Citations

Background: Multiple algorithms with variable performance have been developed to identify dementia using combinations of billing codes and medication data that are widely available from electronic health records (EHR). If the characteristics of misclassified patients are clearly identified, modifying existing algorithms to improve performance may be possible. Objective: To examine the performance of a code-based algorithm to identify dementia cases in the population-based Mayo Clinic Study of Aging (MCSA) where dementia diagnosis (i.e., reference standard) is actively assessed through routine follow-up and describe the characteristics of persons incorrectly categorized. Methods: There were 5,316 participants (age at baseline (mean (SD)): 73.3 (9.68) years; 50.7% male) without dementia at baseline and available EHR data. ICD-9/10 codes and prescription medications for dementia were extracted between baseline and one year after an MCSA dementia diagnosis or last follow-up. Fisher’s exact or Kruskal-Wallis tests were used to compare characteristics between groups. Results: Algorithm sensitivity and specificity were 0.70 (95% CI: 0.67, 0.74) and 0.95 (95% CI: 0.95, 0.96). False positives (i.e., participants falsely diagnosed with dementia by the algorithm) were older, with higher Charlson comorbidity index, more likely to have mild cognitive impairment (MCI), and longer follow-up (versus true negatives). False negatives (versus true positives) were older, more likely to have MCI, or have more functional limitations. Conclusions: We observed a moderate-high performance of the code-based diagnosis method against the population-based MCSA reference standard dementia diagnosis. Older participants and those with MCI at baseline were more likely to be misclassified.

Objective Hearing loss is increasingly recognized as a chronic disease state with important health sequelae. Although considered a central component of routine audiometric testing, the degree to which various patient factors influence speech discrimination is poorly characterized to date. The primary objective of the current work was to describe associations of cognitive performance, sociodemographic factors, and pure-tone audiometry with speech discrimination in older adults. Study Design Prospective study. Setting Olmsted County, Minnesota. Patients There were 1,061 study participants 50 years or older at enrollment in the population-based Mayo Clinic Study of Aging between November 2004 and December 2019 who underwent formal audiometric and cognitive testing included in the current investigation. Main Outcome Measures The primary outcome measure was word recognition scores (WRSs; measured as <100% vs 100% as well as continuous), with pure-tone averages (PTAs; 0.5, 1, 2, and 3 kHz), age, sex, years of education, state area deprivation index (ADI) quintiles, and global cognition z scores as explanatory features. Results The mean (SD) age among the 1,061 participants was 76 (9) years with 528 (50%) males. Participant age [OR (95% CI) for a 10-year increase of 1.8 (1.4–2.3), p < 0.001], male sex [OR = 2.6 (1.9–3.7), p < 0.001], and PTA [OR for a 10-dB hearing loss increase of 2.4 (2.1–2.8), p < 0.001] were all significantly associated with <100% WRSs, with the greatest explanatory ability attributable to the PTA. Years of education (p = 0.9), state ADI quintile (p = 0.6), and global cognitive performance (p = 0.2) were not associated with WRS. The multivariable model demonstrated strong predictive ability for less than perfect WRSs, with a c index of 0.854. Similar results were seen for WRSs analyzed as continuous, with the multivariable model resulting in an R 2 value of 0.433. Conclusions Although PTA exhibited the greatest influence on speech discrimination, advancing age and male sex both independently increased the likelihood of having worse speech discrimination among older adults, even after accounting for years of education, neighborhood-level socioeconomic disadvantage, and cognitive function. These findings help identify patient factors that can be instrumental when designing screening programs for adult-onset hearing loss.

