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 disease, Peripheral vascular disease, Inflammation, Cytokine, Ankle-arm index, Ankle-brachial index, Smoking, Men
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 [2–4] 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 [9–11] as well as its severity [12, 13] even after adjusting for traditional cardiovascular risk factors [10, 13–15]. 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, 12–14, 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.
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.
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
Contributor Information
Jane A. Cauley, Email: jcauley@edc.pitt.edu
Ahmed M. Kassem, Email: ahmed.kassem@pitt.edu
Nancy E. Lane, Email: nelane@ucdavis.edu
Sara Thorson, Email: thorson.sara@gmail.com.
References
- 1.Allison MA, Ho E, Denenberg JO, Langer RD, Newman AB, Fabsitz RR, et al. Ethnic-specific prevalence of peripheral arterial disease in the United States. Am J Prev Med. 2007;32(4):328–333. doi: 10.1016/j.amepre.2006.12.010. [DOI] [PubMed] [Google Scholar]
- 2.McDermott MM, Greenland P, Liu K, Guralnik JM, Celic L, Criqui MH, et al. The ankle brachial index is associated with leg function and physical activity: the Walking and Leg Circulation Study. Ann Intern Med. 2002;136(12):873–883. doi: 10.7326/0003-4819-136-12-200206180-00008. [DOI] [PubMed] [Google Scholar]
- 3.McDermott MM, Liu K, Greenland P, Guralnik JM, Criqui MH, Chan C, et al. Functional decline in peripheral arterial disease: associations with the ankle brachial index and leg symptoms. JAMA. 2004;292(4):453–461. doi: 10.1001/jama.292.4.453. [DOI] [PubMed] [Google Scholar]
- 4.McDermott MM, Guralnik JM, Tian L, Liu K, Ferrucci L, Liao Y, et al. Associations of borderline and low normal ankle-brachial index values with functional decline at 5-year follow-up: the WALCS (Walking and Leg Circulation Study) J Am Coll Cardiol. 2009;53(12):1056–1062. doi: 10.1016/j.jacc.2008.09.063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Diehm C, Lange S, Darius H, Pittrow D, von Stritzky B, Tepohl G, et al. Association of low ankle brachial index with high mortality in primary care. Eur Heart J. 2006;27(14):1743–1749. doi: 10.1093/eurheartj/ehl092. [DOI] [PubMed] [Google Scholar]
- 6.Ankle Brachial Index C, Fowkes FG, Murray GD, Butcher I, Heald CL, Lee RJ, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA. 2008;300(2):197–208. doi: 10.1001/jama.300.2.197. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Resnick HE, Lindsay RS, McDermott MM, Devereux RB, Jones KL, Fabsitz RR, et al. Relationship of high and low ankle brachial index to all-cause and cardiovascular disease mortality: the Strong Heart Study. Circulation. 2004;109(6):733–739. doi: 10.1161/01.CIR.0000112642.63927.54. [DOI] [PubMed] [Google Scholar]
- 8.Heald CL, Fowkes FG, Murray GD, Price JF. Ankle Brachial Index C: Risk of mortality and cardiovascular disease associated with the ankle-brachial index: Systematic review. Atherosclerosis. 2006;189(1):61–69. doi: 10.1016/j.atherosclerosis.2006.03.011. [DOI] [PubMed] [Google Scholar]
- 9.McDermott MM, Guralnik JM, Corsi A, Albay M, Macchi C, Bandinelli S, et al. Patterns of inflammation associated with peripheral arterial disease: the InCHIANTI study. Am Heart J. 2005;150(2):276–281. doi: 10.1016/j.ahj.2004.09.032. [DOI] [PubMed] [Google Scholar]
- 10.Li SB, Yang F, Jing L, Ma J, Jia YD, Dong SY, et al. Correlation between plasma lipoprotein-associated phospholipase A and peripheral arterial disease. Exp Ther Med. 2013;5(5):1451–1455. doi: 10.3892/etm.2013.1005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Wildman RP, Muntner P, Chen J, Sutton-Tyrrell K, He J. Relation of inflammation to peripheral arterial disease in the national health and nutrition examination survey, 1999–2002. Am J Cardiol. 2005;96(11):1579–1583. doi: 10.1016/j.amjcard.2005.07.067. [DOI] [PubMed] [Google Scholar]
- 12.Nylaende M, Kroese A, Stranden E, Morken B, Sandbaek G, Lindahl AK, et al. Markers of vascular inflammation are associated with the extent of atherosclerosis assessed as angiographic score and treadmill walking distances in patients with peripheral arterial occlusive disease. Vasc Med. 2006;11(1):21–28. doi: 10.1191/1358863x06vm662oa. [DOI] [PubMed] [Google Scholar]
- 13.Tzoulaki I, Murray GD, Lee AJ, Rumley A, Lowe GD, Fowkes FG. C-reactive protein, interleukin-6, and soluble adhesion molecules as predictors of progressive peripheral atherosclerosis in the general population: Edinburgh Artery Study. Circulation. 2005;112(7):976–983. doi: 10.1161/CIRCULATIONAHA.104.513085. [DOI] [PubMed] [Google Scholar]
