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. Author manuscript; available in PMC: 2025 Jan 1.
Published in final edited form as: Health Psychol. 2023 Jul 10;43(1):7–18. doi: 10.1037/hea0001310

Association between psychosocial factors and C-reactive protein across income, race, and sex

Asociación entre los factores psicosociales y la proteína C reactiva según los ingresos, la raza y el sexo

Jenny M Cundiff 1, Aleena Bennett 2, Adrian Williams 1, Mary Cushman 3, Virginia J Howard 2
PMCID: PMC10776820  NIHMSID: NIHMS1917476  PMID: 37428772

Abstract

Objective:

A health disparity is a health difference that adversely affects disadvantaged populations, and thus could plausibly be due to social factors. Biopsychosocial processes that contribute to health disparities are not well-understood. Establishing whether candidate biomarkers are similarly associated with biologically relevant psychosocial constructs across health disparity groups is a current gap in our understanding.

Methods:

This study examined associations between perceived stress, depressive symptoms, and social support with CRP and whether associations varied by race, sex or income in 24,395 Black and White adults aged 45 years or older from the REGARDS population-based national cohort.

Results:

The association between depressive symptoms and CRP was slightly larger at higher (vs. lower) income levels and larger for men (vs. women) but did not vary by race. Associations between stress and CRP and social support and CRP were not moderated by income, race, or sex. An interaction between race and income, evidenced that higher income was more strongly associated with lower CRP in White participants compared to Black participants, consistent with the idea of “diminishing returns” of income for the health of Black Americans.

Conclusions:

Basic associations between these psychosocial factors and CRP are small and generally similar across income, race, and sex. Black and lower income Americans likely evidence higher CRP due to greater exposure to psychosocial risk factors rather than increased biological vulnerability to these exposures. Additionally, given small associations, CRP should not be used as a proxy for the construct of psychosocial stress.

Keywords: C-reactive protein, race, income, social support, stress, depressive symptoms

Major disparities in health by race and socioeconomic status (SES) persist in the United States despite enormous health care expenditures and decades of codified commitment to reducing health disparities (e.g., Healthy People 2010, 2020, and 2030). For example, Black Americans are two to three times more likely to die of heart disease compared to White Americans, and lower SES is associated with higher rates of cardiovascular morbidity and mortality independent of race (Kaplan & Keil, 1993; Mensah et al. 2005). Black race is also associated with poorer health independent of SES (Wilson, Thorpe, & LaVeist, 2017). Despite improvements in medical treatments and overall declines in cardiovascular mortality, racial and socioeconomic disparities remain (and may be widening)(Go et al. 2014). Similar patterns are seen for many other common chronic diseases of aging linked to inflammation such as diabetes mellitus and multiple cancers (DeSantis, et al. 2016; Matsushita, Tang & Selvin, 2019).

Low SES (e.g., low income) or Black race do not, themselves, cause individuals to become sick. Instead, the social context and exposures (e.g., stress) associated with race and SES give rise to differential risk (Bailey et al. 2017; Krieger et al. 2005; Williams & Mohammed, 2013). For example, increased exposure to psychological stress can increase risk for disease and this is thought to occur, at least in part, because stress evokes frequent and severe negative affective states which influence physiology and behavior (Cohen, Gianaros, & Manuck, 2016; Cohen & Janicki-Deverts, 2012; Lazarus & Folkman, 1984; Rodgers, Cuevas, Williams, Kawachi, & Subramanian, 2021).

Unsurprisingly then, differences in social contexts and exposures captured by the psychological constructs of stress and negative affect have been, and continue to be, central to psychosocial explanations of both racial and socioeconomic health disparities (Arriola et al., 2021; Cundiff, Boylan, and Muscatell, 2020; Ford, Lewis, Kershaw, Bellamy, & Diez Roux, 2021; Gallo & Matthews, 2003; Matthews & Gallo, 2011). In addition to stress and negative affect, social support is commonly examined as a psychosocial resource (mediator) or buffer (moderator) in psychosocial models of both racial and socioeconomic health disparities (Bell, Thorpe, & LaVeist, 2010; Cundiff, Matthews, & Jennings, 2019; Matthews & Gallo, 2011; Schafer & Vargas, 2016). Together, the investigation of these three psychosocial factors (stress, negative affect, and social support) in racial and socioeconomic health disparities mirror classic theories in health psychology about how the social world may come to influence our biology and ultimately our physical health; stress and its affective correlates wear us down and social support builds us up, with social support perhaps especially important in the face of high exposure to stress (e.g., Cohen and Wills, 1985).

