Reiman, Eric M.

AbstractBackgroundLewy body (LB) diseases can present with overlapping prodromal, cognitive, motor, autonomic or neuropsychiatric symptoms. Intuitively, greater symptom severity should correlate with greater pathological burden, but this has not been consistently shown. LB pathology does not translate to clinical expression in Incidental LB disease. While several composite scores/toolkits utilize overlapping schemes for diagnosis, a composite data‐derived score to predict LB pathologic burden is lacking. We aimed to utilize an autopsy‐confirmed cohort, and validated, brief, and standardized clinical, functional, pathologic assessments, and machine learning (ML)/quantitative modeling techniques to identify clusters and importance rankings among assessments/measures to predict LB pathologic severity and density.MethodA total of 234 subjects with neuropathological finding of LB at autopsy in the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) who were classified as Cognitively Unimpaired or Mild Cognitive Impairment after their first study clinical evaluation were included. Of these, 145 subjects had complete scores for 32 of 46 measures (clinical, cognitive, behavioral and functional) that were utilized as predictors of LB pathology. Olfactory function was assessed using the University of Pennsylvania Smell Identification Test (UPSIT). LB severity was assessed by the Unified Staging System for Lewy Body Disorders (USSLB). Several ML algorithms and quantitative prediction models were explored and compared: artificial neural network (ANN), partial least square regression (PLSR), support vector regression (SVR), relevance vector regression (RVR) and ensemble forest regression (EFR) with leave‐one‐out (LOO) scheme. Game‐theory based Shapley methods assessed the impact, including rankings, consistency and magnitude, of model predictors.ResultRVR predicted aggregate LB density with large effect size (R2 = 0.691, p<0.00001). ANN predicted USSLB severity stage with large effect size (R2 = 0.74, p<2.5e‐43). All other ML algorithms/models provided substantial prediction. Across all models, UPSIT was the most influential predictor (>65%), followed by Controlled Oral Word Association Test (COWAT) and age.ConclusionThese preliminary and exploratory results support the utilization of ML techniques/models to assess LB pathologic burden with key measures collected in relatively small samples. UPSIT was consistently ranked highest impactful among clinical and functional measures/predictors. Integrated together in ML/data‐derived composites, UPSIT, COWAT and other clinical characteristics may be of antemortem utility to predict USSLB stages.

Abstract Former American football players are at risk for developing traumatic encephalopathy syndrome (TES), the clinical disorder associated with neuropathologically diagnosed chronic traumatic encephalopathy (CTE). The objective of this study was to determine whether hyposmia is present in traumatic encephalopathy syndrome. The study included 119 former professional American football players, 60 former college football players, and 58 same-age asymptomatic unexposed men from the DIAGNOSE CTE Research Project. All subjects included in the analysis had completed the Brief Smell Identification Test (B-SIT). Traumatic encephalopathy syndrome and the level of CTE certainty were diagnosed using the 2021 NINDS consensus diagnostic criteria. TES is categorized antemortem by provisional levels of increasing CTE certainty: Suggestive, Possible, and Probable. Former players who had traumatic encephalopathy syndrome and Probable CTE had lower B-SIT scores than those with TES and Suggestive CTE. Hyposmia was more likely in the former players with TES who were either CTE Possible or Probable than in those who did not have TES or had TES but were less likely to have CTE, or CTE Suggestive. There was no difference in B-SIT scores between all former players versus unexposed men nor overall between the football players with and without TES. We conclude that lower B-SIT scores may be a clinical biomarker for underlying CTE in former American football players.

