Baseline levels and longitudinal rates of change in plasma Aβ42/40 among self-identified Black/African American and White individuals

Baseline levels and longitudinal rates of change in plasma Aβ42/40 among self-identified Black/African American and White individuals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Baseline levels and longitudinal rates of change in plasma Aβ42/40 among self-identified Black/African American and White individuals Chengjie Xiong, Suzanne Schindler, Jingqin Luo, John Morris, Randall Bateman, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3783571/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Objective: The use of blood-based biomarkers of Alzheimer disease (AD) may facilitate access to biomarker testing of groups that have been historically under-represented in research. We evaluated whether plasma Aβ42/40 has similar or different baseline levels and longitudinal rates of change in participants racialized as Black or White. Methods: The Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB) is a multi-center longitudinal study to evaluate for potential differences in AD biomarkers between individuals racialized as Black or White. Plasma samples collected at three AD Research Centers (Washington University, University of Pennsylvania, and University of Alabama-Birmingham) underwent analysis with C 2 N Diagnostics’ PrecivityAD™ blood test for Aβ42 and Aβ40 . General linear mixed effects models were used to estimate the baseline levels and rates of longitudinal change for plasma Aβ measures in both racial groups. Analyses also examined whether dementia status, age, sex, education, APOE ε 4 carrier status, medical comorbidities, or fasting status modified potential racial differences. Results: Of the 324 Black and 1,547 White participants, there were 158 Black and 759 White participants with plasma Aβ measures from at least two longitudinal samples over a mean interval of 6.62 years. At baseline, the group of Black participants had lower levels of plasma Aβ40 but similar levels of plasma Aβ42 as compared to the group of White participants. As a result, baseline plasma Aβ42/40 levels were higher in the Black group than the White group, consistent with the Black group having lower levels of amyloid pathology. Racial differences in plasma Aβ42/40 were not modified by age, sex, education, APOE ε 4 carrier status, medical conditions (hypertension and diabetes), or fasting status. Despite differences in baseline levels, the Black and White groups had a similar longitudinal rate of change in plasma Aβ42/40. Interpretation: Black individuals participating in AD research studies had a higher mean level of plasma Aβ42/40, consistent with a lower level of amyloid pathology, which, if confirmed, may imply a lower proportion of Black individuals being eligible for AD clinical trials in which the presence of amyloid is a prerequisite. However, there was no significant racial difference in the rate of change in plasma Aβ42/40, suggesting that amyloid pathology accumulates similarly across racialized groups. Biological sciences/Neuroscience/Cognitive ageing Biological sciences/Neuroscience/Cognitive neuroscience Figures Figure 1 Figure 2 1 INTRODUCTION Biomarkers of Alzheimer disease (AD), including fluid and imaging biomarkers of amyloid and tau pathology, have enabled a better understanding of AD pathophysiology, facilitated clinical trials that have led to the development of amyloid-lowering treatments, and increased the accuracy of clinical dementia diagnosis 1 . While cerebrospinal fluid (CSF)- and positron emission tomography (PET)-based biomarkers accurately detect AD brain pathology, the scale of testing with these modalities is limited by their requirements for specialized personnel and equipment, perceived risks, and high costs 1 – 3 . In contrast, blood is routinely collected in research and clinical care and considered highly accessible, acceptable, and scalable, making blood-based biomarkers ideal tools for research, clinical trials, and clinical practice 4 , 5 . Therefore, blood-based biomarkers of AD may facilitate performing biomarker testing in minoritized groups that have historically been under-represented and systematically excluded in AD research and clinical trials 6 – 9 . Further, Black individuals with cognitive impairment may be less likely to be seen in memory clinics that perform biomarker testing with CSF and PET and serve as an entry point for research studies and clinical trials 10 . The use of blood-based biomarkers may enable testing of individuals in racialized groups who would not be willing to undergo screening with CSF or PET testing, and may enable testing in a community-based setting rather than a major medical center. Multiple epidemiological studies have reported a higher prevalence of dementia in self-identified Black or African American and Hispanic individuals as compared to non-Hispanic White individuals (nHW) 7 , 11 – 13 . Despite the higher reported prevalence of dementia, several research studies have reported a lower rate of AD biomarker abnormalities in Black and Hispanic individuals 14 – 20 , although other studies have found the opposite result or no differences between these groups 21 . The seeming disconnect between the reported prevalence of dementia and the rate of AD biomarker abnormalities has raised concern that the major etiologies of dementia may vary across racial and ethnic groups and/or that biomarkers may not reflect AD pathology consistently across groups, in addition to the possibility that the diagnostic evaluation for dementia may vary across studies. Further, concentrations of some plasma biomarkers can be affected by medical conditions (e.g., chronic kidney disease and obesity) that are more prevalent in some racial and ethnic groups, suggesting that plasma biomarkers may not reflect AD pathology consistently across groups 22 – 25 . However, some evidence indicates that plasma biomarker ratios may normalize for individual-level differences and provide more consistent performance across groups 22 , 24 – 26 . Specifically, we have previously reported that plasma Aβ42/40 as measured by a high precision mass spectrometry-based assay has more consistent performance in classifying amyloid status across racial groups as compared to concentrations of phosphorylated tau 22 . This finding suggests that plasma Aβ42/40 as measured by high precision assays may enable classification of amyloid status in more diverse groups. Almost all studies of racial differences in AD biomarkers have been based on CSF and imaging biomarkers, and the few studies that have reported data on plasma biomarkers only reported cross-sectional data 22 , 27 – 30 .Therefore, it is unknown whether the longitudinal rates of change in plasma biomarkers vary by race or ethnicity. The rate of change is particularly important in clinical trials, as it represents the placebo trajectory of AD pathology that is intended to be modified by treatments. This study utilized one of the largest biracial cohorts with plasma biomarkers assembled thus far to evaluate for potential differences in baseline levels and rates of change in plasma Aβ measures (Aβ42, Aβ40, and Aβ42/40) in self-identified Black and White participants. Participants from three AD Research Centers (Washington University, University of Pennsylvania, and University of Alabama at Birmingham) were included. Samples were analyzed with the C 2 N Diagnostics mass spectrometry-based assay that is currently being used in clinical trials and clinical practice 31 – 32 . General linear mixed effects models were used to estimate the baseline levels and rates of change for plasma Aβ measures in both racialized groups. Analyses also examined whether dementia status, age, sex, education, APOE ε4 carrier status, fasting status, and comorbidities (hypertension and diabetes) modified potential racial differences. 2 METHODS 2.1 Participants The study cohort included individuals with plasma Aβ measures and clinical/cognitive data who participated in the Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB; NIH/NIA R01 AG067505), which aims to understand potential racial differences in harmonized biomarker data collected by multiple research studies of memory and aging in middle-aged and older individuals. Participants in the current study represented three of the SORTOUT-AB sites: the Washington University (WU) Knight Alzheimer Disease Research Center (ADRC), the University of Pennsylvania (UPenn) ADRC, and the University of Alabama at Birmingham (UAB) ADRC. Details of recruitment for these studies have been described previously 16 , 33 . Participants with conditions that could prevent participation or affect long-term participation (e.g., metastatic cancer) were excluded. Participants underwent clinical and/or cognitive assessments within 2 years of their baseline plasma assessments. A subset of the participants also had CSF or imaging assessments within 2 years of their baseline plasma sample collection. All participants provided written informed consent at recruitment from their parent studies. The Washington University Human Research Protection Office approved the current study with additional approvals from the Institutional Review Boards of the other sites. 2.2 Clinical and cognitive assessments Clinical and cognitive assessments from WU, UPenn, and UAB followed protocols consistent with the National Alzheimer’s Coordinating Center Uniform Data Set (UDS) 34 – 35 . Demographic information, body mass index (BMI), and medical history were collected. Race and sex were self-identified by participants. The presence or absence of dementia, and when present, its severity, was determined by the score on the Clinical Dementia Rating®™ (CDR®™) 36 , which was performed at baseline and then annually. Standard criteria were used to diagnose the likely etiology of dementia 37 . The cognitive battery of the UDS included tasks of episodic memory, working memory, semantic knowledge, executive function and attention, and visuospatial ability, and were harmonized across UDS versions 38 : Montreal Cognitive Assessment [MoCA], Animal Fluency (60 seconds), Vegetable Fluency, Wechsler Adult Intelligence Scale (WAIS-R) Digit Symbol, Digit Span, Craft Story Immediate Recall), Craft Story Delayed Recall), Multilingual Naming Test, Free and Cued Selective Reminding, and Trail Making Test A and B. All scales were oriented such that a higher score indicated better cognition and converted to Z-scores using the baseline mean and standard deviation (SD) from the entire cohort. A global cognitive composite score was calculated by averaging Z-scores across all tests. An episodic memory composite score was calculated by averaging the Z-scores from the Craft Story-immediate and Craft Story-delayed tasks. 2.3 Apolipoprotein E genotyping Apolipoprotein E ( APOE ) genotyping was performed as previously described 16 . Participants were classified as APOE ε 4 carriers (one or two ε4 alleles) and non-carriers. 2.4 Blood and CSF collection and analysis At WU, blood was collected at the time of lumbar puncture (fasted) or clinical assessment (non-fasted) 39 . Only non-fasted samples were collected at UPenn, and only fasted samples were collected at UAB. At WU, CSF samples (20–30 mL) were collected at 8 AM after overnight fasting by gravity drip, briefly centrifuged at low speed, and aliquoted into polypropylene tubes prior to freezing at − 80°C. CSF samples from participants enrolled at UAB and UPenn ADRCs were collected in accordance with protocols for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 40 . All plasma samples were analyzed at C 2 N Diagnostics with the PrecivityAD™ assay. Briefly, Aβ40 and Aβ42 were simultaneously immunoprecipitated from plasma via a monoclonal anti-Aβ mid-domain antibody 41 . Proteins were digested into peptides using LysN endoprotease. Liquid chromatography-mass spectrometry was performed on a Thermo Scientific Orbitrap Lumos Tribrid mass spectrometer interfaced with a nano-Acquity chromatography system 41 . An automated immunoassay (LUMIPULSE G1200, Fujirebio, Malverne, PA) was used to measure CSF concentrations of Aβ40, Aβ42, total tau (t-tau), and tau phosphorylated at position 181 (p-tau181) 42 – 43 . A bridging subset of the CSF samples (n = 114) from the UPenn ADRC was selected to represent a wide range of values for all analytes and were run at the same time and with the same reagents as the WU samples to evaluate and adjust for systematic differences between the UPenn and WU sites. A linear regression model fitted on the values of the bridging samples was used to harmonize the CSF biomarker values between UPenn and WU 44 . 2.5 Imaging processing and analysis Details of the structural brain MRI and amyloid PET protocols are provided elsewhere 39 , 45 – 46 , which followed a protocol consistent with that used by the ADNI. A standardized uptake value ratio (SUVR) with correction for partial volume effects was calculated for the FreeSurfer regions of interest (ROIs) for PiB, Florbetapir or Florbetaben 45 . The cerebellum was used as the reference region. A summary measure of amyloid burden was calculated using the averaged SUVR values in the lateral orbitofrontal, medial orbitofrontal, precuneus, rostral middle frontal, superior frontal, superior temporal, and middle temporal regions. To harmonize SUVR values across different tracers (PiB, Florbetapir or Florbetaben), values from the summary measure were converted into Centiloid units 47 , following previously published methods 47 – 48 . 2.6 Statistical analyses The baseline characteristics of participants were summarized with the mean and SD for continuous variables or count and percentage for categorical variables. General linear models were implemented to evaluate for cross-sectional racial differences in levels of plasma Aβ measures using baseline data from either participants with only baseline plasma data or participants with longitudinal plasma data. These models included the main effects of self-identified race and the covariates of age, cognitive BMI, and comorbidity status (hypertension, stroke, and diabetes). Additional analyses included interactions of each variable with racial group to examine whether each of the covariates modified the racial differences. In the subset of participants with CSF and imaging biomarkers, plasma Aβ measures were correlated with the established AD biomarkers by Spearman correlations, and the correlations between racialized groups were compared by a two-sided standard normal test after the Fisher’s Z-transformation 49 . Because of the large number of comparisons in the correlations, we adjusted statistical significance for a False Discovery Rate (FDR) 50 of 5%. General linear mixed models were implemented to evaluate for racial differences in the rate of change of plasma Aβ measures 51 , specifically the random intercept and random slope models that assume linear growth patterns over time 52 . All models included race and a race by time interaction (time 0 = baseline), and the covariates of age, cognitive status (CDR 0 or > 0), sex, years of education, APOE ε 4 status, and type of sample (fasting or non-fasting), BMI, and comorbidities (hypertension, stroke, and diabetes) as fixed effects. The annual rates of change between racialized groups were compared by a two-sided approximate Student t test whose degree of freedom was estimated by the Satterthwaite method. All models were implemented in Rstudio (version 2023.9.1.494 running R version 4.2.1) via the R package lmerTest (version3.1-3). We further assessed whether a linear trend fitted the longitudinal data well, and found no clear nonlinear longitudinal patterns, likely because of small number of plasma samples for most individuals. 