Interleukin-6, Diabetes, and Metabolic Syndrome in a Biracial Cohort: The Reasons for Geographic and Racial Differences in Stroke Cohort

Abstract

OBJECTIVE

Black Americans have a greater risk of type 2 diabetes than White Americans. The proinflammatory cytokine interleukin-6 (IL-6) is implicated in diabetes pathogenesis, and IL-6 levels are higher in Black individuals. This study investigated associations of IL-6 with incident diabetes and metabolic syndrome in a biracial cohort.

RESEARCH DESIGN AND METHODS

The Reasons for Geographic and Racial Differences in Stroke (REGARDS) study enrolled 30,239 Black and White adults age ≥45 years in 2003–2007, with a follow-up ∼9.5 years later. Baseline plasma IL-6 was measured in 3,399 participants at risk of incident diabetes and 1,871 at risk of metabolic syndrome. Relative risk (RR) by IL-6 was estimated with modified Poisson regression for both groups.

RESULTS

Incident diabetes occurred in 14% and metabolic syndrome in 20%; both rates rose across IL-6 quartiles. There was a three-way interaction of IL-6, race, and central adiposity for incident diabetes (P = 8 × 10⁻⁵). In Black participants with and without central adiposity, RRs were 2.02 (95% CI 1.00–4.07) and 1.66 (1.00–2.75) for the fourth compared with first IL-6 quartile, respectively. The corresponding RRs were 1.73 (0.92–3.26) and 2.34 (1.17–4.66) in White participants. The pattern was similar for IL-6 and metabolic syndrome.

CONCLUSIONS

Although IL-6 was higher in Black than in White participants and those with central adiposity, the association of IL-6 with diabetes risk was statistically significant only among White participants without central adiposity. The association with metabolic syndrome risk was similarly stronger in low-risk groups. The results support the concept of interventions to lower inflammation in diabetes prevention, but to reduce race disparities, better biomarkers are needed.

Graphical Abstract

Introduction

The burden of type 2 diabetes in the U.S. disproportionately affects Black individuals, who are 60% more likely than non-Hispanic White adults to develop diabetes and twice as likely to die as a result of diabetes (1). This disparity contributes to disproportionately increased risks of heart disease (2), stroke (3), and end-stage kidney disease (4). Diabetes risk also differs by sex; Black women are more than twice as likely as White women and Black men are 52% more likely than White men to develop diabetes (5). Metabolic syndrome is characterized by a cluster of at least three conditions from among dysglycemia, abdominal obesity, dyslipidemia, and increased blood pressure. Metabolic syndrome increases the risk of diabetes (6), and as with diabetes, there are race–sex differences; non-Hispanic Black women and non-Hispanic White men are more likely than other race–sex groups to have metabolic syndrome (7).

Interleukin-6 (IL-6) is implicated in the pathogenesis of diabetes (8,9). A higher IL-6 level was consistently associated with greater risk of incident diabetes, with a 2020 meta-analysis of 15 published studies finding a 24% greater risk per 1-unit increment of log-transformed IL-6 (10). Adipose tissue is a major source of IL-6, produced in part by activated macrophages, which accumulate in response to the release of hormones, cytokines, and free fatty acids by adipocytes (11). In turn, IL-6 promotes further macrophage infiltration in adipose tissue, mediating the transition from acute to chronic inflammation (12), and induces insulin resistance (8,9). IL-6 increases with obesity (13); thus, this feedback loop is chronically activated in obesity (8) and may indicate a role of IL-6 in diabetes pathogenesis. It is not known whether a higher IL-6 level is a risk factor for future metabolic syndrome, although cross-sectional studies have reported higher IL-6 levels (14) with metabolic syndrome.

Compared with White individuals, Black individuals have higher circulating concentrations of some proinflammatory biomarkers, including IL-6, despite having lower visceral adipose tissue (15). Studies examining the relationship of IL-6 with diabetes incidence by race are limited; we are aware of only one prospective cohort study, which observed participants over 4.7 years and showed no difference in this association by race (16).

In a subcohort of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) (17) study, we studied associations of baseline IL-6 with incident diabetes and metabolic syndrome. We sought to 1) quantify associations of baseline IL-6 with diabetes and metabolic syndrome and 2) identify if associations differed by race, sex, or central adiposity. We hypothesized that a higher IL-6 level would be positively associated with risks of diabetes and metabolic syndrome and that this relationship would differ by race, sex, and central adiposity.

