-
Shanker Swaminathan, Shannon L Risacher,
Karmen K Yoder,
John D West,
Li Shen,
Sungeun Kim,
Mark Inlow,
Tatiana Foroud,
William J Jagust,
Robert A Koeppe,
Chester A Mathis,
Leslie M Shaw,
John Q Trojanowski,
Holly Soares,
Paul S Aisen,
Ronald C Petersen,
Michael W Weiner,
Andrew J Saykin
[show abstract]
[hide abstract]
ABSTRACT: BACKGROUND: Apolipoprotein E (APOE) ε4 allele's role as a modulator of the relationship between soluble plasma amyloid beta (Aβ) and fibrillar brain Aβ measured by Pittsburgh compound B positron emission tomography ([(11)C]PiB PET) has not been assessed. METHODS: Ninety-six Alzheimer's Disease Neuroimaging Initiative participants with [(11)C]PiB scans and plasma Aβ1-40 and Aβ1-42 measurements at the time of PET scanning were included. Regional and voxelwise analyses of [(11)C]PiB data were used to determine the influence of APOE ε4 allele on association of plasma Aβ1-40, Aβ1-42, and Aβ1-40/Aβ1-42 with [(11)C]PiB uptake. RESULTS: In APOE ε4- but not ε4+ participants, positive relationships between plasma Aβ1-40/Aβ1-42 and [(11)C]PiB uptake were observed. Modeling the interaction of APOE and plasma Aβ1-40/Aβ1-42 improved the explained variance in [(11)C]PiB binding compared with using APOE and plasma Aβ1-40/Aβ1-42 as separate terms. CONCLUSIONS: The results suggest that plasma Aβ is a potential Alzheimer's disease biomarker and highlight the importance of genetic variation in interpretation of plasma Aβ levels.
Alzheimer's & dementia: the journal of the Alzheimer's Association 03/2013; · 5.90 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Default mode network (DMN) disruption has been reported in Alzheimer's disease (AD), yet the specific pattern of altered connectivity over the course of prodromal AD remains to be characterized. The aim of this study was to assess DMN connectivity in older adults with informant-verified cognitive complaints (CC) but normal neuropsychological performance compared to individuals with mild cognitive impairment (MCI) and healthy controls (HC). DMN maps were derived from resting-state fMRI using independent component analysis. Group comparisons of DMN connectivity were performed between older adults with MCI (n = 18), CC (n = 23), and HC (n = 16). Both CC and MCI showed decreased DMN connectivity in the right hippocampus compared to HC, with the CC group showing greater connectivity than MCI. These differences survived atrophy correction and correlated with cognitive performance. DMN connectivity appears sensitive to early prodromal neurodegenerative changes associated with AD, notably including pre-MCI individuals with cognitive complaints.
Journal of Alzheimer's disease: JAD 03/2013; · 3.74 Impact Factor
-
Shannon L Risacher,
Sungeun Kim,
Li Shen,
Kwangsik Nho,
Tatiana Foroud,
Robert C Green,
Ronald C Petersen,
Clifford R Jack,
Paul S Aisen,
Robert A Koeppe,
William J Jagust,
Leslie M Shaw,
John Q Trojanowski,
Michael W Weiner,
Andrew J Saykin
[show abstract]
[hide abstract]
ABSTRACT: Objective: Our goal was to evaluate the association of APOE with amyloid deposition, cerebrospinal fluid levels (CSF) of Aβ, tau, and p-tau, brain atrophy, cognition and cognitive complaints in E-MCI patients and cognitively healthy older adults (HC) in the ADNI-2 cohort. Methods: Two-hundred and nine E-MCI and 123 HC participants from the ADNI-2 cohort were included. We evaluated the impact of diagnostic status (E-MCI vs. HC) and APOE ε4 status (ε4 positive vs. ε4 negative) on cortical amyloid deposition (AV-45/Florbetapir SUVR PET scans), brain atrophy (structural MRI scans processed using voxel-based morphometry and Freesurfer version 5.1), CSF levels of Aβ, tau, and p-tau, and cognitive performance and complaints. Results: E-MCI participants showed significantly impaired cognition, higher levels of cognitive complaints, greater levels of tau and p-tau, and subcortical and cortical atrophy relative to HC participants (p < 0.05). Cortical amyloid deposition and CSF levels of Aβ were significantly associated with APOE ε4 status but not E-MCI diagnosis, with ε4 positive participants showing more amyloid deposition and lower levels of CSF Aβ than ε4 negative participants. Other effects of APOE ε4 status on cognition and CSF tau levels were also observed. Conclusions: APOE ε4 status is associated with amyloid accumulation and lower CSF Aβ, as well as increased CSF tau levels in early prodromal stages of AD (E-MCI) and HC. Alternatively, neurodegeneration, cognitive impairment, and increased complaints are primarily associated with a diagnosis of E-MCI. These findings underscore the importance of considering APOE genotype when evaluating biomarkers in early stages of disease.
