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J Gerontol B Psychol Sci Soc Sci. 2011 January; 66B(1): 129–136.
Published online 2010 February 19. doi:  10.1093/geronb/gbq008
PMCID: PMC3001750

Predictors of C-Reactive Protein in the National Social Life, Health, and Aging Project



Inflammation plays an important role in many chronic degenerative diseases associated with aging, and social, economic, and behavioral factors that contribute to inflammation may lead to differential burdens of morbidity and mortality in later life. This study examines socioeconomic status and race/ethnicity as predictors of C-reactive protein (CRP) among older adults in the United States and considers the degree to which health behaviors, medical conditions and medication use, and psychosocial factors account for these associations.


Multiple linear regression analysis of survey data for 1,580 participants, 57–85 years of age, in a population-based nationally representative sample of community-residing older adults in the United States.


Educational attainment, household wealth, and race/ethnicity were independently associated with CRP, with limited evidence for interactions with age. Health-related behaviors and usage of medications related to inflammation accounted for substantial proportions of these associations.


These results highlight the fundamental causes of inflammation among older adults and suggest pathways through which social disparities in inflammation may be reduced.

Keywords: Aging, Health disparities, Inflammation, Social determinants of health

C-REACTIVE protein (CRP) is an acute phase protein produced primarily by hepatocytes in response to proinflammatory cytokines, and the recent application of highly sensitive CRP assays has led to the discovery that relatively minor elevations of CRP—indicating chronic low-grade inflammation—are predictive of a wide range of diseases associated with aging, including cardiovascular disease (Danesh & Pepys, 2000; Ridker, Hennekens, Buring, & Rifai, 2000; Ridker, Rifai, Rose, Buring, & Cook, 2002), type 2 diabetes (Pradhan, Manson, Rifai, Buring, & Ridker, 2001; Thorand et al., 2003), dementia (Engelhart et al., 2004), and late-life disability (Kuo, Bean, Yen, & Leveille, 2006). Elevated CRP has also been prospectively associated with increased mortality risk in a healthy elderly population (Jenny et al., 2007). Although the causal processes linking chronic inflammation to disease and mortality are not fully elucidated, CRP has emerged as an important marker of disease risk in epidemiological research and in clinical research and practice (Pearson et al., 2003).

Dimensions of socioeconomic status (SES) are widely recognized among social scientists as “fundamental causes” of disease that operate through more proximate contextual, behavioral, and psychosocial pathways to shape individual health as well as disparities in population health (Link & Phelan, 1995). From this perspective, exposure to traditional “risk factors” for disease like smoking, or a poor quality diet, is probabilistically linked to social and environmental conditions beyond the individual. Socioeconomic disparities represent “risk regulators” that lead to health disparities by shaping the distributions of risk factors for disease across time and space (Glass & McAtee, 2006). In the context of aging, research on cumulative inequality and cumulative advantage or disadvantage has provided a theoretical framework for studying the dynamic relationships among social and biological factors as they shape trajectories of health across the life course (Dannefer, 2003; Ferraro & Shippee, 2009; Hatch, 2005). These conceptual tools, as well as recent incorporation of minimally invasive biological measures into population-based social science surveys (Lindau & McDade, 2008; McDade, Williams, & Snodgrass, 2007), are providing new opportunities for more comprehensive integrative approaches to health research that draw on the complementary strengths of the social and biomedical sciences.

We attempt to contribute to this effort by investigating the determinants of CRP in a nationally representative population-based survey of aging and health. The objectives of our analyses are threefold: (a) to investigate educational status, household assets, and race/ethnicity as predictors of CRP among older adults in the United States; (b) to consider interactions between age and measures of social stratification; and (c) to evaluate the extent to which health behaviors, medical conditions and medication use, and psychosocial factors account for social disparities in CRP concentration in later life.

