1
|
Vassilaki M, Pittock RR, Aakre JA, Castillo AM, Ramanan VK, Kremers WK, Jack CR, Vemuri P, Lowe VJ, Knopman DS, Petersen RC, Graff-Radford J. Author Response: Eligibility for Anti-Amyloid Treatment in a Population-Based Study of Cognitive Aging. Neurology 2024; 102:e209377. [PMID: 38648608 DOI: 10.1212/wnl.0000000000209377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024] Open
|
2
|
Karstens AJ, Christianson TJ, Lundt ES, Machulda MM, Mielke MM, Fields JA, Kremers WK, Graff-Radford J, Vemuri P, Jack CR, Knopman DS, Petersen RC, Stricker NH. Mayo normative studies: regression-based normative data for ages 30-91 years with a focus on the Boston Naming Test, Trail Making Test and Category Fluency. J Int Neuropsychol Soc 2024; 30:389-401. [PMID: 38014536 PMCID: PMC11014770 DOI: 10.1017/s1355617723000760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
OBJECTIVE Normative neuropsychological data are essential for interpretation of test performance in the context of demographic factors. The Mayo Normative Studies (MNS) aim to provide updated normative data for neuropsychological measures administered in the Mayo Clinic Study of Aging (MCSA), a population-based study of aging that randomly samples residents of Olmsted County, Minnesota, from age- and sex-stratified groups. We examined demographic effects on neuropsychological measures and validated the regression-based norms in comparison to existing normative data developed in a similar sample. METHOD The MNS includes cognitively unimpaired adults ≥30 years of age (n = 4,428) participating in the MCSA. Multivariable linear regressions were used to determine demographic effects on test performance. Regression-based normative formulas were developed by first converting raw scores to normalized scaled scores and then regressing on age, age2, sex, and education. Total and sex-stratified base rates of low scores (T < 40) were examined in an older adult validation sample and compared with Mayo's Older Americans Normative Studies (MOANS) norms. RESULTS Independent linear regressions revealed variable patterns of linear and/or quadratic effects of age (r2 = 6-27% variance explained), sex (0-13%), and education (2-10%) across measures. MNS norms improved base rates of low performance in the older adult validation sample overall and in sex-specific patterns relative to MOANS. CONCLUSIONS Our results demonstrate the need for updated norms that consider complex demographic associations on test performance and that specifically exclude participants with mild cognitive impairment from the normative sample.
Collapse
Affiliation(s)
- Aimee J. Karstens
- Division of Neurocognitive Disorders, Department of
Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Teresa J. Christianson
- Division of Biomedical Statistics and Informatics,
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Emily S. Lundt
- Division of Biomedical Statistics and Informatics,
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Mary M. Machulda
- Division of Neurocognitive Disorders, Department of
Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michelle M. Mielke
- Department of Epidemiology and Prevention, Wake Forest
University School of Medicine, Winston-Salem, NC, USA
- Department of Gerontology and Geriatric Medicine, Wake
Forest University School of Medicine, Winston-Salem, NC, USA
| | - Julie A. Fields
- Division of Neurocognitive Disorders, Department of
Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Walter K. Kremers
- Division of Biomedical Statistics and Informatics,
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | | | | | | | - Nikki H. Stricker
- Division of Neurocognitive Disorders, Department of
Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
3
|
Kouri N, Frankenhauser I, Peng Z, Labuzan SA, Boon BDC, Moloney CM, Pottier C, Wickland DP, Caetano-Anolles K, Corriveau-Lecavalier N, Tranovich JF, Wood AC, Hinkle KM, Lincoln SJ, Spychalla AJ, Senjem ML, Przybelski SA, Engelberg-Cook E, Schwarz CG, Kwan RS, Lesser ER, Crook JE, Carter RE, Ross OA, Lachner C, Ertekin-Taner N, Ferman TJ, Fields JA, Machulda MM, Ramanan VK, Nguyen AT, Reichard RR, Jones DT, Graff-Radford J, Boeve BF, Knopman DS, Petersen RC, Jack CR, Kantarci K, Day GS, Duara R, Graff-Radford NR, Dickson DW, Lowe VJ, Vemuri P, Murray ME. Clinicopathologic Heterogeneity and Glial Activation Patterns in Alzheimer Disease. JAMA Neurol 2024:2817289. [PMID: 38619853 PMCID: PMC11019448 DOI: 10.1001/jamaneurol.2024.0784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/05/2024] [Indexed: 04/16/2024]
Abstract
Importance Factors associated with clinical heterogeneity in Alzheimer disease (AD) lay along a continuum hypothesized to associate with tangle distribution and are relevant for understanding glial activation considerations in therapeutic advancement. Objectives To examine clinicopathologic and neuroimaging characteristics of disease heterogeneity in AD along a quantitative continuum using the corticolimbic index (CLix) to account for individuality of spatially distributed tangles found at autopsy. Design, Setting, and Participants This cross-sectional study was a retrospective medical record review performed on the Florida Autopsied Multiethnic (FLAME) cohort accessioned from 1991 to 2020. Data were analyzed from December 2022 to December 2023. Structural magnetic resonance imaging (MRI) and tau positron emission tomography (PET) were evaluated in an independent neuroimaging group. The FLAME cohort includes 2809 autopsied individuals; included in this study were neuropathologically diagnosed AD cases (FLAME-AD). A digital pathology subgroup of FLAME-AD cases was derived for glial activation analyses. Main Outcomes and Measures Clinicopathologic factors of heterogeneity that inform patient history and neuropathologic evaluation of AD; CLix score (lower, relative cortical predominance/hippocampal sparing vs higher, relative cortical sparing/limbic predominant cases); neuroimaging measures (ie, structural MRI and tau-PET). Results Of the 2809 autopsied individuals in the FLAME cohort, 1361 neuropathologically diagnosed AD cases were evaluated. A digital pathology subgroup included 60 FLAME-AD cases. The independent neuroimaging group included 93 cases. Among the 1361 FLAME-AD cases, 633 were male (47%; median [range] age at death, 81 [54-96] years) and 728 were female (53%; median [range] age at death, 81 [53-102] years). A younger symptomatic onset (Spearman ρ = 0.39, P < .001) and faster decline on the Mini-Mental State Examination (Spearman ρ = 0.27; P < .001) correlated with a lower CLix score in FLAME-AD series. Cases with a nonamnestic syndrome had lower CLix scores (median [IQR], 13 [9-18]) vs not (median [IQR], 21 [15-27]; P < .001). Hippocampal MRI volume (Spearman ρ = -0.45; P < .001) and flortaucipir tau-PET uptake in posterior cingulate and precuneus cortex (Spearman ρ = -0.74; P < .001) inversely correlated with CLix score. Although AD cases with a CLix score less than 10 had higher cortical tangle count, we found lower percentage of CD68-activated microglia/macrophage burden (median [IQR], 0.46% [0.32%-0.75%]) compared with cases with a CLix score of 10 to 30 (median [IQR], 0.75% [0.51%-0.98%]) and on par with a CLix score of 30 or greater (median [IQR], 0.40% [0.32%-0.57%]; P = .02). Conclusions and Relevance Findings show that AD heterogeneity exists along a continuum of corticolimbic tangle distribution. Reduced CD68 burden may signify an underappreciated association between tau accumulation and microglia/macrophages activation that should be considered in personalized therapy for immune dysregulation.
Collapse
Affiliation(s)
- Naomi Kouri
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | - Isabelle Frankenhauser
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
- Paracelsus Medical Private University, Salzburg, Austria
| | - Zhongwei Peng
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | | | | | | | - Cyril Pottier
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | - Daniel P. Wickland
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | | | - Nick Corriveau-Lecavalier
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | | | - Ashley C. Wood
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | - Kelly M. Hinkle
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | | | | | | | - Scott A. Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | | | | | - Rain S. Kwan
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Elizabeth R. Lesser
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Julia E. Crook
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Rickey E. Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Owen A. Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
| | - Christian Lachner
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, Florida
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida
- Department of Neurology, Mayo Clinic, Jacksonville, Florida
| | - Tanis J. Ferman
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, Florida
| | - Julie A. Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota
| | | | - Aivi T. Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - R. Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | | | | | | | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, Florida
| | | | | | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | | |
Collapse
|
4
|
Mak E, Reid RI, Przybelski SA, Lesnick TG, Schwarz CG, Senjem ML, Raghavan S, Vemuri P, Jack CR, Min HK, Jain MK, Miyagawa T, Forsberg LK, Fields JA, Savica R, Graff-Radford J, Jones DT, Botha H, St Louis EK, Knopman DS, Ramanan VK, Dickson DW, Graff-Radford NR, Ferman TJ, Petersen RC, Lowe VJ, Boeve BF, O'Brien JT, Kantarci K. Influences of amyloid-β and tau on white matter neurite alterations in dementia with Lewy bodies. NPJ Parkinsons Dis 2024; 10:76. [PMID: 38570511 PMCID: PMC10991290 DOI: 10.1038/s41531-024-00684-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
Dementia with Lewy bodies (DLB) is a neurodegenerative condition often co-occurring with Alzheimer's disease (AD) pathology. Characterizing white matter tissue microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) may help elucidate the biological underpinnings of white matter injury in individuals with DLB. In this study, diffusion tensor imaging (DTI) and NODDI metrics were compared in 45 patients within the dementia with Lewy bodies spectrum (mild cognitive impairment with Lewy bodies (n = 13) and probable dementia with Lewy bodies (n = 32)) against 45 matched controls using conditional logistic models. We evaluated the associations of tau and amyloid-β with DTI and NODDI parameters and examined the correlations of AD-related white matter injury with Clinical Dementia Rating (CDR). Structural equation models (SEM) explored relationships among age, APOE ε4, amyloid-β, tau, and white matter injury. The DLB spectrum group exhibited widespread white matter abnormalities, including reduced fractional anisotropy, increased mean diffusivity, and decreased neurite density index. Tau was significantly associated with limbic and temporal white matter injury, which was, in turn, associated with worse CDR. SEM revealed that amyloid-β exerted indirect effects on white matter injury through tau. We observed widespread disruptions in white matter tracts in DLB that were not attributed to AD pathologies, likely due to α-synuclein-related injury. However, a fraction of the white matter injury could be attributed to AD pathology. Our findings underscore the impact of AD pathology on white matter integrity in DLB and highlight the utility of NODDI in elucidating the biological basis of white matter injury in DLB.
Collapse
Affiliation(s)
- Elijah Mak
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Hoon Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Manoj K Jain
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Toji Miyagawa
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Rodolfo Savica
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Erik K St Louis
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
- Center for Sleep Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Dennis W Dickson
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Tanis J Ferman
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Ronald C Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| |
Collapse
|
5
|
Vemuri P. Improving Trends in Brain Health Explain Declining Dementia Risk? JAMA Neurol 2024:2816803. [PMID: 38526457 DOI: 10.1001/jamaneurol.2024.0476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
|
6
|
Pradeep A, Raghavan S, Przybelski SA, Preboske G, Schwarz CG, Lowe VJ, Knopman DS, Petersen RC, Jack CR, Graff-Radford J, Cogswell PM, Vemuri P. Can white matter hyperintensities based Fazekas visual assessment scales inform about Alzheimer's disease pathology in the population? Res Sq 2024:rs.3.rs-4017874. [PMID: 38558965 PMCID: PMC10980106 DOI: 10.21203/rs.3.rs-4017874/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background White matter hyperintensities (WMH) are considered hallmark features of cerebral small vessel disease and have recently been linked to Alzheimer's disease pathology. Their distinct spatial distributions, namely periventricular versus deep WMH, may differ by underlying age-related and pathobiological processes contributing to cognitive decline. We aimed to identify the spatial patterns of WMH using the 4-scale Fazekas visual assessment and explore their differential association with age, vascular health, Alzheimer's imaging markers, namely amyloid and tau burden, and cognition. Because our study consisted of scans from GE and Siemens scanners with different resolutions, we also investigated inter-scanner reproducibility and combinability of WMH measurements on imaging. Methods We identified 1144 participants from the Mayo Clinic Study of Aging consisting of older adults from Olmsted County, Minnesota with available structural magnetic resonance imaging (MRI), amyloid, and tau positron emission tomography (PET). WMH distribution patterns were assessed on FLAIR-MRI, both 2D axial and 3D, using Fazekas ratings of periventricular and deep WMH severity. We compared the association of periventricular and deep WMH scales with vascular risk factors, amyloid-PET and tau-PET standardized uptake value ratio, WMH volume, and cognition using Pearson partial correlation after adjusting for age. We also evaluated vendor compatibility and reproducibility of the Fazekas scales using intraclass correlations (ICC). Results Periventricular and deep WMH measurements showed similar correlations with age, cardiometabolic conditions score (vascular risk), and cognition, (p < 0.001). Both periventricular WMH and deep WMH showed weak associations with amyloidosis (R = 0.07, p = < 0.001), and none with tau burden. We found substantial agreement between data from the two scanners for Fazekas measurements (ICC = 0.78). The automated WMH volume had high discriminating power for identifying participants with Fazekas ≥ 2 (area under curve = 0.97). Conclusion Our study investigates risk factors underlying WMH spatial patterns and their impact on global cognition, with no discernible differences between periventricular and deep WMH. We observed minimal impact of amyloidosis on WMH severity. These findings, coupled with enhanced inter-scanner reproducibility of WMH data, suggest the combinability of inter-scanner data assessed by harmonized protocols in the context of vascular contributions to cognitive impairment and dementia biomarker research.
Collapse
|
7
|
Jack CR, Wiste HJ, Algeciras‐Schimnich A, Weigand SD, Figdore DJ, Lowe VJ, Vemuri P, Graff‐Radford J, Ramanan VK, Knopman DS, Mielke MM, Machulda MM, Fields J, Schwarz CG, Cogswell PM, Senjem ML, Therneau TM, Petersen RC. Comparison of plasma biomarkers and amyloid PET for predicting memory decline in cognitively unimpaired individuals. Alzheimers Dement 2024; 20:2143-2154. [PMID: 38265198 PMCID: PMC10984437 DOI: 10.1002/alz.13651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 01/25/2024]
Abstract
BACKGROUND We compared the ability of several plasma biomarkers versus amyloid positron emission tomography (PET) to predict rates of memory decline among cognitively unimpaired individuals. METHODS We studied 645 Mayo Clinic Study of Aging participants. Predictor variables were age, sex, education, apolipoprotein E (APOE) ε4 genotype, amyloid PET, and plasma amyloid beta (Aβ)42/40, phosphorylated tau (p-tau)181, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and p-tau217. The outcome was a change in a memory composite measure. RESULTS All plasma biomarkers, except NfL, were associated with mean memory decline in models with individual biomarkers. However, amyloid PET and plasma p-tau217, along with age, were key variables independently associated with mean memory decline in models combining all predictors. Confidence intervals were narrow for estimates of population mean prediction, but person-level prediction intervals were wide. DISCUSSION Plasma p-tau217 and amyloid PET provide useful information about predicting rates of future cognitive decline in cognitively unimpaired individuals at the population mean level, but not at the individual person level.