Research Design and Methods

Participants

REGARDS is a national prospective cohort study of 30,239 participants from the contiguous U.S. who self-identified as non-Hispanic Black or non-Hispanic White (17). Black adults and those residing in the southeastern stroke belt (18) of the U.S. (North Carolina, South Carolina, Georgia, Tennessee, Mississippi, Alabama, Louisiana, and Arkansas) (19) were oversampled. Participants age ≥45 years were recruited by telephone between 2003 and 2007 from commercially available contact information. They completed a computer-assisted telephone interview and a standardized in-home visit to collect demographic information, clinical measurements, and blood and urine samples (20). REGARDS contacted participants (or proxies) every 6 months to collect health information. Approximately 10 years after enrollment in 2013–2016, 16,150 participants had a second extended telephone interview and in-home visit in which similar data were collected. Institutional review boards of all participating institutions approved the study, and informed consent was obtained from all participants.

BioMedioR Subcohort

The REGARDS Biomarkers as Mediators of Racial Disparities in Risk Factors (BioMedioR) study was designed to study relationships of circulating biomarkers with racial disparities in hypertension and diabetes and is described in detail elsewhere (21). The BioMedioR prospective subcohort was selected from 13,912 REGARDS participants who underwent both in-home assessments and had data on diabetes and hypertension status at both visits. Differences in characteristics of those lost to follow-up in REGARDS (22.0% of White participants and 28.4% of Black participants) were previously found not to have contributed to attrition bias in estimating racial differences (22). BioMedioR included 4,400 participants with 1,100 each of randomly sampled Black men, Black women, White men, and White women. This design allowed excellent power, as evaluated through extensive simulations across multiple sampling scenarios, to detect race and sex interactions with biomarkers (21) and allowed laboratory assays to be completed with cost efficiency (i.e., measurements were not required in 13,912 to answer the research questions). As previously reported, associations of risk factors with incident hypertension and diabetes were similar in the BioMedioR sample and the larger REGARDS sample (21). Two separate analytic samples of BioMedioR participants with baseline IL-6 data were created to study diabetes and metabolic syndrome risk separately; we excluded participants with prevalent disease or who were missing criteria to determine disease status from respective samples.

Laboratory Methods

Fasting blood was drawn from each participant at both visits, locally centrifuged for 10 min, and then sent overnight on ice to the central laboratory at the University of Vermont, where it was centrifuged at 30,000g, aliquoted, and stored at 193 K (20). At both visits, calorimetric reflectance spectrophotometry was used to measure glucose, HDL, and triglycerides with the Ortho Vitros Clinical Chemistry System 950 IRC (Johnson & Johnson Clinical Diagnostics). The HOMA for insulin resistance (HOMA-IR) was calculated as the product of glucose (mmol/L) and insulin (μU/mL) divided by 22.5, with insulin measured by two-step chemiluminescence immunoassay using the Roche Elecsys 2010 system (Roche Diagnostics) (23).

Baseline blood samples from the first in-home visit for BioMedioR participants were retrieved from the biorepository, and IL-6 was measured using ultrasensitive ELISA (V-PLEX Human Proinflammatory Panel I; Meso Scale Diagnostics, LLC, Rockville, MD). The laboratory analytic interassay coefficient of variation ranged from 6.2% to 9.3% at varying concentrations of control samples.

Diabetes and Metabolic Syndrome Definitions

Diabetes was defined as fasting glucose ≥6.99 mmol/L, random glucose ≥11.10 mmol/L, or self-reported use of diabetes medications (24). Incident diabetes was identified in participants who did not have diabetes at the first in-home visit (2003–2007) but were classified as having diabetes at the second (2013–2016).

Metabolic syndrome was defined using the harmonized definition (25) requiring presence of three of the following five risk factors: dysglycemia (fasting glucose ≥5.55 mmol/L or self-reported diabetes medication use), elevated blood pressure (systolic ≥130 and/or diastolic ≥85 mmHg or self-reported antihypertensive use), elevated triglycerides (≥1.69 mmol/L or self-reported fibrate, niacin, or omega-3 fatty acid use), low HDL (<1.04 [men] or <1.29 mmol/L [women] or self-reported fibrate or niacin use), or central adiposity (waist circumference ≥102 [men] or ≥88 cm [women]). Incident metabolic syndrome was identified in participants without metabolic syndrome at the first in-home visit but classified as having metabolic syndrome at the second.

