Life history and individual differences in male testosterone: Mixed evidence for early environmental calibration of testosterone response to first-time fatherhood

Hormones and Behavior 120 (2020) 104684

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Life history and individual differences in male testosterone: Mixed evidence for early environmental calibration of testosterone response to first-time fatherhood☆

T

⁎

Randy Corpuza, , Daphne Bugentalb a b

Department of Psychology, University of Massachusetts Boston, United States of America Department of Psychological and Brain Sciences, University of California, Santa Barbara, United States of America

A R T I C LE I N FO

A B S T R A C T

Keywords: Fatherhood Testosterone Life history theory

Male testosterone (T) decreases in response to childbirth. Longitudinal support for this has come from samples across cultures. In this study, we look at individual differences in this phenomenon. Utilizing a sample of U.S. fathers, we employ life history theory to investigate the influence of a father's early experience on his neuroendocrine response to fatherhood. We conducted three home visits (n = 226 fathers) from the third trimester of pregnancy to when infants were 10 months old. In this sample, T declined from the third trimester of (a partner's) pregnancy to the early months of the postnatal period. T recovered to pre-birth levels by the time infants reached 10 months old. We did not find any evidence that one's subjective experience of their early environment could account for any meaningful variability in T calibration. Objective, “event” measures of early harshness (i.e., death of a sibling/friend) and unpredictability (i.e., parent upheaval) each uniquely predicted a younger age of sexual debut. Neither harshness nor unpredictability had any (direct or indirect) effects on T calibration. Age of sexual debut did predict the rate of T recovery from 3 to 10 months postnatal. The younger one's sexual debut, the more accelerated their T ascent during this period. We discuss the potential reasons for, and implications of our mixed results.

1. Introduction Human males demonstrate declines in testosterone (T) when forming romantic relationships and across their partner's pregnancy and new fathers demonstrate further declines in T from the pre to postnatal period as they transition to fatherhood (Berg and WynneEdwards, 2001; Edelstein et al., 2015; Saxbe et al., 2017; Gettler et al., 2011). These declines in T are thought to reflect a shift toward paternal care and away from mating effort (van Anders et al., 2012). Early influential work used cross-sectional designs with fathers and non-fathers to elucidate the variability in T between males in specific developmental periods—e.g., between single males, mated males, and males with dependent children (Alvergne et al., 2009; Gray et al., 2006; Kuzawa et al., 2009). More recently, scientists have implemented elegantly designed longitudinal studies to investigate within subject change in T across life transitions and evaluate a handful of candidate environmental cues that influence the calibration of T across the lifespan (Edelstein et al., 2015; Gettler et al., 2011; Saxbe et al., 2017;

Sarma et al., 2018). Life history theory (LHT) provides a theoretical foundation from which to explore how and why resource allocation strategies may differ across one's development. Life history strategies are thought to exist along a “slow” to “fast” continuum. Slow strategists are characterized by, among other traits, increased parental investment per child. When the early environment presents as safe and stable, a slow strategy would be most adaptive. Fast life histories are marked by the opposite pattern. Faced with the risk of dying prior to reproduction, fast strategists are rewarded (ultimately) for reproducing early and often across one's reproductive window. These individuals, on average, are expected to reach sexual maturation earlier, reproduce soon after sexual maturation, and favor a “quantity over quality” pattern of parental investment. These strategies may be apparent in the neuroendocrine mechanisms that facilitate how the mating-parenting tradeoff is negotiated (i.e., the hypothalamic pituitary gonadal (HPG) axis—responsible for the regulation and coordination of T; Gettler, 2016). There are notable differences between what one can categorize as a

☆ ⁎

This research was supported by an NSF graduate fellowship to the first author (DGE-1144085), and an NSF research award to the second author (BCS-1147671). Corresponding author at: Department of Psychology, University of Massachusetts Boston, Boston, MA 02125, United States of America. E-mail address: [email protected] (R. Corpuz).

https://doi.org/10.1016/j.yhbeh.2020.104684 Received 1 August 2019; Received in revised form 6 November 2019; Accepted 3 January 2020 0018-506X/ © 2020 Elsevier Inc. All rights reserved.

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“harsh” early environment and an “unpredictable” early environment (Belsky et al., 2012; Simpson et al., 2012). Harshness reflects the extrinsic mortality one faces during early development. Cues that might signal occupancy in a harsh environment can be the death of a sibling during childhood (Gettler et al., 2015) or the context of one's early experience with peers (Brumbach et al., 2009). Early environmental unpredictability can be indexed by paternal transitions (e.g., upheaval of parental relationships early in life; Brumbach et al., 2009). While both classes of early environmental challenges are correlated, current theory suggests that each contributes unique effects toward life history orientation (Belsky et al., 2012; Simpson et al., 2012) and the pace of sexual maturation (Ellis and Essex, 2007; Gettler et al., 2015; Quinlan, 2010; but see Kyweluk et al., 2018). Neuroendocrine hormones, such as testosterone (T) help convey important environmental information throughout the body and across the lifespan to facilitate adaptive responses (Roney and Gettler, 2015). Research on human males in industrialized nations does indeed report lower levels of T in human males that are partnered in committed relationships (Burnham et al., 2003; Gray et al., 2006) and further reductions in circulating T in those males who will soon become or are new fathers (e.g., Edelstein et al., 2015; Gettler et al., 2011; Saxbe et al., 2017; Sarma et al., 2018; Storey et al., 2000). Reduced levels of T are most pronounced in the immediate postnatal period to facilitate paternal care and then steadily increase as this early postnatal period progresses (Berg and Wynne-Edwards, 2001; Fleming et al., 2002; Storey et al., 2000). There is some evidence for a transition back to increased mating effort accompanied by a rise in T in men around the time of divorce (Mazur and Michalek, 1998). Childhood experiences may be instantiated within the neuroendocrine system and contribute to the calibration of downstream adult life history strategies (Gettler, 2016). Some evidence of the influence of one's early environment on later paternal psychobiology comes from Sarma et al. (2018); new fathers who experienced early life harshness had elevated AM T and those who experienced unpredictability showed attenuated declines in AM T across a 4.5 year study period. These effects were only evident when considering a male's age of sexual debut. It is not clear what this might look like with a sample of U.S. fathers. In addition, males in the Sarma et al. (2018) study were asked to report on objective events that may (or may not) have occurred—death of a sibling, paternal instability, and age of sexual debut. These indices certainly fall in line with theory, but much of the psychological literature on life history theory has relied instead on subjective measures of early environmental condition (e.g., Griskevicius et al., 2011). In the current study, we advance the hypothesis that a male's early environment can serve to calibrate his neuroendocrine response in a manner predicted by life history theory. This study includes a large U.S. sample of first-time fathers. Measures of both objective and subjective indices of early environment are drawn from existing human life history literature. We predict that a father's own early childhood experience will predict a significant amount of variance in the slope of his T response to becoming a father. Males that experienced relatively harsh or unpredictable conditions in early childhood will demonstrate an attenuated decline in T following the birth of their child. If these “fast” strategists do experience a decline in the perinatal period at all, then we should see an accelerated return to baseline T for these males specifically.