Collapse
Affiliation(s)
| | - Heather J. Wiste
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | | | - Stephen D. Weigand
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Dan J. Figdore
- Department of Laboratory MedicineMayo ClinicRochesterMinnesotaUSA
| | - Val J. Lowe
- Department of Nuclear MedicineMayo ClinicRochesterMinnesotaUSA
| | | | | | | | | | - Michelle M. Mielke
- Department of Epidemiology and PreventionWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Mary M. Machulda
- Department of Psychiatry and PsychologyMayo ClinicRochesterMinnesotaUSA
| | - Julie Fields
- Department of Psychiatry and PsychologyMayo ClinicRochesterMinnesotaUSA
| | | | | | | | - Terry M. Therneau
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | | |
Collapse
|
8
|
Khandalavala KR, Marinelli JP, Lohse CM, Przybelski SA, Petersen RC, Vassilaki M, Vemuri P, Carlson ML. Neuroimaging Characteristics of Hearing Loss in the Mayo Clinic Study of Aging. Otolaryngol Head Neck Surg 2024; 170:886-895. [PMID: 38018509 PMCID: PMC10922536 DOI: 10.1002/ohn.583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/16/2023] [Accepted: 10/20/2023] [Indexed: 11/30/2023]
Abstract
OBJECTIVE To investigate the association between standard pure tone and speech audiometry with neuroimaging characteristics reflective of aging and dementia in older adults. STUDY DESIGN Prospective population-based study. SETTING Single tertiary care referral center. METHODS Participants from the Mayo Clinic Study of aging 60 years old or older with normal cognition or mild cognitive impairment, baseline neuroimaging, and a behavioral audiogram associated with neuroimaging were eligible for study. Imaging modalities included structural MRI (sMRI) and fluid-attenuated inversion recovery MRI (FLAIR-MRI; N = 605), diffusion tensor imaging MRI (DTI-MRI; N = 444), and fluorodeoxyglucose-positron emission tomography (FDG-PET; N = 413). Multivariable logistic and linear regression models were used to evaluate associations with neuroimaging outcomes. RESULTS Mean (SD) pure tone average (PTA) was 33 (15) dB HL and mean (SD) word recognition score (WRS) was 91% (14). There were no significant associations between audiometric performance and cortical thinning assessed by sMRI. Each 10-dB increase in PTA was associated with increased likelihood of abnormal white-matter hyperintensity (WMH) from FLAIR-MRI (odds ratio 1.26, P = .02). From DTI-MRI, participants with <100% WRSs had significantly lower fractional anisotropy in the genu of the corpus callosum (parameter estimate [PE] -0.012, P = .008) compared to those with perfect WRSs. From FDG-PET, each 10% decrease in WRSs was associated with decreased uptake in the anterior cingulate cortex (PE -0.013, P = .001). CONCLUSION Poorer audiometric performance was not significantly associated with cortical thinning but was associated with white matter damage relevant to cerebrovascular disease (increased abnormal WMH, decreased corpus callosum diffusion). These neuroimaging results suggest a pathophysiologic link between hearing loss and cerebrovascular disease.
Collapse
Affiliation(s)
| | - John P. Marinelli
- Department of Otolaryngology-Head and Neck Surgery, Mayo Clinic, Rochester, MN
| | | | | | - Ronald C. Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
- Department of Neurology, Mayo Clinic, Rochester, MN
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | | | - Matthew L. Carlson
- Department of Otolaryngology-Head and Neck Surgery, Mayo Clinic, Rochester, MN
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN
| |
Collapse
|
9
|
Pavuluri K, Huston J, Ehman RL, Manduca A, Jack CR, Senjem ML, Vemuri P, Murphy MC. Associations between vascular health, brain stiffness and global cognitive function. Brain Commun 2024; 6:fcae073. [PMID: 38505229 PMCID: PMC10950054 DOI: 10.1093/braincomms/fcae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 12/19/2023] [Accepted: 02/27/2024] [Indexed: 03/21/2024] Open
Abstract
Vascular brain injury results in loss of structural and functional connectivity and leads to cognitive impairment. Its various manifestations, including microinfarcts, microhaemorrhages and white matter hyperintensities, result in microstructural tissue integrity loss and secondary neurodegeneration. Among these, tissue microstructural alteration is a relatively early event compared with atrophy along the aging and neurodegeneration continuum. Understanding its association with cognition may provide the opportunity to further elucidate the relationship between vascular health and clinical outcomes. Magnetic resonance elastography offers a non-invasive approach to evaluate tissue mechanical properties, providing a window into the microstructural integrity of the brain. This retrospective study evaluated brain stiffness as a potential biomarker for vascular brain injury and its role in mediating the impact of vascular dysfunction on cognitive impairment. Seventy-five participants from the Mayo Clinic Study of Aging underwent brain imaging using a 3T MR imager with a spin-echo echo-planar imaging sequence for magnetic resonance elastography and T1- and T2-weighted pulse sequences. This study evaluated the effects of vascular biomarkers (white matter hyperintensities and cardiometabolic condition score) on brain stiffness using voxelwise analysis. Partial correlation analysis explored associations between brain stiffness, white matter hyperintensities, cardiometabolic condition and global cognition. Mediation analysis determined the role of stiffness in mediating the relationship between vascular biomarkers and cognitive performance. Statistical significance was set at P-values < 0.05. Diagnostic accuracy of magnetic resonance elastography stiffness for white matter hyperintensities and cardiometabolic condition was evaluated using receiver operator characteristic curves. Voxelwise linear regression analysis indicated white matter hyperintensities negatively correlate with brain stiffness, specifically in periventricular regions with high white matter hyperintensity levels. A negative association between cardiovascular risk factors and stiffness was also observed across the brain. No significant patterns of stiffness changes were associated with amyloid load. Global stiffness (µ) negatively correlated with both white matter hyperintensities and cardiometabolic condition when all other covariables including amyloid load were controlled. The positive correlation between white matter hyperintensities and cardiometabolic condition weakened and became statistically insignificant when controlling for other covariables. Brain stiffness and global cognition were positively correlated, maintaining statistical significance after adjusting for all covariables. These findings suggest mechanical alterations are associated with cognitive dysfunction and vascular brain injury. Brain stiffness significantly mediated the indirect effects of white matter hyperintensities and cardiometabolic condition on global cognition. Local cerebrovascular diseases (assessed by white matter hyperintensities) and systemic vascular risk factors (assessed by cardiometabolic condition) impact brain stiffness with spatially and statistically distinct effects. Global brain stiffness is a significant mediator between vascular disease measures and cognitive function, highlighting the value of magnetic resonance elastography-based mechanical assessments in understanding this relationship.
Collapse
Affiliation(s)
| | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Richard L Ehman
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Armando Manduca
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | |
Collapse
|
10
|
Raghavan S, Przybelski SA, Lesnick TG, Fought AJ, Reid RI, Gebre RK, Windham BG, Algeciras‐Schimnich A, Machulda MM, Vassilaki M, Knopman DS, Jack CR, Petersen RC, Graff‐Radford J, Vemuri P. Vascular risk, gait, behavioral, and plasma indicators of VCID. Alzheimers Dement 2024; 20:1201-1213. [PMID: 37932910 PMCID: PMC10916988 DOI: 10.1002/alz.13540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023]
Abstract
INTRODUCTION Cost-effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non-imaging indicators of VCID using magnetic resonance imaging (MRI)-measured white matter (WM) damage and hypothesized that these indicators differ based on age. METHODS In 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid-attenuated inversion recovery (FLAIR)-MRI, we examined associations between baseline non-imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles. RESULTS VRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata. DISCUSSION Non-imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID. HIGHLIGHTS Non-imaging indicators of VCID can aid in prediction of MRI-measured WM damage but their importance differed by age. Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI. Plasma markers explained variability in WMH across age strata. Most non-imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups. The framework developed here can be used to evaluate new non-imaging VCID indicators proposed in the future.
Collapse
Affiliation(s)
| | | | - Timothy G. Lesnick
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Angela J. Fought
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Robert I. Reid
- Department of Information TechnologyMayo ClinicRochesterMinnesotaUSA
| | | | - B. Gwen Windham
- Department of MedicineUniversity of Mississippi Medical CenterJacksonUSA
| | | | | | - Maria Vassilaki
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | | | | | | | | | | |
Collapse
|
11
|
Corriveau-Lecavalier N, Tosakulwong N, Lesnick TG, Fought AJ, Reid RI, Schwarz CG, Senjem ML, Jack CR, Jones DT, Vemuri P, Rademakers R, Ramos EM, Geschwind DH, Knopman DS, Botha H, Savica R, Graff-Radford J, Ramanan VK, Fields JA, Graff-Radford N, Wszolek Z, Forsberg LK, Petersen RC, Heuer HW, Boxer AL, Rosen HJ, Boeve BF, Kantarci K. Neurite-based white matter alterations in MAPT mutation carriers: A multi-shell diffusion MRI study in the ALLFTD consortium. Neurobiol Aging 2024; 134:135-145. [PMID: 38091751 PMCID: PMC10872472 DOI: 10.1016/j.neurobiolaging.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/28/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023]
Abstract
We assessed white matter (WM) integrity in MAPT mutation carriers (16 asymptomatic, 5 symptomatic) compared to 31 non-carrier family controls using diffusion tensor imaging (DTI) (fractional anisotropy; FA, mean diffusivity; MD) and neurite orientation dispersion and density imaging (NODDI) (neurite density index; NDI, orientation and dispersion index; ODI). Linear mixed-effects models accounting for age and family relatedness revealed alterations across DTI and NODDI metrics in all mutation carriers and in symptomatic carriers, with the most significant differences involving fronto-temporal WM tracts. Asymptomatic carriers showed higher entorhinal MD and lower cingulum FA and patterns of higher ODI mostly involving temporal areas and long association and projections fibers. Regression models between estimated time to or time from disease and DTI and NODDI metrics in key regions (amygdala, cingulum, entorhinal, inferior temporal, uncinate fasciculus) in all carriers showed increasing abnormalities with estimated time to or time from disease onset, with FA and NDI showing the strongest relationships. Neurite-based metrics, particularly ODI, appear to be particularly sensitive to early WM involvement in asymptomatic carriers.
Collapse
Affiliation(s)
- Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA; Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | | | - Timothy G Lesnick
- Departmenf of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Angela J Fought
- Departmenf of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA; Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic Jacksonville, FL, USA; Center for Molecular Neurology, Antwerp University, Belgium
| | | | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Rodolfo Savica
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Hilary W Heuer
- Department of Neurology, University of California San Francisco, CA, USA
| | - Adam L Boxer
- Department of Neurology, University of California San Francisco, CA, USA
| | - Howard J Rosen
- Department of Neurology, University of California San Francisco, CA, USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| |
Collapse
|
12
|
Gunter NB, Gebre RK, Graff-Radford J, Heckman MG, Jack CR, Lowe VJ, Knopman DS, Petersen RC, Ross OA, Vemuri P, Ramanan VK. Machine Learning Models of Polygenic Risk for Enhanced Prediction of Alzheimer Disease Endophenotypes. Neurol Genet 2024; 10:e200120. [PMID: 38250184 PMCID: PMC10798228 DOI: 10.1212/nxg.0000000000200120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/01/2023] [Indexed: 01/23/2024]
Abstract
Background and Objectives Alzheimer disease (AD) has a polygenic architecture, for which genome-wide association studies (GWAS) have helped elucidate sequence variants (SVs) influencing susceptibility. Polygenic risk score (PRS) approaches show promise for generating summary measures of inherited risk for clinical AD based on the effects of APOE and other GWAS hits. However, existing PRS approaches, based on traditional regression models, explain only modest variation in AD dementia risk and AD-related endophenotypes. We hypothesized that machine learning (ML) models of polygenic risk (ML-PRS) could outperform standard regression-based PRS methods and therefore have the potential for greater clinical utility. Methods We analyzed combined data from the Mayo Clinic Study of Aging (n = 1,791) and the Alzheimer's Disease Neuroimaging Initiative (n = 864). An AD PRS was computed for each participant using the top common SVs obtained from a large AD dementia GWAS. In parallel, ML models were trained using those SV genotypes, with amyloid PET burden as the primary outcome. Secondary outcomes included amyloid PET positivity and clinical diagnosis (cognitively unimpaired vs impaired). We compared performance between ML-PRS and standard PRS across 100 training sessions with different data splits. In each session, data were split into 80% training and 20% testing, and then five-fold cross-validation was used within the training set to ensure the best model was produced for testing. We also applied permutation importance techniques to assess which genetic factors contributed most to outcome prediction. Results ML-PRS models outperformed the AD PRS (r2 = 0.28 vs r2 = 0.24 in test set) in explaining variation in amyloid PET burden. Among ML approaches, methods accounting for nonlinear genetic influences were superior to linear methods. ML-PRS models were also more accurate when predicting amyloid PET positivity (area under the curve [AUC] = 0.80 vs AUC = 0.63) and the presence of cognitive impairment (AUC = 0.75 vs AUC = 0.54) compared with the standard PRS. Discussion We found that ML-PRS approaches improved upon standard PRS for prediction of AD endophenotypes, partly related to improved accounting for nonlinear effects of genetic susceptibility alleles. Further adaptations of the ML-PRS framework could help to close the gap of remaining unexplained heritability for AD and therefore facilitate more accurate presymptomatic and early-stage risk stratification for clinical decision-making.
Collapse
Affiliation(s)
- Nathaniel B Gunter
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Robel K Gebre
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Jonathan Graff-Radford
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Michael G Heckman
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Clifford R Jack
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Val J Lowe
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - David S Knopman
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Ronald C Petersen
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Owen A Ross
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Prashanthi Vemuri
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| | - Vijay K Ramanan
- From the Departments of Radiology (N.B.G., R.K.G., C.R.J., V.J.L., P.V.), Neurology (J.G.-R., D.S.K., R.C.P., V.K.R.), and Quantitative Health Sciences (R.C.P.), Mayo Clinic Rochester, MN; and Departments of Quantitative Health Sciences (M.G.H.), Neuroscience (O.A.R.), and Clinical Genomics (O.A.R.), Mayo Clinic Florida, Jacksonville
| |
Collapse
|
13
|
Cogswell PM, Lundt ES, Therneau TM, Wiste HJ, Graff‐Radford J, Algeciras‐Schimnich A, Lowe VJ, Mielke MM, Schwarz CG, Senjem ML, Gunter JL, Knopman DS, Vemuri P, Petersen RC, Jack Jr CR. Modeling the temporal evolution of plasma p-tau in relation to amyloid beta and tau PET. Alzheimers Dement 2024; 20:1225-1238. [PMID: 37963289 PMCID: PMC10916944 DOI: 10.1002/alz.13539] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/02/2023] [Accepted: 10/11/2023] [Indexed: 11/16/2023]
Abstract
INTRODUCTION The timing of plasma biomarker changes is not well understood. The goal of this study was to evaluate the temporal co-evolution of plasma and positron emission tomography (PET) Alzheimer's disease (AD) biomarkers. METHODS We included 1408 Mayo Clinic Study of Aging and Alzheimer's Disease Research Center participants. An accelerated failure time (AFT) model was fit with amyloid beta (Aβ) PET, tau PET, plasma p-tau217, p-tau181, and glial fibrillary acidic protein (GFAP) as endpoints. RESULTS Individual timing of plasma p-tau progression was strongly associated with Aβ PET and GFAP progression. In the population, GFAP became abnormal first, then Aβ PET, plasma p-tau, and tau PET temporal meta-regions of interest when applying cut points based on young, cognitively unimpaired participants. DISCUSSION Plasma p-tau is a stronger indicator of a temporally linked response to elevated brain Aβ than of tau pathology. While Aβ deposition and a rise in GFAP are upstream events associated with tau phosphorylation, the temporal link between p-tau and Aβ PET was the strongest. HIGHLIGHTS Plasma p-tau progression was more strongly associated with Aβ than tau PET. Progression on plasma p-tau was associated with Aβ PET and GFAP progression. P-tau181 and p-tau217 become abnormal after Aβ PET and before tau PET. GFAP became abnormal first, before plasma p-tau and Aβ PET.
