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Published in final edited form as: Psychoneuroendocrinology. 2024 Jul 2;168:107120. doi: 10.1016/j.psyneuen.2024.107120

Salivary Uric Acid Dynamics are Associated with Stress Response Hormones among African Americans in an Urban Sample

Stefan M M Goetz a, Todd Lucas a,*, Douglas A Granger b,c,d
PMCID: PMC11317218  NIHMSID: NIHMS2010380  PMID: 39002453

Abstract

Acute physiological responses to psychosocial stressors are a potential pathway underlying racial disparities in stress-related illnesses. Uric acid (UA) is a potent antioxidant that has been linked to disparities in stress-related illnesses, and recent research has shown that UA is responsive to acute social stress. However, an examination of the relationships between the purinergic system and other commonly measured stress systems is lacking. Here, we measure and characterize associations of salivary uric acid (sUA) with markers of hypothalamic-pituitary-adrenal (HPA) axis activation, sympathetic-adreno-medullar (SAM) axis activation, and acute inflammation. A sample of 103 healthy African Americans (33 male, 70 female) completed the Trier Social Stress Test to induce social-evaluative threat. Passive drool collected before, during, and after the stressor task provided salivary reactivity measures of UA (sUA), cortisol, dehydroepiandrosterone sulfate (DHEAS), salivary alpha amylase (sAA – a surrogate marker of SAM activity) and C-reactive protein (sCRP). Multiple regressions revealed that total activation of cortisol, DHEAS, and sCRP were each associated with higher total activation of sUA. Additionally, DHEAS reactivity was associated with sUA reactivity. Relationships between HPA-axis markers and sUA were especially observed among younger and male participants. Overall, findings suggest potential coordination of stress systems with sUA in response to acute stress, which may further the contributions of biological stress processes to racial health disparities.

Keywords: Stress Reactivity, Uric Acid, Trier Social Stress Test, Health Disparities, Purinergic Stress Response, Multisystem Stress

1. Introduction

African Americans are disproportionately burdened by a broad range of stress-related illnesses (Carnethon et al., 2017). Notably, African Americans have persistently higher rates of cardiovascular disease (CVD) and hypertension; diseases whose etiology is linked to dysregulation of stress and inflammatory systems (Virdis et al., 2014). Stress-related health disparities can be largely attributed to long-standing environmental and social injustices, such as disproportionate exposure to pollution, access to healthcare, and discrimination (Copeland, 2005; Williams & Jackson, 2005). Biological responses to acute psychosocial stress are of particular interest (Lovallo, 2015) and may act both in parallel to and as a mechanism linking environmental and social inputs to racial health disparities (Dimsdale, 2008). Acute stress responses also offer a potential target for psychosocial coping interventions, including those that are culturally targeted to address racial health disparities (Lucas et al., 2023).

The acute stress response encompasses multiple environmentally sensitive biological systems, including the hypothalamic-pituitary-adrenal (HPA) axis, the sympathetic-adreno-medullar (SAM) axis, and the immune/inflammatory system. Although studying these systems individually can produce critical understandings into the links between psychosocial factors, biological stress processes, and racial health disparities, a more recent perspective posits that doing so may offers an incomplete picture, and that attending to how these systems are coordinated may be necessary to fully grasp their contribution to stress-related illness (Lucas, Wegner, et al., 2017). In turn, an important direction for stress and health disparities research concerns identifying how hitherto overlooked biological systems may contribute to multisystem stress responses. Here, we consider the potential role of the purinergic system in multisystem stress reactivity. Specifically, the current study examined the role of the purine, uric acid (UA)—which may contribute to racial disparities (Williams, 2012) in CVD (e.g., Lucas et al., 2020), kidney disease (Johnson et al., 2005), and type 2 diabetes mellitus (Nakanishi et al., 2003)—in relation to other more commonly investigated stress analytes covering the HPA, SAM, and acute inflammatory systems.