Biological variables correlated with psychosocial constructs as well as disease (biomarkers) are often examined in efforts to understand psychobiological stress processes that may contribute to health disparities (e.g., Hackler III, Lew, Gore et al., 2019). C-reactive protein (CRP) is an excellent candidate biomarker for such investigations, because it has not only been convincingly linked to stress, negative affect and social support (Graham-Engeland et al. 2018; Kohler-Forsberg et al. 2017; Marsland et al. 2017; Steptoe, Hamer, & Chida, 2007; Uchino et al. 2018; cf. Horn et al. 2018), but it also predicts multiple common forms of stress-related disease, including those for which racial and socioeconomic disparities have been well established such as cardiovascular and metabolic diseases (Buckley, Fu, Freeman, Rogers, & Helfand, 2009; Lee et al., 2009).

Consistent with the potential role of CRP in understanding cardiometabolic health disparities by race and SES, race differences in CRP have been observed in multiple large diverse cohorts such as the Coronary Artery Risk Development in Young Adults (CARDIA) study and the REasons for Geographic and Racial Differences in Stroke study (REGARDS), with Black Americans showing higher circulating levels of CRP (Cushman et al., 2009; Forrester et al., 2019). In addition, meta-analytic evidence has established that multiple indicators of lower socioeconomic status (income, education) are associated with higher CRP (Muscatell, Brosso, & Humphreys, 2020). Additional investigations have also shown that race and SES may interact in associations with CRP, but the nature of this interaction has not been consistent across previous large cohorts, with some evidence showing that SES is less beneficial (e.g., Farmer, Wray & Haas, 2021), whereas others show a consistent benefit of SES in both Black and White Americans (Gruenwald, Cohen, Matthews, Tracy, & Seeman, 2009).

An important consideration as this literature moves forward is whether CRP is a similarly sensitive correlate of stress, negative affect, and social support across race and SES. For example, CRP is often measured as a proxy for stress exposure and if CRP is differentially associated with stress across demographic factors, this has implications for the construct validity (e.g., meaning) of this biological measure, suggesting a lack of measurement invariance across groups. The ability of measures to capture a construct with similar accuracy across groups is central to obtaining valid empirical conclusions (Hussey & Hughes, 2020), and establishing such invariance may be especially important when group differences are at the heart of the research questions such as with health disparities, and when biological variables begin to supplant self-reports of the psychological constructs they are correlated with (e.g., measuring CRP and interpreting it as stress, rather than acknowledging that CRP is influenced by many other factors such as body mass index). Further, there are potentially interesting insights when the distinction between stress and its biological correlates are made clear. For example, if SES moderates the stress-CRP association, this suggests that the biological impact of stress may differ by socioeconomic context.

We are not aware of any prior research that directly examines whether the perceived stress-CRP association differs by race or socioeconomic status. However, the basic association between perceived stress and CRP has been documented in both low- and high-income samples (Borders et al. 2010; Segerstrom & Miller, 2004) and acute inflammatory reactivity to stress does not appear to differ by socioeconomic status (Owen et al. 2003). For depressive symptoms, there is some research suggesting that associations with CRP may differ by race; multiple large national diverse cohorts show an association between depression and CRP for White men and women but not for Black men and women (Bey, Jesdale, et al., 2018; Case & Stewart, 2014; Chapman & Santos-Lozada, 2020). There is also some evidence that socioeconomic status may moderate associations between negative affect and CRP, with larger associations between negative affect and CRP at lower levels of SES (Appleton et al. 2012; Chapman, Roberts & Duberstein, 2011). For social support, psychological theory suggests that Black Americans and those with lower SES may be more sensitive to social support, because of cultural differences associated with race and SES. It has been argued that, on average, Black Americans and Americans with lower SES are more interdependent, and thus more likely to place greater value on connections with others (Markus & Kitayama, 1991; Kraus et al., 2012). The greater psychological import of social connections may suggest that social support is more germane to health in the cultural context of having lower SES or being African American, and there is some evidence to support this assertion (Uchino et al., 2016).

