reported positive associations of blood bio­ markers of inflammation with PM0.25 but not with larger accumulation­ mode particles 0.25–2.5 µm in diameter (PM0.25–2.5) (Delfino et al. 2009). However, particle mass alone does not provide sufficient information about com­ position or sources. We also previously found positive associations between the biomarkers and PM2.5 EC (Delfino et al. 2009). Based on these findings, we hypothesized that traf­ fic emission sources of organic chemicals in PM0.25 would be positively associated with systemic inflammation. In the present analysis, we aimed to better delineate which underlying PM components may be responsible for the associations we observed for EC and PM0.25 mass using new data on chemical species in the archived PM0.25 filter samples. Materials and Methods Population and design. This was a longitudinal study of repeated measures where each subject acted as his or her own control over time. This limits the impact of confounding by between­ subject characteristics. We recruited subjects from four retirement communities. Subjects were eligible for participation if they had a con­ firmed coronary artery disease history and were ≥ 65 years of age, nonsmokers, and unexposed to environmental tobacco smoke. Of 105 vol­ unteers, 21 were not eligible, 19 dropped out, 2 had too few blood draws or valid biomarker data (> 5 of 12 weeks), and 3 had insufficient biomarker data due to exclusions for frequent infections, leaving 60 subjects. We excluded biomarker measurements during weeks with acute infectious illnesses given their well­ known impact on measured biomarkers. Table 1 lists subject characteristics. Two retirement communities were stud­ ied in 2005–2006 and two in 2006–2007. Subjects were followed for a total of 12 weeks with weekly blood draws for circulating bio­ markers of inflammation in plasma. Each subject contributed 5–12 weekly blood draws (n = 578 total samples). Each community was studied in two 6­week seasonal phases, a warmer period characterized by higher photochemistry fol­ lowed by a cooler period characterized by higher air stagnation and lower mixing heights. This seasonal approach was intended to increase the variability in pollutant char­ acteristics, with higher secondary organic aerosols (SOAs) in the warmer phase and higher primary organic aerosols (POAs) in the cooler phase when trafficr­elated air pollutants increase at ground level. POAs are formed during or shortly after the combustion of fossil fuels. SOAs are largely photochemically pro­ duced from gas­to­particle conversion when volatile reactive organic gases from anthropo­ genic and biogenic sources, and anthropogenic semivolatile organic compounds (SVOCs), are oxidized to form low­volatility products that condense to produce SOAs. There are few data on the importance of variations in this multipollutant characteristic of PM to human health outcomes. In the present study, POAs are represented by PAHs and hopanes, whereas SOAs are represented by waters­oluble organic carbon (WSOC) and organic acids. Most PAHs are considered to be components of POAs. Hopanes are found in the lubricant oils of diesel and gasoline vehicles and are thus tracers of primary vehicular aerosols in the Los Angeles Basin (Schauer et al. 1996, 2000). WSOC (Snyder et al. 2009) and organic acids (Robinson et al. 2006) are tracers of SOAs, although a fraction of WSOC comes from biomass burning (Docherty et al. 2008). The research protocol was approved by the Institutional Review Board of the University of California–Irvine, and we obtained informed written consent from subjects. Biomarkers. We focused on an informative set of biomarkers of inflammation from the previous analysis of peripheral blood biomark­ ers and PM0.25 mass (Delfino et al. 2009). We drew blood samples in ethylenediamine­ tetraacetic acid tubes on Friday afternoons and processed them and froze the plasma on site within 30 min. Samples were stored at –80°C until assayed. Plasma biomarkers were thawed and assayed using 