Abstract
Higher levels of inflammation are associated with adverse outcomes in persons with lower extremity peripheral arterial disease (PAD). This study evaluated associations of physical activity during daily life with levels of inflammatory biomarkers, D-dimer, and homocysteine in persons with PAD. Participants were 244 men and women (mean age 74.4 years ± 8.2) with PAD (ankle brachial index (ABI) < .90). C reactive protein (CRP), Interleukin-6 (IL-6), soluble Intracellular Adhesion Molecule-1 (sICAM-1), soluble Vascular Cellular Adhesion Molecule-1 (sVCAM-1), D-dimer, and homocysteine were assessed at study entry. Physical activity was objectively assessed via a vertical accelerometer, which participants wore continuously for 7 days. After adjusting for age, sex, race, body mass index, smoking, comorbidities, ABI, and other potential confounders, higher physical activity levels were associated linearly and significantly with lower levels of all measured circulating biomarkers: sVCAM-1 (p trend = 0.001); D-Dimer (p trend = 0.005); homocysteine (p trend = 0.006); IL-6 (p trend = 0.010); CRP, (p trend = 0.028); sICAM-1 (p trend = 0.033). In conclusion, higher levels of physical activity were associated independently with lower levels of inflammatory markers, homocysteine, and D-dimer in PAD patients.
Among men and women with lower extremity peripheral arterial disease (PAD), elevated levels of inflammatory biomarkers are associated with increased cardiovascular event rates and more adverse lower extremity outcomes.1–7 Supervised exercise interventions have been shown to lower inflammatory markers in patients with peripheral arterial disease. 8 However, it is unknown whether higher levels of physical activity accrued during daily living, as opposed to intensive, structured exercise programs, are associated with lower levels of inflammation in persons with PAD. Identifying behaviors that are associated with lower levels of biomarkers in persons with PAD may aid the identification of lifestyle interventions that reduce functional decline and cardiovascular event rates in men and women with PAD. This study assessed associations of physical activity during daily life with circulating biomarkers of inflammation [i.e., C-Reactive Protein (CRP), Interleukin-6 (IL-6), soluble Intracellular Adhesion Molecule-1 (sICAM-1), and soluble Vascular Cell Adhesion Molecule-1 (sVCAM-1)]. Homocysteine and D-dimer were also studied. Additionally, we sought to determine whether observed associations were modified by calf muscle characteristics.
METHODS
The protocol was Institutional Review Board-approved by Northwestern University’s Feinberg School of Medicine and Catholic Health Partners Hospitals. Participants gave written informed consent. Participants in this study were enrolled in the Walking and Leg Circulation Study II (WALCS-II), an observational study identifying characteristics associated with functional impairment and decline in persons with PAD. 9
PAD participants were identified consecutively from among patients undergoing lower extremity arterial testing in 3 Chicago-area non-invasive vascular laboratories. All PAD participants were age 59 or older at baseline and had an ABI < 0.90. Characteristics of this population have been described.9 Demented patients, nursing home residents, wheelchair-bound patients, and patients with foot or leg amputations were excluded.10 Non-English-speaking patients were excluded because investigators were not fluent in non-English languages. Patients with recent major surgery were excluded. Patients who had knee and or hip replacements were also excluded.
In the present study, PAD participants in WALCS II who wore a vertical accelerometer continuously for 7 days, had blood drawn, and had calf skeletal muscle measured were included. Systematic data on reasons that some participants did not wear monitors was not collected. Some participants refused to wear monitors, some wore monitors but did not return them and could not be reached at seven-day follow-up, some participants’ monitors malfunctioned, and in some instances all monitors were in use and none were available.
Physical activity was measured over a 7 day period using the Caltrac vertical accelerometer (Torrence, CA). The vertical accelerometer provided an estimate of activity units, primarily a result of walking behavior. Caltrac vertical accelerometers are designed to estimate caloric expenditure. However, for the present study, accelerometers were programmed using identical weight, height, age, and sex for each participant so that activity levels could be compared specifically between all participants.9,11 Programmed in this manner, the accelerometer measures “activity units,” based on vertical movement detected by the Caltrac accelerometer, which is worn at the hip. We have previously validated this accelerometer in patients with PAD.9,11 After wearing activity monitors continuously for 7 days, participants reported the number of activity units displayed on the accelerometer by telephone and mailed their activity monitors back to investigators.
