Abstract
Objective:
To assess the association between allostatic load, as an estimate of chronic stress, and adverse pregnancy outcomes.
Methods:
This was a secondary analysis of the Nulliparous Pregnancy Outcomes Study Monitoring-to-be (nuMoM2b) study, a prospective observational cohort study. Our primary exposure was dichotomous high allostatic load in the first trimester, defined as four or more out of 12 biomarkers in the “worst” quartile. The primary outcome was a composite adverse pregnancy outcome: hypertensive disorders of pregnancy (HDP), preterm birth, small for gestational age (SGA) neonate, and stillbirth. Secondary outcomes included components of the composite. Multivariable logistic regression was used to test the association between high allostatic load and adverse pregnancy outcomes, adjusted for potential confounders. Mediation and moderation analyses were conducted to assess the role of allostatic load along the causal pathway between racial disparities and adverse pregnancy outcomes.
Results:
Among 4,266 individuals, 34.7% had a high allostatic load. Composite adverse pregnancy outcome occurred in 1,171 (27.5%): 14.0% HDP, 8.6% preterm birth (48.0% spontaneous and 52.2% indicated), 11.0% SGA, and 0.3% stillbirth. After adjustment for maternal age, gravidity, smoking, bleeding in the first trimester, and health insurance, high allostatic load was significantly associated with composite adverse pregnancy outcome (aOR 1.5, 95% CI: 1.3, 1.7) and HDP (2.5, 2.0–2.9), but not preterm birth and SGA. High allostatic load partially mediated the association between self-reported race and adverse pregnancy outcomes. The association between allostatic load and HDP differed by self-reported race, but not for composite adverse pregnancy outcome, preterm birth, and SGA.
Conclusion:
High allostatic load in the first trimester is associated with adverse pregnancy outcomes, particularly HDP. Allostatic load was a partial mediator between race and adverse pregnancy outcomes. The association between allostatic load and HDP differed by self-reported race.
Precis:
In a prospectively evaluated nulliparous cohort, allostatic load in early pregnancy is associated with adverse pregnancy outcomes including hypertensive disorders of pregnancy.
INTRODUCTION:
Adverse pregnancy outcomes, such as preterm birth, stillbirth, small for gestational age (SGA), and hypertensive disorders of pregnancy (HDP), occur in approximately a fourth of pregnancies [1–7]. Taken together, these adverse pregnancy outcomes are the leading causes of pregnancy morbidity and mortality [8–11].
The pathophysiology leading to these adverse pregnancy outcomes remains uncertain. One contributing factor may be cumulative stress [12, 13]. Chronic stress exposure or significant wear and tear on the body’s adaptive system, also referred to as weathering, can be estimated with allostatic load and may influence adverse pregnancy outcomes [13, 14]. It has been suggested in prior studies that weathering mediated by chronic stress due to, for example, racism, food or housing insecurity, and or unemployment may contribute to the racial and ethnic disparities observed in pregnancy outcomes [14]. This hypothesis is attractive, since chronic stress is a potentially modifiable risk factor for adverse pregnancy outcomes. However, few data are available regarding the relationship between allostatic load and adverse pregnancy outcomes. Thus, we aimed to assess the relationship between allostatic load and adverse pregnancy outcomes in a large, diverse longitudinal cohort of pregnant individuals. We hypothesize that high allostatic load is associated with higher odds of adverse pregnancy outcomes. Second, we hypothesize that accounting for high allostatic load will explain a significant portion of the racial and ethnic disparities in adverse pregnancy outcomes.
METHODS:
The Nulliparous Pregnancy Outcomes Study: Monitoring mothers-to-be (nuMoM2b) was a prospective cohort study in which 10,038 nulliparous individuals with singleton pregnancies were enrolled between October 2010 and September 2013 from 8 clinical and academic sites. Individuals were eligible for enrollment if they were nulliparous (no prior delivery at 20 weeks or later gestational age), had a viable singleton gestation, had an estimated gestational age of pregnancy between 60–136 weeks, and intended to deliver at a participating clinical site. Individuals were not eligible if they were less than 13 years of age, had three or more prior pregnancy losses, planned to terminate their pregnancy, had any fetal malformations or aneuploidy at the time of enrollment, or had a donor oocyte pregnancy, or were unable to provide informed consent. The study protocol included three study visits during pregnancy and a final visit at the time of delivery. Maternal characteristics were ascertained from baseline clinical assessments, medical record abstraction, and standardized questionnaires by trained chart abstractors. Details of the study procedures have been described elsewhere [15]. The follow-up study, NuMoM2b-Heart Health (HHS), included 4,508 NuMoM2b participants having in-person evaluations 2 to 7 years after completing the nuMoM2b parent study. Biomarker assessment from NuMoM2b early pregnancy samples were performed only for individuals who also participated in HHS. Further data details on the HHS methods have been described elsewhere [16].