Collapse
Affiliation(s)
| | - Emily S. Lundt
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Terry M. Therneau
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Heather J. Wiste
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | | | | | - Val J. Lowe
- Department of RadiologyMayo ClinicRochesterMinnesotaUSA
| | - Michelle M. Mielke
- Department of Epidemiology and PreventionWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | | | - Matthew L. Senjem
- Department of RadiologyMayo ClinicRochesterMinnesotaUSA
- Department of Information TechnologyMayo ClinicRochesterMinnesotaUSA
| | | | | | | | - Ronald C. Petersen
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
- Department of NeurologyMayo ClinicRochesterMinnesotaUSA
| | | |
Collapse
|
14
|
Figdore DJ, Wiste HJ, Bornhorst JA, Bateman RJ, Li Y, Graff‐Radford J, Knopman DS, Vemuri P, Lowe VJ, Jr CRJ, Petersen RC, Algeciras‐Schimnich A. Performance of the Lumipulse plasma Aβ42/40 and pTau181 immunoassays in the detection of amyloid pathology. Alzheimers Dement (Amst) 2024; 16:e12545. [PMID: 38304322 PMCID: PMC10831129 DOI: 10.1002/dad2.12545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 02/03/2024]
Abstract
INTRODUCTION This study evaluated the performance of the Lumipulse plasma beta-amyloid (Aβ) 42/40 and pTau181 compared to other assays to detect an abnormal amyloid-positron emission tomography (PET). METHODS Plasma samples from cognitively unimpaired (N = 179) and MCI/AD dementia (N = 36) individuals were retrospectively evaluated. Plasma Aβ42/40 and pTau181 were measured using the Lumipulse and Simoa immunoassays. An immunoprecipitation mass spectrometry (IP-MS) assay for plasma Aβ42/40 was also evaluated. Amyloid-PET status was the outcome measure. RESULTS Lumipulse and IP-MS Aβ42/40 exhibited the highest diagnostic accuracy for detecting an abnormal amyloid-PET (areas under the curve [AUCs] of 0.81 and 0.84, respectively). The Lumipulse and Simoa pTau181 assays exhibited lower performance (AUCs of 0.74 and 0.72, respectively). The Simoa Aβ42/40 assay demonstrated the lowest diagnostic accuracy (AUC 0.57). Combining Aβ42/40 and pTau181 did not significantly improve performance over Aβ42/40 alone for Lumipulse (AUC 0.83) or over pTau181 alone for Simoa (AUC 0.71). DISCUSSION The Lumipulse Aβ42/40 assay showed similar performance to the IP-MS Aβ42/40 assay for detection of an abnormal amyloid-PET; and both assays performed better than the two p-tau181 immunoassays. The Simoa Aβ42/Aβ40 assay was the least accurate at predicting an abnormal amyloid-PET status. Highlights Lumipulse plasma Aβ42/Aβ40 AUC for abnormal amyloid-PET detection was 0.81.This performance was comparable to previously reported IP-MS and higher than Simoa.Performance of Alzheimer's disease blood biomarkers varies between assays.
Collapse
Affiliation(s)
- Daniel J. Figdore
- Department of Laboratory Medicine and PathologyMayo ClinicRochesterMinnesotaUSA
| | - Heather J. Wiste
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Joshua A. Bornhorst
- Department of Laboratory Medicine and PathologyMayo ClinicRochesterMinnesotaUSA
| | - Randall J. Bateman
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | - Yan Li
- Department of NeurologyWashington University School of MedicineSt. LouisMissouriUSA
| | | | | | | | - Val J. Lowe
- Department of RadiologyMayo ClinicRochesterMinnesotaUSA
| | | | | | | |
Collapse
|
15
|
Switzer A, Charidimou A, McCarter SJ, Vemuri P, Nguyen A, Przybelski SA, Lesnick TG, Rabinstein AA, Brown RD, Knopman DS, Petersen RC, Jack CR, Reichard RR, Graff-Radford J. Boston criteria v2.0 for cerebral amyloid angiopathy without hemorrhage: An MRI-neuropathological validation study. medRxiv 2023:2023.11.09.23298325. [PMID: 37986913 PMCID: PMC10659504 DOI: 10.1101/2023.11.09.23298325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
BACKGROUND Updated criteria for the clinical-MRI diagnosis of cerebral amyloid angiopathy (CAA) have recently been proposed. However, their performance in individuals without intracerebral hemorrhage (ICH) or transient focal neurological episodes (TFNE) is unknown. We assessed the diagnostic performance of the Boston criteria version 2.0 for CAA diagnosis in a cohort of individuals presenting without symptomatic ICH. METHODS Fifty-four participants from the Mayo Clinic Study of Aging or Alzheimer's Disease Research Center were included if they had an antemortem MRI with gradient-recall echo sequences and a brain autopsy with CAA evaluation. Performance of the Boston criteria v2.0 was compared to v1.5 using histopathologically verified CAA as the reference standard. RESULTS Median age at MRI was 75 years (IQR 65-80) with 28/54 participants having histopathologically verified CAA (i.e., moderate-to-severe CAA in at least 1 lobar region). The sensitivity and specificity of the Boston criteria v2.0 were 28.6% (95%CI: 13.2-48.7%) and 65.3% (95%CI: 44.3-82.8%) for probable CAA diagnosis (AUC 0.47) and 75.0% (55.1-89.3) and 38.5% (20.2-59.4) for any CAA diagnosis (possible + probable; AUC: 0.57), respectively. The v2.0 Boston criteria was not superior in performance compared to the prior v1.5 criteria for either CAA diagnostic category. CONCLUSIONS The Boston criteria v2.0 have low accuracy in patients who are asymptomatic or only have cognitive symptoms.. Additional biomarkers need to be explored to optimize CAA diagnosis in this population.
Collapse
|
16
|
Pittock RR, Aakre JA, Castillo AM, Ramanan VK, Kremers WK, Jack CR, Vemuri P, Lowe VJ, Knopman DS, Petersen RC, Graff-Radford J, Vassilaki M. Eligibility for Anti-Amyloid Treatment in a Population-Based Study of Cognitive Aging. Neurology 2023; 101:e1837-e1849. [PMID: 37586881 PMCID: PMC10663008 DOI: 10.1212/wnl.0000000000207770] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/04/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Treatment options for Alzheimer disease (AD) are limited and have focused mainly on symptomatic therapy and improving quality of life. Recently, lecanemab, an anti-β-amyloid monoclonal antibody (mAb), received accelerated approval by the US Food and Drug Administration for treatment in the early stages of biomarker-confirmed symptomatic AD. An additional anti-β-amyloid mAb, aducanumab, was approved in 2021, and more will potentially become available in the near future. Research on the applicability and generalizability of the anti-β-amyloid mAb eligibility criteria on adults with biomarkers available in the general population has been lacking. The study's primary aim was to apply the clinical trial eligibility criteria for lecanemab treatment to participants with early AD of the population-based Mayo Clinic Study of Aging (MCSA) and assess the generalizability of anti-amyloid treatment. The secondary aim of this study was to apply the clinical trial eligibility criteria for aducanumab treatment in MCSA participants. METHODS This cross-sectional study aimed to apply the clinical trial eligibility criteria for lecanemab and aducanumab treatment to participants with early AD of the population-based MCSA and assess the generalizability of anti-amyloid treatment. RESULTS Two hundred thirty-seven MCSA participants (mean age [SD] 80.9 [6.3] years, 54.9% male, and 97.5% White) with mild cognitive impairment (MCI) or mild dementia and increased brain amyloid burden by PiB PET comprised the study sample. Lecanemab trial's inclusion criteria reduced the study sample to 112 (47.3% of 237) participants. The trial's exclusion criteria further narrowed the number of potentially eligible participants to 19 (overall 8% of 237). Modifying the eligibility criteria to include all participants with MCI (instead of applying additional cognitive criteria) resulted in 17.4% of participants with MCI being eligible for lecanemab treatment. One hundred four participants (43.9% of 237) fulfilled the aducanumab clinical trial's inclusion criteria. The aducanumab trial's exclusion criteria further reduced the number of available participants, narrowing those eligible to 12 (5.1% of 237). Common exclusions were related to other chronic conditions and neuroimaging findings. DISCUSSION Findings estimate the limited eligibility in typical older adults with cognitive impairment for anti-β-amyloid mAbs.
Collapse
Affiliation(s)
- Rioghna R Pittock
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Jeremiah A Aakre
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Anna M Castillo
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Vijay K Ramanan
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Walter K Kremers
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Clifford R Jack
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Prashanthi Vemuri
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Val J Lowe
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - David S Knopman
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Ronald C Petersen
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Jonathan Graff-Radford
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN
| | - Maria Vassilaki
- From the Department of Neurology (R.R.P., V.K.R., D.S.K., R.C.P., J.G.-R.), Mayo Clinic, Rochester, MN; The College (R.R.P.), University of Chicago, IL; Departments of Quantitative Health Sciences (J.A.A., A.M.C., W.K.K., R.C.P., M.V.) and Radiology (C.R.J., P.V., V.J.L.), Mayo Clinic, Rochester, MN.
| |
Collapse
|
17
|
Vassilaki M, Syrjanen JA, Krell-Roesch J, Graff-Radford J, Vemuri P, Scharf EL, Machulda MM, Fields JA, Kremers WK, Lowe VJ, Jack CR, Knopman DS, Petersen RC, Geda YE. Association of Cerebrovascular Imaging Biomarkers, Depression, and Anxiety, with Mild Cognitive Impairment. J Alzheimers Dis Rep 2023; 7:1237-1246. [PMID: 38025797 PMCID: PMC10657723 DOI: 10.3233/adr-230073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 10/08/2023] [Indexed: 12/01/2023] Open
Abstract
The study included 1,738 Mayo Clinic Study of Aging participants (≥50 years old; 1,460 cognitively unimpaired and 278 with mild cognitive impairment (MCI)) and examined the cross-sectional association between cerebrovascular (CVD) imaging biomarkers (e.g., white matter hyperintensities (WMH), infarctions) and Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) scores, as well as their association with MCI. High (abnormal) WMH burden was significantly associated with having BDI-II>13 and BAI > 7 scores, and both (CVD imaging biomarkers and depression/anxiety) were significantly associated with MCI when included simultaneously in the model, suggesting that both were independently associated with the odds of MCI.
Collapse
Affiliation(s)
- Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Jeremy A. Syrjanen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Janina Krell-Roesch
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | | | | | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Julie A. Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Walter K. Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Ronald C. Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Yonas E. Geda
- Department of Neurology, and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA
| |
Collapse
|
18
|
Ramanan VK, Gebre RK, Graff-Radford J, Hofrenning E, Algeciras-Schimnich A, Figdore DJ, Lowe VJ, Mielke MM, Knopman DS, Ross OA, Jack CR, Petersen RC, Vemuri P. Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis. Brain 2023; 146:4508-4519. [PMID: 37279785 PMCID: PMC10629762 DOI: 10.1093/brain/awad196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 05/02/2023] [Accepted: 05/14/2023] [Indexed: 06/08/2023] Open
Abstract
Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrolment and treatment monitoring in Alzheimer's disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer's disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analysing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, amyloid-β42/40, glial fibrillary acidic protein or neurofilament light chain. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% versus 68%; P = 0.001). A machine learning approach incorporating plasma biomarkers, demographics and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set) and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity, which could non-invasively enhance the interpretation of blood-based AD biomarker profiles in the population.
Collapse
Affiliation(s)
- Vijay K Ramanan
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Robel K Gebre
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Ekaterina Hofrenning
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Daniel J Figdore
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Michelle M Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
- Department of Clinical Genomics, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | |
Collapse
|
19
|
Mielke MM, Dage JL, Frank RD, Algeciras-Schimnich A, Knopman DS, Lowe VJ, Bu G, Vemuri P, Graff-Radford J, Jack CR, Petersen RC. Author Correction: Performance of plasma phosphorylated tau 181 and 217 in the community. Nat Med 2023; 29:2954. [PMID: 36216947 DOI: 10.1038/s41591-022-02066-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Michelle M Mielke
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
- Department of Neurology, Mayo Clinic, Rochester, MN, USA.
- Department of Epidemiology and Prevention, School of Medicine, Wake Forest University, Winston-Salem, NC, USA.
| | - Jeffrey L Dage
- Stark Neurosciences Research Institute, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Ryan D Frank
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Guojun Bu
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | | | | | - Ronald C Petersen
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
20
|
Nemes S, Logan PE, Manchella MK, Mundada NS, Joie RL, Polsinelli AJ, Hammers DB, Koeppe RA, Foroud TM, Nudelman KN, Eloyan A, Iaccarino L, Dorsant-Ardón V, Taurone A, Maryanne Thangarajah, Dage JL, Aisen P, Grinberg LT, Jack CR, Kramer J, Kukull WA, Murray ME, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Womack KB, Wolk DA, Rabinovici GD, Carrillo MC, Dickerson BC, Apostolova LG. Sex and APOE ε4 carrier effects on atrophy, amyloid PET, and tau PET burden in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S49-S63. [PMID: 37496307 PMCID: PMC10811272 DOI: 10.1002/alz.13403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION We used sex and apolipoprotein E ε4 (APOE ε4) carrier status as predictors of pathologic burden in early-onset Alzheimer's disease (EOAD). METHODS We included baseline data from 77 cognitively normal (CN), 230 EOAD, and 70 EO non-Alzheimer's disease (EOnonAD) participants from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS). We stratified each diagnostic group by males and females, then further subdivided each sex by APOE ε4 carrier status and compared imaging biomarkers in each stratification. Voxel-wise multiple linear regressions yielded statistical brain maps of gray matter density, amyloid, and tau PET burden. RESULTS EOAD females had greater amyloid and tau PET burdens than males. EOAD female APOE ε4 non-carriers had greater amyloid PET burdens and greater gray matter atrophy than female ε4 carriers. EOnonAD female ε4 non-carriers also had greater gray matter atrophy than female ε4 carriers. DISCUSSION The effects of sex and APOE ε4 must be considered when studying these populations. HIGHLIGHTS Novel analysis examining the effects of biological sex and apolipoprotein E ε4 (APOE ε4) carrier status on neuroimaging biomarkers among early-onset Alzheimer's disease (EOAD), early-onset non-AD (EOnonAD), and cognitively normal (CN) participants. Female sex is associated with greater pathology burden in the EOAD cohort compared to male sex. The effect of APOE ε4 carrier status on pathology burden was the most impactful in females across all cohorts.
Collapse
Affiliation(s)
- Sára Nemes
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paige E. Logan
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Mohit K. Manchella
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Department of Chemistry, University of Southern Indiana, Evansville, Indiana, 47712, USA
| | - Nidhi S. Mundada
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Renaud La Joie
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, Indiana, 46202 USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Robert A. Koeppe
- Department of Radiology, University of Michigan Medical School, Ann Arbor, MI, 48105, USA
| | - Tatiana M. Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Kelly N. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Valérie Dorsant-Ardón
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Jeffery L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, 92121, USA
| | - Lea T. Grinberg
- Department of Neurology, University of California, San Francisco, California, 94158, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Joel Kramer
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA, 98195, USA
| | - Melissa E. Murray
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, 90033, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, 85315, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Ranjan Duara
- Department of Neurology, Center for Mind/Brain Medicine, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, 02115, USA
- Wein Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, 33140, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, 10032, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, 559095, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, 77030, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, 02906, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, 60611, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, 02906, USA
| | - Sharon J. Sha
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, 94304, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown Universit, Washington, DC, 20007, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Kyle B. Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - David A. Wolk
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,19104, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, Indiana, 46202 USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | | |
Collapse
|
21
|
Polsinelli AJ, Wonderlin RJ, Hammers DB, Pena Garcia A, Eloyan A, Taurone A, Thangarajah M, Beckett L, Gao S, Wang S, Kirby K, Logan PE, Aisen P, Dage JL, Foroud T, Griffin P, Iaccarino L, Kramer JH, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Soleimani-Meigooni DN, Rumbaugh M, Toga AW, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Womack K, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Baseline neuropsychiatric symptoms and psychotropic medication use midway through data collection of the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort. Alzheimers Dement 2023; 19 Suppl 9:S42-S48. [PMID: 37296082 PMCID: PMC10709525 DOI: 10.1002/alz.13344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION We examined neuropsychiatric symptoms (NPS) and psychotropic medication use in a large sample of individuals with early-onset Alzheimer's disease (EOAD; onset 40-64 years) at the midway point of data collection for the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). METHODS Baseline NPS (Neuropsychiatric Inventory - Questionnaire; Geriatric Depression Scale) and psychotropic medication use from 282 participants enrolled in LEADS were compared across diagnostic groups - amyloid-positive EOAD (n = 212) and amyloid negative early-onset non-Alzheimer's disease (EOnonAD; n = 70). RESULTS Affective behaviors were the most common NPS in EOAD at similar frequencies to EOnonAD. Tension and impulse control behaviors were more common in EOnonAD. A minority of participants were using psychotropic medications, and use was higher in EOnonAD. DISCUSSION Overall NPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants. Future research will investigate moderators and etiological drivers of NPS, and NPS differences in EOAD versus late-onset AD.