Definitions of Covariates

All covariates were from the baseline examination unless otherwise noted. Data were collected using validated tools and methods (17). Demographic variables included self-reported age (years), sex (female or male), and race (Black or White). Geographic region was based on self-reported home location and categorized as the stroke buckle (an area of the U.S. with high stroke mortality in the coastal plains of North Carolina, South Carolina, and Georgia), the stroke belt (an area of the U.S. with high stroke mortality not reaching the same level as that in the stroke buckle; includes nonbuckle parts of North and South Carolinas and Georgia plus Tennessee, Mississippi, Alabama, Louisiana, and Arkansas), or the rest of the country (18,19).

Socioeconomic variables were self-reported and included education (less than high school, high school, some college, or college graduate) and annual household income (<$20,000, $20,000–$35,000, $35,000–$75,000, or ≥$75,000).

Anthropometric variables included waist circumference (measured midway between the lowest rib and iliac crest), BMI (kg/m²), and systolic and diastolic blood pressures (mmHg), calculated as the mean of two resting measurements taken 5 min apart.

Lifestyle variables were self-reported and included tobacco use (current, past, or never) and duration, weekly exercise frequency (none, one to three times, or more than four times), and current alcohol use (none, moderate [one to seven (women) or one to 14 weekly drinks (men)], or heavy [more than seven (women) or >14 weekly drinks (men)]). Regular dietary intake over the previous year was estimated with Block 98 Food Frequency Questionnaires (26) and used to calculate total energy intake (kcal). Dietary pattern scores were defined by factor analysis of intake of 56 food groups (27); a pattern heavy in fried food, processed meats, added fats, egg products, and sweetened beverages similar to dietary patterns observed in the southern U.S. was deemed the southern dietary pattern. Southern diet was considered continuously in analyses, with a higher score indicating greater concordance with this pattern.

Statistical Analysis

Survey weighting of all analyses was applied to account for sample selection, using a Taylor series as a finite population correction (21). In each analytic sample (for incident diabetes and incident metabolic syndrome), participant characteristics were tabulated by IL-6 quartile (28). Categorical variables were reported as percentage and continuous variables as mean (SD) or median (interquartile range [IQR]) based on skewness. IL-6 was log (base e) transformed when used as a continuous variable because of its skewed distribution.

The age-adjusted relative risk (RR) of incident diabetes and metabolic syndrome for Black compared with White participants was assessed for all participants in each analytic sample using Poisson regression with robust variance estimation (29).

Five Poisson regression models were used to calculate RRs of diabetes and metabolic syndrome for each quartile of IL-6, with the first quartile serving as reference, and per a 1-SD increment of log IL-6 (0.77 and 0.76 for the diabetes and metabolic syndrome analyses, respectively). Confounding variables were specified a priori for the association between IL-6 and diabetes and/or metabolic syndrome. Models 1–4 assessed groupings of related variables to provide detailed assessment of which confounding factors were most relevant. Model 1 included age, sex, race, and region. Model 2 included model 1 variables plus triglycerides and HDL. Model 3 included model 2 variables plus baseline tobacco use, tobacco pack-years, weekly exercise frequency, alcohol use, waist circumference, and BMI. Model 4 was our primary model of interest, which included model 3 variables plus education and annual household income. Model 5 included model 4 variables plus HOMA-IR. Across these models, participants missing covariate data were excluded. Visualizations were performed with restricted cubic splines using the Harrell method and model 4 covariates, with the RR expressed relative to the 12.5th percentile IL-6 (28).

Because diet data were frequently missing, a sensitivity analysis was designed to assess confounding by dietary factors. Here, only participants with complete dietary data were included in an analysis with model 4 variables and another with model 4 variables plus southern dietary pattern and total energy intake.

To determine whether there was effect modification by race, sex, or central adiposity (waist circumference ≥88 [women] or ≥102 cm [men]) on the association of log IL-6 with incident diabetes or metabolic syndrome, using the primary adjusted model of interest (model 4), interaction terms for each of these variables with IL-6 (log IL-6 ∗ sex, log IL-6 ∗ race, and log IL-6 ∗ central adiposity) were included in separate models. When testing modification by central adiposity, BMI was removed as a covariate. Interactions were considered significant if the P value for the cross-product term was <0.10 (more liberal than typical to allow interactions of interest with moderate effect to be detected). Poisson regression models were subsequently stratified by those variables showing significant interaction.

Analyses were conducted in R 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). The REGARDS protocol was approved by institutional review boards at all participating institutions. According to REGARDS policy, the aims and analysis plan were prespecified, reviewed, and approved by the REGARDS publications committee, which also reviewed the final manuscript and assured that the a priori plans were followed.