AbstractINTRODUCTIONWe examined associations between plasma‐derived biomarkers of Alzheimer's disease (AD) and neuropsychiatric symptoms (NPS) in community‐dwelling older adults.METHODSCross‐sectional study involving 1005 persons ≥50 years of age (mean 74 years, 564 male, 118 cognitively impaired), who completed plasma‐derived biomarker (amyloid beta 42 [Aβ42]/Aβ40, phosphorylated tau 181 [p‐tau181], p‐tau217, total tau [t‐tau], neurofilament light [NfL]), and NPS assessment.RESULTSP‐tau181 (odds ratio [OR] 2.06, 95% confidence interval [CI] 1.41–3.00, p < 0.001), p‐tau217 (OR 1.70, 95% CI 1.10–2.61, p = 0.016), and t‐tau (OR 1.44, 95% CI 1.08–1.92, p = 0.012) were associated with appetite change. We also found that p‐tau181 and p‐tau217 were associated with increased symptoms of agitation (OR 1.93, 95% CI 1.20–3.11, p = 0.007 and OR 2.04, 95% CI 1.21–3.42, p = 0.007, respectively), and disinhibition (OR 2.39, 95% CI 1.45–3.93, p = 0.001 and OR 2.30, 95% CI 1.33–3.98, p = 0.003, respectively). Aβ42/Aβ40 and NfL were not associated with NPS.CONCLUSIONHigher plasma‐derived p‐tau181 and p‐tau217 levels are associated with increased symptoms of appetite change, agitation, and disinhibition. These findings may support the validity of plasma tau biomarkers for predicting behavioral symptoms that often accompany cognitive impairment.HIGHLIGHTS We studied 1005 community‐dwelling persons aged ≥ 50 years Higher plasma tau levels are associated with increased neuropsychiatric symptoms Aβ42/Aβ40 and NfL are not associated with neuropsychiatric symptoms Clinicians should treat neuropsychiatric symptoms in persons with high plasma‐derived tau

AbstractBackgroundDementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, after Alzheimer’s disease (AD). While alpha‐synuclein pathology is the hallmark of DLB, AD and cerebrovascular pathologies are common in DLB patients. However, the contribution of these pathologies towards neurodegeneration in DLB is largely unknown. We investigated associations of amyloid‐beta, tau, and cerebrovascular biomarkers with regional gray matter (GM) volume in DLB patients and cognitively unimpaired (CU) controls.MethodWe included 30 DLB patients (69.3±10.2 years old, 87% men) and 100 CU individuals balanced on age and sex (Table 1). We used 11C‐Pittsburgh Compound‐B (PiB) and 18F‐Flortaucipir positron emission tomography (PET) to assess amyloid‐beta and tau in‐vivo. We used structural MRI to assess white matter hyperintensity (WMH) volume, a marker of cerebrovascular lesion load, and regional GM volume (a marker of neurodegeneration). We used correlations and ANCOVA in the whole dataset and structural equation modelling in the DLB patients to investigate associations of WMH volume and regional amyloid‐beta and tau PET standardized uptake value ratios (SUVRs) with regional GM volume.ResultDLB patients showed lower GM volume across all cortical and subcortical regions except for cuneus, putamen, and pallidum (Fig.1). Greater WMH volume was associated with lower volume in the medial and orbital frontal cortices, insula, fusiform cortex, and thalamus in DLB patients (Fig.2). Greater PiB SUVR was associated with lower volume in the inferior temporal cortex, while Flortaucipir SUVR did not correlate with GM volume (Fig.2). Structural equation modelling showed that higher age and positivity for the APOE e4 genotype were significant predictors of greater WMH, and WMH in turn was a significant predictor of GM volume in medial and orbital frontal cortices, insula, and inferior temporal cortex (Fig.3). In contrast, we observed two distinct paths for the fusiform cortex, with age having an effect through PiB and Flortaucipir SUVRs on the one path, and through WMH on the other path (Fig.3).ConclusionDLB patients have widespread cortical atrophy, most of which likely is influenced by alpha‐synuclein pathology. Although amyloid‐beta, tau, and cerebrovascular pathologies often coexist in DLB, their contributions to neurodegeneration seem to be region specific.

AbstractBackgroundTamoxifen is used as a selective estrogen receptor modifier (SERM) to treat hormone receptor‐positive breast cancer. Raloxifene, another SERM, has been used for the treatment of osteoporosis in post‐menopausal women. The aim of this cross‐sectional study was to examine whether a history of tamoxifen or raloxifene use was associated with cognitive performance, odds of mild cognitive impairment (MCI), or structural neurodegenerative MRI markers.MethodThe study included women with and without a personal history of breast cancer enrolled in the Mayo Clinic Study of Aging (MCSA). A history of breast cancer and use of SERMs before the MCSA enrollment visit was abstracted using the Rochester Epidemiology Project medical‐linkage system. Participants had a diagnosis of MCI (prevalent) at the time of enrollment into the MCSA. Logistic regression was used to examine associations of SERMs with odds of MCI. Linear regression models were used to examine associations of SERMs with cognitive measures z‐scores (Memory, Executive Function, Language, Visuospatial Skills, Global Cognition), hippocampal volume, and Alzheimer’s disease signature cortical thickness.ResultAmong the women aged ≥ 50 at enrollment in the MCSA, 151 had a history of breast cancer, and 2235 had no prior history of any cancer. Of the 151 women with a history of breast cancer, 42 (28%) had taken tamoxifen and 109 (72%) had not at enrollment in the MCSA; 54 had neuroimaging data (tamoxifen = 16; no tamoxifen = 38). Among the women without prior history of cancer at enrollment in the MCSA, 76 (3%) had taken raloxifene and 2159 (97%) had not; 755 had neuroimaging data (raloxifene = 25; no raloxifene = 730). No significant associations between tamoxifen with global or domain‐specific cognition or odds of MCI were observed among women with a history of breast cancer after adjusting for confounders (Table 1). Likewise raloxifene was not significantly associated with global or domain‐specific cognition or odds of MCI in women without a history of cancer after adjusting for confounders (Table 2). We did not find significant associations between SERMs and any neuroimaging outcome (Table 3).ConclusionOur results suggest use of tamoxifen or raloxifene is not significantly associated with cognition in postmenopausal women.