This study was a secondary analysis of the NuMoM2b cohort. Individuals were included in this analysis if they had biomarkers assessed in serum from 6w0d to 13w6d weeks’ gestation, had not withdrawn from nuMoM2b, had pregnancy outcome data available, agreed to be contacted 2–5 years postpartum, and participated in nuMoM2b-HHS (Figure 1). We excluded individuals who experienced fetal demise < 20 weeks, had a termination of pregnancy, ambiguous or missing allostatic load biomarker measurements, and those who had missing adverse pregnancy outcome (preterm birth, stillbirth, SGA, and HDP). While our inclusion criteria included participation in the HHS study, the subset of NuMoM2b for whom biomarkers are available, this analysis consists of data solely from the NuMoM2b index pregnancy. The study was approved by each institutional review board and all participants gave written informed consent.
Figure 1.
Flow diagram of the study participants included in the analysis. nuMoM2b, Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be; HHS, Heart Health Study.
Allostatic load biomarkers were assessed from serum and urine collected between 6 and 14 weeks of gestation. Creatinine and albumin were assessed from urine, and all others from serum samples were stored at −80 °C at a central core biorepository. Assays were completed in batches at the HHS core laboratory (Lundquist Institute, Torrance, CA) using standard protocols on a Beckman AU480.
We used the definition of the NHANES’s allostatic load, [17, 18] commonly used risk biomarkers focused on health disparities including: serum ((systolic blood pressure (SBP), diastolic blood pressure (DBP) (mmHg), cholesterol (mg/dL), low-density lipoprotein (LDL) (mg/dL), high-density lipoprotein (HDL) (mg/dL), high sensitivity C-reactive protein (hsCRP) (mg/dL), body mass index (BMI) (kg/m2)), urine creatinine (mg/dL), urine albumin (mg/dL) and added 3 additional serum biomarkers (triglycerides (mg/dL), insulin (ulU/mL), and glucose (mg/dL)) measured in the 1st trimester of pregnancy [19]. These biomarkers contribute to or indicate organ and tissue damage within the following physiological pathways: cardiovascular, inflammation, metabolic, and immune [20, 21, and 12]. Numerous variations and definitions of allostatic load have been used and one is not clearly superior to another [18]. As a secondary analysis, we used biomarkers that were available in our dataset and had been used in other definitions of allostatic load.
Similar to prior literature, a high allostatic load score was defined as the ‘worst’ quartile of each biomarker, lowest for HDL and albumin and highest for the rest [22]. For each biomarker, values in the worst quartile (high risk), received a value score of “1” [23, 17]. Values not in the worst quartile were characterized as “low risk” and given a value score of “0” [23, 17]. The allostatic load scores for each biomarker were summed to compute an allostatic load index ranging from 0 to 12. Low allostatic load was reported as an index of < 4, and high allostatic load was an index of ≥ 4, because previous data found this threshold to be discriminatory [24]. Allostatic load was included in the analysis as a dichotomous variable (high vs. low).
Adverse pregnancy outcomes were standard, previously reported in detail, and included: preterm birth (spontaneous and iatrogenic), defined as delivery between 20 weeks and 37 weeks of gestational age; HDP (eclampsia, preeclampsia (superimposed, severe, or mild), or new-onset antepartum HTN), SGA < 10th percentile using the Alexander birthweight norms, and stillbirth. The primary outcome was a composite adverse pregnancy outcome defined by any of preterm birth, HDP, SGA, and stillbirth. Details regarding the definitions of adverse pregnancy outcomes, including adjudication of cases of HDP are available [15, 16].
