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Cytokine. Author manuscript; available in PMC 2012 December 1.
Published in final edited form as:
PMCID: PMC3245985
NIHMSID: NIHMS327953
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Factors Associated with Inflammation Markers, a Cross-Sectional Analysis
Tess V Clendenen,a Karen L Koenig,a Alan A Arslan,abc Annekatrin Lukanova,cd Franco Berrino,e Yian Gu,a Goran Hallmans,f Annika Idahl,g Vittorio Krogh,h Anna E Lokshin,ijk Eva Lundin,l Paola Muti,m Adele Marrangoni,i Brian M Nolen,i Nina Ohlson,l Roy E Shore,an Sabina Sieri,h and Anne Zeleniuch-Jacquotteab
aDepartment of Environmental Medicine, New York University School of Medicine, New York, New York
bNew York University Cancer Institute, New York University School of Medicine, New York, New York
cDepartment of Obstetrics and Gynecology, New York University School of Medicine, New York, New York
dDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
eEtiological and Preventive Epidemiology Unit, National Cancer Institute, Milan, Italy
fDepartment of Public Health and Clinical Medicine/Nutritional Research, Umeå University, Umeå, Sweden
gDepartment of Clinical Sciences, Obstetrics and Gynecology, Umeå University, Umeå, Sweden
hNutritional Epidemiology Unit, National Cancer Institute, Milan, Italy
iDepartment of Medicine, University of Pittsburgh Cancer Institute, Division of Cancer Prevention and Population Science, University of Pittsburgh, Pittsburgh, Pennsylvania
jDepartment of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania
kDepartment of Ob/Gyn Reproductive Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania
lDepartment of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
mEpidemiology and Prevention Unit, Istituto Regina Elena, Rome, Italy
nRadiation Research Effects Foundation, Hiroshima, Japan
Corresponding Author: Tess Clendenen, Department of Environmental Medicine, Division of Epidemiology, New York University School of Medicine, 650 1st Ave., New York, NY, USA. Phone: 212-263-0365, Fax: 212-263-8570, Tess.Clendenen/at/nvumc.org
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Abstract
Epidemiological studies have reported associations between circulating inflammation markers and risk of chronic diseases. It is of interest to examine whether risk factors for these diseases are associated with inflammation. We conducted a cross-sectional analysis to evaluate whether reproductive and lifestyle factors and circulating vitamin D were associated with inflammation markers, including C-reactive protein, cytokines (IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p40, IL-12p70, IL-13, TNFα), and cytokine modulators (IL-1RA, sIL-lRII, sIL-2Ra, sIL-4R, sIL-6R, sTNF-R1/R2), among 616 healthy women. We confirmed associations of several inflammation markers with age and BMI. We also observed significantly higher levels of certain inflammation markers in postmenopausal versus premenopausal women (TNFα, sIL-1RII, sIL-2Ra), with increasing parity (IL-12p40), and with higher circulating 25(OH) vitamin D (IL-13) and lower levels among current users of non-steroidal anti-inflammatory drugs (NSAIDs) (IL-1β, IL-2, IL-10, IL-12p70, and IL-12p40), current smokers (IL-4, IL-13, IL-12p40), and women with a family history of breast or ovarian cancer (IL-4, IL-10, IL-13). Our findings suggest that risk factors for chronic diseases (age, BMI, menopausal status, parity, NSAID use, family history of breast and ovarian cancer, and smoking) are associated with inflammation markers in healthy women.