the infant was 3 months old (T2; average postnatal age = 85.8 days). The timing of the final visit occurred when the infant was 9–10 months old (T3; average postnatal age = 299.4 days). 2.2. Participants We recruited 226 two-parent, first-birth families in their third trimester of pregnancy. One family was excluded (female same-sex pair). This left 225 fathers enrolled in the study and who participated in T1. Of these 225 fathers, 220 contributed full sets of data (including saliva) at T2 and 196 fathers contributed full sets of data (including saliva) at T3. Families were recruited from multiple sources with the majority coming from hospital birthing classes (48.3%). The average age of fathers in this study was M = 32.9, SD = 5.4, 84.1% of this sample was married to their child's mother at T1, and 77.4% of these fathers held at least a college degree. The median income of this sample was $50,000 to $75,000. Fathers self reported their race/ethnicity as Caucasian (70.6%), Latino/Hispanic (12%), Asian American (5.2%), Black/ African American (1.7%), Native American (1.3%), multiracial (2.6%), and other (3.9%). This study is part of larger research project on maternal and paternal postpartum health outcomes; all data was collected between 2014 and 2017. Only data for fathers will be used in the current study. 2.3. Procedure All materials and procedures were reviewed and approved by the University's Institutional Review Board (IRB). Only families who were currently expecting their first child, and were at least 30 days ahead of their due date at the time of sign up were eligible to participate. Upon signing up, participants were asked for their contact information and their due date. Participants were then contacted to schedule an initial home visit approximately 30 days prior to the couple's due date. Prior to the first visit (T1), participants were sent (via email) an information packet detailing the specifics of the study. They were also sent a copy of the consent forms that they were asked to sign at T1. During the T1 visit, the consent forms were explained and signed. Home visitors then trained participants on how to take their own saliva samples. This training session included a simulated saliva collection where participants collected their own saliva using the exact procedure they would be using on their own. Along with pre-labeled saliva kits (which included sterile cotton swabs, polypropylene tubes, written instructions, and Ziploc bags), participants were also given a packet of self-report materials (T1 self-report packet). Participants were directed to complete their written surveys within 7 days of the home visit. Home visitors contacted participants at the 7day mark to schedule an immediate pickup of all self-report material and saliva samples. These procedures were identical for T2 and T3 home visits. Participants were compensated for contributing to each of the three collection periods. 2.4. Materials 2.4.1. Early environment 2.4.1.1. Subjective perceived resources. These data were collected at T1. Availability of resources in childhood was assessed using an established measure of perceived childhood SES (Griskevicius et al., 2011). Participants were asked to indicate their agreement with three statements (α = 0.85) on a 7-point scale from 1, strongly disagree, to 7, strongly agree: “My family consistently had enough money for things when I was growing up,” “I grew up in a relatively wealthy neighborhood,” and “I felt relatively wealthy compared to the other kids in my school.” The mean sum score was 12.32 (SD = 4.44). Sum scores ranged from 3.0 to 21.00. Visual inspection of the boxplot and histogram for this scale indicated a normal distribution. To account for measurement error, this construct was specified as a latent variable (see

2. Method 2.1. Overview and study design This study is part of a larger research program on maternal and paternal postpartum health outcomes. Our research team conducted three home visits that were scheduled across a 9–10-month period. The first home visit took place during the final month of pregnancy (T1; average prenatal age = −27.9 days); the second visit occurred when 2

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were retrieved by a home visitor. Participants were also asked to note any issues with collection or any deviations from the instructions provided. All samples were retrieved from participants, inventoried, and frozen at −50 °C for up to 90 days and were then shipped on dry ice to the Institute for Interdisciplinary Salivary Bioscience Research (IISBR), Arizona State University. Assays for T were conducted by IISBR. All T assays were conducted in duplicate. On average the inter- and intraassay coefficients of variation were < 15% and 10%, respectively. These findings indicate that the assays were conducted within an acceptable margin of error. The average of duplicate assays was used in the statistical analyses. This data was a repeated measure and all data collection occurred at T1, T2, and T3. Testosterone AM raw scores in this sample did not require transformation. Outliers were identified using the outlier-labeling rule3 (Hoaglin and Iglewicz, 1987). There were n = 5 total outliers at T1, n = 2 total outliers at T2, and n = 6 total outliers at T3. All outlying values were winsorized prior to analyses (see Edelstein et al., 2014). Testosterone covariates. The time of one's morning sample was not significantly related to testosterone (or ΔT) and, as a result, this variable was not included as a covariate. Ethnicity was also explored as a covariate; no differences between any of the ethnicity categories were found for testosterone (or ΔT) across collection periods. Due to the relative ethnic homogeneity of our sample, we also divided our sample into Caucasian and non-Caucasian (including multi-racial) to explore any differences that might exist between these two categories. Ethnicity Caucasian/non-Caucasian was also not significantly related to testosterone (or ΔT) and, as a result, this variable was not included as a covariate. Age and BMI have been included in prior research that has looked at testosterone in new fathers (e.g., Gettler et al., 2011). In our sample however, BMI was not significantly related to testosterone (or ΔT) and, as a result, this variable will not be included as a covariate. Age was related to T at baseline but was not related to any other collection periods or slopes of ΔT. Age is retained in subsequent models as a covariate alongside variables predicting T at baseline.