We focus on education and household wealth as measures of SES based on prior work demonstrating their relevance to trajectories of aging and health (Robert & House, 1996; Shuey & Willson, 2008), and we investigate race/ethnicity as a distinct (but likely correlated) dimension of social stratification with dramatic implications for health in our society, particularly among African Americans (Geronimus, Hicken, Keene, & Bound, 2006; Hayward, Miles, Crimmins, & Yang, 2000). We evaluate interactions between age and these three measures of social stratification to explore whether social gradients in CRP strengthen with age, as suggested by “cumulative adversity” and “weathering” approaches, or whether they weaken, as suggested by “age-as-leveler” approaches to understanding the relationships among social adversity, aging, and health (Dupre, 2007). We then consider a set of more proximate factors that link social stratification and CRP in an effort to illuminate the pathways that may contribute to health disparities later in life.


Participants and Data Collection

Analyses are based on data from the National Social Life, Health, and Aging Project (NSHAP; Lindau et al., 2007). Briefly, a national probability sample of community-residing adults 57–85 years of age was selected to study biological and physical function pathways through which social status and relationships influence later life health. Of 4,017 eligible persons, 3,005 (1,550 women and 1,455 men) participated. In-home interviews were conducted between July 2005 and March 2006, and a random five-sixths of respondents (N = 2,494) were asked to provide a blood sample using a minimally invasive fingerstick method (McDade et al., 2007). The refusal rate was 16% (N = 389), and an additional 2% (N = 57) had equipment problems or were otherwise unable to provide a sample (Williams & McDade, 2009). Of the 2,048 providing a blood sample, 2,032 were received by the lab and available for analysis. The protocol was approved by the Institutional Review Boards of the University of Chicago and National Opinion Research Center; all respondents provided written informed consent.


Measures of SES and race/ethnicity.—

Age at the time of interview was calculated based on the date of birth provided by the respondent. Race/ethnicity was based on participant self-report, and for these analyses, participants were assigned to one of four categories: non-Hispanic White, non-Hispanic Black, Hispanic, or other (small numbers of individuals from several groups precluded a finer level of analysis).

Educational attainment was defined by five categories: less than high school, high school graduate/general education diploma, some college/associates degree/vocational certificate, undergraduate degree, and post undergraduate school. Household assets (net worth) were defined as ownership of property (e.g., home, rental property, car, businesses) and financial assets (e.g., savings accounts, stocks, bonds, pensions) after accounting for loans. Individuals were asked to provide an approximate dollar amount. For “don’t know” and “refused” responses, individuals were offered a series of bracketing questions to identify the range of assets.

Health behaviors.—

We assessed waist circumference as a summary measure of energy balance resulting from habitual patterns of physical activity and diet. Waist circumference was measured during the interview using a tape measure placed at the respondent’s natural waist, midway between navel and bottom of ribcage. The distribution of normal waist circumference is higher in men than in women, and internally derived sex-specific waist circumference z-scores were used in the analysis (McCarthy Jarrett, Emmett, Rogers, & Team, 2005; Ostberg et al., 2005).

Frequency of physical activity was rated on a 5-point scale (0 = never to 4 = 3 or more times per week) in response to the question “How often do you participate in physical activity such as walking, dancing, gardening, physical exercise or sports?” Sleep quality was measured by self-reported frequency of feeling rested in the morning (4-point scale, 0 = never to 3 = most of the time). Cigarette smoking status (never, former, or current) was based on self-report. Participants were also queried on whether they ever drink any alcoholic beverages, such as beer, wine, or liquor. In addition, we considered a measure of the cleanliness of the participant’s home based on interviewer assessment. At the end of the interview, the interviewer completed a debriefing questionnaire, which included the following item: “Describe the room(s) in which the interview was conducted.” Options ranged from clean = 1 to dirty = 5. We included this variable because prior research has shown this question to be associated with health outcomes, including CRP (Dunifon, Duncan, & Brooks-Gunn, 2001; McDade, Rutherford, Adair, & Kuzawa, 2008).