High concentrations of UA have been linked to both deleterious and salutary outcomes (El Ridi & Tallima, 2017). For example, UA is a potent antioxidant accounting for half of the antioxidant potential of plasma (Ames et al., 1981) and is associated with neural protective effects (Otani et al., 2023). Simultaneously, high concentrations of UA increase the risk of vascular dementia via its role in hypertension and atherosclerosis (Latourte et al., 2018).

Purine receptors are expressed in numerous limbic structures (Burnstock, 2008), and UA has been linked with mood disorders (Ortiz et al., 2015) and hippocampal processing of psychosocial stress (Goodman et al., 2016); the latter finding indicating one potential route by which UA may influence HPA stress responses given the role that the hippocampus plays in regulating acute cortisol responses (e.g., Dedovic et al., 2009). Buoying this possibility, a recent study found that resting UA was positively associated with acute cortisol stress response (Acevedo et al., 2022).

Immunologically, UA is classified as an alarmin (Boraschi, 2014), a diverse group of endogenous molecules that alert the innate immune system to threats. UA acts as a proinflammatory, particularly with respect to amplification of type 2 cytokines (El Ridi & Tallima, 2017), promotes phagocytosis (Sautin & Johnson, 2008), and allergic inflammation (Kool et al., 2011).

Of current interest, recent studies have documented that UA responds to acute psychosocial stress (Acevedo et al., 2022; Lucas et al., 2020; Manowitz et al., 1993). It is unclear whether these changes represent functional adjustments in the face of a stressor, or are mere byproducts of the activity of other stress-response systems (e.g., the breakdown of ATP utilized by the HPA system: Acevedo et al., 2022). Functionally, when the organism is facing a challenge, UA may acutely increases blood pressure by stimulation of vascular smooth muscle cells and through the capacity to activate the renin-angiotensis system (RAS; Watanabe et al., 2002), and in the near term, the effector peptide angiotensin-II, a downstream output of the RAS, may potentiate stress-related endocrine and cardiovascular responses (Correa et al., 2022; de Kloet et al., 2017).For example, in a previous analysis of the present dataset, stress-related total UA activation was positively associated with total activation of systolic and diastolic blood pressure (Lucas et al., 2020).

Other possibilities include the modulatory role of UA in modulating stress-induced inflammatory responses (e.g., Maslanik et al., 2013), regulation of oxidative stress in the context of acute stress (e.g., Waring et al., 2001; Waring et al., 2003), and modulation of stress appraisals (e.g., Goodman et al., 2016; for a review of potential functions of acute UA responses, see Lucas et al., 2020). Examining the associations between UA stress response and more canonical stress response systems may shed light on the potential functions of acute UA response.

While basal UA has been shown to be a risk factor for disease, and in some, cases a causal contributor (e.g., in vascular and renal disease; Kang & Ha, 2014), the contribution of acute UA response to both resting levels and to the etiology of health disparities remains unclear. However, recent evidence indicates that basal UA is associated with stress-induced cortisol reactivity (Acevedo et al., 2022), greater concurrent systolic and diastolic blood pressure stress reactivity (Woerner et al., 2019) and systolic blood pressure measured 18 months later (Mrug et al., 2017). Furthermore, as mentioned above, total UA activation is correlated with total systolic and diastolic blood pressure (Lucas et al., 2020).

