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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2012 May 15;67(4):503–513. doi: 10.1093/geronb/gbs048

The Social Patterns of a Biological Risk Factor for Disease: Race, Gender, Socioeconomic Position, and C-reactive Protein

Pamela Herd 1,2,✉, Amelia Karraker 2, Elliot Friedman 3
PMCID: PMC3695599  PMID: 22588996

Abstract

Objective.

Understand the links between race and C-reactive protein (CRP), with special attention to gender differences and the role of class and behavioral risk factors as mediators.

Method.

This study utilizes the National Social Life, Health, and Aging Project data, a nationally representative study of older Americans aged 57–85 to explore two research questions. First, what is the relative strength of socioeconomic versus behavioral risk factors in explaining race differences in CRP levels? Second, what role does gender play in understanding race differences? Does the relative role of socioeconomic and behavioral risk factors in explaining race differences vary when examining men and women separately?

Results.

When examining men and women separately, socioeconomic and behavioral risk factor mediators vary in their importance. Indeed, racial differences in CRP among men aged 57–74 are little changed after adjusting for both socioeconomic and behavioral risk factors with levels 35% higher for black men as compared to white men. For women aged 57–74, however, behavioral risk factors explain 30% of the relationship between race and CRP.

Discussion.

The limited explanatory power of socioeconomic position and, particularly, behavioral risk factors, in elucidating the relationship between race and CRP among men, signals the need for research to examine additional mediators, including more direct measures of stress and discrimination.

Keywords: Behavioral risk factors, Gender, Inflammation, Race

There are large and consistent racial differences in morbidity, mortality, and even markers for underlying biological disease processes, such as systemic inflammation (Cummings & Jackson, 2008; Geronimus, Hicken, Keened, & Bound, 2006; Hayward, Miles, Crimmins, & Yang, 2000; McDade, Lindau, & Wroblewski, 2010; Sloan, Ayyagari, Salm, & Grossman, 2010). The consistency of these differences have lead some to call race, in addition to class and gender, a fundamental cause of disease (Link & Phelan, 1995; Phelan & Link, 2010). Specifically in relation to race, the argument is not that race biologically determines differences, but rather that race embodies access to resources, such as income and power, which in turn shape health. In the United States, lacking a high school degree or living below the poverty line is associated with up to 12 fewer years of life and up to 16 fewer years of life without disease (Crimmins & Saito, 2001; Rogot, Sorlie, & Johnson, 1992). In this context, race is a social construct (D. R. Williams, Mohammed, Leavell, & Collins, 2010; D. R. Williams & Sternthal, 2010). Differential access to resources, such as income and education, plays a large role in explaining racial differences in health (D. R. Williams et al., 2010). In addition to class, gender patterns racial differences in health. Racial differences, particularly the role of class and behavioral risk factors as mediators, vary between men and women (Cummings & Jackson, 2008; Liu & Hummer, 2008; Read & Gorman, 2006; Winkleby, Kraemer, Ahn, & Varady, 1998). For example, race differences are larger among women as compared to men for outcomes like cardiovascular mortality and associated behavioral risk factors like obesity (Gallant & Dorn, 2001).

But, although the links between race and morbidity and mortality are well elucidated, more recent evidence linking race to biological disease processes is less well developed, despite the potential utility of examining biological processes that underlie the development of diseases patterned by race. Systemic inflammation is a logical place to start given its links to numerous diseases (i.e., diabetes, cardiac problems) commonly patterned by race and because although it is clear that race and inflammation, specifically C-reactive protein (CRP), are highly correlated, the pathways linking race to CRP are not well elucidated. CRP is an acute phase protein that is considered a marker for systemic inflammation. In hundreds of studies, it has been prospectively associated with disease outcomes ranging from cardiac disease to diabetes and strokes (for reviews see Dehghan et al., 2007; Emerging Risk Factors Collaboration, 2010; Hiekkila et al., 2009). Although there is a dispute regarding the causal processes linking CRP to disease, elevated CRP levels are seen as a risk factor for disease (Lawlor et al., 2008).

Studies have demonstrated that race strongly patterns CRP (Gruenwald, Cohen, Matthews, Tracy, & Seeman, 2009; McDade et al., 2010). Though similar to racial differences in morbidity and mortality, there is evidence that racial differences in CRP may shrink in old age. Explanations for this crossover include mortality selection, only the “hardiest” African Americans survive to the oldest ages, and data quality problems, particularly age misreporting by African Americans (Preston & Elo, 2006).