below) using each of the three items as latent indicators prior to analyses. Creating a subjective perceived resources independent variable. A latent variable (named “Subjective Perceived Resources” henceforth) was created using all three items from the Subjective Perceived Resources scale. Item loadings ranged from 0.77 to.83 (all unstandardized estimates p < .001). Full latent variable statistics for “Subjective Perceived Resources” are presented in Tables S1 and S2 in the supplementary materials. 2.4.1.2. Objective childhood events. These data were also collected at T1. The Childhood Traumatic Events Scale (CTES; Pennebaker and Susman, 1988) is commonly used to assess the subjective impact of childhood traumatic events. The standard CTES assesses childhood traumatic events that occurred prior to the age of 17, however we modified the CTES in the larger study to ask about traumatic events that occurred before the age of 12.1 For the current analyses of “Objective Childhood Events”, traumatic events were limited to: (1) death of sibling or peer and (2) parental divorce or separation. These items on parental instability and exposure to death were chosen to maintain fidelity to theory as well as to facilitate comparison with recent work on objective events and their early environmental influences on T calibration (Sarma et al., 2018). It is important to note that, as part of the larger study, males were asked about the death of a sibling or close friend –a departure from Sarma et al. (2018). It is line with theory however to expect that death of similar aged peers can also be used as an indicator of age-specific rates of extrinsic mortality (Belsky et al., 2012; Ellis et al., 2009; Coall et al., 2016). Age of sexual debut. Participants reported the age at which they first had sexual intercourse. This variable was used as an indicator of sexual maturation and activity. In the current study, we include age of sexual debut as a predictor in longitudinal models of T secretion as well as explore the potential for age of sexual debut to mediate a relationship between childhood events (levels of harshness and unpredictability) and T calibration. 2.4.2. Hormonal measures Testosterone. Each father was asked to contribute an AM sample following each of the three home visits.2 At each home visit, participants were provided with written instructions as well as cotton swabs and polypropylene tubes for saliva collection. At T1, all participants were trained on how to collect their own saliva. At each collection period, fathers were told that they would be submitting a sample collected “within 30 minutes of waking up”. The specific day of sample selection (and subsequent sample retrieval) was agreed upon between the home visitor and the participant. Mean sampling times across all three collection periods was 6:47 am (SD = 1:09). Fathers were instructed to refrain from alcohol 12 h prior to a sample, food 1 h prior to a sample, dairy 20 min prior to a sample, and anything with high sugar, acidity, or caffeine 5 min prior to a sample (Granger et al., 2007). To collect one's own saliva, fathers were directed to place a sterilized absorbent cotton swab underneath their tongue and have it remain there for a full 2–3 min, direct the swab (using one's tongue only) into the appropriately labeled tube, write the down the precise time of sample collection, and place the tube into a freezer safe Ziploc bag (provided) and into the freezer until samples

3. Results 3.1. Preliminary analyses All analyses were run using SPSS (v. 22) and AMOS (v. 22). Correlations, means, and standard deviations for all study variables (raw values) can be seen in Table 1. Growth curve models were built for subjective and objective predictors separately to maintain parsimony and sufficient fit for each model (Kline, 2015). 3.2. Missing values In the longitudinal context, dropout at subsequent occasions can severely bias results, thus care was taken prior to model building to investigate substantive. causes of missingness in this sample. As an initial step, participants were coded as complete (“0”—retained through all three collection periods) or incomplete (“1”—dropped out of study prior to completion of T3) and both groups were compared on all key variables for which data were available. A series of independent-samples t-tests revealed no differences between groups (complete v. incomplete) on any variables measured at T1 (demographic variables, all ps > .72). Variables related to early developmental experience (all ps > .21) were not related

1 This age was selected as the threshold based on a collection of papers that have demonstrated LH strategies are calibrated in response to childhood experience and not adolescent (Simpson et al., 2012) or adult experiences (Griskevicius et al., 2011). See also Sarma et al., 2018. 2 In the current study on early experience and paternal psychobiology, we focus only on the potential role of AM T as similarly designed prior work found that the effects of developmental programming—among the foci of this study—were restricted to AM T (Sarma et al., 2018; Kuzawa et al., 2010).

3 Given FLower and FUpper (approximate quartiles), this technique labels cases as outliers that fall below FL − X(FU — FL) or above FU + X(FU — FL) where X is conventionally set to a value of 2.2 (see Hoaglin et al., 1986; Wilcox, 2017 pp. 14–52).

3

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Table 1 Correlations, means, and standard deviations for primary study variables.

1 Age 2 Sexual debut 3 Subjective childhood SES 4 Death of sibling or close friend (< age 12) 5 Parents divorce/separated (< age 12) 6 AM testosterone T1 7 AM testosterone T2 8 AM testosterone T3 Mean SD % of sample “Yes”

1

2

3

4

5

6

7

8

1 −0.064 0.068 −0.017 0.110 −0.174⁎⁎ −0.068 −0.078 32.98 5.45 –

1 0.027 −0.200⁎ −0.173⁎ 0.103 0.103 −0.170⁎ 18.07 3.34 –

1 −0.093 −0.326⁎⁎ −0.107 −0.015 0.051 12.32 4.44 –

1 0.133⁎ 0.019 0.025 0.011 – – 25.5%

1 0.064 0.023 0.087 – – 26.6%

1 0.461⁎⁎ 0.378⁎⁎ 135.91 pg/ml 32.71 –

1 0.421⁎⁎ 127.10 pg/ml 31.14 –

1 138.00 pg/ml 45.31 –

Note: N = 225, SD = Standard deviation. Means and SD are for raw values. ⁎p < .05,

⁎⁎

p < .01,

⁎⁎⁎

p < .001.

