Publications -- Leigh A. Johnson

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/31193

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.

Browse

Now showing 1 - 16 of 16

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.
Centralizing prescreening data collection to inform data-driven approaches to clinical trial recruitment
(BioMed Central Ltd., 2023-05-03) Kirn, Dylan R.; Grill, Joshua D.; Aisen, Paul; Ernstrom, Karin; Gale, Seth; Heidebrink, Judith; Jicha, Gregory; Jimenez-Maggiora, Gustavo; Johnson, Leigh A.; Peskind, Elaine; McCann, Kelly; Shaffer, Elizabeth; Sultzer, David; Wang, Shunran; Sperling, Reisa; Raman, Rema
BACKGROUND: Recruiting to multi-site trials is challenging, particularly when striving to ensure the randomized sample is demographically representative of the larger disease-suffering population. While previous studies have reported disparities by race and ethnicity in enrollment and randomization, they have not typically investigated whether disparities exist in the recruitment process prior to consent. To identify participants most likely to be eligible for a trial, study sites frequently include a prescreening process, generally conducted by telephone, to conserve resources. Collection and analysis of such prescreening data across sites could provide valuable information to improve understanding of recruitment intervention effectiveness, including whether traditionally underrepresented participants are lost prior to screening. METHODS: We developed an infrastructure within the National Institute on Aging (NIA) Alzheimer's Clinical Trials Consortium (ACTC) to centrally collect a subset of prescreening variables. Prior to study-wide implementation in the AHEAD 3-45 study (NCT NCT04468659), an ongoing ACTC trial recruiting older cognitively unimpaired participants, we completed a vanguard phase with seven study sites. Variables collected included age, self-reported sex, self-reported race, self-reported ethnicity, self-reported education, self-reported occupation, zip code, recruitment source, prescreening eligibility status, reason for prescreen ineligibility, and the AHEAD 3-45 participant ID for those who continued to an in-person screening visit after study enrollment. RESULTS: Each of the sites was able to submit prescreening data. Vanguard sites provided prescreening data on a total of 1029 participants. The total number of prescreened participants varied widely among sites (range 3-611), with the differences driven mainly by the time to receive site approval for the main study. Key learnings instructed design/informatic/procedural changes prior to study-wide launch. CONCLUSION: Centralized capture of prescreening data in multi-site clinical trials is feasible. Identifying and quantifying the impact of central and site recruitment activities, prior to participants signing consent, has the potential to identify and address selection bias, instruct resource use, contribute to effective trial design, and accelerate trial enrollment timelines.
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.
Application of Structural Retinal Biomarkers to Detect Cognitive Impairment in a Primary Care Setting
(IOS Press, 2023-02-02) Mozdbar, Sima; Petersen, Melissa E.; Zhang, Fan; Johnson, Leigh A.; Tolman, Alex; Nyalakonda, Ramyashree; Gutierrez, Alejandra; O'Bryant, Sid E.
BACKGROUND: Despite the diagnostic accuracy of advanced neurodiagnostic procedures, the detection of Alzheimer's disease (AD) remains poor in primary care. There is an urgent need for screening tools to aid in the detection of early AD. OBJECTIVE: This study examines the predictive ability of structural retinal biomarkers in detecting cognitive impairment in a primary care setting. METHODS: Participants were recruited from Alzheimer's Disease in Primary Care (ADPC) study. As part of the ADPC Retinal Biomarker Study (ADPC RBS), visual acuity, an ocular history questionnaire, eye pressure, optical coherence tomography (OCT) imaging, and fundus imaging was performed. RESULTS: Data were examined on n = 91 participants. The top biomarkers for predicting cognitive impairment included the inferior quadrant of the outer retinal layers, all four quadrants of the peripapillary retinal nerve fiber layer, and the inferior quadrant of the macular retinal nerve fiber layer. CONCLUSION: The current data provides strong support for continued investigation into structural retinal biomarkers, particularly the retinal nerve fiber layer, as screening tools for AD.
Water T2 as an early, global and practical biomarker for metabolic syndrome: an observational cross-sectional study
(BioMed Central Ltd., 2017-12-19) Robinson, Michelle D.; Mishra, Ina; Deodhar, Sneha; Patel, Vipulkumar; Gordon, Katrina V.; Vintimilla, Raul; Brown, Kim; Johnson, Leigh A.; O'Bryant, Sid E.; Cistola, David P.
