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The Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to achieve accurate machine learning (ML) if data contain missing values. Therefore, developing a new imputation methodology has become an urgent task for HABS–HD. The three missing data assumptions, (1) missing completely at random (MCAR), (2) missing at random (MAR), and (3) missing not at random (MNAR), necessitate distinct imputation approaches for each mechanism of missingness. Several popular imputation methods, including listwise deletion, min, mean, predictive mean matching (PMM), classification and regression trees (CART), and missForest, may result in biased outcomes and reduced statistical power when applied to downstream analyses such as testing hypotheses related to clinical variables or utilizing machine learning to predict AD or MCI. Moreover, these commonly used imputation techniques can produce unreliable estimates of missing values if they do not account for the missingness mechanisms or if there is an inconsistency between the imputation method and the missing data mechanism in HABS–HD. Therefore, we proposed a three-step workflow to handle missing data in HABS–HD: (1) missing data evaluation, (2) imputation, and (3) imputation evaluation. First, we explored the missingness in HABS–HD. Then, we developed a machine learning-based multiple imputation method (MLMI) for imputing missing values. We built four ML-based imputation models (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and lasso and elastic-net regularized generalized linear model (GLMNET)) and adapted the four ML-based models to multiple imputations using the simple averaging method. Lastly, we evaluated and compared MLMI with other common methods. Our results showed that the three-step workflow worked well for handling missing values in HABS–HD and the ML-based multiple imputation method outperformed other common methods in terms of prediction performance and change in distribution and correlation. The choice of missing handling methodology has a significant impact on the accompanying statistical analyses of HABS–HD. The conceptual three-step workflow and the ML-based multiple imputation method perform well for our Alzheimer’s disease models. They can also be applied to other disease data analyses.

Abstract Introduction To determine if cardiovascular risk factor (CVRF) burden is associated with Alzheimer's disease (AD) biomarkers and whether they synergistically associate with cognition. Methods We cross‐sectionally studied 1521 non‐demented Mexican American (52%) and non‐Hispanic White individuals aged ≥50 years. A composite score was calculated by averaging the z‐scores of five cognitive tests. Plasma β‐amyloid (Aβ) 42/40, total tau (t‐tau), and neurofilament light (NfL) were assayed using Simoa. CVRF burden was assessed using the Framingham Risk Score (FRS). Results Compared to low FRS (< 10% risk), high FRS (≥ 20% risk) was independently associated with increased t‐tau and NfL. High FRS was significantly associated with higher NfL only among Mexican American individuals. Intermediate or high FRS (vs. low FRS) were independently associated with lower cognition, and the association remained significant after adjusting for plasma biomarkers. Hypertension synergistically interacted with t‐tau and NfL (p < 0.05). Discussion CVRFs play critical roles, both through independent and neurodegenerative pathways, on cognition.

BackgroundDue to their low cost, less invasive nature, and ready availability, plasma biomarkers of Alzheimer's disease have been proposed as one-time screening tools for clinical trials and research. The impact of ethnoracial factors on these biomarkers has received little attention. The current cross-sectional study investigated the levels of Aβ40, Aβ42, total tau (t tau), and neurofilament light (NfL) across diagnoses for each of the three major ethnoracial groups in the United States in a community-based cohort of older adults.MethodsA total of 1,862 participants (852 Mexican Americans (MAs); 775 non-Hispanic Whites (NHWs), and 235 African Americans (AAs)) drawn from The Health & Aging Brain Study—Health Disparities (HABS-HD) study were included. Diagnoses were assigned using an algorithm (decision tree) verified by consensus review. Plasma samples were assayed using Simoa technology. Levels of each biomarker were compared for the three ethnoracial groups across cognitive diagnoses using ANOVA covarying sex and age.ResultsSignificant differences were found across the groups at each level of cognitive impairment. Cognitively unimpaired (CU) AA had significantly lower levels of each of the biomarkers than cognitively unimpaired MA or NHW and NHW had higher levels of Aβ40, and NfL than the other two groups. MA had higher t tau than AA or NHW. Mild cognitive impairment (MCI) group NHW had the highest levels on all the biomarkers and AA had the lowest. NHW and MA have higher levels of Aβ40, Aβ42, and t tau there was no difference between the groups for Aβ42. NHW had significantly higher levels of Aβ40, t tau, and NfL than AA. AA had a higher Aβ42/Aβ40 ratio than either NHW or MA for CU MCI.ConclusionsThe use of plasma biomarkers of cognitive decline is promising given their advantages over other biomarkers such as CSF and imaging but as the current research shows, ethnoracial differences must be considered to enhance accuracy and utility. Developing ethnoracial-specific cut points and establishing normative ranges by assay platform for each of the biomarkers are needed. Longitudinal research to assess changes in biomarkers during a cognitive decline is ongoing.