The repeated measures are not treated as independent variables, but as factor loadings associated with the growth factors. In the structural equation framework, models must be “just-identified” or, ideally “overidentified” prior to analysis—situations in which the number of distinct sample moments to be estimated is equal to or greater than the number of distinct parameters to be estimated (Kline, 2015). In constraining measurement error terms to a value extracted from existing literature, fewer parameters are estimated and, as a result, the model can then be over-identified (Bollen, 1990; Hayduk, 1987; Saris and Gallhofer, 2007). To model measurement error, we fixed each error term to 0.35 (for T1, T2, and T3). This fixed error term was calculated using a conservative a priori estimate of reliability (Dabbs, 1990) (Fig. 1).5 Model testing. The optimal functional form of trajectory was identified as curvilinear. On average, testosterone declined from T1 to T2 and then recovered (increased) from T2 to T3. This curvilinear change over time had to be explicitly built into a GCM. Conventional linear GCM feature two growth factors: an intercept (η0) and a slope (η1). In nonlinear curve fitting, however, an additional latent slope factor (η2) is added to estimate the potential curvilinear slope of the growth trajectory. The model that we eventually retained was a piecewise latent GCM.6 This model captures the nonlinear change at the two distinct points (from T1 to T2 and from T2 to T3) as opposed to the expectation that change across all three time points is linear and uniform. Essentially, each “piece” has its own latent slope factor. The end of one piece is the beginning of the next sequential piece. Specification of a piecewise latent GCM is especially well-suited for the study of phenomena related to a response to a significant event embedded within the larger collection period (i.e., birth of a child). All growth factors were initially assumed to covary to allow for the possibility that initial status (η0) is related to the rate of subsequent change at η1 and/ or η2, but we removed the covariance expression from η0 to η2 to help with model identification (i.e., retain sufficient degrees of freedom).7 We used suggested unstandardized pattern coefficients for a piecewise latent GCM with three collection periods (McArdle and Nesselroade, 2014). In a piecewise latent GCM, the unstandardized pattern coefficients for the testosterone intercept factor (η0) remain fixed to 1.0 (i.e., sets baseline measurement to T1 as with linear GCMs).

to whether participants dropped out of the study prior to completion. When comparing groups of “complete (no missing data across any individual items)”, “partial” (missing some individual items, but participated in all three collection periods), and “dropped (missing at least one entire collection period),” no differences on any key variables existed (all ps > .44). A summary of the percentage of participants with missing data at each collection period is presented in Table S3 in the supplementary materials. Overall, missingness was moderate for all variables (0–17%) (Little and Rubin, 2002). We included all variables in models despite missingness, as they increased the precision of the constructs.4 To adjust for biases due to missing data, we fitted all models using full information maximum likelihood (FIML) estimator. Model justification. We used a growth curve model (GCM) specified within a structural equation framework. Current approaches to growth curve modeling can manage a range of complexities including missing data, unequally spaced time points and multivariate growth processes (Bauer and Curran, 2003). The testosterone data met all the criteria needed for using a growth curve model: 1. All testosterone values use the same metric (pg/mL). 2. Participants contributed saliva samples for three distinct days (one AM sample per collection period). 3. Testosterone data are time structured: all participants contributed salivary testosterone at approximately the same intervals (i.e., participants contributed an AM sample for T1 (~30 days prior to childbirth), T2 (~90 days postnatal), and T3 (~10 months postnatal)). Model specification. To determine the optimal functional form of trajectory prior to specification, we inspected AM T scores across all three collection periods. The GCM for testosterone was specified in two steps: 1. The first step is concerned with identifying the best fitting model to accurately depict the trajectory of testosterone change across observations. This step is an attempt to merely identify the mean of the initial observation, the slope of change across observations, and the variance of both the mean and the slope among cases. 2. Given an acceptable GCM is identified, the second step involves adding predictors (i.e., subjective SES or objective events) to the model.

5

Computed as 1-rxx. . Prior to identifying this piecewise latent GCM, we tested a series of models. We began by testing a linear model (η0 and η1, with linear loadings from η1 to each indicator: T1, T2, T3) and confirmed that a basic linear GCM fit this data poorly: [(χ2(3) = 39.58, p < .001); CFI = 0.640, RMSEA = 0.233 (90% CI = 0.17–0.301)] We then tested a series of GCMs with an added growth factor (η2) and quadratic loadings but these models were not identified due to insufficient degrees of freedom. 7 In testing a just-identified model (i.e., 0 degrees of freedom) that retained the covariance expression between η0 and η2, we verified that this model was a poor fit to this data: [(χ2 = insufficient DF); CFI = 0.1.00, RMSEA = 0.261 (90% CI = 0.21–0.31)]. 6

4

Data were assumed to be missing at random (MAR) but further exploration revealed that data were missing completely at random (MCAR): Little's MCAR test (SPSS V. 22) was non-significant (p = .063; retain the null hypothesis that values are indeed MCAR) which provided assurance in using a technique like FIML to handle missing values. 4

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Fig. 1. Mean testosterone AM values from T1, T2, T3. Graph used to identify optimal functional form of trajectory prior to growth curve model fitting. Note: Graph displayed for descriptive purposes only.

The pattern coefficients from the first slope factor (η1) are fixed to constants that estimate the parameter of interest as the slope from T1 to T2 (only) while the pattern coefficients from the second slope factor (η2) are fixed to constants that can estimate the slope from T2 to T3 (only).8 In sum, larger variance of the intercept factor (η0) indicates greater individual difference in AM testosterone at T1, whereas smaller variance of the intercept factor indicates a more similar baseline measurement. Larger variance of the first slope (η1) indicates greater individual difference in testosterone's change from T1 to T2 whereas smaller variance of the first slope factor indicates a more similar pattern of testosterone change during this time. Likewise, larger variance of the second slope (η2) indicates greater individual difference in testosterone's change from T2 to T3. Parameter estimation. This piecewise latent GCM with no predictors is depicted in Fig. 2. Testosterone (AM) measurements were loaded onto three growth factors.9

