Stricker, Nikki H.

Abstract Disclosure: J. Saini: None. R. Nathani: None. C. Zhang: None. M. Suresh: None. K. Thangamuthu: None. S. Singh: None. V. Fell: None. E.J. Atkinson: None. S. Achenbach: None. N.H. Stricker: None. I. Bancos: None. Background: Patients with mild autonomous cortisol secretion (MACS) have impaired cognition compared to patients with non-functioning adrenal adenomas. However, the impact of adrenalectomy on cognitive function in patients with MACS is unclear. Objective: To determine the impact of adrenalectomy on cognitive function in patients with MACS. Methods: Single-center prospective longitudinal study of adults with MACS. MACS was defined as serum cortisol >1.8 mcg/dL after the 1 mg dexamethasone suppression test (DST) in patients with adrenal adenomas without any overt features of hypercortisolism. Patients underwent cognitive evaluation using the standardized National Institute of Health (NIH) Toolbox Cognition Battery (7 tests of attention, episodic and working memory, language, executive function, cognitive flexibility, and processing speed) prior to adrenalectomy and at follow-up at least 10 months post-surgery. Fully corrected T-scores for age, sex, education, and race were compared using a paired t-test before and after adrenalectomy. A T-score of 50 is average performance (SD of 10). Total cognition is composite of fluid cognition (executive function, episodic memory, cognitive flexibility, working memory and processing speed) and crystallized cognition (language and vocabulary). Results: A total of 27 patients with MACS (median age 59.7 years, IQR 51.4-66.9, 17, 63% women) underwent cognitive evaluation prior to and at a median of 1.5 years (IQR, 1.1- 2.0) after adrenalectomy. Before surgery, median post-DST cortisol was 3.8 mcg/dL (IQR, 2.7-6.4) mcg/dL. Of the 27 patients, 21 (78%) patients developed post-operative adrenal insufficiency with 6 (21%) patients still requiring still requiring glucocorticoid replacement at the time of the last follow-up. At baseline, the mean total composite T-score was 50.7 (9.3), with fluid composite T-score of 49.9 (9.4) and crystallized composite T-score of 51.3 (8.7). Following adrenalectomy, all three composite T-scores improved compared to baseline. The crystallized composite T-score (composed of language and vocabulary) increased by a mean of 3.7 (5.5), P=0.001, the fluid composite T-score (composed of episodic, executive, working memory, cognitive flexibility, and processing speed) increased by a mean of 3.1 (8.3), P=0.068, and the total composite T-score improved by a mean of 3.9 (6.2), P=0.004. Conclusion: We showed that adrenalectomy in patients with MACS is associated with improvement in cognition. However, as test results may improve with practice (i.e., improvement due to taking the test the second time), to determine whether the improvements observed represent a reliable change in cognition, the next step is to evaluate for cognition score changes in referent subjects. Presentation: Friday, June 16, 2023

Abstract Disclosure: J. Saini: None. R. Nathani: None. S. Singh: None. K. Thangamuthu: None. M. Suresh: None. C. Zhang: None. A.L. Ebbehøj: None. M. Thomas: None. V. Fell: None. E. Atkinson: None. S. Achenbach: None. N. Stricker: None. I. Bancos: None. Introduction: While patients with overt Cushing syndrome are reported to have impaired cognition, the impact of milder, long-standing hypercortisolism on cognition is incompletely characterized. Objective: To determine the impact of MACS on cognition as compared to age, sex, and education-matched referent subjects. Methods: We conducted a single-center cross-sectional study of adults with MACS and referent subjects. MACS was defined as serum cortisol>1.8 mcg/dL after 1 mg dexamethasone suppression test (DST) in patients with adrenal incidentalomas. Referent subjects were included if they had abdominal imaging and had no adrenal disorders. Exclusion criteria were malignancy and exogenous glucocorticoid use within 1 year. All participants underwent a cognitive evaluation using the NIH Toolbox Cognition Battery between 1/2019 to 7/2022. NIH Toolbox assesses 8 cognitive domains using 7 tests and yields 3 composite scores: fluid cognition (executive function, episodic memory, cognitive flexibility, working memory, and processing speed), crystallized cognition (language and vocabulary), and total composite cognition (composite of all). Results are given in fully corrected T scores (adjusted for age, sex, race, and education) with an average of 50 and a standard deviation (SD) of 10. A subgroup analysis was performed based on DST cortisol. The Kruskal-Wallis test was used to compare scores between groups. Results: A total of 94 patients with MACS (median age 57.2 years, IQR 47.8-66.5, 70% women) and 94 referent subjects (median age 58 years, IQR 49.1-66.4, 70% women) were included. The median post-DST cortisol in patients with MACS was 3.3 mcg/dL (IQR, 2.4-6.2). The mean (SD) total composite T-score was lower in patients with MACS vs referent subjects (51 (9.1) vs 56.3 (9.3), P<0.001). Patients with MACS demonstrated lower mean fluid composite T-score when compared to referent subjects (49.5 (10.4) vs 56.9 (9.7), P<0.001). However, mean crystallized composite T-scores were similar in patients with MACS and referent subjects (52.3 (8.1) vs 53.9 (9.6), P=0.342). A subgroup analysis of patients with post-DST cortisol <3 mcg/dL (n=40) vs patients with post-DST cortisol >3 mcg/dL (n=54) did not show any differences in the total, fluid, or crystalized composite scores. Conclusion: Patients with MACS demonstrate lower cognitive performances than a referent group suggesting MACS has a subtle impact on cognitive functioning. The pattern of cognitive impairment in patients with MACS is similar to that seen with normal cognitive aging: impaired fluid cognitions and unaffected crystallized cognition). Cognitive performance did not differ based on the degree of MACS suggesting that in the absence of overt features of hypercortisolism, the degree of MACS is less important than the presence of MACS. Presentation: Thursday, June 15, 2023

