, 2013)], but
DHEAS can also be converted directly into other androgens/
estrogens from reservoirs in peripheral tissues (Labrie et al.,
2005). Cognitively, DHEA/S is associated with neurological
development, by facilitating learning and enhancing memory
(Majewska, 1995). Given these neurological aspects of
DHEA/S production, it is perhaps not surprising that the adre-
narche event maps neatly onto the 5-to-7-year-old transition
(White, 1996), a time when, cross-culturally, children start to
“make sense”(Lancy & Grove, 2011; Rogoff, Sellers,
Pirrotta, Fox, & White, 1975) due to their increased reasoning
abilities. Socially, onset of DHEA/S production appears to
lead to increased social interactions and reduced fearfulness
(Campbell, 2006). Studies undertaken in western Kenya also
indicate that elevated DHEAS concentrations are associated
with decreased malaria parasite density among pubertal
females (Leenstra et al., 2003) and increased malaria resist-
ance, as well as reduced parasite density, among pubertal
males (Kurtis, Mtalib, Onyango, & Duffy, 2001).
There is individual variation in the timing of the adre-
narche event (Del Giudice et al., 2011; Pratt et al., 1994;
Rotter et al., 1985), and onset can be somewhat earlier
among girls than boys (Sulcova et al., 1997). It is hypothe-
sized that perhaps environmental cues, like increases in body
mass index (Remer & Manz, 1999) or prenatal programming
(Ong et al., 2004), set the timing of adrenarche but it may be
that the onset of DHEA/S production is due solely to the
maturation of the adrenal glands. However, both physical
and social environments have known effects on the timing of
hormonal events; it has been well-established, for example,
that both nutritional (e.g., Kulin, Bwibo, Mutie, & Saniner,
1982) and social (e.g., Ellis & Garber, 2000) stress can affect
pubertal timing. It has also been demonstrated among rhesus
monkeys that calorie restriction can inhibit age-related
declines in DHEAS (Lane, Ingram, Ball, & Roth, 1997), sug-
gesting that environmental inputs may have important effects
on DHEAS production. Ellis and Essex (2007), for example,
found that low parental support is correlated with earlier
onset of adrenarche. As a result, it appears likely that the tim-
ing of adrenarche, within the context of life history pattern-
ing, is tied to early social and environmental inputs (Del
Giudice et al., 2011).
1.3
|
Cortisol production
Cortisol is a steroid hormone produced in the zona fascicu-
lata of the adrenal glands. It is the key glucocorticoid pro-
duced by humans, playing many important roles in both
normal maintenance of the body via its metabolic function as
well as in development, immune function, and maintenance
of homeostasis. Perturbations to homeostasis can be cha-
racterized as stressors, and stress, or allostasis, as the process
by which an organism attempts to return to homeostasis
(Chrousos, 1998; Sapolsky, 1994). These perturbations are
often caused by risk or unpredictability in the physical or
social environment (e.g., Charnov, 1993; Chisholm, Bur-
bank, Coall, & Gemmiti, 2005; Ellis & Essex, 2007; Moore
et al., 1997; Quinlan, 2007).
Effects of allostasis can vary depending on the timing,
patterning, duration, and individual experience of stressors.
For males, prenatal stress can lead to higher disease risk,
whereas the greatest risk for females is associated with stress
during peripubertal or pubertal maturation (Bale & Epperson,
2015). Allostatic load represents the cumulative effects of
extended or repeated stress (McEwen, 1998; McEwen &
Steller, 1993) and is tied to a wide range of negative health
impacts including, among others, immune deficiency, cogni-
tive impairment, inhibited growth, delayed sexual maturity,
damage to the hippocampus, sensitivity of amygdala fear cir-
cuits, and psychological maladjustments (e.g., Flinn, 2006;
McEwen, 1998; McEwen & Steller, 1993; Nepomnaschy &
Flinn, 2009; Sapolsky, 1999). Individuals also vary in both
responsiveness to (Ellis & Boyce, 2008; Ellis, Jackson, &
Boyce, 2006; McEwen, 1998; McEwen & Steller, 1993) and
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perceptions of (McEwen, 1998; McEwen & Steller, 1993;
Piko, 2002) stress and this, coupled with genetic differences,
can result in distinct outcomes despite similar experiences.
An alternative perspective on the allostatic load suggests that
chronic stress provides children information during develop-
ment that, in the context of adaptive plasticity, may inform
appropriate life history strategies (Ellis & Del Giudice, 2014;
Ellis et al., 2006). Regardless, stress clearly has significant
impacts on life history trajectories, and cortisol is one mea-
sure of its effects.