Select risk factors for adverse pregnancy outcomes were identified a priori based on previous literature, including maternal demographics, obstetric, medical history, clinical features of pregnancy, and health behaviors. These included maternal age >=35 vs. <35 years old), \ education (some college or less vs. college), gravidity, any prior miscarriages, previous bleeding in the first trimester, previous abdominal surgery, ever smoking, ever alcohol use, federal poverty level (200% or less vs. more), and health insurance status (government vs. others). Non-Hispanic black race has been associated with chronic stress and allostatic load [31, 43]. Thus, although race is a social construct, we evaluated it as a proxy for social experience, systematic, racism, and other unmeasured social determinants of health that potentially manifest through chronic stress. People of self-reported non-Hispanic Black race were compared to people of races or ethnicities, including non-Hispanic White, Hispanic, Asian, Native American, Native Hawaiian, Multiracial, and other racial backgrounds. It was not possible to analyze some groups separately due to small numbers.
Differences in risk factors associated with adverse pregnancy outcomes were tested using Chi-square and Fisher’s exact test. Our primary analysis assessed the association of high allostatic load and composite adverse pregnancy outcome. As secondary outcomes, we evaluated each adverse pregnancy outcome as an individual outcome in a separate model. Unadjusted odds ratios (ORs) and 95% confidence interval (CIs) for the association of high allostatic load with composite adverse pregnancy outcome were calculated from bi-variable logistic regression models. For multivariable modeling of primary and secondary outcomes, maternal age, smoking status, gravidity, bleeding in the first trimester, and health insurance status were chosen either a priori based on reported associations [25–28, 14] or included due to a between-groups difference with P-value <0.10. As an exploratory analysis, we modeled each allostatic load component with each component of the adverse pregnancy outcome composite to determine which allostatic load components were most associated with each adverse pregnancy outcome; this yielded 48 multivariable comparisons, in which adjustments for multiple comparisons were not made because these analyses were interpreted as exploratory.
We conducted a four-step mediation analysis to test whether allostatic load contributes to racial disparities in adverse pregnancy outcomes, specifically for individuals of the non-Hispanic Black race compared to individuals of all Non-Hispanic White, Hispanic, Asian, Native American, Native Hawaiian, multiracial and additional racial backgrounds. We first examined the association between maternal race and adverse pregnancy outcomes (path c, Figure 2). [29, 30, 44]. Second, we examined the association between maternal race and allostatic load (path a, Figure 2). [29, 30, 44]. Third, we examined the association between allostatic load and adverse pregnancy outcomes (path b, Figure 2). In the final step, we assessed whether the race-adverse pregnancy outcomes relationship was mediated by allostatic load (path c’, Figure 2). [29, 30, 44]. We conducted a sensitivity analysis to examine effect modification by race between allostatic load and adverse pregnancy outcome, limiting the analytical population to individuals of non-Hispanic Black and non-Hispanic White race and ethnicity.
Figure 2.
Causal diagram. Step 1 (path c): effect of maternal race on adverse pregnancy outcomes (race predicting adverse pregnancy outcomes). Step 2 (path a): effect of maternal race on high allostatic load (race predicting high allostatic load). Step 3 (path b and c’): effect of maternal race on adverse pregnancy outcomes mediated by or adjusted for high allostatic load (race and high allostatic load predicting adverse pregnancy outcomes).
We tested whether race moderates the relationship between allostatic load and our primary and secondary outcomes. In unadjusted and adjusted models, we tested for an interaction between race (non-Hispanic Black vs. Non-Hispanic White, Hispanic, Asian, Native American, and Native Hawaiian, multiracial and additional racial backgrounds) and allostatic load. A significant interaction would demonstrate a difference in the association between allostatic load and adverse pregnancy outcomes for individuals of non-Hispanic Black race versus people of Non-Hispanic White, Hispanic, Asian, Native American, Native Hawaiian, multiracial and additional racial backgrounds (i.e., moderation). We conducted a sensitivity analysis of the moderation analysis, limiting the analytical population to individuals of non-Hispanic Black and non-Hispanic White race and ethnicity.
Data analyses were conducted using SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). All tests were performed at a significance level of p < 0.05, and all single degrees of freedom tests were 2-sided.
