The Vanderbilt Memory & Aging Project: Study Design and Baseline Cohort Overview

Study cohort eligibility and selection

The Vanderbilt Memory & Aging Project is a case-control longitudinal study investigating vascular health and brain aging launched in September 2012 at Vanderbilt University Medical Center in Nashville, TN. Participants were recruited through postal mailings, clinical referrals, radio advertisements, newsletters, research distribution emails, community events, websites, and word of mouth. Participants were required to speak English, have adequate auditory and visual acuity for testing, and have a reliable study partner (i.e., an informant the participant knew a minimum of 2 years at study enrollment with weekly contact and knowledge of the participant’s cognitive and functional abilities). Participants were excluded if they had MRI contraindication (i.e., ferrous metal in the body or history of claustrophobia), a history of neurological disease (e.g., dementia, multiple sclerosis), stroke, heart failure, major psychiatric illness (e.g., bipolar disorder, schizophrenia), head injury with loss of consciousness >5 min, chronic obstructive pulmonary disease, or a systemic or terminal illness (e.g., cancer) that could impact participation in follow-up examinations. The protocol was approved by the Vanderbilt University Medical Center Institutional Review Board, and written informed consent was obtained from all participants prior to data collection.

Participants meeting initial inclusion and exclusion criteria completed a 4-h screening visit, including a medical history and record review, a Clinical Dementia Rating (CDR) [13] interview with the participant and informant, an assessment of activities of daily living (Functional Activities Questionnaire) [14], and a comprehensive neuropsychological protocol (see Table 1). This information was used by a consensus team to determine study eligibility, including cognitive diagnosis (i.e., normal cognition (NC) and mild cognitive impairment (MCI)).

Participants labeled as NC were required to have (a) CDR = 0 (no dementia), (b) no deficits in activities of daily living directly attributable to cognitive impairment, and (c) no evidence of neuropsychological impairment defined as standard scores falling 1.5 standard deviations within the age-adjusted normative mean.

MCI determinations are based upon National Institute on Aging/Alzheimer’s Association Workgroup core clinical criteria [15] defined as (a) CDR = 0 or 0.5 (reflecting mild severity of impairment), (b) relatively spared activities of daily living, (c) objective impairment for any neuropsychological test within a cognitive domain (i.e., performances falling greater than 1.5 standard deviations outside the age-adjusted normative mean or pre-morbid level of functioning based on educational attainment, occupational history, and Wide Range Achievement Test 3rd Edition Reading Subtest performance [16]), (d) concern of a cognitive change by the participant, informant or clinician based on information obtained during the clinical interview, and (e) absence of a dementing syndrome.

Given the case-control design, each MCI participant was enrolled first and then an age (defined as ±4 years), sex, and race (defined as White or non-White) NC ‘match’ was enrolled. See Fig. 1 for details on participant selection. Baseline procedures, conducted between September 2012 and November 2014, are described below. Longitudinal follow-up of the cohort is underway with procedures repeating at 18-month intervals for a minimum of 3 years (i.e., baseline, 18-month follow-up, and 36-month follow-up).

Fasting blood work, lumbar puncture, and biochemical analyses

A morning fasting venous blood draw was performed. A series of blood work was immediately performed, including complete blood count with differential and platelet count, comprehensive metabolic panel, lipid profile (with high density lipoprotein), high sensitivity C-reactive protein, anticardiolipin antibody, thyroid stimulating hormone, and hemoglobin A1C. A serum-based insulin assay was also processed. Remaining samples (i.e., 20 mLs of whole blood without anticoagulate reagents added for collecting serum, 10 mLs of EDTA anticoagulated whole blood for collecting plasma, and 2.5 mLs of whole blood in Paxgene tube) were prepared for storage at Vanderbilt. Plasma was separated from whole blood by centrifugation at 2000 g and 4°C for 15 min and stored in ten 0.5 mL aliquots. Remaining blood (including red and white cells) was stored separately in a single 5 mL tube for DNA analysis. Serum was separated by centrifuge at 2000 g and 4°C for 15 min and stored in two 0.5 mL aliquots and seven 1 to 1.5 mL aliquots. The remaining 4 to 5 mL blood clot was stored separately as a backup resource for DNA extraction. After processing and preparation, samples were stored at –80°C, including the Paxgene tube which was immediately frozen.

