Derivation of Indices of Cognitive Change Among Hispanic Adults and Elders.

Importance: Determining the influence of race and ethnicity on change in cognitive test performance has significant implications for clinical practice and research in populations at risk for Alzheimer disease. Objective: To evaluate the significance of race and ethnicity in predicting longitudinal cognitive test performance and to develop models to support evidence-based practice. Design, Setting, and Participants: This prognostic study included baseline and 24-month follow-up data that were obtained from the Health and Aging Brain Study-Health Disparities (HABS-HD) study, an ongoing longitudinal observational study of aging and dementia in a multiracial, multiethnic cohort. Participants included community-dwelling adults and elders living in the Dallas and Fort Worth metropolitan area who were Hispanic and non-Hispanic adults older than the age of 50 years and were cognitively unimpaired. Exposure: The primary exposure of interest was time, measured in months. Main Outcomes and Measures: Demographic variables included age, sex, education, and race and ethnicity. Cognitive domains included attention and working memory, processing speed, language, memory, and executive functioning. Linear regression models predicted follow-up performance from baseline performance and demographic variables for 13 commonly used neuropsychological tests. Follow-up testing was the primary outcome for all domains. Raw scores from 13 standardized tests were used for analyses. Results: This study included 799 adults who were cognitively unimpaired (352 Hispanic individuals [44.1%]; 447 non-Hispanic individuals [55.9%]; 524 female [65.6%]; mean [SD] age, 65.4 [8.1] years). In the regression models, all 13 follow-up scores were significantly predicted from their respective baseline scores and demographic variables. Baseline performance and education were the most consistent predictors of follow-up scores, contributing to all 13 models. Age was significantly associated with follow-up in 11 models, and sex was significant in 5 models. Race and ethnicity contributed to 10 of 13 models, with Hispanic participants predicted to have poorer follow-up scores than their non-Hispanic White counterparts on each test. Conclusions and Relevance: In this longitudinal study of cognitive change in Hispanic and non-Hispanic older adults who were cognitively unimpaired, standardized regression-based models were influenced by multiple demographic variables, including race and ethnicity. These findings highlight the importance of including race and ethnicity in such cognitive change models. This ability to accurately predict cognitive change is expected to become increasingly important as clinical practice and clinical trials need to become more diverse and culturally appropriate in this burgeoning global medical and societal crisis.

Rethinking the residual approach: Leveraging machine learning to operationalize cognitive resilience in Alzheimer's disease.

Cognitive resilience describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals. We demonstrate that this approach makes specific, uncontrollable assumptions and likely leads to biased and erroneous resilience estimates. We propose an alternative strategy which overcomes the standard approach's limitations using machine learning principles. Our proposed approach makes fewer assumptions about the data and construct to be measured and achieves better estimation accuracy on simulated ground-truth data.

Characterization of Ptau181 Among a Diverse Community-Based Cohort: A HABS-HD Study.

Background: Examination of Alzheimer's disease (AD) related biomarkers among diverse communities has remained limited. Objective: The aim of this study was to expand on prior work to provide a characterization of ptau181 among a diverse community sample. Consideration was taken regarding the impact of comorbidities on ptau181 levels including medical. Methods: 3,228 (n = 770 African American [AA], n = 1,231 Hispanic, and n = 1,227 non-Hispanic white [NHW]) Health and Aging Brain Study- Health Disparities (HABS-HD) participants were included in this study. ANCOVAs were conducted to examine differences in ptau181 levels across race and ethnic groups. Violin plots were also generated stratified by APOEɛ4 carrier status, Amyloid PET positivity status, medical comorbidity (hypertension, dyslipidemia, chronic kidney disease [CKD], and diabetes) and by cognitive diagnosis. Results: Ptau181 levels were found to differ between Hispanics and NHW after covarying for age, sex, and APOEɛ4 status. Amyloid PET positivity was associated with higher ptau181 levels across all groups. APOEɛ4 positivity status was only significantly associated with ptau181 levels among AAs. Across all race and ethnic groups, those with a diagnosis of CKD had higher levels of ptau181. When stratified by cognitive diagnosis, cognitively unimpaired Hispanics had higher ptau181 if they also had a diagnosis of CKD or diabetes. p-values ≤0.01. Conclusions: Differences in ptau181 levels were shown in a diverse community sample. Medical comorbidities had a differing effect on ptau181 levels particularly among Hispanics even without cognitive impairment. Findings support the need for future work to consider comorbid conditions when examining the utility of ptau181.