The model was then extended to add objective and subjective measures of early environmental harshness and unpredictability as predictors of growth factors (see Figs. 3 and 4) to explicitly test our hypothesis that early life influences the nature of a male's testosterone response to becoming a father. Growth curve model with no predictors. Parameter estimates for piecewise latent GCM of Testosterone (AM) without predictors can be seen in Table 2. The estimated mean intercept value is η0 = 136.14 (SE = 2.20, p < .001). This is the average value of AM testosterone for fathers at baseline. The variance for var.(η0) = 1079.31 (SE = 102.46) was statistically significant (p < .001) which indicates substantial variability around this mean level of baseline AM testosterone. The estimated mean slope value (between T1 and T2) is η1 = −8.169 (SE = 2.24, p < .001)—indicating that the average rate of change of AM testosterone from T1 to T2 was significant and negative. The variance estimates for this slope factor was var. (η1) = 1081.67 (SE = 102.09, p < .001) which revealed significant individual variation among slope η1 across participants. For the second slope (from T2 to T3), the estimated mean slope value is η2 = 10.21 (SE = 3.02, p < .001)—indicating that the average rate of change of AM testosterone from T2 to T3 was significant and positive. The variance estimate for this second slope factor was var. (η2) = 1750.15 (SE = 180.65, p < .001) which revealed significant individual variation among slope η2 across participants. The η0 and η1 factors significantly covaried: cov(η0, η1) = −575.61, SE = 80.43, p < .001. Fathers that had higher AM testosterone at baseline also had an attenuated decline in AM

8 In conventional growth curve models (i.e., linear), one can model uneven time intervals by assigning unevenly spaced factor loadings that reflect discrepancies in intervals. The piecewise model built for the current study however separates change across two distinct periods separated by one “knot” (inflection point). The loadings used in all models in the current piecewise GCMs indicate the presence of one slope factor on either side of a single knot. It is noteworthy that fixing loadings to more closely resemble linear conventions regarding uneven time intervals would rescale the slope factor's mean and variance by constants and not change the fundamental meaning or affect significance tests of the parameters, correlations, or predictors (Duncan and Duncan, 2009; Weismayer, 2010). 9 In structural equation modeling, single headed arrows are drawn from the latent variable toward the observed variables to indicate a “reflective” model (i.e., the observed variables reflect the underlying latent variable if the model is

(footnote continued) true). This convention is adopted here to maintain continuity with the structural equation literature. 5

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Fig. 2. Piecewise latent growth curve model with three growth factors (η0, η1, η2) with no predictors.

Fig. 3. Piecewise latent growth curve model with three growth factors (η0, η1, η2) and a subjective measure of childhood SES predictor. 6

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Fig. 4. Piecewise latent growth curve model with three growth factors (η0, η1, η2) and objective measures of childhood harshness and instability. Table 2 Parameter estimates for piecewise latent GCM of Testosterone (AM) without predictors. Parameter Latent growth factor means η0 (Intercept/Baseline) η1 (Slope T1 to T2) η2 (Slope T2 to T3) Variances and covariances Latent growth factors η0 η1 η2 η0↔ η1 η1↔ η2

Unstandardized

136.14 −8.169 10.21

1079.31 1081.67 1750.15 −575.61 −400.44

SE

Critical ratio

p

2.20 2.24 3.02

61.784 −3.656 3.377

< .001 < .001 < .001

102.46 102.09 180.65 80.43 88.94

10.534 10.595 9.688 −7.157 −4.502

< .001 < .001 < .001 < .001 < .001

Table 3 Parameter estimates for piecewise latent GCM of AM Testosterone Growth Factors with subjective measure of childhood SES as a predictor. Parameter Latent growth factor means η0 (Intercept/Baseline) η1 (Slope T1 to T2) η2 (Slope T2 to T3) Predictor effect Childhood SES → η0 Childhood SES → η1 Childhood SES → η2 Age (control) → η0

Unstd.

SE

Critical ratio

p

Std.

170.03 −16.73 3.68

12.74 6.58 8.90

13.351 −2.541 0.413

< .001 .01 .68

−0.64 0.70 0.54 −0.79

0.48 0.50 0.68 0.33

−1.311 1.395 0.797 −2.36

.19 .16 .43 .02

−0.087 0.094 0.058 −0.132

Note: This piecewise latent growth curve model fit the data well: p = .40); CFI = 0.1.00, RMSEA = 0.007 (90% [(χ2(4) = 4.039, CI = 0.000–0.101)].

Note: This piecewise latent growth curve model fit the data well: p = .341); CFI = 1.000, RMSEA = 0.000 (90% [(χ2(1) = 0.908, CI = 0.00–0.173)].

The subjective measure of childhood SES used in this study was unable to predict any of the variability of growth factors η0, η1, or η2. Subjective childhood SES was unable to predict the variability in the level of AM testosterone measured at baseline (β = −0.087, p = .19), was unable to predict the variability in slope by which AM testosterone declined from T1 to T2 (β = 0.094, p = .16), nor could subjective childhood SES predict the variability in slope of AM testosterone increase from T2 to T3 (β = 0.058, p = .43). Subjective childhood SES was able to account for < 3% of the variance in η0, η1, and η2 (i.e., all three R2 values with subjective childhood SES as predictor were < 0.03). Model justification: objective childhood events. The growth curve model with objective childhood events predicting T levels across the transition to fatherhood includes three predictors, three latent growth factors, and a multitude of potential paths and locations to estimate covariances among these variables (i.e., 29 parameters to be estimated). The permutations available to which one can identify a parsimonious ad hoc model with good fit—one which still tests our

testosterone from T1 to T2. In addition, the η1 and η2 factors significantly covaried: cov(η1, η2) = −400.44, SE = 88.94, p < .001. Fathers that had a greater AM testosterone decline from T1 to T2 had an attenuated increase in AM testosterone from T2 to T3. Given the pronounced variability across all three growth factors, attention now turns to models that include (1) subjective and (2) objective measures of exposure to unpredictability and harshness as predictors of these growth factors. Both of these models will include male age as a control variable predicting baseline AM T based on zero order correlations (Table 1 above). Growth curve model with subjective measure of childhood SES predictor. This model, whereby perceived childhood SES predicts the piecewise latent GCM for AM testosterone is depicted in Fig. 3. Fit indices for this piecewise latent GCM with predictors are reported in Table 3. This model fits the data well [(χ2(4) = 4.039, p = .40); CFI = 1.000, RMSEA = 0.007 (90% CI = 0.00–0.101)]. Parameter estimates for this final model also appear in Table 3. 7

