Genetic Risk for Alzheimer Disease and Plasma Tau Are Associated With Accelerated Parietal Cortex Thickness Change in Middle-Aged Adults.

Background and Objectives: Neuroimaging and biomarker studies in Alzheimer disease (AD) have shown well-characterized patterns of cortical thinning and altered biomarker concentrations of tau and ß-amyloid (Aß). However, earlier identification of AD has great potential to advance clinical care and determine candidates for drug trials. The extent to which AD risk markers relate to cortical thinning patterns in midlife is unknown. The first objective of this study was to examine cortical thickness change associated with genetic risk for AD among middle-aged military veterans. The second objective was to determine the relationship between plasma tau and Aß and change in brain cortical thickness among veterans stratified by genetic risk for AD. Methods: Participants consisted of post-9/11 veterans (N = 155) who were consecutively enrolled in the Translational Research Center for TBI and Stress Disorders prospective longitudinal cohort and were assessed for mild traumatic brain injury (TBI) and posttraumatic disorder (PTSD). Genome-wide polygenic risk scores (PRSs) for AD were calculated using summary results from the International Genomics of Alzheimer's Disease Project. T-tau and Aß40 and Aß42 plasma assays were run using Simoa technology. Whole-brain MRI cortical thickness change estimates were obtained using the longitudinal stream of FreeSurfer. Follow-up moderation analyses examined the AD PRS × plasma interaction on change in cortical thickness in AD-vulnerable regions. Results: Higher AD PRS, signifying greater genetic risk for AD, was associated with accelerated cortical thickness change in a right hemisphere inferior parietal cortex cluster that included the supramarginal gyrus, angular gyrus, and intraparietal sulcus. Higher tau, but not Aß42/40 ratio, was associated with greater cortical thickness change among those with higher AD PRS. Mild TBI and PTSD were not associated with cortical thickness change. Discussion: Plasma tau, particularly when combined with genetic stratification for AD risk, can be a useful indicator of brain change in midlife. Accelerated inferior parietal cortex changes in midlife may be an important factor to consider as a marker of AD-related brain alterations.

Test-Retest Reliability and Efficacy of Individual Symptoms in Concussion Management.

OBJECTIVE: (1) To determine test-retest reliability of individual Sport Concussion Assessment Tool-Third Edition (SCAT-3) symptom scores and symptom severity scores, (2) to examine the specificity/sensitivity of individual SCAT-3 symptom severity scores acutely (24-48 hours) postconcussion, and (3) to develop a model of symptoms best able to differentiate concussed from nonconcussed student athletes and cadets. DESIGN: Prospective, longitudinal, and cross-sectional. SETTING: Twenty-six civilian schools and 3 US service academies. PARTICIPANTS: Collegiate student athletes (n = 5519) and cadets (n = 5359) from the National Collegiate Athletic Association-Department of Defense Grand Alliance: Concussion Assessment, Research and Education Consortium, including 290 student athletes and 205 cadets, assessed 24 to 48 hours postconcussion. INDEPENDENT VARIABLES: Concussed and nonconcussed student athlete and cadet groups. MAIN OUTCOME MEASURES: Sport Concussion Assessment Tool-Third Edition individual symptom severity scores, total symptom scores, and symptom severity scores. RESULTS: Results indicated poor test-retest reliability across all symptom scores (intraclass correlation coefficient = 0.029-0.331), but several individual symptoms had excellent predictive capability in discriminating concussed from nonconcussed participants (eg, headache, pressure in the head, and don't feel right had area under the curve >0.8, sensitivity >70%, and specificity >85%) regardless of baseline testing. These symptoms were consistent with Chi-square Automatic Interaction Detector classification trees with the highest mean probability. CONCLUSIONS: Findings support the excellent diagnostic accuracy of honest symptom reporting, notwithstanding the known limitations in symptom underreporting, and suggest that there may be added value in examining individual symptoms rather than total symptom scores and symptom severity scores alone. Finally, findings suggest that baseline testing is not necessary for interpreting postconcussion symptom scores.

Posttraumatic stress disorder symptom severity is associated with reduced Montreal Cognitive Assessment scores in a sample of Vietnam War Veterans.