1.4
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DHEA/S as a cortisol antagonist
In addition to its role in children’s social and physical devel-
opment, DHEAS may also act as an antagonist to cortisol,
mitigating some of the effects of allostatic load. High corti-
sol:DHEAS ratios are linked to persistent major depression
among children 8–16 years old (Goodyer et al., 1998). Ade-
quate DHEAS concentrations have been hypothesized to mit-
igate the detrimental effects of cortisol hypersecretion on the
brain (Goodyer, Park, Netherton, & Herbert, 2001). Higher
cortisol:DHEAS ratios are also associated with increased risk
of metabolic syndrome (at least among adult males), while
higher DHEAS appears protective (Phillips et al., 2010).
Increased DHEAS in response to elevated cortisol thus app-
ears to have protective qualities, and likely plays a role
in allostasis. This may be of particular importance during
sensitive developmental periods and in high-stress/high-risk
environments.
1.5
|
Hair hormone analysis
Cortisol and, to a lesser extent, other steroids have been well-
demonstrated to be detectable in hair, both human and nonhu-
man, using commercially available enzyme-linked immu-
nosorbent assay (ELISA) kits (e.g., Davenport, Tiefenbacher,
Lutz, Novak, & Meyer, 2006; Fourie & Bernstein, 2011;
Gow, Thomson, Rieder, Van Uum, & Koren, 2010; Laudensl-
ager, Jorgensen, & Fairbanks, 2012; Sauv
e, Koren, Walsh,
Tokmakejian, & van Uum, 2007). Most prior research quanti-
fying adrenal androgens in hair has focused on DHEA (Kintz,
Cirimele, & Ludes, 1999; Shen, Xiang, Shen, Bu, & Wang,
2009), with only one study including analysis of DHEAS
(Chen et al., 2013). These studies demonstrate that these hor-
mones are present and measurable in hair. None of these stud-
ies used ELISAs for analysis, making this project the first to
do so.
Passive diffusion from blood is considered the most
likely way that hormone is incorporated into hair (Russell,
Koren, Rieder, & Van Uum, 2012; Stalder & Kirschbaum,
2012), but external sources may also contribute to hair corti-
sol concentrations. While topical steroid application is an
obvious confound, sweat (Russell, Koren, Rieder, & Van
Uum, 2014; cf. Noppe et al., 2014) and relative humidity
(Boesch et al., 2015) may increase cortisol concentrations.
The hair follicle itself may also be capable of locally produc-
ing cortisol (Ito et al., 2005). Neither hair color nor hair type
appears to influence cortisol content (Noppe et al., 2014; cf.
Rippe et al., 2016). There is disagreement regarding the
effects of ethnicity on hair cortisol concentrations; some sug-
gest that ethnicity is not correlated with hair cortisol concen-
trations (Vaghri et al., 2013) while others consider it to be an
important control variable (e.g., Rippe et al., 2016).
Hair collection eliminates the need for and stress of hav-
ing multiple spit, blood, or urine collections within and
across several days and weeks (Russell et al., 2012; Van
Uum et al., 2008). Additionally, it does not require any spe-
cial training or context for collection or storage (e.g., Gow
et al., 2010), and the sample remains stable until processing
(even for thousands of years; Webb et al., 2010). In many
populations, but especially among children, use of a noninva-
sive methodology may be preferable when collecting bio-
marker data, as it minimizes the potential for psychological
harm or physical risk.
A single hair sample can be indicative of an individual’s
‘hormone phenotype’and concentration changes can reflect,
for example, developmental inflection points or the experi-
ence of long term stress (Davenport et al., 2006; Russell
et al., 2012). Hair does not reflect circulating hormone con-
centrations at the time of collection, but provides a picture of
an individual’s physiological state during hair growth. This
allows for examination of a time-averaged signal generated
over the time of hair growth (especially cortisol—see Meyer
& Novak, 2012; Sauv
e et al., 2007; Stalder & Kirschbaum,
2012), in contrast to highly variable point samples (e.g.,
saliva or blood collection). By providing both a picture of an
individual’s hormonal phenotype as well as a window into
their experience over several months, hair hormone analysis
extends the range of questions that can be examined within
the constraints of anthropological fieldwork.
1.6
|
Study populations
Research was undertaken among three sub-Saharan popula-
tions: Aka forest foragers, Ngandu horticulturalists, and
Sidama agropastoralists. Aka forest foragers and Ngandu
horticulturalists reside in the southwestern portion of the
Central African Republic within the tropical rainforest of the
Congo Basin. Aka live in association with Ngandu horticul-
turalists. This relationship is one of both economic exchange
(with Aka exchanging forest goods for agricultural foods)
and social and spiritual ties (Bahuchet & Guillaume, 1982;
Hewlett, 1991; Takeuchi, 2005). Although the Aka remain
mobile, time spent living in close proximity to the Ngandu
village has increased in recent years (Meehan, Hagen, &
Hewlett, 2017).
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The Aka social environment is highly supportive. Aka
practice demand sharing; this, along with cooperation and
egalitarianism, is a core value (Bahuchet, 1990; Hewlett,
1992). Aka are indulgent and affectionate parents, instilling
autonomy and independence at an early age (Hewlett, 1991).