Frontiers in aging neuroscience. 01/2013; 5:11.
-
Shannon L Risacher,
Darrell Wudunn,
Susan M Pepin,
Tamiko R Magee,
Brenna C McDonald,
Laura A Flashman,
Heather A Wishart,
Heather S Pixley,
Laura A Rabin,
Nadia Paré,
Jessica J Englert,
Eben Schwartz,
Joshua R Curtain,
John D West,
Darren P O'Neill,
Robert B Santulli,
Richard W Newman,
Andrew J Saykin
[show abstract]
[hide abstract]
ABSTRACT: Deficits in contrast sensitivity (CS) have been reported in Alzheimer's disease (AD). However, the extent of these deficits in prodromal AD stages, including mild cognitive impairment (MCI) or even earlier, has not been investigated. In this study, CS was assessed using frequency doubling technology in older adults with AD (n = 10), amnestic MCI (n = 28), cognitive complaints without performance deficits (CC; n = 20), and healthy controls (HC; n = 29). The association between CS and cognition was also evaluated. Finally, the accuracy of CS measures for classifying MCI versus HC was evaluated. CS deficits were found in AD and MCI, while CC showed intermediate performance between MCI and HC. Upper right visual field CS showed the most significant difference among groups. CS was also associated with cognitive performance. Finally, CS measures accurately classified MCI versus HC. The CS deficits in AD and MCI, and intermediate performance in CC, indicate that these measures are sensitive to early AD-associated changes. Therefore, frequency doubling technology-based measures of CS may have promise as a novel AD biomarker.
Neurobiology of aging 10/2012; · 5.94 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: MOTIVATION: Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, and this longitudinal profile may provide increased power for differentiating genotype groups. How to identify the longitudinal phenotypic markers associated to disease sensitive SNPs is an important and challenging research topic. RESULTS: Taking into account the temporal structure of the longitudinal imaging data and the interrelatedness among the SNPs, we propose a novel 'task-correlated longitudinal sparse regression' model to study the association between the phenotypic imaging markers and the genotypes encoded by SNPs. In our new association model, we extend the widely used ℓ(2,1)-norm for matrices to tensors to jointly select imaging markers that have common effects across all the regression tasks and time points, and meanwhile impose the trace-norm regularization onto the unfolded coefficient tensor to achieve low rank such that the interrelationship among SNPs can be addressed. The effectiveness of our method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected imaging predictors relevant to disease sensitive SNPs. AVAILABILITY: Software is publicly available at: http://ranger.uta.edu/%7eheng/Longitudinal/ CONTACT: heng@uta.edu or shenli@inpui.edu.
Bioinformatics 09/2012; 28(18):i619-i625. · 5.47 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units.