3 RESULTS 3.1 Cohort characteristics at baseline The research-based study cohort included a total of 324 Black and 1,547 White participants with plasma Aβ measures from at least one sample. A sub-cohort of 158 Black and 759 White participants had plasma Aβ measures from at least two samples with a mean interval between the first and last plasma sample of 6.62 (SD = 4.42) years. Participant characteristics at baseline are shown (Table 1 ). Black and White participants had similar ages at baseline (70.2 ± 8.6 and 70.5 ± 9.5 years, respectively, p = 0.26), but Black participants had a shorter average interval between their first and last plasma sample (5.11 ± 3.52 years) compared to White participants (6.93 ± 4.17 years; p < 0.0001). Black participants (72.2%) were more likely to be cognitively normal than White participants (66.4%; p = 0.041) at baseline, but there was no difference in the proportion carrying an APOE ε4 allele (45.1% versus 42.6%, p = 0.35). Most participants completed at least 12 years of education, with Black participants (15.3 ± 2.9 years, p = 0.002) completing slightly fewer years of education on average compared to White participants (15.8 ± 2.8 years). Black participants were more likely than White participants to be female (72.2% versus 53.5%, p < 0.0001). Black participants were more likely than White participants to have a history of hypertension (65.4% versus 42.3%, p < 0.0001) or diabetes (17.3% versus 6.0%, p < 0.0001). Black participants also had a higher average BMI (30.3 ± 6.23) than White participants (27.5 ± 5.16; p < 0.0001). A higher proportion of baseline biomarker data from Black participants were non-fasted (63.9%) than from White participants (34.4%; p < 0.0001). A subset of 110 Black and 1040 White participants had CSF biomarker data at baseline. A subset of 129 Black and 798 White participants had amyloid PET data at baseline. Table 1 Baseline demographics and clinical features White (N = 1547) Black (N = 324) P-value SITE < 0.0001 WashU 1412 (91.3%) 249 (76.9%) UPenn 115 (7.4%) 63 (19.4%) UAB 20 (1.3%) 12 (3.7%) Baseline Age (yr) 0.26 Mean (SD) 70.5 (9.53) 70.2 (8.60) Median [Min, Max] 71.0 [42.7, 98.0] 70.0 [43.5, 91.3] Fasting status < 0.0001 fasting 1015 (65.6%) 117 (36.1%) non-fasting 532 (34.4%) 207 (63.9%) CDR global 0.065 0 1027 (66.4%) 234 (72.2%) 0.5 377 (24.4%) 62 (19.1%) 1 126 (8.1%) 20 (6.2%) 2 16 (1.0%) 6 (1.9%) 3 1 (0.1%) 1 (0.3%) Missing 0 (0%) 1 (0.3%) Sex < 0.0001 M 720 (46.5%) 90 (27.8%) F 827 (53.5%) 234 (72.2%) Education (yr) 0.002 Mean (SD) 15.8 (2.81) 15.3 (2.92) Median [Min, Max] 16.0 [7.00, 29.0] 16.0 [6.00, 25.0] APOE e4 positivity 0.35 Negative 878 (56.8%) 172 (53.1%) Positive 659 (42.6%) 146 (45.1%) Missing 10 (0.6%) 6 (1.9%) BMI (binary) < 0.0001 normal/underweight 426 (27.5%) 52 (16.0%) obese/overweight 805 (52.0%) 225 (69.4%) Missing 316 (20.4%) 47 (14.5%) Hypertension < 0.0001 No 878 (56.8%) 108 (33.3%) Yes 655 (42.3%) 212 (65.4%) Missing 14 (0.9%) 4 (1.2%) Stroke 0.74 No 1401 (90.6%) 258 (79.6%) Yes 25 (1.6%) 6 (1.9%) Missing 121 (7.8%) 60 (18.5%) Diabetes < 0.0001 No 1150 (74.3%) 223 (68.8%) Yes 93 (6.0%) 56 (17.3%) Missing 304 (19.7%) 45 (13.9%) 3.2 Cross-sectional racial differences in plasma Aβ biomarkers Cross-sectional analyses included baseline plasma Aβ measures from either participants with only baseline plasma data or participants with longitudinal plasma data. Groups of Black and White participants were compared with adjustment for age, sex, APOE ε4 carrier status, years of education, fasting status, BMI, cognitive status, and history of diabetes and hypertension (Table 2 ). There were no differences in the covariate-adjusted mean levels of Aβ42 between Black and White groups (25.54 pg/mL vs. 25.70 pg/mL, p = 0.96), but the Black group had a significantly lower level of plasma Aβ40 (166.10 vs. 187.72 pg/mL; p = 0.0004). As a result, the ratio of Aβ42 to Aβ40 (Aβ42/40) was significantly higher in the Black group (0.1214 vs. 0.1168; p < 0.0001). Further analyses examined whether racial differences in the adjusted mean levels of biomarkers were modified by cognitive status, sex, APOE ε4 carrier status, years of education, BMI, or age (Table 3 ). Some of the racial differences between Black and White participants were numerically larger in certain sub-groups, such as the Aβ42/40 ratio in the cognitively normal compared to the cognitively impaired group, and Aβ40 in the younger compared to the older group. However, there were no significant interactions between racial group and any of the covariates, suggesting that regardless of cognitive status, sex, years of education, BMI, or APOE ε4 carrier status, Black participants had a higher mean level of Aβ42/40 compared to White participants. Table 2 Cross-sectional estimates to plasma biomarker levels by race and their differences between self-identified Black and White participants, adjusting for the main effects of covariates Marker Group Estimate Standard Error P Aβ42/40 White 0.1168 0.0030 Black 0.1214 0.0030 Black-White 0.0046 0.0008 < 0.0001 Aβ42 White 25.6966 2.3829 Black 25.5411 2.4237 Black-White -0.1555 0.6065 0.96 Aβ40 White 187.7185 22.5447 Black 166.0956 22.9315 Black-White -21.6229 5.7385 0.00041 * adjusted for the main effects of age (continuous), sex (Male vs. Female), APOE ε4 carrier status (Positive vs. Negative), years of education (continuous), fasting status (fasted vs. non-fasted), cognitive status (CDR 0 vs. ≥0.5), BMI (obese/overweight vs. normal/underweight), and history of diabetes (Yes vs. No), stroke (Yes vs. No), and hypertension (Yes vs. No). Table 3 Estimated cross-sectional differences and SE between Black and White participants in adjusted mean as a function of age (by median split at 70.62 y), sex, APOE ε4 carrier status, years of education, BMI, and cognitive status (CDR) Plasma marker Interacting covariate Baseline adjusted mean difference (Black-White) Standard Error P value on baseline adjusted mean difference P value on interaction* Aβ42/40 CDR 0.0707 = 0 0.0056 0.0009 4.26E-10 ≥ 0.5 0.0017 0.0014 0.6315 APOE ε4 0.97 negative 0.0043 0.0010 9.68E-05 positive 0.0049 0.0011 3.74E-05 Sex 0.999 Male 0.0048 0.0013 0.001125 Female 0.0045 0.0009 3.80E-06 Age 0.999 Younger 0.0046 0.0010 1.05E-05 Older 0.0045 0.0012 0.001 Education 0.999 ≤ 12 yr 0.0046 0.0015 0.0133 > 12 yr 0.0045 0.0009 4.20E-07 BMI 0.997 ≤ 30 0.0049 0.0017 0.0189 > 30 0.0045 0.0008 5.29E-07 Aβ42 CDR 0.988 = 0 -0.0451 0.6900 1.0000 ≥ 0.5 -0.4841 1.1507 0.9761 APOE ε4 0.999 negative -0.0805 0.8054 1.000 positive -0.2403 0.8522 0.993 Sex 0.997 Male 0.0316 1.0354 1.0000 Female -0.2455 0.7286 0.9878 Age 0.117 Younger -1.2192 0.7797 0.3888 Older 1.3430 0.9204 0.4525 Education 0.975 ≤ 12 yr -0.6331 1.2182 0.9583 > 12 yr -0.0319 0.6829 1.0000 BMI 0.210 ≤ 30 2.1860 1.3767 0.3597 > 30 -0.6702 0.6641 0.7281 Aβ40 CDR 0.544 = 0 -25.6400 6.5240 0.0004 ≥ 0.5 -9.6669 10.8806 0.8005 APOE ε4 0.967 negative -19.2822 7.6194 0.0483 positive -24.2676 8.0621 0.0123 Sex 0.996 Male -19.6700 9.7965 0.1673 Female -22.5617 6.8933 0.0049 Age 0.103 Younger -31.9518 7.5148 0.0001 Older -6.6313 8.8709 0.8817 Education 0.989 ≤ 12 yr -25.0114 11.5203 0.12142 > 12 yr -20.5383 6.4582 0.0071 BMI 0.261 ≤ 30 -0.8474 13.0271 1.0000 > 30 -26.1896 6.2839 0.0001 * p value on the interaction between race group and the interacting covariate as indicated in the 2nd column. # All results were adjusted for the main effects of covariates including age, sex, APOE ε4 carrier status, years of education, fasting status, cognitive status (CDR at baseline), BMI, and history of diabetes, stroke, and hypertension, but excluding the interacting covariate in the 2nd column. Table 4 Estimated differences between self-identified Black and White participants in longitudinal rate of change (per year) as a function of baseline age (by median split at 70.62 y) and cognitive status (CDR 0 vs. >0), sex, APOE ε4 carrier status, BMI, and years of education adjusting for the main effects of all the other covariates Plasma marker Interacting covariate Baseline adjusted mean difference (Black-White) Standard Error P value on baseline adjusted mean difference Aβ42/40 CDR 0.466 = 0 7e-05 (0.00016) 0.6499 ≥ 0.5 0.00043 (0.00047) 0.3563 APOE ε4 0.0954 negative 0.0003 (0.00019) 0.1109 positive -0.00021 (0.00025) 0.3854 Sex 0.322 Male 3e-04 (0.00024) 0.2157 Female -1e-05 (2e-04) 0.9610 Age 0.401 Younger 2e-04 (0.00018) 0.2552 Older -8e-05 (0.00029) 0.7733 Education 0.213 ≤ 12 yr 0.00055 (0.00038) 0.1471 > 12 yr 4e-05 (0.00016) 0.8301 BMI 0.342 ≤ 30 -0.00029 (0.00043) 0.5016 > 30 0.00015 (0.00016) 0.3600 Aβ42 CDR 0.975 = 0 0.25649 (0.12978) 0.0488 ≥ 0.5 0.2437 (0.380) 0.5215 APOE ε4 0.963 negative 0.2499 (0.1547) 0.1070 positive 0.2618 (0.2015) 0.1944 Sex 0.199 Male 0.4550 (0.1945) 0.0200 Female 0.1320 (0.1593) 0.4075 Age 0.0056 Younger 0.4817 (0.1408) 0.0007 Older -0.2707 (0.2333) 0.2463 Education 0.652 ≤ 12 yr 0.3834 (0.3080) 0.2139 > 12 yr 0.2318 (0.1339) 0.0843 BMI 0.391 ≤ 30 0.5236 (0.3526) 0.1380 > 30 0.2004 (0.1330) 0.1325 Aβ40 CDR 0.682 = 0 1.5650 (1.1910) 0.1896 ≥ 0.5 0.0636 (3.4732) 0.9854 APOE ε4 0.266 negative 0.4466 (1.4214) 0.7536 positive 3.0208 (1.8434) 0.1018 Sex 0.601 Male 2.2056 (1.7892) 0.2186 Female 0.9987 (1.4582) 0.4937 Age 0.018 Younger 3.3039 (1.2942) 0.0111 Older -2.5717 (2.1290) 0.2275 Education 0.694 ≤ 12 yr 0.3853 (2.8301) 0.8918 > 12 yr 1.5981 (1.2307) 0.1949 BMI 0.152 ≤ 30 5.6108 (3.2231) 0.0823 > 30 0.6663 (1.2188) 0.5849 * p value on the interaction between race group and the interacting covariate as indicated in the 2nd column. # All results were adjusted the main effects of covariates including age, sex, APOE ε4 carrier status, years of education, fasting status, cognitive status (CDR at baseline), BMI, and history of diabetes, stroke, and hypertension but excluding the interacting covariate in the 2nd column. 3.3 Racial differences in the association of plasma Aβ biomarkers with CSF and imaging biomarkers and cognitive scores Spearman correlations of plasma Aβ42, Aβ40, and Aβ42/40 with established CSF and imaging biomarkers and cognitive scores were examined within each racialized group and compared across Black and White groups (Fig. 1 ). Plasma Aβ42 and Aβ40 were only significantly correlated with a few measures in the Black group (likely due to lack of power), but were significantly correlated with almost all CSF and imaging biomarkers and cognitive scores in the larger White group. No significant racial differences were observed in correlations with plasma Aβ42 and Aβ40 except for the correlation between plasma Aβ42 and cognition (p = 0.0035, FDR p = 0.016) and between Aβ40 and cognition (p = 0.0027, FDR p = 0.015), in which Black participants had a stronger correlation. Plasma Aβ42/40 was correlated with CSF Aβ42/40 in both Black (r = 0.48) and White (r = 0.63) groups, and the difference was not significant (FDR p = 0.097). Plasma Aβ42/40 was negatively correlated with CSF total tau, CSF p-tau181, and amyloid PET Centiloid in both Black and White groups, and there were no racial differences in these correlations. Plasma Aβ42/40 was positively correlated with the global cognitive composite and episodic memory composite in both Black and White groups, and there were no racial differences in these correlations. 3.4 Racial differences in the longitudinal changes of plasma Aβ biomarkers Longitudinal trajectories of plasma Aβ42, Aβ40, and Aβ42/40 appeared relatively linear (Fig. 2 ). Black participants had a faster increase than White participants in plasma Aβ42 (∆=0.31 pg/mL/year, SE = 0.12, p = 0.012). However, there was no difference between Black and White participants in the rate of change for plasma Aβ40 (∆=1.89 pg/mL/year, SE = 1.13, p = 0.094). Further, there was no difference between Black and White participants in the rate of change for plasma Aβ42/40 (∆=0.0001, SE = 0.0001, p = 0.35). For plasma Aβ42 and Aβ40, there was a significant interaction between racial group and baseline age such that younger but not older Black participants had a faster increase in Aβ42 (p = 0.0056) and Aβ40 (p = 0.018). For plasma Aβ42/40, there were no significant interactions between racial group and any covariates, suggesting that the rate of change in plasma Aβ42/40 is consistent across racial groups. 4. DISCUSSION This study examined one of the largest biracial AD research cohorts assembled thus far to evaluate for potential differences in baseline levels and rates of longitudinal change in plasma Aβ measures (Aβ42, Aβ40, and Aβ42/40) in self-identified Black and White participants. We found that Black participants had a higher average baseline levels of plasma Aβ42/40 than White participants, which was due to lower average baseline levels of plasma Aβ40. Plasma Aβ42/40 was significantly correlated with almost all CSF and amyloid PET biomarkers as well as cognitive scores in White participants, and the correlations were largely consistent between Black and White participants. There were no significant racial differences in the rate of change in Aβ42/40 and Aβ40, but the Black group had a faster rate of increase in Aβ42 compared to the White group. Our finding that Black research participants had higher average plasma Aβ42/40 levels, which is consistent with less amyloid pathology, aligns with three recent CSF and imaging biomarker studies 14 – 15 , 53 . One imaging study of 144 Black and 3,689 White cognitively normal individuals reported that the Black group had a lower rate of amyloid positivity and lower average amyloid burden 15 . A second imaging study with 635 Black and 15,322 White cognitively impaired individual reported that Black participants were less likely to be amyloid PET positive 14 . In our own recent CSF biomarker study of 266 Black and 1,977 White participants, Black participants had less abnormality of multiple CSF biomarkers including CSF Aβ42/40, total tau, p-tau181, and neurofilament light 53 . In our current study, the lack of significant interactions between racial group and key covariates implies that racial differences in plasma Aβ measures are consistent across age, sex, APOE ε4 carrier status, BMI, and years of education. The consistency of racial differences despite adjustment for key covariates implies that racial group must be evaluated in statistical models analyzing plasma biomarker data. Notably, these biomarker differences, if confirmed by even larger studies on representative cohorts covering the entire spectrum of social determinants of health, may imply that a lower proportion of Black individuals will be eligible for research studies or clinical trials that use plasma biomarkers for amyloid positivity as inclusion criteria. Despite racial differences in the average baseline levels of plasma Aβ42/40, this measure was correlated with most established CSF and amyloid PET biomarkers as well as cognitive composites in both racial groups. Further, the magnitude of correlations between plasma Aβ42/40 and other biomarker and cognitive measures were largely consistent between Black and White participants. One exception was the correlation between cognition and plasma Aβ42 and Aβ40, which was weaker in the White group. Studies with multiple high-accuracy plasma measures of amyloid pathology are needed to better understand potential differences in their relationships with cognitive outcomes. A key finding of this study was that the rate of change in plasma Aβ42/40 did not vary significantly between groups of Black and White individuals, despite racial differences in baseline plasma Aβ42/40. This finding must be replicated and confirmed by even larger studies on representative cohorts covering the entire spectrum of social determinants of health. However, this finding suggests that while higher mean plasma Aβ42/40 levels may result in lower enrollment of Black participants in studies and trials that use biomarkers of amyloid pathology as inclusion criteria, once participants are enrolled and randomized, changes in plasma Aβ42/40 will likely be consistent across racial groups. Furthermore, the lack of interactions between racial group and key covariates in the rate of change implies that the rate of change in plasma Aβ42/40 is not differentially affected by these covariates. Since prevention and treatment trials follow participants to assess the efficacy of treatments, the consistency in rate of change may allow plasma Aβ42/40 to be used in biracial cohorts to establish the efficacy of treatments on biomarker change. Specifically, the placebo arm in future clinical trials may estimate the same rate of change in plasma Aβ42/40 across racial groups to which the active treatment arm may be compared to establish the biomarker efficacy of the treatment. This study has multiple major strengths. Thus far, almost all previous studies of racial differences in AD biomarkers, including those with CSF, imaging, and blood-based biomarkers, were cross-sectional in nature 29 , 54 – 55 , and/or included relatively small numbers of Black participants who were typically enrolled at a single site. In contrast, this study included a relatively large number of Black participants with longitudinal