Objective: Dementia is a devastating neurological disease that may be better managed if diagnosed earlier when subclinical neurodegenerative changes are already present, including subtle cognitive decline and mild cognitive impairment. In this study, we used item-level performance on the Montreal Cognitive Assessment (MoCA) to identify individuals with subtle cognitive decline. Method: Individual MoCA item data from the Alzheimer’s Disease Neuroimaging Initiative was grouped using k-modes cluster analysis. These clusters were validated and examined for association with convergent neuropsychological tests. The clusters were then compared and characterized using multinomial logistic regression. Results: A three-cluster solution had 77.3% precision, with Cluster 1 (high performing) displaying no deficits in performance, Cluster 2 (memory deficits) displaying lower memory performance, and Cluster 3 (compound deficits) displaying lower performance on memory and executive function. Age at MoCA (older in compound deficits), gender (more females in memory deficits), and marital status (fewer married in compound deficits) were significantly different among clusters. Age was not associated with increased odds of membership in the high-performing cluster compared to the others. Conclusions: We identified three clusters of individuals classified as cognitively unimpaired using cluster analysis. Individuals in the compound deficits cluster performed lower on the MoCA and were older and less often married than individuals in other clusters. Demographic analyses suggest that cluster identity was due to a combination of both cognitive and clinical factors. Identifying individuals at risk for future cognitive decline using the MoCA could help them receive earlier evidence-based interventions to slow further cognitive decline.

Background and Purpose Subtle cognitive decline represents a stage of cognitive deterioration in which pathological biomarkers may be present, including early cortical atrophy and amyloid deposition. Using individual items from the Montreal Cognitive Assessment and k‐modes cluster analysis, we previously identified three clusters of individuals without overt cognitive impairment: (1) High Performing (no deficits in performance), (2) Memory Deficits (lower memory performance), and (3) Compound Deficits (lower memory and executive function performance). In this study, we sought to understand the relationships found in our clusters between cortical atrophy on MR and amyloid burden on PET. Methods Data were derived from the Alzheimer's Disease Neuroimaging Initiative and comprised individuals from our previous analyses with available MR and amyloid PET scans (n = 272). Using multiple‐group structural equation modeling, we regressed amyloid standardized uptake value ratio on volumetric regions to simultaneously evaluate unique associations within each cluster. Results In our Compound Deficits cluster, greater whole cerebral amyloid burden was significantly related to right entorhinal cortical and left hippocampal atrophy, rs = –.412 (p = .005) and –.304 (p = .049), respectively. Within this cluster, right entorhinal cortical atrophy was significantly related to greater amyloid burden within multiple frontal regions. Conclusions The Compound Deficits cluster, which represents a group potentially at higher risk for decline, was observed to have significantly more cortical atrophy, particularly within the entorhinal cortex and hippocampus, associated with whole brain and frontal lobe amyloid burden. These findings point to a pattern of early pathological deterioration that may place these individuals at risk for future decline.

Motivation Microbiome datasets are often constrained by sequencing limitations. GenBank is the largest collection of publicly available DNA sequences, which is maintained by the National Center of Biotechnology Information (NCBI). The metadata of GenBank records are a largely understudied resource and may be uniquely leveraged to access the sum of prior studies focused on microbiome composition. Here, we developed a computational pipeline to analyze GenBank metadata, containing data on hosts, microorganisms, and their place of origin. This work provides the first opportunity to leverage the totality of GenBank to shed light on compositional data practices that shape how microbiome datasets are formed as well as examine host-microbiome relationships. Results The collected dataset contains multiple kingdoms of microorganisms, consisting of bacteria, viruses, archaea, protozoa, fungi, and invertebrate parasites, and hosts of multiple taxonomical classes, including mammals, birds, and fish. A human data subset of this dataset provides insights to gaps in current microbiome data collection, which is biased towards clinically relevant pathogens. Clustering and phylogenic analysis reveals the potential to use these data to model host taxonomy and evolution, revealing groupings formed by host diet, environment, and coevolution. Availability GenBank Host-Microbiome Pipeline is available at {{https://github.com/bcbi/genbank_holobiome}}. The GenBank loader is available at {{https://github.com/bcbi/genbank_loader}}. Supplementary information Supplementary data are available at Bioinformatics online.