Keywords: C-Reactive Protein, Cross-sectional Studies, Cytokines, Cytokine Receptors, Epidemiologic Factors
Several studies have shown that elevated inflammation markers, primarily C-reactive protein (CRP), interleukin (IL)-6, and tumor necrosis factor (TNFα), are associated with increased risk of cardiovascular disease, type II diabetes, and other chronic conditions, including cancer [1–9]. We recently conducted a case-control study of ovarian cancer nested in three prospective cohorts, and found that IL-2, IL-4, IL-6, IL-12, and IL-13 were associated with risk [10]. Several inflammation-related markers, including IL-2, IL-5, TNFβ, interferon γ (IFNγ), ICAM, soluble IL-2 receptor (sIL-2R), and soluble TNFα receptor 1 (sTNF-R1), have been found to be positively, and IL-13 inversely, associated with subsequent risk of non-Hodgkin lymphoma (NHL) [11–13]. Given the potential role of inflammation in chronic disease, it is of interest to identify factors that contribute to differences in levels of inflammation markers among healthy people.
The emphasis of previous studies has generally been on the association between a limited number of inflammation markers (usually CRP, IL-6, TNFα) and general lifestyle and/or cardiovascular risk factors [1, 14,15]. The focus of the present study was to assess whether reproductive and lifestyle factors and circulating vitamin D, which is immunomodulatory and has anti-inflammatory properties in vitro [16, 17], are associated with CRP, cytokines (IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p40, IL-12p70, IL-13, TNFα) and cytokine modulators/soluble receptors (IL-1 receptor antagonist (Ra), soluble (s)IL-1RII, sIL-2Ra, sIL-4R, sIL-6R, and sTNF-R1/R2) in healthy women. We selected inflammation markers that showed adequate temporal reproducibility (ICC ≥ 0.5) over a 2–3 year period in preliminary reproducibility studies, thus suggesting that a single inflammation marker measurement can be used to rank women according to their average level [18–20].
2.1 Study Subjects
For these cross-sectional analyses, we included healthy controls from two nested case-control studies in which inflammation markers have been measured: 1) a study of ovarian cancer in three prospective cohorts: the New York University Women’s Health Study (NYUWHS-OVCA), the Northern Sweden Health and Disease Study (NSHDS), and ORDET in Italy [10]; and 2) a study of non-Hodgkin lymphoma in the NYUWHS cohort (NYUWHS-NHL) [11]. The parent cohorts have been described in detail previously [21–24]. Controls were selected to match cases on age (± 6 months), date of blood sampling (± 3 months), and parent cohort (NYUWHS, NSHDS, or ORDET). Controls also had to be alive and free of cancer at the time of the matched case’s cancer diagnosis. Participants were not eligible for inclusion if they were using hormone replacement therapy (HRT) or oral contraceptives (OC) at the time of blood sampling. Up to two controls per case were selected at random from cohort members who met the above criteria. We included all 616 controls from the nested case-control studies in the present cross-sectional analysis (163 controls from NYUWHS-OVCA, 82 from ORDET, 187 from NSHDS, and 184 from NYUWHS-NHL).
Self-reported questionnaires were used to collect data on lifestyle and reproductive variables and height and weight for the NYUWHS and NSHDS subjects. ORDET participants completed in-person interviews with a nurse who also measured height and weight. Although questionnaires varied according to cohort, harmonization of variables was usually straightforward. Participants were considered to have a family history of breast or ovarian cancer only if cancer was reported for a first degree relative (i.e. mother, sister, or daughter). The NSHDS questionnaire asked about “current use” of NSAIDs whereas the NYUWHS questionnaire asked about NSAID use over the previous four weeks. To harmonize NYUWHS and NSHDS data, we restricted NSAID use to use the day of, or one day prior to, blood sampling for the NYUWHS participants, which is more similar to the data that was collected from NSHDS participants. This cut-off is also justified because the half-life of most NSAIDs is less than two days in circulation [25]. ORDET did not collect information on NSAIDs.
2.2 Laboratory Methods
Luminex multiplex bead-based technology was used for measurement of inflammation markers in serum (NYUWHS and ORDET) and EDTA plasma (NSHDS) [26]. The assay kits and procedures and coefficients of variation (CVs) have been described previously in our reproducibility and case-control studies [10, 11,19, 20].