IntroductionDespite tremendous advancements in the research of Alzheimer’s disease (AD), Mexican Americans, who reflect 65% of the US Hispanic community, remain severely underrepresented in research. Our data demonstrate that risk factors for, and biomarkers of, AD are different among Mexican Americans as compared with non-Hispanic whites. Here, we examined the impact of depressive symptoms on cognitive and AD-relevant biomarker outcomes among the Mexican Americans.MethodsData were examined from 1,633 (852 Mexican Americans and 781 non-Hispanic whites) of the Health and Aging Brain Study–Health Disparities (HABS–HD). Depression was assessed using the Geriatric Depression Scale while cognition was measured using detailed neuropsychological testing. Plasma biomarkers of Aβ40, Aβ42, total tau, and NfL were examined in addition to MRI-based neurodegeneration. PET amyloid data were available in a subset of participants.ResultsDepressive symptoms were significantly associated with cognitive testing results among both Mexican Americans and non-Hispanic whites. However, depression was only significantly associated with cognitive outcomes and plasma biomarkers among the Mexican American APOEε4 non-carriers.DiscussionDepressive symptoms are more commonly endorsed by Mexican Americans and these symptoms are more strongly associated with cognitive and AD-biomarker outcomes among this ethnic group. However, depression scores were only related to AD outcomes among APOEε4 non-carriers within the Mexican American group. These findings can aid in the development of a population-informed precision medicine for treating and preventing cognitive loss among the Mexican Americans.

IntroductionDespite the fact that Hispanics are expected to experience the greatest increase in Alzheimer's disease (AD) and related dementias (ADRDs) by 2060, very little data is available regarding the fundamental biomarkers of AD among Mexican Americans who reflect the majority of Hispanics in the U.S. Here we sought to examine the link between APOEε4 genotype and brain amyloid among Mexican Americans as compared to non-Hispanic white participants from the Health & Aging Brain Study – Health Disparities (HABS-HD) cohort.MethodsPET amyloid (florbetaben) data were analyzed from 105 Mexican American and 150 non-Hispanic white participants.ResultsAmong Mexican Americans, APOEε4 genotype presence was associated with Global SUVR (p = 0.003) as well as amyloid burden in the frontal (p < 0.001), lateral parietal (p = 0.003), lateral temporal (p = 0.008) and anterior-posterior cingulate (p = 0.005) regions of interest (ROIs). Among non-Hispanic white participants, APOEε4 genotype presence was associated with Global SUVR (p < 0.001) as well as amyloid burden in the frontal (p < 0.001), lateral parietal (p < 0.001), lateral temporal (p < 0.001) and anterior-posterior cingulate (p < 0.001) regions of interest (ROIs). The association between APOEε4 genotype and cerebral amyloid was strongest among non-Hispanic white participants.Discussion/ConclusionDespite the fact that the APOEε4 genotype is significantly less frequent among Mexican Americans, its presence remains to be a significant risk factor among this group for AD pathological burden across all regions. Additional work is needed to understand the presence, progression, and clinical impact of brain amyloid among Mexican Americans.