Plasma samples were analyzed in batch using the Milliplex Map Kit (EMD Millipore Corporation, Billerica, MA, Cat. No. HCCBP1MAG-58K) to measure leptin, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and vascular endothelial growth factor A (VEGF-A) according to manufacturer instructions. Seven working standards were generated by serial dilution (1 : 3) of the reconstituted standard provided in the kit. Two quality control (QC) samples were included in each plate run. Assay plate was read on Luminex 200 with XPONENT software. Milliplex Analyst 5.0 was used for data analysis. The mean intra-assay coefficients of variation (CVs) were as follows: leptin 5.6%, IL-6 5.9%, TNF-α 6.6%, and VEGF-A 5.9%.

For apolipoprotein E (APOE) genotyping, frozen whole blood was used for white blood cell extraction. Genotyping was performed using the commercially available TaqMan^® single-nucleotide polymorphism genotyping assay from Applied Biosystems (Foster City, California, USA). Polymerase chain reaction (PCR) was carried out in 384-well plates in 5μl reactions containing 5 ng of genomic DNA, Assays-on-Demand probe/primer mix, and TaqMan^® Universal PCR Master Mix. Plates were scanned on a Life Technologies 7900HT real-time PCR machine after 50 cycles of standard PCR on a Life Technologies 9700 Thermal Cycler, and analyzed using Life Technologies SDS 2.4.1 software. Negative and positive controls were included on the plate for assay validation. Genotyping efficiency was >99%, and all duplicates were concordant as expected.

As an optional procedure, 233 participants consented to a morning fasting lumbar puncture. A total of 155 participants successfully completed the procedure to collect a maximum of 25 mLs of CSF with polypropylene syringes using a Sprotte 25-gauge spinal needle in an intervertebral lumbar space in the region between L2 and L5. Two mLs were drawn off for waste. Three mLs were processed for gross appearance, nucleated cells, total cell count, lymphocyte percentage, monocyte/macrophage percentage, glucose, and total protein. The remaining 20 mLs were immediately mixed and centrifuged at 2000 g and 4°C for 10 min. Supernatants were aliquoted in forty 0.5 mL polypropylene tubes and stored at –80°C. Samples were analyzed in batch using commercially available enzyme-linked immunosorbent assays (Fujirebio, Ghent, Belgium) to determine the levels of Aβ1-42 (INNOTEST^® β-AMYLOID_(1 -42) ), total tau (INNOTEST^® hTAU), and tau phosphorylated at threonine 181 (p-tau; INNOTEST^® PHOSPHO-TAU_(181P) ). The INNOTEST Aβ1-42 method employs antibodies that specifically bind the neo-epitopes of the first and last amino acid of the 42 amino acid-long Aβ sequence, thus measuring the full 42 amino acid-long Aβ₄₂ form and not any N-terminally truncated species [17]. Additionally, Aβ forms ending at amino acid 38 and 40 were measured using the Aβ Triplex Assay (Meso Scale Discovery, Rockville, MD). This assay uses end-specific antibodies to capture Aβ peptides ending at amino acid 38, 40, or 42 and 6E10 (specific to amino acids 3 to 8) to detect them (the assay can thus measure both Aβ_{1 - 38/40/42} as well as N-terminally truncated Aβ forms). Meso Scale Discovery Aβ will hereafter be designated Aβ_X - 40 and Aβ_X - 42. Processing was completed by board certified laboratory technicians who were blinded to clinical information, as previously described [18]. Intra-assay CVs were <10%.