Infants are held most of the day by their mothers or another
caregiver, and allomothers (nonmaternal caregivers) consti-
tute an essential component to children’s social environments
(Meehan, 2005). Aka children as young as 3 years old often
act as caregivers, although infants would not be left for
extensive periods with other children until the caregiver
reached approximately 5 years of age. By the age of 10, chil-
dren have already acquired many of the skills necessary to
forest life (Hewlett & Cavalli-Sforza, 1986).
In contrast to their intimate rearing environment, the physical
environment can be considered fairly harsh. Among the Aka,
there are high rates of infant and childhood mortality (Hewlett,
1991). The leading causes of death at all ages are infectious and
parasitic diseases (Hewlett, van de Koppel, & van de Koppel,
1986). Most Aka children have weight-for-age (60.63%) and
height-for-age (80.95%) z-scores 2 SD below WHO reference
means, suggesting that the population is severely underweight
and stunted (Meehan, Helfrecht, & Quinlan, 2014). While nutri-
tional stress is certainly an important issue in this population, it
must also be noted that Aka are genetically distinct from other
Central African peoples (Bozzola et al., 2009; Dietz, Marino,
Peacock, & Bailey, 1989; Jarvis et al., 2012) and are considered
a“pygmy”population, indicating that they will almost always
fall below reference means (Meehan et al., 2014). Despite these
environmental hardships, Aka view their environment—specifi-
cally, the forest—as giving and supportive (Hewlett, Lamb,
Leyendecker, & Scholmerick, 2000).
As noted above, the Ngandu live in the same geographic
region and frequently in close proximity to the Aka but differ
ethnically, linguistically, and culturally. Ngandu parenting
styles have been characterized as more authoritative than
among the Aka (Hewlett, 1991). Infants are typically held
less often than Aka infants and care is less intimate (Hewlett
& Lamb, 2002; Meehan, 2008). Men and women work inde-
pendently and in strongly sex-prescribed activities (Hewlett,
1991; Meehan, 2008). Although the Ngandu practice shar-
ing, it occurs less frequently and is not as widespread
(Hewlett, 1991; Hewlett & Lamb, 2002).
Ngandu are subject to similar disease risks as Aka, partic-
ularly malaria and other parasites. Additional diseases known
to be problematic include bronchitis, sexually-transmitted dis-
eases, and high blood pressure (Hewlett, 1991). Helfrecht and
Meehan (2016) found that fully one-third of Ngandu children
had weight-for-age (33.04%) and height-for-age (34.51%)
z-scores 2 SD below CDC reference means, indicating that
nutritional stress is relatively high in this population.
Sidama agropastoralists reside in the Sidama Zone of the
Southern Nations, Nationalities, and Peoples’Region of
southwestern Ethiopia. Sidamaland, part of the Great Rift
Valley, lies in the area between Lake Awassa and Lake
Abaya, the northern and southern boundaries, respectively
(Br€
ogger, 1986; Hamer, 1987). Both agriculture and cattle
herding are important components of Sidama life but concen-
tration may vary depending on residence elevation (i.e.,
low-, mid-, or high-land environments; Hamer, 1987). The
data here come from Sidama residing in a lowland, peri-
urban environment.
Similar to the Ngandu, the Sidama practice a strict sexual
division of labor (Asefach & Nigatu, 2008; Hamer, 1987).
Children assist their parents in all tasks and, as they mature,
they segregate into sex-proscribed activities (Hamer, 1987).
Due to increased school attendance, however, it was noted
that many children are spending less time obtaining these
skills from their parents. The gerontocracy is male-
dominated (although women gain influence when they
become mothers and particularly when their sons attain eld-
erhood; Br€
ogger, 1986) and infiltrates parenting style—men
are considered responsible for the actions of their wives and
children (Hamer, 1987). Participation in community life,
while generally desirable and pleasant, is obligate; coopera-
tion is essential to social and economic success (Br€
ogger,
1986).
In a survey of disease risks that affect child health,
parents frequently noted malaria, bacteria, parasites (particu-
larly helminths; Ashenafi, Techalew, Mulugeta, Asrat, &
Berhanu, 2011), and pneumonia, as well as influenza and
typhoid (Helfrecht, 2016). They additionally stated that the
prevalence of these is worsened by food shortages. Although
the majority of Sidama children fall within normal ranges for
height- and weight-for-age, 26.8% had height-for-age and
36% had weight-for-age z-scores 2 SD below CDC reference
means (Helfrecht, 2016; see also Yewelsew, Kennedy, Gates,
& Stoecker, 2008). These scores, like those among Aka and
Ngandu, clearly indicate serious potential for increased mor-
bidity and mortality (Martorell & Ho, 1984; Pelletier & Fron-
gillo, 2003; Scrimshaw, Taylor, & Gordon, 1968).
