Lucas, John A.

Abstract INTRODUCTION We describe the reliability of remote self‐administered digital cognitive measures completed via the Mayo Test Drive (MTD) web‐based platform. METHODS A total of 1846 participants (mean age = 70, SD = 12, range 31–101; 48% male; 96% White; 99% non‐Hispanic; 97% cognitively unimpaired) with two to four complete MTD sessions at ∼7.5‐month intervals were included. Test‐retest reliability was assessed using single‐rating, absolute‐agreement, and two‐way mixed intraclass correlation coefficients (ICCs) with 95% confidence intervals. ICCs for in‐person‐administered traditional neuropsychological measures were compared to MTD for a subset of 244 participants. Analyses also compared reliability across demographic, residential, and session context subgroups. RESULTS Reliability was good for the MTD Composite (total ICC = 0.79 [0.77, 0.80]), and moderate‐to‐good for the primary outcome variables for each MTD subtest (total ICCs 0.70–0.83 for Stricker Learning Span and Symbols). The reliability of the remote self‐administered MTD was similar to in‐person‐administered cognitive measures. DISCUSSION MTD showed moderate‐to‐good reliability, supporting its use in longitudinal monitoring.

Abstract INTRODUCTION African American (AA) individuals are underrepresented in biomarker studies for Alzheimer's disease (AD). Biomarkers that reflect the heterogeneity of AD and achieve the greatest accuracy across populations are sorely needed. METHODS Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in AA participants with clinical diagnoses of AD dementia ( n = 181) and cognitively unimpaired (CU, n = 142). Machine learning was used to identify a set of plasma proteins that yielded the best classification accuracy. RESULTS A set of 36 proteins achieved an area under the curve (AUC) of 0.94 to classify AD dementia versus CU, a 16% improvement over age, sex, and apolipoprotein E ( APOE) . This finding was replicated in multiple plasma and brain datasets (AUCs 0.73–0.97). Our findings underscore the importance of matrisome and cerebrovascular dysfunction in AD pathophysiology. DISCUSSION This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning. Highlights Conducted large‐scale plasma proteomics in Alzheimer's disease (AD) versus cognitively unimpaired controls. Machine learning biomarker discovery was replicated in an independent cohort. Novel set of proteins distinguishes AD versus controls with high accuracy (area under the curve [AUC] = 0.94). Achieved reproducibility across multiple replication cohorts (AUC = 0.73–0.97). Network analyses implicates matrisome biology and cerebrovascular dysfunction.

AbstractBackgroundMayo Test DRIVE (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based multi‐device (smartphone, tablet, personal computer) platform optimized for remote self‐administered cognitive assessment that includes a computer‐adaptive word list memory test (Stricker Learning Span; SLS), a measure of processing speed/executive functioning (Symbols Test) and a score combining both the memory and processing speed/executive functioning measures (MTD Composite). We aimed to determine the reliability of MTD measures across follow‐up sessions.MethodIndividuals participating in this ancillary study who completed at least two and up to four complete MTD sessions were included, resulting in 1,293 participants (mean age = 70.6, SD = 11.6; mean education = 15.6, SD = 2.3; 48.6% male; 96.9% White; 99.1% non‐Hispanic; 97.1% cognitively unimpaired) from parent studies (see Table 1). MTD raw scores were winsorized at 1% and 99% to minimize the impact of outliers. Test‐retest reliability was assessed using single‐rating, absolute‐agreement, and two‐way mixed ICCs and results are described using descriptives from Koo et al. (2016). ICCs for in‐person‐administered traditional neuropsychological measures are also reported alongside MTD for a subset of participants newly enrolled into the MCSA (to ensure participants were test naïve to both MTD and in‐person tests).ResultReliability was good for the MTD Composite raw [total ICC = 0.78; see Table 2 for session‐to‐session ICCs and 95% CIs], and moderate‐to‐good for the primary outcome variables for each subtest [total ICC = 0.74 for both SLS and Symbols]. Weighting by accuracy negatively impacted reliability of Symbols and the Composite; without this weighting, MTD Composite and Symbols variables showed higher reliability (MTD Composite z total ICC = 0.80; Symbols response time total ICC = 0.84; see Table 2). Alternative methods of defining response time on Symbols showed nearly identical reliability values. SLS sum of trials (primary variable) had subtly higher reliability than separate measures of learning (1‐5 correct, max span) or delay. Reliability of remote MTD and in‐person‐administered measures and select comparisons are provided in Table 3.ConclusionMTD showed moderate to good reliability, similar to in‐person‐administered traditional neuropsychological tests. Future work will examine the effect of demographics, device type, and session interference on reliability.

