Racial/Ethnic differences in inflammation levels among older adults 56+: an examination of sociodemographic differences across inflammation measure

ABSTRACT

Objective

Chronic inflammation is a key biological risk factor for many widespread adult health conditions. This study examines racial/ethnic differences in inflammation across several inflammatory markers, including selected cytokines that are identified as important for aging and age-related health outcomes.

Methods

Data came from the 2016 Venous Blood Collection Subsample of the Health and Retirement Study. Using logistic regression models, we compared high-risk categories of C-reactive protein and cytokine markers (IL-6, IL-10, IL-1RA, TNFR1, and TGF-Beta), across race/ethnicity and whether these differences persisted among men and women.

Results

The findings provided evidence of significant race/ethnic differences in inflammatory measures, but the patterns differed across marker types.

Conclusions

These findings emphasize that race/ethnic differences are not consistently captured across markers of inflammation and that researchers should proceed with caution when using individual markers of inflammation in an effort to not overlook potential racial/ethnic differences in biological risk.

Introduction

Chronic inflammation is a key biological risk factor for several age-related adult health conditions, including but not limited to cardiovascular risk, cognitive decline, stroke, hypertension, atherosclerosis, and diabetes (Farina et al. 2022; Franceschi and Campisi 2014; Sanada et al. 2018). Over the last few decades, community-based and population studies have made a concerted effort to incorporate inflammation biomarkers, especially C-reactive protein (CRP), to better understand the distribution of biological risk in the population and the role inflammation has on understanding health outcomes and disparities. Chronic low-grade inflammation accounts for approximately 44% of mortality risk among older adults (Bonaccio et al. 2016) and is associated with the heightened risk of morbidities, such as stroke, cardiovascular disease, dementia, hypertension, among others (Farina et al. 2022; Franceschi and Campisi 2014; Sanada et al. 2018).

A large body of research has used inflammatory risk to better understand racial/ethnic health disparities. In general, racially minoritized populations in the United States have been found to have greater levels of inflammation than White populations (Cunningham et al. 2012; García and Ailshire 2019; Herd, Karraker, and Friedman 2012; Schmeer and Tarrence 2018). Prior studies, which largely rely on CRP, have found that Black and Hispanic adults also generally have greater inflammation levels than White adults (Khera et al. 2005; Nazmi and Victora 2007). In addition, some findings suggest inflammatory risk is higher for US-born Hispanics than foreign-born Hispanic adults (Crimmins et al. 2007). These racial/ethnic differences in inflammatory risk have been observed across the life course (Lam et al. 2021; Nazmi and Victora 2007; Schmeer and Tarrence 2018) and tend to continue at older ages (Farmer et al. 2020). Given the prominent racial disparities in mortality (Benjamins et al. 2021; Cunningham et al. 2017) and inflammation-related morbidities among older adults (Calvin et al. 2003; Farina et al. 2022; Hayward et al. 2000; Howard et al. 2019; Read and Gorman 2006), these findings highlight the importance of inflammation as an essential biomarker for understanding population health and health disparities.

Recently, population-based studies of health have expanded the measurement of inflammation beyond CRP. CRP is an acute-phase protein with low-level concentrations found in the blood stream of healthy individuals and rising levels are indicative of inflammation (McDade, Tessler Lindau, and Wroblewski 2011). The use of CRP has been scientifically expedient, because it can be acquired through dried blood spots, can be obtained from harder to reach populations, and may be more cost effective to process than other biomarkers (McDade, Tessler Lindau, and Wroblewski 2011). Nonetheless, scientific advancements have led to the discovery and collection of additional biomarkers of inflammation. At the current time, for example, population-based studies, such as the Health and Retirement Study (HRS), The Irish Longitudinal Study of Ageing (TILDA), Add Health, and the Longitudinal Aging Study in India (LASI), among many others, collect a more comprehensive panel of inflammation markers that include interleukin-6 (IL-6), Interluekin-10 (IL-10), and tumor-necrosis factor receptor 1 (TNFR1). Cytokines, in particular, are chosen because of their association with inflammation, measurement reliability, and accessibility, known relationship with aging (as part of the TAME panel), ability to predict all-cause mortality and morbidities and potential receptiveness to treatment and intervention (Justice et al. 2018; McDade 2011; Meier et al. 2023). Cytokines, such as IL-6 and IL-10, are thought to be less sensitive than CRP to infection and injury and potentially more reflective of exposures to social and environmental conditions across long periods of time (Goldwater et al. 2019; Meier et al. 2002; Tegeler et al. 2016). Due to their relatively recent collection, sociodemographic differences in inflammation levels by biomarkers remain largely unknown. Documenting how these biomarkers differ within and between racial/ethnic groups, this study will provide additional insight into how these markers can be used to further advance research on population health and health inequities.