Physical examination and frailty assessment

Each participant completed a physical examination, including assessment of height, weight, hip circumference, and waist circumference. Frailty was assessed using standardized criteria from the Cardiovascular Health Study [19] as follows

Shrinkage/unintentional weight loss assessed by asking the participant if s/he has lost ≥10 lbs in weight over the previous 12 months or there has been a change >5% in body weight. If yes, the participant met this criterion.

The presence or absence of each frailty criterion was summed for a possible range of 0–5. Participants were categorized as frail ≥3, pre-frail = 1 or 2, and not frail = 0. All frailty criterion were also recorded as continuous or ordinal measurements, including shrinkage (BMI), poor endurance (ordinal sum of exhaustion items ranging 0–6), low physical activity (kilocalories/week), slowness (gait speed in m/s), and weakness (mean dominant hand grip strength across 3 trials). Using these continuous or ordinal measurements, a composite frailty score was also constructed by converting each component raw score to a z-score using the cohort’s baseline mean stratified by sex and standard deviation and then computing an average z-score. Higher z-scores indicated poorer performance (higher frailty) [22].

Lifestyle questionnaires

Participants completed self-report questionnaires assessing physical activity, sleep, and nutrition habits as detailed below:

MLTA estimates leisure time physical activity beyond activities of daily living [21]. We applied the MLTA abbreviated version to assess caloric expenditure [19]. Participants estimated (in min/week) the frequency of physical activities performed in the past 2 weeks. Activity was expressed in expended kilocalories per week.

Subjective cognitive decline and functional activity questionnaires

Participants and their informants completed a series of questionnaires assessing perceived cognitive decline and functional activities. These questionnaires were not used for determination of diagnostic status or eligibility at screening.

Cognitive Changes Questionnaire is derived from frequently used cognitive decline questions [26–29]. Items assess memory and thinking changes, including comparing the participant’s cognition to same-aged peers, comparing the participant’s current cognition to past cognition, and rating the participant’s ability to complete daily tasks. Questions are answered on a 2-item (“yes” or “no”) or 3-item scale (“major problem, ” “minor problem, ” “no problem”). Total scores range 14–85 with higher scores indicating greater perceived change. Both participants and informants completed this questionnaire.

Neuropsychological assessment

At baseline, all participants completed a common, comprehensive neuropsychological protocol assessing global cognition, language, information processing speed, executive functioning, visuospatial ability and episodic learning and memory. Measures were carefully selected to preclude floor or ceiling effects, enabling both detection of early cognitive decline and tracking of longitudinal changes. See Table 2 for details. A subset of participants (n = 231) also completed an addendum protocol assessing episodic learning and memory and visual and verbal working memory. These latter measures were selected to supplement the primary protocol to enhance detection of nuances in neuropsychological performance across the cognitive aging spectrum using an analysis of process and errors as previously suggested [31, 32]. See Table 3 for details. Note, baseline and addendum protocol performances were not used as part of the screening or selection of participants into the study.

Brain magnetic resonance imaging

Participants were scanned on a 3T Phillips Achieva system (Best, The Netherlands) with an 8-channel SENSE receiver head coil [33] at the Vanderbilt University Institute of Imaging Science to gather the following sequences (see Table 4 for acquisition parameters and summary of imaging outcomes):

T1-weighted Anatomical Imaging was acquired for tissue volume quantification and co-registration. T1-weighted images were post-processed using three methods:

FreeSurfer 5.1.0 (http://surfer.nmr.mgh.harvard.edu/): Standard FreeSurfer reconstruction steps were performed to calculate regions of interest (ROIs) and cortical thickness as previously described [34–37]. White and gray matter surfaces were manually inspected and corrected for registration, topological and segmentation defects. After manual intervention, images were re-processed through FreeSurfer to update the transformation template and segmentation information.