Collapse
Affiliation(s)
- Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Ryan J. Wonderlin
- Marian University College of Osteopathic Medicine, Indianapolis, Indiana, 46222, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Alex Pena Garcia
- Marian University College of Osteopathic Medicine, Indianapolis, Indiana, 46222, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, 02912, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, 02912, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, 02912, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, 95616, USA
| | - Sujuan Gao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Sophia Wang
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paige E. Logan
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, 92121, USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Joel H. Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, 98195, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | | | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, 90033, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55123, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, 85351, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, 33140, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, 10032, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55123, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, 77030, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Kyle Womack
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, 63110, USA
| | - Erik Musiek
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, 63110, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205, USA
| | - Meghan Riddle
- Department of Psychiatry, Alpert Medical School, Brown University, Providence, Rhode Island, 02912, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, 60611, USA
| | - Steven Salloway
- Department of Psychiatry, Alpert Medical School, Brown University, Providence, Rhode Island, 02912, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, 94304, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington D.C., 20057, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, 30307, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, 92121, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, 46202, USA
| | - LEADS Consortium
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| |
Collapse
|
22
|
Hammers DB, Eloyan A, Taurone A, Thangarajah M, Beckett L, Gao S, Kirby K, Aisen P, Dage JL, Foroud T, Griffin P, Grinberg LT, Jack CR, Kramer J, Koeppe R, Kukull WA, Mundada NS, Joie RL, Soleimani-Meigooni DN, Iaccarino L, Murray ME, Nudelman K, Polsinelli AJ, Rumbaugh M, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Womack K, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Profiling baseline performance on the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort near the midpoint of data collection. Alzheimers Dement 2023; 19 Suppl 9:S8-S18. [PMID: 37256497 PMCID: PMC10806768 DOI: 10.1002/alz.13160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 06/01/2023]
Abstract
OBJECTIVE The Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) seeks to provide comprehensive understanding of early-onset Alzheimer's disease (EOAD; onset <65 years), with the current study profiling baseline clinical, cognitive, biomarker, and genetic characteristics of the cohort nearing the data-collection mid-point. METHODS Data from 371 LEADS participants were compared based on diagnostic group classification (cognitively normal [n = 89], amyloid-positive EOAD [n = 212], and amyloid-negative early-onset non-Alzheimer's disease [EOnonAD; n = 70]). RESULTS Cognitive performance was worse for EOAD than other groups, and EOAD participants were apolipoprotein E (APOE) ε4 homozygotes at higher rates. An amnestic presentation was common among impaired participants (81%), with several clinical phenotypes present. LEADS participants generally consented at high rates to optional trial procedures. CONCLUSIONS We present the most comprehensive baseline characterization of sporadic EOAD in the United States to date. EOAD presents with widespread cognitive impairment within and across clinical phenotypes, with differences in APOE ε4 allele carrier status appearing to be relevant. HIGHLIGHTS Findings represent the most comprehensive baseline characterization of sporadic early-onset Alzheimer's disease (EOAD) to date. Cognitive impairment was widespread for EOAD participants and more severe than other groups. EOAD participants were homozygous apolipoprotein E (APOE) ε4 carriers at higher rates than the EOnonAD group. Amnestic presentation predominated in EOAD and EOnonAD participants, but other clinical phenotypes were present.
Collapse
Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, USA
| | - Sujuan Gao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Lea T. Grinberg
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Kyle Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Steven Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
| | | |
Collapse
|
23
|
Touroutoglou A, Katsumi Y, Brickhouse M, Zaitsev A, Eckbo R, Aisen P, Beckett L, Dage JL, Eloyan A, Foroud T, Ghetti B, Griffin P, Hammers D, Jack CR, Kramer JH, Iaccarino L, Joie RL, Mundada NS, Koeppe R, Kukull WA, Murray ME, Nudelman K, Polsinelli AJ, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu JC, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Womack K, Carrillo MC, Rabinovici GD, Apostolova LG, Dickerson BC. The Sporadic Early-onset Alzheimer's Disease Signature Of Atrophy: Preliminary Findings From The Longitudinal Early-onset Alzheimer's Disease Study (LEADS) Cohort. Alzheimers Dement 2023; 19 Suppl 9:S74-S88. [PMID: 37850549 PMCID: PMC10829523 DOI: 10.1002/alz.13466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 10/19/2023]
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) research has advanced our understanding of neurodegeneration in sporadic early-onset Alzheimer's disease (EOAD) but studies include small samples, mostly amnestic EOAD, and have not focused on developing an MRI biomarker. METHODS We analyzed MRI scans to define the sporadic EOAD-signature atrophy in a small sample (n = 25) of Massachusetts General Hospital (MGH) EOAD patients, investigated its reproducibility in the large longitudinal early-onset Alzheimer's disease study (LEADS) sample (n = 211), and investigated the relationship of the magnitude of atrophy with cognitive impairment. RESULTS The EOAD-signature atrophy was replicated across the two cohorts, with prominent atrophy in the caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortices, and with relative sparing of the medial temporal lobe. The magnitude of EOAD-signature atrophy was associated with the severity of cognitive impairment. DISCUSSION The EOAD-signature atrophy is a reliable and clinically valid biomarker of AD-related neurodegeneration that could be used in clinical trials for EOAD. HIGHLIGHTS We developed an early-onset Alzheimer's disease (EOAD)-signature of atrophy based on magnetic resonance imaging (MRI) scans. EOAD signature was robustly reproducible across two independent patient cohorts. EOAD signature included prominent atrophy in parietal and posterior temporal cortex. The EOAD-signature atrophy was associated with the severity of cognitive impairment. EOAD signature is a reliable and clinically valid biomarker of neurodegeneration.
Collapse
Affiliation(s)
- Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Yuta Katsumi
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Michael Brickhouse
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexander Zaitsev
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan Eckbo
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Paul Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California - Davis, Davis, California, USA
| | - Jeffrey L Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Bernardino Ghetti
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Dustin Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joel H Kramer
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Renaud La Joie
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Angelina J Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic in Florida, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph C Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - R Scott Turner
- Department of Neurology, Georgetown University, Washington, D.C., USA
| | - Thomas S Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kyle Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Maria C Carrillo
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Gil D Rabinovici
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
24
|
Eloyan A, Thangarajah M, An N, Borowski BJ, Reddy AL, Aisen P, Dage JL, Foroud T, Ghetti B, Griffin P, Hammers D, Iaccarino L, Jack CR, Kirby K, Kramer J, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Musiek E, Onyike CU, Rogalski E, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Womack K, Beckett L, Gao S, Carrillo MC, Rabinovici G, Apostolova LG, Dickerson B, Vemuri P. White matter hyperintensities are higher among early-onset Alzheimer's disease participants than their cognitively normal and early-onset nonAD peers: Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Alzheimers Dement 2023; 19 Suppl 9:S89-S97. [PMID: 37491599 PMCID: PMC10808262 DOI: 10.1002/alz.13402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION We compared white matter hyperintensities (WMHs) in early-onset Alzheimer's disease (EOAD) with cognitively normal (CN) and early-onset amyloid-negative cognitively impaired (EOnonAD) groups in the Longitudinal Early-Onset Alzheimer's Disease Study. METHODS We investigated the role of increased WMH in cognition and amyloid and tau burden. We compared WMH burden of 205 EOAD, 68 EOnonAD, and 89 CN participants in lobar regions using t-tests and analyses of covariance. Linear regression analyses were used to investigate the association between WMH and cognitive impairment and that between amyloid and tau burden. RESULTS EOAD showed greater WMHs compared with CN and EOnonAD participants across all regions with no significant differences between CN and EOnonAD groups. Greater WMHs were associated with worse cognition. Tau burden was positively associated with WMH burden in the EOAD group. DISCUSSION EOAD consistently showed higher WMH volumes. Overall, greater WMHs were associated with worse cognition and higher tau burden in EOAD. HIGHLIGHTS This study represents a comprehensive characterization of WMHs in sporadic EOAD. WMH volumes are associated with tau burden from positron emission tomography (PET) in EOAD, suggesting WMHs are correlated with increasing burden of AD. Greater WMH volumes are associated with worse performance on global cognitive tests. EOAD participants have higher WMH volumes compared with CN and early-onset amyloid-negative cognitively impaired (EOnonAD) groups across all brain regions.
Collapse
Affiliation(s)
- Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Na An
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Bret J Borowski
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Paul Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Jeffrey L Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Bernardino Ghetti
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Pathology & Laboratory Medicine Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Dustin Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California-San Francisco, San Francisco, California, USA
| | - Clifford R Jack
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Joel Kramer
- Department of Neurology, University of California-San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Renaud La Joie
- Department of Neurology, University of California-San Francisco, San Francisco, California, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California-San Francisco, San Francisco, California, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - Raymond S Turner
- Department of Neurology, Georgetown University, Washington D.C., USA
| | - Thomas S Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kyle Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California-Davis, Davis, California, USA
| | - Sujuan Gao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Maria C Carrillo
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Gil Rabinovici
- Department of Neurology, University of California-San Francisco, San Francisco, California, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
| | - Brad Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | |
Collapse
|
25
|
Cho H, Mundada NS, Apostolova LG, Carrillo MC, Shankar R, Amuiri AN, Zeltzer E, Windon CC, Soleimani-Meigooni DN, Tanner JA, Heath CL, Lesman-Segev OH, Aisen P, Eloyan A, Lee HS, Hammers DB, Kirby K, Dage JL, Fagan A, Foroud T, Grinberg LT, Jack CR, Kramer J, Kukull WA, Murray ME, Nudelman K, Toga A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski EJ, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Koeppe R, Iaccarino L, Dickerson BC, La Joie R, Rabinovici GD. Amyloid and tau-PET in early-onset AD: Baseline data from the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Alzheimers Dement 2023; 19 Suppl 9:S98-S114. [PMID: 37690109 PMCID: PMC10807231 DOI: 10.1002/alz.13453] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/12/2023]
Abstract
INTRODUCTION We aimed to describe baseline amyloid-beta (Aβ) and tau-positron emission tomograrphy (PET) from Longitudinal Early-onset Alzheimer's Disease Study (LEADS), a prospective multi-site observational study of sporadic early-onset Alzheimer's disease (EOAD). METHODS We analyzed baseline [18F]Florbetaben (Aβ) and [18F]Flortaucipir (tau)-PET from cognitively impaired participants with a clinical diagnosis of mild cognitive impairment (MCI) or AD dementia aged < 65 years. Florbetaben scans were used to distinguish cognitively impaired participants with EOAD (Aβ+) from EOnonAD (Aβ-) based on the combination of visual read by expert reader and image quantification. RESULTS 243/321 (75.7%) of participants were assigned to the EOAD group based on amyloid-PET; 231 (95.1%) of them were tau-PET positive (A+T+). Tau-PET signal was elevated across cortical regions with a parietal-predominant pattern, and higher burden was observed in younger and female EOAD participants. DISCUSSION LEADS data emphasizes the importance of biomarkers to enhance diagnostic accuracy in EOAD. The advanced tau-PET binding at baseline might have implications for therapeutic strategies in patients with EOAD. HIGHLIGHTS 72% of patients with clinical EOAD were positive on both amyloid- and tau-PET. Amyloid-positive patients with EOAD had high tau-PET signal across cortical regions. In EOAD, tau-PET mediated the relationship between amyloid-PET and MMSE. Among EOAD patients, younger onset and female sex were associated with higher tau-PET.
Collapse
Affiliation(s)
- Hanna Cho
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
- Global Brain Health Institute, University of California, San Francisco, California, USA
| | - Nidhi S Mundada
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Maria C Carrillo
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Ranjani Shankar
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Alinda N Amuiri
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Ehud Zeltzer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Charles C Windon
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - David N Soleimani-Meigooni
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Jeremy A Tanner
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Courtney Lawhn Heath
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Orit H Lesman-Segev
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Diagnostic Imaging, Sheba Medical Center, Tel HaShomer, Israel
| | - Paul Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Rhode Island, USA
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jeffrey L Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Anne Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lea T Grinberg
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Pathology, University of California - San Francisco, San Francisco, California, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joel Kramer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Walter A Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Rhode Island, USA
| | - Emily J Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Thomas S Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert Koeppe
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Leonardo Iaccarino
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Renaud La Joie
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Gil D Rabinovici
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| |
Collapse
|
26
|
Bushnell J, Hammers DB, Aisen P, Dage JL, Eloyan A, Foroud T, Grinberg LT, Iaccarino L, Jack CR, Kirby K, Kramer J, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG, Clark DG. Influence of amyloid and diagnostic syndrome on non-traditional memory scores in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S29-S41. [PMID: 37653686 PMCID: PMC10855009 DOI: 10.1002/alz.13434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 09/02/2023]
Abstract
INTRODUCTION The Rey Auditory Verbal Learning Test (RAVLT) is a useful neuropsychological test for describing episodic memory impairment in dementia. However, there is limited research on its utility in early-onset Alzheimer's disease (EOAD). We assess the influence of amyloid and diagnostic syndrome on several memory scores in EOAD. METHODS We transcribed RAVLT recordings from 303 subjects in the Longitudinal Early-Onset Alzheimer's Disease Study. Subjects were grouped by amyloid status and syndrome. Primacy, recency, J-curve, duration, stopping time, and speed score were calculated and entered into linear mixed effects models as dependent variables. RESULTS Compared with amyloid negative subjects, positive subjects exhibited effects on raw score, primacy, recency, and stopping time. Inter-syndromic differences were noted with raw score, primacy, recency, J-curve, and stopping time. DISCUSSION RAVLT measures are sensitive to the effects of amyloid and syndrome in EOAD. Future work is needed to quantify the predictive value of these scores. HIGHLIGHTS RAVLT patterns characterize various presentations of EOAD and EOnonAD Amyloid impacts raw score, primacy, recency, and stopping time Timing-based scores add value over traditional count-based scores.
Collapse
Affiliation(s)
- Justin Bushnell
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lea T. Grinberg
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Nidhi S. Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Steven Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington D.C., USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - David G. Clark
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | |
Collapse
|
27
|
Dage JL, Eloyan A, Thangarajah M, Hammers DB, Fagan AM, Gray JD, Schindler SE, Snoddy C, Nudelman KNH, Faber KM, Foroud T, Aisen P, Griffin P, Grinberg LT, Iaccarino L, Kirby K, Kramer J, Koeppe R, Kukull WA, Joie RL, Mundada NS, Murray ME, Rumbaugh M, Soleimani-Meigooni DN, Toga AW, Touroutoglou A, Vemuri P, Atri A, Beckett LA, Day GS, Graff-Radford NR, Duara R, Honig LS, Jones DT, Masdeu JC, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Womack KB, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Cerebrospinal fluid biomarkers in the Longitudinal Early-onset Alzheimer's Disease Study. Alzheimers Dement 2023; 19 Suppl 9:S115-S125. [PMID: 37491668 PMCID: PMC10877673 DOI: 10.1002/alz.13399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION One goal of the Longitudinal Early Onset Alzheimer's Disease Study (LEADS) is to define the fluid biomarker characteristics of early-onset Alzheimer's disease (EOAD). METHODS Cerebrospinal fluid (CSF) concentrations of Aβ1-40, Aβ1-42, total tau (tTau), pTau181, VILIP-1, SNAP-25, neurogranin (Ng), neurofilament light chain (NfL), and YKL-40 were measured by immunoassay in 165 LEADS participants. The associations of biomarker concentrations with diagnostic group and standard cognitive tests were evaluated. RESULTS Biomarkers were correlated with one another. Levels of CSF Aβ42/40, pTau181, tTau, SNAP-25, and Ng in EOAD differed significantly from cognitively normal and early-onset non-AD dementia; NfL, YKL-40, and VILIP-1 did not. Across groups, all biomarkers except SNAP-25 were correlated with cognition. Within the EOAD group, Aβ42/40, NfL, Ng, and SNAP-25 were correlated with at least one cognitive measure. DISCUSSION This study provides a comprehensive analysis of CSF biomarkers in sporadic EOAD that can inform EOAD clinical trial design.