plasma samples collected from three sites. Notably, this study used a plasma Aβ assay, PrecivityAD™, that was shown to accurately and consistently classify amyloid status in an overlapping biracial cohort 22 . This test is currently being used in clinical trials as well as in clinical care 32 , making our results of interest to researchers, clinical trialists and clinicians. Finally, significant correlations of plasma Aβ42/40 with CSF Aβ42/40 and amyloid PET demonstrates the potential value of plasma Aβ42/40 as a more acceptable and accessible biomarker of amyloid pathology. Limitations of our study include the limited data on structural and social determinants of health including socioeconomic status, especially life course experience and discrimination, that may correlate with biomarker measurements 56 , and the fact that AD research cohorts are not representative of the general population 57 – 58 . Additionally, in our cohort we do not currently have data on plasma phosphorylated tau measures that have demonstrated very high accuracy in classifying amyloid status 32 . Further, some negative findings must be interpreted with caution: the relatively large sample size of Black participants compared to other studies does not rule out that subtle racial differences may be present. In summary, we found that Black research participants have higher average plasma Aβ42/40 at baseline, which may imply less amyloid pathology, compared to White participants. Interestingly, despite these racial differences at baseline, the rate of change of plasma Aβ42/40 was consistent in both Black and White groups. Further, plasma Aβ42/40 had consistent associations with CSF and imaging biomarkers as well as cognitive measures across racialized groups. These results suggest that plasma Aβ42/40 may be useful in providing a biomarker outcome for research and clinical trials that is consistent across racial groups. Declarations Disclosures DW has served as a paid consultant for Eli Lilly, GE Healthcare and Qynapse, and serves on a DSMB for Functional Neuromodulation. ER serves on a data monitoring committee for Eli Lilly. TB participates as a site investigator in clinical trials sponsored by Avid Radiopharmaceuticals, Eli Lilly and Company, Biogen, Eisai, Janssen, and Roche. DG participates as a site investigator in clinical trials sponsored by Biogen and Janssen. He serves as a consultant to Eisai, Lilly, and Roche. DH and RB co-founded and have equity in C 2 N Diagnostics. DH serves on the scientific advisory board of C2N Diagnostics, Genentech, Denali, Cajal Neurosciences, and Asteroid. SES has served on advisory boards for Eisai. The other Authors declare no Competing Financial or Non-Financial Interests. This work was supported in part by funding from the National Institutes of Health (grant #AG067505 ). Washington University has a financial interest in C 2 N Diagnostics and may financially benefit if the company is successful in marketing its product(s) that is/are related to this research. Author contributions CX, JL, SS, DW, LS, TB, ER, DH, RB, GB, CC, and JM contributed to the conception and design of the study; RH, QB, FA, EG, EG, KM, CM, CX, JL, SS, DW, LS, TB, ER, DH, RB, DG, OC, CC and JM contributed to the acquisition and analysis of data; RH, QB, FA, EG, KM, CM, CX, JL, SS, DW, LS, TB, ER, DH, DG, RB, CC, OC and JM contributed to drafting the text or preparing figures. Acknowledgements This work was supported in part by funding from the National Institutes of Health (grant #AG067505). The authors thank C 2 N Diagnostics for processing the plasma samples and conducting the QC of the data. WU has a financial interest in C 2 N Diagnostics and may financially benefit if the company is successful in marketing its product(s) that is/are related to this research. This study is also supported by NIH/NIA P30 AG066444, P01 AG026276, and P01 AG003991 (PI: John Morris), P30 AG072979 (PI: David Wolk), and P20 AG068024 (PI: Erik Roberson). This work was partially supported by the National Institute of Health (NIH) grant R01 AG070941 (S. Schindler), NIH R44 AG059489 (C 2 N Diagnostics), BrightFocus (CA2016636), The Gerald and Henrietta Rauenhorst Foundation, the Cure Alzheimer’s Fund (K. Moulder), and the Alzheimer’s Drug Discovery Foundation (GC-201711-2013978). C 2 N Diagnostics was co-founded by Drs. Randall Bateman and David Holtzman, who are faculty members at Washington University. The PrecivityAD test was developed in the laboratory of Dr. Randall Bateman at Washington University and licensed to C 2 N Diagnostics. Washington University has a financial interest in the PrecivityAD test. We acknowledge the WU and UPenn and UAB ADRC CSF biospecimen cores for generating the data. We also thank all participants of the WU, UPenn, and UAB ADRCs and their families. Data availability Anonymized data that support the findings of this study are available from the corresponding author and the first author, upon request from any qualified investigator. Code availability We used publicly available software, Rstudio, for the analyses. All software used in this study is described in the Methods section and the accompanying Reporting Summary. References Schindler SE, Atri A (2023) The role of cerebrospinal fluid and other biomarker modalities in the Alzheimer's disease diagnostic revolution. Nat Aging 3(5):460–462 Rafii MS, Aisen PS (2023) Detection and treatment of Alzheimer's disease in its preclinical stage. Nat Aging 3(5):520–531 Zetterberg H, Bendlin BB (2021 Jan) Biomarkers for Alzheimer's disease-preparing for a new era of disease-modifying therapies. Mol Psychiatry 26(1):296–308. 10.1038/s41380-020-0721-9 Epub 2020 Apr 6. PMID: 32251378 Hampel H, Hu Y, Cummings J et al (2023) Blood-based biomarkers for Alzheimer's disease: Current state and future use in a transformed global healthcare landscape. Neuron. Jun 5 Hansson O, Blennow K, Zetterberg H, Dage J (2023) Blood biomarkers for Alzheimer's disease in clinical practice and trials. Nat Aging 3(5):506–519 Karikari TK (2022) Blood Tests for Alzheimer's Disease: Increasing Efforts to Expand and Diversify Research Participation Is Critical for Widespread Validation and Acceptance. Journal of Alzheimer's disease: JAD. Apr 23 Babulal GM, Quiroz YT, Albensi BC, Arenaza-Urquijo E, Astell AJ, Babiloni C, Bahar-Fuchs A, Bell J, Bowman GL, Brickman AM, Chételat G, Ciro C, Cohen AD, Dilworth-Anderson P, Dodge HH, Dreux S, Edland S, Esbensen A, Evered L, Ewers M, Fargo KN, Fortea J, Gonzalez H, Gustafson DR, Head E, Hendrix JA, Hofer SM, Johnson LA, Jutten R, Kilborn K, Lanctôt KL, Manly JJ, Martins RN, Mielke MM, Morris MC, Murray ME, Oh ES, Parra MA, Rissman RA, Roe CM, Santos OA, Scarmeas N, Schneider LS, Schupf N, Sikkes S, Snyder HM, Sohrabi HR, Stern Y, Strydom A, Tang Y, Terrera GM, Teunissen C, van Melo D, Weinborn M, Wesselman L, Wilcock DM, Zetterberg H (2019) O'Bryant SE; International Society to Advance Alzheimer's Research and Treatment, Alzheimer's Association. Perspectives on ethnic and racial disparities in Alzheimer's disease and related dementias: Update and areas of immediate need. Alzheimers Dement 15(2):292–312 Adkins-Jackson PB, George KM, Besser LM, Hyun J, Lamar M, Hill-Jarrett TG, Bubu OM, Flatt JD, Heyn PC, Cicero EC, Zarina Kraal A, Pushpalata Zanwar P, Peterson R, Kim B, Turner RW 2nd, Viswanathan J, Kulick ER, Zuelsdorff M, Stites SD, Arce Rentería M, Tsoy E, Seblova D, Ng TKS, Manly JJ, Babulal G (2023) The structural and social determinants of Alzheimer's disease related dementias. Alzheimers Dement 19(7):3171–3185 Howell JC, Parker MW, Watts KD, Kollhoff A, Tsvetkova DZ, Hu WT (2016) Research Lumbar Punctures among African Americans and Caucasians: Perception Predicts Experience. Front Aging Neurosci 8:296 Lewis A, Gupta A, Oh I et al (2023) Association Between Socioeconomic Factors, Race, and Use of a Specialty Memory Clinic. Neurology 101(14):e1424–e33 Manly JJ, Jones RN, Langa KM et al (2022) Estimating the Prevalence of Dementia and Mild Cognitive Impairment in the US: The 2016 Health and Retirement Study Harmonized Cognitive Assessment Protocol Project. JAMA Neurol 79(12):1242–1249 Power MC, Bennett EE, Turner RW et al (2021) Trends in Relative Incidence and Prevalence of Dementia Across Non-Hispanic Black and White Individuals in the United States, 2000–2016. JAMA Neurol 78(3):275–284 Kornblith E, Bahorik A, Boscardin WJ et al (2022) Association of Race and Ethnicity With Incidence of Dementia Among Older Adults. JAMA 327(15):1488–1495 Wilkins CH, Windon CC, Dilworth-Anderson P et al (2022) Racial and Ethnic Differences in Amyloid PET Positivity in Individuals With Mild Cognitive Impairment or Dementia: A Secondary Analysis of the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS) Cohort Study. JAMA Neurol 79(11):1139–1147 Deters KD, Napolioni V, Sperling RA et al (2021) Amyloid PET Imaging in Self-Identified Non-Hispanic Black Participants of the Anti-Amyloid in Asymptomatic Alzheimer's Disease (A4) Study. Neurology 96(11):e1491–e500 Morris JC, Schindler SE, McCue LM et al (2019) Assessment of Racial Disparities in Biomarkers for Alzheimer Disease. JAMA Neurol 76(3):264–273 Howell JC, Watts KD, Parker MW et al (2017) Race modifies the relationship between cognition and Alzheimer's disease cerebrospinal fluid biomarkers. Alzheimers Res Ther 9(1):88 Garrett SL, McDaniel D, Obideen M et al (2019) Racial Disparity in Cerebrospinal Fluid Amyloid and Tau Biomarkers and Associated Cutoffs for Mild Cognitive Impairment. JAMA Netw Open 2(12):e1917363 Xiong C, Luo J, Schindler SE et al (2022) Racial differences in longitudinal Alzheimer's disease biomarkers among cognitively normal adults. Alzheimers Dement 18(12):2570–2581 Barnes LL (2022) Alzheimer disease in African American individuals: increased incidence or not enough data? Nat Rev Neurol 18:56–62 Gottesman RF, Schneider AL, Zhou Y et al (2016) The ARIC-PET amyloid imaging study: Brain amyloid differences by age, race, sex, and APOE. Neurology 87(5):473–480 Schindler SE, Karikari TK, Ashton NJ et al (2022) Effect of Race on Prediction of Brain Amyloidosis by Plasma Abeta42/Abeta40, Phosphorylated Tau, and Neurofilament Light. Neurology. Apr 21 Mielke MM, Dage JL, Frank RD et al (2022) Performance of plasma phosphorylated tau 181 and 217 in the community. Nat Med. May 26 Schindler SE, Karikari TK (2022) Comorbidities confound Alzheimer's blood tests. Nat Med 28(7):1349–1351 Syrjanen JA, Campbell MR, Algeciras-Schimnich A et al (2021) Associations of amyloid and neurodegeneration plasma biomarkers with comorbidities. Alzheimer's & dementia: the journal of the Alzheimer's Association. Sep 27 Janelidze S, Barthelemy NR, He Y, Bateman RJ, Hansson O (2023) Mitigating the Associations of Kidney Dysfunction With Blood Biomarkers of Alzheimer Disease by Using Phosphorylated Tau to Total Tau Ratios. JAMA Neurol 80(5):516–522 Brickman AM, Manly JJ, Honig LS et al (2021) Plasma p-tau181, p-tau217, and other blood-based Alzheimer's disease biomarkers in a multi-ethnic, community study. Alzheimer's Dement J Alzheimer's Assoc 17(8):1353–1364 Windon C, Iaccarino L, Mundada N et al (2022) Comparison of plasma and CSF biomarkers across ethnoracial groups in the ADNI. Alzheimer's Dement 14(1):e12315 Ramanan VK, Graff-Radford J, Syrjanen J et al (2023) Association of Plasma Biomarkers of Alzheimer Disease With Cognition and Medical Comorbidities in a Biracial Cohort. Neurology 101(14):e1402–e11 Hajjar I, Yang Z, Okafor M et al (2022) Association of Plasma and Cerebrospinal Fluid Alzheimer Disease Biomarkers With Race and the Role of Genetic Ancestry, Vascular Comorbidities, and Neighborhood Factors. JAMA Netw Open 5(10):e2235068. 10.1001/jamanetworkopen.2022.35068 Monane M, Johnson KG, Snider BJ et al (2023) A blood biomarker test for brain amyloid impacts the clinical evaluation of cognitive impairment. Ann Clin Transl Neurol 10(10):1738–1748 Rissman RA, Langford O, Raman R et al (2023) Plasma Abeta42/Abeta40 and phospho-tau217 concentration ratios increase the accuracy of amyloid PET classification in preclinical Alzheimer's disease. Alzheimer's Dement J Alzheimer's Assoc. Nov 6 Wolk DA, Mancuso L, Kliot D, Arnold SE, Dickerson BC Familiarity-based memory as an early cognitive marker of preclinical and prodromal AD Morris JC, Weintraub S, Chui HC et al (2006 Oct-Dec) Alzheimer Dis Assoc Disord 20(4):210–216The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer Disease Centers Weintraub S, Salmon D, Mercaldo N et al (2009 Apr-Jun) The Alzheimer's Disease Centers' Uniform Data Set (UDS): the neuropsychologic test battery. Alzheimer Dis Assoc Disord 23(2):91–101 Morris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43(11):2412–2414 McKhann GM, Knopman DS, Chertkow H et al (2011) The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's Dement J Alzheimer's Assoc 7(3):263–269 Monsell SE, Dodge HH, Zhou XH et al Results From the NACC Uniform Data Set Neuropsychological Battery Crosswalk Study. Alzheimer Dis Assoc Disord 2016 Apr-Jun ;30(2):134–139 Luo J, Agboola F, Grant E et al (2020) Sequence of Alzheimer disease biomarker changes in cognitively normal adults: A cross-sectional study. Neurology 95(23):e3104–e16 Shaw LM, Vanderstichele H, Knapik-Czajka M et al (2009) Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects. Ann Neurol 65(4):403–413 Hu Y, Kirmess KM, Meyer MR et al (2022) Assessment of a Plasma Amyloid Probability Score to Estimate Amyloid Positron Emission Tomography Findings Among Adults With Cognitive Impairment. JAMA Netw Open 5(4):e228392 Alcolea D, Pegueroles J, Muñoz L et al (2019) Agreement of amyloid PET and CSF biomarkers for Alzheimer's disease on Lumipulse. Ann Clin Transl Neurol 6(9):1815–1824 Barthélemy NR, Saef B, Li Y et al (2023) CSF tau phosphorylation occupancies at T217 and T205 represent improved biomarkers of amyloid and tau pathology in Alzheimer’s disease. Nat Aging. 2023/03/13. Henson RL, Volluz K, Saef BA et al (2022) A methodology for normalizing fluid biomarker concentrations across reagent lots. Alzheimer's Dement 18(S5):e066912 Su Y, D'Angelo GM, Vlassenko AG et al (2013) Quantitative analysis of PiB-PET with FreeSurfer ROIs. PLoS ONE 8(11):e73377 Rousset OG, Ma Y, Evans AC (1998) Correction for partial volume effects in PET: principle and validation. J Nucl Med 39(5):904–911 Su Y, Flores S, Hornbeck RC et al (2018) Utilizing the Centiloid scale in cross-sectional and longitudinal PiB PET studies. Neuroimage Clin 19:406–416 Klunk WE, Koeppe RA, Price JC et al (2015) The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimer's Dement J Alzheimer's Assoc 11(1):1–15e1 Graybill FA (1976) Theory and Application of the Linear Model. Duxbury, North Scituate, Ma Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. J R Stat Soc B 57(1):289–300 Diggle PJ, Heagerty P, Liang K-Y, Zeger SL (2002) Analysis of Longitudinal Data, 2nd edn. Oxford University Press, New York Laird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38:963–974 Bonomi S, Lu R, Schindler SE, Bui Q, Lah JJ, Wolk D, Gleason CE, Sperling R, Roberson ED, Levey AI, Shaw L, Van Hulle C, Benzinger T, Adams M, Manzanares C, Qiu D, Hassenstab J, Moulder KL, Balls-Berry JE, Johnson K, Johnson SC, Murchison C, Luo J, Gremminger E, Agboola F, Grant EA, Hornbeck R, Massoumzadeh P, Keefe S, Dierker D, Gray JD, Henson RL, Streitz M, Mechanic-Hamilton D, Morris JC, Xiong C Relationships of Cognitive Measures with Cerebrospinal Fluid but not Imaging Biomarkers of Alzheimer Disease Vary between Black and White Individuals. Ann Neurol 2023; in press. Hall JR, Petersen M, Johnson L, O'Bryant SE (2022) Characterizing Plasma Biomarkers of Alzheimer's in a Diverse Community-Based Cohort: A Cross-Sectional Study of the HAB-HD Cohort. Front Neurol 13:871947. 