The Association of Neighborhood Socioeconomic Status with Executive Function and Processing Speed in Cognitively Normal Mexican American Elders from the Health and Aging Brains Study: Health Disparities Cohort.

INTRODUCTION: Neighborhood socioeconomic status (NSES) has been linked with overall health, and this study will evaluate whether NSES is cross-sectionally associated with cognition in non-Hispanic whites (NHWs) and Mexican Americans (MAs) from the Health and Aging Brain: Health Disparities Study (HABS-HD). METHODS: The HABS-HD is a longitudinal study conducted at the University of North Texas Health Science Center. The final sample analyzed (n = 1,312) were 50 years or older, with unimpaired cognition, and underwent an interview, neuropsychological examination, imaging, and blood draw. NSES was measured using the national area deprivation index (ADI) percentile ranking, which considered socioeconomic variables. Executive function and processing speed were assessed by the trail making tests (A and B) and the digit-symbol substitution test, respectively. Linear regression was used to assess the association of ADI and cognitive measures. RESULTS: MAs were younger, more likely to be female, less educated, had higher ADI scores, performed worse on trails B (all p < 0.05), and had lower prevalence of APOE4 + when compared to NHWs (p < 0.0001). A higher percentage of MAs lived in the most deprived neighborhoods than NHWs. For NHWs, ADI did not predict trails B or DSS scores, after adjusting for demographic variables and APOE4. For MAs, ADI predicted trails A, trails B, and DSS after adjusting for demographic covariates and APOE4 status. CONCLUSION: Our study revealed that living in an area of higher deprivation was associated with lower cognitive function in MAs but not in NHWs, which is important to consider in future interventions to slow cognitive decline.

Comparison of tau spread in people with Down syndrome versus autosomal-dominant Alzheimer's disease: a cross-sectional study.

BACKGROUND: In people with genetic forms of Alzheimer's disease, such as in Down syndrome and autosomal-dominant Alzheimer's disease, pathological changes specific to Alzheimer's disease (ie, accumulation of amyloid and tau) occur in the brain at a young age, when comorbidities related to ageing are not present. Studies including these cohorts could, therefore, improve our understanding of the early pathogenesis of Alzheimer's disease and be useful when designing preventive interventions targeted at disease pathology or when planning clinical trials. We compared the magnitude, spatial extent, and temporal ordering of tau spread in people with Down syndrome and autosomal-dominant Alzheimer's disease. METHODS: In this cross-sectional observational study, we included participants (aged ≥25 years) from two cohort studies. First, we collected data from the Dominantly Inherited Alzheimer's Network studies (DIAN-OBS and DIAN-TU), which include carriers of autosomal-dominant Alzheimer's disease genetic mutations and non-carrier familial controls recruited in Australia, Europe, and the USA between 2008 and 2022. Second, we collected data from the Alzheimer Biomarkers Consortium-Down Syndrome study, which includes people with Down syndrome and sibling controls recruited from the UK and USA between 2015 and 2021. Controls from the two studies were combined into a single group of familial controls. All participants had completed structural MRI and tau PET (18F-flortaucipir) imaging. We applied Gaussian mixture modelling to identify regions of high tau PET burden and regions with the earliest changes in tau binding for each cohort separately. We estimated regional tau PET burden as a function of cortical amyloid burden for both cohorts. Finally, we compared the temporal pattern of tau PET burden relative to that of amyloid. FINDINGS: We included 137 people with Down syndrome (mean age 38·5 years [SD 8·2], 74 [54%] male, and 63 [46%] female), 49 individuals with autosomal-dominant Alzheimer's disease (mean age 43·9 years [11·2], 22 [45%] male, and 27 [55%] female), and 85 familial controls, pooled from across both studies (mean age 41·5 years [12·1], 28 [33%] male, and 57 [67%] female), who satisfied the PET quality-control procedure for tau-PET imaging processing. 134 (98%) people with Down syndrome, 44 (90%) with autosomal-dominant Alzheimer's disease, and 77 (91%) controls also completed an amyloid PET scan within 3 years of tau PET imaging. Spatially, tau PET burden was observed most frequently in subcortical and medial temporal regions in people with Down syndrome, and within the medial temporal lobe in people with autosomal-dominant Alzheimer's disease. Across the brain, people with Down syndrome had greater concentrations of tau for a given level of amyloid compared with people with autosomal-dominant Alzheimer's disease. Temporally, increases in tau were more strongly associated with increases in amyloid for people with Down syndrome compared with autosomal-dominant Alzheimer's disease. INTERPRETATION: Although the general progression of amyloid followed by tau is similar for people Down syndrome and people with autosomal-dominant Alzheimer's disease, we found subtle differences in the spatial distribution, timing, and magnitude of the tau burden between these two cohorts. These differences might have important implications; differences in the temporal pattern of tau accumulation might influence the timing of drug administration in clinical trials, whereas differences in the spatial pattern and magnitude of tau burden might affect disease progression. FUNDING: None.