Lastly, sex may also influence associations between psychosocial variables and inflammation. Individuals born with female sex are more susceptible to the development of autoimmune diseases (Klein & Flannigan, 2016), as well as the development of major depressive disorder (Thapar et al. 2012). Immune cells also have receptors for sex hormones, thus immune system functioning can be directly influenced by biological features of sex (Buskiewicz et al. 2016). Further, the regulation of inflammatory activity by sex hormones has been specifically linked to the primary bodily systems implicated in depression (the central nervous system and HPA axis) (Slavich & Sacher, 2019). Hence, previous literature suggests that both the stress-CRP association and the depressive symptoms-CRP association likely differ by sex, with both thought to be stronger in females. However much of this research is in American samples, and some studies, primarily from non-American samples, have found the opposite, with CRP higher in men compared to women, bringing the presence of this sex difference and the biological argument for it into question (Elovainio et al., 2009; Häfner et al., 2011; Song et al.,2015).

The REGARDS study is a large biracial national cohort that measured CRP, psychological stress, negative affect in the form of depressive symptoms, and social support. Thus it is both well-designed and well-powered to examine whether associations between these psychosocial factors and CRP vary by sex or by race or socioeconomic status, the two primary social determinants of health in the US.

Methods

Participants

The present study is a secondary analysis of data from the REGARDS study, a population-based national cohort of 30,239 non-Hispanic Black and White adults aged 45 years or older residing in the continental US. Those responding yes, don’t know, or refusing the question “Are you Hispanic or Latino?” were not eligible for the study. Participants were randomly selected from a commercially available list (Genesys), and enrolled from 2003 to 2007. The REGARDS study participants provided written consent, and all participating universities’ institutional review boards approved the protocol. For more information about the REGARDS study, please see prior publication (Howard, Cushman, Pulley et al., 2005). The cohort oversampled Black adults and residents of the Southeastern Stroke Belt region of the US (North Carolina, South Carolina, Georgia, Tennessee, Mississippi, Alabama, and Arkansas). In this stratified random sample, 44% were men and 64% were White; 55% were recruited from the southeastern US, while the remaining 45% represented other regions of the contiguous US. Baseline data were collected by trained research staff using a computer-assisted telephone interview (CATI) and an in-home examination in which height was measured using an electronic stadiometer and weight was measured using a digital scale.

We started with the cohort of 30,239 participants, then removed 56 participants with anomalous baseline data for whom the identifying link between the baseline telephone interview and the in-person assessment could not be confirmed as a 100% match. These participants are consistently removed from every analysis from this cohort. We further removed 2,230 participants missing CRP, 174 missing body mass index (BMI), and 3,384 missing income for a sample of 24,395. Due to additional missing data for each psychosocial variable, the final analytical sample was n=24,393 for analyses involving perceived stress, n=24,222 for analyses involving depressive symptoms, and n=24,069 for analyses involving social support (see Table 1).

Table 1.

Descriptive statistics and correlations among primary study variables. Means and standard deviations by subgroups are also reported below the bolded sample means and standard deviations.

Income CESD PSS Social Support lnCRP
(ln mg/L)
Income
CESD −.25
PSS −.19 .44
Social Support .11 −.17 −.16
lnCRP −.17 .09 .07 −.03
N 24,395 24,222 24,393 24,069 24,395
Range 1 - 10 0 - 12 0 - 16 0 - 68.2 −1.9 - 3.6
Sample mean (SD) 6.4 (2.1) 1.1 (2.0) 3.2 (2.9) 19.7 (15.3) 0.8 (1.1)
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Notes: All correlations are spearman correlations and were significant at p<0.05.

Measures

Self-identified race (Black, White) and sex (male, female), and self-reported income (see below) were included in the analyses as potential moderators. Age and BMI, calculated as weight in kg divided by height2 in meters from objective measures during in-home visit, were included as covariates.

Income.

Income was assessed by asking participants a series of questions with appropriate skip patterns to determine the range of their yearly earned household income, including the questions: “Is your annual household income from all sources: 1) Less than $25,000 2) Less than $20,000 3) Less than $15,000 4) Less than $10,000 5) Less than $5,000 6) Greater than $35,000 7) Greater than $50,000 8) Greater than $75,000 or, 9) Greater than $150,000. Analyses treated income as continuous, consistent with previous work in this sample (e.g., Cundiff et al., 2022).

CRP.

At the in-home visit, blood was collected by trained personnel using standardized procedures after a 10–12 hour fast and centrifuged within 2 hours of collection. Plasma and serum were separated and shipped overnight on gel ice packs to a central laboratory. Samples were re-centrifuged and stored for batch processing (Howard et al. 2005). CRP was measured in plasma with a high-sensitivity, particle enhanced immunonephelometric assay (N High Sensitivity CRP, Dade Behring Inc., Deerfield, IL; interassay CVs 2.1–5.7%). Validity of results using this blood collection method was confirmed using a paired samples technique (Gillett et al. 2014) and the assay has reasonable within person variability (Sakinnen et al. 1999). REGARDS uniformly excludes the top 1% of CRP values (CRP>38) before log transformation (skewness before this exclusion is 10.12), and log transformation was necessary given skew (mean=4.05, median=2.18, skewness=2.78) as is typical of this outcome. CRP was evaluated as a continuous variable.