To overcome this limitation, in this article, we propose a new sparse multimodal multitask learning method to reveal complex relationships from gene to brain to symptom. Our main contributions are three-fold: (i) introducing combined structured sparsity regularizations into multimodal multitask learning to integrate multidimensional heterogeneous imaging genetics data and identify multimodal biomarkers; (ii) utilizing a joint classification and regression learning model to identify disease-sensitive and cognition-relevant biomarkers; (iii) deriving a new efficient optimization algorithm to solve our non-smooth objective function and providing rigorous theoretical analysis on the global optimum convergency. Using the imaging genetics data from the Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status. The identified multimodal biomarkers could predict not only disease status but also cognitive function to help elucidate the biological pathway from gene to brain structure and function, and to cognition and disease.
Software is publicly available at: http://ranger.uta.edu/%7eheng/multimodal/.
Bioinformatics 06/2012; 28(12):i127-36. · 5.47 Impact Factor
-
Bioinformatics. 01/2012; 28:229-237.
-
[show abstract]
[hide abstract]
ABSTRACT: Recent advances in high-throughput genotyping and brain imaging techniques enable new approaches to study the influence of genetic variation on brain structures and functions. Traditional association studies typically employ independent and pairwise univariate analysis, which treats single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as isolated units and ignores important underlying interacting relationships between the units. New methods are proposed here to overcome this limitation.
Taking into account the interlinked structure within and between SNPs and imaging QTs, we propose a novel Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) method to identify quantitative trait loci for multiple disease-relevant QTs and apply it to a study in mild cognitive impairment and Alzheimer's disease. Built upon regression analysis, our model uses a new form of regularization, group ℓ(2,1)-norm (G(2,1)-norm), to incorporate the biological group structures among SNPs induced from their genetic arrangement. The new G(2,1)-norm considers the regression coefficients of all the SNPs in each group with respect to all the QTs together and enforces sparsity at the group level. In addition, an ℓ(2,1)-norm regularization is utilized to couple feature selection across multiple tasks to make use of the shared underlying mechanism among different brain regions. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected SNP predictors relevant to the imaging QTs.
Software is publicly available at: http://ranger.uta.edu/%7eheng/imaging-genetics/.
Bioinformatics 12/2011; 28(2):229-37. · 5.47 Impact Factor
-
Jing Wan,
Sungeun Kim,
Mark Inlow,
Kwangsik Nho,
Shanker Swaminathan, Shannon L. Risacher,
Shiaofen Fang,
Michael W. Weiner,
M. Faisal Beg,
Lei Wang,
Andrew J. Saykin,
Li Shen,
ADNI
[show abstract]
[hide abstract]
ABSTRACT: Genetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD
and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal
shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI
scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis
(SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power,
EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified
by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis
identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex
SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic
understanding of complex disorders like AD.
09/2011: pages 376-383;
-
Shanker Swaminathan,
Li Shen, Shannon L Risacher,
Karmen K Yoder,
John D West,
Sungeun Kim,
Kwangsik Nho,
Tatiana Foroud,
Mark Inlow,
Steven G Potkin,
Matthew J Huentelman,
David W Craig,
William J Jagust,
Robert A Koeppe,
Chester A Mathis,
Clifford R Jack,
Michael W Weiner,
Andrew J Saykin
[show abstract]
[hide abstract]
ABSTRACT: Amyloid imaging with [(11)C]Pittsburgh Compound-B (PiB) provides in vivo data on plaque deposition in those with, or at risk for, Alzheimer's disease (AD). We performed a gene-based association analysis of 15 quality-controlled amyloid-pathway associated candidate genes in 103 Alzheimer's Disease Neuroimaging Initiative participants. The mean normalized PiB uptake value across four brain regions known to have amyloid deposition in AD was used as a quantitative phenotype. The minor allele of an intronic SNP within DHCR24 was identified and associated with a lower average PiB uptake. Further investigation at whole-brain voxel-wise level indicated that non-carriers of the minor allele had higher PiB uptake in frontal regions compared to carriers. DHCR24 has been previously shown to confer resistance against beta-amyloid and oxidative stress-induced apoptosis, thus our findings support a neuroprotective role. Pathway-based genetic analysis of targeted molecular imaging phenotypes appears promising to help elucidate disease pathophysiology and identify potential therapeutic targets.
