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Prevalent peripheral arterial disease and inflammatory burden

  • Jane A. Cauley1Email author,
  • Ahmed M. Kassem1,
  • Nancy E. Lane2,
  • Sara Thorson3 and
  • for the Osteoporotic Fractures in Men (MrOS) Study Research Group
BMC GeriatricsBMC series – open, inclusive and trusted201616:213

https://doi.org/10.1186/s12877-016-0389-9

Received: 1 March 2016

Accepted: 6 December 2016

Published: 9 December 2016

Abstract

Background

Strong evidence implicates inflammation in the development of atherosclerotic heart disease but less is known about peripheral arterial disease (PAD). Our objective was to test the hypothesis that a composite index of inflammatory burden is associated with PAD.

Methods

Cross-sectional analysis of a randomly-selected group of 903 community-dwelling men in the MrOS cohort recruited between 2000 and 2002. Using blood samples, we measured seven cytokines and related these levels to prevalent PAD (ankle-brachial index (ABI) <0.9) both individually and as part of an “inflammatory burden score” (a composite sum of the number of pro-inflammatory cytokines in the highest quartile).

Results

Overall, 6.75% of men had ABI <0.9. The odds of prevalent PAD were higher in men with the highest quartile (Q4) levels of interleukin-6 multivariable (MV) adjusted (odds ratio (OR) =3.95 (95% CI, 1.4–11.3), tumor necrosis factor alpha OR = 4.44 (95% confidence interval (CI), 1.5–12.8), and C-reactive protein OR = 3.63 (95% CI, 1.4–9.4) compared to men in Q1. The magnitude of the association of these cytokines with PAD was similar to the effect of being 10 years older, OR = 2.41 (95% CI, 1.16–3.7). These significant effects persisted after additional MV adjustment for smoking except for CRP. Men with the highest inflammatory burden score (≥3) had 3.6 (95% CI, 1.5–8.7) increased odds of PAD, p trend = 0.03. After smoking adjustment the linear trend was borderline statistically significant (p trend = 0.10).

Conclusion

Inflammatory burden is associated with prevalent PAD, an association similar to aging 10 years. The inflammatory effects of smoking contributes to the underlying association between inflammation and PAD.

Keywords

Peripheral arterial diseasePeripheral vascular diseaseInflammationCytokineAnkle-arm indexAnkle-brachial indexSmokingMen

Background

Peripheral arterial disease (PAD) affects more than 8 million Americans and its prevalence is likely to increase as the population ages [1]. Not only is the disease linked to significantly impaired physical function and quality of life [24] but considerable evidence supports a higher risk of mortality in patients with PAD [5, 6], particularly from cardiovascular causes [7, 8]. The ankle brachial index (ABI), which is the ratio of systolic pressure at posterior tibial and/or dorsalis pedis arteries divided by the brachial systolic blood pressure, is commonly used to measure PAD in the legs. Interestingly, incorporation of the ABI [6] into risk stratification tools such as Framingham Risk Score (includes age, total and high density lipoprotein cholesterol, blood pressure, diabetes and smoking) was found to nearly double the accuracy of 10-year predictions of total mortality, cardiovascular mortality, and major coronary events [6].

A number of recent studies have found pro-inflammatory cytokines to be strongly linked to prevalent PAD [911] as well as its severity [12, 13] even after adjusting for traditional cardiovascular risk factors [10, 1315]. While the mechanisms remain unknown, certain pro-inflammatory cytokines, such as interleukin-6 (IL-6) and tumor-necrosis factor-α (TNFα), TNF soluble receptor-II (TNFαSRII), appear to be associated with PAD independent of one another [15]. Some inflammatory markers show different relationships with PAD in the context of disease, as is the case with C-reactive protein (CRP) and diabetes [16].

The exact pathophysiology of PAD remains unclear, but inflammation appears to be involved. Previous studies relating inflammatory markers to PAD had small sample sizes or included only a few inflammatory cytokines. Using data collected in the Osteoporotic Fractures in Men Study (MrOS), we evaluated the relation of 7 inflammatory markers - CRP, IL-6, IL-6 soluble receptor (IL-6SR), TNFα, TNFαSRI, and TNFαSRII as well as interleukin-10 (IL-10, anti-inflammatory), to prevalent PAD in older men and identified characteristics common to those with the highest cytokine inflammatory burden.

Methods

Participants

In 2000–2002, 5994 men enrolled in the Osteoporotic Fractures in Men Study (MrOS), a longitudinal cohort study designed to determine risk factors for osteoporosis, fracture and falls. Men were recruited at six US academic clinical centers primarily through mass mailings targeted to age eligible men. The MrOS study is described in more detail in other publications [17, 18]. Briefly, all men were age ≥ age 65, able to walk independently and did not report bilateral hip replacements; 35% of men were age 65–59 and 11% age 80 or older at baseline. The study was approved by the Institutional Review Boards at each institution. All participants provided written informed consent.

