Stricker, Nikki H.

Abstract Background: Cognitive deficits in memory, language, and executive function have been described in Cushing’s syndrome, but the impact of mild cortisol secretion on cognition is unclear. Rather than overt hypercortisolism, mild autonomous cortisol secretion (MACS) is typically associated with abnormal circadian cortisol production. Aim: To characterize the effect of MACS on cognitive performance. Methods: We conducted a cross-sectional analysis as part of an ongoing cohort study in patients with MACS compared to age and sex-matched referent subjects without cortisol excess. MACS was defined as serum cortisol concentration >1.8 mcg/dL after the 1 mg overnight dexamethasone suppression test (DST), in the absence of signs and symptoms of overt Cushing syndrome. We used the National Institute of Health Toolbox Cognition Battery to assess cognitive performance. A series of seven IPad-based tests were administered to evaluate five key domains: 1) executive function, 2) episodic memory, 3) working memory, 4) language, and 5) processing speed. Performance was reported using fully corrected T-scores for age, sex, education, and race with a normative mean of 50 and a standard deviation of 10. T-scores were generated for the individual components as well as three summary measures: 1) fluid cognition (includes executive function, episodic memory, working memory, and processing speed), 2) crystallized cognition (includes language), and 3) total cognition (composite of fluid and crystalized cognition). Results: A total of 23 patients with MACS and 23 age and sex-matched referent subjects without cortisol excess were enrolled. The median age of diagnosis was 63 years (range, 51–81), and 26 (56%) were women. In the MACS cohort, median cortisol following 1 mg DST was 2.6 ug/dL (range, 1.9–13.0) with median ACTH of 8.5 pg/mL (range, 5.0–38.0) and median DHEA-S of 37 mcg/dL (range, 5.0- 141.0). On cognitive assessment, patients with MACS had lower total cognition (T-scores 50 vs. 54, p=0.05) and fluid cognition (T-scores 48 vs. 53, p=0.01) composite scores compared to referent subjects without cortisol excess. In particular, patients with MACS performed worse on tests of executive function (Dimensional Change Card Sort: T-scores 55 vs. 63, p= 0.02 and Flanker Inhibitory Control and Attention: T-scores 45 vs. 52, p=0.01). There were no significant differences observed in the remaining individual domains of language, processing speed, working memory, and episodic memory, or crystallized cognition. Conclusions: MACS is associated with impaired total cognition, and in particular, executive function and fluid cognition. These findings suggest that patients with MACS are susceptible to cortisol-mediated changes in the brain. Additional studies should examine the contribution of neuropsychiatric symptoms on cognition in MACS, and possible improvement following treatment for cortisol excess.

BackgroundTo operationalize the National Institute on Aging – Alzheimer's Association (NIA‐AA) Research Framework for Alzheimer's Disease 6‐stage continuum of clinical progression for persons with abnormal amyloid.MethodsThe Mayo Clinic Study of Aging is a population‐based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We evaluated persons without dementia having 3 consecutive clinical visits. Measures for cross‐sectional categories included objective cognitive impairment (OBJ) and function (FXN). Measures for change included subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (ΔNBS). We calculated frequencies of the stages using different cutoff points and assessed stability of the stages over 15 months.ResultsAmong 243 abnormal amyloid participants, the frequencies of the stages varied with age: 66 to 90% were classified as stage 1 at age 50 but at age 80, 24 to 36% were stage 1, 32 to 47% were stage 2, 18 to 27% were stage 3, 1 to 3% were stage 4 to 6, and 3 to 9% were indeterminate. Most stage 2 participants were classified as stage 2 because of abnormal ΔOBJ only (44–59%), whereas 11 to 21% had SCD only, and 9 to 13% had ΔNBS only. Short‐term stability varied by stage and OBJ cutoff points but the most notable changes were seen in stage 2 with 38 to 63% remaining stable, 4 to 13% worsening, and 24 to 41% improving (moving to stage 1).InterpretationThe frequency of the stages varied by age and the precise membership fluctuated by the parameters used to define the stages. The staging framework may require revisions before it can be adopted for clinical trials. ANN NEUROL 2021;89:1145–1156

AbstractIntroductionThis study evaluated the diagnostic accuracy of the Cogstate Brief Battery (CBB) for mild cognitive impairment (MCI) and prodromal Alzheimer's disease (AD) in a population‐based sample.MethodsParticipants included adults ages 50+ classified as cognitively unimpaired (CU, n = 2866) or MCI (n = 226), and a subset with amyloid (A) and tau (T) positron emission tomography who were AD biomarker negative (A–T–) or had prodromal AD (A+T+).ResultsDiagnostic accuracy of the Learning/Working Memory Composite (Lrn/WM) for discriminating all CU and MCI was moderate (area under the curve [AUC] = 0.75), but improved when discriminating CU A–T– and MCI A+T+ (AUC = 0.93) and when differentiating MCI participants without AD biomarkers from those with prodromal AD (AUC = 0.86). Conventional cut‐offs yielded lower than expected sensitivity for both MCI (38%) and prodromal AD (73%).DiscussionClinical utility of the CBB for detecting MCI in a population‐based sample is lower than expected. Caution is needed when using currently available CBB normative data for clinical interpretation.