Abstract Introduction Dementia severity can be empirically described by the latent dementia phenotype “δ” and its various composite “homologs”. We have explored δ's blood‐based protein biomarkers in the Texas Alzheimer's Research and Care Consortium (TARCC) study. However, it would be convenient to replicate those associations in the Alzheimer's Disease Neuroimaging Initiative (ADNI). To this end, we recently engineered a δ homolog from observed cognitive performance measures common to both projects (i.e., “dT2A”). Methods We used nine rationally chosen peripheral blood‐based protein biomarkers as indicators of a latent variable “INFLAMMATION”. We then associated that construct with dT2A in structural equation models adjusted for age, gender, depressive symptoms, and apolipoprotein E (APOE) ε4 allelic burden. Significant factor loadings and INFLAMMATION's association with dT2A were confirmed in random splits of TARCC's relatively large sample, and across biofluids in the ADNI. Results Nine proteins measured in serum (TARCC) or plasma (ADNI) explained ≅10% of dT2A's variance in both samples, independently of age, APOE, education, and gender. All loaded significantly on INFLAMMATION, and positively or negatively, depending on their known roles are PRO‐ or ANTI‐inflammatory proteins, respectively. The parameters of interest were confirmed across random 50% splits of the TARCC's sample, and replicated across biofluids in the ADNI. Discussion These results suggest that SEM can be used to replicate biomarker findings across samples and biofluids, and that a substantial fraction of dementia's variance is attributable to peripheral blood‐based protein levels.

Abstract Introduction We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases. Methods Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms. Results The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal. Discussion These data provide evidence of the potential use of a multitiered blood‐based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.

Abstract Introduction We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease would distinguish patients with Lewy body disease from normal controls, and if it would distinguish dementia with Lewy bodies (DLB) from Parkinson's disease (PD). Methods Stored plasma samples were obtained from 145 patients (DLB n = 57, PD without dementia n = 32, normal controls n = 56) enrolled from patients seen in the Behavioral Neurology or Movement Disorders clinics at the Mayo Clinic, Florida. Proteomic assays were conducted and analyzed as per our previously published protocols. Results In the first step, the proteomic profile distinguished the DLB‐PD group from controls with a diagnostic accuracy of 0.97, sensitivity of 0.91, and specificity of 0.86. In the second step, the proteomic profile distinguished the DLB from PD groups with a diagnostic accuracy of 0.92, sensitivity of 0.94, and specificity of 0.88. Discussion These data provide evidence of the potential utility of a multitiered blood‐based proteomic screening method for detecting DLB and distinguishing DLB from PD.

Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer’s disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.

Depression and related neurobehavioral symptoms are common features of Alzheimer’s disease and other dementias. The presence of these potentially modifiable neurobehavioral symptoms in cognitively intact older adults may represent an early indication of pathophysiological processes in the brain. Tau pathology is a key feature of a number of dementias. A number of studies have found an association between tau and neurobehavioral symptoms. The current study investigated the relationship of a blood-based biomarker of tau and symptoms of depression, anxiety, worry, and sleep disturbances in 538 community based, cognitively normal older adults. Logistic regression revealed no significant relationship between plasma total tau and any measures of neurobehavioral symptoms. To assess the impact of level of tau on these relationships, participants were divided into those in the highest quintile of tau and those in the lower four quintiles. Regression analyses showed a significant relationship between level of plasma total tau and measures of depression, apathy, anxiety, worry and sleep. The presence of higher levels of plasma tau and elevated neurobehavioral symptoms may be an early indicator of cognitive decline and prodromal Alzheimer’s disease. Longitudinal research is needed to evaluate the impact of these factors on the development of dementia and may suggest areas for early intervention.

Alzheimer’s disease (AD) can be predicted either by serum or plasma biomarkers, and a combination may increase predictive power, but due to the high complexity of machine learning, it may also incur overfitting problems. In this paper, we investigated whether combining serum and plasma biomarkers with feature selection could improve prediction performance for AD. 150 D patients and 150 normal controls (NCs) were enrolled for a serum test, and 100 patients and 100 NCs were enrolled for the plasma test. Among these, 79 ADs and 65 NCs had serum and plasma samples in common. A 10 times repeated 5-fold cross-validation model and a feature selection method were used to overcome the overfitting problem when serum and plasma biomarkers were combined. First, we tested to see if simply adding serum and plasma biomarkers improved prediction performance but also caused overfitting. Then we employed a feature selection algorithm we developed to overcome the overfitting problem. Lastly, we tested the prediction performance in a 10 times repeated 5-fold cross validation model for training and testing sets. We found that the combined biomarkers improved AD prediction but also caused overfitting. A further feature selection based on the combination of serum and plasma biomarkers solved the problem and produced an even higher prediction performance than either serum or plasma biomarkers on their own. The combined feature-selected serum–plasma biomarkers may have critical implications for understanding the pathophysiology of AD and for developing preventative treatments.