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prediction that objective early events→ T calibration—are profuse. In this situation, a priori theory is used to guide model construction of even the smallest detail of one's model—with each parameter treated as a statement of theory that the modeler(s) must be able to justify (Blunch, 2012; Jaccard and Jacoby, 2010; Kline, 2015). With this sentiment in mind, our team took great care to ensure that the model tested herein was driven strictly by theory and the literature:

Table 4 Parameter estimates for piecewise latent GCM of AM Testosterone Growth Factors with “Objective Exposure” variables: Death of sib/friend (Harshness), parental instability (Unpredictability), and age of sexual debut (pace of sexual development). Parameter Endogenous var. & latent growth factor means η0 (intercept/baseline) η1 (Slope T1 to T2) η2 (Slope T2 to T3) Age of sexual debut Predictor effect Death of sib/friend → η0 Death of sib/friend → η1 Death of sib/friend → η2 Parental instability → η0 Parental instability → η1 Parental instability → η2 Death of sib/friend → Age of sexual debut Parental instability → Age of sexual debut Age of sexual debut→ η0 Age of sexual debut→ η1 Age of sexual debut→ η2 Age (Control) → η0 Modeled covariances Death of sib/friend ↔ Parent instability

1. Early environmental harshness can be indexed by measuring childhood exposure to cues of early morbidity and mortality (Belsky et al., 2012; Ellis et al., 2009; Sarma et al., 2018; Quinlan, 2010). Early environmental unpredictability includes instability in a child's household such as unstable parental relationships (Belsky et al., 2012; Figueredo et al., 2006; Simpson et al., 2012). The predictors in the current model will include our measure of harshness (i.e., death of a sibling or peer before age 12) and unpredictability (i.e., parental upheaval) as separate but correlated predictors of the latent growth curve for AM T. 2. Early environmental experiences with harshness or unpredictability have enduring effects on development such as accelerated sexual maturity (Belsky et al., 2012; Brumbach et al., 2009; Chisholm et al., 2005; Ellis and Essex, 2007; Kuzawa and Bragg, 2012; Quinlan, 2010; Simpson et al., 2012; Sheppard and Sear, 2011). As such, our measure of age of sexual debut is a predictor of the latent growth curve of AM T and, it is also appropriate to treat age of sexual debut simultaneously as an endogenous variable to the measures of harshness and unpredictability. We did not have justification to model age of sexual debut as a third covarying predictor as, in our view, age of sexual debut is an outcome of early environmental challenge. The design of this model did however provide an opportunity to test whether any potential effect of objective childhood events on AM T were transmitted through (i.e., mediation) age of sexual debut.

Unstd.

SE

Critical ratio

p

Std.

143.53 −0.924 62.52 19.01

18.37 14.73 18.06 0.35

7.812 −0.063 3.462 53.631

< .001 .95 < .001 < .001

1.88 1.21 −5.96 6.38 −3.44 4.41 −1.28

4.57 4.61 6.05 4.79 4.96 6.50 0.49

0.423 0.263 −0.985 1.33 −0.695 0.680 −2.642

.67 .79 .33 .18 .49 .50 .01

0.029 0.018 −0.071 0.091 −0.048 0.049 −0.190

−1.17

0.53

−2.20

.03

−0.161

0.94 −0.37 −2.82 −0.83

0.73 0.75 0.92 0.34

1.290 −0.485 −3.06 −2.439

0.03

0.02

1.975

.20 .63 < .001 .02 .05

0.097 −0.037 −0.227 −0.138 0.13

Note: This piecewise latent growth curve model fit the data well: p = .29); CFI = 0.988, RMSEA = 0.031 (90% [(χ2(6) = 7.32, CI = 0.000–0.096)].

was unable to predict variability in the level of AM testosterone measured at baseline (β = −0.097, p = .20) and was unable to predict the variability in slope by which AM testosterone declined from T1 to T2 (β = −0.037, p = .63). Age of sexual debut did predict a portion of the variability in AM testosterone's increase from T2 to T3 (β = −0.227, p < .001). Males who made their sexual debut at younger ages also demonstrated a more pronounced recovery/rebound of AM T from T2 to T3.

Growth curve model with objective childhood events. This model, whereby objective childhood events predicts the age of sexual debut and sexual debut predicts the piecewise latent GCM for AM testosterone is depicted in Fig. 4. Fit indices for this piecewise latent GCM with predictors are reported in Table 4. This model fits the data well [(χ2(6) = 47.32, p = .29); CFI = 0.998, RMSEA = 0.031 (90% CI = 0.00–0.096)]. Parameter estimates for this final model also appear in Table 4. The objective events in childhood items used in this study included a putative measure of early environmental harshness (death of sibling or peer) and uncertainty (upheaval of parents during childhood). These items were unable to predict the variability in the level of AM testosterone measured at baseline (β = 0.029, p = .67 for death of sib/peer and β = 0.091, p = .18 for parental upheaval), did not predict the variability in slope by which AM testosterone declined from T1 to T2 (β = 0.018, p = .79 for death of sib/peer and β = 0.048, p = .18 for parental upheaval) nor could objective events in childhood predict the variability in slope of AM testosterone's increase from T2 to T3 (β = −0.071, p = .33 for death of sib/peer and β = 0.049, p = .50 for parental upheaval). Mediation analysis—to test any mediated effect from harshness and/or unpredictability transmitted through age of sexual debut—was not necessary as neither predictor had any relationship to AM T growth factors (all ps > .23) without (or with) age of sexual debut maintained in this model. The objective events in childhood did account for some variability in the endogenous variable age of sexual debut. The death of a friend or a sibling was able to predict age of sexual debut (β = −0.19, p < .01) and the same was the case for parent upheaval (β = −0.16, p < .05). Having a friend or sibling die in one's youth (< 12) was associated with a younger age of sexual debut. Having parents that separated or divorced in early childhood also reduced the age of one's sexual debut. These two predictors covaried (β = 0.13, p < .05). Age of sexual debut, when used also as a predictor in this model,