96­well immuno ­ assay kits for the proinflammatory cytokine interleukin6­ (IL6­) and the cytokine receptor– soluble tumor necrosis factor­ α (TNF­ α) receptor II (sTNFR­II; Quantikine HS, R&D Systems, Minneapolis, MN). sTNF­RII has a longer halfl­ife than TNF­ α (Aderka 1996) and may thus better reflect sustained or lagged effects. Thawed erythrocyte lysates were assayed spectrophotometrically for activities of the antioxidant enzymes copper/zinc­ superoxide dismutase (Cu,ZnS­OD) and glutathione per ­ oxidase­1 (GPx­1) (Cayman Chemical, Ann Arbor, MI, USA). Cu,Zn­SOD and GPx­ 1 values were normalized to units per gram of hemoglobin. These and related biomarkers are predictive of cardiovascular disease risk (Flores­Mateo et al. 2009; Kritchevsky et al. 2005; Pai et al. 2004). Exposures. The methods used to measure components and their relevance to sources of PM0.25 are described in detail in the Supplemental Material [Chemical Measurement Methods (doi:10.1289/ehp.0901407)] and by Arhami et al. (2010). There we also discuss in detail differences by season and community and describe the relation between indoor and outdoor measurements. Air sampling occurred in the immediate outdoor environment of each retirement com­ munity and at an indoor site located in the common areas of the main community build­ ings. The indoor data are thus representative to some degree of the same indoor environment Environmental Health Perspectives • volume 118 | number 6 | June 2010 Biomarkers and particle components of each subject. Our main interest here is in the effects of outdoors­ource PM components. More than 5 days before each blood draw, we collected indoor and outdoor size­segre ­ gated particle samples using Sioutas Personal Cascade Impactors (SKC Inc., Eighty Four, PA, USA) with Zefluor filters (3­µm pore size; Pall Life Sciences, Ann Arbor, MI, USA). We evaluated components only in the qua­ si­ultrafine fraction (PM 0.25). Mass concen­ trations were determined gravimetrically by weighing the impactor filters and substrates with a microbalance (uncertainty, ± 2 µg; Mettler­ Toledo, Columbus, OH, USA) in a temperature­ controlled and relative humidity– controlled room. The five weekly PM0.25 filters were com­ posited for chemical analyses. These com­ posites were cut into three sections (one half­section and two quarter­sections ). We analyzed the composited half section for 92 different organic compounds using gas chro­ matography/mass spectrometry (GC/MS) (Stone et al. 2008). For the present analy­ sis, compounds are grouped by their struc­ tures, which is the primary control of their chemical interactions. Selected representative organic components were grouped as PAHs, organic (n­alkanoic) acids, n­alkanes, and hopanes [see Supplemental Material, Table 1 (doi:10.1289/ehp.0901407)]. PAHs were fur­ ther subdivided into low­ (two­ to three­ ring), medium­ (four­ ring), and high­ (five­ ring or larger) molecular­ weight PAHs (LMW, MMW, and HMW, respectively), which is loosely connected to volatility and solubility. Table 1. Subjects and biomarker outcomes. Variable Value Age (years) 84.1 ± 5.60 Sex Male 34 (56.7) Female 26 (43.3) Cardiovascular history Confirmation of coronary artery diseasea Myocardial infarction 27 (45.0) Coronary artery bypass graft or 20 (33.3) angioplasty Positive angiogram or stress test 10 (16.7) Clinical diagnosisb 3 (5.0) Congestive heart failure 13 (21.7) Hypertension (by history) 42 (70.0) Hypercholesterolemia (by history) 43 (71.7) Medications Angiotensin-converting enzyme 24 (40.0) inhibitors and angiotensin II receptor antagonists 3-Hydroxy-3-methylglutaryl-coenzyme 31 (51.7) A reductase inhibitors (statins) IL-6 (pg/mL) 2.42 ± 1.85 sTNF-RII (pg/mL) 3,610 ± 1,489 sTNF-RII, soluble tumor necrosis factor-α receptor II. Values are mean ± SD or n (%). aEach category is hierarchical and excludes being in the above diagnostic category. bIncludes subjects with angi- nal symptoms relieved with nitrates plus echocardiogram and electrocardiographic evidence of past infarct. 757