Accurate detection is still a challenge in machine learning (ML) for Alzheimer’s disease (AD). Class imbalance in imbalanced AD data is another big challenge for machine-learning algorithms working under the assumption that the data are evenly distributed within classes. Here, we present a hyperparameter tuning workflow with high-performance computing (HPC) for imbalanced data related to prevalent mild cognitive impairment (MCI) and AD in the Health and Aging Brain Study-Health Disparities (HABS-HD) project. We applied a single-node multicore parallel mode to hyperparameter tuning of gamma, cost, and class weight using a support vector machine (SVM) model with 10 times repeated fivefold cross-validation. We executed the hyperparameter tuning workflow with R’s bigmemory, foreach, and doParallel packages on Texas Advanced Computing Center (TACC)’s Lonestar6 system. The computational time was dramatically reduced by up to 98.2% for the high-performance SVM hyperparameter tuning model, and the performance of cross-validation was also improved (the positive predictive value and the negative predictive value at base rate 12% were, respectively, 16.42% and 92.72%). Our results show that a single-node multicore parallel structure and high-performance SVM hyperparameter tuning model can deliver efficient and fast computation and achieve outstanding agility, simplicity, and productivity for imbalanced data in AD applications.

Abstract Introduction Mexican Americans remain severely underrepresented in Alzheimer's disease (AD) research. The Health & Aging Brain among Latino Elders (HABLE) study was created to fill important gaps in the existing literature. Methods Community‐dwelling Mexican Americans and non‐Hispanic White adults and elders (age 50 and above) were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing, and 3T magnetic resonance imaging (MRI) of the brain. Amyloid and tau positron emission tomography (PET) scans were added at visit 2. Blood samples were stored in the Biorepository. Results Data was examined from n = 1705 participants. Significant group differences were found in medical, demographic, and sociocultural factors. Cerebral amyloid and neurodegeneration imaging markers were significantly different between Mexican Americans and non‐Hispanic Whites. Discussion The current data provide strong support for continued investigations that examine the risk factors for and biomarkers of AD among diverse populations.

BackgroundIndividuals with Alzheimer’s Disease (AD) are often characterized by systemic markers of insulin resistance; however, the broader effects of AD on other relevant metabolic hormones, such as incretins that affect insulin secretion and food intake, remains less clear.MethodsHere, we leveraged a physiologically relevant meal tolerance test to assess diagnostic differences in these metabolic responses in cognitively healthy older adults (CH; n = 32) and AD (n = 23) participants. All individuals also underwent a comprehensive clinical examination, cognitive evaluation, and structural magnetic resonance imaging.ResultsThe meal-stimulated response of glucose, insulin, and peptide tyrosine tyrosine (PYY) was significantly greater in individuals with AD as compared to CH. Voxel-based morphometry revealed negative relationships between brain volume and the meal-stimulated response of insulin, C-Peptide, and glucose-dependent insulinotropic polypeptide (GIP) in primarily parietal brain regions.ConclusionOur findings are consistent with prior work that shows differences in metabolic regulation in AD and relationships with cognition and brain structure.