RESULTS:
The primary analysis included 4,266 pregnant individuals (Figure 1). High allostatic load, 4 or more biomarkers in the worst quartile (Appendix 1, available online at http://links.lww.com/xxx), was identified in 35.0% (n=1,480) of individuals. Baseline characteristics between individuals with high allostatic load and low allostatic load are described in Table 1. Maternal age, race and or ethnicity, education, gravidity, smoking status, prior miscarriage, prior bleeding in the first trimester, and poverty were associated with high allostatic load.
Table 1.
Demographic and clinical characteristics between individuals with high allostatic and low allostatic loads.
| Variables | HH-Study N (%) N = 4,266 | High Allostatic Load ≥ 4 N (%) N = 1,480 |
Low Allostatic Load < 4 N (%) N = 2,786 |
P-value |
|---|---|---|---|---|
| Maternal Age >= 35 | 409 (9.6) | 163 (11.0) | 246 (8.8) | 0.021 |
| Self-reported race (Non-Hispanic Black) | 566 (13.3) | 233 (15.7) | 333 (12.0) | 0.001 |
| Education (Some college or less) | 1625 (38.1) | 651 (44.0) | 974 (35.0) | <0.001 |
| Gravidity >= 3 | 268 (6.3) | 109 (7.4) | 159 (5.7) | 0.033 |
| Smoking | 682 (16.0) | 272 (18.4) | 410 (14.7) | 0.002 |
| Alcohol Use | 2662 (62.4) | 895 (60.5) | 1767 (63.4) | 0.060 |
| Prior Miscarriage | 640 (15.0) | 251 (16.7) | 389 (14.0) | 0.009 |
| Prior Abdominal Surgery | 423 (9.9) | 158 (10.7) | 265 (9.5) | 0.226 |
| Prior Bleeding in First Trimester | 294 (6.9) | 124 (8.4) | 170 (6.1) | 0.005 |
| ≤ 200% of the federal poverty level | 1086 (30.8) | 402 (33.0) | 684 (29.6) | 0.034 |
| Government Health Insurance | 1163 (27.4) | 420 (28.6) | 743 (26.8) | 0.220 |
Data are expressed as binary variables and as n (column %)
Adverse pregnancy outcomes were identified in 27.5 % (n=1,171) of individuals; 586 (14.0%) HDP, 368 (8.6%) preterm birth (48.0% spontaneous and 52.2% medically indicated), 449 (11.0%) SGA, and 12 (0.3%) of stillbirth. 967 individuals had one adverse pregnancy outcome, 165 had two adverse pregnancy outcomes, 38 had three, and one had all four. Baseline demographic characteristics of individuals with and without adverse pregnancy outcomes are presented in Appendix 2, available online at http://links.lww.com/xxx. Maternal age, race and ethnicity (non-Hispanic Black versus Non-Hispanic White, Hispanic, Asian, Native American, Native Hawaiian, multiracial and additional racial backgrounds”), education, smoking status, poverty, and government health insurance were significantly associated with adverse pregnancy outcomes. In contrast, other maternal characteristics were not.
High allostatic load was significantly associated with composite adverse pregnancy outcome (OR= 1.5; 95% CI: 1.3–1.8). After adjustment for maternal age, gravidity, smoking status, bleeding at the first trimester, and government health insurance status, allostatic load remained significantly associated with composite adverse pregnancy outcome (aOR= 1.5; 95% CI: 1.3–1.7) (Table 2). High allostatic load was significantly associated with secondary outcome HDP (OR= 2.5; 95% CI: 2.0–2.9) but not preterm birth, SGA or stillbirth. After adjusting for the same variables as the primary analysis, HDP remained significantly associated with high allostatic load (aOR= 2.5; 95% CI: 2.0–2.9) but not preterm birth, SGA or stillbirth (Table 2). In exploratory analyses, the individual components of allostatic load that were significantly associated with the composite adverse pregnancy outcome were CRP, BMI, DBP, SBP, HDL, and insulin. These individual components, along with triglycerides and glucose, were significantly associated with HDP. BMI, DBP, and SBP were significantly associated with preterm birth. (Appendix 3, available online at http://links.lww.com/xxx).