AbstractBackgroundCharacterizing stress can inform our understanding of direct and indirect pathways impacting mental and physical health outcomes, including age‐related cognitive decline. Chronic stress has been strongly associated with increased risk for cognitive decline, and hair cortisol concentration (HCC) has emerged as a viable neuroendocrine biomarker for its quantification. Cognitive appraisal of stress (CAS) provides key insight into the cognitive framework for responding to a given stressor. We implemented a multi‐pronged approach to evaluate chronic stress and examined its relation to cognition in older adults.MethodParticipants were 22 cognitively normal individuals (x̄ MMSE = 29.33, 0.92; x̄ age: 67.3 ± 8.8 years old, 17.0 ± 2.9 years of education, 54.5% female, 92.6% Non‐Hispanic White). Participants completed the Stress and Adversity Inventory (STRAIN) assessing lifetime stressor exposure and the Primary and Secondary Cognitive Appraisal Scale (PSCAS). HCC was used as a neuroendocrine biomarker of chronic stress. A brief cognitive battery assessing episodic memory and executive functions (Digit Span, HVLT‐R, Trail Making Test, Letter Fluency) and mood/anxiety measures were completed. To correct for skewness, log‐transformed HCC values were used for Pearson and partial correlation analyses controlling for age and education.ResultLifetime stressor exposure and CAS were not significantly related to cognitive performance (all ps > 0.05). There was a marginally significant association between HCC and executive functions (r = ‐0.41, p = 0.08). Total lifetime stressor count, chronicity, and severity were unrelated to symptoms of depression and anxiety (all ps > 0.05). Although lifetime stressor exposure and mood were not significantly related to HCC, higher HCC was associated with higher perceived control of stress (r = 0.45, p = 0.03).ConclusionHCC may be more sensitive to CAS rather than to objective stressor exposure. This highlights the potential of interventions targeted at improving coping in those with chronic stress. Results are preliminary, and the present study will explore impact of social and structural determinants of health on chronic stressor exposure, appraisal, and cognition.

AbstractBackgroundPlasma biomarker development is of increasing interest due to improved accessibility compared to established diagnostic modalities for Alzheimer’s disease (AD). However, current plasma biomarker candidates have not been tested comprehensively in diverse populations, and these markers represent only a fraction of pathways perturbed in AD. Therefore, there is a need to identify novel plasma biomarkers that account for both disease heterogeneity and patient diversity. This study seeks to address these knowledge gaps through untargeted plasma biomarker discovery in African Americans (AA), an underrepresented group in AD research despite having a higher risk of developing AD compared to non‐Hispanic Whites.MethodsUntargeted proteomics were performed using the SomaScan 7k assay on plasma samples from AA study participants clinically diagnosed as AD (n=142) or cognitively unimpaired (CU, n=144). Linear regression models adjusted for age, sex, and batch were implemented to identify differentially expressed proteins (DEPs). APOE was additionally adjusted for in a sensitivity analysis. DEPs with the lowest q‐values were selected as predictors in a receiver operating characteristic (ROC) analysis to assess their accuracy to discriminate AD versus CU individuals.ResultsA total of 153 DEPs were identified (q<0.05) when adjusting for age, sex, and batch, and 100 DEPs were identified when additionally adjusting for APOE. Of the 66 proteins overlapping between these models, SERPINA3, C7, IGFBP‐2, DKK3, SMOC1, and ACES had the lowest q‐values and were included in a ROC model adjusted for age, sex, and APOE. That model classified AD versus CU with an area under the curve (AUC) of 0.87 (95%CI: 0.83‐0.91), a 10.4% improvement over the base model including age, sex, and APOE (AUC=0.78; 95%CI: 0.72‐0.83). Despite the unbiased selection of proteins included in the ROC model, all selected proteins have been associated with AD and implicated in related functional pathways such as neuroinflammation, synaptic function, and insulin signaling.ConclusionThis preliminary plasma biomarker panel demonstrates the potential diagnostic capabilities of biomarker discovery through untargeted proteomics. Future directions include validation of findings in external datasets, concomitant assessment of p‐tau217, and evaluation of machine learning models to define the optimal diagnostic and predictive set of plasma protein biomarkers for AD in AA.