Collapse
Affiliation(s)
- Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Anne M. Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Julia D. Gray
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Casey Snoddy
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kelly N. H. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kelley M. Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Lea T. Grinberg
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Laurel A. Beckett
- Department of Public Health Sciences, University of California-Davis, Davis, California, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | | | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington, D.C., USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kyle B. Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
| | | |
Collapse
|
28
|
Nudelman KNH, Jackson T, Rumbaugh M, Eloyan A, Abreu M, Dage JL, Snoddy C, Faber KM, Foroud T, Hammers DB, Taurone A, Thangarajah M, Aisen P, Beckett L, Kramer J, Koeppe R, Kukull WA, Murray ME, Toga AW, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu JC, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Pathogenic variants in the Longitudinal Early-onset Alzheimer's Disease Study cohort. Alzheimers Dement 2023; 19 Suppl 9:S64-S73. [PMID: 37801072 PMCID: PMC10783439 DOI: 10.1002/alz.13482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 10/07/2023]
Abstract
INTRODUCTION One goal of the Longitudinal Early-onset Alzheimer's Disease Study (LEADS) is to investigate the genetic etiology of early onset (40-64 years) cognitive impairment. Toward this goal, LEADS participants are screened for known pathogenic variants. METHODS LEADS amyloid-positive early-onset Alzheimer's disease (EOAD) or negative early-onset non-AD (EOnonAD) cases were whole exome sequenced (N = 299). Pathogenic variant frequency in APP, PSEN1, PSEN2, GRN, MAPT, and C9ORF72 was assessed for EOAD and EOnonAD. Gene burden testing was performed in cases compared to similar-age cognitively normal controls in the Parkinson's Progression Markers Initiative (PPMI) study. RESULTS Previously reported pathogenic variants in the six genes were identified in 1.35% of EOAD (3/223) and 6.58% of EOnonAD (5/76). No genes showed enrichment for carriers of rare functional variants in LEADS cases. DISCUSSION Results suggest that LEADS is enriched for novel genetic causative variants, as previously reported variants are not observed in most cases. HIGHLIGHTS Sequencing identified eight cognitively impaired pathogenic variant carriers. Pathogenic variants were identified in PSEN1, GRN, MAPT, and C9ORF72. Rare variants were not enriched in APP, PSEN1/2, GRN, and MAPT. The Longitudinal Early-onset Alzheimer's Disease Study (LEADS) is a key resource for early-onset Alzheimer's genetic research.
Collapse
Affiliation(s)
- Kelly N. H. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
| | - Trever Jackson
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, USA, 02912
| | - Marco Abreu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Jeffrey L. Dage
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Casey Snoddy
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Kelley M. Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - DIAN/DIAN-TU Clinical/Genetics Committee
- Washington University School of Medicine in St. Louis, MO, USA, 63110
- Icahn School of Medicine at Mount Sinai, New York, NY, USA, 10029
- Mayo Clinic College of Medicine, Jacksonville, FL, USA, 32224
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, USA, 02912
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, USA, 02912
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, 92121
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, USA, 95616
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, CA, USA, 94143
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA, 48109
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, WA, USA, 98195
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA, 32224
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA, 90033
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, AZ, USA, 85315
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA, 32224
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA, 33140
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA, 10032
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA, 55905
- Department of Neurology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, TX, USA, 77030
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA, 90095
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA, 63110
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21295
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, RI Island, USA, 02912
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA , 60611
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, RI Island, USA, 02912
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA, 94304
| | - R. Scott Turner
- Department of Neurology, Georgetown University, DC, USA, 20057
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, GA, USA, 30307
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 19104
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, IL, USA, 60603
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, 02114
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, CA, USA, 94143
| | - Liana G. Apostolova
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, 92121
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, IN, USA, 46202
| | | |
Collapse
|
29
|
Gebre RK, Rial AM, Raghavan S, Wiste HJ, Johnson Sparrman KL, Heeman F, Costoya-Sánchez A, Schwarz CG, Spychalla AJ, Lowe VJ, Graff-Radford J, Knopman DS, Petersen RC, Schöll M, Jack CR, Vemuri P. Advancing Tau-PET quantification in Alzheimer's disease with machine learning: introducing THETA, a novel tau summary measure. Res Sq 2023:rs.3.rs-3290598. [PMID: 37886506 PMCID: PMC10602128 DOI: 10.21203/rs.3.rs-3290598/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Alzheimer's disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential.
Collapse
Affiliation(s)
- Robel K. Gebre
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Alexis Moscoso Rial
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | | | - Heather J. Wiste
- Department of Qualitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Fiona Heeman
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Alejandro Costoya-Sánchez
- Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
- Nuclear Medicine Department and Molecular Imaging Group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Travesía da Choupana s/n, Santiago de Compostela, 15706, Spain
| | | | | | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - Ronald C. Petersen
- Department of Qualitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Michael Schöll
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Nuclear Medicine Department and Molecular Imaging Group, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Travesía da Choupana s/n, Santiago de Compostela, 15706, Spain
| | | | | | | |
Collapse
|
30
|
Ramanan VK, Graff-Radford J, Syrjanen J, Shir D, Algeciras-Schimnich A, Lucas J, Martens YA, Carrasquillo MM, Day GS, Ertekin-Taner N, Lachner C, Willis FB, Knopman DS, Jack CR, Petersen RC, Vemuri P, Graff-Radford N, Mielke MM. Association of Plasma Biomarkers of Alzheimer Disease With Cognition and Medical Comorbidities in a Biracial Cohort. Neurology 2023; 101:e1402-e1411. [PMID: 37580163 PMCID: PMC10573134 DOI: 10.1212/wnl.0000000000207675] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/06/2023] [Indexed: 08/16/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Recent advances in blood-based biomarkers offer the potential to revolutionize the diagnosis and management of Alzheimer disease (AD), but additional research in diverse populations is critical. We assessed the profiles of blood-based AD biomarkers and their relationships to cognition and common medical comorbidities in a biracial cohort. METHODS Participants were evaluated through the Mayo Clinic Jacksonville Alzheimer Disease Research Center and matched on age, sex, and cognitive status. Plasma AD biomarkers (β-amyloid peptide 1-42 [Aβ42/40], plasma tau phosphorylated at position 181 [p-tau181], glial fibrillary acidic protein [GFAP], and neurofilament light) were measured using the Quanterix SiMoA HD-X analyzer. Cognition was assessed with the Mini-Mental State Examination. Wilcoxon rank sum tests were used to assess for differences in plasma biomarker levels by sex. Linear models tested for associations of self-reported race, chronic kidney disease (CKD), and vascular risk factors with plasma AD biomarker levels. Additional models assessed for interactions between race and plasma biomarkers in predicting cognition. RESULTS The sample comprised African American (AA; N = 267) and non-Hispanic White (NHW; N = 268) participants, including 69% female participants and age range 43-100 (median 80.2) years. Education was higher in NHW participants (median 16 vs 12 years, p < 0.001) while APOE ε4 positivity was higher in AA participants (43% vs 34%; p = 0.04). We observed no differences in plasma AD biomarker levels between AA and NHW participants. These results were unchanged after stratifying by cognitive status (unimpaired vs impaired). Although the p-tau181-cognition association seemed stronger in NHW participants while the Aβ42/40-cognition association seemed stronger in AA participants, these findings did not survive after excluding individuals with CKD. Female participants displayed higher GFAP (177.5 pg/mL vs 157.73 pg/mL; p = 0.002) and lower p-tau181 (2.62 pg/mL vs 3.28 pg/mL; p = 0.001) levels than male participants. Diabetes was inversely associated with GFAP levels (β = -0.01; p < 0.001). DISCUSSION In a biracial community-based sample of adults, we observed that sex differences, CKD, and vascular risk factors, but not self-reported race, contributed to variation in plasma AD biomarkers. Although some prior studies have reported primary effects of race/ethnicity, our results reinforce the need to account for broad-based medical and social determinants of health (including sex, systemic comorbidities, and other factors) in effectively and equitably deploying plasma AD biomarkers in the general population.
Collapse
Affiliation(s)
- Vijay K Ramanan
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC.
| | - Jonathan Graff-Radford
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Jeremy Syrjanen
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Dror Shir
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Alicia Algeciras-Schimnich
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - John Lucas
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Yuka A Martens
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Minerva M Carrasquillo
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Gregory S Day
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Nilüfer Ertekin-Taner
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Christian Lachner
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Floyd B Willis
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - David S Knopman
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Clifford R Jack
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Ronald C Petersen
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Prashanthi Vemuri
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Neill Graff-Radford
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Michelle M Mielke
- From the Department of Neurology (V.K.R., J.G.-R., D.S., D.S.K., R.C.P.), Department of Quantitative Health Sciences (J.S., R.C.P.), and Department of Laboratory Medicine and Pathology (A.A.-S.), Mayo Clinic, Rochester, MN; Department of Psychiatry and Psychology (J.L., C.L.), Department of Neuroscience (Y.A.M., M.M.C., G.S.D., N.E.-T.), Department of Neurology (N.E.-T., C.L., N.G.-R.), and Department of Family Medicine (F.B.W.), Mayo Clinic, Jacksonville, FL; Department of Radiology (C.R.J., P.V.), Mayo Clinic, Rochester, MN; and Department of Epidemiology and Prevention (M.M.M.), Wake Forest University School of Medicine, Winston-Salem, NC
| |
Collapse
|
31
|
Tipton PW, Atik M, Soto-Beasley AI, Day GS, Grewal SS, Chaichana K, Fermo OP, Ball CT, Heckman MG, White LJ, Quicksall ZS, Reddy JS, Ramanan VK, Vemuri P, Elder BD, Ertekin-Taner N, Ross O, Graff-Radford N. CWH43 Variants Are Associated With Disease Risk and Clinical Phenotypic Measures in Patients With Normal Pressure Hydrocephalus. Neurol Genet 2023; 9:e200086. [PMID: 37476022 PMCID: PMC10356132 DOI: 10.1212/nxg.0000000000200086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/25/2023] [Indexed: 07/22/2023]
Abstract
Background and Objectives Variants in the CWH43 gene have been associated with normal pressure hydrocephalus (NPH). We aimed to replicate these findings, identify additional CWH43 variants, and further define the clinical phenotype associated with CWH43 variants. Methods We determined the prevalence of CWH43 variants by whole-genome sequencing (WGS) in 94 patients with NPH. The odds of having CWH43 variant carriers develop NPH were determined through comparison with 532 Mayo Clinic Biobank volunteers without a history of NPH. For patients with NPH, we documented the head circumference, prevalence of disproportionate enlargement of subarachnoid hydrocephalus (DESH), microvascular changes on MRI quantified by the Fazekas scale, and ambulatory response to ventriculoperitoneal shunting. Results We identified rare (MAF <0.05) coding CWH43 variants in 15 patients with NPH. Ten patients (Leu533Terfs, n = 8; Lys696Asnfs, n = 2) harbored previously reported predicted loss-of-function variants, and combined burden analysis confirmed risk association with NPH (OR 2.60, 95% CI 1.12-6.03, p = 0.027). Additional missense variations observed included Ile292Thr (n = 2), Ala469Ser (n = 2), and Ala626Val (n = 1). Though not quite statistically significant, in single variable analysis, the odds of having a head circumference above the 75th percentile of normal controls was more than 5 times higher for CWH43 variant carriers compared with that for noncarriers (unadjusted OR 5.67, 95% CI 0.96-108.55, p = 0.057), and this was consistent after adjusting for sex and height (OR 5.42, 95% CI 0.87-106.37, p = 0.073). DESH was present in 56.7% of noncarriers and only 21.4% of carriers (p = 0.016), while sulcal trapping was also more prevalent among noncarriers (67.2% vs 35.7%, p = 0.030). All 8 of the 15 variant carriers who underwent ventriculoperitoneal shunting at our institution experienced ambulatory improvements. Discussion CWH43 variants are frequent in patients with NPH. Predicted loss-of-function mutations were the most common; we identified missense mutations that require further study. Our findings suggest that congenital factors, rather than malabsorption or vascular dysfunction, are primary contributors to the CWH43-related NPH clinical syndrome.
Collapse
Affiliation(s)
- Philip W Tipton
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Merve Atik
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Alexandra I Soto-Beasley
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Gregory S Day
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Sanjeet S Grewal
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Kaisorn Chaichana
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Olga P Fermo
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Colleen T Ball
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Michael G Heckman
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Launia J White
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Zachary S Quicksall
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Joseph S Reddy
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Vijay K Ramanan
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Prashanthi Vemuri
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Benjamin D Elder
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Nilufer Ertekin-Taner
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Owen Ross
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| | - Neill Graff-Radford
- From the Department of Neurology (P.W.T., G.S.D., O.P.F., N.E.-T., N.G.-R.), Department of Neuroscience (M.A., A.I.S.-B., Z.S.Q., J.S.R., N.E.-T., O.R.), Department of Neurosurgery (S.S.G., K.C.), Division of Clinical Trials and Biostatistics (C.T.B., M.G.H., L.J.W.), Mayo Clinic, Jacksonville, FL; Department of Neurology (V.K.R.), Department of Radiology (P.V.), and Department of Neurosurgery (B.D.E.), Mayo Clinic, Rochester, MN
| |
Collapse
|
32
|
Schwarz CG, Kremers WK, Weigand SD, Prakaashana CM, Senjem ML, Przybelski SA, Lowe VJ, Gunter JL, Kantarci K, Vemuri P, Graff-Radford J, Petersen RC, Knopman DS, Jack CR. Effects of de-facing software mri_reface on utility of imaging biomarkers used in Alzheimer's disease research. Neuroimage Clin 2023; 40:103507. [PMID: 37703605 PMCID: PMC10502400 DOI: 10.1016/j.nicl.2023.103507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/07/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Brain imaging research studies increasingly use "de-facing" software to remove or replace facial imagery before public data sharing. Several works have studied the effects of de-facing software on brain imaging biomarkers by directly comparing automated measurements from unmodified vs de-faced images, but most research brain images are used in analyses of correlations with cognitive measurements or clinical statuses, and the effects of de-facing on these types of imaging-to-cognition correlations has not been measured. In this work, we focused on brain imaging measures of amyloid (A), tau (T), neurodegeneration (N), and vascular (V) measures used in Alzheimer's Disease (AD) research. We created a retrospective sample of participants from three age- and sex-matched clinical groups (cognitively unimpaired, mild cognitive impairment, and AD dementia, and we performed region- and voxel-wise analyses of: hippocampal volume (N), white matter hyperintensity volume (V), amyloid PET (A), and tau PET (T) measures, each from multiple software pipelines, on their ability to separate cognitively defined groups and their degrees of correlation with age and Clinical Dementia Rating (CDR)-Sum of Boxes (CDR-SB). We performed each of these analyses twice: once with unmodified images and once with images de-faced with leading de-facing software mri_reface, and we directly compared the findings and their statistical strengths between the original vs. the de-faced images. Analyses with original and with de-faced images had very high agreement. There were no significant differences between any voxel-wise comparisons. Among region-wise comparisons, only three out of 55 correlations were significantly different between original and de-faced images, and these were not significant after correction for multiple comparisons. Overall, the statistical power of the imaging data for AD biomarkers was almost identical between unmodified and de-faced images, and their analyses results were extremely consistent.
Collapse
Affiliation(s)
| | - Walter K Kremers
- Department of Quantitative Health Sciences, Division of Clinical Trials & Biostatistics, Mayo Clinic, Rochester, MN, USA
| | - Stephen D Weigand
- Department of Quantitative Health Sciences, Division of Clinical Trials & Biostatistics, Mayo Clinic, Rochester, MN, USA
| | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA; Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Division of Clinical Trials & Biostatistics, Mayo Clinic, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | | |
Collapse
|
33
|
Schwarz CG, Kremers WK, Arani A, Savvides M, Reid RI, Gunter JL, Senjem ML, Cogswell PM, Vemuri P, Kantarci K, Knopman DS, Petersen RC, Jack CR. A face-off of MRI research sequences by their need for de-facing. Neuroimage 2023; 276:120199. [PMID: 37269958 PMCID: PMC10389782 DOI: 10.1016/j.neuroimage.2023.120199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/19/2023] [Accepted: 05/25/2023] [Indexed: 06/05/2023] Open
Abstract
It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification ("de-facing") software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96-98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44-45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0-8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a low risk of re-identification and can be shared without de-facing, but this conclusion should be re-evaluated if they are acquired without fat suppression, with a full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.
