Mielke, Michelle M.

Background: Prevention strategies for Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRDs) are urgently needed for reducing incidence. Intra-patient variability in lipid levels is a potentially modifiable risk factor for incident AD/ADRD. Although regular lipid measurements are a part of common clinical practice and longitudinal data routinely available in electronic health records (EHR), research examining this association between AD/ADRD and lipid variability across multiple lipid types remains scarce. Methods: All residents living in Olmsted County, MN on 1/1/2006 age ≥60 years without an AD/ADRD diagnosis were identified using Centers for Medicare & Medicaid Services diagnostic codes. Persons with ≥3 lipid measurements (total cholesterol or triglycerides) in the 5 years prior to index date were retained. Lipid variability was quantified using variability independent of the mean (VIM). Models were adjusted for traditional risk factors. Associations between lipid variability quintiles and incident AD/ADRD were assessed using Cox proportional hazards regression. Multiple imputation was used for missing covariates. Participants were followed through 2018 for incident AD/ADRD. Results: The final analysis included data on 11,551 participants with total cholesterol and 11,502 participants with triglycerides. Participants had a mean age of 71 (range 60-99) years, and were primarily white (96%). Females (54%) were also slightly more frequent than males. Median follow up was 12.9 years (range: 0-13). Participants in the highest quintile of variability for total cholesterol and triglycerides had a 20% increased risk of incident AD/ADRD. Similar results were found in the subset with complete covariate data. Conclusion: In a large EHR derived cohort, persons in the highest quintile of lipid variation had an increased risk of incident AD/ADRD. Further studies to identify the mechanisms behind this risk factor and replication of these results across a more diverse population are needed.

Abstract Introduction Measurement of amyloid beta (Aβ40 and Aβ42) and tau (phosphorylated tau [p‐tau] and total tau [t‐tau]) in cerebrospinal fluid (CSF) can be utilized to differentiate clinical and preclinical Alzheimer's disease dementia (AD) from other neurodegenerative processes. Methods CSF biomarkers were measured in 150 participants from the Mayo Clinic Study of Aging and the Alzheimer's Disease Research Center. P‐tau/Aβ42 (Roche Elecsys, Fujirebio LUMIPULSE) and Aβ42/40 (Fujirebio LUMIPULSE) ratios were compared to one another and to amyloid positron emission tomography (PET) classification. Results Strong correlation was observed between LUMIPULSE p‐tau/Aβ42 and Aβ42/40, as well as Elecsys and LUMIPULSE p‐tau/Aβ42 and Aβ42/40 (Spearman's ρ = –0.827, –0.858, and 0.960, respectively). Concordance between LUMIPULSE p‐tau/Aβ42 and Aβ42/40 was 96% and between Elecsys p‐tau/Aβ42 and both LUMIPULSE ratios was 97%. All ratios had > 94% overall, positive, and negative percent agreement with amyloid PET classification. Discussion These data suggest that p‐tau/Aβ42 and Aβ42/40 ratios provide similar clinical information in the assessment of amyloid pathology.

AbstractConsiderable evidence has accumulated implicating a role for immune mechanisms in moderating the pathology in Alzheimer's disease dementia. However, the appropriate therapeutic target, the appropriate direction of manipulation, and the stage of disease at which to begin treatment remain unanswered questions. Part of the challenge derives from the absence of any selective pressure to develop a coordinated beneficial immune response to severe neural injury in adults. Thus, immune responses to the prevailing stimuli are likely to contain both beneficial and detrimental components. Knowledge gaps include: (1) how a biomarker change relates to the underlying biology, (2) the degree to which pathological stage group differences reflect a response to pathology versus trait differences among individuals regulating risk of developing pathology, (3) the degree to which biomarker levels are predictive of subsequent changes in pathology and/or cognition, and (4) experimental manipulations in model systems to determine whether differences in immune biomarkers are causally related to pathology.