The goal of the present study was to examine associations between posttraumatic stress disorder (PTSD) symptom severity, the number of stressors experienced, and cognitive outcomes in a sample of U.S. Vietnam War Veterans (N = 274). Adults between 60 and 85 years of age completed a Vietnam Veterans Alzheimer's Disease Neuroimaging Initiative Project visit. A modified version of the Life Stressor Checklist-Revised (LSC-R) was used to assess the number of stressful experiences participants experienced, current PTSD severity scores were measured via the Clinician-Administered PTSD Scale for DSM-IV (CAPS-IV), and cognition was assessed using the Montreal Cognitive Assessment (MoCA). Linear regressions were conducted to examine the effect of CAPS-IV and LSC-R scores on cognitive performance. Higher CAPS-IV scores were associated with worse cognitive outcomes on the MoCA, ΔF(1, 264) = 12.686, p < .001, R2 = .142. In contrast, the number of reported stressful experiences was not associated with cognitive outcomes. After accounting for multiple comparisons, findings indicated that CAPS-IV severity scores were significantly associated with the MoCA memory index. In a sample of older Veterans, PTSD symptom severity, but not the number of reported stressors, was associated with poorer performance on a well-established cognitive function screening tool. Analyses of specific MoCA domains indicated that memory may be driving this association. These findings suggest that highly arousing stressors characteristic of PTSD, rather than stressful experiences more broadly, contribute to this association. Future work can use these findings to explore whether treating PTSD symptoms may help maintain cognitive function during the aging process.

The association between blast exposure and transdiagnostic health symptoms on systemic inflammation.

Chronic elevation of systemic inflammation is observed in a wide range of disorders including PTSD, depression, and traumatic brain injury. Although previous work has demonstrated a link between inflammation and various diagnoses separately, few studies have examined transdiagnostic symptoms and inflammation within the same model. The objective of this study was to examine relationships between psychiatric and health variables and systemic inflammation and to determine whether mild traumatic brain injury (mTBI) and/or exposure to blast munitions moderate these relationships. Confirmatory factor analysis in a large sample (N = 357) of post-9/11 Veterans demonstrated a good fit to a four-factor model reflecting traumatic stress, affective, somatic, and metabolic latent variables. Hierarchical regression models revealed that each of the latent variables were associated with higher levels of systemic inflammation. However, the strongest relationship with inflammation emerged among those who had both war-zone blast exposures and metabolic dysregulation, even after adjusting for mental health latent variables. Exploratory analyses showed that blast exposure was associated with metabolic dysregulation in a dose-response manner, with self-reported closer blast proximity associated with the greatest metabolic dysregulation. Together, these results provide a greater understanding of the types of symptoms most strongly associated with inflammation and underscore the importance of maintaining a healthy lifestyle to reduce the impact of obesity and other metabolic symptoms on future chronic disease in younger to middle-aged Veterans.

Machine learning identifies novel markers predicting functional decline in older adults.

The ability to carry out instrumental activities of daily living, such as paying bills, remembering appointments and shopping alone decreases with age, yet there are remarkable individual differences in the rate of decline among older adults. Understanding variables associated with a decline in instrumental activities of daily living is critical to providing appropriate intervention to prolong independence. Prior research suggests that cognitive measures, neuroimaging and fluid-based biomarkers predict functional decline. However, a priori selection of variables can lead to the over-valuation of certain variables and exclusion of others that may be predictive. In this study, we used machine learning techniques to select a wide range of baseline variables that best predicted functional decline in two years in individuals from the Alzheimer's Disease Neuroimaging Initiative dataset. The sample included 398 individuals characterized as cognitively normal or mild cognitive impairment. Support vector machine classification algorithms were used to identify the most predictive modality from five different data modality types (demographics, structural MRI, fluorodeoxyglucose-PET, neurocognitive and genetic/fluid-based biomarkers). In addition, variable selection identified individual variables across all modalities that best predicted functional decline in a testing sample. Of the five modalities examined, neurocognitive measures demonstrated the best accuracy in predicting functional decline (accuracy = 74.2%; area under the curve = 0.77), followed by fluorodeoxyglucose-PET (accuracy = 70.8%; area under the curve = 0.66). The individual variables with the greatest discriminatory ability for predicting functional decline included partner report of language in the Everyday Cognition questionnaire, the ADAS13, and activity of the left angular gyrus using fluorodeoxyglucose-PET. These three variables collectively explained 32% of the total variance in functional decline. Taken together, the machine learning model identified novel biomarkers that may be involved in the processing, retrieval, and conceptual integration of semantic information and which predict functional decline two years after assessment. These findings may be used to explore the clinical utility of the Everyday Cognition as a non-invasive, cost and time effective tool to predict future functional decline.