The current analysis was limited to a cohort of 1,530 randomly-selected participants. Men with at least 5, 1-mL aliquots of archived, unthawed serum were eligible for inclusion. Of these, 980 participants had cytokine data. These men were from a randomly selected subcohort of MrOS men who had inflammatory markers measured as part of a case-cohort study of inflammation and fracture [19]. In order to assign an inflammatory burden score (see below), we excluded 46 participants with missing cytokine data. We further excluded 31 participants with missing ABI values and additional 25 participants with ABI > =1.4, rendering an analytic sample of 878.

Measurements at baseline visit

Biochemical measurements

Fasting morning blood samples were obtained at the baseline visit and were processed and stored at -120C until assay. All cytokine assays were performed at the Laboratory for Cytokine Biochemistry (LCBR), University of Vermont, under the direction of Dr. Russell Tracy.

IL-6 was measured using a high sensitivity ELISA from R&D Systems (Minneapolis, MN) employing a quantitative sandwich enzyme immunoassay technique. The assay range is 0.16–12.0 pg/mL with inter-assay coefficients of variability (CVs) ranging from 6.11–8.47%. Expected values for IL-6 in normal, healthy individuals are <10 pg/mL.

IL-6sR, TNFαSRI and TNFαSRII were measured using an ELISA from R&D Systems (Minneapolis, MN). A monoclonal antibody specific for each cytokine receptor was coated on the assay plate and a polyclonal anti- cytokine receptor antibody used as the sandwich antibody; the amount of cytokine receptor was then determined by colorimetric reaction. The assay range for IL-6sR was 3120–200,000 pg/mL. The manufacturer normal range for IL-6 is approximately 15,000-46,000 pg/mL with inter-assay CVs ranging from 4.68–8.83%. The assay range for TNFα SRI and SRII is 78–6000 pg/mL with inter-assay CVs ranging from 5.42–8.59% for TNFαSRI and 2.87–3.54% for TNFαSRII.

IL-10 and TNFα were measured using the Human Serum CVD3 Multiplex Kit from Millipore Corp. (Billerica, MA), using flow cytometry on the Bio-Rad Bioplex 200 Luminex instrument. The assay range for IL-10 is 0.13-2000 pg/mL with inter-assay CVs ranging from 4.94–10.66%. The TNFα assay range is 0.13-2000 pg/mL with inter-assay CVs ranging from 4.93–9.13%.

CRP was measured using the BNII nephelometer from Dade Behring utilizing a particle enhanced immunonepholometric assay. The assay range is 0.16–1100 ug/mL. Expected values for CRP in normal, healthy individuals are ≤ 3 ug/mL. Inter-assay CVs ranged from 1.52–3.68%.

Participants were assigned a pro-inflammatory burden score –a composite variable that was the sum of the number of pro-inflammatory cytokines (IL-6, IL-6 SR, TNFα, TNFαSRI, TNFαSRII, CRP) in the highest quartile. Of the 878 men in our analytic sample, 851(97%) had data on all 6 inflammatory markers and an additional 26 men had data on 5 cytokines. Based on the pro-inflammatory burden score, participants were classified into 4 groups according to the number of pro-inflammatory cytokines in the highest quartile as follows: 0, 1, 2, and 3–6 (≥3). Other published studies have found relationships between inflammatory burden scores and hip fracture [20].

Outcome measurements

The systolic blood pressure in the right and left posterior tibial artery and the right brachial artery were measured twice after the subjects were supine for at least 5 min. The pulses were detected by using a hand held 8 MHz doppler. The resting ABI was calculated in each leg as the average of the 2 measures of the posterior tibial pressure divided by the average of the two brachial measures. The lower of the right and left index was used as a measure of lower extremity PAD [21]. Participants with an ABI < 0.90 were classified as having PAD, while those with an ABI ≥0.90 were considered free of disease [6].

Covariates

Height was measured by Harpenden stadiometer; weight, by digital or balance beam scale. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Biomarkers included systolic blood pressure (average of two measurements in the arm), fasting blood glucose, total cholesterol and HDL. Walking speed was determined by time to complete a 6-m course at the participant’s usual walking speed [18].

A self-administered questionnaire included demographics, education, self-rated health status, medical history, tobacco use (pack-years and current smoking/past smoking status), and alcohol consumption. Participants were asked to bring all prescription medications they had been taking for at least 1 month to the clinic visit. All medications were entered into an electronic database, verified by pill bottle examination, and each medication was matched to its ingredients based on the Iowa Drug Information Service (IDIS) Drug Vocabulary (College of Pharmacy, University of Iowa, Iowa City, IA) [22]. Participants were classified as diabetic by self-report, a fasting blood glucose (FBG) ≥126 mg/dL, or if they had prescriptions for hypoglycemic agents. Pre-diabetes was defined as FBG ≥100 mg/dL and <126 mg/dL, while normoglycemia was defined as blood glucose <100 mg/dL. Physical activity was quantified using the Physical Activity Scale for the Elderly (PASE) [23].