Below, we present the methods and results of our exami-
nation of DHEAS patterning and onset of adrenarche among
these three sub-Saharan populations. Based on the diverse
ecocultural contexts, we hypothesize that DHEAS patterning
will manifest differently across populations, reflecting
adaptive plasticity in human life history trajectories. We
additionally hypothesize that onset of adrenarche, like other
biological events, will occur later than among Euro-
American populations.
2
|
METHODS
The protocol and procedures presented below were reviewed
and approved by Washington State University’s Institutional
HELFRECHT ET AL.American Journal of Human Biology
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Review Board. Following explanation of the proposed re-
search to participants and prior to hair collection, informed
assent was obtained from child participants and parent per-
mission was obtained from all adults.
2.1
|
Hair collection
Hair was collected from 480 children (80 male, 80 female in
each population), between the ages of approximately 3 and
18 years. Hair samples were obtained by shaving or snipping
a small portion (3 cm diameter) from the posterior vertex
of the participant’s head (left side). Consistency in where
hair is sampled is important as there is research indicating
variation in hormone concentrations in different areas of the
scalp, with the posterior vertex demonstrating the least intra-
sample variation (Sauv
e et al., 2007). If a sample was col-
lected by shaving, a new razor blade was used for each par-
ticipant; scissors, when used, were thoroughly cleaned with
alcohol swabs and allowed to dry between collections. Hair
was collected directly into a labeled paper envelope and
sealed.
As this study was noninvasive, it was not possible to
obtain blood in order to compare circulating hormone to hair
hormone. In addition, blood (or urine/saliva) would have
required several repeated samples to make such a compari-
son. It should be noted, however, that such studies have been
previously done with cortisol and found that hair cortisol
concentrations (HCC) are significantly correlated with
those found in saliva (D’Anna-Hernandez, Ross, Natvig, &
Laudenslager, 2011; Sauv
e et al., 2007), urine (Sauv
e et al.,
2007), and serum (Sauv
e et al., 2007; Yang, Lan, Meng,
Wan, & Han, 1998).
2.2
|
Age
Aka parents do not keep track of children’s birthdates, but
ages of children were accurately calculated by relative aging
to other children in camp and by using seasonal or local
events to determine the child’s birth month and year (see
Helfrecht & Meehan, 2016; Meehan et al., 2014). In addi-
tion, two of the authors (CH & CM) spent 5 and 13 years,
respectively, working with this population, allowing for a
cumulative acquisition of birth dates and demographic data;
several, but not all, of the children participating in this study
have been involved in previous research so their birth month
and year were previously known. As research among this
population of Aka has been ongoing for 301years, both par-
ticipants and researchers are aware of significant events that
have occurred in the village. This allows for year of birth to
be determined with relative ease. From there, seasonal events
—such as when caterpillar season occurred or corn was
planted—can be used to determine the month of birth.
Among the Ngandu and the Sidama, families typically know
the age and date of birth for their children and, if not, birth
certificates are usually available. When this information was
not available, ages were determined to month and year of
birth using relative aging based on season of birth, other fam-
ily members whose birth date was known, and known birth
dates of nearby neighbors. Age is reported here in years.
2.3
|
Hair hormone analysis
Prior to starting extraction, it was necessary to remove for-
eign matter from the hair samples. When working with popu-
lations who live, for example, in mobile camps or mudbrick
homes, such as forest foragers, horticulturalists, and agropas-
toralists, hair collections often include extraneous material.
Some samples had significant quantities of such matter (e.g.,
lice/lice eggs, dirt, or leaf debris) that could affect weights or
interfere with hormone analysis (Cooper, Kronstrand, &
Kintz, 2012) if not inspected and removed prior to the extrac-
tion process. While some researchers are concerned that
washing the hair (especially with water) may remove cortisol
(Hamel et al., 2011), others note that the exterior of the hair
shaft could have been exposed to exogenous sources of hor-
mone and that all samples should undergo decontamination
procedures (Society of Hair Testing, 1997).
After visual examination and debris removal, approxi-
mately 30 mg of hair was weighed into a glass vial. Hair was
then washed twice in 3 ml of isopropanol for 3 min. Isopro-
pranol has been previously determined to be the best choice
of wash medium as it does not extract cortisol from the inte-
rior of the hair shaft (e.g., Davenport et al., 2006) and addi-
tionally serves to remove any remaining debris. Hair was
dried completely (3–5 days minimum) under a fume hood.
More hormone can be recovered from ground than
minced hair (Davenport et al., 2006). Thus, the complete
sample of dry hair was next placed into a stainless steel
microvial (1.8 ml) along with 4 chrome steel beads (size
3.2 mm) and sealed with a silicon rubber cap. These vials
were placed in liquid nitrogen for 1 min and then pulverized
for 1 min at a high speed using a BioSpec (Bartlesville, OK)
mini-beadbeater-16 (set between 3.0 and 3.5). The liquid
nitrogen-pulverization cycle was repeated two additional
times (3 cycles total). The use of liquid nitrogen is not essen-
tial, but freezing the hair led to the greatest consistency in
the processed samples. Following the third cycle, hair sam-
ples had a uniform powder-like consistency.