CRP, as a marker for systemic inflammation, is an important outcome when considering racial differences, especially in the context of the fundamental cause framework. Inflammation is linked to multiple disease outcomes and the link between race and health is not disease specific; the patterns exist across a range of morbidity and mortality outcomes, many of which (i.e., heart disease, strokes, diabetes) are linked to CRP (Gorelick, 1998; Laiyemo et al., 2010; McDade et al., 2010; D. R. Williams et al., 2010). Further, many diseases linked to CRP levels, like cardiovascular disease and diabetes, are racially patterned (Hayward et al., 2000; Wyatt et al., 2005). Thus, the links between CRP and a range of diseases and mortality risks makes CRP relevant for investigations of its links to race.

Class and Behaviors

To date, however, the existing research has not fully elucidated how class and behavioral risk factors mediate the relationship between race and CRP. Though studies have explored whether socioeconomic position (SEP) and behavioral risk factors mediate the relationship between race and CRP, the power of behavioral risk factors, independent of class, is not clear. As already noted, race is a crucial determinant of class—discrimination, both current and institutional, has shaped education and income differences between blacks and whites (Wilson, 2009). In turn, there is a large body of work demonstrating that class—especially education—is a determinant of behaviors (Herd, 2010; Mirowsky & Ross, 2003). Thus, if one simply adjusts for behaviors, without having adjusted for SEP, it is not clear whether the explanatory power of behaviors to explain race differences in CRP simply reflects the explanatory power of class, especially educational attainment. Many studies focused on how behavioral risk factors mediate the relationship between race and CRP, however, have not adjusted for SEP as a mediator (Albert, Glynn, Buring, & Ridker, 2004).

Further, the few studies that have included both SEP and behavioral risk factors, to account for race differences, have generally not adjusted for behavioral risk factors separate from other factors (McDade et al., 2010) or are based on small unrepresentative samples (McDade, Hawkley, & Cacioppo, 2006). For example, McDade and colleagues (2010) add behavioral risk factors to the model simultaneously with measures of chronic disease burden, sleep quality, marital status, depressive symptoms, and household cleanliness. Consequently, it is impossible to establish the extent to which behavioral risk factors, specifically, mediate racial differences in CRP. A study conducted by Kelley-Hedgepeth and colleagues (2008) did find that obesity explained a larger fraction of the race difference in CRP than did SEP, but the sample includes only women ages 42–52 with intact uteruses and ovaries. In sum, prior studies do not make clear the extent to which behaviors explain relationships between race and CRP, after adjusting for SEP.

Gender

But, although attention has been paid to SEP and behavioral risk factors as mediators in the relationship between race and CRP, the literature on racial differences in CRP has paid almost no attention to gender. There is especially limited evidence regarding how the roles of socioeconomic and behavioral risk factors differ when examining men and women separately. Studies that consider gender generally are not based on nationally representative samples and are not explicitly focused on explaining racial differences in CRP (Gruenwald et al., 2009; Khera et al., 2005). Prior research on health and health behaviors, however, would indicate that there may be important gender differences in how racial differences in CRP manifest themselves. For example, some health differences appear to be particularly acute for African-American women including cardiovascular mortality and associated behavioral risk factors like obesity (Cummings & Jackson, 2008; Liu & Hummer, 2008; Read & Gorman, 2006; Winkleby et al., 1998). Generally, racial differences in behavioral risk factors like physical inactivity and obesity, which are tightly linked to CRP and inflammation more broadly, are larger among women than among men (Gallant & Dorn, 2001; Winkleby et al., 1998). Finally, there is evidence that class differences (as measured by income, education, and occupation) in health also vary across gender (Mathews, Manor, & Power, 1999; McDonough, Williams, House, & Duncan, 1999).

Consequently, using the National Social Life, Health, and Aging Project (NSHAP) data, a study of older Americans aged 57–85, we elucidate the relationship between race and CRP by focusing on two questions. First, what is the relative role of socioeconomic versus behavioral risk factors in explaining racial differences in CRP levels? Second, what role does gender play in understanding racial differences? Does the role of socioeconomic and behavioral risk factors differ when examining men and women separately?

Methods

Data

Collected in 2005–2006, the NSHAP is a nationally representative study of noninstitutionalized older adults aged 57–85. The NSHAP data contains information on the demographic characteristics; romantic, sexual, and social relationships; and physical and mental health—including biomarkers—of 3,005 American men and women aged 57–85. The final response rate was 74.8% (O’Muircheartaigh, Eckman, & Smith 2009). Data was collected via in-home interviews and a mail-back questionnaire.

The NSHAP sample was chosen from a multistage area probability design selected by the Institute for Social Research for the Health and Retirement Study (O’Muircheartaigh et al., 2009). NSHAP oversampled blacks and Latinos and balanced age and gender subgroups (O’Muircheartaigh et al., 2009). Although the full NSHAP includes 3,005 respondents, a random 70% sample was asked to provide blood spots of which 1,939 (85% response rate) provided usable samples. Williams and McDade (2009) reported that respondents who refused to provide blood spots did not differ from those who agreed by gender, race, ethnicity, age, education, income, marital status, self-rated physical or mental health, or number of doctor visits in the past year.