Statistical analysis

We calculated descriptive statistics for all variables and tabulated characteristics of participants by level of pro-inflammatory burden score testing for trend. Next, we compared participants with and without PAD. For this, we used chi-squared test (or Fisher's exact test) for categorical variables, two-sample t test for normally distributed continuous data and Wilcoxon-Mann–Whitney test for skewed continuous data. Median tests were used for the cytokines variables because of skewed distributions. Then, a series of crude, age-adjusted and multivariable-adjusted logistic regression models of the relationship between each cytokine and PAD were fit. We modeled quartiles of cytokines as dummy variables (1–4) with quartile 1 as the referent. For the inflammatory burden logistic regressions, we also used dummy variables to account for the number of “high” inflammatory cytokines (0, 1, 2, ≥3). The multivariable models were adjusted for variables that were significantly different between men with and without PAD and variables that are related to both PAD and inflammation. All statistical analyses were conducted with Stata version 13.1 (StataCorp LP, College Station, TX, USA).

Results

Descriptive characteristics of study participants by inflammatory burden score (0 to ≥3) are shown in Table 1. The largest percentage of participants (35%) had a score of 0, followed by a score of 1 (26%), 2 (15%) and ≥3 (24%). With increasing inflammatory burden score, the prevalence of PAD as measured by ABI increased. Men with the highest inflammatory burden also tended to be older and were less likely to rate their health status as good or excellent. Fasting blood glucose tended to increase but total cholesterol and HDL tended to decrease with increasing inflammatory burden. Men with three or more cytokines measured in the highest quartile had a higher prevalence of multiple medical conditions including history of myocardial infarction, hypertension, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF) and diabetes when compared to those with a score of 0 or 1. A higher proportion of participants with the highest inflammatory burden were unable to walk faster than 0.8 m/s. There was no difference in use of aspirin or non-steroidal anti-inflammatory drugs (NSAIDs) across inflammatory burden but use of ACE inhibitors, loop diuretics and antidepressants was greatest among men with the greatest inflammatory burden. On average, men with the highest inflammatory burden also tended to drink less alcohol and have a greater number of smoking pack-years.
Table 1

Characteristics of participants according to number of pro-inflammatory cytokines in the highest quartile

Characteristics

Total N = 878

Pro-inflammatory Burden Scorea

P for trend

0 N = 302

1 N = 230

2 N = 131

≥3 N = 215

PAD, ABI <0.90, n (%)

61 (6.95)

12 (3.97)

14 (6.09)

5 (3.82)

30 (13.95)

<0.001

Demographics

 Age, years, mean ± SD

73.71 ± 5.87

72.82 ± 5.39

72.44 ± 5.13

74.22 ± 6.10

75.99 ± 5.87

<0.001

 Non-white race, n (%)

67 (7.63)

29 (9.60)

21 (9.13)

7 (5.34)

10 (4.65)

0.019

 High school or less, n (%)

217 (24.72)

56 (18.54)

59 (25.65)

40 (30.53)

62 (28.84)

0.003

Health status

 Self-rated health status, good/excellent, n (%)

753 (85.86)

275 (91.06)

201 (87.39)

111 (85.38)

166 (77.21)

<0.001

 Body Mass Index (BMI), mean ± SD

27.32 ± 3.56

27.22 ± 3.17

27.06 ± 3.72

27.39 ± 3.57

27.69 ± 3.86

0.146

 Systolic blood pressure (mmHG), mean ± SD

139.09 ± 18.92

138.94 ± 18.44

138.60 ± 18.59

138.58 ± 18.06

140.13 ± 20.47

0.555

 Fasting blood glucose (mg/dL), mean ± SD

106.68 ± 24.90

105.20 ± 21.53

104.22 ± 22.69

109.37 ± 32.89

109.75 ± 25.53

0.027

 Total cholesterol (mg/dL), mean ± SD

194.58 ± 32.84

196.43 ± 32.52

196.78 ± 32.69

196.30 ± 36.28

188.59 ± 30.62

0.021

 HDL (mg/dL), mean ± SD

48.96 ± 14.65

51.05 ± 14.39

49.85 ± 15.46

48.35 ± 13.75

45.46 ± 14.11

<0.001

Medical conditions

 Myocardial infarction, n (%)

139 (15.83)

41 (13.58)

29 (12.61)

20 (15.27)

49 (22.79)

0.005

 Hypertension, n (%)

383 (43.62)

122 (40.40)

84 (36.52)

56 (42.75)

121 (56.28)

<0.001

 Congestive heart failure, n (%)

36 (4.10)

7 (2.32)

5 (2.17)

4 (3.05)

20 (9.30)

<0.001

 Chronic obstructive pulmonary disease, n (%)

97 (11.05)

17 (5.63)

26 (11.30)

17 (12.98)

37 (17.21)

<0.001

Diabetes status

     

0.002

 Diabetes, n (%)

142 (16.69)

39 (13.36)

25 (11.21)

24 (18.75)

54 (25.96)

 

 Pre-diabetes, n (%)

310 (36.43)

114 (39.04)

79 (35.43)

44 (34.38)

73 (35.10)

 

Medication use

 NSAID, n (%)

130 (15.51)

44 (15.28)

26 (11.82)

19 (15.45)

41 (19.81)