Racial/ethnic differences in inflammation may vary across gender. Research has reported that women tend to have higher levels of inflammation than men (Farmer et al. 2021; Herd, Karraker, and Friedman 2012; Khera et al. 2005; Lakoski et al. 2006), with almost two times greater levels of CRP (Khera et al. 2009). Fewer studies have evaluated how inflammation differs at the intersection of race/ethnicity and gender. One study reported that white women and almost two-thirds of Black women were more likely to have high CRP levels (over >3 mg/l) compared to White men (Khera et al. 2005). In the same study, Black men were more likely to have high CRP levels compared to White men although this difference was not evident when cardiovascular risk, BMI and other factors were adjusted for (Khera et al. 2005). More recently, another study reported that Black men had 35% higher CRP levels compared to White men even after adjusting for behavioral and socioeconomic risk factors (Herd, Karraker, and Friedman 2012). Overall, Black women, when compared to Black men and White men and women, tend to have the highest levels of inflammation when measured with CRP (Farmer et al. 2020, 2021). Given that race/ethnicity and gender often intersect in the health disparities literature (Etherington 2015; Lett, Dowshen, and Baker 2020), we also assess whether racial/ethnic differences in inflammatory risk exist across gender to better understand heterogeneity across different systems of social stratification. When examining racial/ethnic and gender differences in inflammatory risk, it is important to keep in mind that these differences are not inherent biologically but rather emerge from variations in social experiences (i.e. racism and sexism) that are further reinforced by race and gender systems of stratification.

Below, we report racial/ethnic and gender differences in markers of inflammation for a representative sample of US older adults between the ages of 56 and 80 years. We used data from the 2016 Venous Blood Study (VBS) of the Health and Retirement Study, which contained additional markers of inflammation beyond CRP (IL-6, IL-10, IL1RA, TNFR1, and TGF-Beta). We used both individual and summary measures of these markers to evaluate inflammation differences among non-Hispanic White, non-Hispanic Black, and US-born and foreign-born Hispanic men and women. By including multiple markers of chronic inflammation, we provide a more comprehensive assessment of how inflammation risk is distributed across racial/ethnic population subgroups in the United States, which has broad implications on how the inclusion of additional inflammatory markers may help inform population health and health disparities research. Overall, our research indicates that systemic chronic inflammation is a critical biological pathway connecting health with social and environmental factors and may impact health outcomes.

Methods

The Health and Retirement Study (HRS) is a nationally representative, longitudinal study. We restricted our analysis to the 2016 wave obtained from the RAND HRS 2018 Longitudinal File. The HRS tracker file and the core data were merged to the 2016 HRS Venous Blood Study (VBS), which contained the biomarker measures of inflammation. Eligibility for the VBS study was limited to those who were 56 years of age and older and was based on respondents not living in a nursing home, not having a proxy respondent, having completed a prior interview in 2016, and agreeing to and completing subsequent blood collection (Crimmins, Faul, and Thyagarajan 2017). Blood samples were collected by phlebotomists at the respondents’ homes and were taken approximately two months after the respondents completed their 2016 HRS interview. Blood was centrifuged in the field and shipped cold to the Advanced Research & Diagnostics Laboratory at the University of Minnesota, and most samples arrived within a 24-hour window (Crimmins, Faul, and Thyagarajan 2017).