AbstractBackgroundTwo pathways that help explain cognitive health are 1) brain maintenance – referring to the relative absence of brain changes over time; 2) cognitive reserve – a property of the brain that allows for sustained cognitive ability in the presence of brain pathology. Both pathways influence cognitive aging and are best understood using longitudinal data. Our goal was to assess the effect of modifiable vascular/metabolic and lifestyle enrichment factors on these pathways using longitudinal brain changes measured on structural magnetic resonance imaging (MRI) and longitudinal cognitive decline. Lastly, we combined these pathways and their relationships in the same model.MethodWe identified 1250 participants from a well‐characterized sample of older adults from the Mayo Clinic Study of Aging with both longitudinal MRI and cognitive data. Linear mixed models were performed to assess the impact of modifiable factors on brain maintenance and cognitive reserve. Latent growth curve models (SEM) were used to assess the impact of brain structure and cognitive reserve on longitudinal cognition.ResultThe mean age of our sample was 78.8 years, with 45.4% female, and 27.1% APOE4 carriers. Mean follow‐up time was 4.0 years with a mean of 3.2 MRI scans per participant. Linear mixed models showed that poorer vascular/metabolic health (presence of vascular/metabolic conditions) was associated with lower cross‐sectional temporal region cortical thickness (‐0.014 (0.003), p<0.001) and lower global cognition (‐0.042 (0.018), p = 0.024). Higher estimated cognitive reserve at baseline as assessed by education/occupation composite (0.103 (0.01), p<0.001) was associated with higher global cognition. Unexpectedly, late‐life physical activities were related to lower global cognition (‐0.05 (0.007), p = 0.004). Our final latent growth curve SEM fit the data well showing temporal associations between unmodifiable characteristics, cognitive reserve, and brain structural pathways, as well as the influence of modifiable vascular/metabolic factors.ConclusionWe found that brain maintenance and cognitive reserve pathways were related to better cognition over time. We further showed that vascular/metabolic health impacts long‐term cognitive outcomes (i.e. cognitive aging) primarily through the brain maintenance pathway. These data provide a modeling scheme for future studies and can aid in assessing the pathways by which individual factors may influence cognitive aging.

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.

Background: Posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are associated with characteristic patterns of structural network degeneration. Little is known about longitudinal patterns of white matter tract degeneration in these phenotypes. Objective: To assess longitudinal patterns of white matter degeneration and identify phenotype specific cross-sectional and longitudinal diffusion tensor imaging (DTI) biomarkers in PCA and LPA. Methods: Twenty-five PCA, 22 LPA and 25 cognitively unimpaired (CU) individuals were recruited and underwent structural MRI that included a DTI sequence with a follow-up one year later. Cross-sectional and longitudinal mixed effects models were fit to assess the effects of diagnosis on baseline and annualized change in regional DTI metrics. Discriminatory power was investigated using the area under the receiver operating characteristic curves (AUROC). Results: PCA and LPA showed overlapping white matter degeneration profiles predominantly in the left occipital and temporal lobes, the posterior thalamic radiation and sagittal stratum at baseline, as well as the parietal lobe longitudinally. PCA showed degeneration in the occipital and parietal white matter, cross-sectionally and longitudinally, compared to CU, while LPA showed greater degeneration in the temporal and inferior parietal white matter and the inferior fronto-occipital fasciculus cross-sectionally, and in parietal white matter longitudinally compared to CU. Cross-sectionally, integrity of the inferior occipital white matter was best able to differentiate PCA from LPA, with an AUROC of 0.82. Conclusion: These findings contribute to our understanding of white matter degeneration and support usage of DTI as a useful additional diagnostic biomarker for PCA and LPA.