AbstractINTRODUCTIONBlood‐based biomarkers offer a promising approach for the detection of neuropathologies from repetitive head impacts (RHI). We evaluated plasma biomarkers of amyloid, tau, neurodegeneration, and inflammation in former football players.METHODSThe sample included 180 former football players and 60 asymptomatic, unexposed male participants (aged 45–74). Plasma assays were conducted for beta‐amyloid (Aβ) 40, Aβ42, hyper‐phosphorylated tau (p‐tau) 181+231, total tau (t‐tau), neurofilament light (NfL), glial fibrillary acidic protein (GFAP), interleukin‐6 (IL‐6), Aβ42/p‐tau181 and Aβ42/Aβ40 ratios. We evaluated their ability to differentiate the groups and associations with RHI proxies and traumatic encephalopathy syndrome (TES).RESULTSP‐tau181 and p‐tau231(padj = 0.016) were higher and Aβ42/p‐tau181 was lower(padj = 0.004) in football players compared to controls. Discrimination accuracy for p‐tau was modest (area under the curve [AUC] = 0.742). Effects were not attributable to AD‐related pathology. Younger age of first exposure (AFE) correlated with higher NfL (padj = 0.03) and GFAP (padj = 0.033). Plasma GFAP was higher in TES‐chronic traumatic encephalopathy (TES‐CTE) Possible/Probable (padj = 0.008).DISCUSSIONPlasma p‐tau181 and p‐tau231, GFAP, and NfL may offer some usefulness for the characterization of RHI‐related neuropathologies.Highlights Former football players had higher plasma p‐tau181 and p‐tau231 and lower Aβ42/ptau‐181 compared to asymptomatic, unexposed men. Younger age of first exposure was associated with increased plasma NfL and GFAP in older but not younger participants. Plasma GFAP was higher in participants with TES‐CTE possible/probable compared to TES‐CTE no/suggestive.

ImportanceExposure to repetitive head impacts (RHI) is associated with increased risk for neurodegeneration. Accumulation of toxic proteins due to impaired brain clearance is suspected to play a role.ObjectiveTo investigate whether perivascular space (PVS) volume is associated with lifetime exposure to RHI in individuals at risk for RHI-associated neurodegeneration.Design, Setting, and ParticipantsThis cross-sectional study was part of the Diagnostics, Imaging, and Genetics Network for the Objective Study and Evaluation of Chronic Traumatic Encephalopathy (DIAGNOSE CTE) Research Project, a 7-year multicenter study consisting of 4 US study sites. Data were collected from September 2016 to February 2020 and analyses were performed between May 2021 and October 2023. After controlling for magnetic resonance image (MRI) and processing quality, former American football players and unexposed asymptomatic control participants were included in analyses.ExposurePrior exposure to RHI while participating in American football was estimated using the 3 cumulative head impact indices (CHII-G, linear acceleration; CHII-R, rotational acceleration; and CHII, number of head impacts).Main Outcomes and MeasuresIndividual PVS volume was calculated in the white matter of structural MRI. Cognitive impairment was based on neuropsychological assessment. Linear regression models were used to assess associations of PVS volume with neuropsychological assessments in former American football players. All analyses were adjusted for confounders associated with PVS volume.ResultsAnalyses included 224 participants (median [IQR] age, 57 [51-65] years), with 170 male former football players (114 former professional athletes, 56 former collegiate athletes) and 54 male unexposed control participants. Former football players had larger PVS volume compared with the unexposed group (mean difference, 0.28 [95% CI, 0.00-0.56]; P = .05). Within the football group, PVS volume was associated with higher CHII-R (β = 2.71 × 10−8 [95% CI, 0.50 × 10−8 to 4.93 × 10−8]; P = .03) and CHII-G (β = 2.24 × 10−6 [95% CI, 0.35 × 10−6 to 4.13 × 10−6]; P = .03). Larger PVS volume was also associated with worse performance on cognitive functioning in former American football players (β = −0.74 [95% CI, −1.35 to −0.13]; P = .04).Conclusions and RelevanceThese findings suggest that impaired perivascular brain clearance, as indicated by larger PVS volume, may contribute to the association observed between RHI exposure and neurodegeneration.

Background: Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer’s disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques. Objective: To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio. Methods: We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer’s Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results: Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model’s superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors. CONCLUSIONS: Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer’s disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.