Comorbidities Incorporated to Improve Prediction for Prevalent Mild Cognitive Impairment and Alzheimer's Disease in the HABS-HD Study.

BACKGROUND: Blood biomarkers have the potential to transform Alzheimer's disease (AD) diagnosis and monitoring, yet their integration with common medical comorbidities remains insufficiently explored. OBJECTIVE: This study aims to enhance blood biomarkers' sensitivity, specificity, and predictive performance by incorporating comorbidities. We assess this integration's efficacy in diagnostic classification using machine learning, hypothesizing that it can identify a confident set of predictive features. METHODS: We analyzed data from 1,705 participants in the Health and Aging Brain Study-Health Disparities, including 116 AD patients, 261 with mild cognitive impairment, and 1,328 cognitively normal controls. Blood samples were assayed using electrochemiluminescence and single molecule array technology, alongside comorbidity data gathered through clinical interviews and medical records. We visually explored blood biomarker and comorbidity characteristics, developed a Feature Importance and SVM-based Leave-One-Out Recursive Feature Elimination (FI-SVM-RFE-LOO) method to optimize feature selection, and compared four models: Biomarker Only, Comorbidity Only, Biomarker and Comorbidity, and Feature-Selected Biomarker and Comorbidity. RESULTS: The combination model incorporating 17 blood biomarkers and 12 comorbidity variables outperformed single-modal models, with NPV12 at 92.78%, AUC at 67.59%, and Sensitivity at 65.70%. Feature selection led to 22 chosen features, resulting in the highest performance, with NPV12 at 93.76%, AUC at 69.22%, and Sensitivity at 70.69%. Additionally, interpretative machine learning highlighted factors contributing to improved prediction performance. CONCLUSIONS: In conclusion, combining feature-selected biomarkers and comorbidities enhances prediction performance, while feature selection optimizes their integration. These findings hold promise for understanding AD pathophysiology and advancing preventive treatments.

Precision Medicine for Preventing Alzheimer's Disease: Analysis of the ADAPT Study.

BACKGROUND: The Alzheimer's Disease Anti-inflammatory Prevention Trial (ADAPT) was the first-ever large-scale anti-inflammatory prevention trial targeting Alzheimer's disease. OBJECTIVE: The overall goal of this study was to evaluate predictive blood biomarker profiles that identified individuals most likely to be responders on NSAID treatment or placebo at 12 and 24 months. METHODS: Baseline (n = 193) and 12-month (n = 562) plasma samples were assayed. The predictive biomarker profile was generated using SVM analyses with response on treatment (yes/no) as the outcome variable. RESULTS: Baseline (AUC = 0.99) and 12-month (AUC = 0.99) predictive biomarker profiles were highly accurate in predicting response on Celecoxib arm at 12 and 24 months. The baseline (AUC = 0.95) and 12-month (AUC = 0.9) predictive biomarker profile predicting response on Naproxen were also highly accurate at 12 and 24 months. The baseline (AUC = 0.93) and 12-month (AUC = 0.99) predictive biomarker profile was also highly accurate in predicting response on placebo. As with our prior work, the profiles varied by treatment arm. CONCLUSIONS: The current results provide additional support for a precision medicine model for treating and preventing Alzheimer's disease.

Evaluation of Neighborhood-Level Disadvantage and Cognition in Mexican American and Non-Hispanic White Adults 50 Years and Older in the US.