AbstractBackgroundFew normative data for computerized measures administered in unsupervised remote environments are available. We aimed to determine what variables to include in normative models for remote self‐administered assessments, develop normative data for measures administered through Mayo Test Drive (MTD, a multi‐device remote cognitive assessment platform) and evaluate application of norms.Method1240 adults ages 33‐100 (96% White) from the Mayo Clinic Study of Aging and Mayo Alzheimer’s Disease Research Center met normative sample inclusion criteria that included a concordant Cognitively Unimpaired (CU) diagnosis (3 independent raters all diagnosed CU) and CDR = 0 (see Table 1 for sample characteristics). Raw test scores were converted to normalized scaled scores. We derived regression‐based normative data that adjusted for age, age2, sex and education (base model); alternative models are also provided (age+age2+sex; age+ age2). We evaluated whether additional model terms beyond the base model were needed using an a priori cut‐off of 1% variance improvement in adjusted R2 over and above the base model. We examined rates of low test performance defined as performance below ‐1 SD in independent validation samples (n = 167 concordant CU, n = 64 mild cognitive impairment (MCI), n = 14 dementia). Rates are considered significantly different than expected when 95% confidence intervals (CIs) do not include the expected 14.7% base rate frequency.ResultNo additional potential model terms met our a priori cut‐off for inclusion beyond the base model (see Table 2), including other quadratic and cubic terms or interactions, device type, response input type, potential session interference, and national area deprivation index (ADI available in subset; N = 776). Application of norms showed expected rates of low test performance among CU participants (95% CI includes expected base rate) and significantly greater than expected rates of low test performance for all variables in MCI (e.g., 62.5% for composite) and dementia (e.g., 71.4% for composite; see Table 3).ConclusionTypical normative models appear appropriate for remote self‐administered measures and are sensitive to cognitive impairment. Device type and response input did not explain enough variance to warrant inclusion in normative models but are important for individual level interpretation. Future work will improve representation of individuals from under‐represented groups.

AbstractBackgroundFew normative data for computerized measures administered in unsupervised remote environments are available. We aimed to determine what variables to include in normative models for remote self‐administered assessments, develop normative data for measures administered through Mayo Test Drive (MTD, a multi‐device remote cognitive assessment platform) and evaluate application of norms.Method1240 adults ages 33‐100 (96% White) from the Mayo Clinic Study of Aging and Mayo Alzheimer’s Disease Research Center met normative sample inclusion criteria that included a concordant Cognitively Unimpaired (CU) diagnosis (3 independent raters all diagnosed CU) and CDR = 0 (see Table 1 for sample characteristics). Raw test scores were converted to normalized scaled scores. We derived regression‐based normative data that adjusted for age, age2, sex and education (base model); alternative models are also provided (age+age2+sex; age+ age2). We evaluated whether additional model terms beyond the base model were needed using an a priori cut‐off of 1% variance improvement in adjusted R2 over and above the base model. We examined rates of low test performance defined as performance below ‐1 SD in independent validation samples (n = 167 concordant CU, n = 64 mild cognitive impairment (MCI), n = 14 dementia). Rates are considered significantly different than expected when 95% confidence intervals (CIs) do not include the expected 14.7% base rate frequency.ResultNo additional potential model terms met our a priori cut‐off for inclusion beyond the base model (see Table 2), including other quadratic and cubic terms or interactions, device type, response input type, potential session interference, and national area deprivation index (ADI available in subset; N = 776). Application of norms showed expected rates of low test performance among CU participants (95% CI includes expected base rate) and significantly greater than expected rates of low test performance for all variables in MCI (e.g., 62.5% for composite) and dementia (e.g., 71.4% for composite; see Table 3).ConclusionTypical normative models appear appropriate for remote self‐administered measures and are sensitive to cognitive impairment. Device type and response input did not explain enough variance to warrant inclusion in normative models but are important for individual level interpretation. Future work will improve representation of individuals from under‐represented groups.