Ten milligrams (or less) of powdered hair was weighed
into a plastic tube and extracted in 4 ml of methanol. Quanti-
ties greater than 10 mg did not serially dilute consistently,
but dilutions of samples 10 mg were proportional. Samples
were next sonicated at 45 8C for 30 min at 50 KHz using a
Branson 3800 sonicator. Sonication, like milling, can inc-
rease the amount of hormone that can be extracted from hair
(Fourie et al., 2016). Samples were then placed on a plate
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American Journal of Human Biology HELFRECHT ET AL.

shaker at room temperature for 23.5 h at 470 RPM (started at
550 RPM to get the powder into solution, then reduced to
470). At the end of the incubation period, samples were cen-
trifuged at 2620G/3000 RPM for 10 min at 4 8C to separate
the powder from the methanol, and 3.5 ml of the extract was
aliquotted into a polypropylene tube. Samples were dried in
a vacuum concentrator at 37 8C until completely evaporated
(5 h), and then stored at 4 8C if not immediately removed.
The dry extracts were reconstituted in 500 ml of assay buffer
and sonicated for 20 min at 37 8C.
To determine hair hormone concentrations, the samples
were assayed using commercially-available ELISA kits (for
development of this procedure and subsequent analyses,
ALPCO [Salem, NH] Cortisol ELISA [Catalog no. 11-
CORHU-E01-SLV] and Salimetrics [State College, PA] Sali-
vary DHEA-S [Catalog No. 1–1252] kits were used). Cross-
reactivity for the ALPCO cortisol ELISA is 13.6% for pred-
nisolone, 7.6% for corticosterone, 7.2% for deoxycorticoste-
rone, 7.2% for progesterone, 6.2% for cortisone, 5.6% for
deoxycortisol, 5.6% for prednisone, and 1.6% for dexametha-
sone. Crossreactivity for the Salimetrics DHEA-S ELISA is
0.0844% for androsterone and 0.0268% for transandrosterone.
Results for cortisol are returned as ng/ml, while DHEAS
results are returned in pg/ml. These were converted to pg/mg
by dividing the result in half (as only 500 ml of buffer is used
to reconstitute the sample) and then dividing by 0.875
(because only 3.5 ml of the original 4 ml extraction was
dried down). This was then divided by the initial weight of
the powdered hair to determine ng/mg for cortisol and pg/mg
for DHEAS. For cortisol, this was then multiplied by 1000 in
order to determine pg/mg. Both cortisol and DHEAS results
are presented here in pg/mg.
2.4
|
Statistical methods
The hair hormone concentrations for DHEAS were strongly
skewed and were therefore log-transformed to improve nor-
mality; while both transformed and untransformed data are
presented below, transformed data were used for analyses.
Generalized additive models (GAM) were used to evaluate
the associations of age, sex, cortisol, and population with
DHEAS hair hormone concentrations, as well as in examin-
ing the relationship between DHEAS and age by population
to estimate derivatives. GAMs can be a preferable alternative
to linear analyses as they employ a local scoring algorithm
that allows smoothing of predictor variables and identifica-
tion of nonlinear covariate effects (Hastie & Tibshirani,
1986). A one-way ANOVA was used to examine mean pop-
ulation differences in cortisol concentrations and a t-test was
used to evaluate mean differences between the sexes. The
GAM analyses and plots were generated using R 3.3.2 and
the Mixed GAM Computation Vehicle with GCV/AIC/
REML Smoothness Estimation package v. 1.8-1.5; all other
analyses were conducted in Stata 10.1.
3
|
RESULTS
Of the initial 480 participants, we were able to successfully
analyze 449 (221 male; 228 female) for hair DHEAS concen-
trations (Table 1). Across the entire sample, hair DHEAS
concentrations ranged from a minimum of 11.46 pg/mg to a
maximum of 1372.57 pg/mg, with a mean of 231.34 pg/mg
(SD 5231.34) and a median of 163.98 pg/mg. Log
10
-trans-
formation of the data reduces the skew.
Of the 480 participants, 479 (240 male, 239 female) were
included in the cortisol analyses (Table 2). One Sidama girl
had an extremely high HCC (>7000 pg/mg). At the time of
collection, this almost 11-year-old had a skin infection on her
head. Because her HCC well-exceeds the range identified
within the rest of the sample and is likely due to something
external (e.g., the skin infection itself increasing local cortisol
and/or a topical treatment that was not fully eliminated during
the two wash cycles), she was removed from the analyses.
Hair cortisol concentrations across the entire sample ranged
from a minimum of 24.82 pg/mg to a maximum of 3405.42
pg/mg, with a mean of 535.27 pg/mg (SD 5440.01) and a
median of 451.94 pg/mg. Log
10
-transformation of the data
reduces the skew.