In the present exploratory study, we examined the association of a host of stress analytes, representing the HPA, SAM, and inflammatory systems, in relation to basal UA and UA responses in a sample of African Americans who underwent an acute social stressor. We also explored the moderating role of age and sex of these associations given that these systems evince well documented sex and age differences; for instance, basal dehydroepiandrosterone-sulfate (DHEAS) and UA, and cortisol response are higher among males (Kroboth et al., 1999; Liu et al., 2017; Wang & Charchar, 2021) and DHEAS response and concentrations diminish with age (Kroboth et al., 1999). Additionally, differential exposure to stress by age, sex, and their intersection with respect to the type, frequency, and intensity of the stressor (Lewis & Van Dyke, 2018), as well as differences in coping strategies (Drolet & Lucas, 2020) might be reflected in the functioning of the systems under current consideration. Furthermore, past consideration of these data has indicated age and sex differences in the association between UA and cardiovascular measures (Lucas et al., 2020; Woerner et al., 2019). Past research has indicated that UA may be related to HPA hormones, including cortisol (Acevedo et al., 2022), and in the context of diagnosed type 2 diabetes mellitus, basal DHEA (Wan et al., 2020). Additionally, ANS markers (e.g., total blood pressure activation), here indexed by sAA, have been linked to UA (Mrug et al., 2017; Woerner et al., 2019; but see Acevedo et al., 2022), as has CRP (Ruggiero et al., 2006). Here we specifically examined how baseline and acute response measures of salivary cortisol, DHEAS, alpha-amylase (sAA), C-reactive protein (sCRP) are associated with baseline and acute sUA responses.

2. Methods

This study was performed in adjunct to alternate considerations of this data (Lucas et al., 2016), after obtaining the subsequently described measurements of sUA (see also Lucas et al., 2020; Woerner et al., 2019). Procedures for recruiting participants and implementing the stressor task are therefore identical to previous descriptions.

2.1. Participants

Participants for this study were recruited from the Detroit metropolitan community and were required to be 18 years of age or older and identify as African American. Exclusion criteria included taking medications that might interfere with stress physiology or pre-existing medical or psychiatric conditions that would preclude participants from undergoing a minor stress induction. This resulted in a sample of 118 participants, of whom 106 had complete data. Three participants were excluded from the final sample as their sUA levels were below the acceptable threshold. For the analyses incorporating sCRP, an additional participant was removed as their basal sCRP concentration exceeded nine standard deviations from the mean, likely due to the presence of an active infection. The final effective sample therefore included 103 participants (33 male, 70 female)1. Participants’ ages ranged from 18 to 60 (M = 31.41, SD = 13.84). All participants provided informed consent and received modest monetary compensation for a single approximately three-hour session.

2.2. Task Procedures

Participants completed the Trier Social Stress Test (TSST) (Kirschbaum et al., 1993). To minimize the influence of diurnal rhythm, all participants were scheduled for the late morning and early afternoon. Participants were given 10 minutes to acclimate before description of the task and an additional 10 minutes to prepare their speech. The TSST itself was comprised of a five-minute speech and five-minute mental arithmetic task. Participants then completed a battery of questionnaires (Lucas et al., 2016) followed by an hour recovery period during which the participant did not engage in any behavior apart from several saliva collections. This study also included a fully crossed manipulation (i.e., justice condition) implemented ten minutes prior to the fourth salivary collection timepoint, the results of which are considered elsewhere (Lucas et al., 2016; Lucas, Pierce, et al., 2017).

2.3. Saliva Collection and Preparation

Upon arrival, participants drank 2.5 mL of water, and again after each saliva collection. Six 2 mL saliva samples were collected via passive drool. The first was collected after the 10-minute acclimation period. The second, immediately before the TSST; the third, immediately after. Samples four, five, and six, were collected fifteen minutes, thirty minutes, and sixty minutes after the beginning of the recovery period. All samples were stored at −80 °C prior to assay.

2.4. Salivary Measures

Detailed descriptions of all salivary measures are provided elsewhere, where alternate considerations of these data were undertaken (Lucas, Wegner, et al., 2017; Woerner et al., 2019). For parsimony, we provide brief descriptions here. Reactivity of all salivary measures across the TSST are portrayed in Figure 1.

Figure 1.

Figure 1.

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Mean standardized biological response for study participants across time points. Grey portion represents the stress induction period. Sample 1 was collected after a 10-minute acclimation period. Samples 2 and 3 were collected immediately before and after the TSST. Samples 4 through 6 were collected 15 minutes, 30 minutes, and 60 minutes post TSST.