As is standard in this literature, we excluded another 136 cases that had high CRP levels, which may be indicative of acute illness (Pearson et al., 2003). NSHAP collected CRP levels from dried blood spots. Different cutoff criteria for maximum CRP levels have been established for plasma versus blood spot samples (McDade, Burhop, & Dohnal, 2004). A plasma level of 10 mg/L roughly corresponds to a blood spot level of 8.6 mg/L, so CRP levels greater than 8.6 mg/L are excluded from the analytic sample (see McDade et al., 2010). Regardless, sensitivity tests demonstrated no meaningful differences in the pattern of findings when including even the highest CRP values.

We dealt with missing values on covariates by employing multiple imputations (Stata 11), for 516 cases, 359 of which were missing data on income (Rubin, 2004). We did not impute data for an additional 46 cases missing data on race. We imputed 10 values for each missing observation (i.e., income), averaged those coefficients and importantly calculated new standard errors (SEs) that account for the variation across multiple potential imputed values. This is a better strategy for missing data issues than stepwise deletion because it reduces concerns about selection that could bias the findings (Rubin, 2004). Further, multiple imputation also adjusts SEs to reflect the error associated with imputation. We ended with a total sample of 1,757. Because of the randomized modular design of the survey, survey weights used (which include nonresponse by age and urbanicity) are likely adequate to account for additional selection.

Measures

Outcome.—

CRP was obtained in NSHAP via blood spot samples, which were collected on filter paper (Williams & McDade, 2009). This method of CRP data collection has been shown to be precise and reliable (McDade et al., 2004). Because CRP levels were highly skewed, values were (natural) log transformed.

Covariates.—

Demographics: Race is based on self-reported data. We classified respondents as Black non-Hispanic, White non-Hispanic, and Hispanic. We employed an age centered (at age 68) term. Sex is male or female.

SEP.—

We employed two measures of SEP. The first measure is educational attainment, which includes five categories: no high school degree, high school degree, some college, college, and graduate degree. Income includes pretax household income from wages, pensions, social security, and government assistance. Some studies have shown that low-income groups, but not moderate-income groups, had significantly higher levels of inflammatory markers compared to high-income groups (Friedman & Herd, 2010). To allow for such nonlinear associations, we stratified the sample into income quintiles. The income ranges that corresponded to each quintile were: Q1: $17,838 or less; Q2: $17,839–$35,037; Q3: $35,038–$50,161; Q4: $50,162–76,809; Q5: $76,810 or more. Each quintile was dummy coded and included in statistical analyses with the bottom quintile serving as the referent. We did test a linear version of the income measure, but found the results did not vary.

Physical health.—

Health measures are included to help reduce the risk for reverse causality in the SEP–CRP relationship. Significant health problems affect SEP (i.e., disease onset inhibits both educational attainment and labor market participation). Adjusting for existing/known health problems reduces the risk for reverse causality. In short, after adjusting for health, higher CRP levels are more plausibly a risk factor for disease, though we cannot rule out the possibility that there are some underlying unobserved health differences.

Physical health status was assessed using self-reported measures. We classified participants as having a particular condition if a doctor had ever told them that they had the following conditions: hypertension, diabetes or high blood sugar, chronic pulmonary disease/emphysema, heart attack, heart failure, operation to unclog or bypass arteries, asthma, stroke, or arthritis. These measures, which have been linked to inflammation, are included as individual dummy variables for each reported condition. Further, we adjust for general self-assessed health. Respondents answered the following question “How would you rate your physical health: excellent, very good, good, fair, or poor?” Self-reported general health appears to capture much of the health-related variation in CRP.

Prescription drugs.—

Many drugs, both prescription and over-the-counter, including antihypertensive, cholesterol lowering, and antidepressant, have been shown to have anti-inflammatory properties (Jaim & Ridker, 2005; Kenis & Maes, 2002; Pradhan et al., 2002; Ridker, Hennekens, Rifai, Buring, & Manson, 1999). Further, steroid medications, particularly as part of a hormone replacement regimen, have been shown to increase CRP levels. Dummy-coded variables indicating current use of these medications were included in all analyses. Specifically, we included measures for cholesterol absorption inhibitor medication, combination antihyperlipidemic medication, estrogens, progestin, nonsteroidal antiinflammatories, steroids, and antidepressants. These drugs were categorized using the Multum Drug Database: Lexicon Plus version (Qato et al., 2008).

