Reyes, Denise A.

AbstractBackgroundAlzheimer’s disease (AD) patients have decline in cognitive domains including memory, language, visuospatial, and/or executive function and brain pathology including amyloid‐β and tau deposition, neurodegeneration, and frequent vascular co‐pathologies detectable by neuroimaging and/or cerebrospinal fluid biomarkers. However, molecular disease mechanisms are complex and heterogeneous. It is necessary to develop cost‐effective blood‐based biomarkers reflecting brain molecular perturbations in AD. We identified blood‐based gene and co‐expression network level changes associated with AD/mild cognitive impairment (MCI) diagnosis and AD‐related phenotypes.MethodWe performed differential gene expression and weighted gene co‐expression network analysis, followed by meta‐analysis, using blood transcriptome data of 391 participants from the Mayo Clinic Study of Aging and 654 participants from the Alzheimer's Disease Neuroimaging Initiative. The neuroimaging phenotypes include microhemorrhages, infarcts, amyloid burden, hippocampal volume, and white matter hyperintensities. The cognitive phenotypes include standardized cognitive subtest scores and composite scores for memory, language, visuospatial, and executive function.ResultFive out of 18 modules(M) are significantly associated with diagnosis or cognition (FDR‐adjusted p<0.05). M1 and M15 both positively associates with memory, M1 positively associated with language and M15 with visuospatial function. M1 and M15 are enriched in differentially expressed genes (DEGs) associated with language and executive function, respectively. M2 negatively associates with logical memory delayed recall scores(LMDR), memory, executive, and language functions and is enriched in DEGs for these phenotypes. M8 negatively associates with memory, language and executive functions and is enriched in DEGs for memory and language. M12 positively associates with LMDR. M1 and M15 are down‐regulated while M2 and M8 are up‐regulated in AD/MCI patients. Cell‐type enrichment analysis showed M2 is enriched in monocytes and neutrophils; M8 in monocytes; M15 in B cells (FDR <0.05). Gene ontology terms enriched in these modules indicated broad consistency with their cell types.ConclusionWe identified five modules significantly associated with AD/MCI or cognitive phenotypes using blood transcriptome data. These findings nominate blood transcriptome changes and their enriched biological processes as potential pathomechanisms in cognitive decline and AD/MCI development. We aim to investigate these blood transcripts as potential biomarkers for AD or AD‐related phenotypes and therapeutic targets through additional replication and experimental validation studies.

Abstract The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi‐platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1‐weighted and fluid‐attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. Highlights The MRI Core provides multi‐platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF‐fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM‐SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF‐fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

AbstractBackgroundA hallmark of Alzheimer’s disease is amyloid‐β (Aβ) deposition in the brain parenchyma as senile plaques which Aß frequently accompanies in the cerebral vasculature as cerebral amyloid angiopathy (CAA). Previous studies demonstrated that the cerebrovascular system is essential for Aβ clearance and interactions exist between cerebrovascular disease and AD. Neuroimaging data, including measures on small vessel disease, amyloid deposits, and microhemorrhages (MCH), can reflect both cerebrovascular disease and AD pathology, thereby serving as intermediate phenotypes of both conditions. We aim to identify genes and co‐expression networks in blood associated with these neuroimaging endophenotypes and the biological pathways enriched in these perturbed transcriptional networks.MethodWe conducted differential gene expression and Weighted Gene Co‐Expression Network analyses (WGCNA) on blood RNA sequencing data of 407 participants from the longitudinally followed Mayo Clinic Study of Aging cohort. The neuroimaging phenotypes include MCH, infarction, and amyloid deposits measured by Pittsburgh compound B positron emission tomography (PiB PET).ResultWGCNA analysis showed a nominally significant association (p<0.05) between several module eigengenes (MEs) and MCH and PiB PET. One module, M42, remained significantly associated with PiB PET after Bonferroni correction for all 43 modules tested. Pathway enrichment analysis of M42 identified two significant pathways (FDR‐adjusted p<0.05) including phosphodiesterase I activity and IgE binding. These pathways contain genes such as MS4A2 encoding the high‐affinity IgE receptor involved in the immune response. Similarly, ENPP2 in the phosphodiesterase I activity pathway represents an additional candidate gene previously implicated in Alzheimer’s disease.ConclusionWe generated co‐expression networks and identified one network module with a statistically significant association between brain amyloid deposits and blood transcriptome data. Further investigations of this network and its enriched pathways are necessary to understand their roles in amyloidosis, CAA, and neurodegeneration. Future studies will include seeking replication of these results in other datasets. Such studies can help elucidate the biological mechanisms of pathogenesis of cerebrovascular damage and amyloid deposits. In addition, in a clinical setting, blood levels of transcripts discovered in this study may serve as predictive or diagnostic biomarkers with mechanistic implications.