T2-weighted FLAIR was acquired for quantification of WMH. T1 and FLAIR images were post-processed using the Lesion Segmentation Tool toolbox for SPM8 as previously described [41]. Each T1-weighted image voxel was classified as gray matter, white matter, or CSF. FLAIR images were bias-corrected for field inhomogeneities and registered to the T1-weighted images. FLAIR intensity distribution of the three tissue classes were assigned, enabling detection of outliers. Neighboring voxels were classified iteratively and analyzed and assigned to lesion, white matter, or gray matter until no more voxels were assigned to a lesion.

Susceptibility Weighted Image with high resolution 3D T2^* weighted gradient echo was used to capture microbleeds. Both magnitude and phase images were collected. For post-processing, a Hanning filter was first applied to remove slow-varying fluctuations on the unwrapped phase images. Then, a phase mask filter was created from the original phase image and multiplied 4 times with the magnitude image. For the phase mask, a triangular filter was used for vascular enhancement at all vessel orientations [42]. Finally, minimum intensity projection images were formed by combining 5 slices. The images were reviewed by a board-certified neuroradiologist (LTD, blinded to clinical information) to manually detect total number, hemisphere (right, left, both), predominant pattern (deep hemispheric, infratentorial, lobar), location (brainstem, lentiform nucleus, caudate nucleus, cerebellum, frontal, frontal periventricular, lobar, occipital, occipital periventricular, thalamus, parietal periventricular, temporal, temporal periventricular), and distribution (cortical, subcortical) of microbleeds.

Diffusion Tensor Imaging assessed white matter integrity. Images were post-processed with Vanderbilt University Institute for Imaging Science Center of Computational Imaging resources (Vanderbilt University, Nashville, TN) using an established pipeline previously described [43–46]. Standard tensor fitting methods were used to estimate the diffusion tensor and resulting tensor parameters fractional anisotropy (FA) and mean diffusivity (MD) [47]. FA maps were post-processed using two methods:

FSL Tract-Based Spatial Statistics (TBSS) was used for voxel wise analysis [48]. As previously described, TBSS projects participant FA data onto a mean FA tract skeleton [49–51]. Voxel wise cross-participant statistics were generated.

Vessel-encoded Pseudo-Continuous Arterial Spin Labeling (VE-pCASL) assessed cerebral blood flow (CBF; rate of blood delivery to tissue, measured as mL blood/100 g tissue/min) in the microvasculature, separately in left and right internal carotid artery (ICA) and basilar flow territories [53]. Two general images were acquired, including a label image with signal from magnetically labeled blood water and a control image in the absence of a magnetic label. The label scenarios were acquired separately for (i) non-selective labeling of left and right ICAs and basilar artery, (ii) right ICA labeling only and (iii) basilar artery labeling only. This labeling paradigm allows for total CBF to be quantified but also for the CBF in separate flow territories to be quantified. Magnetic labeling was obtained by inverting the spins of incoming blood water using a Hanning-windowed pulse train in the major cervical arteries listed above. Data were corrected for motion applying rigid body transformations and baseline drift with FSL FLIRT. Data were post-processed using MATLAB. Whole brain CBF maps were generated by a sliding window approach subtracting a control image by an adjacent label image. CBF was calculated using a two-compartment perfusion model [54]. See Fig. 2 for illustration of reduced CBF. To allow for quantification of CBF in defined-cortical regions, a separately acquired equilibrium magnetization image (i.e., M₀ image) with identical geometry and readout as the VE-pCASL scan but long repetition time (20 s) was registered to the structural T1 map. The T1 registered M₀ map was registered to the standard MNI space [55]. Transformation matrices for these registrations were then applied to the CBF maps. ROIs were identified using the Multi-Atlas Segmentation, and region specific CBF values were calculated.

