Publications -- James R. Hall

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/32389

This collection is limited to articles published under the terms of a creative commons license or other open access publishing agreement since 2016. It is not intended as a complete list of the author's works.

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Evaluation of Neighborhood-Level Disadvantage and Cognition in Mexican American and Non-Hispanic White Adults 50 Years and Older in the US
(American Medical Association, 2023-08-30) Wong, Christina G.; Miller, Justin B.; Zhang, Fan; Rissman, Robert A.; Raman, Rema; Hall, James R.; Petersen, Melissa E.; Yaffe, Kristine; Kind, Amy J.; O'Bryant, Sid E.; Team, HABS-HD Study
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: beta = -2.50; 95% CI, -4.46 to -0.54), SEVLT Delayed Recall (eg, ADI quintile 3: beta = -1.11; 95% CI, -1.97 to -0.24), WMS-III Digit Span Forward (eg, ADI quintile 4: beta = -1.14; 95% CI, -1.60 to -0.67), TMT part A (ADI quintile 5: beta = 7.85; 95% CI, 1.28-14.42), TMT part B (eg, ADI quintile 5: beta = 31.5; 95% CI, 12.16-51.35), Letter Fluency (ADI quintile 4: beta = -2.91; 95% CI, -5.39 to -0.43), and DSST (eg, ADI quintile 5: beta = -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 (beta = -2.35; 95% CI, -4.40 to -0.30), SEVLT Delayed Recall (beta = -0.95; 95% CI, -1.73 to -0.17), TMT part B (beta = 15.95; 95% CI, 2.47-29.44), and DSST (beta = -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.
Association of Area Deprivation Index and hypertension, diabetes, dyslipidemia, and Obesity: A Cross-Sectional Study of the HABS-HD Cohort
(Sage Publications, 2023-06-26) Vintimilla, Raul; Seyedahmadi, Armin; Hall, James R.; Johnson, Leigh A.; O'Bryant, Sid E.; Team, HABS-HD Study
Objective: This study aims to investigate the association between neighborhood deprivation and the prevalence of major cardiovascular disease (CVD) risk factors (hypertension, diabetes, dyslipidemia, and obesity) in a Mexican American (MA) population compared to NonHispanic Whites (NHW). Method: A cross-sectional analysis was conducted to include 1,867 subjects (971 MA and 896 NHW). Participants underwent a clinical interview, neuropsychological exam battery, functional examination, MRI of the head, amyloid PET scan, and blood draw for clinical and biomarker analysis. We use the Area Deprivation Index (ADI) Model to assign an ADI score to participants based on their neighborhoods. Descriptive, Cochran-Armitage test for trend, and odds ratio statistical analysis were applied. Results: Our results suggest that NHW had higher odds of having HTN, DM, and obesity in the most deprived neighborhoods, while MA showed no increased odds. The study also found that neighborhood deprivation contributed to diabetes in both MA and NHW and was associated with obesity in NHW. Conclusions: These findings highlighted the importance of addressing both individual and societal factors in efforts to reduce cardiovascular risk. Future research should explore the relationship between socio-economic status and cardiovascular risk in more detail to inform the development of targeted interventions.
Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer's Disease Data
(MDPI, 2022-11-17) Zhang, Fan; Petersen, Melissa E.; Johnson, Leigh A.; Hall, James R.; O'Bryant, Sid E.
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.
A Precision Medicine Approach to Treating Alzheimer's Disease Using Rosiglitazone Therapy: A Biomarker Analysis of the REFLECT Trials
(IOS Press, 2021-05-18) O'Bryant, Sid E.; Zhang, Fan; Petersen, Melissa E.; Johnson, Leigh A.; Hall, James R.; Rissman, Robert A.
Background: The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer's disease utilizing a peroxisome proliferator-activated receptor gamma agonist. Objective: To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials. Methods: Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile. Results: A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNFɑ, FABP-3, and PPY) correctly classified treatment response with 100% accuracy across study arms for REFLECT Phase II trial (AVA100193) and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders. Conclusion: A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials.
Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer's Disease With High Performance Computing
(Frontiers Media S.A., 2021-12-08) Zhang, Fan; Petersen, Melissa E.; Johnson, Leigh A.; Hall, James R.; O'Bryant, Sid E.
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.
Neurodegeneration from the AT(N) framework is different among Mexican Americans compared to non-Hispanic Whites: A Health & Aging Brain among Latino Elders (HABLE) Study
(Wiley Periodicals, LLC, 2022-02-09) O'Bryant, Sid E.; Zhang, Fan; Petersen, Melissa E.; Hall, James R.; Johnson, Leigh A.; Yaffe, Kristine; Braskie, Meredith N.; Rissman, Robert A.; Vig, Rocky; Toga, Arthur W.
