9,228 research outputs found
A Machine Learning-Based Multiple Imputation Method for the Health and Aging Brain Study–Health Disparities
No full textThe Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to achieve accurate machine learning (ML) if data contain missing values. Therefore, developing a new imputation methodology has become an urgent task for HABS–HD. The three missing data assumptions, (1) missing completely at random (MCAR), (2) missing at random (MAR), and (3) missing not at random (MNAR), necessitate distinct imputation approaches for each mechanism of missingness. Several popular imputation methods, including listwise deletion, min, mean, predictive mean matching (PMM), classification and regression trees (CART), and missForest, may result in biased outcomes and reduced statistical power when applied to downstream analyses such as testing hypotheses related to clinical variables or utilizing machine learning to predict AD or MCI. Moreover, these commonly used imputation techniques can produce unreliable estimates of missing values if they do not account for the missingness mechanisms or if there is an inconsistency between the imputation method and the missing data mechanism in HABS–HD. Therefore, we proposed a three-step workflow to handle missing data in HABS–HD: (1) missing data evaluation, (2) imputation, and (3) imputation evaluation. First, we explored the missingness in HABS–HD. Then, we developed a machine learning-based multiple imputation method (MLMI) for imputing missing values. We built four ML-based imputation models (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and lasso and elastic-net regularized generalized linear model (GLMNET)) and adapted the four ML-based models to multiple imputations using the simple averaging method. Lastly, we evaluated and compared MLMI with other common methods. Our results showed that the three-step workflow worked well for handling missing values in HABS–HD and the ML-based multiple imputation method outperformed other common methods in terms of prediction performance and change in distribution and correlation. The choice of missing handling methodology has a significant impact on the accompanying statistical analyses of HABS–HD. The conceptual three-step workflow and the ML-based multiple imputation method perform well for our Alzheimer’s disease models. They can also be applied to other disease data analyses
Empirically derived psychosocial-behavioral phenotypes in Black/African American and Hispanic/Latino older adults enrolled in HABS-HD: Associations with AD biomarkers and cognitive outcomes
No full textINTRODUCTION: Identification of psychosocial-behavioral phenotypes to understand within-group heterogeneity in risk and resiliency to Alzheimer's disease (AD) within Black/African American and Hispanic/Latino older adults is essential for the implementation of precision health approaches. METHODS: A cluster analysis was performed on baseline measures of socioeconomic resources (annual income, social support, occupational complexity) and psychiatric distress (chronic stress, depression, anxiety) for 1220 racially/ethnically minoritized adults enrolled in the Health and Aging Brain Study-Health Disparities (HABS-HD). Analyses of covariance adjusting for sociodemographic factors examined phenotype differences in cognition and plasma AD biomarkers. RESULTS: The cluster analysis identified (1) Low Resource/High Distress (n = 256); (2) High Resource/Low Distress (n = 485); and (3) Low Resource/Low Distress (n = 479) phenotypes. The Low Resource/High Distress phenotype displayed poorer cognition and higher plasma neurofilament light chain; differences between the High Resource/Low Distress and Low Resource/Low Distress phenotypes were minimal. DISCUSSION: The identification of psychosocial-behavioral phenotypes within racially/ethnically minoritized older adults is crucial to the development of targeted AD prevention and intervention efforts.The authors thank all the HABS-HD participants and study stafffor their commitment to advancing representative aging researchand for publicly sharing these data with other researchers. Research reported in this publication was supported by the National Institute on Aging (NIA) of the National Institutes of Health (NIH)under Awards R01AG054073 and R01AG058533. The consent isthe sole responsibility of the authors and does not necessarily represent the official views of the NIH. Dr. Clark received supportfrom the HABS-HD Faculty Fellowship (U19AG078109), NIH/NIA(R03 AG085241), Shiley-Marcos Alzheimer's Disease Research Educa-tion Center Grant (P30AG062429), and the Alzheimer's Association (AARG-22-723000). Dr. Thomas was supported by the U.S. Department of Veterans Affairs Clinical Sciences Research and Development Service (1IK2CX001865), NIH/NIA grants (R03 AG070435), and the Alzheimer's Association (AARG-22-723000). Research reported in this publication was also supported by NIA awards (R01AG054073, R01AG058533, P41EB015922, and U19AG078109)
Bilingual neurocognitive resiliency, vulnerability, and Alzheimer's disease biomarker correlates in Latino older adults enrolled in the Health and Aging Brain Study ‐ Health Disparities (HABS‐HD)