Perceived stress.

Perceived stress was measured during the CATI with the 4-item version of the Cohen Perceived Stress Scale (PSS-4) anchored to experiences over the past month (Cohen, 1983). The PSS is a validated measure of participant’s perception that his/her life is unpredictable, uncontrollable, and/or overcommitted (Cohen, 1983). Scores on this measure range from 0 to 16.

Depressive symptoms.

Depressive symptoms were assessed using the validated Centers for Epidemiologic Study of Depression (CES-D) 4-item self-report measure (Melchior et al. 1993), derived from the original CES-D 20-item measure (Radloff, 1977). The 4-item instrument asks how much of the time in the past week the participant has felt depressed, lonely, sad, and had crying spells using the following responses: 0 points—“Rarely or none of the time (less than 1 day)”; 1 point—“Some or a little of the time (1–2 days)”; 2 points—“Occasionally or a moderate amount of the time (3–4 days)”; or 3 points—“Most or all of the time (5–7 days).” A summary score is then calculated with a range from 0 to 12.

Social Support.

A measure of social support was created by aggregating three CATI questions. “How many close friends do you have? That is, people that you feel at ease with, can talk to about private matters, and can call on for help?”, “How many relatives do you have that you feel close to?”, and “How many of these friends or relatives do you see at least once a month?” The responses to each of these questions were summed together (see Mondesir et al. 2018 for similar variable construction in this cohort). To adjust for extreme values, we dropped the highest 1% (values from 112 to 1900) on the premise that people may have been including large groups in their count (e.g., church members) and an additional social support over 112 is unlikely to have significant additional benefit. We then calculated the mean (19.48) and standard deviation (16.25) and winsorized values larger than 3 standard deviations above the mean to that value (68), so that participants with very large numbers were retained at the top of the distribution, but had less influence on the results.

Overview of Analyses

All correlational and regression analyses were performed using SAS® Version 9.4 (Cary, NC). All psychosocial variables were approximately normally distributed and treated as continuous. Analyses are presented in separate tables by psychosocial variable (depressive symptoms, perceived stress, social support). The same set of hierarchical regression models were run for each psychosocial variable. The base model includes main effects of age and BMI, which are well-established correlates of CRP, as well as the psychosocial variable. Next, individual two-way interactions are tested in order to examine whether the association between psychosocial variables and CRP differ by either income, race, or sex. Lastly, a three-way interaction including race, income, and the psychosocial variable are tested in order to examine whether more complex associations may be present at the intersection of race and SES given previous findings with CRP. For significant interactions (p <.05), InterActiv plots were used to visually represent the raw data and simple slope trends (McCabe, Kim, & King, 2018).

Results

Descriptive Statistics

Table 1 reports correlations among the primary study variables as well as means and standard deviations on each of these variables. As expected, higher income was correlated with less stress (r = −.19) and depressive symptoms (r = −.25), more social support (r = .11), and lower CRP (r = −.17). Higher CRP was correlated with greater stress (r = .07) and depressive symptoms (r = .09) and, to a lesser extent, lower social support (r = −.03). Higher social support was also associated with less stress (r = −.16) and depressive symptoms (r = −.17), and stress and depressive symptoms were strongly correlated with one another (r = .44).

Tests of Main Effects and Moderation

Results for depressive symptoms and CRP are shown in Table 2. In the base model, age, BMI, and depressive symptoms all showed the expected positive association with CRP (β’s = .005, .066, and .037, respectively). Adding income to the model resulted in a significant main effect for income (β = −.067) as well as a significant interaction between income and depressive symptoms on CRP (β = .018). Figure 1 shows that as income increases, the depressive symptoms-CRP association becomes larger, and, in fact, even in this large data set, there was no significant association between depressive symptoms and CRP at very low levels of income (−2 standard deviations from the mean). Adding race to the base model also results in a significant main effect (β = .205) showing that Black participants have higher CRP on average compared to White participants, but race does not moderate the association between depressive symptoms and CRP. There was a main effect of sex (β = .313), such that female participants have higher CRP on average compared to male participants. The interaction between sex and depressive symptoms on CRP was also significant (β = −.029). Figure 2 shows that, counter to predictions, the association between depressive symptoms and CRP is larger in male participants compared to female participants. In the final model, race and income both show significant main effects on CRP (β’s = .145 and −.058, respectively), but there is no 3-way interaction. Hence, the association between depressive symptoms and CRP is larger at higher income levels and this association is similar across Black and White participants.