BACKGROUND: Metabolic syndrome (MetS) is a highly prevalent condition that identifies individuals at risk for type 2 diabetes mellitus and atherosclerotic cardiovascular disease. Prevention of these diseases relies on early detection and intervention in order to preserve pancreatic beta-cells and arterial wall integrity. Yet, the clinical criteria for MetS are insensitive to the early-stage insulin resistance, inflammation, cholesterol and clotting factor abnormalities that characterize the progression toward type 2 diabetes and atherosclerosis. Here we report the discovery and initial characterization of an atypical new biomarker that detects these early conditions with just one measurement. METHODS: Water T2, measured in a few minutes using benchtop nuclear magnetic resonance relaxometry, is exquisitely sensitive to metabolic shifts in the blood proteome. In an observational cross-sectional study of 72 non-diabetic human subjects, the association of plasma and serum water T2 values with over 130 blood biomarkers was analyzed using bivariate, multivariate and logistic regression. RESULTS: Plasma and serum water T2 exhibited strong bivariate correlations with markers of insulin, lipids, inflammation, coagulation and electrolyte balance. After correcting for confounders, low water T2 values were independently and additively associated with fasting hyperinsulinemia, dyslipidemia and subclinical inflammation. Plasma water T2 exhibited 100% sensitivity and 87% specificity for detecting early insulin resistance in normoglycemic subjects, as defined by the McAuley Index. Sixteen normoglycemic subjects with early metabolic abnormalities (22% of the study population) were identified by low water T2 values. Thirteen of the 16 did not meet the harmonized clinical criteria for metabolic syndrome and would have been missed by conventional screening for diabetes risk. Low water T2 values were associated with increases in the mean concentrations of 6 of the 16 most abundant acute phase proteins and lipoproteins in plasma. CONCLUSIONS: Water T2 detects a constellation of early abnormalities associated with metabolic syndrome, providing a global view of an individual's metabolic health. It circumvents the pitfalls associated with fasting glucose and hemoglobin A1c and the limitations of the current clinical criteria for metabolic syndrome. Water T2 shows promise as an early, global and practical screening tool for the identification of individuals at risk for diabetes and atherosclerosis.
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.
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.
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.
Circulating mitochondrial DNA: New indices of type 2 diabetes-related cognitive impairment in Mexican Americans
(PLoS, 2019-03-12) Silzer, Talisa K.; Barber, Robert C.; Sun, Jie; Pathak, Gita A.; Johnson, Leigh A.; O'Bryant, Sid E.; Phillips, Nicole
Mitochondrial function has been implicated and studied in numerous complex age-related diseases. Understanding the potential role of mitochondria in disease pathophysiology is of importance due to the rise in prevalence of complex age-related diseases, such as type 2 diabetes (T2D) and Alzheimer's disease (AD). These two diseases specifically share common pathophysiological characteristics which potentially point to a common root cause or factors for disease exacerbation. Studying the shared phenomena in Mexican Americans is of particular importance due to the disproportionate prevalence of both T2D and AD in this population. Here, we assessed the potential role of mitochondria in T2D and cognitive impairment (CI) in a Mexican American cohort by analyzing blood-based indices of mitochondrial DNA copy number (mtDNACN) and cell-free mitochondrial DNA (CFmtDNA). These mitochondrial metrics were also analyzed for correlation with relevant neuropsychological variables and physiological data collected as indicators of disease and/or disease progression. We found mtDNACN to be significantly decreased in individuals with CI, while CFmtDNA was significantly elevated in T2D; further, CFmtDNA elevation was significantly exacerbated in individuals with both diseases. MtDNACN was found to negatively correlate with age and fatty acid binding protein concentration, while positively correlating with CFmtDNA as well as CERAD total recall score. Candidate gene SNP-set analysis was performed on genes previously implicated in maintenance and control of mitochondrial dynamics to determine if nuclear variants may account for variability in mtDNACN. The results point to a single significant locus, in the LRRK2/MUC19 region, encoding leucine rich repeat kinase 2 and mucin 19. This locus has been previously implicated in Parkinson's disease, among others; rs7302859 was the driver SNP. These combined findings further indicate that mitochondrial dysfunction (as assessed by proxy via mtDNACN) is intimately linked to both T2D and CI phenotypes as well as aging.
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.

Browse

Recent Submissions