Abstract Objective Adrenal adenomas are commonly encountered in clinical practice. To date, population-based data on their impact on cognition, mental health, and sleep are lacking. We aimed to study possible associations between adrenal adenomas and dementia, psychiatric or sleep disorders. Design Population-based cohort study, Olmsted County, MN, 1995-2017. Methods Patients with adrenal adenoma and absent overt hormone excess were age- and sex-matched 1:1 to a referent person without adrenal adenoma. Outcomes were baseline and incident diagnoses of dementia, psychiatric or sleep disorders, assessed using ICD codes. Results Of 1004 patients with adrenal adenomas, 582 (58%) were women, and median age at diagnosis was 63 years. At baseline, and after adjusting for age, sex, education, BMI, and tobacco use, patients with adenoma had higher odds of depression (adjusted odds ratio, aOR: 1.3, 95% CI, 1.1-1.6), anxiety (aOR: 1.4, 95% CI, 1.1-1.8), and substance abuse (aOR: 2.4, 95% CI, 1.7-3.4) compared to referents. During a median follow-up of 6.8 years, and after adjusting for age, sex, socioeconomic status, BMI, tobacco, and substance abuse, patients demonstrated a higher risk of psychiatric and sleep disorders [adjusted hazard ratio (95% CI)]: depression [1.7 (1.3-2.2)], anxiety [1.4, CI (1.1-1.7)], insomnia [1.4 (1.0-1.9)], sleep-related breathing disorders [1.5 (1.1-1.9)], hypersomnias [2.1 (1.0-4.2)], parasomnias [2.1 (1.0-4.2)], and sleep-related movement disorders [1.5 (1.0-2.1)], but not dementia. Conclusions Patients with adenomas demonstrate a higher incidence of psychiatric and sleep disorders, possibly due to the underlying subtle increase in cortisol secretion.

Importance The Clinical Dementia Rating (CDR) is a well-validated instrument widely used to detect and stage dementia due to Alzheimer disease. The digital Electronic Clinical Dementia Rating (eCDR) can be remotely self-administered and automatically scored, with potential to facilitate efficient dementia screening and staging. Objective To evaluate the association of the eCDR with the CDR and other in-clinic assessments for screening older adults for cognitive impairment. Design, Setting, and Participants This multisite, cross-sectional study used baseline data from a longitudinal, observational study from 2020 to 2023, including up to 3 years of follow-up. Participants were enrolled from 3 Alzheimer Disease Research Centers and the Brain Health Registry. Participants (aged ≥55 years, with a study partner, and no acute or unstable major medical conditions) were recruited during in-clinic visits or by automated emails. Exposures Participants completed the Uniform Data Set Version 3 (UDS; including the CDR) in supervised clinical research settings, and then completed the eCDR remotely, online and unsupervised, using their own device. Main Outcomes and Measures The primary outcomes were eCDR scores (item; categorical box and global; continuous box and global), CDR scores (item; categorical box and global), and UDS assessment scores. Associations were evaluated using linear and logistic regressions. Results A total of 3565 participants were contacted, and 288 were enrolled. Among 173 participants with item-level data (mean [SD] age, 70.84 [7.65] years; 76 women [43.9%]), eCDR to CDR concordance was 90% or higher for 33 items (63%) and 70% to 89% for 13 items (25%). Box (domain) level concordance ranged from 80% (memory) to 99% (personal care). The global score concordance rate was 81%. κ statistics were fair to moderate. Among 206 participants with box and global scores (mean [SD] age, 71.34 [7.68] years; 95 women [46.1%]), eCDR continuous global score was associated with CDR global (categorical) score with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.70-0.87). Correlations between eCDR and in-clinic UDS assessments were similar to those between CDR sum of box scores and the same in-clinic assessments. Conclusions and Relevance These findings suggest that the eCDR is valid and has potential use for screening and assessment of older adults for cognitive and functional decline related to Alzheimer disease. Instrument optimization and validation in diverse cohorts in remote settings are crucial for evaluating scalability and eCDR utility in clinical research, trials, and health care settings.