25(OH)D was measured using a direct, competitive chemiluminescence immunoassay (DiaSorin LIAISON 25 OH Vitamin D TOTAL Assay) for the NYUWHS-OVCA and NYUWHS-NHL serum samples. NSHDS plasma 25(OH)D was measured using a gamma-B 25-hydroxy vitamin D radioimmunoassay (Immunodiagnostic Systems, Inc.). Details about the 25(OH)D measurements have been described previously [27–29].
2.3 Statistical Methods
Cytokine measurements that were below the lower limit of detection (LLD) were imputed. For eleven markers with less than 5% of the measurements below the lower limit of detection (LLD), we assigned a value equal to the midpoint between the LLD and zero. For seven markers with 5% or more values below the LLD (IL-1β, IL-2, IL-4, IL-5, IL-12p70, IL-13, IL-1RA had 17–25% values below the lower limit of detection), we used a maximum likelihood estimation procedure developed by Lubin et al. to perform multiple imputation in the presence of detection limits [30]. For the NHL study, sIL-1RII and sIL-4R were not measured, sIL-2Ra was measured with a different assay, and sIL-6R measurements were outside of the detection limits of the assay, thus NHL samples are not included in the analysis of these four markers. Cytokine values were log-transformed to reduce departures from the normal distribution.
We estimated geometric means for each cytokine adjusted for study (NYUWHS-OVCA, NYUWHS-NHL, NSHDS, ORDET), age (continuous), and BMI (continuous, log2-transformed) within categories of a number of descriptive variables: age (adjusted for study and BMI only), BMI (adjusted for study and age only), menopausal status (pre/post), number of full term pregnancies, smoking status at blood sampling (current/former/never), first degree family history of breast or ovarian cancer (yes/no), use of NSAIDS at blood sampling (yes/no), use of vitamin supplements at time of blood sampling (yes/no), and ever use of OCs (yes/no). Multivariate regression analyses were conducted to assess which variables were independently associated with each cytokine. Partial Pearson correlations were estimated between log-transformed cytokines and cytokine modulators, adjusting for study, age and BMI. Analyses were conducted using SAS version 9.2 (SAS Institute, Cary, NC). All statistical tests were two-sided and P-values < 0.05 were considered significant.
The Institutional Review Board of New York University School of Medicine, the Ethical Review Board of the National Cancer Institute of Milan (Italy), the Regional Ethical Committee of the University of Umeå, Sweden, and the Swedish Data Inspection Board reviewed and approved this study.
Characteristics of the study subjects are shown in Table 1. The age range at blood sampling was 30–75 years (mean = 55 years), and 66% of the women were post-menopausal. The median BMI was about 25kg/m2. Thirty-five percent of women had previously used oral contraceptives, 23% were nulliparous, and 82% of parous women had 2 or more children. Eighteen percent of women reported having a first-degree family history of breast or ovarian cancer. At the time of blood sampling, 18% were current smokers, 17% were taking NSAIDs, and 42% were using multivitamins.
Table 1
Table 1
Descriptive Characteristics of Study Subjects
Cytokine Distributions by Subject Characteristics
Age-, BMI-, and study-adjusted geometric means and 95% confidence intervals (CI) for selected characteristics are shown in Tables 2 and ​and33 for cytokines and cytokine modulators, respectively. Table 4 shows beta estimates from multivariate models controlling for all characteristics significantly associated with each marker.
Table 2
Table 2
Cytokines: Adjusted Geometric Means and 95% Confidence Intervals Among Categories of Selected Characteristics
Table 3
Table 3
Cytokine Modulators: Adjusted Geometric Means and 95% Confidence Intervals Among Categories of Selected Characteristics
Table 4
Table 4
Multivariate Regression Coefficient Estimates for Factors Independently Associated with Cytokines and Cytokine Modulators
Age and Menopausal Status
There was a significant increasing trend across age groups for CRP, IL-6, and TNFα (Table 2) and for sIL-1RII, sIL-2Ra, sIL-6R, sTNF-R1, and sTNF-R2 (Table 3). After adjusting for menopausal status (Table 4), age was no longer significantly associated with TNFα, sIL-1RII, and sIL-2Ra; these three markers were significantly higher in postmenopausal women (Tables 2–4).