In total, the VBS sample contained 9,188 respondents who were assigned a non-zero weight. Spouses were assigned a zero weight. We further excluded respondents over the age of 80 because biomarker levels tend to plateau after age 80 making these markers less consistent in predicting health outcomes (Bai 2018). Age was centered on the mean, based on birth year, and taken from 2016 wave interview in the HRS tracker data. Only those who self-identified as non-Hispanic White, Black, and Hispanic were kept in the sample. Therefore, we excluded 34 respondents who were missing information about race/ethnicity and 279 respondents who identified as part of a racial/ethnic group other than non-Hispanic White, Black, and Hispanic. Three respondents were dropped due to not having information about their country of birth. After further accounting for missing biomarker data, our sample was reduced to 7,392 respondents.

Inflammatory Markers

The five inflammatory cytokine biomarkers collected in the VBS are: Interleukin-6 (IL-6), Interleukin-10 (IL-10), Interleukin-1 receptor antagonist (IL-1RA), Tumor necrosis factor receptor 1 (TNFR1), and transforming growth factor beta (TGF-beta). These inflammatory markers were selected by aging specialists as appropriate measures to analyze inflammation based on criteria from the TAME assays that included dependability and feasibility of measurement, relevance to aging, reliability to predict health outcomes and all-cause mortality and receptivity to intervention (Justice et al. 2018). In addition to the five cytokine markers, C-reactive protein (CRP) was included in the analysis.

Inflammatory Risk Measure

To measure inflammatory risk, we created a dichotomous variable to indicate high-risk for each inflammation marker. Based on prior work (Farina, Ki Kim, and Crimmins 2023), we first standardized each cytokine to adjust for differences in variances across measures. We then identified quartiles for each cytokine and created a dichotomous indicator reflecting the top 25% with high-risk values (assigned a score of “1”) and the 75% with lower-risk values (assigned a score of “0”). Based upon previous work, elevated levels of CRP, IL-6, IL-10, and TNFR1 correspond with high health risk while low levels of IL-1RA and TGF-Beta are associated with high health risk (Bektas et al. 2018; Farina, Ki Kim, and Crimmins 2023; Milan-Mattos et al. 2019; Rea et al. 2018). Respectively, these markers were top coded to the 99^th percentile, while IL-1RA and TGF-Beta was bottom coded to the 1^st percentile before creating high-risk categories. The cutoff points for each marker to be included in the high-risk group were the following levels (presented as non-standardized measurements): 5.02 and higher for CRP, 5.98 and higher for IL-6, 4.26 and higher for IL-10, 366.03 and lower for IL-1RA, 1,964.57 and higher for TNFR1, and 38,811.64 and lower for TGF-Beta.

As a sensitivity check, we evaluated whether results were sensitive to the specified form for each biomarker: continuous vs. logarithmic. Patterns remained the same. We used the original metric of inflammatory markers when creating high-risk categories for our analysis.

Race/Ethnicity and Gender

Race/ethnicity, gender, US birth, and age were self-reported measures. Respondents were categorized into four racial/ethnic categories: non-Hispanic White, non-Hispanic Black, US born Hispanics and foreign-born Hispanics. We classified Hispanics by US birth because of evidence that health outcomes (Boen and Hummer 2019; Lariscy, Hummer, and Hayward 2015), physiological aging (Farina, Ki Kim, and Crimmins 2023), and inflammation levels (García and Ailshire 2019) differ between foreign born and US born status. Respondents were also categorized by gender (male = 0, female = 1). Age was continuous based on their year of birth.

Analytic Strategy

To examine racial/ethnic differences in inflammation, we estimated several logistic regression models for each inflammation measure. In the first set of models, we evaluated whether Black, US-born Hispanics, and non-US-born Hispanics had greater odds of being classified in the high-risk categories across inflammation measures. These models were adjusted for gender and age. Next, we sought to evaluate whether racial/ethnic differences in being classified in the high-risk inflammation category differed for men and women. We estimated gender-stratified models to evaluate racial differences in inflammatory risk, adjusting for age. Additionally, to evaluate racial differences in the high-risk inflammation markers, we transformed the results into predicted probabilities of being in the high-risk category for each marker for the racial/ethnic and gender groups using the margins command in STATA. Further, to test the foreign-born advantage among Hispanic older adults, we performed an ancillary analysis using logistic regression models to evaluate the odds of being classified as high risk among Hispanic older adults. Finally, correlations were estimated to examine the relationships between the high-risk measures of inflammation. All models are controlled for age and gender. We evaluated the statistical significance at an α-level of .05. To adjust for sample selection and non-response, all models were weighted using the weights provided by the VBS subsection of HRS. The results are thus nationally representative. Model analyses were estimated using STATA 16.1.