Importance: Understanding how socioeconomic factors are associated with cognitive aging is important for addressing health disparities in Alzheimer disease. Objective: To examine the association of neighborhood disadvantage with cognition among a multiethnic cohort of older adults. Design, Setting, and Participants: In this cross-sectional study, data were collected between September 1, 2017, and May 31, 2022. Participants were from the Health and Aging Brain Study-Health Disparities, which is a community-based single-center study in the Dallas/Fort Worth area of Texas. A total of 1614 Mexican American and non-Hispanic White adults 50 years and older were included. Exposure: Neighborhood disadvantage for participants' current residence was measured by the validated Area Deprivation Index (ADI); ADI Texas state deciles were converted to quintiles, with quintile 1 representing the least disadvantaged area and quintile 5 the most disadvantaged area. Covariates included age, sex, and educational level. Main Outcomes and Measures: Performance on cognitive tests assessing memory, language, attention, processing speed, and executive functioning; measures included the Spanish-English Verbal Learning Test (SEVLT) Learning and Delayed Recall subscales; Wechsler Memory Scale, third edition (WMS-III) Digit Span Forward, Digit Span Backward, and Logical Memory 1 and 2 subscales; Trail Making Test (TMT) parts A and B; Digit Symbol Substitution Test (DSST); Letter Fluency; and Animal Naming. Raw scores were used for analyses. Associations between neighborhood disadvantage and neuropsychological performance were examined via demographically adjusted linear regression models stratified by ethnic group. Results: Among 1614 older adults (mean [SD] age, 66.3 [8.7] years; 980 women [60.7%]), 853 were Mexican American (mean [SD] age, 63.9 [7.9] years; 566 women [66.4%]), and 761 were non-Hispanic White (mean [SD] age, 69.1 [8.7] years; 414 women [54.4%]). Older Mexican American adults were more likely to reside in the most disadvantaged areas (ADI quintiles 3-5), with 280 individuals (32.8%) living in ADI quintile 5, whereas a large proportion of older non-Hispanic White adults resided in ADI quintile 1 (296 individuals [38.9%]). Mexican American individuals living in more disadvantaged areas had worse performance than those living in ADI quintile 1 on 7 of 11 cognitive tests, including SEVLT Learning (ADI quintile 5: ß = -2.50; 95% CI, -4.46 to -0.54), SEVLT Delayed Recall (eg, ADI quintile 3: ß = -1.11; 95% CI, -1.97 to -0.24), WMS-III Digit Span Forward (eg, ADI quintile 4: ß = -1.14; 95% CI, -1.60 to -0.67), TMT part A (ADI quintile 5: ß = 7.85; 95% CI, 1.28-14.42), TMT part B (eg, ADI quintile 5: ß = 31.5; 95% CI, 12.16-51.35), Letter Fluency (ADI quintile 4: ß = -2.91; 95% CI, -5.39 to -0.43), and DSST (eg, ADI quintile 5: ß = -4.45; 95% CI, -6.77 to -2.14). In contrast, only non-Hispanic White individuals living in ADI quintile 4 had worse performance than those living in ADI quintile 1 on 4 of 11 cognitive tests, including SEVLT Learning (ß = -2.35; 95% CI, -4.40 to -0.30), SEVLT Delayed Recall (ß = -0.95; 95% CI, -1.73 to -0.17), TMT part B (ß = 15.95; 95% CI, 2.47-29.44), and DSST (ß = -3.96; 95% CI, -6.49 to -1.43). Conclusions and Relevance: In this cross-sectional study, aging in a disadvantaged area was associated with worse cognitive functioning, particularly for older Mexican American adults. Future studies examining the implications of exposure to neighborhood disadvantage across the life span will be important for improving cognitive outcomes in diverse populations.

Comparison of genetic and health risk factors for mild cognitive impairment and Alzheimer's disease between Hispanic and non-Hispanic white participants.

INTRODUCTION: The influence of apolipoprotein E (APOE) genotype on mild cognitive impairment (MCI) and Alzheimer's disease (AD) is well studied in the non-Hispanic white (NHW) population but not in the Hispanic population. Additionally, health risk factors such as hypertension, stroke, and depression may also differ between the two populations. METHODS: We combined three data sets (National Alzheimer's Coordinating Center [NACC], Alzheimer's Disease Neuroimaging Initiative [ADNI], Health and Aging Brain Study: Health Disparities [HABS-HD]) and compared risk factors for MCI and AD between Hispanic and NHW participants, with a total of 24,268 participants (11.1% Hispanic). RESULTS: APOEε4 was associated with fewer all-cause MCI cases in Hispanic participants (Hispanic odds ratio [OR]: 1.114; NHW OR: 1.453), and APOEε2 (Hispanic OR: 1.224; NHW OR: 0.592) and depression (Hispanic OR: 2.817; NHW OR: 1.847) were associated with more AD cases in Hispanic participants. DISCUSSION: APOEε2 may not be protective for AD in Hispanic participants and Hispanic participants with depression may face a higher risk for AD. HIGHLIGHTS: GAAIN allows for discovery of data sets to use in secondary analyses. APOEε2 was not protective for AD in Hispanic participants. APOEε4 was associated with fewer MCI cases in Hispanic participants. Depression was associated with more AD cases in Hispanic participants.