- 14.Brevetti G, Silvestro A, Di Giacomo S, Bucur R, Di Donato A, Schiano V, et al. Endothelial dysfunction in peripheral arterial disease is related to increase in plasma markers of inflammation and severity of peripheral circulatory impairment but not to classic risk factors and atherosclerotic burden. J Vasc Surg. 2003;38(2):374–379. doi: 10.1016/S0741-5214(03)00124-1. [DOI] [PubMed] [Google Scholar]
- 15.Murabito JM, Keyes MJ, Guo CY, Keaney JF, Jr, Vasan RS, D'Agostino RB, Sr, et al. Cross-sectional relations of multiple inflammatory biomarkers to peripheral arterial disease: The Framingham Offspring Study. Atherosclerosis. 2009;203(2):509–514. doi: 10.1016/j.atherosclerosis.2008.06.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Thejaswini KO, Roopakala MS, Prasanna-Kumar KM. A Study of Association of Ankle Brachial Index and the Highly Sensitive C-Reactive Protein in Type 2 Diabetic Patients and in Normal Subjects. J Clin Diagn Res. 2013;7(1):46–50. doi: 10.7860/JCDR/2012/4854.2667. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Blank JB, Cawthon PM, Carrion-Petersen ML, Harper L, Johnson JP, Mitson E, et al. Overview of recruitment for the osteoporotic fractures in men study (MrOS) Contemp Clin Trials. 2005;26(5):557–568. doi: 10.1016/j.cct.2005.05.005. [DOI] [PubMed] [Google Scholar]
- 18.Orwoll E, Blank JB, Barrett-Connor E, Cauley J, Cummings S, Ensrud K, et al. Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study--a large observational study of the determinants of fracture in older men. Contemp Clin Trials. 2005;26(5):569–585. doi: 10.1016/j.cct.2005.05.006. [DOI] [PubMed] [Google Scholar]
- 19.Cauley JA, Barbour KE, Harrison SL, Cloonan YK, Danielson ME, Ensrud KE, et al. Inflammatory Markers and the Risk of Hip and Vertebral Fractures in Men: the Osteoporotic Fractures in Men (MrOS). J Bone Miner Res. 2016. [DOI] [PMC free article] [PubMed]
- 20.Barbour KE, Lui LY, Ensrud KE, Hillier TA, LeBlanc ES, Ing SW, et al. Inflammatory markers and risk of hip fracture in older white women: the study of osteoporotic fractures. J Bone Miner Res. 2014;29(9):2057–2064. doi: 10.1002/jbmr.2245. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Vogt MT, Cauley JA, Newman AB, Kuller LH, Hulley SB. Decreased ankle/arm blood pressure index and mortality in elderly women. JAMA. 1993;270(4):465–469. doi: 10.1001/jama.1993.03510040069031. [DOI] [PubMed] [Google Scholar]
- 22.Pahor M, Chrischilles EA, Guralnik JM, Brown SL, Wallace RB, Carbonin P. Drug data coding and analysis in epidemiologic studies. Eur J Epidemiol. 1994;10(4):405–411. doi: 10.1007/BF01719664. [DOI] [PubMed] [Google Scholar]
- 23.Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol. 1993;46(2):153–162. doi: 10.1016/0895-4356(93)90053-4. [DOI] [PubMed] [Google Scholar]
- 24.Cooke JP, Wilson AM. Biomarkers of peripheral arterial disease. J Am Coll Cardiol. 2010;55(19):2017–2023. doi: 10.1016/j.jacc.2009.08.090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Signorelli SS, Anzaldi M, Fiore V, Simili M, Puccia G, Libra M, et al. Patients with unrecognized peripheral arterial disease (PAD) assessed by ankle-brachial index (ABI) present a defined profile of proinflammatory markers compared to healthy subjects. Cytokine. 2012;59(2):294–298. doi: 10.1016/j.cyto.2012.04.038. [DOI] [PubMed] [Google Scholar]
- 26.Beckman JA, Preis O, Ridker PM, Gerhard-Herman M. Comparison of usefulness of inflammatory markers in patients with versus without peripheral arterial disease in predicting adverse cardiovascular outcomes (myocardial infarction, stroke, and death) Am J Cardiol. 2005;96(10):1374–1378. doi: 10.1016/j.amjcard.2005.07.041. [DOI] [PubMed] [Google Scholar]
- 27.Silvestro A, Scopacasa F, Ruocco A, Oliva G, Schiano V, Zincarelli C, et al. Inflammatory status and endothelial function in asymptomatic and symptomatic peripheral arterial disease. Vasc Med. 2003;8(4):225–232. doi: 10.1191/1358863x03vm503oa. [DOI] [PubMed] [Google Scholar]
- 28.Fiotti N, Giansante C, Ponte E, Delbello C, Calabrese S, Zacchi T, et al. Atherosclerosis and inflammation. Patterns of cytokine regulation in patients with peripheral arterial disease. Atherosclerosis. 1999;145(1):51–60. doi: 10.1016/S0021-9150(99)00013-1. [DOI] [PubMed] [Google Scholar]
- 29.Stoner L, Lucero AA, Palmer BR, Jones LM, Young JM, Faulkner J. Inflammatory biomarkers for predicting cardiovascular disease. Clin Biochem. 2013;46(15):1353–1371. doi: 10.1016/j.clinbiochem.2013.05.070. [DOI] [PubMed] [Google Scholar]
- 30.Chaparala RP, Orsi NM, Lindsey NJ, Girn RS, Homer-Vanniasinkam S. Inflammatory profiling of peripheral arterial disease. Ann Vasc Surg. 2009;23(2):172–178. doi: 10.1016/j.avsg.2008.06.005. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
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.