Systolic pressures in brachial, dorsalis pedis and posterior tibial arteries were measured twice with a hand-held Doppler probe (Nicolet Vascular Pocket Dop II, Golden, CO). The ABI was calculated in each leg by dividing average pressures in each leg by the average of the four brachial pressures.12 Average brachial pressures in the arm with highest pressure were used when one brachial pressure was higher than the opposite brachial pressure in both measurement sets, and the two brachial pressures differed by 10 or more mm Hg in at least one measurement set, since in such cases subclavian stenosis was possible.13 Zero values for the posterior tibial and dorsalis pedis pulses were not included in this calculation. Lowest leg ABI was used in analyses.
Blood draws were completed in a non-fasting state, usually in the morning or afternoon, between 8:00am–1:00pm. Participants rested quietly for 5 minutes in a supine position, then completed the ABI assessment, followed by the blood draw. A 21-gauge butterfly needle was inserted into a large antecubital vein and the tourniquet removed immediately. Blood was collected into EDTA and citrate vacutainer tubes and immediately iced. Tubes were spun at 3,000 revolutions per minute for 20 minutes at 4 degrees Celsius in a refrigerated centrifuge. Samples were immediately frozen at −70 degrees Celsius. Blood was stored for up to 3 years.
CRP levels were determined using an immunotechnique on the Behring BN II analyzer (Dade Behring, Wilmington, DE). Monoclonal anti-CRP antibodies, coated on polystyrene beads, agglutinate with CRP in the serum sample. Intensity of the resulting scattered light in the nephelometer was used to determine the CRP content. This assay detects CRP concentrations as low as 0.015 mg/dL. The intra-assay coefficient of variation for CRP in this study was 2.1%.
Interleukin-6 was measured by an ultrasensitive enzyme-linked immunosorbent assay from R & D Systems (Minneapolis, Minnesota). The assay has a sensitivity of 0.094 pg/ml. The intra-assay coefficient of variation for IL-6 in this study was 4.2%.
Enzyme-linked immunosorbent assays (ELISA) from R & D Systems were used to measure sICAM-1 and sVCAM-1. The intra-assay coefficients of variation for both sICAM-1 and sVCAM-1 in this study were 5.6%.
The concentration of homocysteine was determined using an enzymatic assay on the Hitachi 917 analyzer (Roche Diagnostics), using reagents and calibrators from Catch Inc. (Seattle, Washington). The intra-assay coefficient of variation for homocysteine in this study was 6.9%.
An Asserachrom D-Di kit (Diagnostica Stago, Asnieres-Sur-Seine, France) was used to measure fibrin D-dimer. The Asserachrom D-Di kit uses an ELISA procedure to quantitatively determine D-dimer concentration. The Asserachrom D-Di kit has a lower detection limit of 5 ng/ml. The intra-assay coefficient of variation for D-dimer in this study was 7.2%.
Total cholesterol levels were measured using enzymatic reaction with peroxidase/phenol-4-aminoiphenazone indicator reaction.14 The concentration of HDL-cholesterol was determined using a direct enzymatic colorimetric assay.15
Algorithms developed for the Women’s Health and Aging Study and the Cardiovascular Health Study were used to document comorbidities.16 These algorithms combine data from patient report, physical examination, medical record review, medications, laboratory values, and a primary care physician questionnaire. We assessed diabetes, the number of cardiovascular comorbidities (angina, myocardial infarction, stroke, and heart failure), arthritis (knee arthritis, hip arthritis, hip fracture, spinal stenosis, and disk disease), and the number of other comorbid diseases (pulmonary disease and cancer).
All prescription medications were recorded. The principal investigator (MMM) reviewed and classified medications in a blinded fashion. Height and weight were measured at the study visit. Body mass index (BMI) was calculated as: weight(kg)/height(m2). History of cigarette smoking (total pack-years ever smoked) was determined with patient report.
Using a Computed Tomography (CT) scanner (LightSpeed, General Electric Medical Systems, Waukesha, WI, USA), 2.5 mm cross-sectional images of the calves were obtained at 66.7% of the distance from the distal to the proximal tibia, based on previous study.17 Cross-sectional images were analyzed using BonAlyse (BonAlyse Oy, Jyvaskyla-Fin137 The muscle outline was traced manually and excluded subcutaneous fat, and bone. When quantifying muscle area, the Bon Alyse software quantifies voxels within a range corresponding to muscle density (9 to 271 mg/cm3). Intra-muscular fat is quantified by summing voxels corresponding to fat within muscle tissue (−270 to 8 mg/cm3). Previous cadaver studies demonstrate that these methods provide an estimate of muscle cross-sectional area that is highly correlated with direct anatomic measures.18
Leg symptoms were classified into 1 of 5 groups using the San Diego Claudication Questionnaire, based on previous study.9,19 Leg symptom groups were as follows: 1) classic intermittent claudication (IC); 2) leg pain on exertion and rest; 3) atypical exertional leg pain/carry on; 4) atypical exertional leg pain/stop; 5) asymptomatic (no exertional pain).
