Biomarkers of Inflammation and Thrombosis as Predictors of Near-Term Mortality in Patients with Peripheral Arterial Disease: A Cohort Study

Context Traditional cardiovascular disease risk factors predict distant but not near-term outcomes. Change in thrombogenic and inflammatory biomarkers might predict near-term events. Contribution The authors measured d-dimer, amyloid A protein, and C-reactive protein annually for 3.4 years in 377 patients with peripheral arterial disease. Elevated levelsand increases in levelswere associated with all-cause and cardiovascular diseaserelated deaths occurring 1 to 2 years after their measurement but not with deaths after 2 years. Caution The small number of deaths limits the power to detect patterns. Implication Increases in thrombogenic and inflammatory biomarker levels may signal near-termbut not distantevents. This observation requires confirmation in a larger sample. The Editors Atherosclerotic cardiovascular disease causes more than 19 million deaths per year worldwide and is a major cause of morbidity (1). Traditional atherosclerotic risk factors predict later-term cardiovascular events and mortality but are relatively poor predictors of near-term events (1, 2). Identifying biomarkers whose levels are elevated before an imminent clinical event could provide important prognostic information and elucidate mechanisms of acute events. Thrombogenic and inflammatory factors have been implicated in the pathogenesis of acute cardiovascular events. Elevated levels of inflammatory factors may encourage plaque instability and rupture (3, 4). Elevated d-dimer levels may reflect the degree of ongoing fibrin formation and degradation associated with unstable atherosclerotic plaque (57). The liver rapidly synthesizes the inflammatory markers serum amyloid A and C-reactive protein (CRP) in response to inflammatory stimuli (8). Elevated or increasing levels of these biomarkers may signal increased risk for acute arterial thrombosis or unstable plaque rupture. We studied associations between elevated levels of d-dimer, CRP, and serum amyloid A and near-term mortality versus long-term mortality in patients with lower-extremity peripheral arterial disease (PAD). We also studied associations of increases in these markers with death during the year after measurement. Persons with PAD have increased mortality and elevated levels of these biomarkers compared with persons without PAD (911). Therefore, persons with PAD are an important cohort in which to evaluate temporal associations between death and changes in levels of these biomarkers. We hypothesized that elevated biomarker levels would be more predictive of deaths occurring in the near term after biomarker measurement than of deaths occurring later after biomarker measurement. We also hypothesized that increases in biomarker levels would be associated with a higher risk for death during the first year after the biomarker increase. Methods Overview We designed an observational, prospective study of persons with PAD (12). Participants attended 1 baseline visit and 3 annual follow-up visits. We collected blood at each visit and followed participants for total and cardiovascular disease mortality. We studied the association between biomarker levels at each visit and death occurring during 3 intervals: the year immediately after biomarker measurement, 1 to 2 years after biomarker measurement, and 2 to 3 years after biomarker measurement. Yearly follow-up was defined according to the annual visit dates. We tested the hypothesis that associations of elevated biomarker levels with mortality were strongest for the shortest follow-up interval and weakest for the longest follow-up interval. We used time-dependent analyses for the biomarkers so that participants with blood levels obtained at multiple visits contributed data for each biomarker measurement. We also studied whether death during the first year after changes in biomarker levels was associated with the change in biomarker level between baseline and the 1-year follow-up visit, baseline and the 2-year follow-up visit, and baseline and the 3-year follow-up visit. Participants Participants were consecutive patients undergoing lower-extremity arterial testing in 3 Chicago-area noninvasive vascular laboratories (12). Some participants were identified among consecutive patients in a large, general internal medicine practice because they had a low anklebrachial index. The study protocol was approved by the institutional review boards of Northwestern University's Feinberg School of Medicine and Catholic Health Partners Hospitals (Chicago, Illinois). Participants gave written informed consent. Exclusion Criteria We defined PAD as an anklebrachial index less than 0.90 (13). Exclusion criteria are reported elsewhere (12). We excluded patients with dementia (because they could not answer questions accurately), nursing home residents, wheelchair-bound persons, persons with foot or leg amputations, and patients who had recently had major surgery. We also excluded