10.3389/fneur.2022.871947 PMID: 36062019; PMCID: PMC9435735 Van Hulle CA, Zuelsdorff M, Koscik RL, Ennis GE, Bouges S, Fischer BL, Wyman MF, Lambrou NH, Johnson AL, Umucu E, Salazar H, Chin NA, Meyer MR, Holubasch MS, Kirmess K, Verghese PB, West T, Venkatesh V, Yarasheski KE, Gleason CE (2022) Trajectories of plasma Aβ42/40 among African Americans: Preliminary results from the African American Fighting Alzheimer’s in Midlife (AA-FAIM) study. Alzheimer's Dement 18:e066942. https://doi.org/10.1002/alz.066942 Wilkins CH, Schindler SE, Morris JC (2020) Addressing Health Disparities Among Minority Populations: Why Clinical Trial Recruitment Is Not Enough. JAMA Neurol 77(9):1063–1064 Wilkins CH, Schindler SE, Morris JC Fuller, J. T., … Bettcher, B. M. (2023). Representativeness of samples enrolled in Alzheimer's disease research centers. Alzheimer's & Dementia: Diagnosis, Assessment& Disease Monitoring, 15(2), e12450 Gilmore-Bykovskyi, A., Croff, R., Glover, C. M., Jackson, J. D., Resendez, J., Perez,A., … Manly, J. J. (2022). Traversing the aging research and health equity divide:Toward intersectional frameworks of research justice and participation. The Gerontologist,62(5), 711–720 Additional Declarations Yes there is potential Competing Interest. Bateman: Washington University (co-inventor RJB) submitted the US non-provisional patent application “Blood-Based Methods for Detecting CNS Aβ Deposition" (PCT/US2018/030518). This technology is licensed to C2N Diagnostics and used in the PrecivityAD blood test. Washington University and RJB have equity ownership interest in C2N Diagnostics and receive income based on technology licensed by Washington University to C2N Diagnostics. RJB receives income from C2N Diagnostics for serving on the scientific advisory board. RJB serves on the Roche Gantenerumab Steering Committee as an unpaid member. Benzinger: -All support for the present manuscript; NIH, payments to institution -Grants or contracts from any entity;Siemens, payments to institution -Consulting fees; Biogen, Eli Lilly, Eisai, and Bristol Myers Squibb, payments to me -Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events; Biogen and Eisai, payments to me -Participation on a Data Safety Monitoring Board or Advisory Board; Eisai, Eli Lilly, Bristol Myers Squibb, payments to me -Receipt of equipment, materials, drugs, medical writing, gifts or other services; Avid Radiopharmaceuticals/Eli Lilly (Technology transfer and precursors for radiopharmaceuticals (18F-Florbetapir, 18F-Flortaucipir) LMI (Technology transfer and precursors for radiopharmaceuticals (18F-Pl-2620) Cerveau / Lantheus (Technology transfer and precursors for radiopharmaceuticals (18F-MK-6240)-Consulting fees; Siemens, unpaid ·-Participation on a Data Safety Monitoring Board or Advisory Board; Siemens, no payments made -Participation on a Data Safety Monitoring Board or Advisory Board; NIH sponsored/ External advisor on several grants, No payments other than travel reimbursement -Leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid; ASNR Alzheimer's and ARIA Study Group, QIBA Amyloid PET Working Group, American College of Radiology/AlzNet Work Group, Alzheimer's Assoc. Clinical Tau PET Work Group, Unpaid Cruchaga: NIA-Support for research Michael J Fox Foundation--Support for research Alzheimer's Association-Support for research Alector-Provided consulting fees Circular Genomics--Provided consulting fees; participated in leadership role; provided stock options Somalogics--Supported attendance at ASHG 2022 meeting Geldmacher: receive research funding from Biogen, and Janssen (paid to my institution) I receive consulting fees from Eisai, Lilly, and Roche (paid to me individually). Holtzman: DH co-founded has equity in and serves on the scientific advisory board of C2N Diagnostics. DH serves on the scientific advisory board of Genentech, Denali, Cajal Neurosciences, and Asteroid. Morris: Cure Alzheimer's Fund Research Strategy Counsel Diverse VCID Observational Study Monitoring Board Barcelona Beta Brain Research Foundation Scientific Advisory Board Roberson: Cure Alzheimer's Fund Research Strategy Counsel Diverse VCID Observational Study Monitoring Board Barcelona Beta Brain Research Foundation Scientific Advisory Board Schindler: C2N Diagnostics was co-founded by Ors. Randall Bateman and David Holtzman, who are faculty members at Washington University. The PrecivityAD test was developed in the laboratory of Dr. Ranqall Bateman at Washington University and licensed to C2N Diagnostics. Washington University will receive royalties from the PrecivityAD test. Dr. Schindler does not have any interest in C2N Diagnostics and has not received any direct research funding or compensation from C2N Diagnostics. Dr. Schindler has served on advisory boards for Eisai. She has received travel support from the Alzheimer's Association and USAgainstAlzheimers. Dr. Schindler is an unpaid board member for the Greater Missouri Chapter of the Alz Accociation. Shaw: Leslie M Shaw has served as consultant to: Biogen; Roche, Fujirebio and received research grant support from NIH/NIA(ADNI-Biomarker Core PI; PENN ADRC-Biomarker Core co-leader); MJFox Foundation for Parkinson’s Disease Research. Wolk: D.A.W. has served as a paid consultant to Eli Lilly and Qynapse. He serves on a DSMB for Functional Neuromodulation and GSK. He is a site investigator for a clinical trial sponsored by Biogen with funding paid to his institution. Xiong: NIH Grant AG067505 Payment received by Dr. Chengjie Xiong from Diadem, Dr. Chengjie Xiong is part of an FDA Advisory Committee on Imaging Medical Products Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3783571","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":265445424,"identity":"d48f6a6d-bd36-45bc-859b-eb8f92ae51f4","order_by":0,"name":"Chengjie Xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCRDBI8HAD+YVAPEBYrVINoBUGxCtBQgMDhCrhX9287GHX2Qs5Ixv5Bh//mDAIMd3I4GAJXeOpRvL8EgYm93IMZMA2mIsSUiLgUSOmbQEj0Titts5ZiCHJW4grCX/G0hL/ebZOcYfgFrqidCSwyb5gUciwUA6xwDksAQDgn65kWYmDQxkwxn3n5VJnDGQMJx55gF+Lfwzkp9J/uypk+fvObz5Q0WFjTzfcQK2gAAzbw/CVsLKQYDxxw/iFI6CUTAKRsEIBQBsIEGcCeoygwAAAABJRU5ErkJggg==","orcid":"","institution":"Washington University in St. Louis","correspondingAuthor":true,"prefix":"","firstName":"Chengjie","middleName":"","lastName":"Xiong","suffix":""},{"id":265445425,"identity":"f8e11a4b-7010-40c6-b027-cfa8d951508c","order_by":1,"name":"Suzanne Schindler","email":"","orcid":"","institution":"Washington University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Suzanne","middleName":"","lastName":"Schindler","suffix":""},{"id":265445426,"identity":"4fecc543-62dc-4b98-87aa-4d3a5fdaf537","order_by":2,"name":"Jingqin Luo","email":"","orcid":"https://orcid.org/0000-0003-2759-3072","institution":"Washington University in St. Louis","correspondingAuthor":false,"prefix":"","firstName":"Jingqin","middleName":"","lastName":"Luo","suffix":""},{"id":265445427,"identity":"46515d84-ecfc-42ee-b56d-7f02f043691f","order_by":3,"name":"John Morris","email":"","orcid":"","institution":"Knight Alzheimer Disease Research Center","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Morris","suffix":""},{"id":265445428,"identity":"6af9f711-ede4-4cc9-83b7-6c949fc894d2","order_by":4,"name":"Randall Bateman","email":"","orcid":"https://orcid.org/0000-0002-7729-1702","institution":"Washington University in St. Louis","correspondingAuthor":false,"prefix":"","firstName":"Randall","middleName":"","lastName":"Bateman","suffix":""},{"id":265445429,"identity":"44b40f76-460d-40c7-95cf-d7182d783d59","order_by":5,"name":"David Holtzman","email":"","orcid":"https://orcid.org/0000-0002-3400-0856","institution":"Washington University in St. Louis","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Holtzman","suffix":""},{"id":265445430,"identity":"691f206a-b1bb-4c79-bc1d-f4142577280b","order_by":6,"name":"Carlos Cruchaga","email":"","orcid":"https://orcid.org/0000-0002-0276-2899","institution":"Washington University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Cruchaga","suffix":""},{"id":265445431,"identity":"f6afa3ce-f1b4-4a94-9cb4-52b2fa71b5d0","order_by":7,"name":"Ganesh Babulal","email":"","orcid":"","institution":"Washington University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ganesh","middleName":"","lastName":"Babulal","suffix":""},{"id":265445432,"identity":"d297dba3-b18b-4019-aca1-bb4434e3b6e4","order_by":8,"name":"Rachel Henson","email":"","orcid":"","institution":"Washington University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"","lastName":"Henson","suffix":""},{"id":265445433,"identity":"60554a06-7197-47b8-a97c-5eff78056331","order_by":9,"name":"Tammie Benzinger","email":"","orcid":"https://orcid.org/0000-0002-8114-0552","institution":"Washington University in St. Louis","correspondingAuthor":false,"prefix":"","firstName":"Tammie","middleName":"","lastName":"Benzinger","suffix":""},{"id":265445434,"identity":"a27f0df5-698d-4ecc-8de8-63c0b4f752e4","order_by":10,"name":"Quoc Bui","email":"","orcid":"","institution":"Washington University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Quoc","middleName":"","lastName":"Bui","suffix":""},{"id":265445435,"identity":"683e7f46-f57b-44e4-8446-2632459723ae","order_by":11,"name":"Folasade Agboola","email":"","orcid":"","institution":"Washington University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Folasade","middleName":"","lastName":"Agboola","suffix":""},{"id":265445436,"identity":"660d929f-a887-4dd7-9711-884a515aaaf5","order_by":12,"name":"Elizabeth Grant","email":"","orcid":"","institution":"Washington University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Grant","suffix":""},{"id":265445437,"identity":"7ea5db21-db0e-4d73-a16b-6bcb4978e729","order_by":13,"name":"Emily Gremminger","email":"","orcid":"","institution":"Washington University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Gremminger","suffix":""},{"id":265445438,"identity":"ed23ec6a-5fca-4606-93e8-77553f22ad70","order_by":14,"name":"Krista Moulder","email":"","orcid":"","institution":"Washington University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Krista","middleName":"","lastName":"Moulder","suffix":""},{"id":265445439,"identity":"ff1c40dc-7010-4010-9fe9-34025eff2016","order_by":15,"name":"David Geldmacher","email":"","orcid":"","institution":"University of Alabama Birmingham","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Geldmacher","suffix":""},{"id":265445440,"identity":"ebd4adcc-bce2-43b4-89e9-28f84d869ea2","order_by":16,"name":"Olivio Clay","email":"","orcid":"","institution":"University of Alabama Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Olivio","middleName":"","lastName":"Clay","suffix":""},{"id":265445441,"identity":"66af958b-c820-4fd8-bcb4-b021def84eaa","order_by":17,"name":"Erik Roberson","email":"","orcid":"https://orcid.org/0000-0002-1810-9763","institution":"University of Alabama at Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"","lastName":"Roberson","suffix":""},{"id":265445442,"identity":"9e672d09-e890-40e7-b045-925bbba3c8c4","order_by":18,"name":"Charles Murchison","email":"","orcid":"","institution":"University of Alabama Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Murchison","suffix":""},{"id":265445443,"identity":"f694f712-ef38-42df-8577-f4ba03f76ecb","order_by":19,"name":"David Wolk","email":"","orcid":"","institution":"Department of Neurology, University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Wolk","suffix":""},{"id":265445444,"identity":"8aa33929-49ca-4ba7-987b-a4337cf66c3f","order_by":20,"name":"Leslie Shaw","email":"","orcid":"","institution":"Perelman School of Medicine, University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Leslie","middleName":"","lastName":"Shaw","suffix":""}],"badges":[],"createdAt":"2023-12-20 20:56:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3783571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3783571/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49326965,"identity":"130c6eee-0ac7-43da-b6ac-588b784812f7","added_by":"auto","created_at":"2024-01-08 17:34:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":221515,"visible":true,"origin":"","legend":"\u003cp\u003eSpearman correlations of plasma biomarkers with CSF and imaging biomarkers and cognition and their differences between self-identified Black and White participants. Non-significant (raw P\u0026gt;0.05) correlation were made blank in Panel A and B. Non-significant differences in correlations (FDR P\u0026gt;0.05) were made blank in Panel C\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3783571/v1/c3599d747c984795ab204dc6.png"},{"id":49326964,"identity":"8c658fb4-4405-44ae-b3c4-91deb9fb3dc7","added_by":"auto","created_at":"2024-01-08 17:34:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":274057,"visible":true,"origin":"","legend":"\u003cp\u003eSpaghetti plots of plasma biomarkers against time since baseline between cognitively normal (CDR 0) and impaired (CDR\u0026gt;0) self-identified Black and White participants\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3783571/v1/2761c93f80eb173ac1d298ae.png"},{"id":49327585,"identity":"3f758741-f5c8-4faa-977d-65d8da779cfe","added_by":"auto","created_at":"2024-01-08 17:42:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1193333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3783571/v1/3e5bac1c-ad23-4a9e-b785-0fc83364a352.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nBateman: Washington University (co-inventor RJB) submitted the US non-provisional patent application\r\n“Blood-Based Methods for Detecting CNS Aβ Deposition\" (PCT/US2018/030518). This\r\ntechnology is licensed to C2N Diagnostics and used in the PrecivityAD blood test. Washington\r\nUniversity and RJB have equity ownership interest in C2N Diagnostics and receive income based\r\non technology licensed by Washington University to C2N Diagnostics. RJB receives income from\r\nC2N Diagnostics for serving on the scientific advisory board. RJB serves on the Roche Gantenerumab Steering Committee as an unpaid member.\r\n\r\nBenzinger: -All support for the present manuscript; NIH, payments to institution\r\n-Grants or contracts from any entity;Siemens, payments to institution\r\n-Consulting fees; Biogen, Eli Lilly, Eisai, and Bristol Myers Squibb, payments to me\r\n-Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events; Biogen and Eisai, payments to me\r\n-Participation on a Data Safety Monitoring Board or Advisory Board; Eisai, Eli Lilly, Bristol Myers Squibb, payments to me\r\n-Receipt of equipment, materials, drugs, medical writing, gifts or other services; Avid Radiopharmaceuticals/Eli Lilly (Technology transfer and precursors for radiopharmaceuticals (18F-Florbetapir, 18F-Flortaucipir) LMI (Technology transfer and precursors for radiopharmaceuticals (18F-Pl-2620) Cerveau / Lantheus (Technology transfer and precursors for radiopharmaceuticals (18F-MK-6240)-Consulting fees; Siemens, unpaid\r\n·-Participation on a Data Safety Monitoring Board or Advisory Board; Siemens, no payments made\r\n-Participation on a Data Safety Monitoring Board or Advisory Board; NIH sponsored/ External advisor on several grants, No payments other than travel reimbursement\r\n-Leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid; ASNR Alzheimer's and ARIA Study Group, QIBA Amyloid PET Working Group, American College of Radiology/AlzNet Work Group, Alzheimer's Assoc. Clinical Tau PET Work Group, Unpaid\r\n\r\nCruchaga: NIA-Support for research\r\nMichael J Fox Foundation--Support for research\r\nAlzheimer's Association-Support for research\r\nAlector-Provided consulting fees\r\nCircular Genomics--Provided consulting fees; participated in leadership role; provided stock\r\noptions\r\nSomalogics--Supported attendance at ASHG 2022 meeting\r\n\r\nGeldmacher: receive research funding from Biogen, and Janssen (paid to my institution) I receive consulting fees from Eisai, Lilly, and Roche (paid to me individually). \r\n\r\nHoltzman: DH co-founded has equity in and serves on the scientific advisory board of C2N Diagnostics. DH serves on the scientific advisory board of Genentech, Denali, Cajal Neurosciences, and Asteroid. \r\n\r\nMorris: Cure Alzheimer's Fund Research Strategy Counsel\r\nDiverse VCID Observational Study Monitoring Board\r\nBarcelona Beta Brain Research Foundation Scientific Advisory Board\r\n\r\nRoberson: Cure Alzheimer's Fund Research Strategy Counsel\r\nDiverse VCID Observational Study Monitoring Board\r\nBarcelona Beta Brain Research Foundation Scientific Advisory Board\r\n\r\nSchindler: C2N Diagnostics was co-founded by Ors. Randall Bateman and David Holtzman, who are faculty\r\nmembers at Washington University. The PrecivityAD test was developed in the laboratory of Dr.\r\nRanqall Bateman at Washington University and licensed to C2N Diagnostics. Washington\r\nUniversity will receive royalties from the PrecivityAD test. Dr. Schindler does not have any interest\r\nin C2N Diagnostics and has not received any direct research funding or compensation from C2N\r\nDiagnostics.