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Shaheen H, Melnik R, Singh S. Data-driven Stochastic Model for Quantifying the Interplay Between Amyloid-beta and Calcium Levels in Alzheimer's Disease. Stat Anal Data Min 2024; 17:e11679. [PMID: 38646460 PMCID: PMC11031189 DOI: 10.1002/sam.11679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/23/2024] [Indexed: 04/23/2024]
Abstract
The abnormal aggregation of extracellular amyloid-β ( A β ) in senile plaques resulting in calcium C a + 2 dyshomeostasis is one of the primary symptoms of Alzheimer's disease (AD). Significant research efforts have been devoted in the past to better understand the underlying molecular mechanisms driving A β deposition and C a + 2 dysregulation. Importantly, synaptic impairments, neuronal loss, and cognitive failure in AD patients are all related to the buildup of intraneuronal A β accumulation. Moreover, increasing evidence show a feed-forward loop between A β and C a + 2 levels, i.e. A β disrupts neuronal C a + 2 levels, which in turn affects the formation of A β . To better understand this interaction, we report a novel stochastic model where we analyze the positive feedback loop between A β and C a + 2 using ADNI data. A good therapeutic treatment plan for AD requires precise predictions. Stochastic models offer an appropriate framework for modelling AD since AD studies are observational in nature and involve regular patient visits. The etiology of AD may be described as a multi-state disease process using the approximate Bayesian computation method. So, utilizing ADNI data from 2-year visits for AD patients, we employ this method to investigate the interplay between A β and C a + 2 levels at various disease development phases. Incorporating the ADNI data in our physics-based Bayesian model, we discovered that a sufficiently large disruption in either A β metabolism or intracellular C a + 2 homeostasis causes the relative growth rate in both C a + 2 and A β , which corresponds to the development of AD. The imbalance of C a + 2 ions causes A β disorders by directly or indirectly affecting a variety of cellular and subcellular processes, and the altered homeostasis may worsen the abnormalities of C a + 2 ion transportation and deposition. This suggests that altering the C a + 2 balance or the balance between A β and C a + 2 by chelating them may be able to reduce disorders associated with AD and open up new research possibilities for AD therapy.
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Affiliation(s)
- Hina Shaheen
- Faculty of Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Roderick Melnik
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
| | - Sundeep Singh
- Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
| | - The Alzheimer’s Disease Neuroimaging Initiative
- Data used in preparation of this article were generated by the Alzheimer’s Disease Metabolomics Consortium (ADMC). As such, the investigators within the ADMC provided data, but did not participate in the analysis or writing of this report. A complete listing of ADMC investigators can be found at: https://sites.duke.edu/adnimetab/team/
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Jamalian S, Dolton M, Chanu P, Ramakrishnan V, Franco Y, Wildsmith K, Manser P, Teng E, Jin JY, Quartino A, Hsu JC. Modeling Alzheimer's disease progression utilizing clinical trial and ADNI data to predict longitudinal trajectory of CDR-SB. CPT Pharmacometrics Syst Pharmacol 2023; 12:1029-1042. [PMID: 37101394 PMCID: PMC10349194 DOI: 10.1002/psp4.12974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 04/28/2023] Open
Abstract
There is strong interest in developing predictive models to better understand individual heterogeneity and disease progression in Alzheimer's disease (AD). We have built upon previous longitudinal AD progression models, using a nonlinear, mixed-effect modeling approach to predict Clinical Dementia Rating Scale - Sum of Boxes (CDR-SB) progression. Data from the Alzheimer's Disease Neuroimaging Initiative (observational study) and placebo arms from four interventional trials (N = 1093) were used for model building. The placebo arms from two additional interventional trials (N = 805) were used for external model validation. In this modeling framework, CDR-SB progression over the disease trajectory timescale was obtained for each participant by estimating disease onset time (DOT). Disease progression following DOT was described by both global progression rate (RATE) and individual progression rate (α). Baseline Mini-Mental State Examination and CDR-SB scores described the interindividual variabilities in DOT and α well. This model successfully predicted outcomes in the external validation datasets, supporting its suitability for prospective prediction and use in design of future trials. By predicting individual participants' disease progression trajectories using baseline characteristics and comparing these against the observed responses to new agents, the model can help assess treatment effects and support decision making for future trials.
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Affiliation(s)
| | - Michael Dolton
- Roche Products Australia Pty Ltd.SydneyNew South WalesAustralia
| | | | | | | | | | - Paul Manser
- Genentech, Inc.South San FranciscoCaliforniaUSA
| | - Edmond Teng
- Genentech, Inc.South San FranciscoCaliforniaUSA
| | - Jin Y. Jin
- Genentech, Inc.South San FranciscoCaliforniaUSA
| | | | - Joy C. Hsu
- Genentech, Inc.South San FranciscoCaliforniaUSA
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Sauty B, Durrleman S. Impact of sex and APOE- ε4 genotype on patterns of regional brain atrophy in Alzheimer's disease and healthy aging. Front Neurol 2023; 14:1161527. [PMID: 37333001 PMCID: PMC10272760 DOI: 10.3389/fneur.2023.1161527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/19/2023] [Indexed: 06/20/2023] Open
Abstract
Alzheimer's Disease (AD) is a heterogeneous disease that disproportionately affects women and people with the APOE-ε4 susceptibility gene. We aim to describe the not-well-understood influence of both risk factors on the dynamics of brain atrophy in AD and healthy aging. Regional cortical thinning and brain atrophy were modeled over time using non-linear mixed-effect models and the FreeSurfer software with t1-MRI scans from the Alzheimer's Disease Neuroimaging Initiative (N = 1,502 subjects, 6,728 images in total). Covariance analysis was used to disentangle the effect of sex and APOE genotype on the regional onset age and pace of atrophy, while correcting for educational level. A map of the regions mostly affected by neurodegeneration is provided. Results were confirmed on gray matter density data from the SPM software. Women experience faster atrophic rates in the temporal, frontal, parietal lobes and limbic system and earlier onset in the amygdalas, but slightly later onset in the postcentral and cingulate gyri as well as all regions of the basal ganglia and thalamus. APOE-ε4 genotypes leads to earlier and faster atrophy in the temporal, frontal, parietal lobes, and limbic system in AD patients, but not in healthy patients. Higher education was found to slightly delay atrophy in healthy patients, but not for AD patients. A cohort of amyloid positive patients with MCI showed a similar impact of sex as in the healthy cohort, while APOE-ε4 showed similar associations as in the AD cohort. Female sex is as strong a risk factor for AD as APOE-ε4 genotype regarding neurodegeneration. Women experience a sharper atrophy in the later stages of the disease, although not a significantly earlier onset. These findings may have important implications for the development of targeted intervention.
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Xu L, Wu H, He C, Wang J, Zhang C, Nie F, Chen L. Multi-modal sequence learning for Alzheimer’s disease progression prediction with incomplete variable-length longitudinal data. Med Image Anal 2022; 82:102643. [DOI: 10.1016/j.media.2022.102643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 08/27/2022] [Accepted: 09/23/2022] [Indexed: 11/28/2022]
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Kühnel L, Raket LL, Åström DO, Berger A, Hansen IH, Krismer F, Wenning GK, Seppi K, Poewe W, Molinuevo J. Disease Progression in Multiple System Atrophy-Novel Modeling Framework and Predictive Factors. Mov Disord 2022; 37:1719-1727. [PMID: 35668573 PMCID: PMC9540561 DOI: 10.1002/mds.29077] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/21/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Multiple system atrophy (MSA) is a rare and aggressive neurodegenerative disease that typically leads to death 6 to 10 years after symptom onset. The rapid evolution renders it crucial to understand the general disease progression and factors affecting the disease course. OBJECTIVES The aims of this study were to develop a novel disease-progression model to estimate a population-level MSA progression trajectory and predict patient-specific continuous disease stages describing the degree of progress into the disease. METHODS The disease-progression model estimated a population-level progression trajectory of subscales of the Unified MSA Rating Scale and the Unified Parkinson's Disease Rating Scale using patients in the European MSA natural history study. The predicted disease continuum was validated via multiple analyses based on reported anchor points, and the effect of MSA subtype on the rate of disease progression was evaluated. RESULTS The predicted disease continuum spanned approximately 6 years, with an estimated average duration of 51 months for a patient with global disability score 0 to reach the highest level of 4. The predicted continuous disease stages were shown to be correlated with time of symptom onset and predictive of survival time. MSA motor subtype was found to significantly affect disease progression, with MSA-parkinsonian (MSA-P) type patients having an accelerated rate of progression. CONCLUSIONS The proposed modeling framework introduces a new method of analyzing and interpreting the progression of MSA. It can provide new insights and opportunities for investigating covariate effects on the rate of progression and provide well-founded predictions of patient-level future progressions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Line Kühnel
- H. Lundbeck A/SCopenhagenDenmark
- Department of Mathematical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Lars Lau Raket
- H. Lundbeck A/SCopenhagenDenmark
- Clinical Memory Research Unit, Department of Clinical SciencesLund UniversityLundSweden
| | | | | | | | - Florian Krismer
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
| | | | - Klaus Seppi
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
| | - Werner Poewe
- Department of NeurologyMedical University InnsbruckInnsbruckAustria
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Loeffler DA. Modifiable, Non-Modifiable, and Clinical Factors Associated with Progression of Alzheimer's Disease. J Alzheimers Dis 2021; 80:1-27. [PMID: 33459643 DOI: 10.3233/jad-201182] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
There is an extensive literature relating to factors associated with the development of Alzheimer's disease (AD), but less is known about factors which may contribute to its progression. This review examined the literature with regard to 15 factors which were suggested by PubMed search to be positively associated with the cognitive and/or neuropathological progression of AD. The factors were grouped as potentially modifiable (vascular risk factors, comorbidities, malnutrition, educational level, inflammation, and oxidative stress), non-modifiable (age at clinical onset, family history of dementia, gender, Apolipoprotein E ɛ4, genetic variants, and altered gene regulation), and clinical (baseline cognitive level, neuropsychiatric symptoms, and extrapyramidal signs). Although conflicting results were found for the majority of factors, a positive association was found in nearly all studies which investigated the relationship of six factors to AD progression: malnutrition, genetic variants, altered gene regulation, baseline cognitive level, neuropsychiatric symptoms, and extrapyramidal signs. Whether these or other factors which have been suggested to be associated with AD progression actually influence the rate of decline of AD patients is unclear. Therapeutic approaches which include addressing of modifiable factors associated with AD progression should be considered.