Behavioral risk factors.—

We include body mass index (BMI). We also included smoking measures, which capture whether the respondent is a current smoker, former smoker, or never smoked. Finally, we include measures of physical activity. These include little exercise (<4 times/month) moderate (1–2 times/week), and heavy (≥3 times/week).

Analytic Techniques

The relationship between CRP and SEP, race/ethnicity, and gender was examined using ordinary least-squares regression, which included weights and statistical adjustments for the NSHAP sample frame (O’Muircheartaigh et al., 2009). We based our determination of whether and to what extent covariates mediated the relationship between race and CRP employing standards set by Baron and Kenny (1986) mediation test. Step 1 requires that the independent variable (in this case race) significantly affects the mediator. Step 2 requires that the independent variable significantly affects the dependent variable in the absence of the mediator. Step 3 requires that the mediator have an independent relationship with the dependent variable. Step 4 requires that the mediator reduce the independent variable to zero for full mediation. We consider both the statistical significance and the change in the size of the race coefficient. We are explicit in the results regarding the exact percent size reduction.

The first set of analyses is conducted on the full sample to address two research questions. First, are there racial differences in CRP adjusting for self-reported health measures? Thus, Model 1 includes gender, race/ethnicity, age centered at age 68, self-rated general physical health, medications, and chronic conditions (see Table 1 for detailed health measures). Model 2 adds an age and race interaction term to test whether racial differences in CRP vary across age groups. Second, what explains racial differences in CRP levels? Model 3 tests for the role of SEP by including income and education. Model 4 tests for the role of behavioral risk factors by including BMI, smoking behavior, and exercise frequency.

Table 1.

Analytic Sample Descriptive Statistics. Weighted Means and Proportions (N = 1,757)

Full sample White non-Hispanic (n = 1,338) Black non-Hispanic (n = 233) Significancea
Mean/proportion SE Range Mean/proportion SE Mean/proportion SE
Log CRP (mg/L) 0.23 0.03 0.20 0.04 0.63 0.07 ***
Sociodemographic factors
    Age (years) 68.13 0.22 57–85 68.29 0.25 67.65 0.52
    Gender (female) 0.50 0.01 0.50 0.02 0.58 0.04 †
Race/ethnicity
    White non-Hispanic 0.86 0.01
    Black non-Hispanic 0.08 0.01
    Hispanic (any race) 0.07 0.01
Education ***
    Less than high school 0.20 0.01 0.16 0.01 0.35 0.04
    High school/general education diploma 0.38 0.01 0.40 0.02 0.32 0.04
    Some college/associates/  vocational certificate 0.18 0.01 0.18 0.01 0.17 0.03
    Undergraduate degree 0.14 0.01 0.15 0.01 0.09 0.03
    Postundergraduate degree 0.09 0.01 0.10 0.01 0.08 0.02
Household income ***
    1st quintile <$17,838
    2nd quintile $17,839–$35,037
    3rd quintile $35,038–$50,161
    4th quintile $50,162–76,809
    5th quintile ≥$76,810
Physical health factors
    Comorbidities 0–1
        Heart failure 0.08 0.01 0.08 0.01 0.10 0.02
        Heart attack 0.12 0.01 0.12 0.01 0.12 0.02
        Angioplasty, cardiac catheterization, or angiogram 0.25 0.01 0.26 0.01 0.29 0.04
        Hypertension 0.51 0.01 0.49 0.02 0.71 0.04 ***
        Diabetes 0.20 0.01 0.18 0.01 0.33 0.04 ***
        Arthritis 0.51 0.01 0.52 0.02 0.51 0.04
        Chronic obstructive pulmonary disease/emphysema 0.11 0.01 0.12 0.01 0.10 0.02
        Asthma 0.09 0.01 0.09 0.01 0.10 0.02
        Stroke 0.08 0.01 0.08 0.01 0.12 0.02 *
        Dementia 0.01 0.00 0.01 0.00 n/a n/a
    Self-rated physical health 3.31 0.03 1 = poor, 5 = excellent 3.37 0.04 2.99 0.08 ***
    Medication use 0–1
        Cholesterol absorption inhibitors 0.05 0.01 0.05 0.01 0.02 0.01
        Antihyperlipidemic combinations 0.03 0.00 0.02 0.00 0.02 0.01
        Estrogens 0.07 0.01 0.07 0.01 0.04 0.02
        Progestins 0.01 0.00 0.01 0.00 0.01 0.01 †
        Nonsteroidal antiinflammatory agents 0.11 0.01 0.11 0.01 0.09 0.02
        Steroidsb 0.06 0.01 0.06 0.01 0.03 0.02
        Antidepressantsc 0.13 0.01 0.14 0.01 0.10 0.02
Health Behaviors
    Body mass index (kg/m2) 28.93 0.15 14.84–67.30 28.76 0.17 28.89 0.50 *
    Smoking *
        Never 0.41 0.01 0.40 0.02 0.41 0.04
        Former 0.44 0.01 0.45 0.02 0.39 0.04
        Current 0.16 0.01 0.15 0.01 0.20 0.03
    Exercise ***
        <1–3 times/month 0.19 0.01 0.17 0.01 0.33 0.03
        1–2 times/week 0.15 0.01 0.15 0.01 0.19 0.03
        ≥3 times/week 0.66 0.01 0.68 0.01 0.49 0.03
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Notes. Source: Data are from National Social Life, Health, and Aging Project. CRP = C-reactive protein. Significance tests for differences in proportions are Wilcoxon–Mann–Whitney for education, income quintile, and exercise and χ² for smoking.

