As different signature changes associated with particular stages of disease burden are identified and validated, we anticipate expanded application of qEEG as a reliable and sensitive biomarker(s) of MCI and early Alzheimer's disease.

Background Posttraumatic stress disorder (PTSD) is associated with elevated risk for metabolic syndrome (MetS). However, the direction of this association is not yet established, as most prior studies employed cross-sectional designs. The primary goal of this study was to evaluate bidirectional associations between PTSD and MetS using a longitudinal design. Methods 1,355 male and female veterans of the conflicts in Iraq and Afghanistan underwent PTSD diagnostic assessments and their biometric profiles pertaining to MetS were extracted from the electronic medical record at two time points (spanning ~2.5 years, n = 971 at time 2). Results The prevalence of MetS among veterans with PTSD was just under 40% at both time points and was significantly greater than that for veterans without PTSD; the prevalence of MetS among those with PTSD was also elevated relative to age-matched population estimates. Cross-lagged panel models revealed that PTSD severity predicted subsequent increases in MetS severity (β = .08, p = .002), after controlling for initial MetS severity, but MetS did not predict later PTSD symptoms. Logistic regression results suggested that for every 10 PTSD symptoms endorsed at time 1, the odds of a subsequent MetS diagnosis increased by 56%. Conclusions Results highlight the substantial cardiometabolic concerns of young veterans with PTSD and raise the possibility that PTSD may predispose individuals to accelerated aging, in part, manifested clinically as MetS. This demonstrates the need to identify those with PTSD at greatest risk for MetS and to develop interventions that improve both conditions.

The demand for rapidly administered, sensitive, and reliable cognitive assessments that are specifically designed for identifying individuals in the earliest stages of cognitive decline (and to measure subtle change over time) has escalated as the emphasis in Alzheimer’s disease clinical research has shifted from clinical diagnosis and treatment toward the goal of developing presymptomatic neuroprotective therapies. To meet these changing clinical requirements, cognitive measures or tailored batteries of tests must be validated and determined to be fit-for-use for the discrimination between cognitively healthy individuals and persons who are experiencing very subtle cognitive changes that likely signal the emergence of early mild cognitive impairment. We sought to collect and review data systematically from a wide variety of (mostly computer-administered) cognitive measures, all of which are currently marketed or distributed with the claims that these instruments are sensitive and reliable for the early identification of disease or, if untested for this purpose, are promising tools based on other variables. The survey responses for 16 measures/batteries are presented in brief in this review; full survey responses and summary tables are archived and publicly available on the Campaign to Prevent Alzheimer’s Disease by 2020 Web site (http://pad2020.org). A decision tree diagram highlighting critical decision points for selecting measures to meet varying clinical trials requirements has also been provided. Ultimately, the survey questionnaire, framework, and decision guidelines provided in this review should remain as useful aids for the evaluation of any new or updated sets of instruments in the years to come.

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These results demonstrate the stability of between- and within-subject variability in cognitive performance in schizophrenia, and suggest that variability in cognitive performance may reflect an inherent characteristic of the disorder, rather than differences in test-retest reliability/error of cognitive measures.