0.137

 Aspirin, n (%)

284 (33.89)

97 (33.68)

73 (33.18)

32 (26.02)

82 (39.61)

0.366

 Cox-II inhibitor, n (%)

63 (7.52)

20 (6.94)

11 (5.00)

10 (8.13)

22 (10.63)

0.090

 ACE inhibitors, n (%)

166 (19.81)

53 (18.40)

30 (13.64)

24 (19.51)

59 (28.50)

0.004

 Loop diuretics, n (%)

38 (4.53)

1 (0.35)

6 (2.73)

4 (3.25)

27 (13.04)

<0.001

 Antidepressants, n (%)

55 (6.65)

15 (5.26)

13 (6.07)

3 (2.46)

24 (11.65)

0.022

Physical performance

 Physical Activity Scale for the Elderly score, mean ± SD

149.92 ± 69.95

155.79 ± 66.48

158.76 ± 67.01

147.00 ± 65.49

134.02 ± 77.68

<0.001

 Walking speed <0.8 meters/seconds, n (%)

28 (3.19)

7 (2.32)

3 (1.30)

3 (2.29)

15 (6.98)

0.004

 Average number of drinks/week, mean ± SD

4.52 ± 6.90

5.23 ± 7.20

4.78 ± 7.25

3.61 ± 5.82

3.82 ± 6.60

0.001

Smoking status

     

0.010

 Current smoker, n (%)

33 (3.76)

6 (1.99)

7 (3.04)

7 (5.34)

13 (6.05)

 

 Past smoker, n (%)

514 (58.54)

172 (56.95)

134 (58.26)

74 (56.49)

134 (62.33)

 

 Smoking pack-years, mean ± SD

18.97 ± 25.71

16.09 ± 23.86

18.09 ± 24.74

18.04 ± 22.40

24.53 ± 30.07

0.003

aComposite variable summing number of pro-inflammatory cytokines (IL-6, IL-6 SR, TNFα, TNFαSRI, TNFαSRII, CRP) in the highest quartile

Men with PAD (6.75%) had higher median levels of the pro-inflammatory cytokines IL-6, IL-10, TNFα, TNFαSRI, TNFαSRII, and CRP; they were almost twice as likely to have a CRP level above the clinical cutoff of 3ug/mL (p < 0.05), Table 2. They were also older, and less likely to report good/excellent health status, had higher rates of hypertension, and CHF. Systolic blood pressure and fasting glucose were significantly higher in men with PAD. BMI was lower in the PAD group. There was no difference in NSAIDs, aspirin use, Cox inhibitors or alcohol use by PAD status. Men with ABI <0.9 were more likely to report use of ACE inhibitors, loop diuretics and antidepressants (p = 0.08).
Table 2

Characteristics of men with and without peripheral arterial disease

Characteristics

Total N = 878

Men with PAD (ABI <0.90) N = 61

Men without PAD (ABI ≥0.90) N = 817

p-valuea

Cytokines

 IL-6 pg/ml, median (interquartile range)

2.40 (1.84)

3.38 (2.16)

2.34 (1.71)

<0.001

 IL-6 SR pg/ml, median (interquartile range)

48636 (18103)

50655 (19051)

48494 (18064)

0.232

 IL-10 pg/ml, median (interquartile range)

8.98 (6.94)

10.62 (5.69)

8.87 (6.86)

0.012

 TNFα pg/ml, median (interquartile range)

3.95 (2.43)

4.55 (2.24)

3.91 (2.41)

0.032

 TNFαSRI pg/ml, median (interquartile range)

1918.50 (586.90)

2210.50 (853.10)

1900.70 (570.10)

0.002

 TNFαSRII pg/ml, median (interquartile range)

3499.80 (952.10)

3856.85 (1202.90)

3482.10 (922.70)

0.029

 CRP ug/ml, median (interquartile range)

1.40 (2.12)

2.24 (3.94)

1.37 (2.08)

0.042

 CRP >3 ug/mL, n (%)

209 (23.94)

24 (40.68)

185 (22.73)

0.002

Demographics

 Age, years, mean ± SD

73.71 ± 5.87

76.70 ± 5.99

73.48 ± 5.99

<0.001

 None-white race, n (%)

67 (7.63)

4 (6.56)

63 (7.71)

1.000

 High school or less, n (%)

217 (24.72)

19 (31.15)

198 (24.24)

0.227

Health status

 Self-rated health status, good/excellent, n (%)

753 (85.86)

44 (72.13)

709 (86.89)

0.001

 Body Mass Index (BMI), mean ± SD

27.32 ± 3.56

26.05 ± 3.25

27.42 ± 3.56

0.004

 Systolic blood pressure (mmHG), mean ± SD

139.09 ± 18.92

148.21 ± 24.13

138.41 ± 18.31

<0.001

 Fasting blood glucose (mg/dL), mean ± SD

106.68 ± 24.90

117.80 ± 41.47

105.83 ± 22.97

0.039

 Total cholesterol (mg/dL), mean ± SD

194.58 ± 32.84

191.44 ± 32.46

194.82 ± 32.87

0.442

 HDL (mg/dL), mean ± SD

48.96 ± 14.65

48.32 ± 15.85

49.01 ± 14.57

0.435

Medical conditions

 Myocardial infarction, n (%)