Association of cardiovascular risk factors and blood biomarkers with cognition: The HABS-HD study.

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 ß-amyloid (Aß) 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.

Medical comorbidities and ethnicity impact plasma Alzheimer's disease biomarkers: Important considerations for clinical trials and practice.

INTRODUCTION: Despite the clinical implementation, there remain significant gaps in our knowledge regarding the impact of race/ethnicity or common medical comorbidity on plasma Alzheimer's disease (AD) biomarkers. METHODS: Plasma biomarkers of amyloid beta (Aß)40, Aß42 , total tau, and neurofilament light chain (NfL) were measured across cognitively normal Mexican Americans (n = 445) and non-Hispanic Whites (n = 520). RESULTS: Dyslipidemia was associated with elevated Aß40 (P = .01) and Aß42 (P = .001) while hypertension was associated with elevated Aß40 (P = .003), Aß42 (P < .001), and total tau (P = .002) levels. Diabetes was associated with higher Aß40 (P < .001), Aß42 (P < .001), total tau (P < .001), and NfL (P < .001) levels. Chronic kidney disease (CKD) was associated with elevations in Aß40 (P < .001), Aß42 (P < .001), total tau (P < .001), and NfL (P < .001) levels. Mexican Americans had significantly lower Aß40 (P < .001) and higher total tau (P = .005) levels. DISCUSSION: Plasma AD biomarkers vary significantly in association with common medical comorbidities as well as ethnicity. These findings are important for those using these biomarkers in clinical practice and clinical trials.

Plasma Biomarkers of Alzheimer's Disease Are Associated with Physical Functioning Outcomes Among Cognitively Normal Adults in the Multiethnic HABS-HD Cohort.

In this study, we examined the link between plasma Alzheimer's disease (AD) biomarkers and physical functioning outcomes within a community-dwelling, multiethnic cohort. Data from 1 328 cognitively unimpaired participants (n = 659 Mexican American and n = 669 non-Hispanic White) from the ongoing Health & Aging Brain Study-Health Disparities (HABS-HD) cohort were examined. Plasma AD biomarkers (amyloid beta [Aß]40, Aß42, total tau [t-tau], and neurofilament light chain [NfL]) were assayed using the ultra-sensitive Simoa platform. Physical functioning measures were the Timed Up and Go (TUG) and the Short Physical Performance Battery (SPPB). Cross-sectional linear regression analyses revealed that plasma Aß 40 (p < .001), Aß 42 (p = .003), and NfL (p < .001) were each significantly associated with TUG time in seconds. Plasma Aß 40 (p < .001), Aß 42 (p < .001), t-tau (p = .002), and NfL (p < .001) were each significantly associated with SPPB Total Score. Additional analyses demonstrate that the link between plasma AD biomarkers and physical functioning outcomes were strongest among Mexican Americans. Plasma AD biomarkers are receiving a great deal of attention in the literature and are now available clinically including use in clinical trials. The examination of AD biomarkers and physical functioning may allow for the development of risk profiles, which could stratify a person's risk for neurodegenerative diseases, such as AD, based on plasma AD biomarkers, physical functioning, ethnicity, or a combination of these measures prior to the onset of cognitive impairment.

Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer's Disease Data.

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.

Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer's Disease.

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.

Characterization of Mild Cognitive Impairment and Dementia among Community-Dwelling Mexican Americans and Non-Hispanic Whites.