Results

Sample Characteristics

Table 1 shows the unweighted sample descriptive for the analytical sample. The mean age varied between 64 and 68 years for each group. White adults were the oldest with an average age of 68.2, and US-born Hispanic adults were the youngest with an average age of 64.6. The average age of Black adults was 65.52, and foreign-born US Hispanic adults had an average age of 64.62. Over 50% of the sample identified as White, 19% identified as Black, 5.5% identified as US-born Hispanic, and 10.5% identified as non-US-born Hispanic. Further breakdown by demographic groups showed approximately 28% of the sample were White men, 37% were White women, 7% were Black men, 12% were Black women, 2% are the US-born Hispanic men, 3.5% are US-born Hispanic women, 4.5% are non-US born Hispanic men, and 6% are non-US born Hispanic women. Percentages of people in high-risk categories for the inflammation markers showed some variation by race/ethnicity. For CRP, 36% of Black adults were in the high-risk category, followed by US-Born Hispanics (31%), foreign-born Hispanics (25%) and Whites (21%). For IL-6, US-born Hispanics and Black adults had the highest proportions at high risk (32% and 30% respectively) while Whites and foreign-born Hispanics anchored the low end (23% and 22% respectively). A similar pattern was evident for IL-10 although the magnitude of differences between the groups was smaller compared with the differences for IL-6. Race/ethnic differences for IL-1RA, TNFR1, and IL-6 were less consistent than the results for the other markers, suggesting that race/ethnic differences in inflammatory risk are sensitive to the marker being considered.

Table 1. Unweighted demographic description of the analytic sample (n = 7,392).

	White (n = 4,792)	Black (n = 1,432)	Hispanic, US-born (n = 422)	Hispanic, non-US born (n = 746)
Gender
Men	2,078	539	172	320
	66.84%	17.34%	5.53%	10.29%
Women	2,714	893	250	426
	63.37%	20.85%	5.84%	9.95%
Age (mean, SD)	68.2	65.52	66.33	64.62
	(7.10)	(6.57)	(6.62)	(6.33)
Inflammation Markers
CRP (mg/L)
Top 25% (High Risk)	1,015	516	130	185
	21.18%	36.03%	30.81%	24.80%
Bottom 75% (Low Risk)	3,777	916	292	561
	78.82%	63.97%	69.19%	75.20%
IL-6 (pg/mL)
Top 25% (High Risk)	1,107	436	136	168
	23.10%	30.45%	32.23%	22.52%
Bottom 75% (Low Risk)	3,685	996	286	578
	76.90%	69.55%	67.77%	77.48%
IL-10 (pg/mL)
Top 25% (High Risk)	1,111	411	121	197
	23.18%	28.70%	28.67%	26.41%
Bottom 75% (Low Risk)	3681	1,021	301	549
	76.82%	71.30%	71.33%	73.59%
IL-1RA (pg/mL)
Top 25% (High Risk)	1,116	433	92	207
	23.29%	30.24%	21.80%	27.75%
Bottom 75% (Low Risk)	3,676	999	330	539
	76.71%	69.76%	78.20%	72.25%
TNFR1 (pg/mL)
Top 25% (High Risk)	1,285	324	107	132
	26.82%	22.63%	25.36%	17.69%
Bottom 75% (Low Risk)	3,507	1,108	315	614
	73.18%	77.37%	74.64%	82.31%
TGF-BETA (pg/mL)
Top 25% (High Risk)	1,228	355	107	158
	25.63%	24.79%	25.36%	21.18%
Bottom 75% (Low Risk)	3,564	1,077	315	588
	74.37%	75.21%	74.64%	78.82%