Abstract Introduction We sought to determine if proteomic profiles could predict risk for incident mild cognitive impairment (MCI) and Alzheimer's disease (AD) among adults with Down syndrome (DS). Methods In a cohort of 398 adults with DS, a total of n = 186 participants were determined to be non‐demented and without MCI or AD at baseline and throughout follow‐up; n = 103 had incident MCI and n = 81 had incident AD. Proteomics were conducted on banked plasma samples from a previously generated algorithm. Results The proteomic profile was highly accurate in predicting incident MCI (area under the curve [AUC] = 0.92) and incident AD (AUC = 0.88). For MCI risk, the support vector machine (SVM)‐based high/low cut‐point yielded an adjusted hazard ratio (HR) = 6.46 (P < .001). For AD risk, the SVM‐based high/low cut‐point score yielded an adjusted HR = 8.4 (P < .001). Discussion The current results provide support for our blood‐based proteomic profile for predicting risk for MCI and AD among adults with DS.

Abstract Introduction We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease (AD) in the general population would apply to adults with Down syndrome (DS). Methods Plasma samples were obtained from 398 members of a community‐based cohort of adults with DS. A total of n = 186 participants were determined to be non‐demented and without mild cognitive impairment (MCI) at baseline and throughout follow‐up; n = 50 had prevalent MCI; n = 42 had prevalent AD. Results The proteomic profile yielded an area under the curve (AUC) of 0.92, sensitivity (SN) = 0.80, and specificity (SP) = 0.98 detecting prevalent MCI. For detecting prevalent AD, the proteomic profile yielded an AUC of 0.89, SN = 0.81, and SP = 0.97. The overall profile closely resembled our previously published profile of AD in the general population. Discussion These data provide evidence of the applicability of our blood‐based algorithm for detecting MCI/AD among adults with DS.

Mild traumatic brain injury (mTBI) disproportionately affects military service members and is very difficult to diagnose. To-date, there is currently no blood-based, diagnostic biomarker for mTBI cases with persistent post concussive symptoms. To examine the potential of neuronally-derived (NDE) and astrocytic-derived (ADE) exosome cargo proteins as biomarkers of chronic mTBI in younger adults, we examined plasma exosomes from a prospective longitudinal study of combat-related risk and resilience, marine resiliency study II (MRSII). After return from a combat-deployment participants were interviewed to assess TBI exposure while on deployment. Plasma exosomes from military service members with mTBI (mean age, 21.7 years, n = 19, avg. days since injury 151), and age-matched, controls (deployed service members who did not endorse a deployment-related TBI or a pre-deployment history of TBI; mean age, 21.95 years, n = 20) were precipitated and enriched against a neuronal adhesion protein, L1-CAM, and an astrocyte marker, glutamine aspartate transporter (GLAST) using magnetic beads to immunocapture the proteins and subsequently selected by fluorescent activated cell sorting (FACS). Extracted protein cargo from NDE and ADE preparations were quantified for protein levels implicated in TBI neuropathology by standard ELISAs and on the ultra-sensitive single molecule assay (Simoa) platform. Plasma NDE and ADE levels of Aβ42 were significantly higher while plasma NDE and ADE levels of the postsynaptic protein, neurogranin (NRGN) were significantly lower in participants endorsing mTBI exposure compared to controls with no TBI history. Plasma NDE and ADE levels of Aβ40, total tau, and neurofilament light (NFL), P-T181-tau, P-S396-tau were either undetectable or not significantly different between the two groups. In an effort to understand the pathogenetic potential of NDE and ADE cargo proteins, neuron-like cultures were treated with NDE and ADE preparations from TBI and non-TBI groups. Lastly, we determined that plasma NDE but not ADE cargo proteins from mTBI samples were found to be toxic to neuron-like recipient cells in vitro. These data support the presence of markers of neurodegeneration in NDEs of mTBI and suggest that these NDEs can be used as tools to identify pathogenic mechanisms of TBI.

Background: Recent evidence suggests that increasing dietary intake of minerals reduces the risk of dementia. This study aimed to examine the relationship between potassium and diagnosis of mild cognitive impairment (MCI) in a sample of older Mexican-Americans from rural and urban populations. Methods: The sample was formed of a total of 139 participants with MCI and 371 normal controls from two independent cohorts: a rural cohort (Facing Rural Obstacles to Healthcare Now through Intervention, Education and Research [Project FRONTIER]) and an urban cohort (the Health and Aging Brain among Latino Elders [HABLE] study). Serum electrolytes examined were sodium and potassium. Age and education were entered in the model as covariates. Results: Across both cohorts, the Project FRONTIER (OR = 3.1; p = 0.01) and the HABLE Project (OR = 2.0; p = 0.04), the results indicated that serum potassium levels significantly increased the risk of diagnosis of MCI. Conclusion: Our finding suggested a link between serum potassium levels and a diagnosis of MCI in Mexican-Americans. The results of this study support a previous research which has suggested that the risk factors for MCI may vary by ethnicity.