Table 2:
Adjusted logistic regression estimating the association between adverse pregnancy outcomes and allostatic load.
| High Allostatic Load > 4 (n = 1,480) n (%) |
Low Allostatic Load < 4 (n = 2,786) n (%) |
Odds Ratio (95% Confidence Interval) | ||
|---|---|---|---|---|
| Adverse Pregnancy Outcomes | Unadjusted | Adjusted | ||
| Composite Outcomes | 492 (33.2) | 679 (24.4) | 1.5 (1.3, 1.8) | 1.5 (1.3, 1.7) |
| HDP | 314 (21.2) | 272 (9.8) | 2.5 (2.0, 2.9) | 2.5 (2.0, 2.9) |
| preterm birth | 159 (10.1) | 218 (7.8) | 1.3 (1.0, 1.7) | 1.2 (1.0, 1.5) |
| SGA | 140 (9.5) | 309 (11.1) | 0.9 (0.6, 1.4) | 0.8 (0.6, 1.0) |
| Stillbirth | 7 (0.5) | 5(0.2) | 2.6 (0.8, 8.3) | ** |
Abbreviations: cAPO, composite adverse pregnancy outcome; SGA, small for gestational Age; preterm birth, preterm birth; HDP, hypertensive disorders included: eclampsia, superimposed preeclampsia, severe preeclampsia, mild preeclampsia, and new-onset antepartum hypertension. Data are expressed as n(column %) or Odds Ratio and 95% CI.
Odds ratios and CIs were reported using logistic regression, adjusting for maternal age, gravida, smoking status, bleeding in the first trimester, and health insurance status.
In the absence of any estimates, the prevalence was too low to estimate.
Bold font indicates significant differences.
High allostatic load was a partial mediator in the association between race or ethnicity and composite adverse pregnancy outcome and HDP, but not preterm birth or SGA (Table 3). In greater detail, first, we established a significant association between race and the primary and each secondary outcomes (path c, Table 3). The association between race and high allostatic load was significant (path a, Table 3). The association between allostatic load and the primary outcome was significant, as was the association between allostatic load and HDP, but not preterm birth or SGA (path b, Table 3). Finally, when assessing the association between race and each endpoint in a model including allostatic load, the association was slightly smaller in magnitude for the primary endpoint, HDP (path c’, Table 3). As such, high allostatic load was demonstrated to be a partial mediator in the association between race and primary adverse pregnancy outcome composite and HDP, but not preterm birth or SGA (Table 3). In a sensitivity mediation analysis where we restricted the population to include people of non-Hispanic Black and non-Hispanic White race and ethnicities, results were similar in significance and interpretation, and the partial mediation was slightly smaller in magnitude for adverse pregnancy outcome; (Appendix 4, available online at http://links.lww.com/xxx).
Table 3:
Mediation analysis assessing allostatic load as a mediator of the association between self-reported race and adverse perinatal outcomes.
| Outcome | Total Effect: Step 1 (path c) |
Step 2 (path a) |
Step 3 (path b) |
Direct Effect: Step 4 (path c’) |
|---|---|---|---|---|
| 1. cAPO | 1.8 (1.5–2.1) | 1.4 (1.1–1.6) | 1.5 (1.3–1.7) | 1.7 (1.4–2.1) |
| 2. HDP | 1.5 (1.2–1.9) | 2.5 (2.0–2.9) | 1.4 (1.1–1.8) | |
| 3. preterm birth | 1.6 (1.2–2.1) | 1.3 (1.0–1.6) | 1.5 (1.2–2.0) | |
| 4. SGA | 2.1 (1.7–2.7) | 0.8 (0.7–1.0) | 2.1 (1.7–2.8) |
Abbreviations: cAPO, composite adverse pregnancy outcome; HDP,hypertensive disorders included: eclampsia, superimposed preeclampsia, severe preeclampsia, mild preeclampsia, and new-onset antepartum hypertension; preterm birth, preterm birth; SGA, Small for Gestational Age. Data are expressed as Odds Ratio and 95% CI.
Bold font indicates significant differences.