Hypercapnic VE-pCASL with identical scan parameters as the VE-pCASL acquisition evaluated cerebrovascular reactivity (CVR). A non-rebreathing facemask delivered 5% CO₂ and 95% normoxic normocapnic medical-grade air (approximately 79% N₂, 21% O₂) in a 2-block paradigm from compressed cylinders at a flow rate of 12 L/min. End-tidal CO₂ and estimated arterial oxygen saturation from peripheral pulse oximetry were recorded throughout the protocol. Four scan volumes before the gas challenge were removed from the calculations to allow for blood gases to equilibrate. Percentage change in CBF (from baseline values due to the induced hypercapnia) was normalized by the end-tidal CO₂ measures to calculate cerebral vascular reactivity [56].

Magnetic Resonance Angiography (MRA) assessed the patency of the circle of Willis (CoW). Images were reviewed by a board-certified neuroradiologist (LTD) blinded to clinical information to manually code the patency of each of the seven segments of the CoW (i.e., anterior communicating artery, A1 segments of the left and right anterior cerebral arteries, left and right posterior communicating arteries, and P1 segments of the left and right posterior cerebral arteries). Each segment was coded as normal (≥0.8 mm), hypoplastic (<0.8 mm), or aplastic (invisible by MRA [57]). The entire CoW was further classified as one of 22 variants [58, 59]. See Fig. 3 for illustration of complete versus incomplete CoW.

Vessel Wall Imaging assessed the health of the macrovasculature. This novel 3D sequence uses an anti-DRIVE module with appropriately spaced repetition time to keep CSF magnetization near zero, while nulling the blood water signal using a long turbo-spin-echo pulse train, allowing visualization of the vessel walls. The field of view covers the large cerebral arteries of the CoW and segments of the middle cerebral arteries. Images were reviewed by a board-certified neuroradiologist (LTD) blinded to clinical information to manually measure the vessel wall thickness of the supraclinoid left and right ICA, proximal anterior cerebral artery, proximal middle cerebral artery, and basilar artery. See Fig. 4 for illustration of measurements.

Cardiac magnetic resonance imaging

Cardiac magnetic resonance (CMR) imaging was acquired using a 1.5T Siemens Avanto system (Erlangen, Germany) with a phased-array torso receiver coil. Left (LV) and right ventricular volumes and function were assessed using a breath-hold, electrocardiogram synchronized, cine steady-state free precession sequence with the following parameters: TR = 180 ms, TE = 1.1 ms, flip angle = 80°, field of view = 300–340 mm, and 156×192 matrix. Short and long axis imaging planes were acquired. Under the supervision of a board-certified cardiologist (FLR), a trained rater blinded to all clinical information coded the data. Ventricular mass and volumes were determined from the short axis image stack, 8–10 slices, 8 mm per slice, 0 mm inter-slice gap. Endocardial contours were traced in diastole and systole, and epicardial contours were traced in diastole using QMASS MR 7.6 Enterprise Solution (Medis, Leiden, Netherlands). Velocity encoded flow data was acquired from the proximal and descending thoracic aorta with slice angulation adjusted to maximize perpendicular flow. Flow data was analyzed by QFLOW 5.6 Enterprise Solution (Medis, Leiden, Netherlands), including total flow, net forward flow, and aortic area in systole and diastole. Aortic compliance was quantified from area change in systole and diastole as related to pulse pressure.

Longitudinal, circumferential, and radial strain were assessed for systole and diastole using a semi-automated algorithm in 2D Cardiac Performance Analysis MR (TomTec, Munich, Germany). Steady state free precession cine images from the 4-chamber, 2-chamber, and short axis orientations were post-processed for strain determinations, including peak strain, strain rate, and time to peak strain. Strain results were determined per cardiac segment using the 17-segment model and averaged to yield a global value for each strain orientation. LV epicardial and endocardial contours were also traced in end diastole of standard cine short and long axis images.