Brain Imaging and Behavior 09/2011; 6(1):1-15. · 1.66 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Microdialysis studies report that systemic alcohol increases extracellular dopamine (DA) in the rat striatum. The present study examined whether changes in striatal DA could be detected in rats using small animal positron emission tomography (PET). PET images were acquired in 44 alcohol-naïve male Wistar and alcohol-preferring (P) rats. Subjects received up to three [(11) C]raclopride scans (rest, alcohol, and saline). Animals were anesthetized with isoflurane and secured on a stereotactic-like holder during all scans. Blood samples were collected from the tail or lateral saphenous vein of 12 animals 10 min after tracer injection for determination of blood alcohol concentration (BAC). Time activity curves were extracted from the striatum and the cerebellum and binding potential (BP(ND) ) was calculated as a measure of D(2) receptor availability. Wistars given 1.0 g kg(-1) alcohol (20%v/v) i.v. or 3.0 g kg(-1) alcohol (20%v/v) i.p. showed significant alcohol-induced decreases in BP(ND) . In P rats (given 1.5, 2.25, or 3.0 g kg(-1) alcohol), no individual group showed a statistical effect of alcohol on BP(ND) , but taken together, all P rats receiving i.p. alcohol had significantly lower BP(ND) than rest or saline scans. Large decreases in BP(ND) were primarily observed in rats with BAC above 200 mg%. Also, a significant difference was found between baseline BP(ND) of Wistars who had undergone jugular catheterization surgery for i.v. alcohol administration and those who had not. Preliminary results suggest that alcohol-induced DA release in the rat striatum is detectable using small animal PET given sufficiently large cohorts and adequate blood alcohol levels.
Synapse 02/2011; 65(9):929-37. · 2.94 Impact Factor
-
Shanker Swaminathan,
Sungeun Kim,
Li Shen, Shannon L Risacher,
Tatiana Foroud,
Nathan Pankratz,
Steven G Potkin,
Matthew J Huentelman,
David W Craig,
Michael W Weiner,
Andrew J Saykin,
The Alzheimer's Disease Neuroimaging Initiative Adni
[show abstract]
[hide abstract]
ABSTRACT: Copy number variants (CNVs) are DNA sequence alterations, resulting in gains (duplications) and losses (deletions) of genomic segments. They often overlap genes and may play important roles in disease. Only one published study has examined CNVs in late-onset Alzheimer's disease (AD), and none have examined mild cognitive impairment (MCI). CNV calls were generated in 288 AD, 183 MCI, and 184 healthy control (HC) non-Hispanic Caucasian Alzheimer's Disease Neuroimaging Initiative participants. After quality control, 222 AD, 136 MCI, and 143 HC participants were entered into case/control association analyses, including candidate gene and whole genome approaches. Although no excess CNV burden was observed in cases (AD and/or MCI) relative to controls (HC), gene-based analyses revealed CNVs overlapping the candidate gene CHRFAM7A, as well as CSMD1, SLC35F2, HNRNPCL1, NRXN1, and ERBB4 regions, only in cases. Replication in larger samples is important, after which regions detected here may be promising targets for resequencing.
International journal of Alzheimer's disease. 01/2011; 2011:729478.