Abstract Introduction The present study sought to investigate the measurement invariance of commonly used neuropsychological tests in an ethnically (Hispanic vs. non‐Hispanic) and linguistically (Spanish vs. English) diverse sample. Methods Participants were 736 middle‐aged and older adults (MAge = 62.1, SD = 9.1) assessed at baseline. Measurement invariance testing was performed using multiple‐group confirmatory factor analysis. Results A five‐factor model (memory, attention/executive functioning/processing speed, language, visuospatial, and motor) fit the data well (CFI = 0.979, RMSEA = 0.047) and the composite reliability of the factors ranged from .76 (visuospatial) to .97 (motor). The five‐factor model was found to possess strict measurement invariance for ethnicity and language without a decrement in fit compared to a strong (scalar) invariance model (ΔCFI = .000, ΔRMSEA = .002). Discussion These results indicate that a five‐factor model is suitable for estimating cognitive functioning in Mexican Americans and non‐Hispanic whites without bias by ethnicity or language.

Abstract Introduction This study investigated the comparability of potential Alzheimer's disease (AD) biomarkers across blood fractions and assay platforms. Methods Nonfasting serum and plasma samples from 300 participants (150 AD patients and 150 controls) were analyzed. Proteomic markers were obtained via electrochemiluminescence or Luminex technology. Comparisons were conducted via Pearson correlations. The relative importance of proteins within an AD diagnostic profile was examined using random forest importance plots. Results On the Meso Scale Discovery multiplex platform, 10 of the 21 markers shared >50% of the variance across blood fractions (serum amyloid A R2 = 0.99, interleukin (IL)10 R2 = 0.95, fatty acid‐binding protein (FABP) R2 = 0.94, I309 R2 = 0.94, IL‐5 R2 = 0.94, IL‐6 R2 = 0.94, eotaxin3 R2 = 0.91, IL‐18 R2 = 0.87, soluble tumor necrosis factor receptor 1 R2 = 0.85, and pancreatic polypeptide R2 = 0.81). When examining protein concentrations across platforms, only five markers shared >50% of the variance (beta 2 microglobulin R2 = 0.92, IL‐18 R2 = 0.80, factor VII R2 = 0.78, CRP R2 = 0.74, and FABP R2 = 0.70). Discussion The current findings highlight the importance of considering blood fractions and assay platforms when searching for AD relevant biomarkers.

Abstract Introduction This study combined data across four independent cohorts to examine the positive and negative predictive values of an Alzheimer's disease (AD) blood test if implemented in primary care. Methods Blood samples from 1329 subjects from multiple independent, multiethnic, community‐based, and clinic‐based cohorts were analyzed. A “locked‐down” referent group of 1128 samples was generated with 201 samples randomly selected for validation purposes. Random forest analyses were used to create the AD blood screen. Positive (PPV) and negative (NPV) predictive values were calculated. Results In detecting AD, PPV was 0.81, and NPV was 0.95 while using the full AD blood test. When detecting mild cognitive impairment, PPV and NPV were 0.74 and 0.93, respectively. Preliminary analyses were conducted to detect any “neurodegenerative disease”. The full 21‐protein AD blood test yielded a PPV of 0.85 and NPV of 0.94. Discussion The present study creates the first‐ever multiethnic referent sample that spans community‐based and clinic‐based populations for implementation of an AD blood screen.