139 (15.83)

9 (14.75)

130 (15.91)

0.811

 Hypertension, n (%)

383 (43.62)

39 (63.93)

344 (42.11)

0.001

 Congestive heart failure, n (%)

36 (4.10)

7 (11.48)

29 (3.55)

0.003

 Chronic obstructive pulmonary disease, n (%)

97 (11.05)

8 (13.11)

89 (10.89)

0.593

Diabetes status

   

0.071

Diabetes, n (%)

142 (16.69)

16 (26.67)

126 (15.93)

 

Pre-diabetes, n (%)

310 (36.43)

22 (36.67)

288 (36.41)

 

Medication use

 NSAID, n (%)

130 (15.51)

10 (17.54)

120 (15.36)

0.661

 Aspirin, n (%)

284 (33.89)

20 (35.09)

264 (33.80)

0.843

 Cox-II inhibitor, n (%)

63 (7.52)

1 (1.75)

62 (7.94)

0.115

 ACE inhibitors, n (%)

166 (19.81)

18 (31.58)

148 (18.95)

0.021

 Loop diuretics, n (%)

38 (4.53)

8 (14.04)

30 (3.84)

<0.001

 Antidepressants, n (%)

55 (6.65)

7 (12.28)

48 (6.23)

0.077

Physical performance

 Physical Activity Scale for the Elderly (PASE) score, mean ± SD

149.92 ± 69.95

130.25 ± 79.94

151.39 ± 68.98

0.008

 Walking speed <0.8 meters/seconds, n (%)

28 (3.19)

6 (9.84)

22 (2.69)

0.002

 Average number of drinks/week, mean ± SD

4.52 ± 6.90

4.77 ± 7.79

4.51 ± 6.84

0.275

Smoking status

   

<0.001

 Current smoker, n (%)

33 (3.76)

8 (13.11)

25 (3.06)

 

 Past smoker, n (%)

514 (58.54)

40 (65.57)

474 (58.02)

 

 Smoking pack-years, mean ± SD

18.97 ± 25.71

33.64 ± 32.44

17.87 ± 24.81

<0.001

aBased on median test for cytokines variables, two-sample t test (or Wilcoxon-Mann–Whitney test) for other continuous variables and chi-squared test (or Fisher's exact test) for categorical variables

Men with PAD were 4.4 times more likely to be currently smoking that those without the disease; they also had nearly twice the number of smoking pack-years.

Trends between quartile levels of various cytokines and prevalent PAD are shown in Table 3. Current smoking status was added separately to multivariate adjustment because it was found to have an important influence on odds ratios.
Table 3

Association between cytokines and prevalent peripheral arterial disease (ABI <0.90)

  

Second quarter (25th - 50th percentile)

Third quarter (50th -75th percentile)

Fourth quarter (75th - 100th percentile)

P for trend

Cytokine

Model

Odds Ratio, 95% Confidence Intervala

IL-6

Crude model

1.94 (0.65, 5.87)

3.63 (1.31, 10.01)

6.00 (2.27, 15.91)

<0.001

Age-adjusted model

1.71 (0.57, 5.12)

3.13 (1.12, 8.71)

4.91 (1.83, 13.17)

0.001

Multivariate-adjusted modelb

1.37 (0.42, 4.46)

3.46 (1.19, 10.11)

3.95 (1.38, 11.26)

0.003

Multivariate-adjusted model + smoking status

1.32 (0.40, 4.31)

2.88 (0.98, 8.49)

2.90 (0.99, 8.52)

0.020

Multivariate-adjusted model + CVD risk factorsc

1.17 (0.34, 3.98)

3.39 (1.13,10.18)

3.57 (1.21, 10.50)

0.005

IL-6 SR

Crude model

0.95 (0.42, 2.13)

1.11 (0.51, 2.43)

1.71 (0.84, 3.48)

0.129

Age-adjusted model

0.93 (0.41, 2.10)

1.07 (0.49, 2.35)

1.61 (0.78, 3.31)

0.180

Multivariate-adjusted modelb

1.16 (0.46, 2.89)

1.12 (0.47, 2.71)

1.65 (0.73, 3.74)

0.270

Multivariate-adjusted model + smoking status

1.24 (0.49, 3.18)

1.31 (0.52, 3.28)

1.85 (0.79, 4.36)

0.170

Multivariate-adjusted model + CVD risk factorsc

1.12 (0.44, 2.89)

1.03 (0.41, 2.59)

1.86 (0.79, 4.34)

0.204

IL-10

Crude model

1.30 (0.53, 3.14)

2.73 (1.23, 6.05)

1.89 (0.82, 4.34)

0.045

Age-adjusted model

1.27 (0.52, 3.11)

2.55 (1.14, 5.68)

1.63 (0.70, 3.79)

0.107

Multivariate-adjusted modelb

1.48 (0.56, 3.93)