Introduction: We sought to examine a magnetic resonance imaging (MRI)-based marker of neurodegeneration from the AT(N) (amyloid/tau/neurodegeneration) framework among a multi-ethnic, community-dwelling cohort. Methods: Community-dwelling Mexican Americans and non-Hispanic White adults and elders were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing and 3T MRI of the brain. A neurodegeneration MRI meta-region of interest (ROI) biomarker for the AT(N) framework was calculated. Results: Data were examined from n = 1305 participants. Mexican Americans experienced N at significantly younger ages. The N biomarker was significantly associated with cognitive outcomes. N was significantly impacted by cardiovascular factors (e.g., total cholesterol, low-density lipoprotein) among non-Hispanic Whites whereas diabetes (glucose, HbA1c, duration of diabetes) and sociocultural (household income, acculturation) factors were strongly associated with N among Mexican Americans. Discussion: The prevalence, progression, timing, and sequence of the AT(N) biomarkers must be examined across diverse populations.
Proteomic profiles of prevalent mild cognitive impairment and Alzheimer's disease among adults with Down syndrome
(Wiley Periodicals, Inc., 2020-04-17) Petersen, Melissa E.; Zhang, Fan; Krinsky-McHale, Sharon J.; Silverman, Wayne; Lee, Joseph H.; Pang, Deborah; Hall, James R.; Schupf, Nicole; O'Bryant, Sid E.
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.
Proteomic profiles of incident mild cognitive impairment and Alzheimer's disease among adults with Down syndrome
(Wiley Periodicals, Inc., 2020-05-21) O'Bryant, Sid E.; Zhang, Fan; Silverman, Wayne; Lee, Joseph H.; Krinsky-McHale, Sharon J.; Pang, Deborah; Hall, James R.; Schupf, Nicole
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.
MRI biomarkers of small vessel disease and cognition: A cross-sectional study of a cognitively normal Mexican American cohort
(Wiley Periodicals, LLC, 2021-10-14) Vintimilla, Raul; Hall, James R.; King, Kevin; Braskie, Meredith N.; Johnson, Leigh A.; Yaffe, Kristine; Toga, Arthur W.; O'Bryant, Sid E.
Background: The current project sought to evaluate the impact that white matter hyperintensities (WMH) have on executive function in cognitively normal Mexican Americans, an underserved population with onset and more rapid progression of dementia. Methods: Data from 515 participants (360 female) enrolled in the Health and Aging Brain Study: Health Disparities project were analyzed. Participants underwent clinical evaluation, cognitive testing, and a brain MRI. Linear regression was used to predict the effect of total WMH volume on cognitive test scores. Age, sex, and education were entered as covariates. Results: Regression analysis showed that WMH volume significantly predicted executive function. WMH also predicted global cognition and attention scores, although not significantly after adjusting for age. Conclusion: In this sample of cognitively normal Mexican Americans, we found that WMH volume was associated with lower scores in a measure of executive function, after accounting for age, sex, and education.
Cardiovascular Risk Factors, Cognitive Dysfunction, and Mild Cognitive Impairment
(S. Karger AG, 2020-11-16) Vintimilla, Raul; Balasubramanian, Kishore; Hall, James R.; Johnson, Leigh A.; O'Bryant, Sid E.
Objectives: The present study sought to evaluate the contribution of cardiovascular risk factors to cognitive functioning in a sample of Mexican Americans diagnosed with mild cognitive impairment (MCI). Methods: Hypertension, diabetes, dyslipidemia, and obesity were diagnosed based on self-report and/or standardized procedures. Cognitive function was measured with MMSE, Logical Memory I and II, Trail A & B, FAS, animal naming, and digit span tests. Independent samples t tests and two-way ANOVAs were conducted for analyses, adjusting for relevant covariates. We studied 100 Mexican Americans (65 female) with MCI, ages 50-86, from a longitudinal study of cognitive aging conducted at the University of North Texas Health Science Center. Results: A difference between subjects with and without obesity and memory scores was shown by t tests. Two-way ANOVAs detected an association between the coexistence of hypertension and diabetes with language measures, diabetes and dyslipidemia with executive function, and diabetes and obesity with memory and language measures. Conclusions: This study provides additional evidence about the link between cardiovascular risk factors and cognitive dysfunction in MCI subjects, and also demonstrated that comorbid risk factors increased the degree of cognitive deficit in many areas, which may indicate a higher risk of developing dementia.
Proteomic profiles for Alzheimer's disease and mild cognitive impairment among adults with Down syndrome spanning serum and plasma: An Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study
(Wiley Periodicals, Inc., 2020-06-30) Petersen, Melissa E.; Zhang, Fan; Schupf, Nicole; Krinsky-McHale, Sharon J.; Hall, James R.; Mapstone, Mark; Cheema, Amrita; Silverman, Wayne; Lott, Ira; Rafii, Michael S.; Handen, Benjamin; Klunk, William; Head, Elizabeth; Christian, Bradley; Foroud, Tatiana; Lai, Florence; Rosas, H. Diana; Zaman, Shahid; Ances, Beau M.; Wang, Mei-Cheng; Tycko, Benjamin; Lee, Joseph H.; O'Bryant, Sid E.