Table 2.

Results of regression models examining whether associations differ between depressive symptoms and CRP by income, race, and sex.

Moderator (s) Predictor standardized
beta		 unstandardized
beta (SE)		 Lower
CI		 Upper
CI		 p partial
correlation
Base Model
Age .005 .005 (.001) .004 .007 <.001 .0003
BMI .066 .066 (.001) .064 .068 <.001 .1266
CESD .037 .037 (.003) .030 .044 <.001 .0050
Income
Age .002 .002 (.001) .000 .003 .034 .0003
BMI .064 .064 (.001) .062 .066 <.001 .1283
Income −.067 −.066 (.004) −.073 −.060 <.001 .0185
CESD .019 .019 (.004) .012 .026 <.001 .0012
CESD * income .018 .004 (.002) .001 .007 .006 .0003
Race
Age .006 .006 (.001) .004 .007 <.001 .0003
BMI .063 .063 (.001) .061 .066 <.001 .1275
Race .205 .205 (.014) .177 .233 <.001 .0095
CESD .033 .033 (.003) .026 .040 <.001 .0040
CESD * black −.013 −.013 (.007) −.026 .001 .061 .0001
Sex
Age .006 .006 (.001) .005 .008 <.001 .0003
BMI .064 .064 (.001) .062 .066 <.001 .1289
Female .313 .313 (.014) .286 .340 <.001 .0229
CESD .028 .028 (.003) .021 .035 <.001 .0029
CESD * female −.029 −.029 (.007) −.042 −.015 <.001 .0007
Income*Race
Age .003 .002 (.001) .001 .004 .001 .0003
BMI .062 .062 (.001) .060 .064 <.001 .1287
Income −.058 −.058 (.004) −.065 −.050 <.001 .0186
Race .145 .145 (.015) .117 .174 <.001 .0041
CESD .019 .019 (.004) .012 .026 <.001 .0012
CESD *income .013 .001 (.002) −.003 .005 .527 .0003
CESD * race .001 −.025 (.018) −.059 .010 .167 .0000
Income*race .023 .018 (.008) .002 .033 .028 .0005
CESD* race*income .009 .004 (.003) −.002 .010 .194 .0001
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Note: N=24,222. CI = 95% confidence interval.

Figure 1.

Figure 1.

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The association between depressive symptoms and CRP is moderated by participant income. Plots show the association between depressive symptoms and log CRP across increasing (to the right) income levels. The association between depressive symptoms and CRP is not significant at very low (−2 SD) income levels, but the association is significant and increasingly more positive across the remaining (higher) income levels.

Figure 2.

Figure 2.

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The association between depressive symptoms and CRP is moderated by participant sex. Plots show the association between depressive symptoms and log CRP by sex, with females on the left and males on the right.

Results for perceived stress and CRP are shown in Table 3. In the base model, perceived stress showed the expected positive association with CRP (β = .025). In subsequent models, income, race, and sex all showed significant associations with CRP (β’s = −.067, .204, and .312, respectively); participants with lower income, participants who identify as Black, and participants who were female showed higher levels of CRP. However, there were no significant interactions. Thus the association between perceived stress and CRP did not differ by income, race, or sex. Instead, perceived stress and these demographic variables each showed independent main effects on CRP.

Table 3.

Results of regression models examining whether associations differ between perceived stress and CRP by income, race, and sex.