BACKGROUND: Despite tremendous advancements in the field, our understanding of mild cognitive impairment (MCI) and Alzheimer's disease (AD) among Mexican Americans remains limited. OBJECTIVE: The aim of this study was to characterize MCI and dementia among Mexican Americans and non-Hispanic whites. METHODS: Baseline data were analyzed from n = 1,705 (n = 890 Mexican American; n = 815 non-Hispanic white) participants enrolled in the Health and Aging Brain Study-Health Disparities (HABS-HD). RESULTS: Among Mexican Americans, age (OR = 1.07), depression (OR = 1.09), and MRI-based neurodegeneration (OR = 0.01) were associated with dementia, but none of these factors were associated with MCI. Among non-Hispanic whites, male gender (OR = 0.33), neighborhood deprivation (OR = 1.34), depression (OR = 1.09), and MRI-based neurodegeneration (OR = 0.03) were associated with MCI, while depression (OR = 1.09) and APOEɛ4 genotype (OR = 4.38) were associated with dementia. CONCLUSION: Findings from this study revealed that the demographic, clinical, sociocultural and biomarker characteristics of MCI and dementia are different among Mexican Americans as compared to non-Hispanic whites.

Depression is differentially related to cognitive and biomarker outcomes among Mexican Americans.

Introduction: Despite 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. Methods: Data 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 Aß40, Aß42, total tau, and NfL were examined in addition to MRI-based neurodegeneration. PET amyloid data were available in a subset of participants. Results: Depressive 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. Discussion: Depressive 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.

Characterizing Plasma Biomarkers of Alzheimer's in a Diverse Community-Based Cohort: A Cross-Sectional Study of the HAB-HD Cohort.

Background: Due 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 Aß40, Aß42, 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. Methods: A 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. Results: Significant 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 Aß40, 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 Aß40, Aß42, and t tau there was no difference between the groups for Aß42. NHW had significantly higher levels of Aß40, t tau, and NfL than AA. AA had a higher Aß42/Aß40 ratio than either NHW or MA for CU MCI. Conclusions: The 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.

APOEε4 Genotype Is Related to Brain Amyloid Among Mexican Americans in the HABS-HD Study.

Introduction: Despite 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. Methods: PET amyloid (florbetaben) data were analyzed from 105 Mexican American and 150 non-Hispanic white participants. Results: Among 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/Conclusion: Despite 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.

Metabolic Factors Are Related to Brain Amyloid Among Mexican Americans: A HABS-HD Study.

BACKGROUND: Despite the tremendous amount of research on Alzheimer's disease (AD) biomarkers, very little data is available regarding the fundamental biomarkers of AD among Mexican Americans. OBJECTIVE: Here we sought to examine the link between metabolic markers and brain amyloid among Mexican Americans as compared to non-Hispanic whites from the Health & Aging Brain Study -Health Disparities (HABS-HD) cohort. METHODS: PET amyloid (florbetaben) data was analyzed from 34 Mexican American and 22 non-Hispanic white participants. RESULTS: Glucagon (t = 3.84, p < 0.001) and insulin (t = -2.56, p = 0.02) were both significantly related to global SUVR levels among Mexican Americans. Glucagon and insulin were both related to most ROIs. No metabolic markers were significantly related to brain amyloid levels among non-Hispanic whites. CONCLUSION: Metabolic markers are related to brain amyloid burden among Mexican Americans. Given the increased risk for diabetes, additional research is needed to determine the impact of diabetes on core AD biomarkers among this underserved population.

Vascular risk profile and white matter hyperintensity volume among Mexican Americans and non-Hispanic Whites: The HABLE study.

INTRODUCTION: Among vascular risk factors we hypothesized that an increased prevalence of diabetes in Hispanics would be associated with greater white matter hyperintensity (WMH) volume, which may contribute to cognitive decline. METHODS: A total of 1318 participants (60% female; 49% Hispanic, 51% non-Hispanic White; age 66.2 ± 8.9 years) underwent clinical evaluation and brain magnetic resonance imaging (MRI). WMH volume associations were assessed with age, sex, and ethnicity and then with vascular risk factors in a selective regression model. RESULTS: WMH volume was greater with older age (P < .0001), Hispanic ethnicity (P = .02), and female sex (P = .049). WMH volume was best predicted by age, diastolic blood pressure, hypertension history, hemoglobin A1c (HbA1c), white blood cell count, and hematocrit (P < .01 for all). Elevated HbA1c was associated with greater WMH volume among Hispanics (parameter estimate 0.08 ± 0.02, P < .0001) but not non-Hispanic Whites (parameter estimate 0.02 ± 0.04, P = .5). DISCUSSION: WMH volume was greater in Hispanics, which may be partly explained by increased WMH volume related to elevated HbA1c among Hispanics but not non-Hispanic Whites.