BMI
As shown in Tables 2 and ​and3,3, age-adjusted inflammation marker levels increased significantly with increasing BMI for five markers: CRP, TNFα, sIL-1RII, sIL-6R, and sTNF-R1. Though other cytokines did not show a significant trend with increasing BMI, levels were highest among obese women (BMI > 35 kg/m2) for several markers.
Parity
IL-12p40 showed a significant increasing trend with parity (Table 3), though this association was no longer significant after adjustment for smoking (Table 4).
Family History of Breast or Ovarian Cancer
Several markers were significantly lower in women with a family history of breast or ovarian cancer: IL-4, IL-10, and IL-13 (Table 2). However, the association between family history of breast and ovarian cancer was no longer significant for IL-4 or IL-13 after controlling for smoking (Table 4).
Current use of NSAIDs
Current users of NSAIDs had significantly lower levels of IL-1β, IL-2, IL-10, IL-12p70, and IL-12p40 (Tables 2 and ​and3).3). However, NSAID use was no longer associated with IL-12p40 after controlling for smoking (Table 4).
Smoking Status
Current smokers had significantly lower levels of IL-4, IL-13, and IL-12p40 (Tables 2–4). Former smokers had significantly lower levels of IL-4 and IL-10 (Table 2) and higher levels of sTNF-R2 (Table 3). However, IL-10 was not associated with smoking in multivariate models, while IL-6 was lower among former smokers in the multivariate model adjusted for age (Table 4).
Vitamin D (25(OH)D)
Women with 25(OH)D levels above 50 nmol/L had generally higher levels of inflammatory markers than did women with lower 25(OH)D levels, but the difference was only statistically significant for IL-13 (Tables 2 and ​and3),3), and this difference was no longer significant after adjustment for smoking (Table 4).
Other Variables
We did not observe any significant differences in cytokine levels related to ever use of oral contraceptives or current use of multivitamins. Thus, these characteristics are not shown in Tables 2–4.
Cytokine Correlations
Pearson correlation coefficients adjusted for age, BMI, and study are shown in Table 5. CRP was weakly correlated with the cytokine IL-6 and the cytokine modulator sIL-2Ra (r~ 0.1–0.2). Cytokines were significantly correlated with all other cytokines. The strongest correlations (r > 0.6) were observed between IL-1β and IL-2, IL-10 and IL-12p70, and among the following markers: IL-4, IL-5, IL-6, and IL-13. Correlations between cytokines and cytokine modulators were weak for most pairs (r <0.4), though moderate correlations (r~0.5) were observed between IL-12p40 and two modulators: IL-1RAandsTNF-R1.
Table 5
Table 5
Adjusted Pearson Correlation Coefficients for Cytokines and Cytokine Modulatorsa
Our results support three previous studies that observed higher levels of the pro-inflammatory cytokine TNFα among post-menopausal women [31–33], although others did not observe such a difference [34, 35], or observed lower levels [36]. Two soluble cytokine receptors, sIL-1RII and sIL-2Ra were also higher among post- vs. pre-menopausal women. The associations were apparent after adjustment for age and BMI, which suggests that these markers may be influenced by sex hormones.
Increasing parity was associated with higher levels IL-12p40, although this association was no longer statistically significant after adjustment for smoking. We are unaware of previous reports that have evaluated the association between previous pregnancies and inflammation markers. However, a microarray study of normal breast tissue found that several inflammation-associated genes were upregulated in both recently (0–2 years since pregnancy) and distantly (5–10 years since pregnancy) parous vs. nulliparous women [37]. However, despite adjustment for age and BMI, we cannot exclude the possibility that the association may have been confounded by other factors, including age at each pregnancy and time since last pregnancy.