This study determined whether antioxidant supplementation is a viable complement to exercise regimens in improving cognitive and motor performance in a mouse model of Alzheimer’s disease risk. Starting at 12 months of age, separate groups of male and female mice expressing human Apolipoprotein E3 (GFAP-ApoE3) or E4 (GFAP-ApoE4) were fed either a control diet or a diet supplemented with vitamins E and C. The mice were further separated into a sedentary group or a group that followed a daily exercise regimen. After 8 weeks on the treatments, the mice were administered a battery of functional tests including tests to measure reflex and motor, cognitive, and affective function while remaining on their treatment. Subsequently, plasma inflammatory markers and catalase activity in brain regions were measured. Overall, the GFAP-ApoE4 mice exhibited poorer motor function and spatial learning and memory. The treatments improved balance, learning, and cognitive flexibility in the GFAP-ApoE3 mice and overall the GFAP-ApoE4 mice were not responsive. The addition of antioxidants to supplement a training regimen only provided further benefits to the active avoidance task, and there was no antagonistic interaction between the two interventions. These outcomes are indicative that there is a window of opportunity for treatment and that genotype plays an important role in response to interventions.

Background: Granulocyte colony-stimulating factor (G-CSF) promotes the survival and function of neutrophils. G-CSF is also a neurotrophic factor, increasing neuroplasticity and suppressing apoptosis. Methods: We analyzed G-CSF levels in 197 patients with probable Alzheimer’s disease (AD) and 203 cognitively normal controls (NCs) from a longitudinal study by the Texas Alzheimer’s Research and Care Consortium (TARCC). Data were analyzed by regression with adjustment for age, education, gender and APOE4 status. Results: Serum G-CSF was significantly lower in AD patients than in NCs (β = –0.073; p = 0.008). However, among AD patients, higher serum G-CSF was significantly associated with increased disease severity, as indicated by lower Mini-Mental State Examination scores (β = –0.178; p = 0.014) and higher scores on the global Clinical Dementia Rating (CDR) scale (β = 0.170; p = 0.018) and CDR Sum of Boxes (β = 0.157; p = 0.035). Conclusions: G-CSF appears to have a complex relationship with AD pathogenesis and may reflect different pathophysiologic processes at different illness stages.

Background. Considerable research documents an association between pro- and anti-inflammatory markers and Alzheimer's disease (AD), yet the differential relation between these markers and neuropsychological functioning in AD and nondemented controls has received less attention. The current study sought to evaluate the relationship between peripheral markers of inflammation (both pro- and anti-inflammatory) and neuropsychological functioning through the Texas Alzheimer's Research and Care Consortium (TARCC) cohort. Methods. There were 320 participants (Probable AD n=124, Controls n=196) in the TARCC Longitudinal Research Cohort available for analysis. Regression analyses were utilized to examine the relation between proinflammatory and anti-inflammatory markers and neuropsychological functioning. Follow-up analyses were conducted separately by case versus control status. Results. Proinflammatory and anti-inflammatory markers were found to be associated with neuropsychological testing. Third tertile proinflammatory markers were negatively associated with measures of attention and language, and anti-inflammatory markers were positively associated with measures of immediate verbal memory and delayed verbal and visual memory. Conclusions. These findings support the link between peripheral inflammatory markers and neuropsychological functioning and suggest the utility of examining profiles of inflammatory markers in the future.

Objectives. Determine the relationship between depressive symptom clusters and neuropsychological test performance in an elderly cohort of cognitively normal controls and mild Alzheimer's disease (AD). Design. Cross-sectional analysis. Setting. Four health science centers in Texas. Participants. 628 elderly individuals (272 diagnosed with mild AD and 356 controls) from ongoing longitudinal study of Alzheimer's disease. Measurements. Standard battery of neuropsychological tests and the 30-item Geriatric Depression Scale with regressions model generated on GDS-30 subscale scores (dysphoria, apathy, meaninglessness and cognitive impairment) as predictors and neuropsychological tests as outcome variables. Follow-up analyses by gender were conducted. Results. For AD, all symptom clusters were related to specific neurocognitive domains; among controls apathy and cognitive impairment were significantly related to neuropsychological functioning. The relationship between performance and symptom clusters was significantly different for males and females in each group. Conclusion. Findings suggest the need to examine disease status and gender when considering the impact of depressive symptoms on cognition.