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Affiliation(s)
- David A Loeffler
- Beaumont Research Institute, Department of Neurology, Beaumont Health, Royal Oak, MI, USA
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Karcher H, Savelieva M, Qi L, Hummel N, Caputo A, Risson V, Capkun G, Alzheimer's Disease Neuroimaging Initiative. Modelling Decline in Cognition to Decline in Function in Alzheimer's Disease. Curr Alzheimer Res 2020; 17:635-657. [PMID: 33032508 DOI: 10.2174/1567205017666201008105429] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 08/05/2020] [Accepted: 09/04/2020] [Indexed: 11/22/2022]
Abstract
OBJECTIVES The study aimed to evaluate and quantify the temporal link between cognitive and functional decline, and assess the impact of the apolipoprotein E4 (APOE-e4) genotype on Alzheimer's disease (AD) progression. METHODS A nonlinear mixed-effects Emax model was developed using longitudinal data from 659 patients with dementia due to AD sourced from the Alzheimer's disease neuroimaging initiative (ADNI) database. A cognitive decline model was first built using a cognitive subscale of the AD assessment scale (delayed word recall) as the endpoint, followed by a functional decline model, using the functional assessment questionnaire (FAQ) as the endpoint. Individual and population cognitive decline from the first model drove a functional decline in the second model. The impact of the APOE-e4 genotype status on the dynamics of AD progression was evaluated using the model. RESULTS Mixed-effects Emax models adequately quantified population average and individual disease trajectories. The model captured a higher initial cognitive impairment and final functional impairment in APOE-e4 carriers than non-carriers. The age at cognitive decline and diagnosis of dementia due to AD was significantly lower in APOE-e4 carriers than that of non-carriers. The average [standard deviation] time shift between cognitive and functional decline, i.e. the time span between half of the maximum cognitive decline and half of the maximum functional decline, was estimated as 1.5 [1.6] years. CONCLUSION The present analysis quantifies the temporal link between a cognitive and functional decline in AD progression at the population and individual level, and provides information about the potential benefits of pre-clinical AD treatments on both cognition and function.
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Affiliation(s)
- Helene Karcher
- Vice President, Access Consulting, Modeling & Simulation Unit Head, Parexel, Arnold Böcklin-Str. 29, 4051 Basel, Switzerland
| | | | - Luyuan Qi
- Analytica Laser, Certara Company, Paris, France
| | - Noemi Hummel
- Analytica Laser, Certara Company, Lörrach, Germany
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Disease progression modeling of Alzheimer's disease according to education level. Sci Rep 2020; 10:16808. [PMID: 33033321 PMCID: PMC7544693 DOI: 10.1038/s41598-020-73911-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 09/16/2020] [Indexed: 01/07/2023] Open
Abstract
To develop a disease progression model of Alzheimer’s disease (AD) that shows cognitive decline from subjective cognitive impairments (SCI) to the end stage of AD dementia (ADD) and to investigate the effect of education level on the whole disease spectrum, we enrolled 565 patients who were followed up more than three times and had a clinical dementia rating sum of boxes (CDR-SB). Three cohorts, SCI (n = 85), amnestic mild cognitive impairment (AMCI, n = 240), and ADD (n = 240), were overlapped in two consecutive cohorts (SCI and AMCI, AMCI and ADD) to construct a model of disease course, and a model with multiple single-cohorts was estimated using a mixed-effect model. To examine the effect of education level on disease progression, the disease progression model was developed with data from lower (≤ 12) and higher (> 12) education groups. Disease progression takes 274.3 months (22.9 years) to advance from 0 to 18 points using the CDR-SB. Based on our predictive equation, it takes 116.5 months to progress from SCI to AMCI and 56.2 months to progress from AMCI to ADD. The rate of CDR-SB progression was different according to education level. The lower-education group showed faster CDR-SB progression from SCI to AMCI compared to the higher-education group, and this trend disappeared from AMCI to ADD. In the present study, we developed a disease progression model of AD spectrum from SCI to the end stage of ADD. Our disease modeling provides us with more understanding of the effect of education on cognitive trajectories.
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Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacol Rev 2020; 72:191-217. [PMID: 31843941 DOI: 10.1124/pr.119.017921] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
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Affiliation(s)
- Jhana O Hendrickx
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
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Raket LL. Statistical Disease Progression Modeling in Alzheimer Disease. Front Big Data 2020; 3:24. [PMID: 33693397 PMCID: PMC7931952 DOI: 10.3389/fdata.2020.00024] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 06/24/2020] [Indexed: 01/20/2023] Open
Abstract
Background: The characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration reflects not only the disease state, but also the effect of the cognitive decline on the patient's pre-disease cognitive capability. Patients with high pre-disease cognitive capabilities tend to score better on cognitive tests that are sensitive early in disease relative to patients with low pre-disease cognitive capabilities at a similar disease stage. Thus, a single assessment with a cognitive test is often not adequate for determining the stage of an AD patient. Repeated evaluation of patients' cognition over time may improve the ability to stage AD patients, and such longitudinal assessments in combinations with biomarker assessments can help elucidate the time dynamics of biomarkers. In turn, this can potentially lead to identification of markers that are predictive of disease stage and future cognitive decline, possibly before any cognitive deficit is measurable. Methods and Findings: This article presents a class of statistical disease progression models and applies them to longitudinal cognitive scores. These non-linear mixed-effects disease progression models explicitly model disease stage, baseline cognition, and the patients' individual changes in cognitive ability as latent variables. Maximum-likelihood estimation in these models induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer's Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline that spans ~15 years from the earliest subjective cognitive deficits to severe AD dementia. Subsequent analyses demonstrated how direct modeling of latent factors that modify the observed data patterns provides a scaffold for understanding disease progression, biomarkers, and treatment effects along the continuous time progression of disease. Conclusions: The presented framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments.
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Affiliation(s)
- Lars Lau Raket
- H. Lundbeck A/S, Copenhagen, Denmark.,Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
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Predicting Alzheimer's disease progression using deep recurrent neural networks. Neuroimage 2020; 222:117203. [PMID: 32763427 PMCID: PMC7797176 DOI: 10.1016/j.neuroimage.2020.117203] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 07/22/2020] [Accepted: 07/23/2020] [Indexed: 01/12/2023] Open
Abstract
Early identification of individuals at risk of developing Alzheimer’s disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a “preprocessing” issue, by imputing the missing data using the previous timepoint (“forward filling”) or linear interpolation (“linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing (“model filling”). Our analyses suggest that the minimalRNN with “model filling” compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020.
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12
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Arrington L, Ueckert S, Ahamadi M, Macha S, Karlsson MO. Performance of longitudinal item response theory models in shortened or partial assessments. J Pharmacokinet Pharmacodyn 2020; 47:461-471. [PMID: 32617833 PMCID: PMC7520414 DOI: 10.1007/s10928-020-09697-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/18/2020] [Indexed: 11/21/2022]
Abstract
This work evaluates the performance of longitudinal item response (IR) theory models in shortened assessments using an existing model for part II and III of the MDS-UPDRS score. Based on the item information content, the assessment was reduced by removal of items in multiple increments and the models’ ability to recover the item characteristics of the remaining items at each level was evaluated. This evaluation was done for both simulated and real data. The metric of comparison in both cases was the item information function. For real data, the impact of shortening on the estimated disease progression and drug effect was also studied. In the simulated data setting, the item characteristics did not differ between the full and the shortened assessments down to the lowest level of information remaining; indicating a considerable independence between items. In contrast when reducing the assessment in a real data setting, a substantial change in item information was observed for some of the items. Disease progression and drug effect estimates also decreased in the reduced assessments. These changes indicate a shift in the measured construct of the shortened assessment and warrant caution when comparing results from a partial assessment with results from the full assessment.
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Affiliation(s)
- Leticia Arrington
- Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden.,Merck & Co. Inc, Kenilworth, NJ, USA
| | - Sebastian Ueckert
- Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden
| | | | | | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden.