-
Li Shen,
Sungeun Kim,
Yuan Qi,
Mark Inlow,
Shanker Swaminathan,
Kwangsik Nho,
Jing Wan, Shannon L Risacher,
Leslie M Shaw,
John Q Trojanowski,
Michael W Weiner,
Andrew J Saykin
[show abstract]
[hide abstract]
ABSTRACT: Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on integrative analysis of structural MRI data and proteomic data from an RBM panel to examine their predictive power and identify relevant biomarkers in a large MCI/AD cohort. MRI data included volume and thickness measures of 98 regions estimated by FreeSurfer. RBM data included 146 proteomic analytes extracted from plasma and serum. A sparse learning model, elastic net logistic regression, was proposed to classify AD and MCI, and select disease-relevant biomarkers. A linear support vector machine coupled with feature selection was employed for comparison. Combining RBM and MRI data yielded improved prediction rates: HC vs AD (91.9%), HC vs MCI (90.5%) and MCI vs AD (86.5%). Elastic net identified a small set of meaningful imaging and proteomic biomarkers. The elastic net has great power to optimize the sparsity of feature selection while maintaining high predictive power. Its application to multi-modal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.
Lecture Notes in Computer Science 01/2011; 7012:27-34.
-
[show abstract]
[hide abstract]
ABSTRACT: Genotype imputation is increasingly employed in genome-wide association studies, particularly for integrative and cross-platform analysis. Several imputation algorithms use reference panels with a larger set of genotyped markers to infer genotypes at ungenotyped marker locations. Our objective was to assess which method and reference panel was more accurate when carrying out imputation. We investigated the influence of choice of two most popular imputation methods, IMPUTE and MACH, on two reference panels from the HapMap and the 1000 Genomes Project. Our results indicated that for the HapMap, MACH consistently yielded more accurate imputation results than IMPUTE, while for the 1000 Genomes Project, IMPUTE performed slightly better. The best imputation results were achieved by IMPUTE with the combined reference panel (HapMap + 1000 Genomes Project). IMPUTE with the combined reference panel is a promising strategy for genotype imputation, which should facilitate fine-mapping for discovery as well as known disease-associated candidate regions.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2011; 2011:1013-8.
-
IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6-13, 2011; 01/2011
-
Jing Wan,
Sungeun Kim,
Mark Inlow,
Kwangsik Nho,
Shanker Swaminathan, Shannon L. Risacher,
Shiaofen Fang,
Michael W. Weiner,
Mirza Faisal Beg,
Lei Wang,
Andrew J. Saykin,
Li Shen
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011 - 14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part II; 01/2011
-
[show abstract]
[hide abstract]
ABSTRACT: The goal of the present chapter is to provide an overview of the major findings from studies of neuroimaging in dementia,
particularly from patients with Alzheimer’s disease (AD). The major emphasis is on findings from a variety of imaging modalities
and the use of these measures for early diagnosis and as biomarkers of disease progression. In this chapter, we first describe
the basic neurobiological changes and clinical symptoms associated with AD and related cognitive decline. Next, we discuss
results from studies in AD utilizing structural neuroimaging techniques, including computerized tomography (CT), traditional
structural magnetic resonance imaging (MRI), and other MRI techniques [diffusion tensor imaging (DTI), perfusion MRI, magnetic
resonance spectroscopy (MRS)]. Next, we explore findings from functional MRI studies, including task-related activation studies
and resting and functional connectivity research. We, then, discuss results from the use of nuclear medicine techniques in
AD, including single-photon emission computerized tomography (SPECT) and positron emission tomography (PET) studies. Neuroimaging
in other dementias is also briefly discussed, with particular emphasis on differential diagnosis of dementia type. Finally,
we explore future directions for neuroimaging of early AD and dementia.