Results

Study Population

Supplementary Fig. 1 shows the inclusion flow diagram. Ultimately, 3,399 participants were included in the diabetes analysis; 460 of these (13.5%) developed incident diabetes. For the metabolic syndrome analysis, 1,871 participants were included; 439 (23.5%) had incident metabolic syndrome. Median (IQR) follow-up time was 9.5 (8.6, 9.9) years for both analytic samples.

Baseline characteristics of participants in the diabetes analytic sample by IL-6 quartile are listed in Table 1. Black participants comprised 32% of this sample and represented 55% of those in the highest quartile of IL-6. The mean (SD) BMI was 28.6 (5.5) kg/m², and waist circumference was 93.6 (14.9) cm; both increased with increasing IL-6 quartile, as did other covariates.

Table 1.

Baseline characteristics of diabetes analytic sample by IL-6 quartile, weighted to reflect study design

Characteristic	All or n missing*	IL-6 quartile, pg/mL
		Q1 (0.03 to <0.54)	Q2 (0.54 to <0.78)	Q3 (0.78 to <1.22)	Q4 (1.22 to 378)
n	3,399	831	829	857	881
Weighted n	11,074	2,771	2,768	2,769	2,766
Demographics
Age, years	0	60.9 (8.1)	62.8 (8.2)	63.8 (8.4)	63.6 (8.3)
Black race	0	36	39	47	55
Female sex	0	45	49	52	56
Geographic region	0
Stroke belt		35	32	35	33
Stroke buckle		20	20	21	22
Rest of U.S.		46	48	44	45
Socioeconomic factors
Education	0
Less than high school		4.9	6.2	8.4	12
High school		19	23	25	25
Some college		25	26	27	28
College graduate		51	45	40	36
Household income, $	0
<20,000		8.5	8.9	12	18
20,000–34,000		16	22	25	22
35,000–74,000		33	34	35	32
≥75,000		31	26	18	17
Unwilling to report		12	9.3	9.6	11
Disease
Metabolic syndrome	438	15.4	23.4	35	37.9
Medication use
Antihypertensive	139	31.8	43.7	48.9	54.6
Physical measures
Waist circumference, cm	13	88.4 (12.4)	92.9 (15.4)	96.5 (13.8)	98.6 (15.5)
BMI, kg/m2	10	26.4 (4.2)	28.0 (4.5)	29.6 (5.5)	30.6 (6.7)
Systolic blood pressure, mmHg	3	122 (14)	125 (15)	127 (15)	127 (16)
Diastolic blood pressure, mmHg	3	75 (9)	77 (9)	77 (10)	78 (9)
Laboratory measures
Triglycerides, mmol/L	1	1.3 (0.8)	1.3 (0.8)	1.5 (1.0)	1.5 (1.2)
HDL, mmol/L	13	1.5 (0.4)	1.4 (0.4)	1.3 (0.4)	1.3 (0.4)
Glucose, mmol/L	0	5.1 (0.6)	5.2 (0.8)	5.2 (0.7)	5.2 (0.8)
Insulin, pmol/L	36	50.7 (35.4, 84.7)	62.5 (41.7, 97.9)	79.2 (48.6, 118.8)	79.9 (52.8, 129.2)
HOMA-IR†	385	1.6 (1.1, 2.6)	2.0 (1.3, 3.1)	2.5 (1.5, 3.8)	2.5 (1.6, 4.2)
Lifestyle factors
Current tobacco use	9	6.9	10	13	17
Tobacco use, pack-years	72	0.0 (0.0, 7.8)	0.0 (0.0, 13.5)	0.3 (0.0, 16.5)	1.0 (0.0, 20.0)
Weekly exercise frequency	42
None		21	25	29	35
1–3 times		41	39	39	36
≥4 times		38	36	32	29
Current alcohol use	48
None		49	54	59	59
Moderate		47	41	36	35
Heavy		4.6	5.3	4.6	6.1
Southern diet score	740	−0.3 (1.0)	−0.1 (1.0)	0.0 (1.0)	0.2 (1.1)
Total energy intake, kcal	740	1,611 (1,245, 2,055)	1,636 (1,253, 2,141)	1,589 (1,239, 2,112)	1,645 (1,246, 2,115)

Continuous variables are presented as mean (SD) or median (IQR) based on the skewness of the variable. Proportions are presented as percentage. Percentages may not add to 100% because of rounding.

^*For rows n and Weighted n, column indicates all; for remaining rows, column indicates n missing.

^†n missing is greater than that for those missing glucose or insulin because of nonfasting status for some participants.