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Modified Visual Magnetic Resonance Scale and Neuropsychometric Corelations in Alzheimer's disease. ANADOLU KLINIĞI TIP BILIMLERI DERGISI 2020. [DOI: 10.21673/anadoluklin.737253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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14
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Zhang N, Zheng X, Liu H, Zheng Q, Li L. Testing whether the progression of Alzheimer's disease changes with the year of publication, additional design, and geographical area: a modeling analysis of literature aggregate data. ALZHEIMERS RESEARCH & THERAPY 2020; 12:64. [PMID: 32456710 PMCID: PMC7251914 DOI: 10.1186/s13195-020-00630-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Accepted: 05/10/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND Our objectives were to develop a disease progression model for cognitive decline in Alzheimer's disease (AD) and to determine whether disease progression of AD is related to the year of publication, add-on trial design, and geographical regions. METHODS Placebo-controlled randomized AD clinical trials were systemically searched in public databases. Longitudinal placebo response (mean change from baseline in the cognitive subscale of the Alzheimer's Disease Assessment Scale [ADAS-cog]) and the corresponding demographic information were extracted to establish a disease progression model. Covariate screening and subgroup analyses were performed to identify potential factors affecting the disease progression rate. RESULTS A total of 134 publications (140 trials) were included in this model-based meta-analysis. The typical disease progression rate was 5.82 points per year. The baseline ADAS-cog score was included in the final model using an inverse U-type function. Age was found to be negatively correlated with disease progression rate. After correcting the baseline ADAS-cog score and the age effect, no significant difference in the disease progression rate was found between trials published before and after 2008 and between trials using an add-on design and those that did not use an add-on design. However, a significant difference was found among different trial regions. Trials in East Asian countries showed the slowest decline rate and the largest placebo effect. CONCLUSIONS Our model successfully quantified AD disease progression by integrating baseline ADAS-cog score and age as important predictors. These factors and geographic location should be considered when optimizing future trial designs and conducting indirect comparisons of clinical outcomes.
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Affiliation(s)
- Ningyuan Zhang
- Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Shanghai, 201203, China
| | - Xijun Zheng
- Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Shanghai, 201203, China
| | - Hongxia Liu
- Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Shanghai, 201203, China
| | - Qingshan Zheng
- Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Shanghai, 201203, China.
| | - Lujin Li
- Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Shanghai, 201203, China.
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15
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Sun W, Zhou D, Warner JH, Langbehn DR, Hochhaus G, Wang Y. Huntington's Disease Progression: A Population Modeling Approach to Characterization Using Clinical Rating Scales. J Clin Pharmacol 2020; 60:1051-1060. [DOI: 10.1002/jcph.1598] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 02/03/2020] [Indexed: 11/08/2022]
Affiliation(s)
- Wan Sun
- Quantitative Clinical PharmacologyTakeda Pharmaceuticals Cambridge Massachusetts USA
- Division of PharmacometricsFood and Drug Administration Silver Spring Maryland USA
| | - Di Zhou
- Division of PharmacometricsFood and Drug Administration Silver Spring Maryland USA
| | - John H. Warner
- CHDI Management/CHDI Foundation Princeton New Jersey USA
| | | | | | - Yaning Wang
- Division of PharmacometricsFood and Drug Administration Silver Spring Maryland USA
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16
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NeAT: a Nonlinear Analysis Toolbox for Neuroimaging. Neuroinformatics 2020; 18:517-530. [PMID: 32212063 PMCID: PMC7498484 DOI: 10.1007/s12021-020-09456-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.
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17
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Ahamadi M, Conrado DJ, Macha S, Sinha V, Stone J, Burton J, Nicholas T, Gallagher J, Dexter D, Bani M, Boroojerdi B, Smit H, Weidemann J, Chen C, Yang M, Maciuca R, Lawson R, Burn D, Marek K, Venuto C, Stafford B, Akalu M, Stephenson D, Romero K. Development of a Disease Progression Model for Leucine-Rich Repeat Kinase 2 in Parkinson's Disease to Inform Clinical Trial Designs. Clin Pharmacol Ther 2020; 107:553-562. [PMID: 31544231 PMCID: PMC7939141 DOI: 10.1002/cpt.1634] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 09/05/2019] [Indexed: 11/06/2022]
Abstract
A quantitative assessment of Parkinson's disease (PD) progression is critical for optimizing clinical trials design. Disease progression model was developed using pooled data from the Progression Marker Initiative study and the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in Parkinson's Disease study. Age, gender, concomitant medication, and study arms were predictors of baseline. A mutation in the leucine-rich repeat kinase 2 (LRRK2) encoding gene was associated with the disease progression rate. The progression rate in subjects with PD who carried LRRK2 mutation was slightly slower (~0.170 points/month) than that in PD subjects without the mutation (~0.222 points/month). For a nonenriched placebo-controlled clinical trial, approximately 70 subjects/arm would be required to detect a drug effect of 50% reduction in the progression rate with 80% probability, whereas 85, 93, and 100 subjects/arm would be required for an enriched clinical trial with 30%, 50%, and 70% subjects with LRRK2 mutations, respectively.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Rachael Lawson
- Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in Parkinson’s Disease
| | - David Burn
- Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in Parkinson’s Disease
| | - Kenneth Marek
- Institute of Neurodegenerative Diseases, New Haven, CT, USA
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18
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Ito K, Romero K. Placebo effect in subjects with cognitive impairment. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2020; 153:213-230. [DOI: 10.1016/bs.irn.2020.03.032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
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19
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Gottesman RT, Stern Y. Behavioral and Psychiatric Symptoms of Dementia and Rate of Decline in Alzheimer's Disease. Front Pharmacol 2019; 10:1062. [PMID: 31616296 PMCID: PMC6768941 DOI: 10.3389/fphar.2019.01062] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 08/20/2019] [Indexed: 12/20/2022] Open
Abstract
Alzheimer’s disease causes both cognitive and non-cognitive symptoms. There is increasing evidence that the presentation and course of Alzheimer’s disease is highly heterogenous. This heterogeneity presents challenges to patients, their families, and clinicians due to the difficulty in prognosticating future symptoms and functional impairment. Behavioral and psychiatric symptoms are emerging as a significant contributor to this clinical heterogeneity. These symptoms have been linked to multiple areas of neurodegeneration, which may suggest that they are representative of network-wide dysfunction in the brain. However, current diagnostic criteria for Alzheimer’s disease focus exclusively on the cognitive aspects of disease. Behavioral and psychiatric symptoms have been found in multiple studies to be related to disease severity and to contribute to disease progression over time. A better understanding of how behavioral and psychiatric symptoms relate to cognitive aspects of Alzheimer’s disease would help to refine the models of disease and hopefully lead to improved ability to develop therapeutic options for this devastating disease.
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Affiliation(s)
- Reena T Gottesman
- Division of Aging and Dementia, Department of Neurology, Columbia University Medical Center, New York, NY, United States
| | - Yaakov Stern
- Division of Cognitive Neuroscience, Department of Neurology, Columbia University Medical Center, New York, NY, United States
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20
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Watanabe M, Nakamura Y, Yoshiyama Y, Kagimura T, Kawaguchi H, Matsuzawa H, Tachibana Y, Nishimura K, Kubota N, Kobayashi M, Saito T, Tamura K, Sato T, Takahashi M, Homma A. Analyses of natural courses of Japanese patients with Alzheimer's disease using placebo data from placebo-controlled, randomized clinical trials: Japanese Study on the Estimation of Clinical course of Alzheimer's disease. ALZHEIMERS & DEMENTIA-TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS 2019; 5:398-408. [PMID: 31517028 PMCID: PMC6727219 DOI: 10.1016/j.trci.2019.07.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Introduction Symptomatic anti-Alzheimer's disease (AD) drugs have been commonly used for the treatment of AD. Knowing the natural courses of patients with AD on placebo is highly relevant for clinicians to understand their efficacy and for investigators to design clinical studies. Methods The data on rating scales for dementia such as Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog) and Severe Impairment Battery were extracted from eight previous Japanese Phase II and III studies. Natural courses of Japanese AD patients in placebo groups were evaluated and statistically analyzed in a pooled and retrospective fashion. Results Decreases in ADAS-cog and Severe Impairment Battery was larger at week 22 or 24 than at week 12. Scores of ADAS-cog appeared to deteriorate faster in moderate AD than in mild AD. Discussion The present data will provide clinicians following up patients with AD with helpful information on how to manage AD patients and investigators with instruction for clinical study design.
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Affiliation(s)
- Mitsunori Watanabe
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Yu Nakamura
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan.,Department of Neuropsychiatry, Kagawa University School of Medicine, Kagawa, Japan
| | - Yasumasa Yoshiyama
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan.,Inage Neurology and Memory Clinic, Chiba, Japan
| | - Tatsuo Kagimura
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan.,Translational Research Center for Medical Innovation (TRI), Foundation for Biomedical Research and Innovation at Kobe, Kobe, Japan
| | - Hiroyuki Kawaguchi
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Hiroshi Matsuzawa
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Yosuke Tachibana
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Kazuma Nishimura
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Naoki Kubota
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Masato Kobayashi
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Takayuki Saito
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Kaoru Tamura
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Takayuki Sato
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | - Masayoshi Takahashi
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan
| | | | - Akira Homma
- Japanese Society of Scaling Keys of Evaluation Techniques for CNS Disorders Heterogeneity (SKETCH), Tokyo, Japan.,Otafuku Memory Clinic, Ibaraki, Japan
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21
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Gao F, Wang Y, Zeng D. EARLY DIAGNOSIS OF NEUROLOGICAL DISEASE USING PEAK DEGENERATION AGES OF MULTIPLE BIOMARKERS. Ann Appl Stat 2019; 13:1295-1318. [PMID: 31673303 PMCID: PMC6822567 DOI: 10.1214/18-aoas1236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Neurological diseases are due to the loss of structure or function of neurons that eventually leads to cognitive deficit, neuropsychiatric symptoms, and impaired activities of daily living. Identifying sensitive and specific biological and clinical markers for early diagnosis allows recruiting patients into a clinical trial to test therapeutic intervention. However, many biomarker studies considered a single biomarker at one time that fails to provide precise prediction for disease age at onset. In this paper, we use longitudinally collected measurements from multiple biomarkers and measurement error-corrected clinical diagnosis ages to identify which biomarkers and what features of biomarker trajectories are useful for early diagnosis. Specifically, we assume that the subject-specific biomarker trajectories depend on unobserved states of underlying latent variables with the conditional mean follows a nonlinear sigmoid shape. We show that peak degeneration age of the biomarker trajectory is useful for early diagnosis. We propose an Expectation-Maximization (EM) algorithm to obtain the maximum likelihood estimates of all parameters and conduct extensive simulation studies to examine the performance of the proposed methods. Finally, we apply our methods to studies of Alzheimer's disease and Huntington's disease and identify a few important biomarkers that can be used for early diagnosis.