A comparison of cognitive performance in the Suffolk County cohort and their unaffected siblings.

People diagnosed with schizophrenia and other psychoses demonstrate impaired neuropsychological performance. Their unaffected siblings exhibit mild impairments relative to unrelated controls, suggesting genetic and shared environmental risk for psychosis account for some portion of cognitive impairments observed in cases. However, most sibling studies were conducted early in illness course. Studying cases and unaffected siblings later in life is valuable because diagnostic misclassification is common early in illness, possibly leading to spurious conclusions. This study compared neuropsychological performance of individuals with psychotic disorders (schizophrenia and other psychoses), their unaffected siblings, and controls. Assessments were conducted 20 years after case enrollment in the Suffolk County Mental Health Project, when siblings and controls were added to the protocol. Results showed individuals with schizophrenia and other psychoses performed worse than their matched siblings across domains. Relative to controls, siblings of participants with schizophrenia showed mild deficits in executive function and processing speed, while no significant differences were observed between siblings of those with other psychoses and controls. These findings suggest pre- and post-onset factors impact cognitive deficits in psychosis, but pre-onset factors are more salient in schizophrenia. Additionally, schizophrenia and other psychoses exist on a neurodevelopmental continuum, with schizophrenia being a more severe manifestation.

Body Mass Index and Polygenic Risk for Alzheimer's Disease Predict Conversion to Alzheimer's Disease.

Body mass index (BMI) is a risk factor for Alzheimer's disease (AD) although the relationship is complex. Obesity in midlife is associated with increased risk for AD, whereas evidence supports both higher and lower BMI increasing risk for AD in late life. This study examined the influence of individual differences in genetic risk for AD to further clarify the relationship between late-life BMI and conversion to AD. Participants included 52 individuals diagnosed as having mild cognitive impairment (MCI) at baseline who converted to AD within 24 months and 52 matched MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. BMI was measured at baseline. Genetic risk for AD was assessed via genome-wide polygenic risk scores. Conditional logistic regression models were run to determine if BMI and polygenic risk predicted conversion to AD. Results showed an interaction between BMI and genetic risk, such that individuals with lower BMI and higher polygenic risk were more likely to convert to AD relative to individuals with higher BMI. These results remained significant after adjusting for cerebrospinal fluid biomarkers of AD. Exploratory sex-stratified analyses revealed this relationship only remained significant in males. These results show that higher genetic risk in the context of lower BMI predicts conversion to AD in the next 24 months, particularly among males. These findings suggest that genetic risk for AD in the context of lower BMI may serve as a prodromal risk factor for future conversion to AD.

Genetic Risk for Alzheimer's Disease Moderates the Association Between Medial Temporal Lobe Volume and Episodic Memory Performance Among Older Adults.

BACKGROUND: A complex set of interactions between biological, genetic, and environmental factors likely underlies the development of Alzheimer's disease (AD). Identifying which of these factors is most associated with AD is important for early diagnosis and treatment. OBJECTIVE: We sought to examine genetic risk and structural brain volume on episodic memory in a sample of older adults ranging from cognitively normal to those diagnosed with AD. METHODS: 686 adults (55-91 years old) completed a 3T MRI scan, baseline cognitive assessments, and biospecimen collection through the Alzheimer's Disease Neuroimaging Initiative. Hierarchical linear regression analyses examined main and interaction effects of medial temporal lobe (MTL) volume and polygenic hazard score (PHS), indicating genetic risk for AD, on a validated episodic memory composite score. RESULTS: Genetic risk moderated the relationship between MTL volume and memory, such that individuals with high PHS and lower hippocampal and entorhinal volume had lower memory composite scores [ΔF (1,677) = 4.057, p = 0.044, ΔR2 = 0.002]. Further analyses showed this effect was driven by the left hippocampus [ΔF(1,677) = 5.256, p = 0.022, ΔR2 = 0.003] and right entorhinal cortex [ΔF (1,677) = 6.078, p = 0.014, ΔR2 = 0.003]. CONCLUSIONS: Among those with high genetic risk for AD, lower volume was associated with poorer memory. Results suggest that the interaction between AD genetic risk and MTL volume increases the likelihood for memory impairment among older adults. Results from this study suggest that genetic risk and brain volume should be considered key factors in tracking cognitive decline.