Echocardiogram

Standard 2D, M-mode, and Doppler transthoracic echocardiography, including measurements of cardiac structure and function, was performed by a single research sonographer (JG) at the Vanderbilt University Medical Center Clinical Research Center on a Phillips IE33 cardiac ultrasound machine (Phillips Medical, Andover, MD). Digital images with measurements were sent to the Vanderbilt Heart Imaging Core Laboratory and confirmed by one of two board certified cardiologists (LAM, DKG) using commercially available software (HeartLab, AGFA Healthcare, Greenville, SC). All raters were blinded to clinical information.

Image acquisition and quantification was performed according to American Society of Echocardiography (ASE) guidelines [60–67]. Briefly, LV volumes and ejection fraction were calculated by the biplane Simpson’s method. LV mass was calculated from LV linear dimensions using the ASE recommended formula (0.8×[1.04[(LV internal diameter during diastole+posterior wall thickness during diastole+septum wall thickness during diastole)3 - (LV internal diameter during diastole)3]]+0.6 g) and indexed to body surface area. LV hypertrophy (LVH) was defined as LV mass index >115 g/m² in men or >95 g/m² in women. Left atrial (LA) maximum volume was measured by the modified Simpson’s method using apical 4- and 2-chamber views at the end-systolic frame preceding mitral valve opening and indexed to body surface area to derive LA volume index. Each valve was assessed for the presence and severity of stenosis and regurgitation with the over-reading cardiologist assigning one of the following grades: normal, trace, mild, mild to moderate, moderate, moderate to severe, or severe.

Early (E) and late (A) transmitral velocities and the E deceleration time were measured from pulsed wave spectral Doppler images acquired in the apical 4-chamber view with the sample volume positioned at the tip of the mitral leaflets. Peak lateral and septal mitral annular early relaxation and atrial contraction velocities were assessed using tissue Doppler imaging (TDI). LV filling pressures were estimated by E wave divided by average early relaxation velocities. Diastolic function was graded by the over-reading cardiologist as normal, grade I (impaired relaxation), grade II (pseudonormal), or grade III (restrictive) according to ASE recommendations. Right ventricular systolic function was assessed using TDI systolic velocity of the lateral tricuspid annulus and tricuspid annulus planar systolic excursion. Cardiac output was calculated as stroke volume×heart rate, where stroke volume was calculated from the LV outflow tract velocity time integral and diameter. The right ventricular systolic pressure was estimated from the peak tricuspid regurgitant velocity using the modified Bernoulli equation plus right atrial pressure. Right atrial pressure was estimated from the inferior vena cavamaximal diameter and change in size with inspiration.

Final values were taken from measurements of a single cardiac cycle for participants in normal sinus rhythm or the average of three cardiac cycles for those participants in atrial fibrillation. Atrial fibrillation at the time of echocardiography was determined by the absence of A waves on transmitral spectral Doppler flow and tissue Doppler mitral annular velocity profiles, as well as the lack of organized electrocardiographic P waves.

Ambulatory blood pressure monitoring

As an optional study procedure, a subset of participants (n = 301) wore a SpaceLabs 90207 monitor (SpaceLabs Inc., Redmond, WA) on the left arm for 24-consecutive hours of ambulatory blood pressure monitoring. Systolic blood pressure, diastolic blood pressure, pulse pressure, and heart rate readings were captured every 30 min for a maximum of 49 readings. Data were uploaded to a workstation using the SpaceLabs Healthcare ABP Report Management System (Version 3.0.5, 2010). For QC purposes, at least 39 readings in the 24-h monitoring period were required for inclusion, resulting in 34 exclusions. Datasets with >3 h gap between readings (n = 19) were excluded, 17 of whom were excluded for <39 readings, resulting in 265 datasets meeting QC.