Medical factors.—

Current medical conditions were assessed by asking participants “has a medical doctor ever told you that you have any of the following conditions?” diabetes or high blood sugar; Alzheimer’s disease or another form of dementia; arthritis; emphysema, chronic bronchitis or chronic obstructive lung disease; asthma; stroke, cerebrovascular accident, blood clot or bleeding in the brain, transient ischemic attack, heart failure, or heart attack. Interviewers directly observed participants’ medications by asking to see all medications used “on a regular schedule, like every day or every week.” Our analysis considers commonly used prescription and over-the-counter medications with established effects on inflammation, categorized according to mechanism of action using Multum Drug Database: Lexicon Plus version (Qato et al., 2008), as follows: statins (HMG-CoA reductase inhibitors), immunosuppressive agents (antipsoriatics, antihistamines, leukotriene modifiers, and mast cell stabilizers), glucocorticoids (steroidal anti-inflammatories and inhaled corticosteroids), sex hormones (estrogens, progestin, and sex hormone combinations), nonsteroidal anti-inflammatories (analgesics, analgesic combinations, cox-2 inhibitors, miscellaneous analgesics, narcotic analgesic combinations, and antirheumatics), and coagulation modifiers (salicylates, 5-aminosalicylates, and antiplatelet agents).

Psychosocial factors.—

An 11-item short version of the Center for Epidemiological Studies-Depression scale was used as a measure of depression (Kohout, Berkman, Evans, & Cornoni-Huntley, 1993). A modified version of the seven-item anxiety subscale of the Hospital Anxiety and Depression Scale was used to assess anxiety levels (Snaith, 2002). Perceived stress was assessed using a modified version of the four-item Perceived Stress Scale (Pbert, Doerfler, & DeCosimo 1992). Marital status was based on the response to the following question: “Are you currently married, living with a partner, separated, divorced, widowed, or have you never been married?” We recognize that marital status is commonly considered a demographic factor, but we included it with other psychosocial factors following prior work evaluating relationship status and quality as psychosocial factors that influence immune function and inflammation in experimental settings (Kiecolt-Glaser et al., 2005; Robles & Kiecolt-Glaser, 2003)

Measurement of CRP.—

Details on the collection of dried blood spot samples and the analysis of CRP have been presented elsewhere (Williams & McDade, 2009). Briefly, free flowing capillary blood was collected on filter paper commonly used for neonatal screening (#903; Schleicher & Schuell, Keene, NH). Samples were analyzed in the Laboratory for Human Biology Research at Northwestern University using a high-sensitivity enzyme immunoassay protocol previously developed for use with blood spots (McDade, Burhop, & Dohnal, 2004). Prior validation of assay performance indicates that the blood spot CRP method has good sensitivity, precision, and reliability and a high correlation between matched plasma and blood spot samples (Pearson r = .96) (McDade et al., 2004). Of the 2,032 participants with blood samples available for testing, 1,939 (95%) had usable CRP data (exclusions were primarily due to an insufficient volume of blood).

Data Analysis

The distribution of CRP was highly skewed and was therefore log-transformed prior to analysis. In addition, because we are using CRP as an indicator of chronic low-grade inflammation that may increase risk for cardiovascular disease, we eliminated individuals with evidence of an acute inflammatory condition (e.g., infection). A recent joint scientific statement issued by the American Heart Association and the Centers for Disease Control and Prevention recommends that plasma CRP concentrations more than 10 mg/L be discarded (Pearson et al., 2003). Using a conversion formula based on prior comparison of matched plasma/blood spot samples (McDade et al., 2004), this plasma value corresponds to a blood spot CRP concentration of 8.6 mg/L. We therefore removed individuals with blood spot CRP greater than 8.6 mg/L from our analyses due to the likelihood that these high concentrations represented acute inflammation.