2.02 (0.81, 5.04)

1.57 (0.62, 3.96)

0.263

Multivariate-adjusted model + smoking status

1.52 (0.56, 4.13)

2.13 (0.83, 5.44)

1.75 (0.68, 4.53)

0.184

Multivariate-adjusted model + CVD risk factorsc

1.74 (0.63, 4.81)

2.52 (0.96, 6.57)

1.89 (0.72, 4.95)

0.139

TNFα

Crude model

2.33 (0.93, 5.83)

2.52 (1.02, 6.21)

3.24 (1.35, 7.79)

0.010

Age-adjusted model

2.43 (0.97, 6.13)

2.54 (1.02, 6.28)

3.09 (1.27, 7.47)

0.015

Multivariate-adjusted modelb

3.02 (0.99, 9.18)

3.69 (1.26, 10.78)

4.44 (1.54, 12.80)

0.005

Multivariate-adjusted model + smoking status

2.95 (0.96, 9.06)

3.40 (1.15, 10.08)

3.95 (1.35, 11.58)

0.012

Multivariate-adjusted model + CVD risk factorsc

3.37 (1.05, 10.83)

4.13 (1.35, 12.67)

5.34 (1.77, 16.16)

0.003

TNFαSRI

Crude model

1.15 (0.47, 2.84)

1.54 (0.65, 3.64)

3.34 (1.53, 7.29)

0.002

Age-adjusted model

1.01 (0.41, 2.51)

1.25 (0.52, 2.99)

2.24 (0.97, 5.15)

0.051

Multivariate-adjusted modelb

1.24 (0.46, 3.34)

1.28 (0.48, 3.42)

2.26 (0.89, 5.75)

0.104

Multivariate-adjusted model + smoking status

1.16 (0.42, 3.16)

1.07 (0.39, 2.89)

1.80 (0.69, 4.69)

0.285

Multivariate-adjusted model + CVD risk factorsc

1.43 (0.50, 4.13)

1.59 (0.56, 4.52)

3.20 (1.15, 8.85)

0.029

TNFαSRII

Crude model

1.83 (0.72, 4.68)

1.95 (0.77, 4.94)

3.74 (1.57, 8.88)

0.004

Age-adjusted model

1.64 (0.64, 4.21)

1.64 (0.64, 4.20)

2.65 (1.08, 6.54)

0.042

Multivariate-adjusted modela

1.79 (0.63, 5.06)

1.70 (0.60, 4.83)

2.50 (0.91, 6.85)

0.095

Multivariate-adjusted model + smoking status

1.50 (0.52, 4.33)

1.46 (0.50, 4.20)

2.11 (0.75, 5.91)

0.181

Multivariate-adjusted model + CVD risk factorsc

2.23 (0.72, 6.93)

2.66 (0.85, 8.32)

3.81 (1.25, 11.63)

0.019

CRP

Crude model

1.78 (0.73, 4.32)

1.53 (0.61, 3.82)

3.37 (1.48, 7.64)

0.008

Age-adjusted model

1.92 (0.78, 4.71)

1.66 (0.66, 4.18)

3.43 (1.50, 7.85)

0.008

Multivariate-adjusted modelb

2.61 (0.95, 7.15)

1.88 (0.66, 5.36)

3.63 (1.41, 9.36)

0.020

Multivariate-adjusted model + smoking status

2.41 (0.87, 6.66)

1.52 (0.52, 4.44)

2.80 (1.06, 7.37)

0.092

Multivariate-adjusted model + CVD risk factorsc

2.02 (0.71, 5.73)

1.62 (0.56, 4.71)

3.24 (1.23, 8.55)

0.035

aReference group = lowest quartile

bMultivariate-adjusted models were adjusted for age, MI, hypertension, CHF, COPD, diabetes status, NSAID, Aspirin, cox-II inhibitor, ACE inhibitors, loop diuretics, antidepressants, self-rated health status, BMI, PASE score and walking speed

cCVD risk factors included systolic blood pressure, fasting blood glucose, total cholesterol and HDL

There were higher odds of prevalent PAD among participants with increasing levels of IL-6, TNFα, and CRP. These trends remained statistically significant (p <0.05) in MV models, except in the case of CRP, which was attenuated after multivariate plus smoking status adjustment. For IL-6, in MV models, the odds ratios ranged from 1.37 (quartile 2) to 3.95 (quartile 4), p trend = 0.003; for TNFα, 3.02 (quartile 2) to 4.44 (quartile 4), p trend = 0.05; and for CRP, 2.61 (quartile 2) to 3.63 (quartile 4), p trend = 0.02. These increased odds of prevalent PAD were similar to aging 10 years (OR per ten year increase in age = 2.41 (1.57 - 3.70). A positive trend between TNFαSRI and TNFαSRII was found with PAD for crude and age-adjusted models; however, this was no longer statistically significant after multivariate adjustment or smoking adjustment. The association with IL-6 SR and Il-10 with PAD were no longer significant in the age adjusted models. Adjustment for cardiovascular risk factors had little effect on the association between inflammatory burden and PAD.