Collapse
Affiliation(s)
- Christopher G Schwarz
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
| | - Walter K Kremers
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Arvin Arani
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Marios Savvides
- CyLab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | | | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| |
Collapse
|
34
|
Carvalho DZ, McCarter SJ, St Louis EK, Przybelski SA, Johnson Sparrman KL, Somers VK, Boeve BF, Petersen RC, Jack CR, Graff-Radford J, Vemuri P. Association of Polysomnographic Sleep Parameters With Neuroimaging Biomarkers of Cerebrovascular Disease in Older Adults With Sleep Apnea. Neurology 2023; 101:e125-e136. [PMID: 37164654 PMCID: PMC10351545 DOI: 10.1212/wnl.0000000000207392] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/23/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Our objective was to determine whether polysomnographic (PSG) sleep parameters are associated with neuroimaging biomarkers of cerebrovascular disease (CVD) related to white matter (WM) integrity in older adults with obstructive sleep apnea (OSA). METHODS From the population-based Mayo Clinic Study of Aging, we identified participants without dementia who underwent at least 1 brain MRI and PSG. We quantified 2 CVD biomarkers: WM hyperintensities (WMHs) from fluid-attenuated inversion recovery (FLAIR)-MRI, and fractional anisotropy of the genu of the corpus callosum (genu FA) from diffusion MRI. For this cross-sectional analysis, we fit linear models to assess associations between PSG parameters (NREM stage 1 percentage, NREM stage 3 percentage [slow-wave sleep], mean oxyhemoglobin saturation, and log of apnea-hypopnea index [AHI]) and CVD biomarkers (log of WMH and log of genu FA), respectively, while adjusting for age (at MRI), sex, APOE ε4 status, composite cardiovascular and metabolic conditions (CMC) score, REM stage percentage, sleep duration, and interval between MRI and PSG. RESULTS We included 140 participants with FLAIR-MRI (of which 103 had additional diffusion MRI). The mean ± SD age was 72.7 ± 9.6 years at MRI with nearly 60% being men. The absolute median (interquartile range [IQR]) interval between MRI and PSG was 1.74 (0.9-3.2) years. 90.7% were cognitively unimpaired (CU) during both assessments. For every 10-point decrease in N3%, there was a 0.058 (95% CI 0.006-0.111, p = 0.030) increase in the log of WMH and 0.006 decrease (95% CI -0.012 to -0.0002, p = 0.042) in the log of genu FA. After matching for age, sex, and N3%, participants with severe OSA had higher WMH (median [IQR] 0.007 [0.005-0.015] vs 0.006 [0.003-0.009], p = 0.042) and lower genu FA (median [IQR] 0.57 [0.55-0.63] vs 0.63 [0.58-0.65], p = 0.007), when compared with those with mild/moderate OSA. DISCUSSION We found that reduced slow-wave sleep and severe OSA were associated with higher burden of WM abnormalities in predominantly CU older adults, which may contribute to greater risk of cognitive impairment, dementia, and stroke. Our study supports the association between sleep depth/fragmentation and intermittent hypoxia and CVD biomarkers. Longitudinal studies are required to assess causation.
Collapse
Affiliation(s)
- Diego Z Carvalho
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN.
| | - Stuart J McCarter
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Erik K St Louis
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Scott A Przybelski
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Kohl L Johnson Sparrman
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Virend K Somers
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Bradley F Boeve
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Ronald C Petersen
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Clifford R Jack
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Jonathan Graff-Radford
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| | - Prashanthi Vemuri
- From the Department of Neurology (D.Z.C., S.J.M., E.K.S.L., B.F.B., R.C.P., J.G.-R.), Center for Sleep Medicine (D.Z.C., S.J.M., E.K.S.L., B.F.B.), Division of Pulmonary and Critical Care, Department of Internal Medicine, Department of Quantitative Health Sciences (S.A.P., R.C.P.), Department of Radiology (K.L.J.S., C.R.J., P.V.), and Department of Cardiovascular Medicine (V.K.S.), Mayo Clinic, Rochester, MN
| |
Collapse
|
35
|
Duering M, Biessels GJ, Brodtmann A, Chen C, Cordonnier C, de Leeuw FE, Debette S, Frayne R, Jouvent E, Rost NS, Ter Telgte A, Al-Shahi Salman R, Backes WH, Bae HJ, Brown R, Chabriat H, De Luca A, deCarli C, Dewenter A, Doubal FN, Ewers M, Field TS, Ganesh A, Greenberg S, Helmer KG, Hilal S, Jochems ACC, Jokinen H, Kuijf H, Lam BYK, Lebenberg J, MacIntosh BJ, Maillard P, Mok VCT, Pantoni L, Rudilosso S, Satizabal CL, Schirmer MD, Schmidt R, Smith C, Staals J, Thrippleton MJ, van Veluw SJ, Vemuri P, Wang Y, Werring D, Zedde M, Akinyemi RO, Del Brutto OH, Markus HS, Zhu YC, Smith EE, Dichgans M, Wardlaw JM. Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol 2023; 22:602-618. [PMID: 37236211 DOI: 10.1016/s1474-4422(23)00131-x] [Citation(s) in RCA: 80] [Impact Index Per Article: 80.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/03/2023] [Accepted: 03/28/2023] [Indexed: 05/28/2023]
Abstract
Cerebral small vessel disease (SVD) is common during ageing and can present as stroke, cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive and other symptoms and affect activities of daily living. Standards for Reporting Vascular Changes on Neuroimaging 1 (STRIVE-1) categorised and standardised the diverse features of SVD that are visible on structural MRI. Since then, new information on these established SVD markers and novel MRI sequences and imaging features have emerged. As the effect of combined SVD imaging features becomes clearer, a key role for quantitative imaging biomarkers to determine sub-visible tissue damage, subtle abnormalities visible at high-field strength MRI, and lesion-symptom patterns, is also apparent. Together with rapidly emerging machine learning methods, these metrics can more comprehensively capture the effect of SVD on the brain than the structural MRI features alone and serve as intermediary outcomes in clinical trials and future routine practice. Using a similar approach to that adopted in STRIVE-1, we updated the guidance on neuroimaging of vascular changes in studies of ageing and neurodegeneration to create STRIVE-2.
Collapse
Affiliation(s)
- Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Medical Image Analysis Center, University of Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
| | - Geert Jan Biessels
- Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher Chen
- Department of Pharmacology, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Psychological Medicine, Memory Aging and Cognition Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Charlotte Cordonnier
- Université de Lille, INSERM, CHU Lille, U1172-Lille Neuroscience and Cognition (LilNCog), Lille, France
| | - Frank-Erik de Leeuw
- Department of Neurology, Donders Center for Medical Neuroscience, Radboudumc, Nijmegen, Netherlands
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, University of Bordeaux, INSERM, UMR 1219, Bordeaux, France; Department of Neurology, Institute for Neurodegenerative Diseases, CHU de Bordeaux, Bordeaux, France
| | - Richard Frayne
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Seaman Family MR Research Centre, Foothills Medical Centre, University of Calgary, Calgary, AB, Canada
| | - Eric Jouvent
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Walter H Backes
- School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, Netherlands; School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Hee-Joon Bae
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea; Cerebrovascular Disease Center, Seoul National University Bundang Hospital, Seongn-si, South Korea
| | - Rosalind Brown
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hugues Chabriat
- Centre Neurovasculaire Translationnel, CERVCO, INSERM U1141, FHU NeuroVasc, Université Paris Cité, Paris, France
| | - Alberto De Luca
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Charles deCarli
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Thalia S Field
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada; Vancouver Stroke Program, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Aravind Ganesh
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | - Steven Greenberg
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Karl G Helmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Athinoula A Martinos Center for Biomedical Imaging, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Saima Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Angela C C Jochems
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
| | - Hanna Jokinen
- Division of Neuropsychology, HUS Neurocenter, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Hugo Kuijf
- Image Sciences Institute, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bonnie Y K Lam
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Jessica Lebenberg
- AP-HP, Lariboisière Hospital, Translational Neurovascular Centre, FHU NeuroVasc, Université Paris Cité, Paris, France; Université Paris Cité, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Bradley J MacIntosh
- Sandra E Black Centre for Brain Resilience and Repair, Hurvitz Brain Sciences, Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Computational Radiology and Artificial Intelligence Unit, Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Pauline Maillard
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA, USA
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Margaret KL Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lui Che Woo Institute of Innovative Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Leonardo Pantoni
- Department of Biomedical and Clinical Science, University of Milan, Milan, Italy
| | - Salvatore Rudilosso
- Comprehensive Stroke Center, Department of Neuroscience, Hospital Clinic and August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Neurology, Boston University Medical Center, Boston, MA, USA; Framingham Heart Study, Framingham, MA, USA
| | - Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Colin Smith
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Julie Staals
- School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, Netherlands; Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Edinburgh Imaging and Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | | | | | - Yilong Wang
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - David Werring
- Stroke Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Marialuisa Zedde
- Neurology Unit, Stroke Unit, Department of Neuromotor Physiology and Rehabilitation, Azienda Unità Sanitaria-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Rufus O Akinyemi
- Neuroscience and Ageing Research Unit, Institute for Advanced Medical Research and Training, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Oscar H Del Brutto
- School of Medicine and Research Center, Universidad de Especialidades Espiritu Santo, Ecuador
| | - Hugh S Markus
- Stroke Research Group, Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Eric E Smith
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada; Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada; Department of Radiology, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; German Centre for Cardiovascular Research (DZHK), Munich, Germany
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK.
| |
Collapse
|
36
|
Bermudez C, Graff-Radford J, Syrjanen JA, Stricker NH, Algeciras-Schimnich A, Kouri N, Kremers WK, Petersen RC, Jack CR, Knopman DS, Dickson DW, Nguyen AT, Reichard RR, Murray ME, Mielke MM, Vemuri P. Plasma biomarkers for prediction of Alzheimer's disease neuropathologic change. Acta Neuropathol 2023; 146:13-29. [PMID: 37269398 PMCID: PMC10478071 DOI: 10.1007/s00401-023-02594-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 05/14/2023] [Accepted: 05/24/2023] [Indexed: 06/05/2023]
Abstract
While plasma biomarkers for Alzheimer's disease (AD) are increasingly being evaluated for clinical diagnosis and prognosis, few population-based autopsy studies have evaluated their utility in the context of predicting neuropathological changes. Our goal was to investigate the utility of clinically available plasma markers in predicting Braak staging, neuritic plaque score, Thal phase, and overall AD neuropathological change (ADNC).We utilized a population-based prospective study of 350 participants with autopsy and antemortem plasma biomarker testing using clinically available antibody assay (Quanterix) consisting of Aβ42/40 ratio, p-tau181, GFAP, and NfL. We utilized a variable selection procedure in cross-validated (CV) logistic regression models to identify the best set of plasma predictors along with demographic variables, and a subset of neuropsychological tests comprising the Mayo Clinic Preclinical Alzheimer Cognitive Composite (Mayo-PACC). ADNC was best predicted with plasma GFAP, NfL, p-tau181 biomarkers along with APOE ε4 carrier status and Mayo-PACC cognitive score (CV AUC = 0.798). Braak staging was best predicted using plasma GFAP, p-tau181, and cognitive scores (CV AUC = 0.774). Neuritic plaque score was best predicted using plasma Aβ42/40 ratio, p-tau181, GFAP, and NfL biomarkers (CV AUC = 0.770). Thal phase was best predicted using GFAP, NfL, p-tau181, APOE ε4 carrier status and Mayo-PACC cognitive score (CV AUC = 0.754). We found that GFAP and p-tau provided non-overlapping information on both neuritic plaque and Braak stage scores whereas Aβ42/40 and NfL were mainly useful for prediction of neuritic plaque scores. Separating participants by cognitive status improved predictive performance, particularly when plasma biomarkers were included. Plasma biomarkers can differentially inform about overall ADNC pathology, Braak staging, and neuritic plaque score when combined with demographics and cognitive variables and have significant utility for earlier detection of AD.
Collapse
Affiliation(s)
- Camilo Bermudez
- Department of Neurology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55902, USA.
| | | | - Jeremy A Syrjanen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Nikki H Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | | | - Naomi Kouri
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Walter K Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55902, USA
| | | | - David S Knopman
- Department of Neurology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55902, USA
| | | | - Aivi T Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - R Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | - Michelle M Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | | |
Collapse
|
37
|
Tian J, Raghavan S, Reid RI, Przybelski SA, Lesnick TG, Gebre RK, Graff-Radford J, Schwarz CG, Lowe VJ, Kantarci K, Knopman DS, Petersen RC, Jack CR, Vemuri P. White Matter Degeneration Pathways Associated With Tau Deposition in Alzheimer Disease. Neurology 2023; 100:e2269-e2278. [PMID: 37068958 PMCID: PMC10259272 DOI: 10.1212/wnl.0000000000207250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 02/16/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The dynamics of white matter (WM) changes are understudied in Alzheimer disease (AD). Our goal was to study the association between flortaucipir PET and WM health using neurite orientation dispersion and density imaging (NODDI) and evaluate its association with cognitive performance. Specifically, we focused on NODDI's Neurite Density Index (NDI), which aids in capturing axonal degeneration in WM and has greater specificity than single-shell diffusion MRI methods. METHOD We estimated regional flortaucipir PET standard uptake value ratios (SUVRs) from 3 regions corresponding to Braak stage I, III/IV, and V/VI to capture the spatial distribution pattern of the 3R/4R tau in AD. Then, we evaluated the associations between these measurements and NDIs in 29 candidate WM tracts using Pearson correlation and multiple regression models. RESULTS Based on 223 participants who were amyloid positive (mean age of 78 years and 57.0% male, 119 cognitively unimpaired, 56 mild cognitive impairment, and 48 dementia), the results showed that WM tracts NDI decreased with increasing regional Braak tau SUVRs. Of all the significant WM tracts, the uncinate fasciculus (r = -0.274 for Braak I, -0.311 for Braak III/IV, and -0.292 for Braak V/VI, p < 0.05) and cingulum adjoining hippocampus (r = -0.274, -0.288, -0.233, p < 0.05), both tracts anatomically connected to areas of early tau deposition, were consistently found to be within the top 5 distinguishing WM tracts associated with flortaucipir SUVRs. The increase in tau deposition measurable outside the medial temporal lobes in Braak III-VI was associated with a decrease in NDI in the middle and inferior temporal WM tracts. For cognitive performance, WM NDI had similar coefficients of determination (r 2 = 31%) as regional Braak flortaucipir SUVRs (29%), and together WM NDI and regional Braak flortaucipir SUVRs explained 46% of the variance in cognitive performance. DISCUSSION We found spatially dependent WM degeneration associated with regional flortaucipir SUVRs in Braak stages, suggesting a spatial pattern in WM damage. NDI, a specific marker of axonal density, provides complementary information about disease staging and progression in addition to tau deposition. Measurements of WM changes are important for the mechanistic understanding of multifactorial pathways through which AD causes cognitive dysfunction.
Collapse
Affiliation(s)
- Jianqiao Tian
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Sheelakumari Raghavan
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Robert I Reid
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Scott A Przybelski
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Timothy G Lesnick
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Robel K Gebre
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Jonathan Graff-Radford
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Christopher G Schwarz
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Val J Lowe
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Kejal Kantarci
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - David S Knopman
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Ronald C Petersen
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Clifford R Jack
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN
| | - Prashanthi Vemuri
- From the Department of Radiology (J.T., S.R., R.K.G., C.G.S., V.J.L., K.K., C.R.J., P.V.), Mayo Clinic; Mayo Clinic Graduate School of Biomedical Sciences (J.T.); and Department of Information Technology (R.I.R.), Department of Quantitative Health Sciences (S.A.P., T.G.L.), and Department of Neurology (J.G.-R., D.S.K., R.C.P.), Mayo Clinic, Rochester, MN.
| |
Collapse
|
38
|
Cogswell PM, Lundt ES, Therneau TM, Mester CT, Wiste HJ, Graff-Radford J, Schwarz CG, Senjem ML, Gunter JL, Reid RI, Przybelski SA, Knopman DS, Vemuri P, Petersen RC, Jack CR. Evidence against a temporal association between cerebrovascular disease and Alzheimer's disease imaging biomarkers. Nat Commun 2023; 14:3097. [PMID: 37248223 PMCID: PMC10226977 DOI: 10.1038/s41467-023-38878-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 05/15/2023] [Indexed: 05/31/2023] Open
Abstract
Whether a relationship exists between cerebrovascular disease and Alzheimer's disease has been a source of controversy. Evaluation of the temporal progression of imaging biomarkers of these disease processes may inform mechanistic associations. We investigate the relationship of disease trajectories of cerebrovascular disease (white matter hyperintensity, WMH, and fractional anisotropy, FA) and Alzheimer's disease (amyloid and tau PET) biomarkers in 2406 Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center participants using accelerated failure time models. The model assumes a common pattern of progression for each biomarker that is shifted earlier or later in time for each individual and represented by a per participant age adjustment. An individual's amyloid and tau PET adjustments show very weak temporal association with WMH and FA adjustments (R = -0.07 to 0.07); early/late amyloid or tau timing explains <1% of the variation in WMH and FA adjustment. Earlier onset of amyloid is associated with earlier onset of tau (R = 0.57, R2 = 32%). These findings support a strong mechanistic relationship between amyloid and tau aggregation, but not between WMH or FA and amyloid or tau PET.
