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

Blood Markers in Relation to a History of Traumatic Brain Injury Across Stages of Cognitive Impairment in a Diverse Cohort

BACKGROUND

Traumatic brain injury (TBI) has been linked to multiple pathophysiological processes that could increase risk for Alzheimer's disease and related dementias (ADRD). However, the impact of prior TBI on blood biomarkers for ADRD remains unknown.

OBJECTIVE

Using cross-sectional data, we assessed whether a history of TBI influences serum biomarkers in a diverse cohort (approximately 50% Hispanic) with normal cognition, mild cognitive impairment, or dementia.

METHODS

Levels of glial fibrillary acidic protein (GFAP), neurofilament light (NFL), total tau (T-tau), and ubiquitin carboxy-terminal hydrolase-L1 (UCHL1) were measured for participants across the cognitive spectrum. Participants were categorized based on presence and absence of a history of TBI with loss of consciousness, and study samples were derived through case-control matching. Multivariable general linear models compared concentrations of biomarkers in relation to a history of TBI and smoothing splines modelled biomarkers non-linearly in the cognitively impaired groups as a function of time since symptom onset.

RESULTS

Each biomarker was higher across stages of cognitive impairment, characterized by clinical diagnosis and Mini-Mental State Examination performance, but these associations were not influenced by a history of TBI. However, modelling biomarkers in relation to duration of cognitive symptoms for ADRD showed differences by history of TBI, with only GFAP and UCHL1 being elevated.

CONCLUSIONS

Serum GFAP, NFL, T-tau, and UCHL1 were higher across stages of cognitive impairment in this diverse clinical cohort, regardless of TBI history, though longitudinal investigation of the timing, order, and trajectory of the biomarkers in relation to prior TBI is warranted.

Top Alzheimer's disease risk allele frequencies differ in HABS-HD Mexican- versus Non-Hispanic White Americans

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.

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.

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.