nonEnglish-speaking patients because investigators were not fluent in non-English languages. AnkleBrachial Index Measurement After participants rested supine for 5 minutes, we used a hand-held Doppler probe (Pocket-Dop II, Nicolet Vascular, Golden, Colorado) to measure systolic pressures in the right brachial artery, right dorsalis pedis and posterior tibial arteries, left dorsalis pedis and posterior tibial arteries, and left brachial artery. Each pressure was measured twice (12). We calculated the anklebrachial index in each leg by dividing average pressures in each leg by the average of the 4 brachial pressures (14). We used average brachial pressures in the arm with highest pressure instead of the average of all 4 brachial pressures when 1 brachial pressure was higher than the other in both measurement sets, and the 2 brachial pressures differed by 10 mm Hg or more in at least 1 measurement set. These participants may have had subclavian stenosis (1416). The lowest leg anklebrachial index was used in analyses. Comorbid Conditions We used algorithms developed for the Women's Health and Aging Study to document comorbid conditions at baseline (17). These algorithms combine data from patient report, physical examination, medical record review, medications, laboratory values, and a primary care physician questionnaire. Blood Collection At each visit, we asked all participants to undergo blood sample collection and storage and to verify their assent by initialing the consent form. Phlebotomy was attempted for all consenting participants. The principal investigator certified health interviewers to do phlebotomy before beginning blood collection and again every 6 months to ensure continued adherence to protocol. A 10% randomly selected sample of participants underwent split sample testing for additional quality-control monitoring. Blood was collected into Vacutainer (Becton Dickenson, Franklin Lakes, New Jersey) tubes containing EDTA and sodium citrate and was put on ice immediately. Tubes were spun at 3000 rpm for 20 minutes at 4C in a refrigerated centrifuge. Blood was stored at 70C until analyses were completed, which was up to 3 years after blood collection. Serum Amyloid A and CRP Levels We measured serum amyloid A and CRP by using an immunotechnique on the Behring BN II analyzer (Dade Behring, Wilmington, Delaware). This method detects CRP concentrations as low as 0.15 mg/L (18). Coefficients of variability were 7.94% for serum amyloid A and 4.26% for CRP. d-Dimer Levels We used an Asserachrom D-Di kit (Diagnostica Stago, Asnires-sur-Seine, France) to measure d-dimer with an enzyme-linked immunosorbent assay procedure. The coefficient of variability was 9.4%. Total and High-Density Lipoprotein Cholesterol Levels Total cholesterol levels were measured by using enzymatic reaction with peroxidasephenol-4-aminophenazone indicator reaction (19). High-density lipoprotein cholesterol levels were measured by using direct enzymatic colorimetric assay (20). Cigarette Smoking We determined cigarette smoking history by patient report using a structured interview. Pack-years of smoking were calculated on the basis of the number of years smoked and the average number of packs smoked per day. Death We ascertained deaths from the Social Security Death Index. Survival status was available for all participants. We obtained death certificates from the state of Illinois or medical records. Cause of death was determined by a certified nosologist who had completed advanced training in identifying causes of death from death certificates. Cardiovascular deaths were those with International Statistical Classification of Diseases and Related Health Problems, 10th Revision, codes in the range I01.0 through I99.9, which includes deaths due to coronary heart disease, stroke, peripheral vascular disease, and other cardiovascular diseases. Statistical Analysis We compared baseline characteristics between decedents and survivors with PAD by using general linear models for continuous variables and chi-square tests for categorical variables. We used proportional hazards regression analyses to relate baseline biomarker levels to all-cause and cardiovascular mortality at 4-year follow-up,adjusting for age, sex, race, diabetes, number of cardiovascular diseases, smoking, and anklebrachial index. Analytic Approaches Used to Address Primary Study Aims We used 2 analytic approaches to model associations of biomarker levels with mortality. To assess the marginal associations of a given biomarker level with near-term versus later-term mortality, we used a 3-model approach in which we performed 3 proportional hazard analyses, each corresponding to 1 of 3 intervals: the first, second, and third years after the biomarker measurement. To assess longitudinal associations of changes in biomarker levels with mortality, we combined the data into 1 model (combined model approach). The Appendix presents detailed descriptions of these 2 analytic approaches. The 3-model approach provides more statistical power than the combined mode