AbstractBackgroundPreclinical Alzheimer’s cognitive composites (PACC) are designed to detect subtle cognitive changes in clinical trials. PACCs often include measures of memory, attention, and semantic fluency, with variations based on available measures. We created a Mayo Clinic PACC (Mayo‐PACC) to address limitations of existing PACCs by prioritizing parsimony, non‐proprietary measures, clinical applicability, and psychometric properties. We developed a Mayo‐PACC and examined sensitivity to detecting longitudinal cognitive change in amyloid positive (A+) older adults relative to the Preclinical Alzheimer’s Cognitive Composite‐Revised (PACC‐R). Exploratory analyses included comparison to other composites.MethodParticipants included 614 individuals in the Mayo Clinic Study of Aging (428 A‐ and 186 A+) age 65‐85 who were cognitively unimpaired at baseline with 7 years mean follow‐up. A+ was defined as PiB‐PET meta‐ROI SUVR ≥ 1.48 (centiloid 22). The Mayo‐PACC included the AVLT sum of trials (trials 1‐5, short delay, long delay), Trails B and Animal fluency. Measures included in the PACC‐R and other comparison composites are listed in Table 1. We calculated study‐specific z‐scores and examined psychometric properties of component measures including test‐retest reliability, practice effects, and distributional properties (ceiling/floor effects, skew, kurtosis). We examined sensitivity to preclinical cognitive change using slope estimates (difference in annualized change across A+ and A‐ groups) from linear mixed models (LMMs) for each composite. A jackknife procedure was used to calculate the standard error and subsequent confidence intervals of differences in rates of change (A+>A‐ indicating more significant decline in A+). Significance was determined based on whether the 95% Confidence Interval (CI) contained 0 (alpha = .05).ResultAll composites showed sensitivity to amyloid‐related longitudinal cognitive decline (A+>A‐ annualized change p<.05; see Table 2). Jack‐knife comparisons revealed comparable utility of Mayo‐PACC and PACC‐R (CI contained 0, see Table 3). Exploratory analyses revealed Mayo‐PACC performed similarly to an adaptation of the ADCS‐PACC, Global‐z, Memory‐z and Attention‐z. The Mayo‐PACC performed better than a 2‐measure Mayo‐PACC‐remote and a single memory measure.ConclusionThe Mayo‐PACC provides a parsimonious composite score that has comparable utility for detecting A+>A‐ cognitive decline compared to the PACC‐R and other composites and can be widely used in both research and clinical settings.

Abstract Introduction We aimed to define a Mayo Preclinical Alzheimer's disease Cognitive Composite (Mayo‐PACC) that prioritizes parsimony and use of public domain measures to facilitate clinical translation. Methods Cognitively unimpaired participants aged 65 to 85 at baseline with amyloid PET imaging were included, yielding 428 amyloid negative (A‐) and 186 amyloid positive (A+) individuals with 7 years mean follow‐up. Sensitivity to amyloid‐related cognitive decline was examined using slope estimates derived from linear mixed models (difference in annualized change across A+ and A‐ groups). We compared differences in rates of change between Mayo‐PACC and other composites (A+ > A‐ indicating more significant decline in A+). Results All composites showed sensitivity to amyloid‐related longitudinal cognitive decline (A+ > A‐ annualized change p < 0.05). Comparisons revealed that Mayo‐PACC (AVLT sum of trials 1‐5+6+delay, Trails B, animal fluency) showed comparable longitudinal sensitivity to other composites. Discussion Mayo‐PACC performs similarly to other composites and can be directly translated to the clinic.

AbstractBackgroundMayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform optimized for remote self‐administered assessment that includes a computer adaptive word list memory test (Stricker Learning Span; SLS) and a measure of processing speed (Symbols Test). Study aims were to determine 1) convergent validity of MTD through correlations with in‐person neuropsychological measures and 2) criterion validity of MTD by qualitatively comparing the ability of Mayo Test Drive and in‐person administered neuropsychological measures to differentiate biomarker‐defined groups, independent of clinical diagnosis.MethodsMayo Clinic Study of Aging participants (N = 282) completed a brief remote cognitive assessment (mean age = 74, SD = 12; 262 CU and 20 MCI per consensus conference diagnosis; see Table 1). A subset of participants had brain amyloid and/or brain tau PET scans available within 3 years; overlapping groups were formed for 1) those on the Alzheimer’s disease (AD) continuum (A+, n = 31) or not (A‐, n = 57) and for 2) those with biological AD (A+T+, n = 19) or without any AD biomarkers (A‐T‐, n = 47). Primary outcome variables were SLS sum of trials, AVLT sum of trials, SYM Correct Trials response time, Digit Symbol Coding, MTD composite and Mayo‐PACC (AVLT sum of trials, Trails B, animals). Bivariate correlations were performed across all participants. Linear model ANOVAs were used to investigate biomarker subgroup differences; Hedge’s g effect sizes are reported.ResultsConvergent validity was supported through significant correlation (p’s < .001) of SLS and AVLT (r = .63), SYM and Coding (r = ‐.64), and MTD composite with Mayo‐PACC (r = .67); see Table 2. MTD subtests showed expected relationships with age and education (p’s < .05; Table 3). All MTD performances were significantly lower for A+ and A+T+ participants relative to A‐ or A‐T‐ (p’s<.001; Table 4). All MTD outcome measures showed a similar or greater group difference effect size relative to traditional in‐person administered neuropsychological measures for separating A+ vs. A‐ and A+T+ vs. A‐T‐ groups. See Figure 1 and Table 4.ConclusionsMTD subtests show evidence of convergent validity through strong correlations with traditional in‐person cognitive measures. Preliminary analyses suggest MTD has promise for differentiating PET biomarker groups.

AbstractBackgroundRemote assessment tools offer significant promise for aiding early detection of cognitive impairment. Mayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for remote self‐administered assessment that includes a computer adaptive word list memory test (Stricker Learning Span; SLS) and a measure of processing speed (Symbols Test). We examined the diagnostic accuracy of the SLS and a MTD composite (SLS max span, SLS trials 1–5 total correct, SLS delay correct, [Symbols correct item response time*‐1]) for amnestic mild cognitive impairment (aMCI). We also explored diagnostic accuracy for a broader group that included individuals with possible MCI (pMCI; see Figure 1).MethodsParticipants were recruited from the Mayo Clinic Study of Aging for this ancillary remote study. 226 were cognitively unimpaired (CU; concordant CU diagnosis by 3 independent raters). Fifty‐six participants had possible MCI (at least 1 of 3 raters indicated MCI) and 16 had a consensus diagnosis of aMCI. Primary outcome variables were SLS sum of trials, AVLT sum of trials, Symbols correct items response time, and MTD composite. Mean difference analyses used linear model ANOVAs (alpha = .05). Receiver operating characteristic (ROC) curves were applied; we derived optimal cutoff scores based on the Youden index method.ResultsBoth aMCI and possible MCI groups showed significantly lower performance than the CU group on SLS (Hedge’s g aMCI = ‐1.72, pMCI = ‐1.04), Symbols (Hedge’s g aMCI = 1.38, pMCI = 0.64), and MTD composite (Hedge’s g aMCI = ‐1.89, pMCI = ‐1.09); see Tables 1 and 2 (all p‘s<.01 even when additionally covarying age, education, sex). Total area under the curve was high for differentiating of CU vs. aMCI (MTD Composite = 0.91, SLS = 0.91) and acceptable for CU vs. pMCI (MTD Composite = 0.77, SLS = 0.77); see Table 2. Consistent with our flexible platform, a variety of device types were used to complete remote testing (32% smartphone, 12% tablet, 56% PC).ConclusionsMTD and the SLS show high diagnostic accuracy for aMCI. MTD is a flexible, brief, and easy‐to‐use remote cognitive assessment tool with great potential for scalable use in future studies seeking to maximize inclusion of individuals with aMCI in clinical trials or for cognitive screening in clinical settings.

AbstractBackgroundMayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for self‐administered assessment for novel (Stricker Learning Span) and open access (Symbols Test) cognitive measures with previously demonstrated validity and reliability. Our study aim was to examine the usability of MTD, defined as the extent to which MTD was used to achieve the desired study goal of completing a testing session among participants willing to engage in an all‐remote study.Methods248 Mayo Clinic Study of Aging and Mayo Alzheimer’s Disease Research Center participants were invited to participate in this ancillary, uncompensated remote pilot study via email. To examine 1) willingness to participate in a remote cognitive assessment study and 2) usability of MTD, the initiation and completion rates of MTD were examined by cognitively unimpaired (CU; n = 142) and cognitively impaired (CI; n = 106) groups (see Figure 1). These rates and other factors associated with MTD feasibility were further explored across age in CU (i.e., 50–69, 70–79, and 80+ years) and specific diagnostic groups in CI (i.e., individuals with dementia, mild cognitive impairment (MCI), and possible MCI).ResultsSession completion rate (usability) was 98% across all participants who chose to participate, and, surprisingly, completion rates did not vary by age or diagnostic groups (p = .69; see Table 1). As expected, more individuals chose to participate in the CU (77%) versus CI (52%) group (p < .001). In terms of feasibility, individuals over 80 and with CI were more likely to have some contact with the study coordinator. Use of smartphones decreased with age, with most CU individuals over 70 using a personal computer (69‐73%; Table 2).ConclusionsThough the CI group was less likely to choose to participate than the CU group, out of those that did choose to participate the rate of completion was 98%. This demonstrates high usability of MTD. Study coordinator support and a flexible testing platform is critical for facilitating participation in remote testing across the aging population, and more supports and incentives may be needed for individuals with cognitive impairment to improve participation rates.

AbstractBackgroundDyadic (self and study partner) subjective reports of an individual’s recent cognitive and functional decline are an efficient approach to facilitate identification of older adults with, and at risk for, Mild Cognitive Impairment (MCI). Such methods can be easily adapted for remote administration, but few studies have investigated the ability of remotely‐collected, unsupervised dyadic subjective reports to accurately detect MCI and cognitive decline.MethodsIn 289 older adults enrolled in the Brain health Registry (BHR), with clinically‐confirmed diagnoses of Cognitively‐unimpaired (CU) or MCI, we identified the optimal combination of remote measures to accurately distinguish CU from MCI. We then externally validated our predictive model in 217 participants enrolled in a separate cohort, Validation of Online Methods to Predict Cognitive Decline (eVAL). Predictors included Cogstate Brief Battery One Card Learning (OCL) accuracy; self‐ and study partner‐reported memory concern (SMCs); performance on online adaptations of Everyday Cognition Scale (ECog, self‐ and study partner‐report versions), Geriatric Depression Scale Short Form (GDS); family history of AD, age, gender, and education. We calculated 10‐fold cross validated area under the ROC curves (cvAUC), 95% confidence intervals (CI), and the percentage of participants correctly classified (accuracy) for all models. Likelihood ratio (LR) tests were used to compare model fits.ResultsIn the BHR cohort, models including dyad‐report ECog, family history of AD, GDS, and Cogstate OCL scores distinguished CU from MCI with the highest accuracy (cvAUC = 0.90; 95% CI = 0.83, 0.92; accuracy = 85.5%). Models excluding study partner‐report ECog distinguished diagnostic groups with moderate accuracy (cvAUC = 0.83; 95% CI = 0.78, 0.85; accuracy = 75.9%) When applied to the validation cohort, the optimal training model including dyadic report performed with moderate accuracy ( Table 2 ). In the validation cohort, inclusion of study partner (but not self) report SMC, in addition to dyadic‐report ECog, improved model fit (LR test p<0.01).ConclusionsRemotely‐collected dyadic subjective measures, along with other remote measures, accurately distinguish CU from MCI. Subjective cognitive and functional decline and concerns about memory problems independently contribute to diagnostic discrimination. Results support future utilization of remote dyadic subjective measures to efficiently identify older adults with MCI for prodromal AD clinical trials, and in clinical care settings.