Based on loess regression plots, each population
appears to have its own DHEAS and cortisol patterning
(Figure 1). Surprisingly, very high DHEAS levels were
found among the youngest in all three samples, but espe-
cially in the Aka and Sidama populations. In general,
DHEAS patterning does not reflect expectations based on
the literature cited above; the high concentrations among
young children coupled with the relatively gradual rise
after the adrenarche event deviate from previously identi-
fied trajectories. For the most part, cortisol concentrations
mirrored the DHEAS trajectory, albeit with visible varia-
tion at the tails of the age distribution.
Results from a one-way ANOVA indicated that there are
significant differences in cortisol concentrations between
populations [F(2,236)58.52, P<.001]. Post hoc compari-
sons using the Scheffe test indicate that there is no significant
difference between Aka and Ngandu, but both of these popu-
lations have significantly (P<.001) higher HCC than
Sidama children (see Table 2 for means and SD). Sex differ-
ences among children were not expected but it has been
noted in some other studies that males have higher cortisol
responses to stress than females (see Kudielka & Kirsch-
baum, 2005 for a review) so this was tested here. Among the
Aka and the Sidama, but not the Ngandu, males have higher
mean cortisol concentrations. This only reaches significance
for the Sidama (t5–1.88, P5.031).
HELFRECHT ET AL.American Journal of Human Biology
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A GAM was used to evaluate the effects of age, sex, cor-
tisol, and population on DHEAS concentrations; based on
the loess regression plots, an interaction term for sex and
population was also included (Table 3). The intercept for this
model represents Aka females. Controlling for age within
each population, results indicate that Aka have significantly
higher DHEAS concentrations than Sidama, but not Ngandu
(although there does appear to be a trend in this direction as
well). In general, there is no difference in DHEAS concentra-
tions between males and females. Sidama children have the
lowest hair DHEAS concentrations, controlling for age, sex,
cortisol, and population. Within populations, there is no dif-
ference between Aka males and females or between Sidama
males and females, but Ngandu males have significantly
lower DHEAS concentrations than females. For both Aka
and Sidama children, age is a significant predictor of
DHEAS hair concentrations. Because GAMs allow the data
to determine the fit of the curve, the estimated degrees of
freedom (edf) allow interpretation of the variation that exists
across the sample. Here, the relatively high edf indicate that
there are evident changes across the age range of the sample.
Age is not a significant predictor for Ngandu children, and
the regression line is relatively flat in comparison to Aka and
Sidama, albeit somewhat curvilinear (Figure 2). HCC also
has a significant (though relatively minor) effect on DHEAS
concentrations, with higher cortisol being associated with
higher DHEAS concentrations. The model accounts for 47%
of the variance in DHEAS hair concentrations.
TABLE 1 Summary statistics for age, DHEAS, and log
10
-transformed DHEAS by population and sex
Aka Ngandu Sidama
Girls Boys Girls Boys Girls Boys
N71 63 77 79 80 79
Age (years) Mean 9.82 8.69 7.89 7.99 8.18 7.94
Range 2.99-18 3.06–17.42 3.02–18.26 3.13–17.10 2.76-18.12 2.96-15.14
SD 3.88 4.02 3.48 3.38 3.63 3.06
DHEAS (pg/mg) Mean 359.65 356.03 293.56 184.54 109.11 126.55
Range 78.19–853.93 89.94–905.26 43.80–1372.57 33.08–736.52 24.07–598.46 11.46–585.97
SD 226.26 209.99 222.64 131.07 108.74 114.57
Log
10
DHEAS Mean 2.46 2.48 2.37 2.18 1.90 1.97
Range 1.89-2.93 1.95-2.96 1.64-3.14 1.52-2.87 1.38-2.78 1.06-2.77
SD 0.30 0.25 0.29 0.27 0.32 0.34
TABLE 2 Summary statistics for age, cortisol, and log
10
-transformed cortisol by population and sex
Aka Ngandu Sidama
Girls Boys Girls Boys Girls Boys
N80 80 80 80 79 80
Age (years) Mean 9.83 9.41 7.94 8.05 8.14 7.88
Range 2.99-18.00 3.06–18.00 3.02–18.26 3.13–17.1 2.76-18.12 2.96-15.14
SD 4.25 4.33 3.53 3.40 3.64 3.09
Cortisol (pg/mg) Mean 577.65 624.07 681.91 641.59 279.03 404.17
Range 112.30–1488.00 97.32–2728.16 97.67–1957.35 34.23–3072.48 24.82–1921.89 29.10–3405.42
SD 342.02 447.36 383.86 451.96 313.14 528.99
Log
10
cortisol Mean 2.68 2.70 2.76 2.72 2.25 2.37
Range 2.05-3.17 1.99-3.44 1.99-3.29 1.53-3.49 1.39-3.28 1.46-3.53
SD 0.28 0.29 0.26 0.30 0.40 0.43
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Average age of adrenarcheal onset was determined by
taking the derivative of the curve for age regressed on log
10
-
transformed DHEAS concentrations using a GAM. The zero
point of the curve is the age at which DHEAS production
changes from declining concentrations to rising production
and is illustrated for each population in Figure 3. Based on
these samples, the age at which the early childhood decline
in DHEAS transitions to an increase in DHEAS concentra-
tions—representing onset of adrenarche—is approximately
9 years among Aka and Ngandu children, and around 8 years
for Sidama children.