12/2010: pages 309-339;
-
Li Shen,
Sungeun Kim, Shannon L Risacher,
Kwangsik Nho,
Shanker Swaminathan,
John D West,
Tatiana Foroud,
Nathan Pankratz,
Jason H Moore,
Chantel D Sloan,
Matthew J Huentelman,
David W Craig,
Bryan M Dechairo,
Steven G Potkin,
Clifford R Jack,
Michael W Weiner,
Andrew J Saykin
[show abstract]
[hide abstract]
ABSTRACT: A genome-wide, whole brain approach to investigate genetic effects on neuroimaging phenotypes for identifying quantitative trait loci is described. The Alzheimer's Disease Neuroimaging Initiative 1.5 T MRI and genetic dataset was investigated using voxel-based morphometry (VBM) and FreeSurfer parcellation followed by genome-wide association studies (GWAS). One hundred forty-two measures of grey matter (GM) density, volume, and cortical thickness were extracted from baseline scans. GWAS, using PLINK, were performed on each phenotype using quality-controlled genotype and scan data including 530,992 of 620,903 single nucleotide polymorphisms (SNPs) and 733 of 818 participants (175 AD, 354 amnestic mild cognitive impairment, MCI, and 204 healthy controls, HC). Hierarchical clustering and heat maps were used to analyze the GWAS results and associations are reported at two significance thresholds (p<10(-7) and p<10(-6)). As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. Detailed image analyses of rs6463843 (flanking NXPH1) revealed reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. A genome-wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication.
NeuroImage 11/2010; 53(3):1051-63. · 5.89 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Atrophic changes in early Alzheimer's disease (AD) and amnestic mild cognitive impairment (MCI) have been proposed as biomarkers for detection and monitoring. We analyzed magnetic resonance imaging (MRI) atrophy rate from baseline to 1 year in 4 groups of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI): AD (n = 152), converters from MCI to probable AD (MCI-C, n = 60), stable MCI (MCI-S, n = 261), and healthy controls (HC, n = 200). Scans were analyzed using multiple methods, including voxel-based morphometry (VBM), regions of interest (ROIs), and automated parcellation, permitting comparison of annual percent change (APC) in neurodegeneration markers. Effect sizes and the sample required to detect 25% reduction in atrophy rates were calculated. The influence of APOE genotype on APC was also evaluated. AD patients and converters from MCI to probable AD demonstrated high atrophy APCs across regions compared with minimal change in healthy controls. Stable MCI subjects showed intermediate atrophy rates. APOE genotype was associated with APC in key regions. In sum, APC rates are influenced by APOE genotype, imminent MCI to AD conversion, and AD-related neurodegeneration.
Neurobiology of aging 08/2010; 31(8):1401-18. · 5.94 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The ADNI (Alzheimer's Disease Neuroimaging Initiative) is a large longitudinal study of patients with probable Alzheimer's disease (AD), patients with mild cognitive impairment (MCI) and healthy elderly controls followed for at least 2-3 years. Many participants in the ADNI are being treated with medications, and these may have beneficial or deleterious effects.
The goal of the study was to characterize baseline medication use in the ADNI.
Diagnosis, demographics, medication status, psychometric data and MRI measures of hippocampal volume and entorhinal cortex thickness were obtained for 818 participants from the ADNI cohort. Total number of medications, Beers list (potentially dangerous) medications and AD treatments were also tabulated. ANOVA and logistic regression were used to assess associations between baseline pharmacotherapy and diagnosis, demographics, and selected clinical and MRI variables.
Of the 818 enrolled ADNI participants, 809 were available for analysis in the present study, including 184 patients with AD, 399 patients with MCI and 226 healthy elderly controls. Significant gender differences in recruitment were observed in the MCI group. The average number of medications per participant was 8 (SD 4) and 22% reported treatment with one or more Beers list medications. For symptomatic treatment of MCI or AD, donepezil and memantine were the most commonly reported drugs. As expected, MCI and AD patients with more severe impairment were more likely to be treated. Men received treatment more frequently than women. Older subjects and those with less education were less likely to receive treatment.
AD and MCI participants from the ADNI cohort were being treated with polypharmacy and many were also taking one or more medications with the potential for adverse effects. Off-label use of cholinesterase inhibitors and/or memantine for MCI was common, with more severely affected patients most likely to receive treatment. Differences in the frequency of symptomatic treatment were also observed as a function of age, years of education, gender and disease severity.
Drugs & Aging 08/2010; 27(8):677-86. · 2.67 Impact Factor