Analyses proceeded in four stages using a series of linear regression models with log-transformed CRP concentration as the dependent variable. We first investigated the bivariate relationships between CRP concentration and health behaviors, medical, and psychosocial factors. A p value <.05 was set as the criterion for statistical significance. Second, we considered models evaluating gender, race/ethnicity, and age as predictors of CRP (Model 1). We isolated these variables to evaluate whether associations were modified by the inclusion of education and assets. Third, we considered a model that included education, assets, race/ethnicity, gender, and age to document the extent to which disparities in CRP exist along these dimensions (Model 2). We also tested for interactions between age and education, age and assets, as well as age and race/ethnicity. We considered separate models stratified by age group (57–64, 65–74, and 75–85 years) to aid interpretation if the interaction terms reached statistical significance. Fourth, we examined health behaviors, medical, and psychosocial factors (found in the first stage) in a final model including education, assets, race/ethnicity, gender, and age. We investigated whether these proximate factors accounted for social disparities in CRP by evaluating the degree to which they modified regression coefficients for education, assets, and race/ethnicity in comparison to the base model. Analyses accounted for the sampling strata and clustering, and weights were employed to adjust for differential probabilities of selection and differential nonresponse. Standard errors were calculated using the linearization method (Binder, 1983). Statistical analyses were conducted with Stata Version 10.0 (StataCorp, 2008).

Complete sociodemographic and CRP data were available for 1,701 participants. Analyses excluded 121 participants with CRP >8.6 mg/L, resulting in an analytic sample of 1,580. The final model loses 35 observations due to missing values across different variables. The 35 participants not included in the final model did not differ from other participants with respect to CRP (p > .9), and results were very similar when these 35 observations were also excluded from the models evaluated in Stages 1 through 3.


The weighted response rate for the NSHAP study was 75.5% (74.8% unweighted). Distributions for the variables considered in our analyses are presented in Table 1. The demographic characteristics of the subset of respondents with complete sociodemographic and CRP data closely match those of respondents to the 2002 Current Population Survey (U.S. Census Bureau, 2003) as is true for the NSHAP sample overall (Lindau et al., 2007). The median blood spot CRP concentration was 1.5 mg/L (25th percentile: 0.6 and 75th percentile: 3.5). Individuals with CRP >8.6 mg/L excluded from subsequent analyses did not differ significantly from the rest of the sample with respect to age. However, Blacks compared with Whites (odds ratio [OR] = 2.95, 95% confidence interval [CI] = 1.84–4.72, p < .01) as well as females (OR = 1.96, 95% CI = 1.31–2.95, p < .01) and individuals with lower levels of educational attainment (OR = 1.42, 95% CI = 1.21–1.68, p < .01) and higher waist circumference (OR = 1.03; 95% CI = 1.02–1.04, p < .01) were more likely to be excluded due to acute elevations in CRP.

Table 1.
Descriptive Statistics for Entire Sample (N = 1,701). Population Mean (SE) Values are presented for continuous variables; population proportions (SE) are presented for categorical variables. Median (25%–75%) values are presented for CRP. All estimates ...

CRP concentration was significantly higher in non-Hispanic Black participants compared with Whites and among women compared with men (Table 2, Model 1). However, these differences were substantially attenuated when education and assets were considered (Table 2, Model 2). Higher educational attainment was associated with reduced CRP. Higher levels of household assets were also associated with lower CRP, but this association was significant only for individuals with the highest levels of assets (>$500,000). Compared with non-Hispanic Whites, non-Hispanic Blacks had marginally higher concentrations of CRP, and Hispanics had significantly lower concentrations. There was no significant gender difference in CRP after controlling for other sociodemographic factors.

Table 2.
Multiple Linear Regression Models Predicting Log-Transformed C-Reactive Protein Concentration.

We found no evidence for significant interactions between age and educational status (p = .57) or household assets (p = .18). However, associations between CRP and race/ethnicity were significantly moderated by age (p < .01). In the youngest age group, a similar pattern in CRP across race/ethnicity was seen as reported above, with higher CRP among non-Hispanic blacks (B = 0.105, SE = .077) and lower CRP among Hispanics (B = −0.197, SE = 0.069) compared with Whites. In the middle age group, the association between race/ethnicity and CRP was weaker (p = .07). In the oldest age group, differences in CRP concentrations across the racial/ethnic groups were further attenuated (p = .58).