Moderator (s) Predictor standardized
beta		 unstandardized
beta (SE)		 Lower
CI		 Upper
CI		 p partial
correlation
Base Model
Age .006 .006 (.001) .004 .007 <.001 0.0003
BMI .067 .067 (.001) .064 .069 <.001 0.1266
PSS .025 .025 (.002) .020 .030 <.001 0.0046
Income
Age .002 .002 (.001) .000 .003 .026 0.0003
BMI .064 .064 (.001) .062 .066 <.001 0.1283
Income −.067 −.067 (.003) −.073 −.060 <.001 0.0184
PSS .014 .014 (.002) .009 .019 <.001 0.0014
PSS * income .002 .000 (.001) −.002 .003 .709 0.0000
Race
Age .006 .006 (.001) .005 .007 <.001 0.0003
BMI .064 .064 (.001) .061 .066 <.001 0.1275
Race .204 .204 (.014) .177 .232 <.001 0.0095
PSS .022 .022 (.002) .017 .027 <.001 0.0036
PSS * race −.008 −.006 (.005) −.015 .004 .224 0.0000
Sex
Age .006 .006 (.001) .005 .008 <.001 0.0003
BMI .064 .064 (.001) .062 .066 <.001 0.1289
Female .312 .312 (.014) .285 .339 <.001 0.0229
PSS .017 .017 (.002) .013 .022 <.001 0.0022
PSS * female −.011 −.008 (.005) −.017 .002 .109 0.0001
Income*Race
Age .003 .003 (.001) .001 .004 .001 0.0003
BMI .062 .062 (.001) .060 .064 <.001 0.1287
Income −.058 −.058 (.004) −.065 −.051 <.001 0.0184
Race .144 .144 (.015) .116 .173 <.001 0.0041
PSS .013 .013 (.002) .009 .018 <.001 0.0013
PSS * income −.001 .000 (.002) −.003 .003 .917 0.0000
PSS * race −.005 −.003 (.014) −.030 .023 .810 0.0000
Income * race .026 .025 (.010) .005 .045 .000 0.0006
PSS * race * income .000 .000 (.002) −.004 .004 .987 0.0000
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Note: N=24,395. CI = 95% confidence interval.

Results for social support and CRP are shown in Table 4. In the base model, social support showed the expected negative association with CRP (β = −.001). In subsequent models, income, race, and sex all again showed significant associations with CRP (β’s = −.072, .218, and .328, respectively); however, there were no significant interactions. Thus the association between social support and CRP did not differ by income, race, or sex. Unlike results for depressive symptoms and perceived stress, social support did not show an independent main effect on CRP once other demographic variables were entered into the model. Because main effects can be obscured when a non-significant interaction is included in the model, we ran an additional model including only the main effects (age, BMI, social support, income, race). The effect of social support was not significant in this model either, though each of the other variables maintained significant independent effects. Thus, while higher social support was associated with lower CRP independent of age and BMI, the effect of social support became nonsignificant after adjusting for income and race.

Table 4.

Results of regression models examining whether associations differ between social support and CRP by income, race, and sex.

Moderator (s) Predictor standardized
beta		 unstandardized
beta (SE)		 Lower
CI		 Upper
CI		 p partial
correlation
Base Model
Age .005 .005 (.001) .004 .006 <.001 .0003
BMI .067 .067 (.001) .065 .069 <.001 .1265
Social Support −.001 −.001 (.001) −.002 .000 .020 .0002
Income
Age .001 .001 (.001) .000 .003 .119 .0003
BMI .064 .064 (.001) .062 .066 <.001 .1286
income −.072 −.072 (.003) −.078 −.065 <.001 .0185
Social Support .000 .000 (.000) −.001 .001 .608 .0000
Social Support* income −.002 .000 (.000) .000 .000 .822 .0000
Race
Age .006 .006 (.001) .004 .007 <.001 .0003
BMI .064 .064 (.001) .062 .066 <.001 .1276
Race .218 .218 (.014) .190 .246 <.001 .0097
Social Support −.001 −.001 (.001) −.002 .000 .206 .0001
Social Support *race .006 .001 (.001) −.001 .002 .414 .0000
Sex
Age .006 .006 (.001) .005 .007 <.001 .0003
BMI .065 .064 (.001) .062 .067 <.001 .1291
Female .328 .328 (.014) .301 .355 <.001 .0231
Social Support .000 .000 (.000) −.001 .001 .674 .0000
Social Support * female −.004 −.001 (.001) −.002 .001 .578 .0000
Income*Race
Age .002 .002 (.001) .000 .004 .008 .0003
BMI .062 .062 (.001) .060 .065 <.001 .1290
Income −.062 −.062 (.004) −.069 −.056 <.001 .0186
Race .150 .150 (.015) .121 .178 <.001 .0043
Social Support .000 .000 (.000) −.001 .001 .989 .0000
Social Support * income .002 .000 (.000) .000 .001 .376 .0000
Social Support * race .004 .004 (.003) −.002 .010 .215 .0000
Income x race .025 .034 (.011) .013 .055 .000 .0005
Social Support *race * income −.008 −.001 (.001) −.001 .000 .287 .0000
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Note: N=24,069. CI = 95% confidence interval.

