Aging / Alzheimer's
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/32538
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Browsing Aging / Alzheimer's by Author "Hall, James"
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Item A Study on the Utility of Blood-Based Biomarkers for Alzheimer’s Disease in Predicting Cerebral Amyloid among Individuals with Down Syndrome(2024-03-21) Awasthi, Shubhangi; Zhang, Fan; Hall, James; Mapstone, Mark; O'Bryant, Sid; Petersen, MelissaPurpose: Down Syndrome (DS) is one of the most common genetic disorders. Individuals with DS show Alzheimer’s Disease (AD)-related neuropathology at a younger age, placing them at an increased risk for developing dementia. Limited research has explored the relationship between blood-based biomarkers and cerebral amyloid positivity. Our study examines the association between blood-based biomarkers and the presence of cerebral amyloid detected by PET scans in participants with DS. Methods: Data were analyzed on a cohort of n=368 participants with DS aged 30 and above. Proteomic assays of amyloid beta 40 (Aꞵ40) and 42 (Aꞵ42), total tau, neurofilament light (NfL), and phosphorylated tau181 (pTau181) were performed on plasma samples using Single Molecule Array (Simoa). Cerebral amyloid levels were obtained through PET Amyloid imaging. Covariates included age, gender, and presence of at least one APOε4 allele. Correlations were run using R statistical software. Random Forest analyses were conducted to examine the link between the select biomarkers and cerebral amyloid SUVR levels. Logistic regressions were also used in examining the utility of AD biomarkers in detecting cerebral amyloid positivity. Significance was set at p<0.05. Results: The biomarkers that significantly correlated with cerebral amyloid SUVR levels were Aꞵ42 (p=0.020), total tau (p<0.001), NfL (p<0.001), and pTau181 (p<0.001). There was no significant correlation between Aꞵ40 (p=0.888) and levels of cerebral amyloid. Regression analysis demonstrated a high correlation (R2 = 0.956) between the biomarkers and cerebral amyloid positivity. Our profile was accurate in detecting the presence of cerebral amyloid, yielding an area under the curve (AUC) of 0.9984, a positive predictive value (PPV/Precision) of 0.9571, sensitivity of 0.9853, and specificity of 0.9404. Conclusion: Our findings demonstrate that our proteomic profile consisting of the biomarkers Aꞵ40, Aꞵ42, total tau, NfL, and pTau181, and select demographics was highly accurate in predicting the presence of cerebral amyloid in our cohort. Having a less invasive and less costly screening tool, such as a blood-based biomarker profile, will allow for earlier detection of dementia in individuals who are at risk. Future research should explore these findings in the context of a larger cohort for increased generalizability.Item Utility of Blood Based Biomarkers in Detecting Cerebral Amyloid among Mexican Americans: A HABS-HD Study(2024-03-21) Alexander, Angel; Zhang, Fan; Hall, James; O'Bryant, Sid; Petersen, MelissaBackground: Alzheimer's disease (AD) is the leading cause of dementia in our nation's aging population. Minority groups, such as Hispanics, are 1.5 times more likely than non-Hispanic whites (NHW) to develop AD during their lifetime. Several studies have shown that blood biomarkers can be used to detect dementia related to AD. However, limited research has analyzed the relationship between blood biomarkers and cerebral amyloid, an AD biomarker, in minority groups. This study aims to address this gap and examine the utility of blood-based biomarkers to detect cerebral amyloid and predict AD among NHW and Mexican Americans (MA). Methods: Data were analyzed on 232 participants (n=148 NHW; n=84 MA) from the Health & Aging Brain Study – Health Disparities (HABS-HD) study. Of those selected for inclusion in this study, 46 participants had a positive cerebral amyloid scan, while 186 had a negative scan. Plasma samples were assayed for amyloid beta (Aβ)40, Aβ42, total tau (t-tau), phosphorylated tau (p-tau181), and neurofilament light (NfL) using the Simoa (single molecule array) technology platform (Quanterix.com). PET amyloid imaging was performed using a Neuraceq (florbetaben) tracer to measure cerebral amyloid positivity based on a clinical read and global standardized uptake value ratios (SUVRs). Covariates included age, gender, and education. Certain models were also split by ethnic groups. Correlation models were run separately for each blood biomarker and total cerebral amyloid SUVR. The plasma proteomic profiles were generated without transformations using Random forest (RF) analysis in R package. Regression coefficients examined the relationship between