Springer Science and Business Media LLC

Simultaneous consideration of two neuropathological traits related to Alzheimer’s disease (AD) has not been attempted in a genome-wide association study. We conducted genome-wide pleiotropy analyses using association summary statistics from the Beecham et al. study (PLoS Genet 10:e1004606, 2014) for AD-related neuropathological traits, including neuritic plaque (NP), neurofibrillary tangle (NFT), and cerebral amyloid angiopathy (CAA). Significant findings were further examined by expression quantitative trait locus and differentially expressed gene analyses in AD vs. control brains using gene expression data. Genome-wide significant pleiotropic associations were observed for the joint model of NP and NFT (NP + NFT) with the single-nucleotide polymorphism (SNP) rs34487851 upstream of C2orf40 (alias ECRG4, P = 2.4 × 10−8) and for the joint model of NFT and CAA (NFT + CAA) with the HDAC9 SNP rs79524815 (P = 1.1 × 10−8). Gene-based testing revealed study-wide significant associations (P ≤ 2.0 × 10−6) for the NFT + CAA outcome with adjacent genes TRAPPC12, TRAPPC12-AS1, and ADI1. Risk alleles of proxy SNPs for rs79524815 were associated with significantly lower expression of HDAC9 in the brain (P = 3.0 × 10−3), and HDAC9 was significantly downregulated in subjects with AD compared with control subjects in the prefrontal (P = 7.9 × 10−3) and visual (P = 5.6 × 10−4) cortices. Our findings suggest that pleiotropy analysis is a useful approach to identifying novel genetic associations with complex diseases and their endophenotypes. Functional studies are needed to determine whether ECRG4 or HDAC9 is plausible as a therapeutic target.

Disease-modifying treatments for Alzheimer’s disease highlight the need for early detection of cognitive decline. However, at present, most primary care providers do not perform routine cognitive testing, in part due to a lack of access to practical cognitive assessments, as well as time and resources to administer and interpret the tests. Brief and sensitive digital cognitive assessments, such as the Digital Clock and Recall (DCR™), have the potential to address this need. Here, we examine the advantages of DCR over the Mini-Mental State Examination (MMSE) in detecting mild cognitive impairment (MCI) and mild dementia. We studied 706 participants from the multisite Bio-Hermes study (age mean ± SD = 71.5 ± 6.7; 58.9% female; years of education mean ± SD = 15.4 ± 2.7; primary language English), classified as cognitively unimpaired (CU; n = 360), mild cognitive impairment (MCI; n = 234), or probable mild Alzheimer’s dementia (pAD; n = 111) based on a review of medical history with selected cognitive and imaging tests. We evaluated cognitive classifications (MCI and early dementia) based on the DCR and the MMSE against cohorts based on the results of the Rey Auditory Verbal Learning Test (RAVLT), the Trail Making Test-Part B (TMT-B), and the Functional Activities Questionnaire (FAQ). We also compared the influence of demographic variables such as race (White vs. Non-White), ethnicity (Hispanic vs. Non-Hispanic), and level of education (≥ 15 years vs. < 15 years) on the DCR and MMSE scores. The DCR was superior on average to the MMSE in classifying mild cognitive impairment and early dementia, AUC = 0.70 for the DCR vs. 0.63 for the MMSE. DCR administration was also significantly faster (completed in less than 3 min regardless of cognitive status and age). Among 104 individuals who were labeled as “cognitively unimpaired” by the MMSE (score ≥ 28) but actually had verbal memory impairment as confirmed by the RAVLT, the DCR identified 84 (80.7%) as impaired. Moreover, the DCR score was significantly less biased by ethnicity than the MMSE, with no significant difference in the DCR score between Hispanic and non-Hispanic individuals. DCR outperforms the MMSE in detecting and classifying cognitive impairment—in a fraction of the time—while being not influenced by a patient’s ethnicity. The results support the utility of DCR as a sensitive and efficient cognitive assessment in primary care settings. ClinicalTrials.gov identifier NCT04733989.

The concept of dependence has been proposed as a unified representation of disease severity to quantify and stage disease progression in a manner more informative to patients, caregivers, and healthcare providers. This paper provides a review of the Dependence Scale (DS) as a quantitative measure of Alzheimer’s disease severity, its properties as an outcome measure, a metric of disease progression, and a correlate of medical costs. The literature supports the notion that the DS is related to, but distinct from, key severity measures, including cognition, function, and behavior, and captures the full spectrum of patient needs. It also presents as a useful measure for assessing disease progression. Results underscore the importance of the DS as a unique endpoint in Alzheimer’s disease clinical trials, providing important information about the impact of therapeutic interventions. The DS also is a useful measure for economic evaluation of novel interventions aimed at delaying progression.

With the recent publication of new criteria for the diagnosis of preclinical Alzheimer's disease (AD), there is a need for neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the neuropsychological tests of the National Alzheimer's Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets. Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all neuropsychological tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model. For each neuropsychological test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output. An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer's disease.

The Genetic Frontotemporal Initiative Staging Group has proposed clinical criteria for the diagnosis of prodromal frontotemporal dementia (FTD), termed mild cognitive and/or behavioral and/or motor impairment (MCBMI). The objective of the study was to validate the proposed research criteria for MCBMI-FTD in a cohort of genetically confirmed FTD cases against healthy controls. A total of 398 participants were enrolled, 117 of whom were carriers of an FTD pathogenic variant with mild clinical symptoms, while 281 were non-carrier family members (healthy controls (HC)). A subgroup of patients underwent blood neurofilament light (NfL) levels and anterior cingulate atrophy assessment. The core clinical criteria correctly classified MCBMI vs HC with an AUC of 0.79 (p < 0.001), while the addition of either blood NfL or anterior cingulate atrophy significantly increased the AUC to 0.84 and 0.82, respectively (p < 0.001). The addition of both markers further increased the AUC to 0.90 (p < 0.001). The proposed MCBMI criteria showed very good classification accuracy for identifying the prodromal stage of FTD.