AbstractBackgroundAfrican Americans experience a high dementia burden yet are critically underrepresented in observational studies and clinical trials in memory and aging. Lack of adequate representation increases the likelihood that research findings may not generalize to African Americans. A deeper understanding of the barriers and facilitators that influence participation by younger and older African American adults is needed to support recruitment and retention in studies focused on early‐ and late‐onset neurodegenerative diseases.MethodAfrican American community leaders collaborated in design of an electronic survey that was distributed via social media focused on local zip codes. Survey respondents rated barriers and facilitators to research participation on a 0–3‐point Likert scale. Responses ≥2 corresponded with barriers/facilitators likely to influence participation decisions. Differences across age groups were evaluated using nonparametric Kruskal‐Wallis tests. Strategies to mitigate barriers and reinforce facilitators were further explored via community‐based focus groups.ResultSurvey data included responses from 240 African Americans (18‐44‐years, n = 76; 45‐64‐years, n = 83; ≥65‐years, n = 81). Requirement to undergo a diagnostic lumbar puncture was a substantial barrier to research participation across all age groups. Attending weekday visits (median = 2.0), including visits ≥2 hours (median 2.0), was a substantial barrier for individuals 18‐44‐years, but not older respondents (p = 0.001). Compensation for meals/transportation was beneficial across all age groups (median = 2.0; p<0.05). The option to attend visits in the local community or on weekends and access to door‐to‐door transportation were substantial facilitators for respondents <65‐years (median = 2.0) vs ≥65‐years (median = 0; p = 0.001). The option to complete some tests at home was a substantial facilitator for younger (18‐44‐years, median = 2.0) but not older respondents (≥65‐years, median = 0; p = 0.001). Focus group results supported survey findings and showed slight variations in priorities across age groups. Older participants (≥65‐years‐old) more frequently cited the longstanding effects of racism as a barrier to research participation compared to younger participants (45‐64‐years‐old; p = 0.008), who more frequently cited concerns regarding testing requirements (p = 0.034).ConclusionPerceived barriers and facilitators to research participation vary with age. Age‐specific strategies are needed to increase engagement, address disparities in recruitment, and promote retention of African American participants in studies of memory and aging across the lifespan.

AbstractBackgroundAfrican Americans (AA) represent 12% of the United States population ≥65 years‐old, but 19% of persons with dementia. Inequalities in social determinants of health may influence access to healthcare resources contributing to increased dementia risk. Modifiable risk factors, including vascular risk factors and depression, may contribute up to 40% of dementia risk and are overrepresented in AA. Accordingly, risk factor modification in AA may improve disparities and decrease dementia risk.MethodDementia risk factors were evaluated in community‐dwelling AA (n = 261) and non‐Hispanic White (NHW; n = 193) participants who completed ≥2 research visits in studies of memory and aging at the Mayo Clinic Florida Alzheimer Disease Research Center. The association between modifiable risk factors and cognitive impairment (global Clinical Dementia Rating® [CDR] ≥0.5), and rates of decline in impaired participants (measured using the CDR Sum‐of‐Boxes; CDR‐sb) were compared between AA and NHW, while controlling for demographics, APOEe4 status, and Area Deprivation Index.ResultModifiable dementia risk factors were common in AA and NHW participants. The ADI was higher in AA denoting greater socioeconomic and environmental deprivation [7.8 (2.3) vs. 4.0 (2.1); p<0.001]. History of stroke/transient ischemic attack (OR, CI95% = 2.52, 1.08‐5.83) was associated with an increased odds of dementia in AA participants only, while depression was associated with an increased odds of dementia in both AA (3.10, 1.52‐6.30) and NHW (3.46, 1.14‐10.48) participants. Moreover, depression (β, CI95% = 1.55, 0.5‐2.6), p = 0.004) was associated with a faster rate of decline (CDR‐sb) in cognitively impaired AA participants. AA participants with depression were disproportionately less likely to report use of antidepressant medications than NHW counterparts (40/45, 89% vs. 21/44, 48%); p<0.001).ConclusionModifiable risk factors associated with dementia were common in AA and NHW participants. Depression was associated with dementia in both AA and NHW but uniquely associated with an accelerated rate of cognitive decline in AA participants. Disparities in use of antidepressant medications may contribute to these differences. Optimizing depression treatment in AA communities may improve cognitive trajectories and dementia risk in this population.