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 0.69 (95% CI 0.66-0.71) for CHARGE-AF and 0.72 (95% CI 0.69-0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

Background and Purpose: Cerebral microbleeds (CMBs) are represented by small areas of hemosiderin deposition, detected on brain magnetic resonance imaging (MRI), and found in ≈23% of the cognitively normal population over age of 60 years. CMBs predict risk of hemorrhagic and ischemic stroke. They correlate with increased cardiovascular mortality. In this article, we sought to determine in a population-based study whether antithrombotic medications correlate with CMBs and, if present, whether the association was direct or mediated by another variable. Methods: The study consisted of 1253 participants from the population-based Mayo Clinic Study of Aging who underwent T2* gradient-recalled echo magnetic resonance imaging. We tested the relationship between antithrombotic medications and CMB presence and location, using multivariable logistic-regression models. Ordinal logistic models tested the relationship between antithrombotics and CMB frequency. Using structural equation models, we assessed the effect of antithrombotic medications on presence/absence of CMBs and count of CMBs in the CMB-positive group, after considering the effects of age, sex, vascular risk factors, amyloid load by positron emission tomography, and apoE. Results: Two hundred ninety-five participants (26.3%) had CMBs. Among 678 participants taking only antiplatelet medications, 185 (27.3%) had CMBs. Among 95 participants taking only an anticoagulant, 43 (45.3%) had CMBs. Among 44 participants taking an anticoagulant and antiplatelet therapy, 21 (48.8%) had CMBs. Anticoagulants correlated with the presence and frequency of CMBs, whereas antiplatelet agents were not. Structural equation models showed that predictors for presence/absence of CMBs included older age at magnetic resonance imaging, male sex, and anticoagulant use. Predictors of CMB count in the CMB-positive group were male sex and amyloid load. Conclusions: Anticoagulant use correlated with presence of CMBs in the general population. Amyloid positron emission tomography correlated with the count of CMBs in the CMB-positive group.

Abstract Background: Elevated urine cortisol (<1% of urinary steroid metabolome) was reported to predict future development of dementia. Our objective was to determine the association of urine steroid metabolome and its diurnal variation with cognitive function in men and women. Methods: Cross-sectional study of community-dwelling adults ≥ 50 years. Participants with adrenal disorders, end-stage renal or liver disease, on exogenous steroids or drugs affecting steroid metabolism were excluded. All participants completed day and night separate urine collection. A series of seven IPad-based tests using the National Institute of Health Toolbox Cognition Battery were administered to evaluate five key domains; performance was reported using fully corrected T-scores for age, sex, education, and race with a national normative mean of 50. T-scores were generated for the two summary measures: 1) fluid cognition (includes executive function, episodic memory, working memory, and processing speed), and 2) total composite (composite of fluid and language score). Urine samples were analyzed with the liquid-chromatography, high-resolution, accurate-mass mass spectrometry for 25 urine steroid metabolites. Results: Of 109 participants, 56 (51%) were women, and age and educational status were similar in men and women. On cognitive assessment, men and women had similar median composite cognition (T-score of 53 vs 54, p=0.74) and fluid cognition (T-score of 53 vs 51, p-value 0.96). Urine steroid metabolome analysis demonstrated 21/25 steroids were higher in men vs women. In both women and men, the ratio of total cortisol metabolites/total androgen metabolites (TCM/TAM) was associated with lower fluid cognition (women: ρ= -0.34, p=0.01, men: ρ= -0.43, p=0.001) and composite cognition (women: ρ= -0.27, p=0.04, men: ρ= -0.39, p=0.004). Higher ratio of day to night TCM were associated with a better fluid cognition in men (ρ= 0.35, p=0.01), but not in women (ρ= -0.11, p=0.41). Steroid ratios suggesting a relative enzymatic deficiency of 5α-Reductase type 2 was associated with lower fluid cognition in women (ρ= -0.29, p=0.03). In men, the fluid composite score was associated with a relative deficiency in 21-Hydroxylase (ρ= 0.42, p=0.002), 3β-Hydroxysteroid dehydrogenase (ρ= 0.43, p=0.001), and P450oxidoreductase (ρ= -0.35, p=0.01). Conclusion: We showed that a higher glucocorticoid to androgen ratio and a flattened circadian steroid variation were associated with lower global and fluid cognition score. Steroid ratios reflecting steroidogenesis enzymatic activity demonstrated sex differences in relation to cognition. Additional studies should examine whether the steroid fingerprint associated with lower cognition is predictive of a future dementia onset.