\r\nDr. Schindler has served on advisory boards for Eisai. She has received travel support from the\r\nAlzheimer's Association and USAgainstAlzheimers. Dr. Schindler is an unpaid board member for the Greater Missouri Chapter of the Alz Accociation.\r\n\r\nShaw: Leslie M Shaw has served as consultant to: Biogen; Roche, Fujirebio and received research grant support from NIH/NIA(ADNI-Biomarker Core PI; PENN ADRC-Biomarker Core co-leader); MJFox Foundation for Parkinson’s Disease Research. \r\n\r\nWolk: D.A.W. has served as a paid consultant to Eli Lilly and Qynapse. He\r\nserves on a DSMB for Functional Neuromodulation and GSK. He is a site investigator\r\nfor a clinical trial sponsored by Biogen with funding paid to his institution.\r\n\r\nXiong: NIH Grant AG067505\r\nPayment received by Dr. Chengjie Xiong from Diadem, Dr. Chengjie Xiong is part of an FDA Advisory Committee on Imaging Medical Products","formattedTitle":"Baseline levels and longitudinal rates of change in plasma Aβ42/40\r\namong self-identified Black/African American and White individuals","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eBiomarkers of Alzheimer disease (AD), including fluid and imaging biomarkers of amyloid and tau pathology, have enabled a better understanding of AD pathophysiology, facilitated clinical trials that have led to the development of amyloid-lowering treatments, and increased the accuracy of clinical dementia diagnosis\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While cerebrospinal fluid (CSF)- and positron emission tomography (PET)-based biomarkers accurately detect AD brain pathology, the scale of testing with these modalities is limited by their requirements for specialized personnel and equipment, perceived risks, and high costs\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In contrast, blood is routinely collected in research and clinical care and considered highly accessible, acceptable, and scalable, making blood-based biomarkers ideal tools for research, clinical trials, and clinical practice\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, blood-based biomarkers of AD may facilitate performing biomarker testing in minoritized groups that have historically been under-represented and systematically excluded in AD research and clinical trials\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Further, Black individuals with cognitive impairment may be less likely to be seen in memory clinics that perform biomarker testing with CSF and PET and serve as an entry point for research studies and clinical trials\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The use of blood-based biomarkers may enable testing of individuals in racialized groups who would not be willing to undergo screening with CSF or PET testing, and may enable testing in a community-based setting rather than a major medical center.\u003c/p\u003e \u003cp\u003eMultiple epidemiological studies have reported a higher prevalence of dementia in self-identified Black or African American and Hispanic individuals as compared to non-Hispanic White individuals (nHW)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Despite the higher reported prevalence of dementia, several research studies have reported a lower rate of AD biomarker abnormalities in Black and Hispanic individuals\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, although other studies have found the opposite result or no differences between these groups\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The seeming disconnect between the reported prevalence of dementia and the rate of AD biomarker abnormalities has raised concern that the major etiologies of dementia may vary across racial and ethnic groups and/or that biomarkers may not reflect AD pathology consistently across groups, in addition to the possibility that the diagnostic evaluation for dementia may vary across studies. Further, concentrations of some plasma biomarkers can be affected by medical conditions (e.g., chronic kidney disease and obesity) that are more prevalent in some racial and ethnic groups, suggesting that plasma biomarkers may not reflect AD pathology consistently across groups\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, some evidence indicates that plasma biomarker ratios may normalize for individual-level differences and provide more consistent performance across groups \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Specifically, we have previously reported that plasma Aβ42/40 as measured by a high precision mass spectrometry-based assay has more consistent performance in classifying amyloid status across racial groups as compared to concentrations of phosphorylated tau \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This finding suggests that plasma Aβ42/40 as measured by high precision assays may enable classification of amyloid status in more diverse groups.\u003c/p\u003e \u003cp\u003eAlmost all studies of racial differences in AD biomarkers have been based on CSF and imaging biomarkers, and the few studies that have reported data on plasma biomarkers only reported cross-sectional data \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.Therefore, it is unknown whether the longitudinal rates of change in plasma biomarkers vary by race or ethnicity. The rate of change is particularly important in clinical trials, as it represents the placebo trajectory of AD pathology that is intended to be modified by treatments. This study utilized one of the largest biracial cohorts with plasma biomarkers assembled thus far to evaluate for potential differences in baseline levels and rates of change in plasma Aβ measures (Aβ42, Aβ40, and Aβ42/40) in self-identified Black and White participants. Participants from three AD Research Centers (Washington University, University of Pennsylvania, and University of Alabama at Birmingham) were included. Samples were analyzed with the C\u003csub\u003e2\u003c/sub\u003eN Diagnostics mass spectrometry-based assay that is currently being used in clinical trials and clinical practice \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. General linear mixed effects models were used to estimate the baseline levels and rates of change for plasma Aβ measures in both racialized groups. Analyses also examined whether dementia status, age, sex, education, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, fasting status, and comorbidities (hypertension and diabetes) modified potential racial differences.\u003c/p\u003e"},{"header":"2 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eThe study cohort included individuals with plasma Aβ measures and clinical/cognitive data who participated in the Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB; NIH/NIA R01 AG067505), which aims to understand potential racial differences in harmonized biomarker data collected by multiple research studies of memory and aging in middle-aged and older individuals. Participants in the current study represented three of the SORTOUT-AB sites: the Washington University (WU) Knight Alzheimer Disease Research Center (ADRC), the University of Pennsylvania (UPenn) ADRC, and the University of Alabama at Birmingham (UAB) ADRC. Details of recruitment for these studies have been described previously\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Participants with conditions that could prevent participation or affect long-term participation (e.g., metastatic cancer) were excluded. Participants underwent clinical and/or cognitive assessments within 2 years of their baseline plasma assessments. A subset of the participants also had CSF or imaging assessments within 2 years of their baseline plasma sample collection. All participants provided written informed consent at recruitment from their parent studies. The Washington University Human Research Protection Office approved the current study with additional approvals from the Institutional Review Boards of the other sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical and cognitive assessments\u003c/h2\u003e \u003cp\u003eClinical and cognitive assessments from WU, UPenn, and UAB followed protocols consistent with the National Alzheimer\u0026rsquo;s Coordinating Center Uniform Data Set (UDS)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Demographic information, body mass index (BMI), and medical history were collected. Race and sex were self-identified by participants. The presence or absence of dementia, and when present, its severity, was determined by the score on the Clinical Dementia Rating\u0026reg;\u0026trade; (CDR\u0026reg;\u0026trade;)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, which was performed at baseline and then annually. Standard criteria were used to diagnose the likely etiology of dementia\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The cognitive battery of the UDS included tasks of episodic memory, working memory, semantic knowledge, executive function and attention, and visuospatial ability, and were harmonized across UDS versions\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e: Montreal Cognitive Assessment [MoCA], Animal Fluency (60 seconds), Vegetable Fluency, Wechsler Adult Intelligence Scale (WAIS-R) Digit Symbol, Digit Span, Craft Story Immediate Recall), Craft Story Delayed Recall), Multilingual Naming Test, Free and Cued Selective Reminding, and Trail Making Test A and B. All scales were oriented such that a higher score indicated better cognition and converted to Z-scores using the baseline mean and standard deviation (SD) from the entire cohort. A global cognitive composite score was calculated by averaging Z-scores across all tests. An episodic memory composite score was calculated by averaging the Z-scores from the Craft Story-immediate and Craft Story-delayed tasks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Apolipoprotein E genotyping\u003c/h2\u003e \u003cp\u003eApolipoprotein E (\u003cem\u003eAPOE\u003c/em\u003e) genotyping was performed as previously described\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Participants were classified as \u003cem\u003eAPOE ε\u003c/em\u003e4 carriers (one or two ε4 alleles) and non-carriers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Blood and CSF collection and analysis\u003c/h2\u003e \u003cp\u003eAt WU, blood was collected at the time of lumbar puncture (fasted) or clinical assessment (non-fasted)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Only non-fasted samples were collected at UPenn, and only fasted samples were collected at UAB. At WU, CSF samples (20\u0026ndash;30 mL) were collected at 8 AM after overnight fasting by gravity drip, briefly centrifuged at low speed, and aliquoted into polypropylene tubes prior to freezing at \u0026minus;\u0026thinsp;80\u0026deg;C. CSF samples from participants enrolled at UAB and UPenn ADRCs were collected in accordance with protocols for the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. All plasma samples were analyzed at C\u003csub\u003e2\u003c/sub\u003eN Diagnostics with the PrecivityAD\u0026trade; assay. Briefly, Aβ40 and Aβ42 were simultaneously immunoprecipitated from plasma via a monoclonal anti-Aβ mid-domain antibody\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Proteins were digested into peptides using LysN endoprotease. Liquid chromatography-mass spectrometry was performed on a Thermo Scientific Orbitrap Lumos Tribrid mass spectrometer interfaced with a nano-Acquity chromatography system\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn automated immunoassay (LUMIPULSE G1200, Fujirebio, Malverne, PA) was used to measure CSF concentrations of Aβ40, Aβ42, total tau (t-tau), and tau phosphorylated at position 181 (p-tau181)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. A bridging subset of the CSF samples (n\u0026thinsp;=\u0026thinsp;114) from the UPenn ADRC was selected to represent a wide range of values for all analytes and were run at the same time and with the same reagents as the WU samples to evaluate and adjust for systematic differences between the UPenn and WU sites. A linear regression model fitted on the values of the bridging samples was used to harmonize the CSF biomarker values between UPenn and WU\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Imaging processing and analysis\u003c/h2\u003e \u003cp\u003eDetails of the structural brain MRI and amyloid PET protocols are provided elsewhere\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, which followed a protocol consistent with that used by the ADNI. A standardized uptake value ratio (SUVR) with correction for partial volume effects was calculated for the FreeSurfer regions of interest (ROIs) for PiB, Florbetapir or Florbetaben\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The cerebellum was used as the reference region. A summary measure of amyloid burden was calculated using the averaged SUVR values in the lateral orbitofrontal, medial orbitofrontal, precuneus, rostral middle frontal, superior frontal, superior temporal, and middle temporal regions. To harmonize SUVR values across different tracers (PiB, Florbetapir or Florbetaben), values from the summary measure were converted into Centiloid units\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, following previously published methods\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analyses\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of participants were summarized with the mean and SD for continuous variables or count and percentage for categorical variables. General linear models were implemented to evaluate for cross-sectional racial differences in levels of plasma Aβ measures using baseline data from either participants with only baseline plasma data or participants with longitudinal plasma data. These models included the main effects of self-identified race and the covariates of age, cognitive BMI, and comorbidity status (hypertension, stroke, and diabetes). Additional analyses included interactions of each variable with racial group to examine whether each of the covariates modified the racial differences. In the subset of participants with CSF and imaging biomarkers, plasma Aβ measures were correlated with the established AD biomarkers by Spearman correlations, and the correlations between racialized groups were compared by a two-sided standard normal test after the Fisher\u0026rsquo;s Z-transformation\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Because of the large number of comparisons in the correlations, we adjusted statistical significance for a False Discovery Rate (FDR)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e of 5%.\u003c/p\u003e \u003cp\u003eGeneral linear mixed models were implemented to evaluate for racial differences in the rate of change of plasma Aβ measures\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, specifically the random intercept and random slope models that assume linear growth patterns over time\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. All models included race and a race by time interaction (time 0\u0026thinsp;=\u0026thinsp;baseline), and the covariates of age, cognitive status (CDR 0 or \u0026gt;\u0026thinsp;0), sex, years of education, \u003cem\u003eAPOE ε\u003c/em\u003e4 status, and type of sample (fasting or non-fasting), BMI, and comorbidities (hypertension, stroke, and diabetes) as fixed effects. The annual rates of change between racialized groups were compared by a two-sided approximate Student t test whose degree of freedom was estimated by the Satterthwaite method. All models were implemented in Rstudio (version 2023.9.1.494 running R version 4.2.1) via the R package \u003cem\u003elmerTest\u003c/em\u003e (version3.1-3). We further assessed whether a linear trend fitted the longitudinal data well, and found no clear nonlinear longitudinal patterns, likely because of small number of plasma samples for most individuals.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Cohort characteristics at baseline\u003c/h2\u003e \u003cp\u003e The research-based study cohort included a total of 324 Black and 1,547 White participants with plasma Aβ measures from at least one sample. A sub-cohort of 158 Black and 759 White participants had plasma Aβ measures from at least two samples with a mean interval between the first and last plasma sample of 6.62 (SD\u0026thinsp;=\u0026thinsp;4.42) years. Participant characteristics at baseline are shown (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Black and White participants had similar ages at baseline (70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6 and 70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 years, respectively, p\u0026thinsp;=\u0026thinsp;0.26), but Black participants had a shorter average interval between their first and last plasma sample (5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52 years) compared to White participants (6.