Women with a first degree family history of breast or ovarian cancer had lower levels of several cytokines. While inflammation marker levels are not likely to be directly related to family history, this risk factor may be associated with adoption of healthy lifestyle elements.
Current use of NSAIDs was inversely associated with a number of inflammatory markers (IL-1β, IL-2, IL-10 and IL-12p70), in line with our expectations. However, NSAID use was not associated with levels of CRP. Prior reports on the association between NSAID use and CRP are conflicting [38–43], which may be due to differences in duration, types, and doses of NSAID evaluated, but also because some studies were limited in sample size [38–40, 42], included subjects with existing chronic disease [43], or reported associations for regular users who were asked to abstain from use prior to blood sampling [41].
Somewhat contrary to our expectations, smoking status was not significantly associated with CRP, IL-6, TNFα or other pro-inflammatory cytokines. In our study, current smokers generally had lower levels of cytokines than non-smokers, though these associations were only statistically significant for the anti-inflammatory markers, IL-4, IL-13 and IL-12p40. Lower levels of these cytokines among smokers vs. non-smokers were observed in all four sub-studies (NYUWHS-OVCA, NYUWHS-NHL, ORDET, and NSHDS). Previous studies on current smoking and cytokines among healthy individuals have found positive [34, 44–50], inverse [51, 52], and null [49, 52–55] associations. Inconsistent findings may be a result of incomplete control for lifetime exposure to cigarette smoke (e.g. pack-years), which may be a more relevant measure of exposure than current smoking status.
CRP was not correlated with most cytokines. Given the role of IL-6, and to a lesser extent TNFα and IL-1β, in the induction of CRP, we expected to observe moderate to strong correlations of CRP with these cytokines. In models adjusted for study and age (but not BMI), CRP was positively correlated with TNFα (r = 0.1, P < 0.005) and IL-6 (r= 0.1, P=0.001), but not IL-1β. Others have found a moderate correlation between CRP and IL-6, some of which adjusted for BMI, with most estimating a correlation coefficient between 0.3–0.5 [2, 14, 34, 41, 50, 56, 57]. However, data from the Women’s Health Initiative trial suggests that there could be IL-6-independent pathways for regulating CRP, as CRP and IL-6 had different associations with several cardiovascular risk factors: HRT use, alcohol use, and exercise [44]. For example, the consistent association between CRP and HRT [58] vs. the weak or null association between IL-6 and HRT, may be due to direct (IL-6-independent) hepatic induction of CRP [44].
The observed significant correlation between TNFα, IL-6, and IL-1β was expected due to the regulatory inter-relationship of these cytokines [34, 41, 56, 59, 60]. Cytokines were correlated with each of the other cytokines, and less so with cytokine modulators. In physiological conditions, elevations in pro-inflammatory cytokines trigger elevations in anti-inflammatory cytokines to resolve the inflammatory response. Thus, we expected all pro- and anti-inflammatory cytokines to be positively correlated with each other in healthy women. However, since all the cytokines were measured using the same assay kit, we cannot rule out the possibility that imperfect antibody specificity could have contributed to the strength of the correlations. The relationship between cytokines and their modulators is complex, because soluble cytokine receptors can act as both cytokine agonists and/or antagonists [61]. Possible reasons for the lack of correlation between cytokines and their modulators have been suggested by others, such as the longer half-life of soluble receptors in circulation and/or the inability to detect all unbound and bound forms of cytokines with immunoassays [62–64].
The lack of association between cytokines and 25(OH)D, the best representation of an individual’s vitamin D status, is in agreement with a study of vitamin D supplementation in overweight and obese individuals that did not find an association between circulating 25(OH)D and CRP or cytokines (including IL-2, IL-4, IL-5, IL-10, IL-12, IL-13) [52]. We found some evidence of a positive association for IL-13 and 25(OH)D, though the association was not significant in multivariate analyses. Others have reported a lack of association between 25(OH)D and several inflammation markers (CRP, IL-1β, IL-6, IL-10, SIL-2R, TNFα, sTNF-R1, and sTNF-R2) [65–70], though one small study found an inverse association between 25(OH)D and TNFα [65] and two others found that vitamin D supplementation stabilized [71] or reduced [72] TNFα among patients with congestive heart failure or obesity, respectively. We note that the median 25(OH)D level in our study group (median: 48 nmoI/L, interquartile range: 25, 81 nmoI/L) is considered to be in the “insufficient” range (~<50 nmoI/L) based on some recommendations [73, 74].