2.4.1. Salivary Uric Acid

Salivary samples were assayed in duplicate using commercially available highly sensitive enzymatic reaction kits designed for saliva (Catalog # 1–3802, Salimetrics, Carlsbad, CA). This kit requires 10 μL as the test volume and has a limit of detection (LLD) of 0.07 mg/dL. The average intra-assay coefficient of variation (CV) was less than 5% and the inter-assay CV was less than 10%. At baseline, participants sUA was found to be 2.68 mg/dL (SD = 1.77 mg/dL), similar to that of healthy individuals (2.25 mg/dL; Vernerová et al., 2021).

2.4.2. Salivary Cortisol

Salivary cortisol was quantified in duplicate using a highly sensitive (LLD of 0.007 μg/dL) enzyme immunoassay (Catalog # 1–3002, Salimetrics, Carlsbad, CA), requiring a test volume of 25 μL per sample. The average intra-assay CV was slightly higher than 5% and the inter-assay CV was less than 10%. We observed an average baseline cortisol value of 0.214 μg/dL (SD = 0.166 μg/dL),

2.4.3. Salivary Dehydroepiandrosterone-sulfate

Saliva samples were assayed in duplicate using a highly sensitive (LLD of 43 pg/mL) enzyme immunoassay (Catalog # 1–2212, Salimetrics, Carlsbad, CA), requiring 100 μL of saliva per determination. The average intra-assay CV was slightly above 5% and the average inter-assay CV was less than 10%. According to Granger et al. (1999), DHEAS enters oral fluid by ultrafiltration and salivary concentrations may therefore be influenced by salivary flow rate. We therefore corrected for salivary flow rate by estimating sample volumes by weight and then dividing by the time it took the participant to produce the sample. We observed an average concentration at baseline of 1420.13 pg/mL (SD = 1324.68 pg/mL).

2.4.4. Salivary Alpha Amylase

Salivary alpha-amylase was quantified using commercially available kinetic reaction assays (Catalog # 1–1902, Salimetrics, Carlsbad, CA), which provided high sensitivity (LLD of 0.4 U/mL). The test volume per determination was 8 μL. Unlike the other analytes, sAA was assayed in singlet, however 30 replicates were assayed to determine intra-assay CV, which was less than 7.5% and the inter-assay CV was less than 6%. Since flow rate may influence sAA (Rohleder et al., 2006), we used flow rate adjusted values. At baseline, we observed an average salivary concentration of 38.80 U/mL (SD = 28.62 U/mL).

2.4.5. Salivary C-Reactive Protein

Salivary CRP was quantified using a commercially available (Catalog # 1–2102, Salimetrics, Carlsbad, CA), highly sensitive immunoassay (LLD of 10 pg/mL). The test volume for determination was 15 μL. The average intra-assay CV was 4% and the inter-assay CV was less than 10%. Baseline concentration of sCRP after four outliers were removed (Z > 3.3), was 3007.04 pg/mL (SD = 2886.94 pg/mL).

2.4. Statistical analysis

Time-point one constituted our baseline measure for each analyte. However, due to the heterogeneity in analyte peak timing, following the recommendations of Miller et al. (2018), reactivity was calculated by selecting the peak reactivity measure post-TSST and subtracting the analyte nadir prior to the TSST. See figure 1 for an overview of the analyte dynamics.

Recovery was calculated by first selecting the peak analyte value post-TSST and subtracting the subsequent analyte minimum. Some participants did not evince a recovery response. Thus, for the regression analyses that examined recovery (described below), the sample sizes were reduced and varied between 51 participants and 62 participants.

We considered associations between sUA and each analyte within each summative measure by conducting a series of three-step hierarchical multiple regressions. Significance was assessed using R2 change and individual regression weights of predictors newly entered at each step. Sex and age were entered as a covariate on step-one, analyte serving as predictor on step-two, and analyte-by-age/sex entered on step-three. Salivary UA served as the criterion variable. All the variables were first mean centered.