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17 years; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Black participants (72.2%) were more likely to be cognitively normal than White participants (66.4%; p\u0026thinsp;=\u0026thinsp;0.041) at baseline, but there was no difference in the proportion carrying an \u003cem\u003eAPOE\u003c/em\u003e ε4 allele (45.1% versus 42.6%, p\u0026thinsp;=\u0026thinsp;0.35). Most participants completed at least 12 years of education, with Black participants (15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 years, p\u0026thinsp;=\u0026thinsp;0.002) completing slightly fewer years of education on average compared to White participants (15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 years). Black participants were more likely than White participants to be female (72.2% versus 53.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Black participants were more likely than White participants to have a history of hypertension (65.4% versus 42.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) or diabetes (17.3% versus 6.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Black participants also had a higher average BMI (30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23) than White participants (27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A higher proportion of baseline biomarker data from Black participants were non-fasted (63.9%) than from White participants (34.4%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A subset of 110 Black and 1040 White participants had CSF biomarker data at baseline. A subset of 129 Black and 798 White participants had amyloid PET data at baseline.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographics and clinical features\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1547)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;324)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSITE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWashU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1412 (91.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249 (76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUPenn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline Age (yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.5 (9.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.2 (8.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.0 [42.7, 98.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.0 [43.5, 91.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFasting status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1015 (65.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117 (36.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-fasting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e532 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207 (63.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCDR global\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1027 (66.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e720 (46.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90 (27.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e827 (53.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234 (72.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (yr)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.8 (2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.3 (2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.0 [7.00, 29.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.0 [6.00, 25.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPOE e4\u003c/b\u003e \u003cb\u003epositivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e878 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172 (53.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e659 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (binary)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enormal/underweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eobese/overweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e805 (52.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e316 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e878 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e655 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212 (65.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStroke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1401 (90.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e258 (79.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1150 (74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223 (68.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Cross-sectional racial differences in plasma Aβ biomarkers\u003c/h2\u003e \u003cp\u003eCross-sectional analyses included baseline plasma Aβ measures from either participants with only baseline plasma data or participants with longitudinal plasma data. Groups of Black and White participants were compared with adjustment for age, sex, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, years of education, fasting status, BMI, cognitive status, and history of diabetes and hypertension (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There were no differences in the covariate-adjusted mean levels of Aβ42 between Black and White groups (25.54 pg/mL vs. 25.70 pg/mL, p\u0026thinsp;=\u0026thinsp;0.96), but the Black group had a significantly lower level of plasma Aβ40 (166.10 vs. 187.72 pg/mL; p\u0026thinsp;=\u0026thinsp;0.0004). As a result, the ratio of Aβ42 to Aβ40 (Aβ42/40) was significantly higher in the Black group (0.1214 vs. 0.1168; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Further analyses examined whether racial differences in the adjusted mean levels of biomarkers were modified by cognitive status, sex, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, years of education, BMI, or age (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Some of the racial differences between Black and White participants were numerically larger in certain sub-groups, such as the Aβ42/40 ratio in the cognitively normal compared to the cognitively impaired group, and Aβ40 in the younger compared to the older group. However, there were no significant interactions between racial group and any of the covariates, suggesting that regardless of cognitive status, sex, years of education, BMI, or \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, Black participants had a higher mean level of Aβ42/40 compared to White participants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCross-sectional estimates to plasma biomarker levels by race and their differences between self-identified Black and White participants, adjusting for the main effects of covariates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.6966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.3829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.5411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187.7185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.5447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166.0956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-21.6229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003e*\u003c/b\u003e adjusted for the main effects of age (continuous), sex (Male vs. Female), \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status (Positive vs. Negative), years of education (continuous), fasting status (fasted vs. non-fasted), cognitive status (CDR 0 vs. \u0026ge;0.5), BMI (obese/overweight vs. normal/underweight), and history of diabetes (Yes vs. No), stroke (Yes vs. No), and hypertension (Yes vs. No).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated cross-sectional differences and SE between Black and White participants in adjusted mean as a function of age (by median split at 70.62 y), sex, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, years of education, BMI, and cognitive status (CDR)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteracting covariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaseline adjusted mean difference\u003c/p\u003e \u003cp\u003e(Black-White)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value on baseline adjusted mean difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value on interaction*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.26E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e ε4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.68E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.74E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.80E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYounger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.20E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.29E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e ε4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.2403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.2455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYounger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.2192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.6331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.6702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-25.6400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.5240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.6669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.8806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e ε4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-19.2822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.6194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-24.2676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-19.6700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.7965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-22.5617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.8933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYounger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-31.9518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.5148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.6313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.8709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-25.0114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.5203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-20.5383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.8474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-26.1896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.2839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* p value on the interaction between race group and the interacting covariate as indicated in the 2nd column.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e#\u003c/sup\u003e All results were adjusted for the main effects of covariates including age, sex, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, years of education, fasting status, cognitive status (CDR at baseline), BMI, and history of diabetes, stroke, and hypertension, but excluding the interacting covariate in the 2nd column.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated differences between self-identified Black and White participants in longitudinal rate of change (per year) as a function of baseline age (by median split at 70.62 y) and cognitive status (CDR 0 vs. \u0026gt;0), sex, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, BMI, and years of education adjusting for the main effects of all the other covariates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteracting covariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBaseline adjusted mean difference\u003c/p\u003e \u003cp\u003e(Black-White)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value on baseline adjusted mean difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7e-05 (0.00016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00043 (0.00047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e ε4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003 (0.00019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00021 (0.00025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3e-04 (0.00024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1e-05 (2e-04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYounger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2e-04 (0.00018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8e-05 (0.00029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00055 (0.00038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4e-05 (0.00016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00029 (0.00043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00015 (0.00016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25649 (0.12978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0488\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2437 (0.380)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e ε4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2499 (0.1547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2618 (0.2015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4550 (0.1945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1320 (0.1593)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0056\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYounger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4817 (0.1408)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2707 (0.2333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3834 (0.3080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2318 (0.1339)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5236 (0.3526)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2004 (0.1330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5650 (1.1910)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0636 (3.4732)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e ε4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4466 (1.4214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0208 (1.8434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2056 (1.7892)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9987 (1.4582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYounger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3039 (1.2942)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.5717 (2.1290)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3853 (2.8301)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12 yr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5981 (1.2307)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6108 (3.2231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6663 (1.2188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* p value on the interaction between race group and the interacting covariate as indicated in the 2nd column.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e#\u003c/sup\u003eAll results were adjusted the main effects of covariates including age, sex, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, years of education, fasting status, cognitive status (CDR at baseline), BMI, and history of diabetes, stroke, and hypertension but excluding the interacting covariate in the 2nd column.