Overall Racial/Ethnic Differences in Inflammation Markers

Table 2 shows a greater likelihood for racial/ethnic minoritized groups to be in the high-risk categories for the inflammation markers, which is similar to the patterns found in Table 1. Compared to White respondents, Black adults were more likely to be in the high-risk category for CRP (OR = 1.89, p < 0.001), IL-6 (OR = 1.47, p < 0.001), IL-10 (OR = 1.58, p < 0.001), and IL-1RA (OR = 1.48, p < 0.001). Our results also showed similar patterns for US-born Hispanic adults. US-born Hispanic adults were 1.75 times (p < 0.001) more likely to be in the high-risk category for CRP, 1.80 times more likely to be in the high-risk category for IL-6, and 1.54 times (p < 0.01) more likely to be in the high-risk category for IL-10 than white adults. These patterns were less consistent for foreign-born Hispanic adults. While foreign-born Hispanic adults were 1.36 times (p < 0.05) more likely to be in the high-risk category for IL-10, they were less likely than White adults to be in the high-risk category for IL-6 (OR = 0.77, p < 0.05).

Table 2. Odds ratios from weighted logistic regression models predicting high-risk inflammation levels, HRS VBS 2016.

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	CRP	IL-6	IL-10	IL-1RA	TNFR1	TGF-Beta
Black	1.89***	1.48***	1.58***	1.49***	1.14	1.11
	(0.17)	(0.14)	(0.15)	(0.13)	(0.12)	(0.10)
Hispanic, US born	1.76***	1.80***	1.54**	0.97	1.14	1.08
	(0.26)	(0.26)	(0.23)	(0.16)	(0.18)	(0.17)
Hispanic, Foreign-born	1.07	0.77*	1.36*	1.22	0.90	0.83
	(0.14)	(0.10)	(0.18)	(0.16)	(0.14)	(0.11)
Female	1.48***	1.00	0.81**	0.81**	1.06	0.78***
	(0.11)	(0.07)	(0.06)	(0.06)	(0.08)	(0.05)
Age	0.99*	1.03***	1.03***	0.99*	1.09***	1.05***
	(0.00)	(0.00)	(0.01)	(0.00)	(0.01)	(0.01)
Constant	0.21***	0.29***	0.32***	0.36***	0.29***	0.38***
	(0.01)	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)

Referent group: White Men.

*** p < 0.001, ** p < 0.01, * p < 0.05.

Racial/Ethnic Differences in Inflammation Risk within Gender Groups

Next, we examined racial/ethnic differences in inflammation within gender groups to evaluate whether racial/ethnic differences were consistent for men and women (Table 3). Overall, we found that minoritized populations often had higher rates of inflammation for men and women. However, findings varied across inflammation markers. Among women, the likelihood to be in the high-risk category for inflammation was especially notable for Black and US-born Hispanic women. Both groups had higher odds of being in the high-risk category for IL-6 (OR = 1.71, p < 0.001, OR = 1.81, p < 0.01) and IL-10 (OR = 1.46, p < 0.001; OR = 1.69, p < 0.01). Black women were also 1.95 times (p < 0.001) more likely to be at high-risk for CRP and 1.28 times (p < 0.05) more likely to be in the high-risk category for TGF-Beta compared to White women. For IL-10, foreign-born Hispanic women were also more likely to be in the high-risk category (OR = 1.45, p < 0.05) than White women.

Table 3. Odds ratios from unweighted logistic regression models predicting racial/ethnic differences of high-risk inflammation levels by gender, HRS VBS 2016.

	CRP		IL6		IL10		IL1RA		TNFR1		TGF-BETA
VARIABLES	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women
Black	1.800***	1.97***	1.24	1.70***	1.73***	1.46***	1.81***	1.24	1.22	1.08	0.94	1.28*
	(0.287)	(0.21)	(0.21)	(0.18)	(0.27)	(0.17)	(0.25)	(0.15)	(0.24)	(0.12)	(0.14)	(0.15)
Hispanic, US born	2.237***	1.45*	1.78*	1.82**	1.37	1.69**	1.29	0.74	1.37	0.97	0.94	1.20
	(0.524)	(0.28)	(0.41)	(0.33)	(0.33)	(0.33)	(0.32)	(0.16)	(0.33)	(0.21)	(0.22)	(0.24)
Hispanic, Foreign-born	0.831	1.28	0.73	0.81	1.27	1.45*	1.72**	0.83	1.09	0.77	0.89	0.80
	(0.189)	(0.21)	(0.14)	(0.14)	(0.23)	(0.27)	(0.33)	(0.14)	(0.25)	(0.16)	(0.19)	(0.14)
Age	1.002	0.98***	1.03***	1.02***	1.03***	1.03***	0.99+	0.99	1.10***	1.08***	1.07***	1.03***
	(0.00803)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
Constant	0.215***	0.30***	0.29***	0.28***	0.31***	0.26***	0.34***	0.31***	0.28***	0.31***	0.39***	0.29***
	(0.0141)	(0.02)	(0.02)	(0.02)	(0.02)	(0.01)	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)

Referent group: White.