We next investigated the extent to which associations between CRP and social status were independent of health behaviors, psychosocial factors, and medical factors that bivariate analyses revealed as significant predictors of CRP in our sample (Table 2, full model). Waist circumference and smoking (both currently and in the past) were positively associated with CRP. The interviewer’s rating of the cleanliness of the respondent’s household was also associated with CRP, with lower CRP concentrations for respondents living in a household evaluated as cleaner. The use of sex hormones and immunosuppressive medications were associated with elevated CRP, whereas statin use predicted lower CRP. Physical activity levels, sleep quality, alcohol use, history of heart failure or arthritis, depressive symptoms, and being married or cohabitating were significantly associated with CRP in bivariate analyses but not in the fully adjusted model. Remaining behavioral, psychosocial, and medical factors were not significantly related to CRP in bivariate analyses.

Educational status remained as a significant predictor of CRP in the fully adjusted model, although the strength of this association was substantially reduced. The interaction between race/ethnicity and age was also statistically significant in the full model (p < .01). Level of assets was not a significant predictor of CRP after controlling for more proximate behavioral, psychosocial, and medical factors.


CRP is an important marker of morbidity and mortality risk in older populations, and chronic inflammation may play a role in contributing to social and economic gradients in a wide range of diseases. In this study, we find evidence for substantial social gradients in CRP in a nationally representative cohort of older Americans: Educational status, assets, and race/ethnicity are all significant predictors of variation in CRP concentration. Education and race/ethnicity are significant predictors of acute (>8.6 mg/L) as well as chronic low-grade inflammation in our study. Health-related behaviors and medication usage represent more proximate predictors of CRP that account for substantial portions of these associations. These results highlight the fundamental causes of inflammation among older adults and suggest pathways through which social disparities in inflammation may be reduced. A better understanding of the determinants of population variation in CRP may be particularly important for guiding primary and secondary prevention efforts to reduce later life risk for diseases with an inflammatory component.

Our results are consistent with several studies reporting socioeconomic gradients in CRP, with higher concentrations of CRP found in groups with lower levels of education and income (Alley et al., 2006; Nazmi & Victora, 2007; Ranjit et al., 2007). Higher concentrations of CRP have also been consistently reported in African Americans compared with European Americans (McDade, Hawkley, & Cacioppo, 2006; Patel et al., 2006). Of note is the extent to which asset-based and race/ethnic differences in CRP, and substantial portions of educational differences in CRP, can be explained by behavioral and medical factors, suggesting that differences in lifestyle associated with broader socio-structural conditions are major contributors to social gradients in CRP. In particular, attenuation of the difference in CRP between non-Hispanic Blacks and Whites after adjustment for SES indicates that reduced opportunities for education and wealth may represent important mechanisms contributing to elevated CRP among non-Hispanic Blacks.

Associations with waist circumference and smoking are consistent with prior population-based research (McDade et al., 2006). Waist circumference was a particularly strong predictor of CRP, likely reflecting the importance of visceral adipose tissue as a source of proinflammatory cytokines, such as interleukin-6 (Lyon, Law, & Hsueh, 2003). Few prior studies finding social disparities in CRP have accounted comprehensively for medication use, an issue that is likely to be particularly important when conducting research on inflammation among older adults (Qato et al., 2008). We included several commonly used drug classes with mechanisms of action known to affect inflammation generally or CRP specifically. As has been previously reported in younger samples, people using cholesterol-lowering drugs, or statins, had significantly lower CRP concentrations (Jenkins et al., 2003). Exogenous sex hormone and immunosuppressive drug use have also been associated with higher CRP (Ridker, Hennekens, Buring, Kundsin, & Shih, 1999).