This study has a number of strengths. First, participants were healthy and not using HRT or OCs for at least 6 months prior to blood sampling, thus minimizing the effects of existing disease and exogenous hormones on cytokine levels. Second, we evaluated associations between risk factors and a large number of cytokines and cytokine modulators which have not been evaluated in previous studies. Third, all inflammation marker measurements were performed in the same laboratory, which minimizes assay variability and allows individuals to be ranked relative to others.
Our study also has some limitations. First, questionnaires differed between cohorts. However, results were usually consistent across cohorts, providing confidence in our findings. Second, it is possible that measured levels of a single cytokine are not reflective of their biologically relevant concentration. This may be due to assay limitations in quantification of absolute levels and/or the presence of circulating cytokine inhibitors, including soluble receptors and other binding proteins. Finally, the exploratory nature of our analyses resulted in a substantial number of statistical tests, thus it is likely that some associations were significant due to chance. However, we did not adjust for multiple comparisons because our goal was to identify general patterns of association that may explain risk factor associations and to aid in the selection of potential confounders for future studies of cytokines and chronic disease risk. The associations observed here should be evaluated in independent studies.
In conclusion, we observed the expected relationships between inflammation markers and age and BMI. Higher levels of certain markers were observed among postmenopausal vs. premenopausal women (TNFα, sIL-1RII, and sIL-2Ra) and with increasing parity (IL-12p40). Lower levels were observed among current versus non-users of NSAIDs (IL-10, IL-β, IL-10, IL-12p70, and IL-12p40), women with a family history of breast or ovarian cancer (IL-4, IL-10, and IL-13), and current versus non-smokers (IL-4, IL-13, and IL-12p40). These findings suggest that one mechanism underlying the relationship between reproductive and lifestyle factors and chronic diseases may involve inflammation mediators.
​
Highlights
  • We assessed potential determinants of inflammation markers among 616 healthy women.
  • Observed associations between lifestyle and reproductive factors and inflammation markers.
  • Associations: age, BMI, menopause, parity, NSAIDs, family history of cancer, smoking.
Acknowledgments
Funding
This work was supported by research grants from the National Cancer nstitute (R21 CA116585, R01 CA098661, and P30CA016087) and the National nstitute of Environmental Health Sciences Center Grant (ES000260) at the National nstitutes of Health.
Abbreviations
25(OH)D25-hydroxyvitamin D
BMIbody mass index
CVcoefficient of variation
CIconfidence interval
CRPC-reactive protein
ORDETdiet and hormones in the etiology of cancer study
EDTAethylenediaminetetraacetic acid
HRThormone replacement therapy
ICAMinter-cellular adhesion molecule
IFNinterferon
ILinterleukin
IL-1Rainterleukin-1 receptor antagonist
ICCintra-class correlation coefficient
LLDlower limit of detection
METmetabolic equivalent tasks
NYUWHSNew York University Women’s Health Study
NHLnon-Hodgkin lymphomas
NSAIDnon-steroidal anti-inflammatory drug
NSHDSNorthern Sweden Health and Disease Study
OCoral contraceptives
PBMCperipheral blood mononuclear cell
R- suffixreceptor
s -prefixsoluble
sIL-1RIIsoluble IL-1 receptor
sIL-2Rasoluble IL-2 receptor alpha
sIL-4Rsoluble IL-4 receptor
sIL-6Rsoluble IL-6 receptor
TNFtumor necrosis factor

Footnotes
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