Baseline characteristics by IL-6 quartile in the metabolic syndrome analytic sample are listed in Table 2. Black participants comprised 32% of this sample and 57% of those in the highest quartile of IL-6. Mean (SD) BMI was 27.1 (4.7) kg/m², and mean waist circumference was 89.7 (14.5) cm; as in the diabetes sample, risk factor prevalence increased with increasing IL-6 quartile.

Table 2.

Baseline characteristics of metabolic syndrome analytic sample by IL-6 quartile, weighted to reflect study design

Characteristic	All or n missing*	IL-6 quartile, pg/mL
		Q1 (0.03 to <0.50)	Q2 (0.50 to <0.72)	Q3 (0.72 to <1.08)	Q4 (1.08 to 378)
n	1,871	455	455	468	493
Weighted n	6,037	1,513	1,508	1,507	1,509
Demographics
Age, years	0	60.4 (7.8)	62.4 (8.2)	64.0 (8.5)	63.3 (8.3)
Black race	0	36	39	46	57
Female sex	0	46	49	49	52
Geographic region	0
Stroke belt		34	31	33	33
Stroke buckle		21	24	21	24
Rest of U.S.		45	46	46	43
Socioeconomic factors
Education	0
Less than high school		4.6	5.1	6.4	8.9
High school		20	24	22	26
Some college		25	23	25	28
College graduate		50	48	47	38
Household Income, $	0
<20,000		8.6	5.3	12	14
20,000–35,000		16	20	23	25
35,000–75,000		36	36	34	35
≥75,000		28	28	22	16
Unwilling to report		12	10	9.4	10
Disease
Diabetes	0	5.3	5.9	7.9	6.1
Medication use
Antihypertensive†	18	25	32	40	42
Fibrate	0	0.7	0.0	0.4	0.0
Niacin	0	0.4	0.4	0.0	0.2
Omega-3 fatty acid	0	6.8	3.7	2.8	3.2
Physical measures
Waist circumference, cm	0	86.3 (11.8)	89.0 (11.8)	92.3 (16.6)	93.9 (14.9)
BMI, kg/m2	5	25.8 (3.9)	26.7 (4.0)	27.8 (4.6)	28.5 (5.7)
Systolic blood pressure, mmHg†	1	121 (14)	122 (15)	124 (15)	125 (15)
Diastolic blood pressure, mmHg†	1	75 (9)	76 (8)	76 (9)	76 (9)
Laboratory measures
Triglycerides, mmol/L	0	1.1 (0.5)	1.2 (0.5)	1.2 (0.5)	1.3 (1.4)
HDL, mmol/L	0	1.5 (0.4)	1.5 (0.4)	1.4 (0.4)	1.4 (0.4)
Glucose, mmol/L	0	5.1 (0.7)	5.2 (0.8)	5.2 (1.2)	5.1 (1.2)
Insulin, pmol/L	111	44.7 (32.4, 64.5)	51.6 (34.8, 75.4)	60.1 (39.2, 88.3)	62.6 (42.0, 92.5)
HOMA-IR	111	1.5 (1.0, 2.1)	1.7 (1.1, 2.5)	1.9 (1.2, 2.9)	2.0 (1.3, 3.0)
Lifestyle factors
Current tobacco use	7	5.1	10	9.0	19
Tobacco use, pack-years	36	0.0 (0.0, 6.2)	0.0 (0.0, 10.0)	0.0 (0.0, 13.2)	1.0 (0.0, 21.8)
Weekly exercise frequency	26
None		21	22	30	34
1–3 times		42	40	35	37
≥4 times		37	38	35	29
Current alcohol use	23
None		51	51	57	57
Moderate		43	44	37	37
Heavy		5.1	4.2	5.8	6.6
Southern diet score	389	−0.3 (1.0)	−0.2 (1.0)	−0.1 (0.9)	0.2 (1.1)
Total energy intake, kcal	389	1,594 (1,216, 2,078)	1,633 (1,297, 2,144)	1,528 (1,180, 2,086)	1,620 (1,225, 2,106)

Continuous variables are presented as mean (SD) or median (IQR) based on the skewness of the variable; proportions are presented as percentage. Percentages may not add to 100% because of rounding.

^*For rows n and Weighted n, column indicates all; for remaining rows, column indicates n missing.

^†Participants missing either antihypertensive medication use status or blood pressure measurement remained in the sample only if they met the elevated blood pressure criterion of the metabolic syndrome definition through the other.