To test for linear relationships between physical activity and blood biomarkers, participants were categorized into quartiles according to their physical activity level. Mean biomarker levels and other patient characteristics were compared between the physical activity quartiles using general linear models. For continuous variables, the tests for linear trend across the physical activity quartiles were based on general linear models using the actual physical activity level as an independent variable. Proportions for dichotomous variables were estimated using general linear models and the test for linear trend was based on age-adjusted logistic models.
In Model I, we adjusted for age, race, and sex. In Model II, we added adjustment for BMI, ABI, cigarette smoking (pack-years), comorbidities, and leg symptoms. In Model III, we adjusted for all variables in Model II plus calf muscle area and percent fat in the calf muscle.20 Because statin drugs lower CRP levels, mean CRP levels across the physical activity categories were adjusted for statin use (yes/no). Analyses were performed using SAS statistical software, version 9.1 (SAS Institute Inc. Cary, NC).
Results
Among 478 participants with PAD (defined as ABI < .90 at baseline) in the WALCS-II cohort, biomarker calf skeletal muscle data were available for 403 PAD patients (84%). Of these, complete accelerometer data were available from 244 (61%). Characteristics of participants with vs. without activity monitor data were similar (Table 1). Age-adjusted characteristics of the study population according to physical activity quartiles are presented in Table 2.
Table 1.
Characteristics of peripheral arterial disease participants with and without Caltrac accelerometer data among participants with blood markers (N = 403).
| Variable | Without Activity Monitor (N = 159) | With Activity Monitor (N = 244) | P value |
|---|---|---|---|
| Age (years) | 75.6 (8.1) | 74.4 (8.2) | 0.17 |
| Men | 88 (55%) | 127 (52%) | 0.52 |
| African-American | 25 (16%) | 40 (16%) | 0.86 |
| Ankle brachial index | 0.63(0.17) | 0.63 (0.15) | 0.73 |
| Cigarette smoking ever (pack years) | 38.7 (39.9) | 32.9 (32.7) | 0.11 |
| Body mass index (kg/m2) | 27.6 (5.1) | 27.9 (4.9) | 0.56 |
| Total cholesterol (mg/dl) | 175 (38) | 175 (43) | 0.99 |
| High-density lipoprotein cholesterol (mg/dl) | 50.7 (17.9) | 52.3 (19.0) | 0.41 |
| Diabetes mellitus | 49 (31%) | 78 (32%) | 0.81 |
| Number of cardiac or cerebrovascular events | 1.1 (1.2) | 1. (1.2) | 0.69 |
| Number of other comorbid diseases | 0.8 (0.8) | 0.7 (0.8) | 0.09 |
| C-Reactive Protein (mg/L) | 5.2 (11.5) | 3.2 (4.1) | 0.02 |
| Interleukin-6 (pg/mL) | 4.4 (4.1) | 3.9 (3.5) | 0.12 |
| Intracellular Adhesion Molecule-1 (ng/mL) | 313 (93) | 302 (92) | 0.23 |
| Vascular Cellular Adhesion Molecule-1 (ng/mL) | 1204 (460) | 1132 (421) | 0.11 |
| Homocysteine (umol/L) | 12.0 (5.1) | 11.3 (4.1) | 0.15 |
| D-Dimer (ug/mL) | 1.0 (1.2) | 0.9 (1.0) | 0.43 |
Values are expressed as mean (standard deviation) unless otherwise indicated.
Table 2.
Age-adjusted characteristics of study participants according to quartile of physical activity level.