No full textAbstract INTRODUCTION The effects of bilingualism on neuropsychological test performance in bilinguals with and without cognitive impairment are not well‐understood and are relatively limited by small sample sizes of Latinos. METHODS Using analysis of covariance (ANCOVA), we explored patterns of cognitive performance and impairment across a large sample of community‐dwelling bilingual and monolingual Latino older adults with (n = 180) and without (n = 643) mild cognitive impairment (MCI) enrolled in HABS‐HD. RESULTS Bilinguals demonstrated cognitive resiliency in the form of significantly better performance on the Trail Making Test and Digit Symbol Substitution Test, observed across the cognitively unimpaired and MCI groups. In contrast, bilinguals demonstrated cognitive vulnerability in the form of significantly poorer performance and higher impairment rates on phonemic fluency in the MCI phase, only. Follow‐up analyses revealed less balanced bilinguals demonstrated poorer performance and higher impairment rates on this measure, supported by lower levels of plasma Aβ 42/40. DISCUSSION Patterns of cognitive performance and impairment differ as a function of bilingualism. Bilingualism must be considered when evaluating cognitive and biomarker outcomes in Latino older adults. Highlights Latino bilinguals perform better on measures of processing speed and coding. Latino bilinguals with MCI demonstrate cognitive vulnerability in verbal fluency. Less balanced bilinguals demonstrate greatest vulnerability anchored by Aβ 42/40
The effects of team-skills training on transactive memory and performance
No full textThe existence of effective Transactive Memory Systems (TMS) in teams has been found to enhance task performance. Methods of developing Transactive Memory (TM) are therefore an important focus of research. This study aimed to explore one such method, the use of a generic team-skills training programme to develop TM and subsequent task performance. Sixteen three-member teams were all trained to complete a complex collaborative task, prior to which half the teams (n=8), completed a team-skills training programme. Results confirmed that those teams who had been trained to develop a range of team skills such as problem-solving, interpersonal relationships, goal setting and role allocation, evidenced significantly higher team skill, TM and performance than those who were not trained in such skills. Results are discussed with reference to the wider TM literature and the mechanisms through which team-skills training could facilitate the more rapid development of TM
Predictive value and weight of factors associated with cognitive performance in Hispanics/Latinos enrolled in the Health and Aging Brain Study: Health Disparities
No full textAbstract INTRODUCTION In this analysis of cognitively unimpaired (CU) Hispanic participants from the Health and Aging Brain Study: Health Disparities (HABS‐HD), we aimed to identify the main predictor factors for cognitive performance and their relative importance (weight). METHODS The HABS‐HD is a community‐based longitudinal cohort study. Data from 952 CU Hispanics, enrolled from 2017 to February 2024, were analyzed. Random forest, an assembly learning method based on decision trees, was used to cross‐sectionally forecast the predictive value of 42 risk factors (4 demographic variables, 4 socioeconomic variables, 6 psychosocial variables, 17 health variables, and 11 plasma and magnetic resonance imaging biomarkers) together, and the weighting of each factor for different cognitive domains (global cognition, memory, language, executive function, attention, and processing speed). RESULTS Participants included in the analyses had a mean age of 61.3 years (9.14), 69.4% were female, and had a mean of 10.52 (4.61) years of education. Income, glucose levels, plasma amyloid beta (Aβ)42, total tau, and neurofilament light chain were in the top 10 predictors in six cognitive domains. Age, education years, Penn State Worry Questionnaire, body mass index, and C‐reactive protein were the main predictors in four cognitive domains, while plasma Aβ40 was in the top 10 list for five cognitive domains. DISCUSSION Results support the notion that cognitive performance depends on interactions among social, economic, biological, and functional factors. The effects of factors together, and the weight of each factor in various cognitive domains may be different in Hispanics. More studies comparing different ethnic groups are necessary to help in the development of tailored interventions to prevent cognitive decline. Highlights Numerous factors have been associated with cognitive decline and dementia. Research on these factors has relied on a meta‐analysis of their individual association with cognition, consolidating data from different non‐Hispanic White populations. Hispanics are the largest minority group in the United States, and only a few studies have analyzed the overall impact of these factors together, and their individual relative effect in different cognitive domains. We found that cognitive performance in Hispanics may be a result of interactions among social, economic, biological, and functional factors
Supplementary Material for: The association of neighborhood socioeconomic status with executive function and processing speed in cognitively normal Mexican Americans elders from the Health and Aging Brains Study – Health Disparities cohort
