Petersen, Ronald C.

ImportanceNomenclature in the field of neurodegenerative diseases presents a challenging problem. Inconsistent use of terms such as Alzheimer disease and dementia has compromised progress in clinical care, research, and development of therapeutics. Dementia-associated stigma further contributes to inconsistent and imprecise language. The result is a lack of clarity that produces confusion with patients and the general public and presents communication challenges among researchers. Therefore, the Advisory Council on Research, Care, and Services of the National Plan to Address Alzheimer’s Disease authorized a committee to make recommendations for improvement.ObjectiveTo establish a systematic neurodegenerative disease framework for information collection and communication to standardize language usage for research, clinical, and public health purposes.Evidence ReviewThe Dementia Nomenclature Initiative organized into 3 major stakeholder working groups: clinicians, researchers, and the public (including individuals living with dementia and family caregivers). To inform the work, the initiative completed a narrative literature review of dementia nomenclature evolution over the last century across the PubMed, CINAHL, PsycInfo, and Scopus databases (January 1, 2000, through July 31, 2020). Initiative working groups used the results as a foundation for understanding current challenges with dementia nomenclature and implications for research, clinical practice, and public understanding. The initiative obtained additional input via focus groups with individuals living with dementia and caregivers, with separate groups for race and ethnicity (American Indian or Alaska Native, Asian or Pacific Islander, Black or African American, Hispanic or Latino, and White) as an initial assessment of the meaning of dementia-related terms to these groups.FindingsFrom working group deliberations, the literature review, and focus group input, the initiative developed a framework clearly separating the clinical syndromic presentation experienced by affected individuals from possible underlying pathophysiologies. In the framework, domains of clinical impairment, such as cognitive, behavioral, motor, and other neurologic features, are graded by level of impairment between none and severe. Next, biomarker information describes underlying disease processes, explains the syndrome, and identifies possible disease labels: Alzheimer disease, frontotemporal degeneration, dementia with Lewy bodies, or vascular cognitive impairment dementia.Conclusions and RelevanceThe Dementia Nomenclature Initiative established a framework to guide communication about cognitive impairment among older adults. Wider testing and refinement of the framework will subsequently improve the information used in communicating about cognitive impairment and the way in which the information is used in clinical, research, and public settings.

Abstract There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer’s disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer’s disease pathology (behavioural Alzheimer’s disease) and 28 had amnestic Alzheimer’s disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as ‘eigenbrains’. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer’s disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer’s disease, then amnestic Alzheimer’s disease. Dysexecutive Alzheimer’s disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer’s disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer’s disease, temporo-parietal areas in amnestic Alzheimer’s disease and frontotemporal areas in bvFTD and behavioural Alzheimer’s disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer’s disease patient for behavioural Alzheimer’s disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Abstract Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer’s disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging’s use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

Abstract Background Blood-based biomarkers (BBBs) are a growing area of study for improving Alzheimer’s disease (AD) clinical evaluation. BBBs have been demonstrated to correlate with AD CSF biomarkers as well as neuroimaging results for assessment of amyloid pathology. Currently BBBs may be used as a screening tool for patient selection in clinical trials, and alongside more well characterized biomarkers like amyloid-PET and CSF markers for evaluation of individuals presenting with cognitive impairment. A comparative study of several AD BBBs assays was performed to assess bias between two platforms, as well as correlation to markers of amyloid pathology. Methods The study included 146 plasma samples from 112 cognitively normal (CN), and 34 mild cognitive impairment (MCI) or AD dementia individuals. Plasma Aβ42, Aβ40, and pTau181 concentrations were obtained using the Fujirebio Lumipulse and Quanterix Simoa immunoassays. Assay comparison between Fujirebio and Quanterix were performed for Aβ42, Aβ40, Aβ42/Aβ40 ratio, pTau181, and pTau181/Aβ42 ratio using Passing-Bablok regression and regression slopes were obtained. Amyloid-PET was performed using Pittsburg Compound B, and a standard uptake value ratio (SUVR) of 1.48 was used to classify amyloid status. In the CN group, 69 were amyloid negative, and 43 were amyloid positive. In the MCI/AD dementia group, 8 were amyloid negative and 26 were amyloid positive. Spearman’s rank correlation coefficient (rho) was used to determine correlation between assays and correlation of each with Amyloid-PET SUVRs. Results Between Fujirebio (y) and Quanterix (x) assays, measured Aβ42 and Aβ40 concentrations showed a large bias with y = 5.23x − 14.25, and y = 4.42x − 264.87, respectively. A moderate to strong correlation was observed between assays for Aβ42 and Aβ40 with rho values of 0.641 (P < 0.0001), and 0.498 (P < 0.0001), respectively. The ratio of Aβ42/Aβ40 corrected this bias with y = 0.96x + 0.03 and rho = 0.509 (P < 0.0001). pTau181 showed little bias between methods with y = 0.92x + 0.48, and a rho = 0.592 (P < 0.0001). However, the pTau181/Aβ42 ratio showed a large bias driven mostly by differences in the Aβ42 concentrations with y = 0.27x + 0.04 and rho = 0.574 (P < 0.0001). Quanterix assay results correlated with amyloid-PET SUVR yielded rho coefficients of AB42/40 ratio = −0.219 (P = 0.079), pTau181 = 0.380 (P < 0.0001), and pTau181/Aβ42 ratio = 0.466 (P < 0.0001). Fujirebio assay results correlated with amyloid-PET SUVR showed AB42/40 ratio rho = −0.551 (P < 0.0001), pTau181 rho = 0.370 (P < 0.0001), and pTau181/Aβ42 ratio rho = 0.391 (P < 0.0001). Conclusion This study demonstrates that some BBBs methods differ significantly between manufacturers. Little bias was observed between the Aβ42/Aβ40 and pTau181 determinations for the evaluated platforms; however, significant bias was observed in Aβ42, Aβ40, and the pTau181/Aβ42 ratio. The Fujirebio platform demonstrated stronger correlation of the Aβ42/Aβ40 ratio with amyloid-PET SUVR, while Quanterix showed better correlation of the pTau181/Aβ42 ratio. The pTau181 assays showed similar correlation to amyloid-PET SUVR. Bias between assays as well as clinical correlation differences may preclude the generalization of clinical performance of AD BBBs assays between platforms.

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.

AbstractThe Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co‐pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI‐4 aims to address by enrolling a diverse cohort.

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.