AbstractBackgroundElevated systolic blood pressure (SBP) and diastolic blood pressure (DBP) are risk factors for white matter (WM) injury associated with cognitive decline and dementia. White matter hyperintensities (WMH) on MRI, markers of WM injury, increase with aging and are more common in women than in men after the age of 60. We examined SBP and DBP in relation to WMH volume and diffusion MRI measures of WM integrity in postmenopausal women.MethodWomen (n=144) with median age of 67 (range 58‐72) and of good cardiovascular health who were previously enrolled in the multi‐site randomized clinical trial, Kronos Early Estrogen Prevention Study (KEEPS), participated in the present observational KEEPS Continuation Study. SBP, DBP, and MRI data were collected approximately 9 years after the end of KEEPS menopausal hormones versus placebo 4‐year interventions. WM integrity was assessed with WMH volume measurements (log transformed) on structural MRI and with measures of WM microstructure on diffusion MRI. Multiple linear regression analyses, adjusted for age, were used to test for associations of SBP and DBP with MRI measures.ResultThe median SBP was 128 (range 86‐180) and DBP was 77 (range 47‐106). Higher SBP (r=0.332;p=0.015) and DBP (r=0.346;p=0.007) were associated with higher WMH volumes. Among the WM tracts, higher SBP was associated with lower fractional anisotropy (FA) in lateral orbitofrontal gyrus WM (r=0.198;p=0.039) and sagittal stratum (r=0.226;p=0.043), and higher DBP was associated with lower FA in the superior occipital gyrus WM (r=0.370;p=0.045). Higher SBP and DBP were associated with higher mean diffusivity (MD) in the external capsule [(r=0.311;p=0.007);(r=375;p<0.001)] and the fronto‐occipital fasciculus [(r=0.286;p=0.016); (r=0.303;p=0.008)]. Furthermore, higher DBP was associated with higher MD in the anterior corona radiata (r=0.233;p=0.039), internal capsule (r=0.303;p=0.042), and the middle (r=0.275; p=0.03) and superior frontal gyrus (r=0.269;p=0.03) WM.ConclusionElevated SBP and DBP measurements are associated with greater WM injury in postmenopausal women. In particular, the frontal lobe WM and its connecting tracts may be more vulnerable to elevation in SBP and DBP. Whether BP control reduces the risk for WM injury, a risk factor for dementia, in post‐menopausal women warrants study.

Background Changes in white matter microstructural integrity are detectable before appearance of white matter lesions on magnetic resonance imaging as a manifestation of cerebral small‐vessel disease. The information relating poor white matter microstructural integrity to aortic stiffness, a hallmark of aging, is limited. We aimed to examine the association between aortic stiffness and white matter microstructural integrity among older adults. Methods and Results We conducted a cross‐sectional study to examine the association between aortic stiffness and white matter microstructural integrity among 1484 men and women (mean age, 76 years) at the 2011 to 2013 examination of the ARIC‐NCS (Atherosclerosis Risk in Communities Neurocognitive Study). Aortic stiffness was measured as carotid‐femoral pulse wave velocity. Cerebral white matter microstructural integrity was measured as fractional anisotropy and mean diffusivity using diffusion tensor imaging. Multivariable linear regression was used to examine the associations of carotid‐femoral pulse wave velocity with fractional anisotropy and mean diffusivity of the overall cerebrum and at regions of interest. Each 1‐m/s higher carotid‐femoral pulse wave velocity was associated with lower overall fractional anisotropy (β=−0.03; 95% CI , −0.05 to −0.02) and higher overall mean diffusivity (β=0.03; 95% CI , 0.02–0.04). High carotid‐femoral pulse wave velocity (upper 25th percentile) was associated with lower fractional anisotropy (β=−0.40; 95% CI , −0.61 to −0.20) and higher overall mean diffusivity (β=0.27; 95% CI , 0.10–0.43). Similar associations were observed at individual regions of interest. Conclusions High aortic stiffness is associated with low cerebral white matter microstructural integrity among older adults. Aortic stiffness may serve as a target for the prevention of poor cerebral white matter microstructural integrity.

Abstract Introduction Our goal was to develop cut points for amyloid positron emission tomography (PET), tau PET, flouro‐deoxyglucose (FDG) PET, and MRI cortical thickness. Methods We examined five methods for determining cut points. Results The reliable worsening method produced a cut point only for amyloid PET. The specificity, sensitivity, and accuracy of cognitively impaired versus young clinically normal (CN) methods labeled the most people abnormal and all gave similar cut points for tau PET, FDG PET, and cortical thickness. Cut points defined using the accuracy of cognitively impaired versus age‐matched CN method labeled fewer people abnormal. Discussion In the future, we will use a single cut point for amyloid PET (standardized uptake value ratio, 1.42; centiloid, 19) based on the reliable worsening cut point method. We will base lenient cut points for tau PET, FDG PET, and cortical thickness on the accuracy of cognitively impaired versus young CN method and base conservative cut points on the accuracy of cognitively impaired versus age‐matched CN method.