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Racial differences in the association of plasma Aβ biomarkers with CSF and imaging biomarkers and cognitive scores\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSpearman correlations of plasma Aβ42, Aβ40, and Aβ42/40 with established CSF and imaging biomarkers and cognitive scores were examined within each racialized group and compared across Black and White groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Plasma Aβ42 and Aβ40 were only significantly correlated with a few measures in the Black group (likely due to lack of power), but were significantly correlated with almost all CSF and imaging biomarkers and cognitive scores in the larger White group. No significant racial differences were observed in correlations with plasma Aβ42 and Aβ40 except for the correlation between plasma Aβ42 and cognition (p\u0026thinsp;=\u0026thinsp;0.0035, FDR p\u0026thinsp;=\u0026thinsp;0.016) and between Aβ40 and cognition (p\u0026thinsp;=\u0026thinsp;0.0027, FDR p\u0026thinsp;=\u0026thinsp;0.015), in which Black participants had a stronger correlation. Plasma Aβ42/40 was correlated with CSF Aβ42/40 in both Black (r\u0026thinsp;=\u0026thinsp;0.48) and White (r\u0026thinsp;=\u0026thinsp;0.63) groups, and the difference was not significant (FDR p\u0026thinsp;=\u0026thinsp;0.097). Plasma Aβ42/40 was negatively correlated with CSF total tau, CSF p-tau181, and amyloid PET Centiloid in both Black and White groups, and there were no racial differences in these correlations. Plasma Aβ42/40 was positively correlated with the global cognitive composite and episodic memory composite in both Black and White groups, and there were no racial differences in these correlations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Racial differences in the longitudinal changes of plasma Aβ biomarkers\u003c/h2\u003e \u003cp\u003eLongitudinal trajectories of plasma Aβ42, Aβ40, and Aβ42/40 appeared relatively linear (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Black participants had a faster increase than White participants in plasma Aβ42 (∆=0.31 pg/mL/year, SE\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;=\u0026thinsp;0.012). However, there was no difference between Black and White participants in the rate of change for plasma Aβ40 (∆=1.89 pg/mL/year, SE\u0026thinsp;=\u0026thinsp;1.13, p\u0026thinsp;=\u0026thinsp;0.094). Further, there was no difference between Black and White participants in the rate of change for plasma Aβ42/40 (∆=0.0001, SE\u0026thinsp;=\u0026thinsp;0.0001, p\u0026thinsp;=\u0026thinsp;0.35). For plasma Aβ42 and Aβ40, there was a significant interaction between racial group and baseline age such that younger but not older Black participants had a faster increase in Aβ42 (p\u0026thinsp;=\u0026thinsp;0.0056) and Aβ40 (p\u0026thinsp;=\u0026thinsp;0.018). For plasma Aβ42/40, there were no significant interactions between racial group and any covariates, suggesting that the rate of change in plasma Aβ42/40 is consistent across racial groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003e This study examined one of the largest biracial AD research cohorts assembled thus far to evaluate for potential differences in baseline levels and rates of longitudinal change in plasma Aβ measures (Aβ42, Aβ40, and Aβ42/40) in self-identified Black and White participants. We found that Black participants had a higher average baseline levels of plasma Aβ42/40 than White participants, which was due to lower average baseline levels of plasma Aβ40. Plasma Aβ42/40 was significantly correlated with almost all CSF and amyloid PET biomarkers as well as cognitive scores in White participants, and the correlations were largely consistent between Black and White participants. There were no significant racial differences in the rate of change in Aβ42/40 and Aβ40, but the Black group had a faster rate of increase in Aβ42 compared to the White group.\u003c/p\u003e \u003cp\u003eOur finding that Black research participants had higher average plasma Aβ42/40 levels, which is consistent with less amyloid pathology, aligns with three recent CSF and imaging biomarker studies\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. One imaging study of 144 Black and 3,689 White cognitively normal individuals reported that the Black group had a lower rate of amyloid positivity and lower average amyloid burden \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A second imaging study with 635 Black and 15,322 White cognitively impaired individual reported that Black participants were less likely to be amyloid PET positive\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In our own recent CSF biomarker study of 266 Black and 1,977 White participants, Black participants had less abnormality of multiple CSF biomarkers including CSF Aβ42/40, total tau, p-tau181, and neurofilament light\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. In our current study, the lack of significant interactions between racial group and key covariates implies that racial differences in plasma Aβ measures are consistent across age, sex, \u003cem\u003eAPOE\u003c/em\u003e ε4 carrier status, BMI, and years of education. The consistency of racial differences despite adjustment for key covariates implies that racial group must be evaluated in statistical models analyzing plasma biomarker data. Notably, these biomarker differences, if confirmed by even larger studies on representative cohorts covering the entire spectrum of social determinants of health, may imply that a lower proportion of Black individuals will be eligible for research studies or clinical trials that use plasma biomarkers for amyloid positivity as inclusion criteria.\u003c/p\u003e \u003cp\u003eDespite racial differences in the average baseline levels of plasma Aβ42/40, this measure was correlated with most established CSF and amyloid PET biomarkers as well as cognitive composites in both racial groups. Further, the magnitude of correlations between plasma Aβ42/40 and other biomarker and cognitive measures were largely consistent between Black and White participants. One exception was the correlation between cognition and plasma Aβ42 and Aβ40, which was weaker in the White group. Studies with multiple high-accuracy plasma measures of amyloid pathology are needed to better understand potential differences in their relationships with cognitive outcomes.\u003c/p\u003e \u003cp\u003eA key finding of this study was that the rate of change in plasma Aβ42/40 did not vary significantly between groups of Black and White individuals, despite racial differences in baseline plasma Aβ42/40. This finding must be replicated and confirmed by even larger studies on representative cohorts covering the entire spectrum of social determinants of health. However, this finding suggests that while higher mean plasma Aβ42/40 levels may result in lower enrollment of Black participants in studies and trials that use biomarkers of amyloid pathology as inclusion criteria, once participants are enrolled and randomized, changes in plasma Aβ42/40 will likely be consistent across racial groups. Furthermore, the lack of interactions between racial group and key covariates in the rate of change implies that the rate of change in plasma Aβ42/40 is not differentially affected by these covariates. Since prevention and treatment trials follow participants to assess the efficacy of treatments, the consistency in rate of change may allow plasma Aβ42/40 to be used in biracial cohorts to establish the efficacy of treatments on biomarker change. Specifically, the placebo arm in future clinical trials may estimate the same rate of change in plasma Aβ42/40 across racial groups to which the active treatment arm may be compared to establish the biomarker efficacy of the treatment.\u003c/p\u003e \u003cp\u003eThis study has multiple major strengths. Thus far, almost all previous studies of racial differences in AD biomarkers, including those with CSF, imaging, and blood-based biomarkers, were cross-sectional in nature\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, and/or included relatively small numbers of Black participants who were typically enrolled at a single site. In contrast, this study included a relatively large number of Black participants with longitudinal plasma samples collected from three sites. Notably, this study used a plasma Aβ assay, PrecivityAD\u0026trade;, that was shown to accurately and consistently classify amyloid status in an overlapping biracial cohort \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This test is currently being used in clinical trials as well as in clinical care \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, making our results of interest to researchers, clinical trialists and clinicians. Finally, significant correlations of plasma Aβ42/40 with CSF Aβ42/40 and amyloid PET demonstrates the potential value of plasma Aβ42/40 as a more acceptable and accessible biomarker of amyloid pathology. Limitations of our study include the limited data on structural and social determinants of health including socioeconomic status, especially life course experience and discrimination, that may correlate with biomarker measurements \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, and the fact that AD research cohorts are not representative of the general population\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Additionally, in our cohort we do not currently have data on plasma phosphorylated tau measures that have demonstrated very high accuracy in classifying amyloid status \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Further, some negative findings must be interpreted with caution: the relatively large sample size of Black participants compared to other studies does not rule out that subtle racial differences may be present.\u003c/p\u003e \u003cp\u003e In summary, we found that Black research participants have higher average plasma Aβ42/40 at baseline, which may imply less amyloid pathology, compared to White participants. Interestingly, despite these racial differences at baseline, the rate of change of plasma Aβ42/40 was consistent in both Black and White groups. Further, plasma Aβ42/40 had consistent associations with CSF and imaging biomarkers as well as cognitive measures across racialized groups. These results suggest that plasma Aβ42/40 may be useful in providing a biomarker outcome for research and clinical trials that is consistent across racial groups.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDisclosures\u003c/h2\u003e \u003cp\u003eDW has served as a paid consultant for Eli Lilly, GE Healthcare and Qynapse, and serves on a DSMB for Functional Neuromodulation. ER serves on a data monitoring committee for Eli Lilly. TB participates as a site investigator in clinical trials sponsored by Avid Radiopharmaceuticals, Eli Lilly and Company, Biogen, Eisai, Janssen, and Roche. DG participates as a site investigator in clinical trials sponsored by Biogen and Janssen. He serves as a consultant to Eisai, Lilly, and Roche. DH and RB co-founded and have equity in C\u003csub\u003e2\u003c/sub\u003eN Diagnostics. DH serves on the scientific advisory board of C2N Diagnostics, Genentech, Denali, Cajal Neurosciences, and Asteroid. SES has served on advisory boards for Eisai. The other Authors declare no Competing Financial or Non-Financial Interests. This work was supported in part by funding from the National Institutes of Health (grant #AG067505 ). Washington University has a financial interest in C\u003csub\u003e2\u003c/sub\u003eN Diagnostics and may financially benefit if the company is successful in marketing its product(s) that is/are related to this research.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eCX, JL, SS, DW, LS, TB, ER, DH, RB, GB, CC, and JM contributed to the conception and design of the study; RH, QB, FA, EG, EG, KM, CM, CX, JL, SS, DW, LS, TB, ER, DH, RB, DG, OC, CC and JM contributed to the acquisition and analysis of data; RH, QB, FA, EG, KM, CM, CX, JL, SS, DW, LS, TB, ER, DH, DG, RB, CC, OC and JM contributed to drafting the text or preparing figures.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was supported in part by funding from the National Institutes of Health (grant #AG067505). The authors thank C\u003csub\u003e2\u003c/sub\u003eN Diagnostics for processing the plasma samples and conducting the QC of the data. WU has a financial interest in C\u003csub\u003e2\u003c/sub\u003eN Diagnostics and may financially benefit if the company is successful in marketing its product(s) that is/are related to this research. This study is also supported by NIH/NIA P30 AG066444, P01 AG026276, and P01 AG003991 (PI: John Morris), P30 AG072979 (PI: David Wolk), and P20 AG068024 (PI: Erik Roberson). This work was partially supported by the National Institute of Health (NIH) grant R01 AG070941 (S. Schindler), NIH R44 AG059489 (C\u003csub\u003e2\u003c/sub\u003eN Diagnostics), BrightFocus (CA2016636), The Gerald and Henrietta Rauenhorst Foundation, the Cure Alzheimer\u0026rsquo;s Fund (K. Moulder), and the Alzheimer\u0026rsquo;s Drug Discovery Foundation (GC-201711-2013978). C\u003csub\u003e2\u003c/sub\u003eN Diagnostics was co-founded by Drs. Randall Bateman and David Holtzman, who are faculty members at Washington University. The PrecivityAD test was developed in the laboratory of Dr. Randall Bateman at Washington University and licensed to C\u003csub\u003e2\u003c/sub\u003eN Diagnostics. Washington University has a financial interest in the PrecivityAD test. We acknowledge the WU and UPenn and UAB ADRC CSF biospecimen cores for generating the data. We also thank all participants of the WU, UPenn, and UAB ADRCs and their families.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eAnonymized data that support the findings of this study are available from the corresponding author and the first author, upon request from any qualified investigator.\u003c/p\u003e\u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eWe used publicly available software, Rstudio, for the analyses. All software used in this study is described in the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section and the accompanying Reporting Summary.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchindler SE, Atri A (2023) The role of cerebrospinal fluid and other biomarker modalities in the Alzheimer's disease diagnostic revolution. Nat Aging 3(5):460\u0026ndash;462\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRafii MS, Aisen PS (2023) Detection and treatment of Alzheimer's disease in its preclinical stage. Nat Aging 3(5):520\u0026ndash;531\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZetterberg H, Bendlin BB (2021 Jan) Biomarkers for Alzheimer's disease-preparing for a new era of disease-modifying therapies. Mol Psychiatry 26(1):296\u0026ndash;308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41380-020-0721-9\u003c/span\u003e\u003cspan address=\"10.1038/s41380-020-0721-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2020 Apr 6. PMID: 32251378\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHampel H, Hu Y, Cummings J et al (2023) Blood-based biomarkers for Alzheimer's disease: Current state and future use in a transformed global healthcare landscape. Neuron. Jun 5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansson O, Blennow K, Zetterberg H, Dage J (2023) Blood biomarkers for Alzheimer's disease in clinical practice and trials. Nat Aging 3(5):506\u0026ndash;519\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarikari TK (2022) Blood Tests for Alzheimer's Disease: Increasing Efforts to Expand and Diversify Research Participation Is Critical for Widespread Validation and Acceptance. Journal of Alzheimer's disease: JAD. Apr 23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabulal GM, Quiroz YT, Albensi BC, Arenaza-Urquijo E, Astell AJ, Babiloni C, Bahar-Fuchs A, Bell J, Bowman GL, Brickman AM, Ch\u0026eacute;telat G, Ciro C, Cohen AD, Dilworth-Anderson P, Dodge HH, Dreux S, Edland S, Esbensen A, Evered L, Ewers M, Fargo KN, Fortea J, Gonzalez H, Gustafson DR, Head E, Hendrix JA, Hofer SM, Johnson LA, Jutten R, Kilborn K, Lanct\u0026ocirc;t KL, Manly JJ, Martins RN, Mielke MM, Morris MC, Murray ME, Oh ES, Parra MA, Rissman RA, Roe CM, Santos OA, Scarmeas N, Schneider LS, Schupf N, Sikkes S, Snyder HM, Sohrabi HR, Stern Y, Strydom A, Tang Y, Terrera GM, Teunissen C, van Melo D, Weinborn M, Wesselman L, Wilcock DM, Zetterberg H (2019) O'Bryant SE; International Society to Advance Alzheimer's Research and Treatment, Alzheimer's Association. Perspectives on ethnic and racial disparities in Alzheimer's disease and related dementias: Update and areas of immediate need. Alzheimers Dement 15(2):292\u0026ndash;312\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdkins-Jackson PB, George KM, Besser LM, Hyun J, Lamar M, Hill-Jarrett TG, Bubu OM, Flatt JD, Heyn PC, Cicero EC, Zarina Kraal A, Pushpalata Zanwar P, Peterson R, Kim B, Turner RW 2nd, Viswanathan J, Kulick ER, Zuelsdorff M, Stites SD, Arce Renter\u0026iacute;a M, Tsoy E, Seblova D, Ng TKS, Manly JJ, Babulal G (2023) The structural and social determinants of Alzheimer's disease related dementias. Alzheimers Dement 19(7):3171\u0026ndash;3185\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowell JC, Parker MW, Watts