We conducted moderation analyses using the reduced model following the recommendation of Hayes (2017, p. 237) via simple slopes analyses using PROCESS version 4.3. We also conducted a regions of significance probe of significant interactions using the Johnson-Neyman approach so as to consider the age at which the analyte affected the measure of sUA (Hayes & Montoya, 2017). Following the recommendation of Miller et al. (2018), the regression for total activation included baseline sUA in step-one.

Given the relatively small samples, extreme values observed in any one participant analyte measures could exert an outsized influence on the effect estimations. We therefore first excluded cases iteratively in which one of the two analytes exceeded 3.3 standard deviations from the analyte’s mean value before conducting each regression. For the regressions involving response analyses, we included a control for the two justice manipulations and their interaction in step-one. See Lucas et al., (2020) for age and sex estimates, omitted here for brevity. Sex was contrast coded (−1 = male, 1 = female).

3. Results

Results of all subsequently presented multiple regressions are summarized in Table 1, while significant interactions are presented in Figure 2. Zero-order correlations between all the respective summative measures and variables can be found in Table S1 in the supplemental material.

Table 1.

Summative Stress Analyte Associations with Summative Uric Acid.

Baseline Total Activation Reactivity Recovery
Age & Sex (N) N = 101 N = 100 N = 101 N = 76
Step 1 Model Δ r 2 .020 .730 *** .049 .211 ***
 Age .073 .148 ** .146 .206 †
 Sex −.115 −.087 −.103 −.138
 Baseline sUA .805 ***
Step 2 Model Δ r 2 .015 .002 .004 .022
 Age x Sex .125 .048 .068 .154
Cortisol (N) N = 100 N = 99 N = 100 N = 61
Step 2 Model Δ r 2 .001 .015 * .009 .006
 Cortisol .029 .131 * .102 .079
Step 3 Model Δ r 2 .003 .014 † .059 * .014
 Age x Analyte −.055 −.103† −.233* .118
 Sex x Analyte .001 −.074 −.191† −.051
DHEAS (N) N = 96 N = 94 N = 97 N = 54
Step 2 Model Δ r 2 <.001 .010 † .085 ** .039
 DHEAS .004 .110 † .300 ** .215
Step 3 Model Δ r 2 .022 .007 .031 .054
 Age x Analyte −.043 −.080 .258 .107
 Sex x Analyte −.149 .025 .094 −.248†
Alpha-Amylase (N) N = 100 N = 96 N = 97 N = 58
Step 2 Model Δ r 2 .012 .006 .006 .001
 Alpha-Amylase −.109 .077 .082 −.035
Step 3 Model Δ r 2 .002 .018 * .031 .030
 Age x Analyte −.038 −.030 −.017 .140
 Sex x Analyte −.030 .146 * .250 † .154
C-Reactive Protein (N) N = 96 N = 94 N = 98 N = 48
Step 2 Model Δ r 2 .001 .009 † .013 .015
 C-Reactive Protein −.031 .097 † .117 −.133
Step 3 Model Δ r 2 .026 .005 .053 † .008
 Age x Analyte .060 −.045 −.223* −.115
 Sex x Analyte .127 .079 .122 −.092
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Notes. Age, sex, and condition were always entered in step 1 of the model but are largely redundant so are first reported in a separate hierarchical regression analysis and omitted in the subsequent analyte regressions.

Coefficients are standardized regression weights.

***

p < .001,

**

p < .01,

*

p < .05,

†

p <.10.

For sex, −1 = male, 1 = female.

Figure 2.

Figure 2.

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Age and sex moderator effects on salivary uric acid. Coefficients are standardized regression weights. ***p < .001, **p < .01, *p < .05.

3.1. Multisystem Associations of Baseline Uric Acid

sUA baseline was unassociated with all the analyte baseline measures (|β’s| < .109, p’s > .280). Additionally, the inclusion of the interaction terms did not explain any additional variance in sUA baseline (ΔR2’s < .026, p’s > .297) nor was any specific interaction term significant (|β’s| < .149, p’s > .158).