Introduction: Previously generated serum and plasma proteomic profiles were examined among adults with Down syndrome (DS) to determine whether these profiles could discriminate those with mild cognitive impairment (MCI-DS) and Alzheimer's disease (DS-AD) from those cognitively stable (CS). Methods: Data were analyzed on n = 305 (n = 225 CS; n = 44 MCI-DS; n = 36 DS-AD) enrolled in the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS). Results: Distinguishing MCI-DS from CS, the serum profile produced an area under the curve (AUC) = 0.95 (sensitivity [SN] = 0.91; specificity [SP] = 0.99) and an AUC = 0.98 (SN = 0.96; SP = 0.97) for plasma when using an optimized cut-off score. Distinguishing DS-AD from CS, the serum profile produced an AUC = 0.93 (SN = 0.81; SP = 0.99) and an AUC = 0.95 (SN = 0.86; SP = 1.0) for plasma when using an optimized cut-off score. AUC remained unchanged to slightly improved when age and sex were included. Eotaxin3, interleukin (IL)-10, C-reactive protein, IL-18, serum amyloid A , and FABP3 correlated fractions at r2 > = 0.90. Discussion: Proteomic profiles showed excellent detection accuracy for MCI-DS and DS-AD.
Potential two-step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study
(Elsevier Inc., 2019-05-02) O'Bryant, Sid E.; Edwards, Melissa; Zhang, Fan; Johnson, Leigh A.; Hall, James R.; Kuras, Yuliya; Scherzer, Clemens R.
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.
Depression, inflammation, and memory loss among Mexican Americans: analysis of the HABLE cohort
(Cambridge University Press, 2017-06-20) Johnson, Leigh A.; Edwards, Melissa; Gamboa, Adriana; Hall, James R.; Robinson, Michelle; O'Bryant, Sid E.
Background: This study explored the combined impact of depression and inflammation on memory functioning among Mexican-American adults and elders. Methods: Data were analyzed from 381 participants of the Health and Aging Brain study among Latino Elders (HABLE). Fasting serum samples were collected and assayed in duplicate using electrochemiluminesce on the SECTOR Imager 2400A from Meso Scale Discovery. Positive DepE (depression endophenotype) was codified as any score >1 on a five-point scale based on the GDS-30. Inflammation was determined by TNFɑ levels and categorized by tertiles (1st, 2nd, 3rd). WMS-III LMI and LMII as well as CERAD were utilized as measures of memory. ANOVAs examined group differences between positive DepE and inflammation tertiles with neuropsychological scale scores as outcome variables. Logistic regressions were used to examine level of inflammation and DepE positive status on the risk for MCI. Results: Positive DepE as well as higher inflammation were both independently found to be associated with lower memory scores. Among DepE positive, those who were high in inflammation (3rd tertile) were found to perform significantly worse on WMS-III LM I (F = 4.75, p = 0.003), WMS-III LM II (F = 8.18, p < 0.001), and CERAD List Learning (F = 17.37, p < 0.001) when compared to those low on inflammation (1st tertile). The combination of DepE positive and highest tertile of inflammation was associated with increased risk for MCI diagnosis (OR = 6.06; 95% CI = 3.9-11.2, p < 0.001). Conclusion: Presence of elevated inflammation and positive DepE scores increased risk for worse memory among Mexican-American older adults. Additionally, the combination of DepE and high inflammation was associated with increased risk for MCI diagnosis. This work suggests that depression and inflammation are independently associated with worse memory among Mexican-American adults and elders; however, the combination of both increases risk for poorer memory beyond either alone.
A proteomic signature for dementia with Lewy bodies
(Elsevier Inc., 2019-03-15) O'Bryant, Sid E.; Ferman, Tanis J.; Zhang, Fan; Hall, James R.; Pedraza, Otto; Wszolek, Zbigniew K.; Como, Tori; Julovich, David A.; Mattevada, Sravan; Johnson, Leigh A.; Edwards, Melissa; Graff-Radford, Neill R.
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.
Plasma Total Tau and Neurobehavioral Symptoms of Cognitive Decline in Cognitively Normal Older Adults
(Frontiers Media S.A., 2021-11-05) Hall, James R.; Petersen, Melissa E.; Johnson, Leigh A.; O'Bryant, Sid E.
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.
The Health & Aging Brain among Latino Elders (HABLE) study methods and participant characteristics
(Wiley Periodicals, LLC, 2021-06-21) O'Bryant, Sid E.; Johnson, Leigh A.; Barber, Robert C.; Braskie, Meredith N.; Christian, Bradley; Hall, James R.; Hazra, Nalini; King, Kevin; Kothapalli, Deydeep; Large, Stephanie; Mason, David; Matsiyevskiy, Elizabeth; McColl, Roderick; Nandy, Rajesh; Palmer, Raymond; Petersen, Melissa E.; Philips, Nicole; Rissman, Robert A.; Shi, Yonggang; Toga, Arthur W.; Vintimilla, Raul; Vig, Rocky; Zhang, Fan; Yaffe, Kristine
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.

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