a

Significance tests for differences in means are two-tailed t tests.

b

includes androgens and anabolic steroids, nasal steroids, and inhaled corticosteroids.

c

includes ssri, tricyclic, phenylpiperazine, tetracyclic, ssnri, and other miscellaneous antidepressants.

†p < 0.1. *p < .05. **p < .01. ***p < .001. Weights adjust for differential probabilities of selection and for nonresponse.

The second set of analyses breaks the sample down by age (ages 57–74 and 75–85) and gender. Both prior research on CRP (McDade et al., 2010; Mitka, 2003) and our own sensitivity analyses testing interaction terms, indicate that subanalyses by age are appropriate. As we already discussed, findings of racial convergence in morbidity and mortality at older ages is nearly universal, likely due to survivor selection (Johnson, 2000). Further, separate analyses for men and women are important given prior research, discussed in the Introduction, that indicates important gender differences in how racial differences in CRP may manifest themselves via SEP and behavioral risk factors. Model 1 includes race, age, self-rated general physical health, medications, and chronic conditions to establish baseline racial differences for women as compared to men. Model 2 adds SEP variables (education and income) to establish the extent to which SEP mediates the relationship between race and CRP separately for men versus women. Model 3 adds BMI, smoking behavior, and exercise frequency to test the extent to which behavioral risk factors mediate the relationship between race and CRP separately for men versus women.

Results

Table 1 presents descriptive statistics on the dependent variable and covariates, in addition to differences in the covariates by race. As expected there are significant racial differences in CRP levels, socioeconomic covariates, behavioral risk covariates, and health covariates. Blacks have higher CRP levels, lower SEP levels, and more behavioral risk factors as compared to Whites.

CRP levels remain significantly higher amongst blacks as compared to whites, after adjusting for sex, age, health covariates, and prescription drug use, in this sample of those aged 57–85 (Table 2, Model 1). Black respondents had CRP levels 33% points higher than comparable whites. But that variation is age graded (Table 2, Model 2). At older ages (sensitivity analyses indicate above age 75–80), those differences become insignificant, both statistically and in terms of coefficient size (results shown in greater detail in Table 3). In short, there is racial crossover in CRP levels among the oldest sample respondents. This finding is robust given that NSHAP oversampled blacks over age 80. Although the main effects for Hispanics were not significant, there was an interaction between ethnicity and age. At older ages, the differences between whites and Hispanic CRP levels widen. Because this is a cross section and because who comprises Hispanics (nation of origin/immigration status/SES) has changed over time, it is difficult to make a meaningful interpretation of the positive interaction between age and Hispanic ethnicity.

Table 2.

Weighted Ordinary Least-Squares Coefficients for Natural Logged C-Reactive Protein Among Older Adults, All Ages

Model 1 Model 2 Model 3 Model 4
Race/ethnicity
    White, non-Hispanic (reference) – – – –
    Black, non-Hispanic 0.329*** 0.322*** 0.263** 0.220**
    Hispanic −0.097 −0.058 −0.148 −0.108
Female 0.191** 0.192** 0.158* 0.212**
Age (centered at 68) −0.010* −0.011* −0.015** −0.003
Race/ethnicity × Age (centered at 68)
    White, non-Hispanic*Age (reference) – – –
    Black, non-Hispanic*Age −0.020† −0.017 0.025*
    Hispanic*Age 0.031 **0.032 **0.026*
Education
    Less than high school (reference) – –
    High school/general education diploma −0.171* −0.153†
    Some college/associate's degree −0.095 −0.076
    College degree −0.223† −0.162
    Professional degree −0.377** −0.248*
Income quintile
    1 (reference) – –
    2 0.137 0.109
    3 0.075 0.047
    4 −0.037 −0.025
    5 −0.144 −0.115
Body mass index 0.059***
Smoking
    Never (reference) –
    Former 0.162*
    Current 0.434***
Exercise
    Once a week or less (reference) –
    1–2 times/week −0.057
    3+ times/week −0.227**
Constant 0.661*** 0.668*** 0.773*** −1.054***
N 1,757 1,757 1,757 1,757
F statistic 5.05*** 5.21*** 4.81*** 8.85***
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Notes. Sources: Data are from National Social Life, Health, and Aging Project (NSHAP). Significance levels for two-tailed tests of coefficients: †p < 0.1. *p < .05. **p < .01. ***p < .001. All models control for self-reported hypertension, diabetes, heart failure, cardiac arrest, heart surgery, arthritis, chronic pulmonary disease/emphysema, asthma, stroke, and general self-assessed health. They also include controls for the following drugs: cholesterol absorption inhibitor medication, combination antihyperlipidemic medication, estrogens, progestin, nonsteroidal antiinflammatories, steroids, and antidepressants. Weights adjust for differential probabilities of selection and for nonresponse.