3.2. Multisystem Associations of Salivary Uric Acid Total Activation

After controlling for covariates, total activation of sUA was significantly associated with total activation of cortisol (β = .131, p = .022), and marginally associated with total activation of DHEAS (β = .110, p = .068), as well as total activation of sCRP (β = .097, p = .074). The main effect of cortisol was qualified by a trending age x cortisol interaction (ΔR2 = .014, β = −.103, p = .072). Cortisol was positively associated with sUA total activation among younger African Americans (β = .229, t(91) = 3.203, p = .002), but was unrelated among older African Americans (β = .004, t(91) = 0.041, p = .968). The regions of significance probe showed that cortisol total activation x age interaction was positive and significant when age was 30.84 years or younger but not significant above this age. Finally, although the main effect of sAA on sUA was not significant (β = .077, p = .154), there was a significant sex x sAA interaction (ΔR2 = .018, p = .039, β = .146, p = .012). sAA was significantly positively associated with sUA total activation among females (β = .173, t(88) = 2.70, p = .008), but was unrelated among males (β = −.117, t(88) = −1.27, p = .207).

3.3. Multisystem Associations of Salivary Uric Acid Reactivity

sUA reactivity was positively and significantly associated with DHEAS reactivity (β = .300, p = .004). However, there were no significant age or sex moderators for DHEAS reactivity (ΔR2 = .031, p = .201). Main effects of cortisol reactivity and sCRP reactivity on sUA reactivity were likewise not significant (cortisol: ΔR2 = .009, p = .338; sCRP: ΔR2 = .013, p = .261). However, there was a significant age x cortisol reactivity interaction on sUA reactivity (ΔR2 = .059, p = .049, β = −.233, p = .028), and a significant age x sCRP reactivity interaction on sUA reactivity (ΔR2 = .053, p = .072, β = −.223, p = .031). Cortisol reactivity was positively associated with sUA reactivity among younger African Americans (β = .350, t(93) = 2.329, p = .022), but not among older African Americans (β = −.111, t(93) = −0.781, p = .437). The region of significance analysis indicated that cortisol reactivity was positively and significantly associated with sUA reactivity for those aged 23.60 and younger. Similarly, sCRP reactivity was positively associated with sUA reactivity among younger African Americans (β = .336, t(91) = 2.348, p = .021), but not among older African Americans (β = −.062, t(91) = −0.481, p = .632). Here, the region of significance analysis indicated that sCRP reactivity was positively and significantly associated with sUA reactivity for those aged 24.47 and younger.

Additionally, the sex x cortisol reactivity interaction approached significance (β = −.191, p = .072). Cortisol reactivity was positively associated with sUA reactivity among males (β = .317, t(93) = 2.058, p = .042), but not among females (β = −.077, t(93) = −0.532, p = .596). Finally, there were no significant main or interaction effects of sAA reactivity on sUA reactivity, save a trending sex x sAA interaction (β = .250, p = .097); however, sAA reactivity was not significantly associated with sUA reactivity in either males (β = −.351, t(90) = −1.27, p = .207) nor in females (β = .151, t(90) = 1.36, p = .177).

3.4. Multisystem Associations of Salivary Uric Acid Recovery

For sUA recovery, only a trending sex x DHEAS recovery interaction emerged (ΔR2 = .054, p = .187, β = −.248, p = .075). DHEAS recovery was positively associated with sUA recovery among males (β = .615, t(47) = 2.405, p = .020), but not among females (β = .111, t(47) = 0.652, p = .518). There were no significant main or interaction effects on sUA recovery for any cortisol, sCRP, or sAA recovery.

4. Discussion

In this study we investigated the correlates of stress reactivity across the HPA, SAM, and acute inflammatory systems with respect to sUA parameters among healthy African Americans. There has been growing interest in viewing the connection of health disparities linked to psychosocial stress through a multisystem lens (e.g., Lucas, Wegner, et al., 2017) and a growing recognition that sUA may contribute to acute stress responses (Acevedo et al., 2022; Lucas et al., 2020; Mrug et al., 2017; Woerner et al., 2019). Several findings provide novel insights and suggest future directions for multisystem stress and health disparities research.