KD, Kollhoff A, Tsvetkova DZ, Hu WT (2016) Research Lumbar Punctures among African Americans and Caucasians: Perception Predicts Experience. Front Aging Neurosci 8:296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis A, Gupta A, Oh I et al (2023) Association Between Socioeconomic Factors, Race, and Use of a Specialty Memory Clinic. Neurology 101(14):e1424\u0026ndash;e33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManly JJ, Jones RN, Langa KM et al (2022) Estimating the Prevalence of Dementia and Mild Cognitive Impairment in the US: The 2016 Health and Retirement Study Harmonized Cognitive Assessment Protocol Project. JAMA Neurol 79(12):1242\u0026ndash;1249\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePower MC, Bennett EE, Turner RW et al (2021) Trends in Relative Incidence and Prevalence of Dementia Across Non-Hispanic Black and White Individuals in the United States, 2000\u0026ndash;2016. JAMA Neurol 78(3):275\u0026ndash;284\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKornblith E, Bahorik A, Boscardin WJ et al (2022) Association of Race and Ethnicity With Incidence of Dementia Among Older Adults. JAMA 327(15):1488\u0026ndash;1495\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkins CH, Windon CC, Dilworth-Anderson P et al (2022) Racial and Ethnic Differences in Amyloid PET Positivity in Individuals With Mild Cognitive Impairment or Dementia: A Secondary Analysis of the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS) Cohort Study. JAMA Neurol 79(11):1139\u0026ndash;1147\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeters KD, Napolioni V, Sperling RA et al (2021) Amyloid PET Imaging in Self-Identified Non-Hispanic Black Participants of the Anti-Amyloid in Asymptomatic Alzheimer's Disease (A4) Study. Neurology 96(11):e1491\u0026ndash;e500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris JC, Schindler SE, McCue LM et al (2019) Assessment of Racial Disparities in Biomarkers for Alzheimer Disease. JAMA Neurol 76(3):264\u0026ndash;273\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowell JC, Watts KD, Parker MW et al (2017) Race modifies the relationship between cognition and Alzheimer's disease cerebrospinal fluid biomarkers. Alzheimers Res Ther 9(1):88\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarrett SL, McDaniel D, Obideen M et al (2019) Racial Disparity in Cerebrospinal Fluid Amyloid and Tau Biomarkers and Associated Cutoffs for Mild Cognitive Impairment. JAMA Netw Open 2(12):e1917363\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong C, Luo J, Schindler SE et al (2022) Racial differences in longitudinal Alzheimer's disease biomarkers among cognitively normal adults. Alzheimers Dement 18(12):2570\u0026ndash;2581\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnes LL (2022) Alzheimer disease in African American individuals: increased incidence or not enough data? Nat Rev Neurol 18:56\u0026ndash;62\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGottesman RF, Schneider AL, Zhou Y et al (2016) The ARIC-PET amyloid imaging study: Brain amyloid differences by age, race, sex, and APOE. Neurology 87(5):473\u0026ndash;480\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchindler SE, Karikari TK, Ashton NJ et al (2022) Effect of Race on Prediction of Brain Amyloidosis by Plasma Abeta42/Abeta40, Phosphorylated Tau, and Neurofilament Light. Neurology. Apr 21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMielke MM, Dage JL, Frank RD et al (2022) Performance of plasma phosphorylated tau 181 and 217 in the community. Nat Med. May 26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchindler SE, Karikari TK (2022) Comorbidities confound Alzheimer's blood tests. Nat Med 28(7):1349\u0026ndash;1351\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSyrjanen JA, Campbell MR, Algeciras-Schimnich A et al (2021) Associations of amyloid and neurodegeneration plasma biomarkers with comorbidities. Alzheimer's \u0026amp; dementia: the journal of the Alzheimer's Association. Sep 27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanelidze S, Barthelemy NR, He Y, Bateman RJ, Hansson O (2023) Mitigating the Associations of Kidney Dysfunction With Blood Biomarkers of Alzheimer Disease by Using Phosphorylated Tau to Total Tau Ratios. JAMA Neurol 80(5):516\u0026ndash;522\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrickman AM, Manly JJ, Honig LS et al (2021) Plasma p-tau181, p-tau217, and other blood-based Alzheimer's disease biomarkers in a multi-ethnic, community study. Alzheimer's Dement J Alzheimer's Assoc 17(8):1353\u0026ndash;1364\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWindon C, Iaccarino L, Mundada N et al (2022) Comparison of plasma and CSF biomarkers across ethnoracial groups in the ADNI. Alzheimer's Dement 14(1):e12315\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamanan VK, Graff-Radford J, Syrjanen J et al (2023) Association of Plasma Biomarkers of Alzheimer Disease With Cognition and Medical Comorbidities in a Biracial Cohort. Neurology 101(14):e1402\u0026ndash;e11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHajjar I, Yang Z, Okafor M et al (2022) Association of Plasma and Cerebrospinal Fluid Alzheimer Disease Biomarkers With Race and the Role of Genetic Ancestry, Vascular Comorbidities, and Neighborhood Factors. JAMA Netw Open 5(10):e2235068. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2022.35068\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2022.35068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonane M, Johnson KG, Snider BJ et al (2023) A blood biomarker test for brain amyloid impacts the clinical evaluation of cognitive impairment. Ann Clin Transl Neurol 10(10):1738\u0026ndash;1748\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRissman RA, Langford O, Raman R et al (2023) Plasma Abeta42/Abeta40 and phospho-tau217 concentration ratios increase the accuracy of amyloid PET classification in preclinical Alzheimer's disease. Alzheimer's Dement J Alzheimer's Assoc. Nov 6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolk DA, Mancuso L, Kliot D, Arnold SE, Dickerson BC Familiarity-based memory as an early cognitive marker of preclinical and prodromal AD\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris JC, Weintraub S, Chui HC et al (2006 Oct-Dec) Alzheimer Dis Assoc Disord 20(4):210\u0026ndash;216The Uniform Data Set (UDS): clinical and cognitive variables and descriptive data from Alzheimer Disease Centers\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeintraub S, Salmon D, Mercaldo N et al (2009 Apr-Jun) The Alzheimer's Disease Centers' Uniform Data Set (UDS): the neuropsychologic test battery. Alzheimer Dis Assoc Disord 23(2):91\u0026ndash;101\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris JC (1993) The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43(11):2412\u0026ndash;2414\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKhann GM, Knopman DS, Chertkow H et al (2011) The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's Dement J Alzheimer's Assoc 7(3):263\u0026ndash;269\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonsell SE, Dodge HH, Zhou XH et al Results From the NACC Uniform Data Set Neuropsychological Battery Crosswalk Study. Alzheimer Dis Assoc Disord 2016 Apr-Jun ;30(2):134\u0026ndash;139\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo J, Agboola F, Grant E et al (2020) Sequence of Alzheimer disease biomarker changes in cognitively normal adults: A cross-sectional study. Neurology 95(23):e3104\u0026ndash;e16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaw LM, Vanderstichele H, Knapik-Czajka M et al (2009) Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects. Ann Neurol 65(4):403\u0026ndash;413\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Kirmess KM, Meyer MR et al (2022) Assessment of a Plasma Amyloid Probability Score to Estimate Amyloid Positron Emission Tomography Findings Among Adults With Cognitive Impairment. JAMA Netw Open 5(4):e228392\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlcolea D, Pegueroles J, Mu\u0026ntilde;oz L et al (2019) Agreement of amyloid PET and CSF biomarkers for Alzheimer's disease on Lumipulse. Ann Clin Transl Neurol 6(9):1815\u0026ndash;1824\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarth\u0026eacute;lemy NR, Saef B, Li Y et al (2023) CSF tau phosphorylation occupancies at T217 and T205 represent improved biomarkers of amyloid and tau pathology in Alzheimer\u0026rsquo;s disease. Nat Aging. 2023/03/13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenson RL, Volluz K, Saef BA et al (2022) A methodology for normalizing fluid biomarker concentrations across reagent lots. Alzheimer's Dement 18(S5):e066912\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu Y, D'Angelo GM, Vlassenko AG et al (2013) Quantitative analysis of PiB-PET with FreeSurfer ROIs. PLoS ONE 8(11):e73377\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRousset OG, Ma Y, Evans AC (1998) Correction for partial volume effects in PET: principle and validation. J Nucl Med 39(5):904\u0026ndash;911\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu Y, Flores S, Hornbeck RC et al (2018) Utilizing the Centiloid scale in cross-sectional and longitudinal PiB PET studies. Neuroimage Clin 19:406\u0026ndash;416\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlunk WE, Koeppe RA, Price JC et al (2015) The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimer's Dement J Alzheimer's Assoc 11(1):1\u0026ndash;15e1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraybill FA (1976) Theory and Application of the Linear Model. Duxbury, North Scituate, Ma\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. J R Stat Soc B 57(1):289\u0026ndash;300\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiggle PJ, Heagerty P, Liang K-Y, Zeger SL (2002) Analysis of Longitudinal Data, 2nd edn. Oxford University Press, New York\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaird NM, Ware JH (1982) Random-effects models for longitudinal data. Biometrics 38:963\u0026ndash;974\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonomi S, Lu R, Schindler SE, Bui Q, Lah JJ, Wolk D, Gleason CE, Sperling R, Roberson ED, Levey AI, Shaw L, Van Hulle C, Benzinger T, Adams M, Manzanares C, Qiu D, Hassenstab J, Moulder KL, Balls-Berry JE, Johnson K, Johnson SC, Murchison C, Luo J, Gremminger E, Agboola F, Grant EA, Hornbeck R, Massoumzadeh P, Keefe S, Dierker D, Gray JD, Henson RL, Streitz M, Mechanic-Hamilton D, Morris JC, Xiong C Relationships of Cognitive Measures with Cerebrospinal Fluid but not Imaging Biomarkers of Alzheimer Disease Vary between Black and White Individuals. Ann Neurol 2023; in press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall JR, Petersen M, Johnson L, O'Bryant SE (2022) Characterizing Plasma Biomarkers of Alzheimer's in a Diverse Community-Based Cohort: A Cross-Sectional Study of the HAB-HD Cohort. Front Neurol 13:871947. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2022.871947\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2022.871947\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 36062019; PMCID: PMC9435735\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Hulle CA, Zuelsdorff M, Koscik RL, Ennis GE, Bouges S, Fischer BL, Wyman MF, Lambrou NH, Johnson AL, Umucu E, Salazar H, Chin NA, Meyer MR, Holubasch MS, Kirmess K, Verghese PB, West T, Venkatesh V, Yarasheski KE, Gleason CE (2022) Trajectories of plasma Aβ42/40 among African Americans: Preliminary results from the African American Fighting Alzheimer\u0026rsquo;s in Midlife (AA-FAIM) study. Alzheimer's Dement 18:e066942. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/alz.066942\u003c/span\u003e\u003cspan address=\"10.1002/alz.066942\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkins CH, Schindler SE, Morris JC (2020) Addressing Health Disparities Among Minority Populations: Why Clinical Trial Recruitment Is Not Enough. JAMA Neurol 77(9):1063\u0026ndash;1064\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkins CH, Schindler SE, Morris JC Fuller, J. T., \u0026hellip; Bettcher, B. M. (2023). Representativeness of samples enrolled in Alzheimer's disease research centers. Alzheimer's \u0026amp; Dementia: Diagnosis, Assessment\u0026amp; Disease Monitoring, 15(2), e12450\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilmore-Bykovskyi, A., Croff, R., Glover, C. M., Jackson, J. D., Resendez, J., Perez,A., \u0026hellip; Manly, J. J. (2022). Traversing the aging research and health equity divide:Toward intersectional frameworks of research justice and participation. The Gerontologist,62(5), 711\u0026ndash;720\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3783571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3783571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e The use of blood-based biomarkers of Alzheimer disease (AD) may facilitate access to biomarker testing of groups that have been historically under-represented in research. We evaluated whether plasma Aβ42/40 has similar or different baseline levels and longitudinal rates of change in participants racialized as Black or White.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe Study of Race to Understand Alzheimer Biomarkers (SORTOUT-AB) is a multi-center longitudinal study to evaluate for potential differences in AD biomarkers between individuals racialized as Black or White. Plasma samples collected at three AD Research Centers (Washington University, University of Pennsylvania, and University of Alabama-Birmingham) underwent analysis with C\u003csub\u003e2\u003c/sub\u003eN Diagnostics’ PrecivityAD™ blood test for Aβ42 and Aβ40\u003ca href=\"\" target=\"_blank\"\u003e.\u003c/a\u003e General linear mixed effects models were used to estimate the baseline levels and rates of longitudinal change for plasma Aβ measures in both racial groups. Analyses also examined whether dementia status, age, sex, education, \u003cem\u003eAPOE\u003c/em\u003e \u003cem\u003eε\u003c/em\u003e4 carrier status, medical comorbidities, or fasting status modified potential racial differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e \u0026nbsp;Of the 324 Black and 1,547 White participants, there were 158 Black and 759 White participants with plasma Aβ measures from at least two longitudinal samples over a mean interval of 6.62 years. At baseline, the group of Black participants had lower levels of plasma Aβ40 but similar levels of plasma Aβ42 as compared to the group of White participants. As a result, baseline plasma Aβ42/40 levels were higher in the Black group than the White group, consistent with the Black group having lower levels of amyloid pathology. Racial differences in plasma Aβ42/40 were not modified by age, sex, education, \u003cem\u003eAPOE\u003c/em\u003e \u003cem\u003eε\u003c/em\u003e4 carrier status, medical conditions (hypertension and diabetes), or fasting status. Despite differences in baseline levels, the Black and White groups had a similar longitudinal rate of change in plasma Aβ42/40.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation: \u003c/strong\u003eBlack individuals participating in AD research studies had a higher mean level of plasma Aβ42/40, consistent with a lower level of amyloid pathology, which, if confirmed, may imply a lower proportion of Black individuals being eligible for AD clinical trials in which the presence of amyloid is a prerequisite. However, there was no significant racial difference in the rate of change in plasma Aβ42/40, suggesting that amyloid pathology accumulates similarly across racialized groups.\u003c/p\u003e","manuscriptTitle":"Baseline levels and longitudinal rates of change in plasma Aβ42/40\namong self-identified Black/African American and White individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 17:34:23","doi":"10.21203/rs.3.rs-3783571/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d0d230ba-079b-43d7-a50f-07e79899f302","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":27980246,"name":"Biological sciences/Neuroscience/Cognitive ageing"},{"id":27980247,"name":"Biological sciences/Neuroscience/Cognitive neuroscience"}],"tags":[],"updatedAt":"2024-06-21T11:35:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 17:34:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3783571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3783571","identity":"rs-3783571","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}