Men with CRP >3ug/mL were more likely to have prevalent PAD than those with a lower level, MV models, OR = 2.0, (95% CI, 1.06–3.79). Nevertheless, this association was no longer significant after adjusting for pack-years or smoking status (Table 4). Participants with an inflammatory burden score ≥3 (Table 5) were 3.6 times more likely to have PAD compared to those with a score of 0, OR = 3.59 (95% CI, 1.48–8.71). This trend was attenuated slightly after adjustment for smoking, (p trend = 0.09).
Table 4

Association between CRP >3 ug/mL and prevalent peripheral arterial disease (ABI <0.90)

Model

CRP >3 ug/mL

Odds Ratio (95% Confidence Interval)a

Crude model

2.33 (1.35, 4.02)

Age-adjusted model

2.24 (1.29, 3.90)

Conditions-adjusted modelb

2.23 (1.26, 3.96)

Medications-adjusted modelc

2.20 (1.23, 3.96)

Multivariate-adjusted modeld

2.00 (1.06, 3.79)

Multivariate-adjusted model + smoking status

1.69 (0.88, 3.25)

Multivariate-adjusted model + smoking pack-years

1.69 (0.87, 3.26)

Multivariate-adjusted model + CVD risk factorse

2.02 (1.04, 3.92)

aReference group = CRP ≤3 ug/mL

bConditions-adjusted model was adjusted for MI, hypertension, CHF, COPD, diabetes status

cMedications-adjusted model was adjusted for NSAID, Aspirin, cox-II inhibitor, ACE inhibitors, loop diuretics, antidepressants

dMultivariate-adjusted models were adjusted for age, MI, hypertension, CHF, COPD, diabetes status, NSAID, Aspirin, cox-II inhibitor, ACE inhibitors, loop diuretics, antidepressants, self-rated health status, BMI, PASE score, walking speed

eCVD risk factors included systolic blood pressure, fasting blood glucose, total cholesterol and HDL

Table 5

Association of pro-inflammatory burden scores with prevalent peripheral arterial disease

Model

Pro-inflammatory burden score

Odds Ratio (95% Confidence Interval)a

1

2

≥3

P for trend

Crude model

1.57 (0.71, 3.45)

0.96 (0.33, 2.78)

3.92 (1.96, 7.85)

0.002

Age-adjusted model

1.62 (0.73, 3.59)

0.85 (0.29, 2.48)

3.11 (1.52, 6.35)

0.025

Conditions-adjusted modelb

1.79 (0.79, 4.06)

1.01 (0.34, 2.98)

3.65 (1.75, 7.63)

0.008

Medications-adjusted modelc

2.13 (0.90, 5.06)

1.30 (0.42, 3.98)

4.29 (1.93, 9.54)

0.004

Multivariate-adjusted modeld

2.40 (0.95, 6.07)

1.27 (0.39, 4.15)

3.59 (1.48, 8.71)

0.031

Multivariate-adjusted model + smoking status

2.25 (0.87, 5.78)

1.15 (0.35, 3.79)

2.88 (1.17, 7.13)

0.097

Multivariate-adjusted model + smoking pack-years

2.42 (0.93, 6.27)

1.28 (0.38, 4.25)

3.29 (1.33, 8.15)

0.052

Multivariate-adjusted model + CVD risk factorse

2.87 (1.06, 7.79)

1.31 (0.37, 4.67)

4.88 (1.86, 12.79)

0.013

aReference group = pro-inflammatory burden score of zero

bConditions-adjusted model was adjusted for MI, hypertension, CHF, COPD, diabetes status

cMedications-adjusted model was adjusted for NSAID, Aspirin, cox-II inhibitor, ACE inhibitors, loop diuretics, antidepressants

dMultivariate-adjusted models were adjusted for age, MI, hypertension, CHF, COPD, diabetes status, NSAID, Aspirin, cox-II inhibitor, ACE inhibitors, loop diuretics, antidepressants, self-rated health status, BMI, PASE score, walking speed

eCVD risk factors included systolic blood pressure, fasting blood glucose, total cholesterol and HDL

Discussion

In the present study men with the highest levels of IL-6, TNF-α, or CRP had a higher odds of prevalent PAD compared to men with the lowest levels. Higher inflammation was associated with slower walking speed as well as a higher prevalence of hypertension, diabetes mellitus, COPD, CHF and higher fasting blood glucose. Nevertheless, the association between inflammation and PAD was independent of these risk factors. Smoking is a well-established risk factor for PAD and was also associated with greater inflammation. Adjustment for smoking attenuated the relationship between CRP and PAD, suggesting that smoking may influence risk of PAD through inflammatory pathways. Conversely, the association between IL-6 and TNFα and PAD remained statistically significant even after adjusting for smoking, supporting previous studies suggesting that inflammation itself is an independent risk factor for PAD [9, 10, 1214, 24]. In our study, the magnitude of the association between highest inflammatory burden and PAD was similar to the increased odds of PAD associated with being 10 years older.