4. Discussion In this large U.S. sample of first-time fathers, AM T declined from the third trimester of (a partner's) pregnancy to the early months of the postnatal period and recovered by the time infants reached 10 months old. Using a growth curve model containing three growth factors for T—baseline (3rd trimester), slope 1 (Δ in T from baseline to 3 months postnatal), and slope 2 (Δ in T from 3 months to 10 months postnatal)—we were able to identify significant amounts of variation in all three growth factors. To our knowledge, there are no other short-term longitudinal studies with a U.S. sample of this size with pre and post birth collection periods for male T. Our detection of individual differences in the descent of male T from pre to post birth and then the subsequent T rebound within the first year of an infant's life will contribute to extant research that has uncovered related findings with smaller sample sizes (Berg and Wynne-Edwards, 2001; Storey et al., 2000), T samples through the prenatal period (Edelstein et al., 2015; Saxbe et al., 2017), and non-U.S. samples with T collected over the course of years (Gettler et al., 2011). We found only partial support for our prediction that one's developmental experiences has the capacity to influence T calibration across the transition to fatherhood. Utilizing a widely used (e.g., Griskevicius et al., 2011; Piff, 2014; Hill et al., 2012) and often cited subjective measure of childhood SES, we did not find any evidence that one's subjective experience of their early environment could account for any variability in T calibration. However, objective, “event” measures of 8

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(Edelstein et al., 2015; Gettler et al., 2011; Holmboe et al., 2017; Mazur and Michalek, 1998), the exclusion of measures on relationship functioning prevent us from ruling out the influence of pair-bonding on T. Future work should include a measure of relationship satisfaction or functioning to clarify this association. On average, T levels are at their peak at the moment of wakeup and decline throughout the day. Kuzawa et al. (2016) found that 60% of this decline may occur in the first 30 min of waking in a sample of fathers from Cebu (Philippines). Fathers in the current study were instructed to provide a saliva sample “within 30 minutes of waking”. While fathers self-reported the exact time that they provided their morning saliva sample, we did not collect data on how close (or far) that collection time was from the moment of wakeup. However, Kuzawa et al. (2016) did point out that the decline from wake up to 30-minutes post-wakeup was two times greater than that observed using identical protocols with older fathers. The average age of those males in Kuzawa et al. (2016) is 21.5 while the average age of males in the current study is 32.9; these scientists point out that the magnitude of the decline in T from wake up to 30-mins post-wakeup declines with age due to less pronounced overnight increases in T. If true, concerns regarding the absence of the precise time between first waking and one's AM sample is partially mitigated in the current sample of older males. The average drop in T from our prenatal samples to our first postnatal samples was 6%–a decline on par with T declines across studies with comparable collection periods (6% PM T decline from 12 to 36 weeks gestation, Edelstein, 2015). However, it is not clear where the divide might be in AM or PM T regarding statistical significance and biological meaningfulness. It is difficult to tease apart what an x% increase in T might indicate across such a short time period. This also goes for the 9% increase in T from 3 to 10 months postnatal. Kuo et al., 2018 found that a 13% T difference was enough to predict paternal direct and indirect care. The future inclusion of parenting and/or mating measures in this research would be one way to determine whether the changes in T observed in this sample are biologically meaningful events. In terms of the objective events that we asked participants to report on, there are other adverse childhood events that influence development aside from those we ask in this study. For example, death of a parent influences child development across numerous domains (e.g., physical development, school performance, risk for mental illnesses; Rostila et al., 2019). The same could be said about early exposure to poverty, violence, abuse, neglect, and sexual trauma (Gur et al., 2019). Future paternal neurobiological research should include data on a more diverse set of early stressors and advance theoretically sound predictions as to how each stressor might influence early development and/or how these stressors may interact. One other limitation is the homogenous demographics of our sample: first-time fathers who are mostly well-educated, Caucasian, and married/cohabitating with their partner. The sample reflects the local community from which the sample was drawn and is demographically similar to comparable research (Berg and Wynne-Edwards, 2001; Edelstein et al., 2015; Saxbe et al., 2017; Storey et al., 2000) but our findings must be viewed in context of this homogeny as our results may not generalize to populations that may include more diversity. It is noteworthy however that despite difference in baseline T across populations, T modulation occurs at similar magnitudes (Trumble et al., 2012). It is also possible that the current homogeny deviates from heterogeneity that existed when participants were younger. The experiences recorded from these fathers were from childhood while their SES data (educational attainment) is limited to their present-day SES. The percentage of fathers in this sample who experienced adverse events in childhood (from 1980 to 1995, when most in this sample were ages 0–12) is comparable to samples that are more representative of the general population (e.g., cdc.gov/acestudy/about.html). It may be the case then that: (1) this sample of fathers experienced traumatic events above what would be expected of the current demographics of this