Analytical plan

Prior to analysis, medical and vascular characteristics were calculated as follows. Systolic blood pressure was the mean of two measurements obtained prior to the echocardiogram. Diabetes mellitus included current fasting blood glucose ≥126 mg/dL, hemoglobin A1C ≥6.5%, or current oral hypoglycemic or insulin medication usage. Dyslipidemia included total cholesterol ≥200 mg/dL, high-density lipoprotein cholesterol <40 mg/dL, or lipid-lowering medication usage. Self-report was used to ascertain current cigarette smoking (i.e., yes/no within the year prior to baseline examination), prior cigarette smoking (i.e., yes/no ever prior to baseline examination), medication use, history of cancer, history of transient ischemic attack, and history of head injury with loss of consciousness < 5 minutes. Cardiovascular disease (CVD) included coronary heart disease, angina, or myocardial infarction (note, heart failure was a study exclusion). History of sleep disorder included restless leg syndrome, sleep apnea, or REM sleep behavior disorder. Thyroid disease included self-report, thyroid stimulating hormone <0.45 or >4.5 mcunit/mL, prior thyroid surgery, or thyroid medication usage. Atrial fibrillation included self-report with supporting evidence from echocardiogram, CMR, or prior surgery or medication usage to treat atrial fibrillation. Framingham Stroke Risk Profile score included age, sex, systolic blood pressure, anti-hypertensive medication usage, diabetes mellitus, current cigarette smoking, LVH, CVD, and atrial fibrillation [68]. APOE ɛ4 carrier status was defined as positive (ɛ2/ɛ4, ɛ3/ɛ4, ɛ4/ɛ4) or negative (ɛ2/ɛ2, ɛ2/ɛ3, ɛ3/ɛ3).

Descriptive statistics for baseline demographic, genetic, biomarker, medical, and neuropsychological characteristics were calculated separately for NC and MCI participants. Between-group characteristics were statistically compared using Pearson chi-square test for categorical variables and Wilcoxon rank sum test for continuous variables. Significance was set a priori at p <  0.05. All analyses were conducted using R 3.2.0 (http://www.r-project.org).

Demographic characteristics

Of the 566 participants who completed eligibility visits, 335 were enrolled. At baseline, the cohort was age 73±7 years with an education level of 16±3 years, 41% of participants being female, and 13% self-identifying as a racial or ethnic minority. Sample characteristics for the entire cohort as well as between-group differences for NC and MCI participants are presented in Table 5.

All 335 participants completed required elements of the protocol, including fasting blood work, physical examination/frailty assessment, questionnaires, neuropsychological assessment, brain MRI, cardiac MRI, and echocardiogram. The exception is that 8 participants did not complete either the cardiac MRI (n = 3) or the brain MRI (n = 7). Of note, there were some differences in sample characteristics for those participants who consented to optional study components and those participants who did not consent. Specifically, when compared to participants who did not complete the optional lumbar puncture, participants completing the procedure were more likely to be male (p = 0.006) and self-identify as White non-Hispanic (p = 0.002) with lower FSRP scores (p = 0.005), anti-hypertensive medication usage (p = 0.001), history of cigarette smoking (p = 0.006), and history of atrial fibrillation (p = 0.02). As compared to participants who did not complete the optional ambulatory blood pressure monitoring protocol, participants completing the protocol were younger (p = 0.008) with lower education levels (p = 0.04, though means for both groups were at or above the college graduate level) and history of cigarette smoking (p = 0.04).

AD genetic, family history, and biomarker characteristics

There was a between-group difference for APOE ɛ4 (x² = 5.7, p = 0.017), such that the MCI group had a higher prevalence of APOE ɛ4 carriers (40%) compared to the NC group (28%). However, the participant groups were comparable for self-reported family history of both dementia (x² = 0.01, p = 0.91) and AD (x² = 0.25, p = 0.61).

The groups differed on CSF biomarkers of AD in the expected direction, including Aβ_X - 42 (W_(1,153)  = 6.9, p = 0.009), Aβ_1 - 42 (W_(1,153)  = 5.7, p = 0.018), total tau (W_(1,153)  = 8.4, p = 0.004), and p-tau levels (W_(1,328)  = 5.5, p = 0.02). The groups did not differ for CSF Aβ_X - 40 levels (W_(1,153)  = 0.04, p = 0.85).