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Affiliation(s)
- Fei Gao
- Department of Biostatistics, University of Washington, Seattle, Washington 98195,
| | - Yüanjia Wang
- Department of Biostatistics, Columbia University, New York, New York 10032,
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599,
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22
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D'Antonio F, Reeves S, Sheng Y, McLachlan E, de Lena C, Howard R, Bertrand J. Misidentification Subtype of Alzheimer's Disease Psychosis Predicts a Faster Cognitive Decline. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:308-315. [PMID: 30779330 PMCID: PMC6533361 DOI: 10.1002/psp4.12389] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 01/05/2019] [Indexed: 12/16/2022]
Abstract
The presence of psychosis is associated with a more rapid decline in Alzheimer's disease (AD), but the impact of paranoid (persecutory delusions) and misidentification (misperceptions and/or hallucinations) subtypes of psychosis on the speed of decline in AD is still unclear. We analyzed data on Alzheimer's Disease Neuroimaging Initiative 2 participants with late mild cognitive impairment or AD, and we described individual trajectories of Alzheimer's Disease Assessment Scale-Cognitive Subscale scores using a semimechanistic logistic model with a mixed effects-based approach, which accounted for dropout and adjusted for baseline Mini Mental State Examination scores. The covariate model included psychosis subtypes, age, gender, education, medications, and Apolipoprotein E epsilon 4 (Apo-e ε4 genotype). We found that the Alzheimer's Disease Assessment Scale-Cognitive Subscale rate of increase was doubled in misidentification (βr,misid_subtype = 0.63, P = 0.031) and mixed (both subtypes; βr,mixed_subtype = 0.70, P = 0.003) when compared with nonpsychotic (or paranoid) patients, suggesting that the misidentification subtype may represent a distinct AD sub-phenotype associated with an accelerated pathological process.
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Affiliation(s)
- Fabrizia D'Antonio
- Division of Psychiatry, University College London, London, UK.,Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Suzanne Reeves
- Division of Psychiatry, University College London, London, UK
| | - Yucheng Sheng
- Department of Pharmaceutics, School of Pharmacy, University College London, London, UK
| | - Emma McLachlan
- Department of Old Age Psychiatry, King's College London, London, UK
| | - Carlo de Lena
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Robert Howard
- Division of Psychiatry, University College London, London, UK
| | - Julie Bertrand
- UMR 1137 Infection, Antimicrobials, Modelling, Evolution (IAME) French Institute for Medical Research (INSERM), University Paris, Paris, France.,Genetics Institute, University College London, London, UK
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23
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Analysis of ordinal longitudinal data under nonignorable missingness and misreporting: An application to Alzheimer’s disease study. J MULTIVARIATE ANAL 2018. [DOI: 10.1016/j.jmva.2018.02.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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24
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Maudsley S, Devanarayan V, Martin B, Geerts H. Intelligent and effective informatic deconvolution of “Big Data” and its future impact on the quantitative nature of neurodegenerative disease therapy. Alzheimers Dement 2018; 14:961-975. [DOI: 10.1016/j.jalz.2018.01.014] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Revised: 10/03/2017] [Accepted: 01/18/2018] [Indexed: 12/31/2022]
Affiliation(s)
- Stuart Maudsley
- Department of Biomedical ResearchUniversity of AntwerpAntwerpBelgium
- VIB Center for Molecular NeurologyAntwerpBelgium
| | | | - Bronwen Martin
- Department of Biomedical ResearchUniversity of AntwerpAntwerpBelgium
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25
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Hadjichrysanthou C, Ower AK, de Wolf F, Anderson RM. The development of a stochastic mathematical model of Alzheimer's disease to help improve the design of clinical trials of potential treatments. PLoS One 2018; 13:e0190615. [PMID: 29377891 PMCID: PMC5788351 DOI: 10.1371/journal.pone.0190615] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 12/18/2017] [Indexed: 01/08/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterised by a slow progressive deterioration of cognitive capacity. Drugs are urgently needed for the treatment of AD and unfortunately almost all clinical trials of AD drug candidates have failed or been discontinued to date. Mathematical, computational and statistical tools can be employed in the construction of clinical trial simulators to assist in the improvement of trial design and enhance the chances of success of potential new therapies. Based on the analysis of a set of clinical data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) we developed a simple stochastic mathematical model to simulate the development and progression of Alzheimer's in a longitudinal cohort study. We show how this modelling framework could be used to assess the effect and the chances of success of hypothetical treatments that are administered at different stages and delay disease development. We demonstrate that the detection of the true efficacy of an AD treatment can be very challenging, even if the treatment is highly effective. An important reason behind the inability to detect signals of efficacy in a clinical trial in this therapy area could be the high between- and within-individual variability in the measurement of diagnostic markers and endpoints, which consequently results in the misdiagnosis of an individual's disease state.
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Affiliation(s)
- Christoforos Hadjichrysanthou
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Alison K. Ower
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
| | - Frank de Wolf
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
- Janssen Prevention Center, Leiden, The Netherlands
| | - Roy M. Anderson
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom
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26
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Peck RW. Precision Medicine Is Not Just Genomics: The Right Dose for Every Patient. Annu Rev Pharmacol Toxicol 2018; 58:105-122. [DOI: 10.1146/annurev-pharmtox-010617-052446] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Richard W. Peck
- Pharma Research and Exploratory Development, Roche Innovation Center Basel, 4070 Basel, Switzerland
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27
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Das K, Rana S, Roy S. Evaluation of Alzheimer's disease progression based on clinical dementia rating scale with missing responses and covariates. J Biopharm Stat 2017; 28:893-908. [PMID: 29173033 DOI: 10.1080/10543406.2017.1402780] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
In clinical trials, patient's disease severity is usually assessed on a Likert-type scale. Patients, however, may miss one or more follow-up visits (non-monotone missing). The statistical analysis of non-Gaussian longitudinal data with non-monotone missingness is difficult to handle, particularly when both response and time-dependent covariates are subject to such missingness. Even when the number of patients with intermittent missing data is small, ignoring those patients from analysis seems to be unsatisfactory. The focus of the current investigation is to study the progression of Alzheimer's disease by incorporating a non-ignorable missing data mechanism for both response and covariates in a longitudinal setup. Combining the cumulative logit longitudinal model for Alzheimer's disease progression with the bivariate binary model for the missing pattern, we develop a joint likelihood. The parameters are then estimated using the Monte Carlo Newton Raphson Expectation Maximization (MCNREM) method. This approach is quite easy to handle and the convergence of the estimates is attained in a reasonable amount of time. The study reveals that apolipo-protein plays a significant role in assessing a patient's disease severity. A detailed simulation has also been carried out for justifying the performance of our approach.
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Affiliation(s)
- Kalyan Das
- a Department of Statistics , University of Calcutta, Ballygunge Science College , Kolkata , India
| | - Subrata Rana
- a Department of Statistics , University of Calcutta, Ballygunge Science College , Kolkata , India
| | - Surupa Roy
- b Department of Statistics , St. Xavier's College , Kolkata , India
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28
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Karelina T, Demin O, Demin O, Duvvuri S, Nicholas T. Studying the Progression of Amyloid Pathology and Its Therapy Using Translational Longitudinal Model of Accumulation and Distribution of Amyloid Beta. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:676-685. [PMID: 28913897 PMCID: PMC5658285 DOI: 10.1002/psp4.12249] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 07/28/2017] [Accepted: 08/24/2017] [Indexed: 11/16/2022]
Abstract
Long‐term effects of amyloid targeted therapy can be studied using a mechanistic translational model of amyloid beta (Aβ) distribution and aggregation calibrated on published data in mouse and human species. Alzheimer disease (AD) pathology is modeled utilizing age‐dependent pathological evolution for rate constants and several variants of explicit functions for Aβ toxicity influencing cognitive outcomes (Adas‐cog). Preventive Aβ targeted therapies were simulated to minimize the Aβ difference from healthy physiological levels. Therapeutic targeted simulations provided similar predictions for mouse and human studies. Our model predicts that: (1) at least 1 year (2 years for preclinical AD) of treatment is needed to observe cognitive effects; (2) under the hypothesis with functional importance of Aβ, a 15% decrease in Aβ (using an imaging biomarker) is related to 15–20% cognition improvement by immunotherapy. Despite negative outcomes in clinical trials, Aβ continues to remain a prospective target demanding careful assessment of mechanistic effect and duration of trial design.