early harshness (i.e., death of a sibling/friend) and unpredictability (i.e., parent upheaval) each had unique effects on the age of a male's sexual debut; the death of a sibling/friend and the upheaval of one's parental relationships each predict a younger age of sexual debut. Neither harshness nor unpredictability had any (direct or indirect) effects on T growth factors however. While age of sexual debut could not account for variability in baseline T or the rate of T decline from baseline to 3 months postnatal, a novel finding is that one's age of sexual debut did predict the rate of T recovery from 3 months postnatal to 10 months postnatal. The younger one's sexual debut, the more accelerated their T “rebound” from postnatal months 3 to 10. Little attention has been allocated to the potential transition of a “back to mating” endocrine shift and when one might expect to detect this (see Rosenbaum et al., 2018 for one example). A rebound in male T at any point in one's reproductive years might indicate a return to mating effort (Booth and Dabbs, 1993; Mazur and Michalek, 1998). Thus, it is feasible to view a rebound in T from 3 to 10 months postnatal as an indication that there are important differences among some males shifting back into a more mating oriented strategy sooner as part of a “fast” strategy (indexed by an earlier age of sexual debut). This mating effort can be in the form of: (1) seeking new mates or (2) diverting a partner's energetic resources toward producing additional offspring during her reproductive years. Either strategy can serve to reduce the interval between one's offspring. It is not clear why an objective report of unfavorable events but not a male's subjective measure of childhood SES had the ability to predict the age of sexual debut (or any growth factors) in our community sample of first-time fathers. Findings from studies that used only this subjective measure of childhood SES to identify one's life history strategy have been referenced often (e.g., Miller et al., 2011; Ellis and Bjorklund, 2012; Simpson et al., 2012; Piff, 2014). However, the threeitem measure was validated with samples of undergraduates that, for many reasons, are quite different from the samples of males utilized in paternal psychobiology. In addition, the dependent variables that appear in this body of work often focus on calibrational effects of life history strategy on cognitive tasks, not neuroendocrine outcomes. This convenient self-report measure may not be an optimal method to detect relationships between early environment and the resulting neurobiological embededness of that experience. 4.1. Limitations This sample is, comparatively speaking (Grebe et al., 2019), a large sample to explore these effects longitudinally. Prior work similar in design to the current study has uncovered meaningful insights from smaller samples of North American males (i.e., 31 fathers in Storey et al., 2000; 29 fathers in Edelstein et al., 2015) as well as larger samples (Kuo et al., 2018). The sample size in the current study provided an opportunity to increase our ability to detect modest changes in T during the transition to fatherhood. Nonetheless, we were limited in our ability to test more complicated growth models (e.g., including subjective and objective predictors in one model) that included additional variables that are highly relevant to theory. While we are among the few studies to capture T at time points both prior to childbirth and up to first 10 months of the postnatal period, the timing of our prenatal T collection was not optimal. The average prenatal T sample in the current study was collected roughly one month prior to the arrival of offspring. Using a smaller samples of U.S. males, Edelstein et al., (2015) and Saxbe et al. (2017) collected T throughout the prenatal period (up to gestational age week 36). These studies do not include a postnatal sample of male T, but it is clear that T declines across a partner's pregnancy. If our own prenatal sample was further from birth (i.e., gestational age weeks 20–28), we may have been in a better position to detect larger pre-to-post birth T differences than the modest ones we measured here. In addition, since T is also modulated through exposure to one's partner across the pre and postnatal periods 9

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sample or (2) the current demographics of this sample conceals a widerthan-expected range of childhood experiences that cannot be detected without objective measures of childhood SES. In either case, we are only left to speculate. We advocate that future research include an assortment of objective measures of childhood conditions such as childhood SES (e.g., fathers' own parent(s) education as a proxy measure of family socioeconomic status; Greenfield and Moorman, 2018).

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5. Conclusion In the current study, T declined from the pre to postnatal period as would be predicted by extant literature. This longitudinal study featured a large sample of first-time fathers in the U.S. Novel to this research is the presence of a T rebound only 10-months removed from childbirth. We predicted that we would find a relationship between one's early environment and the T response to first-time fatherhood. While we found only partial support for our predictions, we do hope that we were able to stimulate further discussion on how the early environment might have enduring influence on how males negotiate the mating-parenting tradeoff and what neuroendocrine mechanisms facilitate that relationship. The influences on male endocrine calibration are numerous. Progress will continue from additional (short-term and long-term) longitudinal research across cultures, an increase in sample size and diversity, and the inclusion of well-validated measures of childhood environmental conditions. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.yhbeh.2020.104684. References Alvergne, A., Faurie, C., Raymond, M., 2009. Variation in testosterone levels and male reproductive effort: insight from a polygynous human population. Horm. Behav. 56 (5), 491–497. Bauer, D.J., Curran, P.J., 2003. Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. Psychol. Methods 8 (3), 338. Belsky, J., Schlomer, G.L., Ellis, B.J., 2012. Beyond cumulative risk: distinguishing harshness and unpredictability as determinants of parenting and early life history strategy. Dev. Psychol. 48 (3), 662. Berg, S.J., Wynne-Edwards, K.E., 2001. Changes in testosterone, cortisol, and estradiol levels in men becoming fathers. In: Mayo Clinic Proceedings. Vol. 76, No. 6. Elsevier, pp. 582–592. Blunch, N., 2012. Introduction to Structural Equation Modeling Using IBM SPSS Statistics and AMOS. Sage. Bollen, K.A., 1990. Overall fit in covariance structure models: two types of sample size effects. Psychol. Bull. 107 (2), 256. Booth, A., Dabbs Jr., J.M., 1993. Testosterone and men's marriages. Soc. Forces 72 (2), 463–477. Brumbach, B.H., Figueredo, A.J., Ellis, B.J., 2009. Effects of harsh and unpredictable environments in adolescence on development of life history strategies. Hum. Nat. 20 (1), 25–51. Burnham, T.C., Chapman, J.F., Gray, P.B., McIntyre, M.H., Lipson, S.F., Ellison, P.T., 2003. Men in committed, romantic relationships have lower testosterone. Horm. Behav. 44 (2), 119–122. Chisholm, J.S., Quinlivan, J.A., Petersen, R.W., Coall, D.A., 2005. Early stress predicts age at menarche and first birth, adult attachment, and expected lifespan. Hum. Nat. 16 (3), 233–265. Coall, D.A., Tickner, M., McAllister, L.S., Sheppard, P., 2016. Developmental influences on fertility decisions by women: an evolutionary perspective. Philos. Trans. R. Soc., B Biol. Sci. 371 (1692), 20150146. Dabbs, J.M., 1990. Salivary testosterone measurements: reliability across hours, days, and weeks. Physiol. Behav. 48 (1), 83–86. Duncan, T.E., Duncan, S.C., 2009. The ABC's of LGM: an introductory guide to latent variable growth curve modeling. Soc. Personal. Psychol. Compass 3 (6), 979–991. Edelstein, R.S., Wardecker, B.M., Chopik, W.J., Moors, A.C., Shipman, E.L., Lin, N.J., 2015. Prenatal hormones in first-time expectant parents: longitudinal changes and within-couple correlations. Am. J. Hum. Biol. 27 (3), 317–325. Edelstein, R.S., van Anders, S.M., Chopik, W.J., Goldey, K.L., Wardecker, BM., 2014. Dyadic associations between testosterone and relationship quality in couples. Horm Behav. 65, 401–407. Ellis, B.J., Bjorklund, D.F., 2012. Beyond mental health: an evolutionary analysis of development under risky and supportive environmental conditions: an introduction to

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