Collapse
Affiliation(s)
- Petrice M Cogswell
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
| | - Emily S Lundt
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Carly T Mester
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Heather J Wiste
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | | | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
- Department of Information Technology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Jeffrey L Gunter
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Ronald C Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
- Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA
| |
Collapse
|
39
|
Jack CR, Wiste HJ, Algeciras-Schimnich A, Figdore DJ, Schwarz CG, Lowe VJ, Ramanan VK, Vemuri P, Mielke MM, Knopman DS, Graff-Radford J, Boeve BF, Kantarci K, Cogswell PM, Senjem ML, Gunter JL, Therneau TM, Petersen RC. Predicting amyloid PET and tau PET stages with plasma biomarkers. Brain 2023; 146:2029-2044. [PMID: 36789483 PMCID: PMC10151195 DOI: 10.1093/brain/awad042] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/20/2022] [Accepted: 01/21/2023] [Indexed: 02/16/2023] Open
Abstract
Staging the severity of Alzheimer's disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer's Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer's clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1-2, 3-4, 5-6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1-42 and Aβ1-40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78-0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72-0.85 versus C = 0.73-0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer's disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.
Collapse
Affiliation(s)
- Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Heather J Wiste
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Dan J Figdore
- Department of Laboratory Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Val J Lowe
- Department of Nuclear Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Vijay K Ramanan
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Michelle M Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | |
Collapse
|
40
|
Ali M, Archer DB, Gorijala P, Western D, Timsina J, Fernández MV, Wang TC, Satizabal CL, Yang Q, Beiser AS, Wang R, Chen G, Gordon B, Benzinger TLS, Xiong C, Morris JC, Bateman RJ, Karch CM, McDade E, Goate A, Seshadri S, Mayeux RP, Sperling RA, Buckley RF, Johnson KA, Won HH, Jung SH, Kim HR, Seo SW, Kim HJ, Mormino E, Laws SM, Fan KH, Kamboh MI, Vemuri P, Ramanan VK, Yang HS, Wenzel A, Rajula HSR, Mishra A, Dufouil C, Debette S, Lopez OL, DeKosky ST, Tao F, Nagle MW, Hohman TJ, Sung YJ, Dumitrescu L, Cruchaga C. Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease. Acta Neuropathol Commun 2023; 11:68. [PMID: 37101235 PMCID: PMC10134547 DOI: 10.1186/s40478-023-01563-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.
Collapse
Affiliation(s)
- Muhammad Ali
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Daniel Western
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Maria V Fernández
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Ting-Chen Wang
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health, San Antonio, TX, 78229, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | | | - Gengsheng Chen
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | - Brian Gordon
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University, St Louis, MO, USA
| | - Chengjie Xiong
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Department of Neurology, Washington University, St Louis, MO, USA
| | - Randall J Bateman
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA
- Department of Neurology, Washington University, St Louis, MO, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Celeste M Karch
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
| | - Eric McDade
- Department of Neurology, Washington University, St Louis, MO, USA
| | - Alison Goate
- Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Richard P Mayeux
- The Department of Neurology, Columbia University, New York, NY, USA
| | - Reisa A Sperling
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Brigham and Women's Hospital and Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel F Buckley
- Brigham and Women's Hospital and Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Keith A Johnson
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hong-Hee Won
- Department of Digital Health, Samsung Medical Center, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Digital Health, Samsung Medical Center, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hang-Rai Kim
- Department of Neurology, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Digital Health, Samsung Medical Center, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Elizabeth Mormino
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, 270 Joondalup Dr, Joondalup, WA, 6027, Australia
| | - Kang-Hsien Fan
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - M Ilyas Kamboh
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Vijay K Ramanan
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN, 55905, USA
| | - Hyun-Sik Yang
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, USA
| | - Allen Wenzel
- Wisconsin Alzheimer's Institute, Madison, WI, USA
| | - Hema Sekhar Reddy Rajula
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
| | - Aniket Mishra
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
| | - Carole Dufouil
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
| | - Stephanie Debette
- UMR 1219, University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, Team ELEANOR, 33000, Bordeaux, France
- Department of Neurology, Boston University School of Medicine, Boston, MA, 2115, USA
- Department of Neurology, CHU de Bordeaux, 33000, Bordeaux, France
| | - Oscar L Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Steven T DeKosky
- Department of Neurology and McKnight Brain Institute, University of Florida, Gainesville, FL, USA
| | - Feifei Tao
- Neurogenomics, Genetics-Guided Dementia Discovery, Eisai, Inc, Cambridge, MA, USA
| | - Michael W Nagle
- Neurogenomics, Genetics-Guided Dementia Discovery, Eisai, Inc, Cambridge, MA, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University, St. Louis, MO, 63110, USA.
- NeuroGenomics and Informatics, Washington University, St. Louis, MO, 63110, USA.
- Knight Alzheimer's Disease Research Center, Washington University, St Louis, MO, USA.
- Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, 63110, USA.
- Department of Genetics, Washington University School of Medicine, St Louis, MO, 63110, USA.
| |
Collapse
|
41
|
Gebre RK, Senjem ML, Raghavan S, Schwarz CG, Gunter JL, Hofrenning EI, Reid RI, Kantarci K, Graff-Radford J, Knopman DS, Petersen RC, Jack CR, Vemuri P. Cross-scanner harmonization methods for structural MRI may need further work: A comparison study. Neuroimage 2023; 269:119912. [PMID: 36731814 PMCID: PMC10170652 DOI: 10.1016/j.neuroimage.2023.119912] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/26/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023] Open
Abstract
The clinical usefulness MRI biomarkers for aging and dementia studies relies on precise brain morphological measurements; however, scanner and/or protocol variations may introduce noise or bias. One approach to address this is post-acquisition scan harmonization. In this work, we evaluate deep learning (neural style transfer, CycleGAN and CGAN), histogram matching, and statistical (ComBat and LongComBat) methods. Participants who had been scanned on both GE and Siemens scanners (cross-sectional participants, known as Crossover (n = 113), and longitudinally scanned participants on both scanners (n = 454)) were used. The goal was to match GE MPRAGE (T1-weighted) scans to Siemens improved resolution MPRAGE scans. Harmonization was performed on raw native and preprocessed (resampled, affine transformed to template space) scans. Cortical thicknesses were measured using FreeSurfer (v.7.1.1). Distributions were checked using Kolmogorov-Smirnov tests. Intra-class correlation (ICC) was used to assess the degree of agreement in the Crossover datasets and annualized percent change in cortical thickness was calculated to evaluate the Longitudinal datasets. Prior to harmonization, the least agreement was found at the frontal pole (ICC = 0.72) for the raw native scans, and at caudal anterior cingulate (0.76) and frontal pole (0.54) for the preprocessed scans. Harmonization with NST, CycleGAN, and HM improved the ICCs of the preprocessed scans at the caudal anterior cingulate (>0.81) and frontal poles (>0.67). In the Longitudinal raw native scans, over- and under-estimations of cortical thickness were observed due to the changing of the scanners. ComBat matched the cortical thickness distributions throughout but was not able to increase the ICCs or remove the effects of scanner changeover in the Longitudinal datasets. CycleGAN and NST performed slightly better to address the cortical thickness variations between scanner change. However, none of the methods succeeded in harmonizing the Longitudinal dataset. CGAN was the worst performer for both datasets. In conclusion, the performance of the methods was overall similar and region dependent. Future research is needed to improve the existing approaches since none of them outperformed each other in terms of harmonizing the datasets at all ROIs. The findings of this study establish framework for future research into the scan harmonization problem.
Collapse
Affiliation(s)
- Robel K Gebre
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA; Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | | | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | |
Collapse
|
42
|
Nho K, Risacher SL, Apostolova L, Bice PJ, Brosch J, Deardorff R, Faber K, Farlow MR, Foroud T, Gao S, Rosewood T, Kim JP, Nudelman K, Yu M, Aisen P, Sperling R, Hooli B, Shcherbinin S, Svaldi D, Jack CR, Jagust WJ, Landau S, Vasanthakumar A, Waring JF, Doré V, Laws SM, Masters CL, Porter T, Rowe CC, Villemagne VL, Dumitrescu L, Hohman TJ, Libby JB, Mormino E, Buckley RF, Johnson K, Yang HS, Petersen RC, Ramanan VK, Vemuri P, Cohen AD, Fan KH, Kamboh MI, Lopez OL, Bennett DA, Ali M, Benzinger T, Cruchaga C, Hobbs D, De Jager PL, Fujita M, Jadhav V, Lamb BT, Tsai AP, Castanho I, Mill J, Weiner MW, Saykin AJ. Novel CYP1B1-RMDN2 Alzheimer's disease locus identified by genome-wide association analysis of cerebral tau deposition on PET. medRxiv 2023:2023.02.27.23286048. [PMID: 36993271 PMCID: PMC10055458 DOI: 10.1101/2023.02.27.23286048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Determining the genetic architecture of Alzheimer's disease (AD) pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we performed a genome-wide association study of cortical tau quantified by positron emission tomography in 3,136 participants from 12 independent studies. The CYP1B1-RMDN2 locus was associated with tau deposition. The most significant signal was at rs2113389, which explained 4.3% of the variation in cortical tau, while APOE4 rs429358 accounted for 3.6%. rs2113389 was associated with higher tau and faster cognitive decline. Additive effects, but no interactions, were observed between rs2113389 and diagnosis, APOE4 , and Aβ positivity. CYP1B1 expression was upregulated in AD. rs2113389 was associated with higher CYP1B1 expression and methylation levels. Mouse model studies provided additional functional evidence for a relationship between CYP1B1 and tau deposition but not Aβ. These results may provide insight into the genetic basis of cerebral tau and novel pathways for therapeutic development in AD.
Collapse
|
43
|
Murray AM, Vemuri P. Kidney Disease and Brain Health: Current Knowledge and Next Steps. Am J Kidney Dis 2023; 81:253-255. [PMID: 36428134 PMCID: PMC9993350 DOI: 10.1053/j.ajkd.2022.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 09/29/2022] [Indexed: 11/24/2022]
Affiliation(s)
- Anne M Murray
- Geriatrics Division, Department of Medicine, Hennepin HealthCare, and Berman Center for Outcomes and Clinical Research, Hennepin HealthCare Research Institute, Minneapolis, Minnesota; Department of Radiology, Mayo Clinic, Rochester, Minnesota.
| | - Prashanthi Vemuri
- Geriatrics Division, Department of Medicine, Hennepin HealthCare, and Berman Center for Outcomes and Clinical Research, Hennepin HealthCare Research Institute, Minneapolis, Minnesota; Department of Radiology, Mayo Clinic, Rochester, Minnesota
| |
Collapse
|
44
|
Windham BG, Griswold ME, Ranadive R, Sullivan KJ, Mosley TH, Mielke MM, Jack CR, Knopman D, Petersen R, Vemuri P. Relationships of Cerebral Perfusion With Gait Speed Across Systolic Blood Pressure Levels and Age: A Cohort Study. J Gerontol A Biol Sci Med Sci 2023; 78:514-520. [PMID: 35640170 PMCID: PMC9977228 DOI: 10.1093/gerona/glac120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND This study aimed to examine if the association of cerebral perfusion with gait speed differs across systolic blood pressure (SBP) and age. METHODS Cerebral perfusion was measured via arterial spin labeled (ASL)-MRI among community-dwelling adults aged 31-94 years in the population-based Mayo Clinic Study of Aging. Usual gait speed was assessed over 5.6 meters on an electronic mat. Sex- and body mass index (BMI)-adjusted linear regression models estimated cross-sectional gait speed associations with ASL and modifying effects of age and SBP using 3-way and 2-way interaction terms between continuous age, SBP, and ASL. Results report estimated differences in gait speed per standard deviation (SD) lower ASL for exemplar SBPs and ages. RESULTS Among 479 participants (mean age 67.6 years; 44% women; mean gait speed 1.17 m/s), ASL relations to gait speed varied by age (ASL-x-age interaction: p = .001) and SBP (ASL-x-SBP interaction: p = .009). At an SBP of 120 mmHg, each SD lower ASL was associated with a 0.04 m/s (95% confidence interval [CI]: 0.01, 0.07) slower gait speed at 65 years, 0.07 m/s (0.04, 0.10) at 75 years, and 0.09 m/s (0.05, 0.13) at 85 years. At an SBP of 140 mmHg, ASL associations with gait speed were attenuated to 0.01 (-0.01, 0.04), 0.04 (0.02, 0.06), and 0.06 (0.04, 0.09) m/s slower gait speed at ages 65, 75, and 85, respectively. CONCLUSION Poorer cerebral perfusion is associated with clinically meaningful slower gait speeds, particularly with older age, while higher perfusion markedly attenuates age differences in gait speed.
Collapse
Affiliation(s)
- B Gwen Windham
- Address correspondence to: B. Gwen Windham, MD, MHS, Department of Medicine/Geriatrics, The Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, 2500 N. State Street, Jackson, MS 39216, USA. E-mail:
| | - Michael E Griswold
- Department of Medicine/Geriatrics, The Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Radhikesh Ranadive
- Department of Medicine/Geriatrics, The Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Kevin J Sullivan
- Department of Medicine/Geriatrics, The Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Thomas H Mosley
- Department of Medicine/Geriatrics, The Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Michelle M Mielke
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Clifford R Jack
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Dave Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ron Petersen
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Prashanthi Vemuri
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
45
|
Sprung J, Laporta ML, Knopman DS, Petersen RC, Mielke MM, Jack CR, Martin DP, Hanson AC, Schroeder DR, Schulte PJ, Przybelski SA, Valencia Morales DJ, Weingarten TN, Vemuri P, Warner DO. Association of Indication for Hospitalization With Subsequent Amyloid Positron Emission Tomography and Magnetic Resonance Imaging Biomarkers. J Gerontol A Biol Sci Med Sci 2023; 78:304-313. [PMID: 35279026 PMCID: PMC9951063 DOI: 10.1093/gerona/glac064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Hospitalization in older age is associated with accelerated cognitive decline, typically preceded by neuropathologic changes. We assess the association between indication for hospitalization and brain neurodegeneration. METHODS Included were participants from the Mayo Clinic Study of Aging, a population-based longitudinal study, with ≥1 brain imaging available in those older than 60 years of age between 2004 and 2017. Primary analyses used linear mixed-effects models to assess association of hospitalization with changes in longitudinal trajectory of cortical thinning, amyloid accumulation, and white matter hyperintensities (WMH). Additional analyses were performed with imaging outcomes dichotomized (normal vs abnormal) using Cox proportional hazards regression. RESULTS Of 2 480 participants, 1 966 had no hospitalization and 514 had ≥1 admission. Hospitalization was associated with accelerated cortical thinning (annual slope change -0.003 mm [95% confidence interval (CI) -0.005 to -0.001], p = .002), but not amyloid accumulation (0.003 [95% CI -0.001 to 0.006], p = .107), or WMH increase (0.011 cm3 [95% CI -0.001 to 0.023], p = .062). Interaction analyses assessing whether trajectory changes are dependent on admission type (medical vs surgical) found interactions for all outcomes. While surgical hospitalizations were not, medical hospitalizations were associated with accelerated cortical thinning (-0.004 mm [95% CI -0.008 to -0.001, p = .014); amyloid accumulation (0.010, [95% CI 0.002 to 0.017, p = .011), and WMH increase (0.035 cm3 [95% CI 0.012 to 0.058, p = .006). Hospitalization was not associated with developing abnormal cortical thinning (p = .407), amyloid accumulation (p = .596), or WMH/infarctions score (p = .565). CONCLUSIONS Medical hospitalizations were associated with accelerated cortical thinning, amyloid accumulation, and WMH increases. These changes were modest and did not translate to increased risk for crossing the abnormality threshold.