It is worth noting that household cleanliness was an independent predictor of CRP. The significance of this association is not clear. One possibility is that household cleanliness reflects personal traits related to organization and efficiency and that these traits promote health directly, or indirectly through their association with economic outcomes, as has been shown in prior research with the Panel Study of Income Dynamics (Dunifon et al., 2001). Alternatively, a less tidy home environment may contribute to chronic inflammation if it is associated with exposure to pollutants or increases the frequency or intensity of pathogen exposure. Prior research has documented significant associations among seropositivity to common infectious agents, inflammation, and the development of cardiovascular disease (Adiloglu et al., 2005; Zhu et al., 2000), and recent findings from a cohort of women in Philippines indicate that a household-based measure of pathogen exposure is an important predictor of CRP (McDade et al., 2008).

Psychosocial stressors have direct effects on multiple aspects of immune function and inflammation, and prior research has documented significant associations with CRP (Ford & Erlinger, 2004; Kiecolt-Glaser et al., 2003; Segerstrom & Miller, 2004). We find evidence for significant bivariate associations between CRP concentration and marital status and symptoms of depression, but these associations were not significant in the fully adjusted model. Although these results suggest that psychosocial factors do not play major roles in contributing to variation in CRP among older adults, the impact of psychosocial factors may operate in part through health behaviors. Smoking, for example, is more likely for individuals under stress (Kouvonen, Kivimaki, Virtanen, Pentti, & Vahtera, 2005). Our attempts to isolate independent associations between psychosocial factors and CRP thus represent a conservative test of their contribution to inflammation. Nonetheless, our results suggest that future population-based investigations into the psychosocial determinants of inflammation should consider a wide range of variables that may mediate associations with CRP.

CRP levels have been found to increase with age for men and women, but findings are inconsistent (Hutchinson et al., 2000; Khor et al., 2004). In our sample, age was negatively associated with CRP for non-Hispanic Blacks and Whites but positively associated with CRP for Hispanics. And in contrast to prior research using other indicators of health (e.g., House, Lantz, & Herd, 2005), we find no evidence to suggest that age modifies the association between CRP and education or assets, with some evidence to suggest that age attenuates differences in CRP associated with race/ethnicity. This pattern of results provides limited evidence in support of the “age-as-leveler” hypothesis with respect to race/ethnic differences in CRP, although the absence of a similar pattern with education or assets cautions against overinterpretation. Rather, these results suggest that mortality selection or cohort effects contribute to differences across age in our sample with respect to inflammation, and they underscore the need for prospective data to identify individual trajectories of aging, including how and why these trajectories differ across social groups.

A strength of this study is that we were able to evaluate a comprehensive set of behavioral, medical, and psychosocial predictors of CRP in a large sample representative of older Americans. Yet educational disparities in CRP remain unaccounted for by these variables, drawing attention to the limitations associated with the cross-sectional analysis of a single CRP measure. Conditions early in life may also make significant contributions to social gradients in inflammation: Recent research suggests that prenatal nutrition, growth in infancy, and infectious disease exposure in infancy and childhood have long-term implications for the regulation of inflammation and CRP production in adulthood (McDade, Kuzawa, Rutherford, & Adair, 2009; Sattar et al., 2004). Nonetheless, our findings contribute to a fuller understanding of inflammation as a physiological mechanism through which social factors contribute to disparities in health. Our focus on older adults complements prior research and further underscores the value of biosocial approaches to understanding the determinants of health across the life course.


National Institutes of Health (5R01AG021487); University of Chicago Center on Aging Core on Biomeasures in Population-Based Health and Aging Research (5P30AG 012857).


All authors report no financial conflicts of interest.


The authors would like to acknowledge research assistance provided by Dima Qato, PharmD, MPH, in relation to classification of the medication data. Dr. Qato is a paid employee of S.T.L. whose effort on this manuscript was supported by the National Opinion Research Center—University of Chicago Center on Aging Core on Biomeasures in Population-Based Health and Aging Research. We also thank Bhairavi Nallanathan, Andreea Mihai, and Alina Fomovska, paid undergraduate staff members in the laboratory of S.T.L., for research assistance. Author Contributions: T.W.M. conceptualized the analyses and prepared the manuscript. S.T.L. was involved in the design and execution of data collection for the NSHAP and contributed to data interpretation and manuscript preparation. K.W. implemented statistical analyses and assisted with manuscript preparation.


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