Participants
The study sample consisted of 680 community-dwelling seniors living in the Boston area who participated in the MOBILIZE Boston Study (MBS). The design and methodology for this study have been previously described in detail [19, 20]. In brief, 765 persons were enrolled using door-to-door population based recruitment. To be included, individuals had to be > 65 years, able to understand and communicate in English, able to walk 20 feet without personal assistance (walking aids permitted), and expected to live in the area for at least 2 years. Exclusion criteria included terminal disease, severe vision or hearing deficits, and Mini-Mental State Examination score <18 [21, 22]. All subjects underwent a complete home and laboratory assessment of demographic characteristics, medical conditions, medications, functional status, gait speed, smoking status, alcohol use, blood pressure, and cerebral hemodynamics at baseline.
Depressive Symptoms Assessment
Depressive symptoms were assessed at study enrollment using the Revised Center for Epidemiologic Studies Depression scale (CESD-R) [23]. The CESD-R is a 20-item self-administered instrument designed to measure the presence of depressive symptoms over the previous two weeks in community studies. Note that the CESD-R does not capture information on a patient's clinical or treatment history and is not useful as a diagnostic tool for depression. The CESD-R has been validated [24, 25]. As in previous work [26], we chose a priori to use scores of <16 to indicate no or minimal depressive symptoms and ≥16 to indicate the presence of moderate or severe symptoms.
Biomarker measures
Plasma concentrations of sICAM-1, sVCAM-1, and interleukin-6 (IL-6) were measured by ELISA assay (R&D Systems, Minneapolis, MN). For sICAM-1 this assay has a sensitivity of 0.35 ng/mL, and the day-to-day variability of the assay at concentrations of 64.2, 117, 290 and 453 ng/mL are 10.1, 7.4, 6.0 and 6.1 %, respectively. For sVCAM-1 the assay has a sensitivity of 2.0 ng/mL and the day-to-day variability of the assay at concentrations of 9.8, 24.9 and 49.6 ng/mL is 10.2, 8.5 and 8.9 %, respectively. For IL-6, the assay has a sensitivity of 0.094 pg/mL, and the day-to-day variability of the assay at concentrations of 0.49, 2.78 and 5.65 pg/mL are 9.6, 7.2 and 6.5 %, respectively. In addition, the concentration of high sensitivity C-reactive protein (hsCRP) was determined using an immunoturbidimetric assay on a Hitachi 917 analyzer (Roche Diagnostics - Indianapolis, IN), using reagents and calibrators from DiaSorin (Stillwater, MN). This high-sensitivity assay has a limit of detection of 0.03 mg/L. The day-to-day variability of the assay at concentrations of 0.91, 3.07 and 13.38 mg/L are 2.81, 1.61 and 1.1 %, respectively. All assays were performed by Dr. Nader Rifai’s group at Boston Children’s Hospital.
Magnetic Resonance Image data
Volumes of cerebral white matter hyperintensities (WMH) normalized by intracranial volume were estimated from the MRI data in a subset of 25 MBS participants who completed MRI substudy. Eligible and willing participants were imaged using a Siemens Trio 3 Tesla system (Erlangen, Germany) employing a 12-channel phased-array head coil for reception and body coil for transmission. Cerebral WMH were measured with Free Surfer (http://surfer.nmr.mgh.harvard.edu) using a multispectral procedure that classifies white matter as normal or abnormal from signal intensities from the T1, PD, and T2 images at each voxel. The Free Surfer procedure for lesion segmentation combines the initial standard segmentation with an extension of the subcortical segmentation procedure that incorporates information from a co registered T2 and PD image for the segmentation of signal abnormalities within the white matter. Although the procedure with T1-weighted images alone tends to underestimate white matter lesion volumes, the incorporation of information from T2/PD provides more robust estimation of the lesion volumes. The multiple image modalities were registered to the T1 with boundary-based registration (BBR) and the segmentation of WMH from healthy WM was accomplished with a multispectral Gaussian classifier for each subject based on the atlas values.
Other covariates
Covariates included sociodemographic characteristics, health status, and amount of physical activity. Sociodemographic characteristics assessed in the home interview included age, sex, race (self-identified), and years of education. We used the validated Physical Activity Scale for the Elderly (PASE) to measure physical activity in the previous week [27]. Participants were asked about physician-diagnosed major medical conditions. Details of the study variables have been published previously [19, 20]. Diabetes was defined using an algorithm based on self-reported diabetes, use of antidiabetic medications, and laboratory measures from the baseline clinic visit, including random glucose (≥200 mg/dL) and hemoglobin A1c (≥7 %). Body mass index (calculated as weight in kilograms divided by height in meters squared) was calculated from measured height and weight. Comorbidity index was the number of comorbidities or medical conditions. Medication use (antihypertensive, antidepressant, and benzodiazepine) was also assessed.
Ethics Statement
The MOBILIZE Boston Study was reviewed and approved by the Hebrew SeniorLife Institutional Review Board (IRB). Written informed consent was obtained from each participant. The study was conducted according to the principles of the Helsinki Declaration.
Data Analysis
Concentrations of sVCAM-1 and sICAM-1 were natural log-transformed to approximate a normal distribution prior to modeling as continuous variables. We also divided the distributions of sVCAM-1 and sICAM-1 into quintiles according to the distribution in the entire study population for categorical analyses. We compared baseline characteristics of different groups of study participants by using t tests, χ2 tests, or Wilcoxon rank-sum tests, as appropriate.
We used multivariate logistic regression to estimate the odds ratio and 95 % confidence intervals (CIs) for quintiles of sVCAM-1 and sICAM-1 and depressive symptoms (CESD-R Score ≥16). Analyses were adjust for the following groups of confounders: 1) other biomarkers (IL6, C-Reactive Protein); 2) socio-demographic conditions (age, gender, white race, education level, BMI, current smoker, alcohol use); 3) Health conditions (diabetes, hypertension, congestive heart failure, hyperlipidemia, cognitive status, any cardiovascular medications, coronary disease, previous stroke), and 4) physical activity level.
Subjects with missing data for depressive symptoms, sVCAM-1 and sICAM-1 were excluded. All analyses were performed using SAS software, version 9.3 (SAS Institute Inc, Cary, North Carolina). A two-sided P value of less than 0.05 was considered indicative of statistical significance.