Table 3.

Weighted Ordinary Least-Squares Coefficients for Natural Logged C-Reactive Protein Among Older Women and Men by Age 57–74 and Age 75–85

Model 1a Model 1b Model 1c Model 1d Model 2a Model 2b Model 2c Model 2d Model 3a Model 3b Model 3c Model 3d
Women Men Women Men Women Men Women Men Women Men Women Men
Ages 57–74 Ages 57–74 Ages 75–85 Ages 75–85 Ages 57–74 Ages 57–74 Ages 75–85 Ages 75–85 Ages 57–74 Ages 57–74 Ages 75–85 Ages 75–85
Race/ethnicity
    White non-Hispanic (reference) – – – – – – – – – – – –
    Black non-Hispanic 0.353** 0.407** 0.170 0.018 0.308* 0.322* 0.193 −0.006 0.210† 0.356 * −0.017 −0.033
    Hispanic −0.221 −0.214 0.365 0.347 −0.303† −0.340* 0.328 0.274 −0.306† −0.226 0.260 0.279
Age 0.000 −0.017† −0.044† 0.043† −0.004 −0.021* −0.044* 0.045† 0.009 −0.007 −0.043* 0.053*
Education
    Less than high school (reference) – – – – – – – –
    High school/general education diploma −0.300* −0.286* −0.025 0.321 −0.239† −0.209 −0.063 0.232
    Some college/associate's degree 0.026 −0.307* −0.337 0.110 0.074 −0.248 −0.347 0.076
    College degree −0.351† −0.219 −0.214 −0.041 −0.311 −0.111 −0.196 −0.031
    Professional degree −0.316 −0.456* −0.843* −0.442 0.028 −0.264 −0.700† −0.409
Income quintile
    1 (reference) – – – – – – – –
    2 0.144 0.209 0.295 −0.168 0.048 0.134 0.265 −0.146
    3 0.103 0.183 0.062 −0.187 0.059 0.121 0.044 −0.197
    4 −0.002 0.092 −0.004 −0.690 * 0.024 0.063 −0.028 −0.581
    5 −0.006 −0.102 −0.337 −0.397 0.052 −0.108 −0.309 −0.367
Body mass index 0.076*** 0.048*** 0.045*** 0.059***
Smoking
    Never (reference) – – – –
    Former 0.112 0.251* 0.033 0.236
    Current 0.283* 0.710*** 0.032 0.105
Exercise
    Once a week or less (reference) – – – –
    1–2 times/week –0.148* 0.049 −0.334 0.097
    3+ times/week −0.280 −0.289* −0.194 −0.254
Constant 1.209 1.741* 3.928* −3.069 1.597* 2.096** 3.964* −2.960 −1.611† −0.466 2.927† −5.196*
N 597 622 306 232 597 622 306 232 597 622 306 232
F statistic 2.99*** 12.55*** 3.07*** 2.15** 2.49*** 3.82** 2.76*** 2.28*** 4.78*** 6.14*** 3.1*** 3.55***
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Notes. Sources: Data are from National Social Life, Health, and Aging Project (NSHAP). Significance levels for two-tailed tests of coefficients: †p < .1. *p < .05. **p < .01. ***p < .001. All models control for self-reported hypertension, diabetes, heart failure, cardiac arrest, heart surgery, arthritis, chronic pulmonary disease/emphysema, asthma, stroke, and general self-assessed health. They also include controls for the following drugs: cholesterol absorption inhibitor medication, combination antihyperlipidemic medication, estrogens, progestin, nonsteroidal antiinflammatories, steroids, and antidepressants. Weights adjust for differential probabilities of selection and for nonresponse.