No full textIntroduction: 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 (NHW) and Mexican Americans (MA) 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=1312) 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: MA were younger, more likely to be female, less educated, had higher ADI scores, performed worse on trails B (all p<0.05), and have lower prevalence of APOE4+ (p<0.001), when compared to NHW. A higher percentage of MA lived in the most deprived neighborhoods than NHW. For NHW, ADI did not predict trails B or DSS scores, after adjusting for demographic variables and APOE4. For MA, 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 MA but not in NHW, which is important to consider in future interventions to slow cognitive decline
Team perfectionism and team performance: A prospective study
No full textPerfectionism is a personality characteristic that has been found to predict sports performance in athletes. To date, however, research has exclusively examined this relationship at an individual level (i.e., athletes’ perfectionism predicting their personal performance). The current study extends this research to team sports by examining whether, when manifested at team level, perfectionism predicts team performance. A sample of 231 competitive rowers from 36 boats completed measures of self-oriented, team-oriented, and team-prescribed perfectionism prior to competing against one another in a 4-day rowing competition. Strong within-boat similarities in the levels of team members’ team-oriented perfectionism supported the existence of collective team-oriented perfectionism at the boat level. Two-level latent growth curve modeling of day-by-day boat performance showed that team-oriented perfectionism positively predicted the position of the boat in mid-competition and the linear improvement in position. The findings suggest that imposing perfectionistic standards on team members may drive teams to greater levels of performance
Sex differences in the association of cardiometabolic risk scores and blood pressure measurements with white matter hyperintensities in diverse older adults-HABS-HD
No full textIntroduction: We aimed to determine whether cardiometabolic risk factors and blood-pressure (BP) metrics were differentially associated with white matter hyperintensities volume (WMHV) in males versus females in the Health and Aging Brain Study-Health Disparities.
Methods: We analyzed 3,585 community-dwelling adults (2,207 females) from non-Hispanic White, non-Hispanic Black, and Hispanic groups who underwent BP measurement and WMHV quantification. Linear regression models assessed (i) individual risk factors (diabetes, hypertension, dyslipidemia, obesity, tobacco dependence), (ii) a composite risk score, and (iii) four BP metrics (systolic, diastolic, pulse pressure, mean arterial pressure), each including a sex-interaction term and adjusting for age, education, race/ethnicity, and scanner. A second BP model also controlled for all five risk factors.
Results: Diabetes (β = 0.46, 95% CI 0.28-0.64), hypertension (β = 0.47, 0.30-0.64), and higher composite risk (β = 0.19, 0.12-0.26) were associated with greater WMHV. Diastolic BP (β = 0.18, 0.11-0.26) and mean arterial pressure (β = 0.14, 0.07-0.21) related to larger WMHV, with diastolic BP remaining significant after full adjustment (β = 0.14, 0.07-0.22). No sex interactions survived correction.
Discussion: These findings underscore the importance of aggressive cardiometabolic and BP control, particularly diastolic BP, to mitigate WMHV in both sexes
Top Alzheimer's disease risk allele frequencies differ in HABS‐HD Mexican‐ versus Non‐Hispanic White Americans
No full textAbstract INTRODUCTION: Here we evaluate frequencies of the top 10 Alzheimer's disease (AD) risk alleles for late‐onset AD in Mexican American (MA) and non‐Hispanic White (NHW) American participants enrolled in the Health and Aging Brain Study–Health Disparities Study cohort. METHODS: Using DNA extracted from this community‐based diverse population, we calculated the genotype frequencies in each population to determine whether a significant difference is detected between the different ethnicities. DNA genotyping was performed per manufacturers’ protocols. RESULTS: Allele and genotype frequencies for 9 of the 11 single nucleotide polymorphisms (two apolipoprotein E variants, CR1, BIN1, DRB1, NYAP1, PTK2B, FERMT2, and ABCA7) differed significantly between MAs and NHWs. DISCUSSION: The significant differences in frequencies of top AD risk alleles observed here across MAs and NHWs suggest that ethnicity‐specific genetic risks for AD exist. Given our results, we are advancing additional projects to further elucidate ethnicity‐specific differences in AD
Association of Area Deprivation Index and hypertension, diabetes, dyslipidemia, and Obesity: A Cross-Sectional Study of the HABS-HD Cohort
No full textObjective: 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.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported on this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Numbers R01AG054073, R01AG058533, P41EB015922, U19AG078109, and R35AG071916. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health
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