*** p < 0.001, ** p < 0.01, * p < 0.05.

Racial/ethnic differences in inflammatory risk were also prominent among men, most notably for Black men. Compared with White men, Black men were 1.8 (p < 0.001) times more likely to be in the high-risk category for CRP, 1.73 times (p < 0.001) more likely to be in the high-risk category for IL-10, and 1.81 times (p < 0.001) more likely to be in the high-risk category for IL-1RA. Further, US-born Hispanic men were more likely to be in the high-risk category for CRP (OR = 2.24, p < 0.001) and IL-16 (OR = 1.78, p < 0.05) compared to White men. Non-US born Hispanic men had 1.71 times (p < 0.01) greater odds of being in the high-risk category for IL-1RA compared to White men.

Differences in Inflammatory Risk for US-Born and Foreign-Born Hispanics

Differences in inflammatory risk were also evident among Hispanics by nativity status (see Table 4). Compared to US-born Hispanics, foreign-born Hispanics were less likely to be in the high-risk category for both CRP (OR = 0.62, p < 0.05) and IL-6 (OR = 0.43, p < 0.001). For the other inflammatory markers, we did not find any statistically significant differences.

Table 4. Odds Ratios from logistic regression models predicting high-risk inflammation levels for Hispanics, HRS VBS 2016 (n = 1,168).

	(1)	(2)	(3)	(4)	(5)	(6)
VARIABLES	CRP	IL-6	IL-10	IL-1RA	TNFR1	TGF-Beta
Foreign-born Hispanic	0.62*	0.43***	0.89	1.25	0.79	0.77
	(0.12)	(0.08)	(0.17)	(0.25)	(0.17)	(0.15)
Female	1.52*	1.02	0.95	0.46***	0.79	0.81
	(0.29)	(0.19)	(0.18)	(0.09)	(0.18)	(0.16)
Age	1.01	1.04**	1.03*	1.00	1.09***	1.05***
	(0.01)	(0.01)	(0.01)	(0.02)	(0.02)	(0.01)
Constant	0.37***	0.52***	0.45***	0.47***	0.38***	0.40***
	(0.07)	(0.09)	(0.08)	(0.09)	(0.07)	(0.07)

Referent group: US-Born Hispanic Men.

*** p < 0.001, ** p < 0.01, * p < 0.05.

Predicted Probabilities of Racial/Ethnic Differences in Inflammation Risk within Gender Groups

Next, to better understand overall racial/ethnic differences in “absolute” levels of high-risk inflammation we used gender-stratified logistic regression models to obtain the predicted probabilities of being classified into high-risk inflammation (shown in Table 5). Unlike odds ratios that are relative measures that are dependent on the overall level of the referent population, the predicted probabilities highlight the differences in magnitude across racial/ethnic groups. For several markers, Black and US-born Hispanic men had larger predicted probabilities than White and foreign-born Hispanic men. For CRP, the predicted probabilities for US-born Hispanics were nearly twice the size of that for foreign-born Hispanics (15% compared to 32%). IL-6 patterns had a similar range as CRP with foreign-born and US-born Hispanics again defining the lower and upper bounds. For IL-10, White men had the lowest predicted probability at 23% while 34% of Black men were classified into the high-risk category. For IL-1RA, TNFR1, and TGF-Beta, patterns were mixed and had smaller differences in magnitude. In fact, there are no discernable differences in being at high risk across the racial/ethnic groups for TNFR1 and TGF-BETA.

Table 5. Predicted probabilities from logistic regression models predicting racial/ethnic differences of high-risk inflammation levels by gender, HRS VBS 2016.