| Variable | Quartile of Activity: Caltrac Accelerometer (activity units) | P trend | |||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | ||
| <459 (N=62) | 459–660 (N=60) |
661–914 (N=62) |
>914 (N = 61) |
||
| Men | 34 (57%) | 29 (49%) | 31 (49%) | 33 (55%) | 0.72 |
| African-American | 9 (15%) | 9 (15%) | 13 (21%) | 9 (15%) | 0.94 |
| Ankle brachial index | 0.63(0.02) | 0.65(0.02) | 0.64(0.02) | 0.62(0.02) | 0.67 |
| Cigarette smoking ever (pack years) | 31.9(4.2) | 31.7(4.2) | 33.2(4.2) | 34.6(4.2) | 0.62 |
| Body mass index (kg/m2) | 28.7 (0.6) | 28.3 (0.6) | 27.4 (0.6) | 27.2 (0.6) | 0.07 |
| Total cholesterol (mg/dl) | 166(5.6) | 174(5.8) | 187(5.9) | 176(5.7) | 0.10 |
| High-density lipoprotein cholesterol (mg/dl) | 47.5(2.4) | 55.8(2.5) | 54.1(2.6) | 52.4(2.5) | 0.24 |
| Diabetes mellitus | 26 (44%) | 17 (29%) | 16 (24%) | 19 (30%) | 0.04 |
| Number of cardiac or cerebrovascular events | 1.7(0.2) | 1.1(0.2) | 0.96(0.2) | 0.67(0.2) | <0.001 |
| Number of other comorbid diseases | 0.8(0.1) | 0.7(0.1) | 0.6(0.1) | 0.6 (0.1) | 0.19 |
| Statin use | 30 (49%) | 34 (57%) | 28 (45%) | 35 (53%) | 0.62 |
| Anti-platelet therapy use | 35 (58%) | 35 (59%) | 27 (43%) | 36 (58%) | 0.58 |
| Angiotensin-Converting Enzyme Inhibitor use | 23 (38%) | 20 (33%) | 23 (37%) | 12 (26%) | 0.07 |
| Calf muscle area (mm2) | 5050(163) | 5495(163) | 5726(162) | 5896(164) | <0.001 |
| Calf muscle percent fat | 16.8(1.4) | 9.7(1.4) | 8.3(1.4) | 6.5(1.4) | <0.001 |
| C-Reactive Protein (mg/L) | 4.4 (0.5) | 3.4 (0.5) | 2.6(0.5) | 2.3 (0.5) | 0.00 |
| Interleukin-6 (pg/mL) | 5.2(0.4) | 4.1(0.5) | 3.1 (0.5) | 3.0(0.5) | <0.001 |
| Intracellular Adhesion Molecule-1 (ng/mL) | 325(11.8) | 296(12.1) | 304(12.0) | 281(12.1) | 0.02 |
| Vascular Cellular Adhesion Molecule-1 (ng/mL) | 1352(51) | 1097(52) | 1050(51) | 1021(52) | <0.001 |
| Homocysteine (umol/L) | 13.2(0.5) | 10.9(0.5) | 10.3(0.5) | 10.7(0.5) | 0.00 |
| D-Dimer (ug/mL) | 1.3(0.1) | 0.96(0.1) | 0.67(0.1) | 0.76(0.1) | 0.00 |
Values are expressed as mean (standard error) unless otherwise indicated. Cardiac or cerebrovascular disease was defined as the history of myocardial infarction or heart failure or angina pectoris or stroke.
Adjusting for age, sex, and race (Model I), higher physical activity quartiles were associated with lower levels of CRP, IL-6, sICAM-1, sVCAM-1, homocysteine, and D-dimer (Figure 1). After additional adjustment for BMI, smoking, ABI, comorbidities, and leg symptoms (Model II), associations between higher levels of physical activity and lower biomarker levels were attenuated but remained statistically significant (Table 3). After additional adjustment for calf muscle area and percent fat (Model III), associations of physical activity and sICAM-1, sVCAM-1, homocysteine, and D-dimer were further attenuated, but remained statistically significant (Table 3). Associations for IL-6 and CRP were also attenuated and of only borderline significance.
Figure 1. Associations between quartiles of Caltrac assessed physical activity and baseline blood markers.
Caltrac (activity units): 1st quartile (<459), 2nd quartile (459-<660), 3rd quartile (660–914), 4th quartile (> 914).
Model I: Adjusted for age, sex, and race.
Adjusted quartile means and upper 95% confidence intervals presented
Table 3.
Associations between baseline quartiles of physical activity and biomarkers.