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29
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Vandemeulebroecke M, Bornkamp B, Krahnke T, Mielke J, Monsch A, Quarg P. A Longitudinal Item Response Theory Model to Characterize Cognition Over Time in Elderly Subjects. CPT Pharmacometrics Syst Pharmacol 2017; 6:635-641. [PMID: 28643388 PMCID: PMC5613212 DOI: 10.1002/psp4.12219] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Revised: 06/06/2017] [Accepted: 06/06/2017] [Indexed: 11/06/2022] Open
Abstract
For drug development in neurodegenerative diseases such as Alzheimer's disease, it is important to understand which cognitive domains carry the most information on the earliest signs of cognitive decline, and which subject characteristics are associated with a faster decline. A longitudinal Item Response Theory (IRT) model was developed for the Basel Study on the Elderly, in which the Consortium to Establish a Registry for Alzheimer's Disease - Neuropsychological Assessment Battery (with additions) and the California Verbal Learning Test were measured on 1,750 elderly subjects for up to 13.9 years. The model jointly captured the multifaceted nature of cognition and its longitudinal trajectory. The word list learning and delayed recall tasks carried the most information. Greater age at baseline, fewer years of education, and positive APOEɛ4 carrier status were associated with a faster cognitive decline. Longitudinal IRT modeling is a powerful approach for progressive diseases with multifaceted endpoints.
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30
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Henneges C, Reed C, Chen YF, Dell'Agnello G, Lebrec J. Describing the Sequence of Cognitive Decline in Alzheimer's Disease Patients: Results from an Observational Study. J Alzheimers Dis 2017; 52:1065-80. [PMID: 27079700 PMCID: PMC4927893 DOI: 10.3233/jad-150852] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Improved understanding of the pattern of cognitive decline in Alzheimer’s disease (AD) would be useful to assist primary care physicians in explaining AD progression to patients and caregivers. Objective: To identify the sequence in which cognitive abilities decline in community-dwelling patients with AD. Methods: Baseline data were analyzed from 1,495 patients diagnosed with probable AD and a Mini-Mental State Examination (MMSE) score ≤ 26 enrolled in the 18-month observational GERAS study. Proportional odds logistic regression models were applied to model MMSE subscores (orientation, registration, attention and concentration, recall, language, and drawing) and the corresponding subscores of the cognitive subscale of the Alzheimer’s Disease Assessment Scale (ADAS-cog), using MMSE total score as the index of disease progression. Probabilities of impairment start and full impairment were estimated at each MMSE total score level. Results: From the estimated probabilities for each MMSE subscore as a function of the MMSE total score, the first aspect of cognition to start being impaired was recall, followed by orientation in time, attention and concentration, orientation in place, language, drawing, and registration. For full impairment in subscores, the sequence was recall, drawing, attention and concentration, orientation in time, orientation in place, registration, and language. The sequence of cognitive decline for the corresponding ADAS-cog subscores was remarkably consistent with this pattern. Conclusion: The sequence of cognitive decline in AD can be visualized in an animation using probability estimates for key aspects of cognition. This might be useful for clinicians to set expectations on disease progression for patients and caregivers.
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Affiliation(s)
| | - Catherine Reed
- Eli Lilly and Company Limited, Lilly Research Centre, Windlesham, UK
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31
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Baker E, Iqbal E, Johnston C, Broadbent M, Shetty H, Stewart R, Howard R, Newhouse S, Khondoker M, Dobson RJB. Trajectories of dementia-related cognitive decline in a large mental health records derived patient cohort. PLoS One 2017; 12:e0178562. [PMID: 28591196 PMCID: PMC5462385 DOI: 10.1371/journal.pone.0178562] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 05/15/2017] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Modeling trajectories of decline can help describe the variability in progression of cognitive impairment in dementia. Better characterisation of these trajectories has significant implications for understanding disease progression, trial design and care planning. METHODS Patients with at least three Mini-mental State Examination (MMSE) scores recorded in the South London and Maudsley NHS Foundation Trust Electronic Health Records, UK were selected (N = 3441) to form a retrospective cohort. Trajectories of cognitive decline were identified through latent class growth analysis of longitudinal MMSE scores. Demographics, Health of Nation Outcome Scales and medications were compared across trajectories identified. RESULTS Four of the six trajectories showed increased rate of decline with lower baseline MMSE. Two trajectories had similar initial MMSE scores but different rates of decline. In the faster declining trajectory of the two, a higher incidence of both behavioral problems and sertraline prescription were present. CONCLUSIONS We find suggestive evidence for association of behavioral problems and sertraline prescription with rate of decline. Further work is needed to determine whether trajectories replicate in other datasets.
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Affiliation(s)
- Elizabeth Baker
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Ehtesham Iqbal
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Caroline Johnston
- National Institute for Health Research (NIHR) Biomedical Research for mental health and Dementia Unit at South London and Maudlsey NHS Foundation Trust, London, United Kingdom
| | - Matthew Broadbent
- National Institute for Health Research (NIHR) Biomedical Research for mental health and Dementia Unit at South London and Maudlsey NHS Foundation Trust, London, United Kingdom
| | - Hitesh Shetty
- South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Robert Stewart
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Robert Howard
- Division of Psychiatry, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Stephen Newhouse
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- National Institute for Health Research (NIHR) Biomedical Research for mental health and Dementia Unit at South London and Maudlsey NHS Foundation Trust, London, United Kingdom
- Farr Institute of Health Informatics Research, UCL institute of Health Informatics, University College London, London, United Kingdom
| | - Mizanur Khondoker
- Department of Population Health and Primary Care, Norwich Medical School, University of East Anglia, Norwich, United Kingdom
| | - Richard J. B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- National Institute for Health Research (NIHR) Biomedical Research for mental health and Dementia Unit at South London and Maudlsey NHS Foundation Trust, London, United Kingdom
- Farr Institute of Health Informatics Research, UCL institute of Health Informatics, University College London, London, United Kingdom
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Chen H, Zeng D, Wang Y. Penalized nonlinear mixed effects model to identify biomarkers that predict disease progression. Biometrics 2017; 73:1343-1354. [PMID: 28182831 DOI: 10.1111/biom.12663] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Revised: 12/01/2016] [Accepted: 01/01/2017] [Indexed: 12/29/2022]
Abstract
Precise modeling of disease progression in neurodegenerative disorders may enable early intervention before clinical manifestation of a disease, which is crucial since early intervention at the premanifest stage is expected to be more effective. Neuroimaging biomarkers are indicative of the underlying disease pathology and may be used to predict future disease occurrence at the premanifest stage. As observed in many pivotal studies, longitudinal measurements of clinical outcomes, such as motor or cognitive symptoms, often present nonlinear sigmoid shapes over time, where the inflection points of the trajectories mark a meaningful time in disease progression. Therefore, to identify neuroimaging biomarkers predicting disease progression, we propose a nonlinear mixed effects model based on a sigmoid function to predict longitudinal clinical outcomes, and associate a linear combination of neuroimaging biomarkers with subject-specific inflection points. Based on an expectation-maximization (EM) algorithm, we propose a method that can fit a nonlinear model with many potentially correlated biomarkers for random inflection points while achieving computational stability. Variable selection is introduced in the algorithm in order to identify important biomarkers of disease progression and to reduce prediction variability. We apply the proposed method to the data from the Predictors of Huntington's Disease study to select brain subcortical regional volumes predictive of the inflection points of the motor and cognitive function trajectories. Our results reveal that brain atrophy in the striatum and expansion of the ventricular system are highly predictive of the inflection points. Furthermore, these inflection points may precede clinically defined disease onset by as early as a decade and thus may be useful biomarkers as early signs of Huntington's Disease onset.
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Affiliation(s)
- Huaihou Chen
- Department of Biostatistics, University of Florida, Gainesville, Florida 32611, U.S.A
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A
| | - Yuanjia Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York 10032, U.S.A
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Abstract
Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic-pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer's disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.