	CRP		IL6		IL10		IL1RA		TNFR1		TGF-BETA
VARIABLES	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women	Men	Women
White	0.18	0.24	0.22	0.22	0.23	0.20	0.26	0.24	0.20	0.22	0.26	0.22
	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
Black	0.28	0.38	0.26	0.32	0.34	0.27	0.39	0.28	0.23	0.24	0.25	0.26
	(0.03)	(0.02)	(0.03)	(0.02)	(0.03)	(0.02)	(0.03)	(0.02)	(0.03)	(0.02)	(0.02)	(0.02)
US-born Hispanic	0.32	0.31	0.33	0.33	0.29	0.30	0.31	0.19	0.25	0.22	0.25	0.25
	(0.05)	(0.04)	(0.05)	(0.04)	(0.05)	(0.04)	(0.05)	(0.03)	(0.04)	(0.04)	(0.04)	(0.04)
Foreign-born Hispanic	0.15	0.28	0.17	0.18	0.28	0.27	0.37	0.21	0.21	0.18	0.24	0.18
	(0.03)	(0.03)	(0.03)	(0.02)	(0.03)	(0.03)	(0.04)	(0.03)	(0.04)	(0.03)	(0.04)	(0.02)

The patterns among women were similar to those for men. White and foreign-born Hispanic women had lower predicted probabilities for CRP, IL6, and IL-10. The magnitude of the race/ethnic differences is smaller for the remaining markers and the patterns become less consistent across the markers.

Discussion

While prior research based on CRP levels has shown greater levels of inflammation for minoritized populations, we extended this work to determine whether elevated inflammatory risk would also be evident when using more novel inflammation markers that have been recently added to several nationally, representative studies. Understanding how inflammatory risk may differ based on the choice of inflammation marker has broad implications for understanding differences in biological risk in the population and their application to examine racial/ethnic disparities in health. Overall, we find patterns that point to greater inflammation risk for Black and US-born Hispanic older adults when compared to White older adults and foreign-born Hispanics for CRP, IL6, and IL-10. Racial/ethnic differences in inflammation were also similar for men and women. However, we also found some inconsistencies in the pattern of race/ethnic differentials across inflammatory markers, which may influence the utility of these markers for examining population health and health disparities.

Overall, the increased levels of inflammation risk among racial/ethnic minorities provides additional evidence that Black and US-born Hispanic adults exhibit greater levels of high-risk inflammation compared to White adults. This is consistent with other findings (Crimmins et al. 2007; García and Ailshire 2019; Khera et al. 2005; Nazmi and Victora 2007). These differences were most evident with CRP, IL-6, and IL-10. However, it is important to note that we did not find statistically significant differences for TNFR1 or TGF-Beta for US-born Hispanic and Black adults, and we also did not see statistically significant differences for IL-1RA among US-born Hispanics. These differences point to some important considerations when evaluating disparities in inflammation-related health outcomes. While some inflammation markers may be useful in capturing differences in inflammatory biological risk across race/ethnic groups, other markers may obscure or be irrelevant to a better understanding of differences in inflammation-related health outcomes. Further research is required to better clarify these patterns. Specifically, two areas of research are needed. First, whether racial/ethnic differences for these markers are also observed at younger ages. Second, how inflammation may be tied to different life experiences that may differ by race/ethnicity (e.g., experiences of discrimination or work opportunities). A better understanding of both the patterns and drivers of inflammation is of significant importance for population health research.

Our study also provided additional evidence that racial/ethnic differences in inflammatory risk were evident for both men and women. Prior research has largely focused on racial/ethnic differences for women. Black women, in particular, experience the highest levels of inflammation across race/ethnic groups (Chinn, Martin, and Redmond 2021; Farmer et al. 2020; Khera et al. 2005; Read and Gorman 2006). In this study, we found evidence that racial differences across gender groups were comparable. Black and US-born Hispanic men and women had higher levels of inflammation than their White counterparts. However, it is important to note that Black women had higher levels across several inflammation biomarkers, whereas the other groups were not as consistent, which may suggest that Black women, in particular, are more likely to have higher inflammation that extends across various biological inputs.