Distribution of IL-6

Supplementary Fig. 2 presents baseline log IL-6 distribution for both analytic samples by incident disease status. In the diabetes sample, median (IQR) baseline IL-6 was 0.79 (0.54, 1.24) pg/mL. In the metabolic syndrome sample, it was 0.73 (0.51, 1.12) pg/mL. For both analytic samples, median log IL-6 at baseline was higher in the group that went on to develop disease.

Incidence of Diabetes or Metabolic Syndrome

Supplementary Table 1 summarizes risk of incident diabetes or metabolic syndrome by race. In the diabetes analytic sample, 14% of participants developed incident diabetes, with a greater percentage seen in Black than in White participants (18% vs. 10%). The age-adjusted RR of diabetes for Black compared with White participants was 1.90 (95% CI 1.59–2.27). The rate of incident diabetes increased across successive IL-6 quartiles overall and in both race groups.

In the metabolic syndrome analytic sample, 23% of participants developed incident metabolic syndrome at follow-up. The age-adjusted RR of metabolic syndrome for Black compared with White participants was 1.06 (95% CI 0.90–1.25). As with diabetes, the rate increased across IL-6 quartiles.

Association of IL-6 With Incident Diabetes

Strong interactions were observed for log IL-6 ∗ race and log IL-6 ∗ central adiposity on incident diabetes (P = 6 × 10⁻⁵ and 0.001, respectively). A three-way interaction for log IL-6 ∗ race ∗ central adiposity was therefore tested (including all two-way interactions), and this was highly statistically significant (P = 8 × 10⁻⁵). There was no sex interaction (P = 0.80). Because of these interactions, stratified analyses were pursued, with model 4 (primary model) results including spline plots shown in Fig. 1, which shows a log-linear relationship for White participants without central adiposity. Similarly, in Supplementary Table 2, both in the quartile-based analysis and per SD increment of log IL-6, the association of IL-6 with incident diabetes was greatest in White participants without central adiposity. Specifically, in model 4, they had an RR of 2.34 (95% CI 1.17–4.66) for the fourth compared with first IL-6 quartile; per SD increment of log IL-6, this RR was 1.44 (1.26–1.64). Black participants with central adiposity had the next strongest association in the quartile-based analysis (RR 2.02 [1.00–4.07]) (Supplementary Table 2); however, this and associations in other stratified groups did not maintain statistical significance. For each SD increment log IL-6, White participants with central adiposity had the second greatest association (RR 1.34 [1.11–1.63]) (Supplementary Table 2).

Figure 1.

A–C: RR (95% CI) of incident diabetes by quartile of baseline IL-6 and per SD log IL-6 stratified by race and central adiposity (A) and restricted cubic splines showing the RR (95% CI) for incident diabetes by baseline IL-6 relative to 12.5th percentile of the IL-6 distribution, stratified by race and central adiposity (B and C). A: RR (95% CI) for incident diabetes estimated with modified Poisson regression, adjusting for model 4 covariates: age, sex, region, triglycerides, HDL, baseline tobacco use status, tobacco pack-years, weekly exercise frequency, alcohol use, education, and annual household income. Central adiposity was defined using the metabolic syndrome criterion for elevated waist circumference. B and C: Restricted cubic splines showing the RR (95% CI) for incident diabetes by baseline IL-6 stratified by central adiposity among Black (B) and White (C) participants, adjusting for covariates in model 4. Shaded areas represent 95% CIs. Splines are relative to the median of the first quartile (12.5th percentile) of IL-6, with knots (fifth, 50th, 87.5th, and 95th percentiles) established by a modified version of the Harrell method (28). Knot locations described in Supplementary Table 6. Kernel density plots depict distribution of IL-6 by baseline central adiposity and incident diabetes status. The x-axes are presented in a log scale.

Supplementary Table 2 summarizes similar patterns across all five models; in model 1, the RR gradually rose across quartiles in all four race/central adiposity groups, with the steepest rise and highest RRs across quartiles in White participants without central adiposity (RR 1.27 [95% CI 0.65–2.48], 2.04 [1.08–3.86], and 3.04 [1.65–5.60] in model 1 quartiles 2, 3, and 4, respectively). Adding covariates in successive models modestly attenuated the RRs across quartiles. The P values for trend across quartiles were statistically significant from models 1 to 4 among White participants, whereas statistical significance was lost for Black participants without central adiposity. Trends were similar per SD of baseline log IL-6.

For model 5 adding HOMA-IR, the RRs among White participants regardless of central adiposity were attenuated; there was a modest impact among Black participants with central adiposity, but among Black participants without central adiposity, the RR was accentuated (Supplementary Table 2).