Collapse
Affiliation(s)
- Juraj Sprung
- Address correspondence to: Juraj Sprung, MD, PhD, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. E-mail:
| | - Mariana L Laporta
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Michelle M Mielke
- Division of Epidemiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - David P Martin
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew C Hanson
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota, USA
| | - Darrell R Schroeder
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota, USA
| | - Phillip J Schulte
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Toby N Weingarten
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | - David O Warner
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
46
|
Pink A, Krell‐Roesch J, Syrjanen JA, Christenson LR, Lowe VJ, Vemuri P, Fields JA, Stokin GB, Kremers WK, Scharf EL, Jack CR, Knopman DS, Petersen RC, Vassilaki M, Geda YE. Interactions Between Neuropsychiatric Symptoms and Alzheimer's Disease Neuroimaging Biomarkers in Predicting Longitudinal Cognitive Decline. Psychiatr Res Clin Pract 2023; 5:4-15. [PMID: 36909142 PMCID: PMC9997077 DOI: 10.1176/appi.prcp.20220036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 01/22/2023] Open
Abstract
Objective To examine interactions between Neuropsychiatric symptoms (NPS) with Pittsburgh Compound B (PiB) and fluorodeoxyglucose positron emission tomography (FDG-PET) in predicting cognitive trajectories. Methods We conducted a longitudinal study in the setting of the population-based Mayo Clinic Study of Aging in Olmsted County, MN, involving 1581 cognitively unimpaired (CU) persons aged ≥50 years (median age 71.83 years, 54.0% males, 27.5% APOE ɛ4 carriers). NPS at baseline were assessed using the Neuropsychiatric Inventory Questionnaire (NPI-Q). Brain glucose hypometabolism was defined as a SUVR ≤ 1.47 (measured by FDG-PET) in regions typically affected in Alzheimer's disease. Abnormal cortical amyloid deposition was measured using PiB-PET (SUVR ≥ 1.48). Neuropsychological testing was done approximately every 15 months, and we calculated global and domain-specific (memory, language, attention, and visuospatial skills) cognitive z-scores. We ran linear mixed-effect models to examine the associations and interactions between NPS at baseline and z-scored PiB- and FDG-PET SUVRs in predicting cognitive z-scores adjusted for age, sex, education, and previous cognitive testing. Results Individuals at the average PiB and without NPS at baseline declined over time on cognitive z-scores. Those with increased PiB at baseline declined faster (two-way interaction), and those with increased PiB and NPS declined even faster (three-way interaction). We observed interactions between time, increased PiB and anxiety or irritability indicating accelerated decline on global z-scores, and between time, increased PiB and several NPS (e.g., agitation) showing faster domain-specific decline, especially on the attention domain. Conclusions NPS and increased brain amyloid deposition synergistically interact in accelerating global and domain-specific cognitive decline among CU persons at baseline.
Collapse
Affiliation(s)
- Anna Pink
- First Department of MedicineParacelsus Medical UniversitySalzburgAustria
| | - Janina Krell‐Roesch
- Department of Quantitative Health SciencesMayo Clinic RochesterRochesterMinnesotaUSA
- Institute of Sports and Sports ScienceKarlsruhe Institute of TechnologyKarlsruheGermany
| | - Jeremy A. Syrjanen
- Department of Quantitative Health SciencesMayo Clinic RochesterRochesterMinnesotaUSA
| | - Luke R. Christenson
- Department of Quantitative Health SciencesMayo Clinic RochesterRochesterMinnesotaUSA
| | - Val J. Lowe
- Department of RadiologyMayo Clinic RochesterRochesterMinnesotaUSA
| | | | - Julie A. Fields
- Department of Psychiatry and PsychologyMayo Clinic RochesterRochesterMinnesotaUSA
| | - Gorazd B. Stokin
- International Clinical Research Center/St. Anne HospitalBrnoCzech Republic
| | - Walter K. Kremers
- Department of Quantitative Health SciencesMayo Clinic RochesterRochesterMinnesotaUSA
| | - Eugene L. Scharf
- Department of NeurologyMayo Clinic RochesterRochesterMinnesotaUSA
| | - Clifford R. Jack
- Department of RadiologyMayo Clinic RochesterRochesterMinnesotaUSA
| | - David S. Knopman
- Department of NeurologyMayo Clinic RochesterRochesterMinnesotaUSA
| | - Ronald C. Petersen
- Department of Quantitative Health SciencesMayo Clinic RochesterRochesterMinnesotaUSA
- Department of NeurologyMayo Clinic RochesterRochesterMinnesotaUSA
| | - Maria Vassilaki
- Department of Quantitative Health SciencesMayo Clinic RochesterRochesterMinnesotaUSA
| | - Yonas E. Geda
- Department of NeurologyFranke Global Neuroscience Education CenterBarrow Neurological InstitutePhoenixArizonaUSA
| |
Collapse
|
47
|
Beba A, Peterson SM, Brennan PC, O’Byrne J, Machulda MM, Jannetto PI, Vemuri P, Lewallen DG, Kremers HM, Vassilaki M. Correlation of Blood Metal Concentrations with Cognitive Scores and Neuroimaging Findings in Patients with Total Joint Arthroplasty. J Alzheimers Dis 2023; 94:1335-1342. [PMID: 37393495 PMCID: PMC10481381 DOI: 10.3233/jad-221182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2023]
Abstract
Total joint arthroplasty (TJA) implants are composed of metals, ceramics, and/or polyethylene. Studies suggest that the debris released from metal implants may possess neurotoxic properties with reports of neuropsychiatric symptoms and memory deficits, which could be relevant to Alzheimer's disease and related dementias. This exploratory study examined the cross-sectional correlation of blood metal concentrations with cognitive performance and neuroimaging findings in a convenience sample of 113 TJA patients with history of elevated blood metal concentrations of either titanium, cobalt and/or chromium. Associations with neuroimaging measures were observed but not with cognitive scores. Larger studies with longitudinal follow-up are warranted.
Collapse
Affiliation(s)
- Alican Beba
- George Washington University, Columbian College of Arts and Sciences, Washington, DC, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Peter C. Brennan
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Jamie O’Byrne
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Paul I. Jannetto
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Hilal Maradit Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Maria Vassilaki
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
48
|
Harrison TM, Ward TJ, Murphy A, Baker SL, Dominguez PA, Koeppe R, Vemuri P, Lockhart SN, Jung Y, Harvey DJ, Lovato L, Toga AW, Masdeu J, Oh H, Gitelman DR, Aggarwal N, Snyder HM, Baker LD, DeCarli C, Jagust WJ, Landau SM. Optimizing quantification of MK6240 tau PET in unimpaired older adults. Neuroimage 2023; 265:119761. [PMID: 36455762 PMCID: PMC9957642 DOI: 10.1016/j.neuroimage.2022.119761] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/28/2022] [Accepted: 11/21/2022] [Indexed: 11/29/2022] Open
Abstract
Accurate measurement of Alzheimer's disease (AD) pathology in older adults without significant clinical impairment is critical to assessing intervention strategies aimed at slowing AD-related cognitive decline. The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (POINTER) is a 2-year randomized controlled trial to evaluate the effect of multicomponent risk reduction strategies in older adults (60-79 years) who are cognitively unimpaired but at increased risk for cognitive decline/dementia due to factors such as cardiovascular disease and family history. The POINTER Imaging ancillary study is collecting tau-PET ([18F]MK6240), beta-amyloid (Aβ)-PET ([18F]florbetaben [FBB]) and MRI data to evaluate neuroimaging biomarkers of AD and cerebrovascular pathophysiology in this at-risk sample. Here 481 participants (70.0±5.0; 66% F) with baseline MK6240, FBB and structural MRI scans were included. PET scans were coregistered to the structural MRI which was used to create FreeSurfer-defined reference regions and target regions of interest (ROIs). We also created off-target signal (OTS) ROIs to examine the magnitude and distribution of MK6240 OTS across the brain as well as relationships between OTS and age, sex, and race. OTS was unimodally distributed, highly correlated across OTS ROIs and related to younger age and sex but not race. Aiming to identify an optimal processing approach for MK6240 that would reduce the influence of OTS, we compared our previously validated MRI-guided standard PET processing and 6 alternative approaches. The alternate approaches included combinations of reference region erosion and meningeal OTS masking before spatial smoothing as well as partial volume correction. To compare processing approaches we examined relationships between target ROIs (entorhinal cortex (ERC), hippocampus or a temporal meta-ROI (MetaROI)) SUVR and age, sex, race, Aβ and a general cognitive status measure, the Modified Telephone Interview for Cognitive Status (TICSm). Overall, the processing approaches performed similarly, and none showed a meaningful improvement over standard processing. Across processing approaches we observed previously reported relationships with MK6240 target ROIs including positive associations with age, an Aβ+> Aβ- effect and negative associations with cognition. In sum, we demonstrated that different methods for minimizing effects of OTS, which is highly correlated across the brain within subject, produced no substantive change in our performance metrics. This is likely because OTS contaminates both reference and target regions and this contamination largely cancels out in SUVR data. Caution should be used when efforts to reduce OTS focus on target or reference regions in isolation as this may exacerbate OTS contamination in SUVR data.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - William J Jagust
- University of California Berkeley, USA; Lawrence Berkeley National Laboratory, USA
| | | |
Collapse
|
49
|
Shir D, Mielke MM, Hofrenning EI, Lesnick TG, Knopman DS, Petersen RC, Jack CR, Algeciras-Schimnich A, Vemuri P, Graff-Radford J. Associations of Neurodegeneration Biomarkers in Cerebrospinal Fluid with Markers of Alzheimer's Disease and Vascular Pathology. J Alzheimers Dis 2023; 92:887-898. [PMID: 36806507 PMCID: PMC10193844 DOI: 10.3233/jad-221015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
BACKGROUND The National Institute on Aging-Alzheimer's Association Research Framework proposes defining Alzheimer's disease by grouping imaging and fluid biomarkers by their respective pathologic processes. The AT(N) structure proposes several neurodegenerative fluid biomarkers (N) including total tau (t-tau), neurogranin (Ng), and neurofilament light chain (NfL). However, pathologic drivers influencing each biomarker remain unclear. OBJECTIVE To determine whether cerebrospinal fluid (CSF)-neurodegenerative biomarkers (N) map differentially to Alzheimer's disease pathology measured by Aβ42 (an indicator of amyloidosis, [A]), p-tau (an indicator of tau deposition, [T]), and MRI vascular pathology indicators (measured by white-matter integrity, infarcts, and microbleeds [V]). METHODS Participants were from Mayo Clinic Study of Aging (MCSA) with CSF measures of NfL, Ng, t-tau, Aβ42, and p-tau and available MRI brain imaging. Linear models assessed associations between CSF neurodegeneration (N) markers, amyloid markers (A), tau (T), and vascular pathology (V). RESULTS Participants (n = 408) had a mean age of 69.2±10.7; male, 217 (53.2%); cognitively unimpaired, 359 (88%). All three neurodegeneration biomarkers correlated with age (p < 0.001 for NfL and t-tau, p = 0.018 for Ng). Men had higher CSF-NfL levels; women had higher Ng (p < 0.001). NfL and t-tau levels correlated with infarcts (p = 0.009, p = 0.034 respectively); no biomarkers correlated with white-matter integrity. N biomarkers correlated with p-tau levels (T, p < 0.001). Higher Aβ42 levels associated with higher N-biomarker levels but only among cognitively unimpaired (A, p < 0.001). CONCLUSION The influence of vascular pathology in the general population on CSF (N) biomarkers is modest, with greater influence of infarcts than white-matter disruption. Neurodegeneration markers more closely correlated with tau than amyloid markers.
Collapse
Affiliation(s)
- Dror Shir
- Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Michelle M. Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota 55905, USA
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, 27101
| | | | - Timothy G. Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - David S. Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Ronald C. Petersen
- Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905, USA
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905, USA
| | | | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905, USA
| | | |
Collapse
|
50
|
Murray ME, Moloney CM, Kouri N, Syrjanen JA, Matchett BJ, Rothberg DM, Tranovich JF, Sirmans TNH, Wiste HJ, Boon BDC, Nguyen AT, Reichard RR, Dickson DW, Lowe VJ, Dage JL, Petersen RC, Jack CR, Knopman DS, Vemuri P, Graff-Radford J, Mielke MM. Global neuropathologic severity of Alzheimer's disease and locus coeruleus vulnerability influences plasma phosphorylated tau levels. Mol Neurodegener 2022; 17:85. [PMID: 36575455 PMCID: PMC9795667 DOI: 10.1186/s13024-022-00578-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/26/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Advances in ultrasensitive detection of phosphorylated tau (p-tau) in plasma has enabled the use of blood tests to measure Alzheimer's disease (AD) biomarker changes. Examination of postmortem brains of participants with antemortem plasma p-tau levels remains critical to understanding comorbid and AD-specific contribution to these biomarker changes. METHODS We analyzed 35 population-based Mayo Clinic Study of Aging participants with plasma p-tau at threonine 181 and threonine 217 (p-tau181, p-tau217) available within 3 years of death. Autopsied participants included cognitively unimpaired, mild cognitive impairment, AD dementia, and non-AD neurodegenerative disorders. Global neuropathologic scales of tau, amyloid-β, TDP-43, and cerebrovascular disease were examined. Regional digital pathology measures of tau (phosphorylated threonine 181 and 217 [pT181, pT217]) and amyloid-β (6F/3D) were quantified in hippocampus and parietal cortex. Neurotransmitter hubs reported to influence development of tangles (nucleus basalis of Meynert) and amyloid-β plaques (locus coeruleus) were evaluated. RESULTS The strongest regional associations were with parietal cortex for tau burden (p-tau181 R = 0.55, p = 0.003; p-tau217 R = 0.66, p < 0.001) and amyloid-β burden (p-tau181 R = 0.59, p < 0.001; p-tau217 R = 0.71, p < 0.001). Linear regression analysis of global neuropathologic scales explained 31% of variability in plasma p-tau181 (Adj. R2 = 0.31) and 59% in plasma p-tau217 (Adj. R2 = 0.59). Neither TDP-43 nor cerebrovascular disease global scales independently contributed to variability. Global scales of tau pathology (β-coefficient = 0.060, p = 0.016) and amyloid-β pathology (β-coefficient = 0.080, p < 0.001) independently predicted plasma p-tau217 when modeled together with co-pathologies, but only amyloid-β (β-coefficient = 0.33, p = 0.021) significantly predicted plasma p-tau181. While nucleus basalis of Meynert neuron count/mm2 was not associated with plasma p-tau levels, a lower locus coeruleus neuron count/mm2 was associated with higher plasma p-tau181 (R = -0.50, p = 0.007) and higher plasma p-tau217 (R = -0.55, p = 0.002). Cognitive scores (Adj. R2 = 0.25-0.32) were predicted by the global tau scale, but not by the global amyloid-β scale or plasma p-tau when modeled simultaneously. CONCLUSIONS Higher soluble plasma p-tau levels may be the result of an intersection between insoluble deposits of amyloid-β and tau accumulation in brain, and may be associated with locus coeruleus degeneration.
Collapse
Affiliation(s)
- Melissa E. Murray
- Department of Neuroscience, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Christina M. Moloney
- Department of Neuroscience, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Naomi Kouri
- Department of Neuroscience, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Jeremy A. Syrjanen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN USA
| | - Billie J. Matchett
- Department of Neuroscience, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Darren M. Rothberg
- Department of Neuroscience, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Jessica F. Tranovich
- Department of Neuroscience, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Tiffany N. Hicks Sirmans
- Department of Neuroscience, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Heather J. Wiste
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN USA
| | - Baayla D. C. Boon
- Department of Neuroscience, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Aivi T. Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN USA
| | - R. Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN USA
| | - Dennis W. Dickson
- Department of Neuroscience, Mayo Clinic Florida, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University, Indianapolis, IN USA
| | | | | | | | | | | | - Michelle M. Mielke
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN USA
- Wake Forest University School of Medicine, Winston-Salem, NC USA
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, 525 Vine, 5th floor, Winston-Salem, NC 27157 USA
| |
Collapse
|