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Venuto CS, Potter NB, Dorsey ER, Kieburtz K. A review of disease progression models of Parkinson's disease and applications in clinical trials. Mov Disord 2016; 31:947-956. [PMID: 27226141 PMCID: PMC4931998 DOI: 10.1002/mds.26644] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 02/19/2016] [Accepted: 03/04/2016] [Indexed: 12/31/2022] Open
Abstract
Quantitative disease progression models for neurodegenerative disorders are gaining recognition as important tools for drug development and evaluation. In Parkinson's disease (PD), several models have described longitudinal changes in the Unified Parkinson's Disease Rating Scale (UPDRS), one of the most utilized outcome measures for PD trials assessing disease progression. We conducted a literature review to examine the methods and applications of quantitative disease progression modeling for PD using a combination of key words including "Parkinson disease," "progression," and "model." For this review, we focused on models of PD progression quantifying changes in the total UPDRS scores against time. Four different models reporting equations and parameters have been published using linear and nonlinear functions. The reasons for constructing disease progression models of PD thus far have been to quantify disease trajectories of PD patients in active and inactive treatment arms of clinical trials, to quantify and discern symptomatic and disease-modifying treatment effects, and to demonstrate how model-based methods may be used to design clinical trials. The historical lack of efficiency of PD clinical trials begs for model-based simulations in planning for studies that result in more informative conclusions, particularly around disease modification. © 2016 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Charles S. Venuto
- Center for Human Experimental Therapeutics, University of Rochester, Rochester, NY, USA
- Department of Neurology, University of Rochester, Rochester NY USA
| | - Nicholas B. Potter
- Center for Human Experimental Therapeutics, University of Rochester, Rochester, NY, USA
| | - E. Ray Dorsey
- Center for Human Experimental Therapeutics, University of Rochester, Rochester, NY, USA
- Department of Neurology, University of Rochester, Rochester NY USA
| | - Karl Kieburtz
- Center for Human Experimental Therapeutics, University of Rochester, Rochester, NY, USA
- Department of Neurology, University of Rochester, Rochester NY USA
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Wendland JR, Ehlers MD. Translating Neurogenomics Into New Medicines. Biol Psychiatry 2016; 79:650-6. [PMID: 26140822 DOI: 10.1016/j.biopsych.2015.04.027] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2014] [Revised: 02/27/2015] [Accepted: 04/16/2015] [Indexed: 10/23/2022]
Abstract
Brain disorders remain one of the defining challenges of modern medicine and among the most poorly served with new therapeutics. Advances in human neurogenetics have begun to shed light on the genomic architecture of complex diseases of mood, cognition, brain development, and neurodegeneration. From genome-wide association studies to rare variants, these findings hold promise for defining the pathogenesis of brain disorders that have resisted simple molecular description. However, the path from genetics to new medicines is far from clear and can take decades, even for the most well-understood genetic disorders. In this review, we define three challenges for the field of neurogenetics that we believe must be addressed to translate human genetics efficiently into new therapeutics for brain disorders.
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Affiliation(s)
- Jens R Wendland
- PharmaTherapeutics Clinical Research, Worldwide Research and Development, Pfizer Inc., Cambridge, Massachusetts
| | - Michael D Ehlers
- Neuroscience Research Unit, Worldwide Research and Development, Pfizer Inc., Cambridge, Massachusetts.
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Verma N, Beretvas SN, Pascual B, Masdeu JC, Markey MK. New scoring methodology improves the sensitivity of the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) in clinical trials. ALZHEIMERS RESEARCH & THERAPY 2015; 7:64. [PMID: 26560146 PMCID: PMC4642693 DOI: 10.1186/s13195-015-0151-0] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 09/28/2015] [Indexed: 01/11/2023]
Abstract
Introduction As currently used, the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) has low sensitivity for measuring Alzheimer’s disease progression in clinical trials. A major reason behind the low sensitivity is its sub-optimal scoring methodology, which can be improved to obtain better sensitivity. Methods Using item response theory, we developed a new scoring methodology (ADAS-CogIRT) for the ADAS-Cog, which addresses several major limitations of the current scoring methodology. The sensitivity of the ADAS-CogIRT methodology was evaluated using clinical trial simulations as well as a negative clinical trial, which had shown an evidence of a treatment effect. Results The ADAS-Cog was found to measure impairment in three cognitive domains of memory, language, and praxis. The ADAS-CogIRT methodology required significantly fewer patients and shorter trial durations as compared to the current scoring methodology when both were evaluated in simulated clinical trials. When validated on data from a real clinical trial, the ADAS-CogIRT methodology had higher sensitivity than the current scoring methodology in detecting the treatment effect. Conclusions The proposed scoring methodology significantly improves the sensitivity of the ADAS-Cog in measuring progression of cognitive impairment in clinical trials focused in the mild-to-moderate Alzheimer’s disease stage. This provides a boost to the efficiency of clinical trials requiring fewer patients and shorter durations for investigating disease-modifying treatments. Electronic supplementary material The online version of this article (doi:10.1186/s13195-015-0151-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nishant Verma
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton Street Stop C0800, Austin, TX, 78712, USA. .,NeuroTexas Institute Research Foundation, St. David's HealthCare, 1015 E. 32nd Street Suite 404, Austin, TX, 78705, USA.
| | - S Natasha Beretvas
- Department of Educational Psychology, The University of Texas at Austin, 1 University Station D5800, Austin, TX, 78712, USA.
| | - Belen Pascual
- Nantz National Alzheimer Center, Houston Methodist Neurological Institute, 6560 Fannin Street, Houston, TX, 77030, USA.
| | - Joseph C Masdeu
- Nantz National Alzheimer Center, Houston Methodist Neurological Institute, 6560 Fannin Street, Houston, TX, 77030, USA.
| | - Mia K Markey
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keeton Street Stop C0800, Austin, TX, 78712, USA. .,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street FCT14.50000, Houston, TX, 77030, USA.
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Samtani MN, Xu SX, Russu A, Adedokun OJ, Lu M, Ito K, Corrigan B, Raje S, Brashear HR, Styren S, Hu C. Alzheimer's disease assessment scale-cognitive 11-item progression model in mild-to-moderate Alzheimer's disease trials of bapineuzumab. ALZHEIMERS & DEMENTIA-TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS 2015; 1:157-169. [PMID: 29854935 PMCID: PMC5975060 DOI: 10.1016/j.trci.2015.09.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Introduction The objective of this study was to estimate longitudinal changes in disease progression (measured by Alzheimer's disease assessment scale-cognitive 11-item [ADAS-cog/11] scale) after bapineuzumab treatment and to identify covariates (demographics or baseline characteristics) contributing to the variability in disease progression rate and baseline disease status. Methods A population-based disease progression model was developed using pooled placebo and bapineuzumab data from two phase-3 studies in APOE ε4 noncarrier and carrier Alzheimer's disease (AD) patients. Results A beta regression model with the Richard's function as the structural component best described ADAS-cog/11 disease progression for mild-to-moderate AD population. This analysis confirmed no effect of bapineuzumab exposure on ADAS-cog/11 progression rate, consistent with the lack of clinical efficacy observed in the statistical analysis of ADAS-cog/11 data in both studies. Assessment of covariates affecting baseline severity revealed that men had a 6% lower baseline ADAS-cog/11 score than women; patients who took two AD concomitant medications had a 19% higher (worse) baseline score; APOE ε4 noncarriers had a 5% lower baseline score; and patients who had AD for a longer duration had a higher baseline score. Furthermore, shorter AD duration, younger age, APOE ε4 carrier status, and use of two AD concomitant medications were associated with faster disease progression rates. Patients who had an ADAS-cog/11 score progression rate that was not statistically significantly different from 0 typically took no AD concomitant medications. Discussion The beta regression model is a sensible modeling approach to characterize cognitive decline in AD patients. The influence of bapineuzumab exposure on disease progression measured by ADAS-cog/11 was not significant. Trial Registration ClinicalTrials.gov identifier: NCT00575055 and NCT00574132.
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Affiliation(s)
| | - Steven X Xu
- Janssen Research & Development, LLC, NJ, USA
| | | | | | - Ming Lu
- Janssen Research & Development, LLC, NJ, USA
| | | | | | | | | | | | - Chuanpu Hu
- Janssen Research & Development, LLC, NJ, USA
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Xu SX, Samtani MN, Russu A, Adedokun OJ, Lu M, Ito K, Corrigan B, Raje S, Brashear HR, Styren S, Hu C. Alzheimer's disease progression model using disability assessment for dementia scores from bapineuzumab trials. ALZHEIMERS & DEMENTIA-TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS 2015; 1:141-149. [PMID: 29854934 PMCID: PMC5975025 DOI: 10.1016/j.trci.2015.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Objective Disability assessment for dementia (DAD) measurements from two phase-3 studies of bapineuzumab in APOE ε4 noncarrier and carrier Alzheimer's disease (AD) patients were integrated to develop a disease progression model. Methods We evaluated longitudinal changes in DAD scores, baseline factors affecting disease progression, and bapineuzumab effect on disease progression. Results A beta regression model best described DAD disease progression. The estimated treatment effect of bapineuzumab was not significant, consistent with lack of clinical efficacy observed in the primary analysis. The model suggested that progression of DAD tended to decrease with increase in bapineuzumab exposure. The exposure-response relationship was similar regardless of APOE ε4 status but more pronounced in patients with mild AD. Baseline disease status, age, memantine use, and years since onset (YSO) had significant effects on baseline DAD scores. AD concomitant medication use, baseline disease status, and YSO had significant effects on disease progression rate, measured by DAD score. Conclusions The beta regression model is a sensible modeling approach to characterize functional decline in AD patients. This analysis suggested a possible effect of bapineuzumab exposure on DAD progression. Further evaluation may be warranted in future studies. Trial Registration ClinicalTrials.gov identifier: NCT00575055 and NCT00574132.
