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Background: Repetitive head impacts (RHI) from American football can lead to tau and non‐tau pathologies that might present as white matter hyperintensities (WMH) on FLAIR MRI. In 2022, we published a study in Alzheimer's & Dementia that examined WMH and their association with risk factors and clinical function in former elite football players. That study will be presented during this symposium along with new unpublished data that used imaging and fluid biomarkers to examine etiological correlates (tau, amyloid, neurodegeneration, axonal injury, neuroinflammation) of WMH in the same cohort. Methods: The multi‐site DIAGNOSE CTE Research Project recruited 180 football players and 60 asymptomatic males without RHI ("controls"), all 45‐74 years (Table 1). Participants completed MRI, lumbar puncture, and neuropsychological testing. Lesion Segmentation Toolbox estimated FLAIR WMH, FreeSurfer derived total cortical thickness, and a diffusion pipeline derived average global FA. CSF was analyzed for p‐tau181, Aβ1‐42, NfL, and sTREM2. Tobit regressions compared football players (n = 149) and controls (n = 53) on total and regional log‐WMH and estimated the effects of years of football and age of first exposure (AFE) to football on log‐WMH. Linear regressions evaluated log‐WMH and clinical associations in football players. Structural equation modeling (SEM) examined effects between log‐WMH and cortical thickness, FA, and the CSF biomarkers. Analyses accounted for age, race, revised Framingham Stroke Risk Profile (rFSRP), body mass index, APOE ε4, and evaluation site. Results: Alosco et al. (2022) found older (i.e., 60+) but not younger football players had greater total, frontal, temporal and parietal log‐WMH compared to controls (FDR‐adjusted p‐values<0.05; Table 2). Among older football players, younger AFE was associated with greater log‐WMH (beta = ‐0.13, 95% CI = ‐0.23,‐0.02). Greater log‐WMH corresponded to worse Trails A‐B (beta = ‐4.32, 95% CI = ‐8.59,‐0.05) and List Learning Long Delay scores (beta = ‐0.56, 95% CI = ‐0.93,‐0.19). Subsequent unpublished SEM analyses in football players (Figure 1) showed the following direct effects on log‐WMH: higher rFSRP, higher CSF p‐tau181, lower FA, and reduced cortical thickness. There were no indirect effects. Compared to controls, SEM associations were stronger in football players exception for cortical thickness. Conclusions: FLAIR WMH might have unique imaging characteristics, risk factors, and pathological underpinnings in people exposed to RHI. [ABSTRACT FROM AUTHOR]

Background: Within older Veterans, multiple factors may contribute to cognitive difficulties. Beyond Alzheimer's disease (AD), military‐related exposures leading to vascular disease (e.g., agent orange), posttraumatic stress disorder (PTSD), and traumatic brain injury (TBI) may also impact cognition. This study aimed to derive groups based on objective cognition, subjective cognition, and amyloid burden, and then compare groups on demographic, clinical, and biomarker characteristics. Method: A hierarchical cluster analysis was conducted on 233 Vietnam‐Era Veterans without dementia from the Department of Defense‐Alzheimer's Disease Neuroimaging Initiative. Variables entered in the cluster analysis included: immediate and delayed memory, language, and attention/executive functioning domain scores; subjective cognitive decline (SCD) severity; and cortical amyloid PET SUVR. A discriminate function analysis (DFA) tested the extent to which the individual variables predicted cluster‐group membership. ANOVAs and chi‐squared tests examined demographic, clinical, and biomarker characteristics by group. Result: Cluster analysis identified 4 groups: Group 1 with high cognition, low SCD, and average amyloid (n = 41); Group 2 with average cognition, average SCD, and low amyloid (n = 121); Group 3 with low attention/executive functioning, average SCD, and high amyloid (n = 28); and Group 4 with low memory/language, high SCD, and average amyloid (n = 43; Figure 1). The DFA correctly classified 88.8% of participants. Group 3 had the greatest proportion of APOE ε4 carriers and individuals with TBI, highest tau burden, and lowest hippocampal volume. Group 4 had the fewest years of education, highest proportion of Hispanic/Latino participants, PTSD, and worst sleep quality. Groups did not differ on age, depressive symptoms, hypertension, or substance use (Table 1). Conclusion: Within Vietnam‐Era Veterans, there were high and average cognition groups (Groups 1 and 2), both with low amyloid burden. Group 3 was most consistent with biological AD, yet attention/executive function was the lowest score. The complexity of older Veterans' co‐morbid conditions may interact with AD pathology to show attention/executive dysfunction (rather than memory) as a prominent early symptom. Group 4 had the worst cognitive scores and highest SCD despite low amyloid; education, PTSD symptoms, sleep quality, and perhaps sociocultural factors may be contributing to this profile. Future work will investigate longitudinal cognitive and functional declines by group. [ABSTRACT FROM AUTHOR]

Background: Compared to conventional Petersen/Winblad criteria for mild cognitive impairment (MCI), our prior work has shown that neuropsychological or "actuarial" criteria more accurately predicts progression to dementia, and is more strongly associated with Alzheimer's disease (AD) biomarkers and imaging profiles. However, research to date has been conducted in relatively healthy samples with few comorbidities. Given that history of traumatic brain injury (TBI) and post‐traumatic stress disorder (PTSD) is common in older veteran populations, and both conditions are well‐documented risk factors for AD, we compared Jak/Bondi neuropsychological and ADNI MCI criteria to examine cross‐sectional cognitive and biomarker profiles in a well‐characterized sample of Vietnam‐era veterans with history of TBI and PTSD. Method: 186 veterans (mean age = 70.11) from the DoD‐ADNI study with history of TBI were evaluated for MCI using (1) ADNI criteria: subjective memory concern, objective memory impairment using WMS‐R Logical Memory, and Clinical Dementia Rating Global = 0.5; and (2) neuropsychological criteria: >1 standard deviation (SD) below age‐appropriate norms on two tests within any cognitive domain, or >1 SD below age‐appropriate norms on one test across all domains. Accounting for age and education, linear regression evaluated relationships between MCI diagnosis and AD biomarkers (Aβ42, total tau [t‐tau], and phosphorylated tau [p‐tau]). Logistic regression evaluated whether PTSD symptoms (CAPS total score) predicted MCI. Result: Roughly 17% of the sample met neuropsychological criteria for MCI, while 13% met ADNI criteria. Agreement between criteria was poor (κ =.17). APOE4 carrier status did not predict diagnosis by either criteria. MCI diagnosis using neuropsychological criteria was associated with higher t‐tau (p =.049) and p‐tau (p =.04), but not Aβ42 (p =.15), while ADNI diagnosis was not associated with any biomarkers (p>.05). PTSD symptoms predicted MCI diagnosis using ADNI (p =.005) but not neuropsychological criteria (p =.19). Conclusion: Findings showed that MCI diagnosis using sensitive neuropsychological methods is more strongly associated with AD biomarkers than conventional diagnostic methods. Additionally, ADNI criteria may be capturing false‐positive diagnoses secondary to PTSD. MCI diagnostics benefit from the incorporation of comprehensive neuropsychological methods, particularly in complex populations with medical and psychiatric comorbidities. [ABSTRACT FROM AUTHOR]