the proteomic biomarkers, the independent variable, and cerebral amyloid, the dependent variable. Logistic regressions were conducted to examine the ability of the proteomic profiles to predict cerebral amyloid positivity status. Results: Biomarkers Aβ40, Aβ42, p-tau181, and NfL were all significantly correlated with cerebral amyloid (ps< 0.05). The entire cohort had a high regression performance (R2=0.905) between the proteomic biomarkers and cerebral amyloid, with p-tau181 as the driving biomarker. Among the MA group, the biomarkers comprising the proteomic profile yielded excellent accuracy in detecting cerebral amyloid (Area Under the Curve [AUC] = 1.00, Sensitivity [SN] = 1.00, Specificity [SP] = 0.97, and positive predictive value [PPV] = 88%). Among the NHW group, the biomarkers also detected cerebral amyloid with a high level of accuracy (AUC = 0.99, SN = 1, SP = 0.99, and PPV = 92%). Cerebral amyloid positivity was predicted with 100% accuracy in both groups, with false positive rates of 1.19% and 2.03% in the MA and NHW groups, respectively. Conclusions: This study supports the application of plasma proteomic profiles to predict cerebral amyloid-related to AD. The findings highlighted that p-tau181 is the most important biomarker for cerebral amyloid detection in the MA population. This is relevant as prior work in NHW have focused on Aβ being the primary biomarker for AD detection. Future work should examine these findings in a larger population subset in order to validate the predictive utility of blood biomarkers as a screening tool in clinical settings for early AD detection.Item Vascular and metabolic profiles related to white matter hyperintensities in a multiethnic cohort from the HABS-HD study(2024-03-21) Taylor, Douglas; Vintimilla, Raul; Hall, James; Johnson, Leigh; O'Bryant, SidPurpose: There are more than 6 million people living with Alzheimer’s disease (AD) in the United States. Mexican-Americans (MA) and African-Americans (AA) are disproportionally affected by AD and related dementias, and it is expected that these disparities will increase in the coming years. AD commonly presents with vascular dementia and research has shown the relationship between the two to be complex, with many individuals presenting with mixed dementia. Vascular dementia is commonly related to small vessel disease. Small vessel disease occurs when endothelial damage in cerebrovascular circulation causes ischemia, leading to microinfarcts. The microinfarcts show up as white matter hyperintensities (WMH) in MRI. Most research using WMH to study dementia has been completed with non-Hispanic whites (NHW), though studies have shown a higher incidence of metabolic factors related to AD in MA. It is our goal to use WMH to find further differences in vascular and metabolic factors related to AD among a cohort of NHW, MA, and AA. Method: A cross-sectional analysis of 2363 subjects from the HABS-HD cohort was conducted (967 NHW, 410 AA, and 986 MA). Participants underwent a clinical evaluation and a 3T brain MRI (Siemens Skyra). WMH volume was measured from FLAIR using the Statistical Parametric Mapping (SPM) Lesion Segmentation Tool. WMH were Log transform to achieve normality, and were adjusted for intracranial volume derived from Free3Surfer V6.0 analysis of T1MPRAGE. Fasting blood samples were collected, and clinical measures were conducted using standard procedures. Clinical, vascular, and metabolic risk factors (table 1) were used in linear regression models as predictors of WMH volume adjusted by intracranial volume (ICV). Age, sex, and education were entered as covariates. Results: The total sample was 62.3 percent female with a mean age of 65.4 years and 13.07 years of education. NHW were older, had more years of education, had lower BMI, lower systolic and diastolic blood pressure, and levels of triglycerides, HA1c, and EGFR when compared to AA and MA (p ≤0.005). In NHW, age, sex, education, SBP, DBP, and hypertension significantly predicted WMH volumes (p ≤ 0.005). Age, years of education and BMI were the only significant predictors of WMH volume in AA (p ≤ 0.005), while age, total cholesterol and T4 levels were significant predictors of WMH volume in MA (p ≤ 0.005). Having a diagnosis of diabetes or dyslipidemia, also predicted WMH volume in MA. Conclusion: Results showed that different factors contribute to WMH volume among different ethnicities. Results suggest that in NHW, a vascular profile is most relevant, while in MA and AA, a metabolic profile seems to be driven the association with WMH. Prospective studies are needed to further understand the how the different profiles among different ethnicities affect the presentation of WMH and pathology of SVD.