Across the models incorporating different psychosocial variables, we also found a consistent interaction between income and race on CRP (β’s ranged from .023 to .026). The interaction is shown in Figure 3. Consistent with the diminishing returns hypothesis, the nature of the interaction revealed that higher income was more strongly associated with lower CRP in White (b=−.07) participants compared to Black (b=−.04) participants. The slope of the relationship was almost twice as large for White participants, but the confidence intervals also abutted one another at b= −.06.

Figure 3.

Figure 3.

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The association between income and CRP is moderated by participant race. Plots show the association between income and log CRP by participant’s self-reported race, with Black participants on the left and White participants on the right.

Discussion

Whether the basic associations between stress, depressive symptoms, and social support with CRP differ by racial and socioeconomic factors relevant to health has rarely been examined and was the primary goal of the current study. Although two significant interactions emerged in the association between depressive symptoms and CRP (income by depressive symptoms on CRP and sex by depressive symptoms on CRP), overall CRP appears to have very similar associations with psychological stress, depressive symptoms, and social support across race and income, as well as sex, in this large national sample.

The consistent association between perceived stress and CRP in this sample suggests that CRP is an equally sensitive biomarker of perceived stress across sex, race and income. Although stress has a similar association with CRP across these demographic factors, Black Americans and Americans with lower income experience greater stress exposure (Cundiff et al. 2021; Cohen & Janicki-Deverts, 2012; Turner & Avison, 2003; Almeida et al. 2005). These results are inconsistent with the stress-vulnerability model of health disparities but consistent with the stress exposure hypothesis and prior research (e.g., Sternthal, Slopen & Williams, 2011).

CRP was also similarly related to social support across race and income, as well as sex, though we note that the association was quite small (r=−.03) and became nonsignificant after adjustment for race and income. Although small, this effect size is consistent with the confidence interval (Zr between −.02 and −.09) from a recent meta-analysis of social support and inflammation (Uchino et al., 2018). Null findings for interactions between race and income with social support on CRP are counter to hypotheses suggesting that social support may be more protective in individuals with lower SES and Black Americans due to less individualistic and more interdependent values on average (Markus & Kitayama, 1991; Stephens et al. 2012; Kraus et al. 2012). Thus, although interdependence may be more highly valued, it does not appear to be more germane to stress biology, at least in the form of CRP. Additionally, social support has been conceptualized as a buffer of the stress-disease relationship, such that social support is most health-protective at high levels of stress (Cohen & Wills, 1985; Uchino, Bowen, Carlisle, & Birmingham, 2012). This stress buffering hypothesis suggests an interaction between stress and social support on CRP that was not tested here. However, consistent associations between social support and CRP across race and income suggest that insomuch as Black race and lower income are associated with greater stress exposure (as they are in this sample), social support did not have a larger protective effect in groups with higher levels of stress, and so is counter to the stress buffering hypothesis.

CRP was differentially associated with depressive symptoms across income and sex, but not race. The interaction between sex and depressive symptoms on CRP (Figure 1) was directly counter to prior research; the association between depressive symptoms and CRP was larger for men, whereas previous findings often found evidence of a larger association in women (e.g., Slavich & Sacher, 2019). Lack of moderation by race is also not consistent with past findings from multiple large national cohorts, wherein White men and women have shown associations between CRP and depressive symptom severity but Black men and women have not (Bey, Jesdale, et al., 2018; Case & Stewart, 2014; Chapman & Santos-Lozada, 2020). The significant interaction between income and depressive symptoms on CRP (Figure 2) showed that the association was larger at higher levels of income. This finding is not consistent with vulnerability models suggesting that negative affect may have a larger influence on health for individuals living in more vulnerable, poorer resourced environments (Redmond et al. 2013; Chapman, et al. 2011).

Although not a primary aim of the study, analytic models also revealed an interaction between race and income on CRP consistent with the diminishing returns hypothesis (Farmer & Ferraro, 2005). Higher income was more strongly associated with lower CRP in White participants compared to Black participants. The slope of the relationship was almost twice as large for White participants, but the confidence intervals abutted each other. So while there was evidence of diminishing returns of higher SES on CRP for Black participants, it is possible that this difference is so small that it is not clinically significant. Notably, other analyses in this same sample have revealed an interaction between race and income on perceived stress, suggesting that income is potentially a more potent stress-reducer for Black Americans (Cundiff et al. 2021). Thus although Black participants in this sample appear to benefit more from higher income in terms of lowered stress, they benefit less from higher income in terms of systemic inflammation. The fact that income can be stress dampening but inflammation promoting in Black participants, is an additional example of why CRP is not simply a biological operationalization of perceived stress and so should not be conflated with stress. Additionally, standardized betas for the association between all three psychosocial factors and CRP ranged between −.001 and .037. These are small effects and further call into question the use of CRP as a biomarker of these psychosocial factors.