We tested whether race moderates the relationship between the composite adverse pregnancy outcome, HDP, preterm birth, and SGA by including an interaction term between race and allostatic load in a model of each endpoint. For people of non-Hispanic Black compared to Non-Hispanic White, Hispanic, Asian, Native American, Native Hawaiian, multiracial and additional racial backgrounds, the association of allostatic load and the composite adverse pregnancy outcome, preterm birth, or SGA were not significantly different; however, the association between allostatic load and HDP differed considerably by race (p=0.02) (Table 4). Thus, race was a significant moderator of the association between allostatic load and HDP, with the association between allostatic load and HDP significantly smaller in magnitude for people of non-Hispanic Black compared to individuals of Non-Hispanic White, Hispanic, Asian, Native American, Native Hawaiian, multiracial and additional racial backgrounds(Table 4). In a sensitivity moderation analysis where we restricted the population to include only people of non-Hispanic Black and non-Hispanic White race and ethnicities, results were similar in magnitude, significance, and interpretation (Appendix 5, available online at http://links.lww.com/xxx).
Table 4:
Moderation of self-reported race in the association between adverse pregnancy outcomes and high allostatic load (≥ 4), unadjusted and adjusted logistic regression model odds ratios
| Outcome | Non-Hispanic Black N = 566 | Non-Hispanic White and additional race and ethnicities N = 3,700 | Interaction P-value |
|---|---|---|---|
| Composite adverse outcome | 246 (43.5) | 955 (25.8) | |
| uOR (95%CI) | 1.4 (1.0–2.0) | 1.5 (1.3–1.8) | 0.640 |
| aOR (95%CI) | 1.3 (0.9–1.9) | 1.5 (1.3–1.8) | 0.511 |
| Hypertensive disorders of pregnancy | 104 (18.4) | 482 (13.0) | |
| uOR (95%CI) | 1.6 (1.0–2.4) | 2.7 (2.2–3.3) | 0.020 |
| aOR (95%CI) | 1.5 (1.0–2.4) | 2.7 (2.2–3.2) | 0.020 |
| Preterm birth | 69 (12.2) | 299 (8.1) | |
| uOR (95%CI) | 1.4 (0.8–2.3) | 1.3 (1.0–1.6) | 0.855 |
| aOR (95%CI) | 1.4 (0.8–2.3) | 1.2 (0.9–1.5) | 0.680 |
| Small for Gestational age | 102 (18.0) | 347(9.4) | |
| uOR (95%CI) | 0.9 (0.6–1.4) | 0.8 (0.6–1.0) | 0.554 |
| aOR (95%CI) | 0.8 (0.5–1.3) | 0.8 (0.6–1.0) | 0.729 |
| Stillbirth | 2 (0.4) | 10 (0.27) |
Data are expressed as binary variables and as n (column %); n = proportion of individuals with outcomes in each racial category.
Moderation not estimated in models for stillbirth in the presence of low frequency of outcomes. uOR, unadjusted odds ratios; aOR, adjusted odds ratio; CI, confidence interval;
Adjusted Odds ratios and CIs were reported using unconditional logistic regression after adjusting for maternal age, education level, gravida, smoking status, bleeding at first trimester, health insurance status.
DISCUSSION:
In our study, high allostatic load during early pregnancy was associated with 50% greater odds for adverse pregnancy outcomes compared to low allostatic load. This association was most pronounced with HDP. The components of allostatic load most strongly associated with adverse pregnancy outcomes were BMI, SBP, DBP, triglycerides, insulin, and hsCRP. These biomarkers involve the endocrine, inflammatory, cardiovascular, and metabolic systems that may shift in response to dysregulated stress response and potentially lead to adverse pregnancy outcomes [12, 20]. Other smaller studies examining the relationship between allostatic load and adverse pregnancy outcomes reported mixed results [31, 32, 17, and 23]. However, our findings support growing evidence that cumulative stress is associated with adverse pregnancy outcomes [32, 17, and 21].