AbstractBackgroundPractice effects can obscure interpretation of longitudinal cognitive performance. Reliable change indices (RCIs) provide data for determining a significant change, typically for a single repeat assessment, but multiple follow‐up visits are common clinically and in research cohorts. Further, regression‐based or linear mixed effects models improve prediction of future cognitive decline and have higher correlations with neuroimaging biomarkers than RCI approaches. We present norms for change for the Auditory Verbal Learning Tests (AVLT) with conditional normative z‐scores for up to 7 repeat assessments, and demonstrate application of this method to individuals with preclinical Alzheimer’s disease (AD).MethodNormative Sample: Robust conditional norms were derived from 1001 adults ages 50‐90 from the Mayo Clinic Study of Aging (MCSA) who completed the AVLT across 4‐8 visits approximately 15 months apart and remained cognitively unimpaired (CU) across all available visits. Linear mixed effects models were used to compute a conditional normative z‐score for AVLT 30‐minute recall and sum of trials. Models included adjustment for baseline performance, number of prior test exposures, demographic factors (age, sex, education), and interactions (baseline*time; sex*time). Test Sample: We used a preclinical AD sample with positive amyloid and tau imaging (n= 27 CU A+T+; Jack et al. 2017) expected to have a higher rate of intra‐individual cognitive decline relative to CU A‐T‐ individuals (n = 268) to compare mean performances of conditional normative z‐scores to typical age‐corrected Mayo’s Older Americans Normative Studies (MOANS) norms. Participants had at least 1 and up to 4 follow‐up visits, with their most recent AT data used for classification.ResultAVLT performance based on typical norms did not differ across A+T+ and A‐T‐ groups. Specifically, across 2‐4 follow‐up visits, the typical norms mean z‐score was similar across groups (p=0.945; see Table 1). However, the conditional norms mean z‐score that accounts for practice effects and typical trajectories of AVLT performance was lower in the A+T+ relative to the A‐T‐ group for 30‐minute recall (p=0.055) and sum of trials (p=0.060).ConclusionConditional normative methods that account for practice effects are more sensitive to preclinical AD than typical norms, and show promise for identifying transitional cognitive decline.

AbstractBackgroundThe numeric clinical staging scheme described by the NIA‐AA workgroup proposes evidence of subtle decline on longitudinal cognitive testing as one option to define transitional cognitive decline for individuals in the Alzheimer’s continuum. Few studies have compared longitudinal and cross‐sectional definitions of subtle objective cognitive impairment (sOBJ). The study aim was to compare the diagnostic accuracy of cross‐sectional sOBJ and longitudinal objective cognitive decline (ΔOBJ) for preclinical Alzheimer’s disease (AD) based on Cogstate One Card Learning (OCL) performance.MethodWe included Mayo Clinic Study of Aging cognitively unimpaired (CU) participants who were A+T+ by amyloid and tau PET assessment (n=32) and A‐T‐ (n=211); all had at least two follow‐up visits. sOBJ was defined as performance ≤ ‐1SD on OCL accuracy using age‐corrected normative data. ΔOBJ was measured using two different methods: (1) within subjects’ standard deviation (WSD) using a ≤ ‐1 z‐score for change to define decline based on Cogstate’s normative change data, and (2) extracted subject‐specific slopes from a linear mixed effects model (LME) which can be interpreted as approximate annual change; decline was defined as slope < 10%ile among a reference sample of CU participants aged 50‐65 at baseline (N=732).ResultssOBJ and ΔOBJ‐WSD both showed low sensitivity in detection of A+T+ (9.4% and 12.5%, respectively). Total AUC values did not differ significantly (p=.31) across sOBJ (AUC = .64, CI = .53‐.75) and ΔOBJ‐WSD (AUC = .53, CI = .42‐.65) methods. Sensitivity of the ΔOBJ‐LME method to predict A+T+ was improved (37.5%) when using a conventional cutoff of <10%ile slope and was further improved (56%) when using an optimal derived cutoff equivalent to <17%ile slope within the reference group. Like the sOBJ method, the ΔOBJ‐LME method differentiated groups better than chance (AUC = .69, CI = .58‐.80). Despite increased sensitivity, total AUC values were not significantly different across sOBJ and ΔOBJ‐LME methods (p=.34). However, total AUC of ΔOBJ‐LME was significantly better than ΔOBJ‐WSD (p=.03).ConclusionsΔOBJ may be more sensitive to preclinical AD than sOBJ and may serve as a helpful method for identifying at risk CU individuals if advanced statistical methods are used for defining change.