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Affiliation(s)
- Steven X Xu
- Janssen Research & Development, LLC, Raritan, NJ, USA
| | | | | | | | - Ming Lu
- Janssen Research & Development LLC, Spring House, PA, USA
| | | | | | | | - H Robert Brashear
- Janssen Alzheimer Immunotherapy Research & Development, LLC, South San Francisco, CA, USA
| | | | - Chuanpu Hu
- Janssen Research & Development LLC, Spring House, PA, USA
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39
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ. 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement 2015; 11:e1-120. [PMID: 26073027 PMCID: PMC5469297 DOI: 10.1016/j.jalz.2014.11.001] [Citation(s) in RCA: 203] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/18/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Johan Luthman
- Neuroscience Clinical Development, Neuroscience & General Medicine Product Creation Unit, Eisai Inc., Philadelphia, PA, USA
| | - John C Morris
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam Schwarz
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Romero K, Ito K, Rogers JA, Polhamus D, Qiu R, Stephenson D, Mohs R, Lalonde R, Sinha V, Wang Y, Brown D, Isaac M, Vamvakas S, Hemmings R, Pani L, Bain LJ, Corrigan B. The future is now: model-based clinical trial design for Alzheimer's disease. Clin Pharmacol Ther 2015; 97:210-4. [PMID: 25669145 PMCID: PMC6463482 DOI: 10.1002/cpt.16] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2014] [Accepted: 10/24/2014] [Indexed: 11/08/2022]
Abstract
Failures in trials for Alzheimer's disease (AD) may be attributable to inadequate dosing, population selection, drug inefficacy, or insufficient design optimization. The Coalition Against Major Diseases (CAMD) was formed in 2008 to develop drug development tools (DDT) to expedite drug development for AD and Parkinson's disease. CAMD led a process that successfully advanced a clinical trial simulation (CTS) tool for AD through the formal regulatory review process at the US Food and Drug Administration (FDA) and European Medicines Agency (EMA).
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Affiliation(s)
- K Romero
- Critical Path Institute, Tucson, Arizone, USA
| | - K Ito
- Pfizer, Groton, Connecticut, USA
| | - JA Rogers
- Metrum Research Group, Tariffville, Connecticut, USA
| | - D Polhamus
- Metrum Research Group, Tariffville, Connecticut, USA
| | - R Qiu
- Pfizer, Groton, Connecticut, USA
| | | | - R Mohs
- Eli Lilly, Lilly Corporate Center, Indianapolis, Indiana, USA
| | | | - V Sinha
- US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Y Wang
- US Food and Drug Administration, Silver Spring, Maryland, USA
| | - D Brown
- European Medicines Agency, London, UK
| | - M Isaac
- European Medicines Agency, London, UK
| | | | | | - L Pani
- European Medicines Agency, London, UK
| | - LJ Bain
- Critical Path Institute, Tucson, Arizone, USA
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Holford N. Clinical pharmacology = disease progression + drug action. Br J Clin Pharmacol 2015; 79:18-27. [PMID: 23713816 PMCID: PMC4294073 DOI: 10.1111/bcp.12170] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2013] [Accepted: 04/30/2013] [Indexed: 01/20/2023] Open
Abstract
Clinical pharmacology is concerned with understanding how to use medicines to treat disease. Pharmacokinetics and pharmacodynamics have provided powerful methodologies for describing the time course of concentration and effect in individuals and in populations. This population approach may also be applied to describing the progression of disease and the action of drugs to change disease progress. Quantitative models for symptomatic and disease-modifying effects of drugs are valuable not only for describing drugs and diseases but also for identifying criteria to distinguish between types of drug actions, with implications for regulatory decisions and long-term patient care.
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Affiliation(s)
- Nick Holford
- Department of Pharmacology & Clinical Pharmacology, University of Auckland, Auckland, New Zealand
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42
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Lorenzi M, Pennec X, Frisoni GB, Ayache N. Disentangling normal aging from Alzheimer's disease in structural magnetic resonance images. Neurobiol Aging 2015; 36 Suppl 1:S42-52. [PMID: 25311276 DOI: 10.1016/j.neurobiolaging.2014.07.046] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2013] [Revised: 07/25/2014] [Accepted: 07/28/2014] [Indexed: 12/31/2022]
Affiliation(s)
- Marco Lorenzi
- Asclepios Research Project, INRIA Sophia Antipolis, Sophia Antipolis, France.
| | - Xavier Pennec
- Asclepios Research Project, INRIA Sophia Antipolis, Sophia Antipolis, France
| | - Giovanni B Frisoni
- IRCCS Fatebenefratelli, Brescia, Italy; Memory Clinic, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Nicholas Ayache
- Asclepios Research Project, INRIA Sophia Antipolis, Sophia Antipolis, France
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Shi J, Stonnington CM, Thompson PM, Chen K, Gutman B, Reschke C, Baxter LC, Reiman EM, Caselli RJ, Wang Y. Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry. Neuroimage 2015; 104:1-20. [PMID: 25285374 PMCID: PMC4252650 DOI: 10.1016/j.neuroimage.2014.09.062] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2014] [Revised: 09/20/2014] [Accepted: 09/29/2014] [Indexed: 11/29/2022] Open
Abstract
Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Boris Gutman
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Cole Reschke
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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Qiu Y, Li L, Zhou TY, Lu W. Alzheimer's disease progression model based on integrated biomarkers and clinical measures. Acta Pharmacol Sin 2014; 35:1111-20. [PMID: 25088003 DOI: 10.1038/aps.2014.57] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Accepted: 05/16/2014] [Indexed: 12/15/2022] Open
Abstract
AIM Biomarkers and image markers of Alzheimer's disease (AD), such as cerebrospinal fluid Aβ42 and p-tau, are effective predictors of cognitive decline or dementia. The aim of this study was to integrate these markers with a disease progression model and to identify their abnormal ranges. METHODS The data of 395 participants, including 86 normal subjects, 108 early mild cognitive impairment (EMCI) subjects, 120 late mild cognitive impairment (LMCI) subjects, and 81 AD subjects were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. For the participants, baseline and long-term data on cerebrospinal fluid Aβ42 and p-tau, hippocampal volume, and ADAS-cog were available. Various linear and nonlinear models were tested to determine the associations among the ratio of Aβ42 to p-tau (the Ratio), hippocampal volume and ADAS-cog. RESULTS The most likely models for the Ratio, hippocampal volume, and ADAS-cog (logistic, Emax, and linear models, respectively) were used to construct the final model. Baseline disease state had an impact on all the 3 endpoints (the Ratio, hippocampal volume, and ADAS-cog), while APOEε4 genotype and age only influence the Ratio and hippocampal volume. CONCLUSION The Ratio can be used to identify the disease stage for an individual, and clinical measures integrated with the Ratio improve the accuracy of mild cognitive impairment (MCI) to AD conversion forecasting.
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Conrado DJ, Denney WS, Chen D, Ito K. An updated Alzheimer's disease progression model: incorporating non-linearity, beta regression, and a third-level random effect in NONMEM. J Pharmacokinet Pharmacodyn 2014; 41:581-98. [PMID: 25168488 DOI: 10.1007/s10928-014-9375-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 08/11/2014] [Indexed: 11/26/2022]
Abstract
Our objective was to expand our understanding of the predictors of Alzheimer's disease (AD) progression to help design a clinical trial on a novel AD medication. We utilized the Coalition Against Major Diseases AD dataset consisting of control-arm data (both placebo and stable background AD medication) from 15 randomized double-blind clinical trials in mild-to-moderate AD patients (4,495 patients; July 2013). Our ADAS-cog longitudinal model incorporates a beta-regression with between-study, -subject, and -residual variability in NONMEM; it suggests that faster AD progression is associated with younger age and higher number of apolipoprotein E type 4 alleles (APOE*4), after accounting for baseline disease severity. APOE*4, in particular, seems to be implicated in the AD pathogenesis. In addition, patients who are already on stable background AD medications appear to have a faster progression relative to those who are not receiving AD medication. The current knowledge does not support a causality relationship between use of background AD medications and higher rate of disease progression, and the correlation is potentially due to confounding covariates. Although causality has not necessarily been demonstrated, this model can inform inclusion criteria and stratification, sample size, and trial duration.
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Affiliation(s)
- Daniela J Conrado
- Pharmatherapeutics Clinical Pharmacology, Pfizer Inc., Cambridge, MA, 02139, USA,
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Shen L, Thompson PM, Potkin SG, Bertram L, Farrer LA, Foroud TM, Green RC, Hu X, Huentelman MJ, Kim S, Kauwe JSK, Li Q, Liu E, Macciardi F, Moore JH, Munsie L, Nho K, Ramanan VK, Risacher SL, Stone DJ, Swaminathan S, Toga AW, Weiner MW, Saykin AJ. Genetic analysis of quantitative phenotypes in AD and MCI: imaging, cognition and biomarkers. Brain Imaging Behav 2014; 8:183-207. [PMID: 24092460 PMCID: PMC3976843 DOI: 10.1007/s11682-013-9262-z] [Citation(s) in RCA: 121] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The Genetics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI), formally established in 2009, aims to provide resources and facilitate research related to genetic predictors of multidimensional Alzheimer's disease (AD)-related phenotypes. Here, we provide a systematic review of genetic studies published between 2009 and 2012 where either ADNI APOE genotype or genome-wide association study (GWAS) data were used. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We also discuss the diverse analytical strategies used in these studies, including univariate and multivariate analysis, meta-analysis, pathway analysis, and interaction and network analysis. Finally, we perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. Major ADNI findings included all the top 10 AD genes and several of these (e.g., APOE, BIN1, CLU, CR1, and PICALM) were corroborated by ADNI imaging, fluid and cognitive phenotypes. ADNI imaging genetics studies discovered novel findings (e.g., FRMD6) that were later replicated on different data sets. Several other genes (e.g., APOC1, FTO, GRIN2B, MAGI2, and TOMM40) were associated with multiple ADNI phenotypes, warranting further investigation on other data sets. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies employing next-generation sequencing and convergent multi-omics approaches, and for clinical drug and biomarker development.