Men with PAD tended to have higher levels of every pro-inflammatory cytokine except IL-6 SR. Current literature supports that the strongest positive associations between PAD and cytokines are found with IL-6 [9, 13, 15, 25, 26] and CRP [9, 11, 13, 15, 26, 27] although there is some evidence for IL-6SR [9], TNFαSRI/II [15, 28] and a number of cytokines we did not measure in our study [9, 13, 29]. Our finding that TNFα was positively and significantly related with PAD has some strong support [26], although certain studies lack statistical significance [15] and others have actually reported the opposite [9]. Although previous studies have found conflicting results [9, 10, 30] we found a positive relationship between IL-10 and PAD in unadjusted analyses, which may represent a mechanism of overall inflammatory dysregulation in the disease. We found no association between IL-6SR and PAD, and the positive association between TNFαSRI/II with PAD was attenuated after multivariate adjustment. Nevertheless, our metric of inflammatory burden included all of the pro-inflammatory markers. Men with the greatest inflammatory burden had a >3-fold increased odds of PAD, but this was slightly attenuated after adjusting for smoking. These results suggest that overall inflammatory burden is related to PAD; however, smoking at least partially mediates this association.

Pro-inflammatory cytokines are implicated in other disease processes but the exact mechanism of their pathophysiology remains unclear. If particular inflammatory profiles are specific to one disease process over another (i.e. PAD versus CVD) it could lead to new screening tools, parameters for risk stratification, and targeted therapies. With an aging population and large anticipated influx of new “health care utilizers”, it is of paramount importance that screening tests be developed and exploited to curb morbidity and mortality from PAD and other diseases. Development of a screening cytokine panel to identify patients at risk of multiple diseases would have an important public health impact.

There are a number of strengths to our study. Our study consists of a population-based cohort and we measured seven cytokines with well-established, repeatable assays. Our use of ABI as a proxy for PAD is widely accepted [6] and we accounted for many possible confounders, including standard cardiovascular risk factors, physical activity, comorbidities and medications. Previous literature on PAD and inflammation often used a case–control approach or measured only a small number of cytokines.

Limitations of our study include the cross-sectional design. Our study population consisted of primarily Caucasian men and may not be generalizable to non-white men and women. We developed an inflammatory burden score by calculating the number of pro-inflammatory cytokines in the highest quartile. However, this approach yielded an uneven number of men in each group. For example, the number of men with 2 high inflammatory cytokines was much smaller than the other groups and CI were wide. Finally, we had no information on clinical signs and symptoms of PAD.

Conclusions

In conclusion, individual pro-inflammatory cytokines and overall inflammatory burden are associated with prevalent PAD, an association similar to aging 10 years. The inflammatory effects of smoking at least partially mediates this association. To our knowledge we are the first to relate inflammatory burden to prevalent PAD, which could be expanded upon in the future as more cytokines/biomarkers are discovered, and the assays become faster, more sensitive, and less expensive. Future studies on incident PAD in the setting of various inflammatory profiles may allow us predict disease and intervene before there is significant morbidity.

Abbreviations

ABI: 

ankle-arm index

CHF: 

congestive heart failure

CI: 

confidence interval

COPD: 

chronic obstructive pulmonary disease

CRP: 

C-reactive protein

CVs: 

coefficients of variability

FBG: 

fasting blood glucose

IDIS: 

Iowa Drug Information Service

IL-10: 

interleukin-10

IL-6SR: 

IL-6 soluble receptor

LCBR: 

Laboratory for Cytokine Biochemistry

MrOS: 

Osteoporotic Fractures in Men Study

MV: 

multivariable

OR: 

odds ratio

PAD: 

peripheral arterial disease

PASE: 

Physical Activity Scale for the Elderly

TNFα: 

tumor-necrosis factor-α

TNFαSRII: 

soluble receptor-II

Declarations

Acknowledgements

Not applicable.

Funding

The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), the NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, UL1 TR000128 and The Multidisciplinary Clinical Research Center for Rheumatic and Musculoskeletal Diseases (MCRC) under grant number: P60AR054731.

Availability of data and materials

The de-identified data is stored on a secure server at the University of Pittsburgh. The data will not be shared unless investigators submit a written application to the Osteoporotic Fractures in Men Steering Committee for approval.

Authors’ contribution

ST, AMK, NEL, JAC have made substantial contributions to conception and design of the manuscript. JAC oversaw the acquisition of data. AMK performed the statistical analysis. ST, AMK, NEL, JAC contributed to the interpretation of data. ST was responsible for drafting the manuscript. ST, AMK, NEL, JAC have participated in revising the manuscript critically for important intellectual content. ST, AMK, NEL, JAC read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The protocol for the study was approved by the Institutional Review Boards at all participating institutions, including the University of Pittsburgh, University of Minnesota at Minneapolis, University of Alabama at Birmingham, University California- San Diego, Stanford University, Oregon Health Sciences University and California Pacific Medical Center. All subjects provided written informed consent.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh
(2)
University of California Davis Health System
(3)
Family Medicine of Southwest, Peacehealth Southwest Medical Center

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Copyright

© The Author(s). 2017

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