In a sensitivity analysis to understand confounding by diet, which was missing in 28.1% of participants, we reanalyzed model 4 in each subgroup, including only participants with complete diet data for comparison with the same model with diet added (Supplementary Table 3). Here, there was no confounding observed by diet.

Association of IL-6 With Incident Metabolic Syndrome

For metabolic syndrome, the interaction P values for log IL-6 ∗ race and log IL-6 ∗ central adiposity were 0.07 and 9 × 10⁻⁴, respectively; there was no three-way interaction (P = 0.73) or sex interaction (P = 0.23). Figure 2 shows the RR of metabolic syndrome stratified by race and central adiposity separately in model 4. For those with central adiposity, a higher IL-6 level was associated with greater metabolic syndrome risk in the spline regressions. Examined by quartile and per SD increment, the relationship was less clear in those with central adiposity, whereas for those without central adiposity, all three analyses were concordant, with the RR for participants in the fourth IL-6 quartile relative to the first being 1.69 (95% CI 1.21–2.37) (Supplementary Table 4). With race stratification, the per SD increment and spline regression demonstrated a greater association in White than in Black participants; this was less apparent considering IL-6 quartile.

Figure 2.

A–D: RR (95% CI) of incident metabolic syndrome by quartile of baseline IL-6 and per SD log IL-6 stratified by central adiposity (A) and separately stratified by race (B) and restricted cubic spline plots showing the RR (95% CI) for incident metabolic syndrome by baseline IL-6 relative to the 12.5th percentile of the IL-6 distribution stratified by central adiposity (C) and separately stratified by race (D). A and B: RR (95% CI) for incident metabolic syndrome estimated with modified Poisson regression, adjusting for model 4 covariates: age, sex, race (excluded from race-stratified analyses), region, triglycerides, HDL, baseline tobacco use status, tobacco pack-years, weekly exercise frequency, alcohol use, waist circumference (excluded from central adiposity–stratified analyses), BMI (excluded from central adiposity–stratified analyses), education, and annual household income. Central adiposity was defined using the metabolic syndrome criterion for elevated waist circumference. C and D: Restricted cubic splines showing the RR (95% CI) for incident metabolic syndrome by baseline IL-6, stratified by central adiposity (C) and separately stratified by race (D), adjusting for covariates in model 4. Shaded areas represent 95% CIs. Splines are relative to the median of the first quartile (12.5th percentile) of IL-6, with knots (fifth, 50th, 87.5th, and 95th percentiles) established by a modified version of the Harrell method (28). Knot locations described in Supplementary Table 6. Kernel density plots depict distribution of IL-6 by baseline central adiposity and incident metabolic syndrome status (B) and race and incident metabolic status (C). The x-axes are presented in log scale.

Supplementary Table 4 shows similar patterns across all five models; for participants without central adiposity, IL-6 in the third and fourth quartiles compared with quartile 1 showed statistically significant elevated risk in all models, with modest attenuation in models 1–4. The only other subgroup with significant elevated risk was White participants, only in models 1 and 2. The P values for trend across quartiles were statistically significant in all models among participants without central adiposity only. Trends were similar per SD of baseline log IL-6, although associations became statistically significant among participants with central adiposity.

For model 5 adding HOMA-IR, the RRs attenuated among those with central adiposity and remained essentially the same among those without central adiposity. Model 5 RRs were accentuated among Black participants and attenuated among White participants (Supplementary Table 4).

Diet data were missing in 20.8%, so we analyzed those with complete diet data in a sensitivity analysis (Supplementary Table 5); no confounding by diet was observed in this analysis.

Conclusions

In this biracial U.S. cohort, higher IL-6 level was a risk factor for both diabetes and metabolic syndrome; however, associations were generally greater in lower-risk subgroups (i.e., in White compared with Black participants) and in those without central adiposity.

Our findings are consistent with the understanding that IL-6–mediated inflammation plays a causal role in the development of insulin resistance (10). Chronic inflammation is implicated in the insulin resistance seen in diabetes and metabolic syndrome (30). This is likely a result of IL-6 production by activated macrophages in adipose tissue, which accumulates in proportion to adipocyte size (8,11).