Medical characteristics

The NC and MCI groups were similar for history of cancer (x² = 0.001, p = 0.97), head injury with loss of consciousness <5 min (x² = 1.04, p = 0.31), sleep disorder (x² = 0.03, p = 0.85), transient ischemic attack (x² = 0.23, p = 0.64), and thyroid disease (x² = 2.34, p = 0.13).

The groups differed on Framingham Stroke Risk Profile (W_(1,330)  = 7.1, p = 0.008) with MCI participants having a higher mean risk profile score (13.0±4.2) than NC participants (11.9±4.1). The groups differed on LVH (x² = 4.1, p = 0.04) with the MCI participants having a higher prevalence of LVH than the NC participants. The groups differed on systolic blood pressure (W_(1,333) =7.1, p = 0.008) with MCI participants having higher mean values (144.9±18.6) than NC participants (139.9±17.6) despite comparable anti-hypertensive medication utilization (x² = 0.86, p = 0.35). The groups did not differ on other vascular health characteristics, including dyslipidemia (x² = 0.03, p = 0.87), diabetes (x² = 2.76, p = 0.10), prevalent CVD (x² = 0.36, p = 0.55), or atrial fibrillation (x² = 0.05, p = 0.82). There were no group differences for prior smoking history (x² = 0.10, p = 0.81) or current smoking prevalence (x² = 0.14, p = 0.71). See Table 5 for details.

CDR, activities of daily living, and neuropsychological test performances

As designed, CDR global score differed between the two participant groups with 100% of NC participants having a CDR = 0 and 86% of MCI participants having a CDR = 0.5 (x² = 251.0, p <  0.001). Activities of daily living, as assessed by the Functional Activities Questionnaire, were compromised in MCI participants as compared to NC participants (W_(1,333)  = 264.0, p <  0.001).

Between-group comparisons yielded baseline neuropsychological performance differences in all cognitive domains assessed such that MCI participants performed worse on all measures as compared to NC participants. Specifically, differences emerged for Montreal Cognitive Assessment (W_(1,332)  = 129.0, p <  0.001), Animal Naming (W_(1,332)  = 65.5, p <  0.001), Boston Naming Test-30 Item Even Version (W_(1,332)  = 46.7, p <  0.001), California Verbal Learning Test-II (CVLT-II) Trials 1–5 Total Learning (W_(1,332)  = 159.0, p <  0.001), CVLT-II Long Delay Free Recall (W_(1,332)  = 186.0, p <  0.001), Biber Figure Learning Test (BFLT) Trials 1–5 Total Learning (W_(1,330)  = 180.0, p <  0.001), BFLT Delayed Free Recall (W_(1,330)  = 148.0, p <  0.001), Coding (W_(1,332)  = 59.1, p <  0.001), Delis Kaplan Executive Function System (DKEFS) Color-Word Interference Test Word Reading (W_(1,327)  = 34.5, p <  0.001), DKEFS Color-Word Interference Test Color Naming (W_(1,324)  = 50.9, p <  0.001), DKEFS Number Sequencing (W_(1,332) - = 47.8, p <  0.001), DKEFS Number Letter Sequencing (W_(1,331)  = 100.0, p <  0.001), DKEFS Color-Word Interference Test Inhibition (W_(1,327)  = 81.5, p <  0.001), DKEFS Tower Test (W_(1,331)  = 20.4, p <  0.001), Letter Fluency (W_(1,333)  = 63.3, p <  0.001), and Hooper Visual Organization Test (W_(1,332)  = 24.5, p <  0.001). Wide Range Achievement Test 3rd Edition Reading Subtest performance also differed between the two groups (W_(1,333)  = 20.3, p <  0.001). See Table 6 for details, including effect sizes.