Due to a lack of reliable biomarkers, physiological stress has been hard to measure. Different aspects of stress can be reliably measured using questionnaires and instruments during pregnancy [33]. However, such measures only capture a “snapshot” in time. Moreover, serial assessment is difficult and expensive to accomplish. Consequently, studies have not consistently measured a relationship between self-reported psychosocial burden and adverse pregnancy outcomes [33, 34]. It is noteworthy that psychosocial stress measured by self-reported instruments was not associated with adverse pregnancy outcomes in the overall NuMom2b cohort and psychosocial stress did not influence or mediate the link between non-Hispanic Black race and adverse pregnancy outcomes [35]. Further, acknowledging that each component of allostatic load could convey stress and other potential physiologic pathways over the life course [13, 36], makes components of allostatic load attractive as biomarkers because they include widely available, well-characterized, inexpensive measures and assays.
Substantial racial disparities in birth outcomes persist in the US, with higher rates of adverse pregnancy outcome and mortality for non-Hispanic Black individuals [37–43, 25]. Allostatic load is a potential pathway to explain some disparities because stressors due to structural racism, such as poverty, educational disadvantage, and perceived stress are more likely to be experienced in Black individuals [43]. Indeed, allostatic load partially mediated the relationship between race and adverse pregnancy outcomes. Race did not significantly moderate the relationship between allostatic load and composite adverse pregnancy outcome, preterm birth, or SGA. However race was a moderator between allostatic load and HDP; this association was significantly different in magnitude for people of non-Hispanic Black than Non-Hispanic White, Hispanic, Asian, Native American, Native Hawaiian, multiracial and additional racial backgrounds(Table 4), and as well between people of non-Hispanic Black and non-Hispanic White race (Appendix 5, http://links.lww.com/xxx). This “moderation” may be due to social factors that accumulate based on racism that have a complex interplay in the relationship of race, allostatic load, and adverse pregnancy outcomes.
Our study had several limitations. We were limited in our ability to draw robust conclusions regarding stillbirth due to the small numbers of occurrences. Also, we only evaluated allostatic load during the first trimester. Thus, we could not assess the relationships between allostatic load later in pregnancy or trends in allostatic load most proximal to adverse pregnancy outcomes. In addition, we used the 10-factor allostatic load index. We did not assess additional metabolic and cardiovascular indicators that make up a broader allostatic load index, which may be more robust for evaluating allostatic load and adverse pregnancy outcomes [43]. Further work is needed to assess the optimal components for determining allostatic load. Our cohort included only nulliparous individuals with access to tertiary care centers and, therefore, may not be generalizable to other populations. Finally, we were unable to individually assess several self-reported racial and ethnic groups due to small numbers.
A major strength of this study was using a large, well-characterized prospective cohort with standardized data collection by trained research personnel, thereby limiting bias and enhancing accuracy. Another strength is that each allostatic load biomarker was weighted equally, which was a scientifically sound approach because evidence suggests no significant difference between empirical and clinical cut-off assessments [44–47], and assessing allostatic load in the first trimester allows for early intervention. Also, our outcomes were well-documented, used standard and rigorous criteria, and had physician adjudication of uncertain cases or adverse pregnancy outcomes [15]. Lastly, our population was geographically, racially, and ethnically diverse.
Our study suggests that high allostatic load in early pregnancy is associated with subsequent adverse pregnancy outcomes, particularly HDP. Allostatic load was also a partial mediator between race and adverse pregnancy outcomes. These findings support evaluating components of allostatic load and chronic stress as potentially modifiable risk factors to improve pregnancy outcomes.
Supplementary Material
Acknowledgments
This study is supported by cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development: 32 U10-HL119991; U10-HL119989; U10-HL120034; U10-HL119990; U10-HL120006; U10-HL119992;U10-HL120019; U10-HL119993; and U10-HL120018. U10 HD063036, RTI International; U10 HD063072, Case Western Reserve University; U10 HD063047, Columbia University; U10 HD063037, Indiana University; U10 HD063041, University of Pittsburgh; U10 HD063020, Northwestern University; U10 HD063046, University of California Irvine; U10 HD063048, University of Pennsylvania; and U10 HD063053, University of Utah. In addition, support was provided by respective Clinical and Translational Science Institutes to Indiana University (UL1TR001108) and University of California Irvine (UL1TR000153).
Footnotes
Presented at the Society for Maternal Fetal Medicine 2022 annual meeting, February 3, 2022, Orlando, Florida.
Financial Disclosure
The authors did not report any potential conflicts of interest.
Each author has confirmed compliance with the journal’s requirements for authorship.
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