4
|
DISCUSSION
We were successfully able to use hair hormone analysis to
examine patterns of DHEAS and cortisol production among
children in three sub-Saharan populations, providing an alter-
nate methodology for life history research in areas where tra-
ditional methods are challenging. We additionally provide
baseline data for these two biomarkers, useful to both intra-
and cross-cultural analyses. Most importantly, the DHEAS
patterns presented here reflect distinct variation from known
patterns of development, with implications for studies of
human ontogeny and life history trajectories. That differences
exist was expected, but the high levels observed across early
childhood and the comparatively flat pattern observed among
Ngandu children were both surprising. Although all three
populations appear to have delayed involution of the fetal
zone in comparison to Euro-American populations, the
Sidama and Aka both reflect a decline across early childhood
followed by a rise across middle childhood and adolescence,
whereas Ngandu only trend towards a positive association
between DHEAS and age. We also found that while there
was, in general, no sex difference in DHEAS concentrations,
Ngandu males had significantly lower DHEAS than Ngandu
females.
Two factors of the physical environment, malnutrition
and disease burden, may be of relevance here in relation to
both DHEAS and cortisol patterning. Although previous
sample sizes for hair cortisol analyses using ELISAs and
conducted among children are smaller, potentially obscuring
FIGURE 1 Scatter plot and loess regression of age (in years) on cortisol and DHEAS hair concentrations (pg/mg) by population and sex. Each dot
represents an individual data point
TABLE 3 Significance values of the GAM predictors (population,
sex, log
10
-transformed cortisol, population 3sex, and age by popula-
tion) for log
10
-transformed DHEAS concentrations (n5449; adj. R-
sq 50.471)
Parametric coefficients Estimate P-value
(Intercept) 2.191 .000
Ngandu 20.083 .075
Sidama 20.511 .000
Male 0.009 .845
Log
10
-transformed cortisol 0.096 .016
Ngandu 3male 20.198 .002
Sidama 3male 0.055 .395
Smooth terms edf P-value
Age 3Aka 2.606 .000
Age 3Ngandu 1.863 .258
Age 3Sidama 3.632 .000
HELFRECHT ET AL.American Journal of Human Biology
|
9of17

extant variation, the concentrations reported are also gener-
ally much lower than those found here (see Boesch et al.,
2015; Groenveld et al., 2013; Grunau et al., 2013; Karl
en,
Frostell, Theodorsson, Faresj€
o, & Ludvigsson, 2013; Karl
en,
Ludvigsson, Frostell, Theodorsson, & Faresj€
o, 2011; Noppe
et al., 2014; Steudte et al., 2011; Vaghri et al., 2013). It is
possible that our methodology is more effective at extracting
hair cortisol, as we both milled and sonicated our samples,
methods known to improve extraction (see Fourie et al.,
2016). It is also probable that our results are a reflection of
the greater environmental stress in these geographies. In all
three populations, the majority of children fall below interna-
tional reference means for height- and weight-for age, sug-
gesting extensive experience with both acute and chronic
nutritional stress (Helfrecht, 2016; Helfrecht & Meehan,
2016; Meehan et al., 2014). Malnutrition is associated with
elevated cortisol (e.g., Alleyne & Young, 1967; Fernald &
Grantham-McGregor, 1998; Jaya Rao, Srikantia, & Gopalan,
1968; Smith et al., 1981), and this is a likely explanation for
the high levels of cortisol seen across all three populations.
Given the comparatively lower rates of malnutrition among
the Sidama in this study (Helfrecht, 2016), it is not surprising
that their cortisol concentrations are significantly lower than
among Aka and Ngandu children.
Disease burden is also high in both these geographies and
some risks, such as malaria and other parasitic diseases, are
shared. This also likely contributes to the high HCC observed,
but may also be a factor in DHEAS patterning. As noted
above, higher concentrations of DHEAS are associated with
reduced parasite density and increased resistance to malaria
among adolescents in Kenya (Kurtis et al., 2001; Leenstra
et al., 2003). The coupled impact of disease and malnutrition
has potentially led to an allostatic load requiring modification
of DHEAS production, particularly among Aka and Ngandu
FIGURE 2 GAM plots by population (A, Aka; B, Ngandu; C, Sidama). Each tick on the x-axis represents an individual data point
10 of 17
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children, who face greater risk of death from malaria than
Sidama children (WHO, 2010). The loess regression plots
suggest that this is a feasible explanation, as the DHEAS tra-
jectories largely mirror cortisol concentrations.