| N | CRP | IL-6 | Homocysteine | |||||
|---|---|---|---|---|---|---|---|---|
| Mol II | Mol III | Mol II | Mol III | Mol II | Mol III | |||
| CALTRAC | 1st quartile (<459) | 62 | 4.3 | 4.1 | 4.9 | 4.6 | 13.2 | 13.0 |
| 2nd quartile (459–660) | 60 | 3.3 | 3.2 | 4.0 | 4.0 | 10.8 | 10.8 | |
| 3rd quartile (661–914) | 61 | 2.6 | 2.7 | 3.3 | 3.4 | 10.4 | 10.4 | |
| 4th quartile (>914) | 61 | 2.6 | 2.7 | 3.2 | 3.4 | 10.9 | 11.0 | |
|
| ||||||||
| p-value | 0.13 | 0.31 | 0.06 | 0.23 | 0.00 | 0.01 | ||
| p-trend | 0.03 | 0.09 | 0.01 | 0.06 | 0.00 | 0.02 | ||
| N | ICAM | VCAM | DDIMER | |||||
|---|---|---|---|---|---|---|---|---|
| Mol II | Mol III | Mol II | Mol III | Mol II | Mol III | |||
| CALTRAC | 1st quartile (<459) | 62 | 325.5 | 327.4 | 1326 | 1312 | 1.3 | 1.2 |
| 2nd quartile (459–660) | 60 | 293.2 | 293.0 | 1085 | 1087 | 1.0 | 1.0 | |
| 3rd quartile (661–914) | 61 | 306.2 | 305.5 | 1074 | 1078 | 0.7 | 0.7 | |
| 4th quartile (>914) | 61 | 280.7 | 279.4 | 1036 | 1044 | 0.8 | 0.8 | |
|
| ||||||||
| p-value | 0.07 | 0.07 | 0.00 | 0.01 | 0.02 | 0.07 | ||
| p-trend | 0.03 | 0.03 | 0.00 | 0.00 | 0.01 | 0.03 | ||
Model II: Adjusted for age, sex, race, BMI, smoking, comorbidities, ABI, and leg symptoms
Model III: Adjusted for all the covariates in Model II + calf muscle area and percent fat
Discussion
This study demonstrates that in persons with PAD, higher levels of physical activity during daily living assessed by vertical accelerometer are associated with lower levels of inflammatory markers (CRP, IL-6, sICAM-1, sVCAM-1), homocysteine and D-dimer, adjusting for confounders including age, sex, race, ABI, comorbidities, BMI, and smoking. However, when we additionally adjusted for calf muscle area and calf muscle percent fat, many of these relationships were attenuated or no longer statistically significant, indicating that associations of higher biomarker levels with smaller calf muscle area and greater calf muscle percent fat may explain some proportion of the associations of higher physical activity with lower biomarker levels.
Our results are important because elevated levels of inflammatory markers are associated with higher mortality and more lower extremity adverse outcomes in those with PAD.1–3,21–22 Further, among PAD patients, lower physical activity levels are associated with higher rates of all-cause and cardiovascular mortality.23 Future studies should test physical activity interventions aimed at increasing daily activity to determine if they can lower biomarker levels and whether this results in lower rates of adverse outcomes.
Associations of higher physical activity levels with lower biomarker levels may be related, in part, to associations of higher levels of circulating biomarkers with more adverse skeletal muscle characteristics. Our prior work demonstrates that higher levels of the biomarkers studied here are associated with more adverse calf muscle characteristics in people with PAD.24 Findings reported here indicate that although adjustment for calf muscle characteristics attenuates some associations, many associations remain statistically significant, suggesting that muscle characteristics do not completely explain the observed associations. In this cross-sectional study, we cannot discern the role of calf muscle characteristics as potential mediators of associations of higher physical activity levels with reductions in biomarker levels. It may be that while exercise is the important variable, calf muscle and fat serve as markers for exercise activity and give a better indication of integrated exercise than does an activity monitor worn for a brief period of time. Longitudinal research is needed to clarify these relationships.
There are several limitations to the current study. First, because this is a cross-sectional study, rather than a randomized controlled trial, we cannot determine a causal relationship between physical activity and blood biomarkers. Further, although we adjusted for potential confounders, other non-measured factors such as better clinical care or a healthier lifestyle may account for the associations observed between physical activity during daily living and biomarker levels. Alternatively, body composition, which may not be entirely encompassed by the BMI may be related to both physical activity and biomarker levels, potentially influencing our findings. We are also limited by the fact that only slightly more than 60% of participants with complete blood and calf muscle data wore the physical activity monitors. However, our data indicate that while individuals were not randomly chosen to wear accelerometers, those who did vs. did not wear the activity monitors were largely similar on baseline characteristics. Finally, we did not collect data on the intensity of the physical activities in which the participants engaged. Consequently, we cannot determine how intensity of physical activity is associated with inflammatory markers in these patients.
Acknowledgments
FUNDING SOURCES
Supported by grants R01-HL58099, R01-HL64739, R01-HL71223, and R01-HL076298 from the National Heart Lung and Blood Institute and by grant #RR-00048 from the National Center for Research Resources, National Institutes of Health (NIH). Supported in part by the Intramural Research Program, National Institute on Aging, NIH.
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