Limitations

The current study has many strengths including a large population-based national cohort that oversampled Black adults and residents of the Southeastern Stroke Belt region of the US. However, there are interpretative limitations of this study to consider. This study examined middle-aged and older adults, and results could be specific to age group or cohort. Perceived stress and depressive symptoms were both measured using brief 4-item assessments and social support was measured using face-valid items regarding felt social connection and regular contact with others. Longer, more thorough assessments of these psychological constructs could show different associations. Similarly, perceived stress is just one measure of stress and other forms of stress may show different patterns of associations. This possibility seems especially likely given the number of ways stress can be operationalized and the fact that even the basic association between stress and CRP can differ by type of stressor in the same sample (e.g., Kershaw et al., 2017; Ranjit et al., 2007). SES was operationalized as income and other operationalizations may show different patterns of association (Braveman, 2005; Cundiff et al., 2022). This study was cross-sectional, and there is still some question about whether the stress-CRP association is causal. In a recent review of the literature examining measures of stress and inflammatory markers, the authors found no reliable association between acute exposure to stress in the laboratory and acute changes in circulating levels of CRP, but there were very few studies to draw from (k=6) (Marsland et al. 2017). Notably, other studies have found within-person increases in CRP related to within-person increases in perceived stress outside the laboratory (Chiang et al. 2019). Relatedly, the presence of physical illness or existing health conditions was not controlled in analyses, so associations with CRP may also be attributable to frank differences in disease driving differences in both inflammation and psychosocial factors. Lastly, although results revealed three statistically significant interactions, the study had a large sample size and interaction effects were small (Figures 1, 2, and 3, respectively).

Conclusions & Future Directions

CRP is an excellent candidate biomarker for understanding how biologically-relevant psychosocial factors such as stress, negative affect, and social support may manifest in biology so as to increase risk for multiple diseases with known racial and socioeconomic disparities. However, biomarkers do not have a 1-to-1 association with psychosocial factors and may be more sensitive for some groups than others. Results here show that the basic associations between these psychosocial factors and CRP are, in general, small but similar (e.g., invariant) across race and income. Thus, CRP is not a very sensitive or specific measure of these psychosocial constructs but it is similarly sensitive across these two health disparity groups. Previous research on these psychosocial factors and CRP, in which White Americans and those with higher income are more represented, may readily apply to Black Americans and those with lower income. However, measuring CRP and calling it stress is clearly not warranted. Examination of potential differences by sex were also examined given strong evidence that associations may be stronger in women, but our results suggest a small difference in only the depressive symptom-CRP association, which was slightly stronger in men, not women.

Consistency in associations between psychosocial factors and CRP across race and SES is counter to many theories and past findings in smaller samples, and suggests that associations between stress, depressive symptoms, and social support with CRP are not confounded by sociodemographic factors and health disparity groups are not more vulnerable to these psychosocial stressors. Instead, there is similar biological vulnerability but greater exposure to stress, negative affect and lower social support among Black Americans and those with lower SES. Future research may benefit from focusing more on increased exposures rather than differential susceptibility in Black (vs White) Americans and those with lower income. Continuing to examine whether (and why) higher SES may be associated with a slightly smaller protective effect in Black (vs. White) Americans in terms of systemic inflammation, also warrants additional research. Examining additional mechanisms beyond those associated with psychosocial stress may be wise given previous findings in this sample showing that income protects Black Americans from stress more than White Americans, but from inflammation less.

Public Significance:

Health disparities by race and socioeconomic status are a leading public health and social justice concern. This study provides evidence that contributing psychosocial factors (stress, depressive symptoms, social support) show similar associations with inflammation across race and income. Results suggest that greater exposure to risk factors likely contributes to health disparities, rather than differential biological vulnerability across social categories.

Acknowledgments:

This research project is supported by cooperative agreement U01 NS041588 co-funded by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA), National Institutes of Health, Department of Health and Human Service. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS or the NIA. Representatives of the NINDS were involved in the review of the manuscript but were not directly involved in the collection, management, analysis or interpretation of the data. The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at: https://www.uab.edu/soph/regardsstudy/. Additional support was provided by the Deep South Resource Center for Minority Aging Research (RCMAR) P30 AG031054 from the NIA. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS or the NIA.

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