Does SEP explain racial differences? In model 3, after including SEP covariates, the size of the race effect drops from .32 to .26, a 15% reduction or a moderate change. Compared to those without a high school degree, those with high school degrees, college degrees, and professional degrees had CRP levels 17, 22, and 38% points lower, respectively, though the difference between those with college degrees and high school degrees was marginally statistically significant (p < .10). Finally, the gender coefficient shrinks from .19 to .16, with women having higher levels compared to men indicating that SEP differences between men and women play some role in explaining gender differences.

The inclusion of behavioral risk factors further elucidates racial variation in CRP (Table 2, Model 4). The inclusion of BMI, physical activity, and smoking patterns moderately reduces the race coefficient from .26 to .22, a 15% reduction. Though both SEP and behavioral risk factors moderately weaken the relationship between race and CRP, the differences remain statistically significant.

Table 3 tackles our second research question. What role does gender play in understanding racial differences? Does the role of socioeconomic and behavioral risk factors differ when examining men and women separately? Given both prior evidence and evidence in Table 2 of weakening racial differences in CRP at older ages, we further broke the analyses down by age (those over and under age 75).

Table 3 demonstrates that racial differences shrink significantly after age 75. Basic racial differences in CRP are just slightly larger for men than for women under age 75 (Model 1a and Model 1b), whereas the racial differences are not significant for either women or men age 75–85 (Model 1c and Model 1d).

Separate analyses for men and women in Models 2a–2d indicate that SEP explains 16%–18% of the racial variation in CRP for both men and women. Among women, the race difference dropped by about 16%, once adjusting for SEP. Among women under age 75, those with high school degrees (−.30, p < .05) and those with college degrees (−.35, p < .10) had lower CRP levels than those without high school degrees (Model 2a). Among women over age 75, the only statistically significant difference was those with professional degrees compared to those without high school degrees (−.84, p < .05; Model 2c). It should be noted, however, that even though NSHAP oversampled individuals aged 75 and older there are some groups, such as women with professional degrees that are quite small. Among men under age 75, the race coefficient shrinks by 18% (Model 2b). For men younger than age 75, when compared to those without a high school degree, those with a high school degree (−.29, p < .05), those with an associate’s degree (−.31, p < .05), and those with a professional degree (−.46, p < .05) all had lower levels of CRP. Among men over age 75, the only coefficient that was significant was the difference between those in the 4th income quintile compared to the bottom income quintile (−.69, p < .05).

Models 3a–3d demonstrate that behavioral risk factors explain a moderate amount of the racial variation among women under age 75, but not for men under age 75. For women under age 75, behaviors (BMI, smoking, and exercise) explain 33% of the racial variation in CRP (Model 3a), though race remains marginally significant with CRP levels among black women 21% higher than among white women. Contrastingly, among men under age 75, although behavioral risk factors are both significant predictors of CRP levels and help explain educational variation in CRP levels, they do not help explain the racial differences (Model 3b). Indeed, the race coefficient increases in both size and significance once behaviors (BMI, exercise, and smoking) are included in the models with black men aged 57–75 having CRP levels 36% higher than white men. This coefficient is little different than Model 1, where SEP and behavioral risk factors are not included. Among women and men aged 75–85 (Models 3c and 3d), racial differences in CRP are not significant, though BMI is independently associated with CRP.

Discussion

This study emphasizes the importance of gender, class, and behavioral risk factors, in addition to age, in understanding racial differences in CRP levels. In the full sample, SEP and behavioral risk factors explain relatively equivalent amounts of the relationship between race and CRP. Racial differences are not fully explained by socioeconomic and behavioral risk factor mediators. Examining women and men separately produced some interesting findings, however. First, behavioral risk factors explained nearly 30% of the relationship between race and CRP for women aged 57–74, as compared to SEP, which explained 13% of the relationship. However for men, although SEP did account for a fraction of the relationship, behavioral risk factors actually suppressed racial differences in CRP among men aged 57–74. Thus after adjusting for both SEP and behavioral risk factors, race differences in CRP were little different than models accounting for neither factor.

Similar to prior studies, we find diminishing racial differences in CRP at the oldest ages. It is unlikely that this diminishment is a cohort effect resulting from cross-sectional data. Indeed, racial differences should be larger in older cohorts given general improvements in economic well being and declining discrimination for younger cohorts. Healthy survivor selection likely plays a role in these declining differences at the oldest ages (Preston & Elo, 2006).

We also find that SEP, particularly education, and behavioral risk factors help explain racial differences in CRP levels. In the full sample, SEP and behavioral risk factors each explain approximately 15%–20% of the relationship between race and CRP, though the relationship remains statistically significant even after adjusting for both. The inclusion of behavioral risk factors also mediated SEP differences in CRP, which parallels findings in the existing literature (Alley et al., 2006; Friedman & Herd, 2010; Kershaw, Mezuk, Abdou, Rafferty, & Jackson , 2010; Lubbock, Goh, Ali, Ritchie, & Whooley, 2005). And similar to our findings, existing research generally does not find that behaviors fully explain relationship between SEP and inflammation (Khera et al., 2005; Kivimaki et al., 2005), though Pollitt and colleagues (2008) do find it explains much of the relationship for CRP, though not for an alternative marker for inflammation, fibrinogen.