First, we found a lack of association between stress systems and sUA at baseline. This finding may indicate that multisystem alignment with sUA may primarily occurs in the acute phase of the stress response; thus, future research that links these associations to health outcomes will likely benefit from assessing these systems in the context of acute stress rather than relying on measures of basal alignment (Laurent et al., 2016; Lucas et al., 2020). Indeed, we found that across stress systems, response measures, particularly total activation, evinced alignment with sUA. Specifically, we found that sUA total activation was positively associated with cortisol, DHEAS, sAA, and CRP total activation, though the cortisol and sAA associations were qualified by an age-by-analyte and a sex-by-analyte interaction, such that cortisol alignment only occurred among younger participants and sAA alignment only occurred among females. A similar pattern emerged for reactivity alignments, though in addition to the previous age-by-cortisol interaction, a sex-by-cortisol interaction emerged indicating that only among males were the two aligned. Additionally, CRP alignment only occurred among younger participants. Salivary UA recovery was largely unrelated to the other analytes apart from DHEAS, which was qualified by a sex-by-DHEAS interaction such that alignment was only observed among men.

Second, the positive alignments between sUA, cortisol, and DHEAS responses hint at several potential functions. Given that cortisol is a catabolic hormone and causes oxidative stress (Aschbacher et al., 2013), the simultaneous release of cortisol and sUA may represent an adaptive coordinated response optimizing energy mobilization while compensating for the oxidative stress associated with cortisol release. However, both sUA and cortisol are also associated with insulin resistance (Anagnostis et al., 2009; Copur et al., 2022; Kanbay et al., 2016; Yoo et al., 2005; Zhu et al., 2014). DHEAS release also co-occurred with sUA release which may indicate a parallel compensatory process as DHEAS increases insulin sensitivity and protects against inflammation (Kroboth et al., 1999; Maninger et al., 2009; Prall & Muehlenbein, 2018), consistent with UA’s role as a proinflammatory (Ruggiero et al., 2006) and in the present data, UA’s association with an increase in sCRP. Alternatively, UA may simply be associated with HPA activity.

In contrast to previous work which did not find an association between resting sUA and markers of ANS stress response (Acevedo et al., 2022), we found that sUA total activation and reactivity were associated with a surrogate marker of ANS activity, sAA, but only among females. This is consistent with our prior analyses of these data using a different marker of ANS activity, systolic and diastolic blood pressure (Lucas et al., 2020; Woerner et al., 2019). These results are consistent with data indicating that UA is more strongly linked to CVD among women relative to men (e.g., Tuttle et al., 2001) and suggests the heighted SNS activation may underline this sex difference.

Several important limitations are worth noting. First, we only considered a single population, healthy African Americans from a single midwestern city. It is uncertain whether the associations can be generalized to non-African Americans, African Americans residing in other urban and non-urban locales, or individuals with health conditions, particularly those associated with the stress systems under present consideration. Nevertheless, we feel justified in our choice of study population, given that African Americans experience a disproportionate burden of illnesses linked to the dysregulation of stress systems (Lucas, Wegner, et al., 2017). Future research should therefore incorporate comparison groups that vary along these dimensions.

The present research does not link any of the observed patterns to indices of long-term health. It is therefore crucial that future research prospectively examines these stress response profiles in relation to disease risk. Moreover, the cross-sectional design of the present study and the exclusion of unhealthy participants obscure the interpretation of age-related differences in analyte alignments due to potential selection effects. While speculative, the observed variations in analyte/sUA profiles between younger and older participants may stem from the exclusion of older individuals with profiles resembling those of the younger cohort if they are deleterious. This exclusion could be attributed to the fact that such profiles may eventuate in disease pathology, an outcome that served as a screening criterion for participation in the current study. Consequently, the observed age-related differences might not solely reflect genuine physiological changes associated with aging but could also be influenced by the selective inclusion of healthier individuals. Contrary to this speculation, past research concerning multisystem coupling tends to indicate greater decoupling in the context of exposures linked to pathology (King et al., 2020). Alternatively, it should be noted that the paucity of older individuals in our sample may have diminished our ability to detect significant analyte-by-sUA associations at the upper end of the age distribution. When age did emerge as a significant moderator, the analyte-sUA associations were always significant among the young and non-significant among the old. It remains possible that analyte-sUA associations are uniform across age (i.e., age does not moderate the effect) and the moderation effects are a byproduct of our having fewer observations for older individuals.