Abstract Exposure to repetitive head impacts in contact sports is associated with neurodegenerative disorders including chronic traumatic encephalopathy (CTE), which currently can be diagnosed only at post-mortem. American football players are at higher risk of developing CTE given their exposure to repetitive head impacts. One promising approach for diagnosing CTE in vivo is to explore known neuropathological abnormalities at post-mortem in living individuals using structural MRI. MRI brain morphometry was evaluated in 170 male former American football players ages 45–74 years (n = 114 professional; n = 56 college) and 54 same-age unexposed asymptomatic male controls (n = 54, age range 45–74). Cortical thickness and volume of regions of interest were selected based on established CTE pathology findings and were assessed using FreeSurfer. Group differences and interactions with age and exposure factors were evaluated using a generalized least squares model. A separate logistic regression and independent multinomial model were performed to predict each traumatic encephalopathy syndrome (TES) diagnosis, core clinical features and provisional level of certainty for CTE pathology using brain regions of interest. Former college and professional American football players (combined) showed significant cortical thickness and/or volume reductions compared to unexposed asymptomatic controls in the hippocampus, amygdala, entorhinal cortex, parahippocampal gyrus, insula, temporal pole and superior frontal gyrus. Post hoc analyses identified group-level differences between former professional players and unexposed asymptomatic controls in the hippocampus, amygdala, entorhinal cortex, parahippocampal gyrus, insula and superior frontal gyrus. Former college players showed significant volume reductions in the hippocampus, amygdala and superior frontal gyrus compared to the unexposed asymptomatic controls. We did not observe Age × Group interactions for brain morphometric measures. Interactions between morphometry and exposure measures were limited to a single significant positive association between the age of first exposure to organized tackle football and right insular volume. We found no significant relationship between brain morphometric measures and the TES diagnosis core clinical features and provisional level of certainty for CTE pathology outcomes. These findings suggested that MRI morphometrics detect abnormalities in individuals with a history of repetitive head impact exposure that resemble the anatomic distribution of pathological findings from post-mortem CTE studies. The lack of findings associating MRI measures with exposure metrics (except for one significant relationship) or TES diagnosis and core clinical features suggested that brain morphometry must be complemented by other types of measures to characterize individuals with repetitive head impacts.

AbstractBackgroundThe NINDS criteria define Traumatic Encephalopathy Syndrome (TES) as the clinical manifestation of the neuropathologically diagnosed chronic traumatic encephalopathy (CTE). The core clinical features of TES include both cognitive and neuropsychiatric symptoms, the latter termed neurobehavioral dysregulation (NBD). We explored potential biological correlates of NBD by investigating fluid biomarkers of neuronal damage, astrogliosis, phosphorylated tau (pTau), neuroinflammation, and catecholamines in former American football players.MethodOur cohort consisted of former professional (PRO) and college (COL) American football players from the DIAGNOSE CTE Research Project. NBD was measured by four subscales (i.e., explosivity, emotional dyscontrol, impulsivity, affective lability) and a total NBD score, derived by factor analysis of multiple self‐report neuropsychiatric measures. Plasma neurofilament light (NfL) and glial fibrillary acidic protein (GFAP), CSF total tau and pTau181/231/217, as well as CSF biomarkers related to neuroinflammation (IL‐6, CRP, TNF‐α, VEGF‐A), were measured with various well‐validated immunoassay methods. Catecholamines in CSF (dopamine, 3,4‐dihydroxyphenylacetic acid, 3,4‐dihydroxyphenylalanine, noradrenaline, 3,4‐dihydroxyphenylglycol) were measured with HPLC. Multivariate linear regression models assessed the relationships between biomarkers and NBD scales, adjusted for age, BMI, race, and APOE e4, including stratification by exposure group, i.e., PRO, COL.ResultDemographics and biomarker levels are displayed in Table 1. High levels of IL‐6 were significantly associated with higher scores of all NBD subscales, independent of exposure group. Other significant relationships included plasma NfL with emotional dyscontrol and impulsivity. Within the college group, CSF pTau181 was significantly associated with emotional dyscontrol and CSF noradrenaline with explosivity, emotional dyscontrol, impulsivity, and total NBD score. (Table 2 & Figure 1). IL‐6 models were repeated with measures of cognitive dysfunction (another core feature of TES) added as covariates; these analyses again found significant relationships between IL‐6 and NBD, irrespective of cognitive functioning.ConclusionWe observed specific fluid biomarkers related to NBD in former American football players. Of note were the associations between IL‐6 and all NBD subscales (and not with cognitive functioning), suggesting that neuroinflammation may be a specific component in the expression of NBD symptoms. Future studies will investigate how these neuroinflammatory processes are related to other biomarkers, level of RHI exposure, and neuropathology.