Our findings do not support the literature evidence that IL-6 might be a suitable marker for risk assessment for future diabetes in clinical settings. A meta-analysis of 15 prospective studies including 5,421 incident cases and 31,562 noncases showed a 24% greater hazard of developing diabetes per 1-unit increment log-transformed higher IL-6 (hazard ratio 1.24; 95% CI 1.17–1.32) but presented no information on racial differences (10). The current findings address this gap and detected differences in the association by race and central adiposity. Specifically, although our findings among White participants were similar to the literature (10), there was no robust association in Black participants, and there was a race difference that was highly statistically significant, so general clinical applicability of IL-6 measurement is unlikely. This finding contrasts with results of the Multi-Ethnic Study of Atherosclerosis, which reported an association of higher IL-6 level with risk of incident diabetes over 4.7 years but found no significant difference in the association by race (16). This study had shorter follow-up and four ethnic groups which were all free of clinical cardiovascular disease at baseline. These factors and low power may have affected the ability to detect race differences.

In cross-sectional and case–control studies, IL-6 level was higher among patients with versus without metabolic syndrome (14). We are aware of no prospective studies on IL-6 and risk of incident metabolic syndrome and no previous data comparing this association in Black and White participants.

The generally greater association of higher IL-6 level with risk of incident diabetes or metabolic syndrome in participants without versus with central adiposity at baseline may suggest that IL-6 from sources other than central adipose tissue may more effectively induce insulin resistance than that from central adipose tissue. However, in light of existing models describing insulin resistance resulting from inflammation of central adipose tissue, it is possible that those with central adiposity and a relatively high IL-6 level at baseline may have been excluded from our analysis because they developed diabetes at a younger age, leading to the observed findings. Alternatively, the observed difference may be due to other types of bias or confounding not accounted for in the analysis or to chance.

Interventions that might alter IL-6–related diabetes and metabolic syndrome include lifestyle changes (e.g., improved physical activity (31) and healthier diet (32)) or pharmacologic agents (e.g., biguanides (33), sodium–glucose cotransporter 2 inhibitors (34), and monoclonal antibodies (35)). However, findings here suggest that targeting IL-6 in middle and older age may not help to reduce the racial disparity in diabetes affecting Black individuals, because the association of IL-6 with diabetes risk was less apparent in Black compared with White individuals. Future studies should focus on extending our findings to younger individuals, because it is possible inflammation-related diabetes emerges at a younger age in Black individuals than the age group considered here. Existing research shows that the experience of persistent discrimination contributes to elevated inflammation in Black individuals (36,37), and higher IL-6 levels have been observed in racially segregated populations (38), so further study on the relationship between inflammation and glucose disorders in Black individuals is warranted. This should include evaluation of other inflammation-sensitive biomarkers that might reveal possible interventions to narrow the racial disparity.

The present analysis has limitations. As with all studies, the roles of bias, incomplete control for confounding, and chance must be considered in the interpretation of results. Diabetes status was determined on a single fasting or random glucose measurement or by self-reported use of hypoglycemic medication. This may have misclassified some participants, especially because hemoglobin A_1c was not available. Diabetes and metabolic syndrome status were assessed using threshold criteria that incompletely account for the continuous nature of the underlying disease. IL-6 was assessed once at baseline and might have changed over time before the development of disease. It is also possible that acute illness misclassified some participants as having a higher IL-6 level; this was likely to occur with random distribution among participants and thus is unlikely to have affected results. Bias related to participants dying or being lost to follow-up between the two in-home visits could have affected results in unpredictable ways, but previous work has demonstrated minimal evidence of selection bias resulting from attrition of racial disparities in REGARDS (22). There were subtle differences considering results from spline plots and the RR per SD or quartile of IL-6, likely reflecting smaller sample sizes in stratified analyses. Strengths of the present analysis include the prospective study design minimizing reverse causality in associations, the geographic diversity of the cohort supporting generalizability to Black and White U.S. adults, and the oversampling of Black participants. Additionally, this large cohort was observed for >9 years, with satisfactory retention and rigorous data collection.

In conclusion, we confirm that IL-6 is a marker for diabetes risk and describe a similar association between IL-6 and metabolic syndrome. Important race and central adiposity subgroup findings suggest directions for future study. These findings may inform further research to reduce the burden of these conditions and reduce the racial disparity in diabetes.

This article contains supplementary material online at https://doi.org/10.2337/figshare.24913368.

Funding Statement

REGARDS is supported by cooperative agreement U01 NS041588 cofunded by the National Institute of Neurological Disorders and Stroke and the National Institute on Aging, National Institutes of Health (NIH), U.S. Department of Health and Human Services. Additional support was provided by the Larner College of Medicine Dean’s Office (B.J.P.). Funding for support from the Study Design and Molecular Epidemiology Core, Vermont Center for Cardiovascular and Brain Health, was provided by grant P20 GM135007 from the National Institute of General Medical Sciences, NIH.