The current research is also limited by a relatively modest sample size and one skewed towards females and younger individuals (see Figure S1). Given that the observed associations were modest in magnitude, these results should be considered preliminary, and replications and extensions should aim to recruit larger and more sex and age balanced samples. Nonetheless, in a recent study, Williams and colleagues provide a partial conceptual replication with respect to sUA response using a discrimination-based stressor. This response was associated with a host of cardiometabolic variables including a long-term index of sugar metabolism (Williams et al., 2022).

We chose sUA as our outcome variable rather than a predictor given the copious literature implicating UA in disease processes (El Ridi & Tallima, 2017); as such, we were interested in how sUA relates to other, well established stress systems, viewing UA as a potential mechanism through which these systems, when chronically activated, might eventuate in deleterious health outcomes. However, we could have considered the reverse case. Although these relationships might prove to be bidirectional, some research indicates that, for example, CRP expression, is clearly induced by UA rather than the CRP influencing UA release (e.g., Kang et al., 2005). Future research should aim to address this issue, along with elucidating the associations across summative analyte measures (e.g., basal UA and DHEAS response, or vice versa).

Acknowledging these limitations, the findings of this study contribute valuable insights into the complex interplay between multisystem stress physiology and UA dynamics. The concomitant alignment of canonical stress systems with that of sUA offers new mechanistic insights towards a functional understanding of stress responses and potentially stress response system dysregulation. A focus on modifiable biological and behavioral risk factors holds particular promise in reducing racial health disparities (Bancks et al., 2017). The increasing availability of commercially available assay panels makes implementation of multisystem approaches more feasible and should offer a clearer picture of when and how UA is associated with disease. The evaluation of interventions designed around manipulating UA, e.g., dietary, and pharmacological interventions, will likely benefit by considering the impact of such interventions on multiple stress systems, particularly with respect to HPA axis stress responsivity.

Supplementary Material

1

Highlights.

  • Salivary uric acid responds to acute psychosocial stress

  • Basal cortisol, DHEAS, salivary alpha amylase, and C-reactive protein are unassociated with basal sUA

  • Cortisol, DHEAS, and sCRP dynamics are associated with sUA dynamics

  • Multisystem alignment with sUA was stronger among male and younger participants

Acknowledgements

This research was supported by the National Heart, Lung, and Blood Institute (R21HL097191) awarded to Todd Lucas. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute. In the interest of full disclosure, DAG is founder and chief scientific and strategy advisor at Salimetrics LLC and Salivabio LLC and these relationships are managed by the policies of the committees on conflict of interest at the Johns Hopkins University School of Medicine and University of California at Irvine. We thank Mercedes Hendrickson, Nathan Weidner, Lenwood Hayman, Edyta Debowska, Kaitlyn Simmonds, Kevin Wynne, Rhiana Wegner, and the Clinical Research Center at Wayne State University for assistance with data collection. Finally, we appreciate biotechnical support with salivary assays provided by Carla Slike, Becky Zavacky, and Jessica Acevedo.

Footnotes

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Declaration of interests

Douglas A. Granger reports a relationship with Salimetrics LLC that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

1

In accordance with the recommendations of the Sex and Gender Equity in Research (SAGER) guidelines (Heidari et al., 2016), we use the term ‘sex,’ ‘male,’ and ‘female’ rather than ‘gender’ or gender-related terms, as the focus of the current research was on physiological attributes rather than socio-cultural construction of gender.

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