Maintaining good cognitive function at older age is important, but our knowledge of patterns and predictors of cognitive aging is still limited. We used Bayesian model-based clustering to group 5064 participants of the Long Life Family Study (ages 49-110 years) into clusters characterized by distinct trajectories of cognitive change in the domains of episodic memory, attention, processing speed, and verbal

Objective Impairments in cognition and everyday functioning are common in schizophrenia and bipolar disorder. Based on two studies of schizophrenia (SCZ) and bipolar I disorder (BPI) with similar methods, this paper presents factor analyses of cognitive and functional capacity (FC) measures. The overall goal of these analyses was to determine whether performance-based assessments should be examined individually, or aggregated on the basis of the correlational structure of the tests and as well as to evaluate the similarity of factor structures in SCZ and BPI. Method Veterans Affairs (VA) Cooperative Studies Program study #572, evaluated cognitive and FC measures among 5,414 BPI and 3,942 SZ patients. A second study evaluated similar neuropsychological (NP) and FC measures among 368 BPI and 436 SZ patients. Principal components analysis, as well as exploratory and confirmatory factor analyses, were used to examine the data. Results Analyses in both datasets suggested that NP and FC measures were explained by of a single underlying factor in BPI and SCZ patients, both when analyzed separately or as in a combined sample. The factor structure in both studies was similar, with or without inclusion of FC measures; homogeneous loadings were observed for that single factor across cognitive and FC domains across the samples. Conclusions The empirically derived factor model suggests that NP performance and FC are best explained as a single latent trait applicable to people with schizophrenia and bipolar illness. This single measure may enhance the robustness of the analyses relating genomic data to performance-based phenotypes.

Identifying sickle cell disease patients at high risk of complications could lead to personalized treatment and better prognosis but despite many advances prediction of the clinical course of these patients remains elusive. We propose a system-type approach to discover profiles of multiple, common biomarkers that correlate with morbidity and mortality in sickle cell disease. We used cluster analysis to discover 17 signatures of 17 common circulating biomarkers in 2320 participants of the Cooperative Study of Sickle Cell Disease, and evaluated the association of these signatures with risk for stroke, pain, leg ulceration, acute chest syndrome, avascular necrosis, seizure, death, and trend of fetal hemoglobin and hemolysis using longitudinally collected data. The analysis shows that some of the signatures are associated with reduced risk for complications, while others are associated with increased risk for complications. We also show that these signatures repeat in two more contemporary studies of sickle cell disease and correlate with recently discovered biomarkers of pulmonary vascular disease. With replication and further study, these biomarker signatures could become an important and affordable precision medicine tool to aid treatment and management of the disease.

Deployment-related TBI may not be reported reliably over time, particularly among war-zone veterans with greater PTSD symptoms. Results of screening evaluations for TBI history should be viewed with caution in the context of PTSD symptom history.

The Trail Making Test (TMT) is a neuropsychological test used to assess cognitive dysfunction. The TMT consists of two parts: TMT‐A requires connecting numbers 1 to 25 sequentially; TMT‐B requires connecting numbers 1 to 12 and letters A to L sequentially, alternating between numbers and letters. We propose using a digitally recorded version of TMT to capture cognitive or physical functions underlying test performance. We analyzed digital versions of TMT‐A and ‐B to derive time metrics and used Bayesian hidden Markov models to extract additional metrics. We correlated these derived metrics with cognitive and physical function scores using regression. On both TMT‐A and ‐B, digital metrics associated with graphomotor processing test scores and gait speed. Digital metrics on TMT‐B were additionally associated with episodic memory test scores and grip strength. These metrics provide additional information of cognitive state and can differentiate cognitive and physical factors affecting test performance.