Finally, we evaluated whether differences in inflammation were observed for foreign-born and US-born Hispanics. Prior research has shown slower biological aging, lower inflammation, and a health advantage when evaluating disability and all-cause mortality for foreign-born Hispanics when compared to US-born Hispanics (Crimmins et al. 2007; Garcia et al. 2018; Lariscy, Hummer, and Hayward 2015). We find some evidence of lower inflammatory risk among foreign-born Hispanics. Compared to US-born Hispanics, foreign-born Hispanics had lower odds of being classified as having high-risk for CRP and IL-6. Given the importance of inflammation for numerous health conditions, this evidence is suggestive that some of the foreign-born health advantage of Hispanics may be driven by lower biological risk.

Limitations

Limitations are important to consider for this study. Prior studies have shown that racial/ethnic differences in biological aging profiles decline with older ages, specifically for those older than 75, which is most likely due to selective survival as those with greater biological risk have a greater mortality risk (Crimmins, Kim, and Seeman 2009; Farina, Ki Kim, and Crimmins 2023; Levine and Crimmins 2014). Therefore, racial/ethnic differences in inflammatory risk may differ in part due to variations in mortality processes for each group. The cross-sectional nature of the data also limits our understanding of inflammatory change for individuals and populations. For example, with reference to the comparison of foreign-born and US-born Hispanics, it is important to note that both acculturation processes and migration patterns may impact the cross-sectional association that foreign-born advantage observed in this data (Blue and Fenelon 2011; Cunningham et al. 2012; Niño and Hearne 2022; Scholaske, Wadhwa, and Entringer 2021). Longitudinal research that accounts for these changes at the individual and population level will better elucidate why we observe lower levels of high inflammation among foreign-born Hispanics. Additionally, due to sample limitations, we are unable to evaluate foreign-born differences by place of origin. For Hispanic older adults in the HRS, a large percentage self-identified as Mexican Americans as well as Cubans. Future work assessing within-Hispanic differences would be useful in providing a more holistic understanding of inflammatory differences. Finally, we evaluated inflammation markers individually, due to the research questions posed. However, these markers are interrelated (see Appendix: Supplementary Figure 1) and their dynamics are complex and may each perform a variety of functions within the immune system (Del Giudice and Gangestad 2018). A better understanding of how these markers can be combined and analyzed is necessary to fully understand how variability in inflammatory pathways and underlying inflammation-related biological risk, impact health and health disparities. Overall, while our study documents that differences in inflammatory risk exist, given that the differences observed are not inherent but rather arise from variations in social experiences (i.e. sexism and racism), future work should consider how systems of structural inequity impact these differences in biological risk.

Conclusion

Our study evaluates race/ethnic differences in inflammation across several inflammation markers, including selected cytokines that are identified as important for aging and age-related health outcomes (Justice et al. 2018) and are being added into several national longitudinal surveys. Evidence points to the significance of systemic chronic inflammation as an important biological pathway linking social and environmental exposures to health and may contribute to health inequalities. Here, we have focused on race/ethnicity to better understand how inflammation may or may not contribute to emergent research that seeks to understand racial/ethnic inequalities. This area of research will continue to be of great importance given that minoritized populations will be an increasingly larger share of the older population in the coming decades (US Census Bureau 2020). The inconsistency of racial/ethnic differences across markers raises two important considerations for biodemographic and biosocial research. First, scholars should be aware that markers documenting differences in inflammation-related biological risk are not equivalent. The choice of marker may impact the ability for researchers to detect differences in biological risk. Second, as studies add more biomarkers with improvements in biomarker collection and processing, it is important to include markers that both contribute to the large body of research already established (i.e. CRP) and to reinvestigate patterns based on prior findings with newer biomarkers to evaluate potential nuances across them and consider why they may arise.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19485565.2024.2356672

Additional information

Funding

This research was partially supported by infrastructure grants from the National Institute of Child Health and Human Development [P2CHD042849) and the National Institute on Aging (P30AG066614], awarded to the Population Research Center at The University of Texas at Austin by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The study was also supported by a training grant from the National Institute on Aging (K99AG07694) and an NIA research grant R56 AG057778. NIA also provided support for the collection of the study data [U01 AG009740]. The study was also supported by the National Institutes of Health grant [T32AG000037]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.