Abstract Background: Adrenal adenomas are incidentally diagnosed in 7% of adults undergoing abdominal imaging. Mild autonomous cortisol secretion is present in 50% of adrenal adenomas, and even “nonfunctioning” adrenal adenomas demonstrate abnormal steroid profile. We aimed to 1) investigate the prevalence of mental and sleep disorders in patients with adrenal adenomas and to 2) determine the risk of mental and sleep disorders after the diagnosis of adrenal adenoma in patients compared to the referent subjects from the same population Methods: Using a medical records linkage system, we identified adult patients living in the Olmsted County, MN diagnosed with an adrenal adenoma during 1995–2017. Patients with overt hormone excess were excluded. Every patient with adenoma was matched by sex and age to a referent subject from the same population. Subjects were followed until death or end of the study. Mental health related comorbidities and sleep disorders were assessed at baseline and during follow up. Results: Our cohort included 1004 patients with adrenal adenomas and 1004 referent subjects (58% women, median age of 63 years). Patients were more likely to smoke (70% vs 54%, p <0.001) and had a higher BMI (30 kg/m2 vs 28 kg/m2, p < 0.001). Within 5 years prior to the index date (diagnosis of adenoma), and after adjusting for BMI and smoking, patients demonstrated a higher prevalence of depression (Odds ratio, OR of 1.3 (CI95% 1.1–1.6), p=0.02), anxiety (OR of 1.4 (CI95% 1.1–1.8, p=0.003), substance abuse disorders (OR of 2.4 (CI 95% 1.7–3.4), p<0.001), but not insomnia (OR of 1.2 (CI95% 0.9–1.7) and sleep related breathing disorders (OR of 1.3 (CI 95% 0.9–1.7). During follow-up, starting 1 year after the diagnosis, patients demonstrated a higher risk of new onset depression (HR of 1.9, CI95%1.5–2.4), anxiety (HR of 1.5,CI95% 1.2–1.9), schizophrenia (HR of 1.7, CI95% 1.2–2.4), and substance abuse disorders (HR of 1.6, CI95% 1.2–2.0). Risk of sleep disorders 1 year after diagnosis was also high for insomnia (HR of 1.4, CI95% 1.1–1.9), sleep-related breathing disorders (HR of 1.8, CI95% 1.4–2.3), hypersomnias of central origin (HR of 2.0, CI95%1.04–3.96), parasomnias (HR of 2.4, CI95%1.2–4.7), and sleep-related movement disorders(HR of 1.9, CI95%1.3–2.6). Conclusion: Patients with adenomas are at increased risk for mental and sleep disorders, possibly explained by the underlying subtle cortisol secretion. Further prospective studies with an in-depth characterization of both hormonal secretion and mental/sleep disorders are needed. Reversibility or improvement of mental health and sleep disorders with adrenalectomy should be investigated.

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.

Abstract While cerebrovascular disease can be observed in vivo using MRI, the multiplicity and heterogeneity in the mechanisms of cerebrovascular damage impede accounting for these measures in ageing and dementia studies. Our primary goal was to investigate the key sources of variability across MRI markers of cerebrovascular disease and evaluate their impact in comparison to amyloidosis on cognitive decline in a population-based sample. Our secondary goal was to evaluate the prognostic utility of a cerebrovascular summary measure from all markers. We included both visible lesions seen on MRI (white matter hyperintensities, cortical and subcortical infarctions, lobar and deep microbleeds) and early white matter damage due to systemic vascular health using diffusion changes in the genu of the corpus callosum. We identified 1089 individuals aged ≥60 years with concurrent amyloid-PET and MRI scans from the population-based Mayo Clinic Study of Aging. We divided these into discovery and validation datasets. Using the discovery dataset, we conducted principal component analyses and ascertained the main sources of variability in cerebrovascular disease markers. Using linear regression and mixed effect models, we evaluated the utility of these principal components and combinations of these components for the prediction of cognitive performance along with amyloidosis. Our main findings were (i) there were three primary sources of variability among the CVD measures—white matter changes are driven by white matter hyperintensities and diffusion changes; number of microbleeds (lobar and deep); and number of infarctions (cortical and subcortical); (ii) Components of white matter changes and microbleeds but not infarctions significantly predicted cognition trajectories in all domains with greater contributions from white matter; and (iii) The summary vascular score explained 3–5% of variability in baseline global cognition in comparison to 3–6% variability explained by amyloidosis. Across all cognitive domains, the vascular summary score had the least impact on memory performance (∼1%). Though there is mechanistic heterogeneity in the cerebrovascular disease markers measured on MRI, these changes can be grouped into three components and together explain variability in cognitive performance equivalent to the impact of amyloidosis on cognition. White matter changes represent dynamic ongoing damage, predicts future cognitive decline across all domains and diffusion measurements help capture white matter damage due to systemic vascular changes. Therefore, measuring and accounting for white matter changes using diffusion MRI and white matter hyperintensities along with microbleeds will allow us to capture vascular contributions to cognitive impairment and dementia.