Although behavioral risk factors play an important role in explaining racial differences in CRP levels, racial differences in behavioral risk factors may reflect coping mechanisms to manage stress and distress (Kershaw et al., 2009; Kim, Bursac, DiLillo, White, & West, 2009; Lewis, Aiello, Leurgans, Kelly, Barnes, 2010; D. R. Williams et al., 2010). For example, stress from discrimination may affect eating patterns and substance abuse (Borrell et al., 2007). This distinction is important because it implies that these behaviors may not be individualized and easily modifiable, but are a reaction to the stress associated with deprivation and discrimination (Geronimus et al., 2006; Phelen & Link, 2010).

When testing mediation effects separately for men and for women, the models indicated that SEP explained relatively similar amounts of the racial difference in CRP. Indeed, research examining the relationship between SEP and CRP has tended to find either no gender difference or a slightly larger relationship for women as compared to men (Gruenwald et al., 2009; Kivimaki et al., 2005; Rathmann et al., 2006). It is worth noting, however, that these studies were based on samples that were either younger than our sample (Gruenwald et al., 2009; Kivimaki et al., 2005) or racially homogenous (Rathmann et al., 2006).

Most striking was that among men aged 57–75, after adjusting for both SEP and behavioral risk factors, racial differences in CRP levels were almost unchanged. SEP did explain 18% of the difference, but behavioral risk factors were actually suppressing racial differences. So after adjusting for both SEP and behavioral risk factors, racial differences were nearly the same as when not adjusting for SEP and behavioral risk factors. These findings parallel prior work on young black men (Gruenwald et al., 2009).

Unlike for men, behavioral risk factors explained 33% of the racial differences in CRP among women and their inclusion reduced the race coefficient to marginal statistical significance. The difference in BMI as a mediator for racial differences for women as compared to men is likely rooted in the fact that there are no meaningful racial differences in BMI among men, but relatively large racial differences in BMI among women. Indeed, Kelley-Hedgepeth and colleagues (2008) find that BMI explains much of the racial differences among women, though they did not analyze men. Further, there is existing evidence that BMI is a more important mediator between SEP and CRP for women than for men (Gruenwald et al. 2009) and that BMI helps explain most of the higher CRP levels in women compared to men (Cartier et al. 2009). For example, Kershaw and colleagues (2010) find the mediated effect of diet was stronger for women than for men. Khera and colleagues (2009) find that body fat is a more important predictor of CRP in women than in men.

What explains the remaining racial differences in CRP levels, especially among men? One hypothesis that should be pursued in future work is the potential effects of stress, particularly discrimination. Indeed, fundamental cause theory would argue that race is a fundamental cause, separate from, though related to SEP precisely because of racial discrimination. As noted earlier, the fact that behavioral differences play a role in explaining racial differences, at least for women, may reflect coping patterns to manage stress and distress. There may be other ways that stress operates for black men that are not measured in this study. There is growing evidence that stress is linked to higher levels of inflammation (Fuligni et al., 2009; Kiecolt-Glaser et al., 2010; McDade et al., 2006; Taylor, Lehman, Kiefe, & Seeman, 2006). For example, a recent meta-analysis of laboratory studies concluded that acute psychological stressors produce increases in circulating levels of inflammatory proteins, including CRP (Steptoe, Hamer, & Chida, 2007).

Racial discrimination is thought to represent a particularly noxious type of chronic stress that is linked to a range of adverse health outcomes in blacks, and there is recent evidence that chronic discrimination is associated with higher levels of CRP in black adults (Lewis et al., 2010) and in E-selectin in a sample of older adults (Friedman, Williams, Singer, & Ryff, 2009). Further, discrimination is linked to heart disease—coronary calcification and behavioral risk factors (Borrell et al., 2007; Lewis et al., 2010). Future research must consider chronic stress generally, and experiences of discrimination specifically, as potential contributors to racial differences in systemic inflammation.

Although this study does not fully explain what mediates race difference in CRP, it does make clear that the pathways mediating racial differences in CRP may be different for men and women. And that for men, in particular, behavioral risk factors do little to explain the racial patterning of CRP.

Acknowledgments

Pamela Herd planned the study, supervised the data analysis, and wrote the paper. Amelia Karraker performed all statistical analyses and contributed to revising the paper. Elliot Friedman helped plan the study, including instrumentation, and revise the manuscript.

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