Little data on DHEAS patterning currently exist for
majority world populations (but see Worthman, 1993), but
fairly extensive data exist on puberty. Although puberty and
adrenarche are not related events, these data may be informa-
tive for interpreting the timing and patterning of DHEAS
production observed here. For example, examination of age
at menarche in nonindustrial populations allowed for an
understanding of the historically-novel trend toward earlier
puberty (e.g., Parent et al., 2003). It is possible that the
“delayed”adrenarcheal onset observed in these three popula-
tions is actually more reflective of timing throughout our
evolutionary history, and earlier onset in minority world pop-
ulations is due to greater environmental stability/less nutri-
tional stress, biasing our previous interpretations.
FIGURE 3 Derivatives (dotted lines) of the GAM for DHEAS by age (years)
HELFRECHT ET AL.American Journal of Human Biology
|
11 of 17

This lens may also aid in our understanding of the pat-
terning of DHEAS production. Worthman (1999) found that
the amount of time between onset of puberty (as marked hor-
monally) and the menarche event is greater among Kikuyu
girls than among British girls; Kikuyu girls exhibited onset
of puberty an average of 2.9 years prior to reaching men-
arche, whereas this difference was only 2.3 years among
British girls. The slow rise of DHEAS production following
adrenarche observed here may reflect a comparable—yet
independent—biological process, demonstrating a LH event
adaptively modified to address the harshness of the environ-
ment by ensuring survival prior to reproduction.
The relatively flat pattern observed among Ngandu chil-
dren is challenging to address, as is the higher DHEAS
concentrations among females. One potential explanation
may lie in the pattern of nutritional decline experienced by
Ngandu boys and girls across childhood (Helfrecht &
Meehan, 2016). The stress of malnutrition may be impacting
DHEAS production, as nutritional status is a hypothesized
factor influencing DHEAS production (Ong et al., 2004;
Remer & Manz, 1999; Shi, Wudy, Buyken, Hartmann, &
Remer, 2009). Although Ngandu girls experience significant
declines in their HAZ and WAZ scores between certain
developmental phases, Ngandu boys start and remain lower
across development (Helfrecht & Meehan, 2016). This may,
in part, explain girls’higher DHEAS concentrations, as their
HAZ scores (a measure of chronic nutritional stress) average
higher than those of boys. In contrast, however, Ngandu girls
have higher HCC, which would suggest the potential for
greater nutritional stress. It is possible that their HCC reflects
more acute nutritional stress, as their WAZ scores are more
comparable to those of boys, and girls often do more allocare
and resource acquisition while boys are in school. Additional
research including anthropometric and health measures, as
well as allostatic load, is necessary to better evaluate this
outcome.
Although this is the largest sample to date making use of
hair hormone analysis among children and in sub-Saharan
populations, the age range of the sample is not evenly distrib-
uted, which necessitates some caution when drawing conclu-
sions surrounding the youngest (<5 years old; n592 for
DHEAS) and oldest (12 and above; n575 for DHEAS) par-
ticipants (see also Figures 1 and 2). A wider age range is
needed to fully understand why DHEAS concentrations are so
high in young children, as well as to complete the picture of
DHEAS patterning in non-Western populations. Future
research should be longitudinal, commencing data collection
in infancy and extending into early adulthood in order to bet-
ter evaluate adrenal maturation and DHEAS patterning across
development. In addition, anthropometrics were not collected
in all three populations, preventing a deeper exploration into
the relationship between environment and genetics at this
time. Explorations of HAZ, WAZ, and BMIZ in relation to
hair DHEAS concentrations among Sidama children, how-
ever, indicated that these measures may be less predictive
than HCC (Helfrecht, 2016). We have here provided evidence
of the usefulness of this non-invasive method to explore ques-
tions related to biomarkers and life history, but it should be
noted that cultural or religious restrictions might make this
approach challenging in some regions, particularly in areas
where hair is associated with sorcery (such as in the Central
African Republic). Despite their beliefs in sorcery, Aka and
Ngandu parents and children were willing to participate and
no concerns were expressed by the communities.
This study offers a significant contribution towards un-
covering the full range of “normal”DHEAS and cortisol pat-
terning among populations where it has traditionally been
difficult to collect biomarkers. The variation from expected
trajectories of DHEAS production provides some of the first
empirical data on adaptive responses to the local environ-
ments during early and middle childhood. These data further
demonstrate the importance of situating development within
local ecocultural contexts and the need for continued
research in majority world populations.
AUTHOR CONTRIBUTIONS
CH designed the study and collected the data with input
from CM, RB, EH, and SD. CM and SD provided logisti-
cal support. DDA and CH conducted the hair hormone
analyses. EH and CH conducted the statistical analyses.
CH authored the manuscript. CM, RB, EH, SD, and DDA
edited for intellectual content and provided critical feed-
back on the manuscript.
ORCID
Courtney L. Meehan http://orcid.org/0000-0003-2034-652X
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