MicroRNAs (miRNAs) are noncoding RNAs that are highly relevant as disease biomarkers. Several studies that explored the role of miRNAs in Alzheimer’s disease (AD) demonstrated their usefulness in clinical identification. Nevertheless, miRNAs that may act as endogenous controls (ECs) have not yet been established. The identification of ECs would contribute to the standardization of these biomarkers in AD. The objective of the study was to identify miRNAs that can be used as ECs in AD. We evaluated 145 patients divided into two different cohorts. One was a discovery cohort of 19 women diagnosed with mild to moderate AD (Mini-Mental State Examination (MMSE) score ≥ 20) and with confirmed pathologic levels of Aβ42 in CSF. The stability assessment cohort consisted of 126 individuals: 24 subjects without AD or any kind of dementia and negative for all core CSF biomarkers of AD, 25 subjects with MCI and negative for CSF biomarkers (MCI −), 22 subjects with MCI and positive for CSF biomarkers (MCI +), and 55 subjects with AD and positive for CSF biomarkers. In the discovery cohort, a profile of 384 miRNAs was determined in the plasma by TaqMan low-density array. The best EC candidates were identified by mean-centering and concordance correlation restricted normalization methods. The stability of the EC candidates was assessed using the GeNorm, BestKeeper, and NormFinder algorithms. Nine miRNAs (hsa-miR-324-5p, hsa-miR-22-5p, hsa-miR-103a-2-5p, hsa-miR-362-5p, hsa-miR-425-3p, hsa-miR-423-5p, hsa-let-7i-3p, hsa-miR-532-5p, and hsa-miR-1301-3p) were identified as EC candidates in the discovery cohort. The validation results indicated that hsa-miR-103a-2-5p was the best EC, followed by hsa-miR-22-5p, hsa-miR-1301-3p, and hsa-miR-425-3p, which had similar stability values in all three algorithms. We identified a profile of four miRNAs as potential plasma ECs to be used for normalization of miRNA expression data in studies of subjects with cognitive impairment.

Pathogenic heterozygous mutations in the progranulin gene (GRN) are a key cause of frontotemporal dementia (FTD), leading to significantly reduced biofluid concentrations of the progranulin protein (PGRN). This has led to a number of ongoing therapeutic trials aiming to treat this form of FTD by increasing PGRN levels in mutation carriers. However, we currently lack a complete understanding of factors that affect PGRN levels and potential variation in measurement methods. Here, we aimed to address this gap in knowledge by systematically reviewing published literature on biofluid PGRN concentrations. Published data including biofluid PGRN concentration, age, sex, diagnosis and GRN mutation were collected for 7071 individuals from 75 publications. The majority of analyses (72%) had focused on plasma PGRN concentrations, with many of these (56%) measured with a single assay type (Adipogen) and so the influence of mutation type, age at onset, sex, and diagnosis were investigated in this subset of the data. We established a plasma PGRN concentration cut-off between pathogenic mutation carriers and non-carriers of 74.8 ng/mL using the Adipogen assay based on 3301 individuals, with a CSF concentration cut-off of 3.43 ng/mL. Plasma PGRN concentration varied by GRN mutation type as well as by clinical diagnosis in those without a GRN mutation. Plasma PGRN concentration was significantly higher in women than men in GRN mutation carriers (p = 0.007) with a trend in non-carriers (p = 0.062), and there was a significant but weak positive correlation with age in both GRN mutation carriers and non-carriers. No significant association was seen with weight or with TMEM106B rs1990622 genotype. However, higher plasma PGRN levels were seen in those with the GRN rs5848 CC genotype in both GRN mutation carriers and non-carriers. These results further support the usefulness of PGRN concentration for the identification of the large majority of pathogenic mutations in the GRN gene. Furthermore, these results highlight the importance of considering additional factors, such as mutation type, sex and age when interpreting PGRN concentrations. This will be particularly important as we enter the era of trials for progranulin-associated FTD.

Mild cognitive impairment (MCI) has an uncertain etiology and prognosis and may be challenging for clinicians to discuss with patients and families. Amyloid imaging may aid specialists in determining MCI etiology and prognosis, but creates novel challenges related to disease labeling. We convened a workgroup to formulate recommendations for clinicians providing care to MCI patients. Clinicians should use the MCI diagnosis to validate patient and family concerns and educate them that the patient’s cognitive impairment is not normal for his or her age and education level. The MCI diagnosis should not be used to avoid delivering a diagnosis of dementia. For patients who meet Appropriate Use Criteria after standard-of-care clinical workup, amyloid imaging may position specialists to offer more information about etiology and prognosis. Clinicians must set appropriate expectations, including ensuring that patients and families understand the limitations of amyloid imaging. Communication of negative results should include that patients remain at elevated risk for dementia and that negative scans do not indicate a specific diagnosis or signify brain health. Positive amyloid imaging results should elicit further monitoring and conversations about appropriate advance planning. Clinicians should offer written summaries, including referral to appropriate social services. In patients with MCI, there is a need to devote considerable time and attention to patient education and shared decision-making. Amyloid imaging may be a tool to aid clinicians. Careful management of patient expectations and communication of scan results will be critical to the appropriate use of amyloid imaging information.