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Affiliation(s)
- Li Shen
- Center for Neuroimaging and Indiana Alzheimer’s Disease Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - Paul M. Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
| | - Steven G. Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA 92617 USA
| | - Lars Bertram
- Neuropsychiatric Genetics Group, Max-Planck Institute for Molecular Genetics, Berlin, Germany
| | - Lindsay A. Farrer
- Biomedical Genetics L320, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118 USA
| | - Tatiana M. Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Robert C. Green
- Division of Genetics and Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115 USA
| | - Xiaolan Hu
- Clinical Genetics, Exploratory Clinical & Translational Research, Bristol-Myers Squibbs, Pennington, NJ 08534 USA
| | - Matthew J. Huentelman
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ 85004 USA
| | - Sungeun Kim
- Center for Neuroimaging and Indiana Alzheimer’s Disease Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - John S. K. Kauwe
- Departments of Biology, Neuroscience, Brigham Young University, 675 WIDB, Provo, UT 84602 USA
| | - Qingqin Li
- Department of Neuroscience Biomarkers, Janssen Research and Development, LLC, Raritan, NJ 08869 USA
| | - Enchi Liu
- Biomarker Discovery, Janssen Alzheimer Immunotherapy Research and Development, LLC, South San Francisco, CA 94080 USA
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA 92617 USA
- Department of Sciences and Biomedical Technologies, University of Milan, Segrate, MI Italy
| | - Jason H. Moore
- Department of Genetics, Computational Genetics Laboratory, Dartmouth Medical School, Lebanon, NH 03756 USA
| | - Leanne Munsie
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN 46285 USA
| | - Kwangsik Nho
- Center for Neuroimaging and Indiana Alzheimer’s Disease Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - Vijay K. Ramanan
- Center for Neuroimaging and Indiana Alzheimer’s Disease Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th Street, Suite 4100, Indianapolis, IN 46202 USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Shannon L. Risacher
- Center for Neuroimaging and Indiana Alzheimer’s Disease Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - David J. Stone
- Merck Research Laboratories, 770 Sumneytown Pike, WP53B-120, West Point, PA 19486 USA
| | - Shanker Swaminathan
- Center for Neuroimaging and Indiana Alzheimer’s Disease Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th Street, Suite 4100, Indianapolis, IN 46202 USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
| | - Michael W. Weiner
- Departments of Radiology, Medicine and Psychiatry, UC San Francisco, San Francisco, CA 94143 USA
| | - Andrew J. Saykin
- Center for Neuroimaging and Indiana Alzheimer’s Disease Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th Street, Suite 4100, Indianapolis, IN 46202 USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Center for Neuroimaging and Indiana Alzheimer’s Disease Center, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th Street, Suite 4100, Indianapolis, IN 46202 USA
- Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA 92617 USA
- Neuropsychiatric Genetics Group, Max-Planck Institute for Molecular Genetics, Berlin, Germany
- Biomedical Genetics L320, Boston University School of Medicine, 72 East Concord Street, Boston, MA 02118 USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202 USA
- Division of Genetics and Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115 USA
- Clinical Genetics, Exploratory Clinical & Translational Research, Bristol-Myers Squibbs, Pennington, NJ 08534 USA
- Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ 85004 USA
- Departments of Biology, Neuroscience, Brigham Young University, 675 WIDB, Provo, UT 84602 USA
- Department of Neuroscience Biomarkers, Janssen Research and Development, LLC, Raritan, NJ 08869 USA
- Biomarker Discovery, Janssen Alzheimer Immunotherapy Research and Development, LLC, South San Francisco, CA 94080 USA
- Department of Sciences and Biomedical Technologies, University of Milan, Segrate, MI Italy
- Department of Genetics, Computational Genetics Laboratory, Dartmouth Medical School, Lebanon, NH 03756 USA
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN 46285 USA
- Merck Research Laboratories, 770 Sumneytown Pike, WP53B-120, West Point, PA 19486 USA
- Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095 USA
- Departments of Radiology, Medicine and Psychiatry, UC San Francisco, San Francisco, CA 94143 USA
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Braskie MN, Thompson PM. A focus on structural brain imaging in the Alzheimer's disease neuroimaging initiative. Biol Psychiatry 2014; 75:527-33. [PMID: 24367935 PMCID: PMC4019004 DOI: 10.1016/j.biopsych.2013.11.020] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2013] [Revised: 11/05/2013] [Accepted: 11/06/2013] [Indexed: 01/18/2023]
Abstract
In recent years, numerous laboratories and consortia have used neuroimaging to evaluate the risk for and progression of Alzheimer's disease (AD). The Alzheimer's Disease Neuroimaging Initiative is a longitudinal, multicenter study that is evaluating a range of biomarkers for use in diagnosis of AD, prediction of patient outcomes, and clinical trials. These biomarkers include brain metrics derived from magnetic resonance imaging (MRI) and positron emission tomography scans as well as metrics derived from blood and cerebrospinal fluid. We focus on Alzheimer's Disease Neuroimaging Initiative studies published between 2011 and March 2013 for which structural MRI was a major outcome measure. Our main goal was to review key articles offering insights into progression of AD and the relationships of structural MRI measures to cognition and to other biomarkers in AD. In Supplement 1, we also discuss genetic and environmental risk factors for AD and exciting new analysis tools for the efficient evaluation of large-scale structural MRI data sets such as the Alzheimer's Disease Neuroimaging Initiative data.
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Affiliation(s)
- Meredith N Braskie
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, California; Department of Neurology, University of Southern California, Los Angeles, California
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, California; Department of Neurology, University of Southern California, Los Angeles, California; Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, California; Department of Radiology, University of Southern California, Los Angeles, California; Department of Pediatrics, University of Southern California, Los Angeles, California; Department of Ophthalmology, University of Southern California, Los Angeles, California; Keck School of Medicine, and Viterbi School of Engineering, University of Southern California, Los Angeles, California.
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Samtani MN, Raghavan N, Novak G, Nandy P, Narayan VA. Disease progression model for Clinical Dementia Rating-Sum of Boxes in mild cognitive impairment and Alzheimer's subjects from the Alzheimer's Disease Neuroimaging Initiative. Neuropsychiatr Dis Treat 2014; 10:929-52. [PMID: 24926196 PMCID: PMC4049432 DOI: 10.2147/ndt.s62323] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The objective of this analysis was to develop a nonlinear disease progression model, using an expanded set of covariates that captures the longitudinal Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) scores. These were derived from the Alzheimer's Disease Neuroimaging Initiative ADNI-1 study, of 301 Alzheimer's disease and mild cognitive impairment patients who were followed for 2-3 years. METHODS The model describes progression rate and baseline disease score as a function of covariates. The covariates that were tested fell into five groups: a) hippocampal volume; b) serum and cerebrospinal fluid (CSF) biomarkers; c) demographics and apolipoprotein Epsilon 4 (ApoE4) allele status; d) baseline cognitive tests; and e) disease state and comedications. RESULTS Covariates associated with baseline disease severity were disease state, hippocampal volume, and comedication use. Disease progression rate was influenced by baseline CSF biomarkers, Trail-Making Test part A score, delayed logical memory test score, and current level of impairment as measured by CDR-SB. The rate of disease progression was dependent on disease severity, with intermediate scores around the inflection point score of 10 exhibiting high disease progression rate. The CDR-SB disease progression rate in a typical patient, with late mild cognitive impairment and mild Alzheimer's disease, was estimated to be approximately 0.5 and 1.4 points/year, respectively. CONCLUSIONS In conclusion, this model describes disease progression in terms of CDR-SB changes in patients and its dependency on novel covariates. The CSF biomarkers included in the model discriminate mild cognitive impairment subjects as progressors and nonprogressors. Therefore, the model may be utilized for optimizing study designs, through patient population enrichment and clinical trial simulations.
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Affiliation(s)
| | | | - Gerald Novak
- Janssen Research and Development, LLC, Raritan, New Jersey, USA
| | - Partha Nandy
- Janssen Research and Development, LLC, Raritan, New Jersey, USA
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Hall JR, Wiechmann AR, Johnson LA, Edwards M, Barber RC, Cunningham R, Singh M, O'Bryant SE. Total cholesterol and neuropsychiatric symptoms in Alzheimer's disease: the impact of total cholesterol level and gender. Dement Geriatr Cogn Disord 2014; 38:300-9. [PMID: 25011444 PMCID: PMC4201880 DOI: 10.1159/000361043] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/04/2014] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD) are a major factor in nursing home placement and a primary cause of stress for caregivers. Elevated cholesterol has been linked to psychiatric disorders and has been shown to be a risk factor for AD and to impact disease progression. The present study investigated the relationship between cholesterol and NPS in AD. METHODS Data on cholesterol and NPS from 220 individuals (144 females, 76 males) with mild-to-moderate AD from the Texas Alzheimer's Research and Care Consortium (TARCC) cohort were analyzed. The total number of NPS and symptoms of hyperactivity, psychosis, affect and apathy were evaluated. Groups based on total cholesterol (TC; ≥200 vs. <200 mg/dl) were compared with regard to NPS. The impact of gender was also assessed. RESULTS Individuals with high TC had lower MMSE scores as well as significantly more NPS and more symptoms of psychosis. When stratified by gender, males with high TC had significantly more NPS than females with high TC or than males or females with low TC. CONCLUSION The role of elevated cholesterol in the occurrence of NPS in AD appears to be gender and symptom specific. A cross-validation of these findings will have implications for possible treatment interventions, especially for males with high TC.
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Affiliation(s)
- James R. Hall
- Institute of Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, Texas, USA, Department of Psychiatry and Behavioral Health, University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - April R. Wiechmann
- Institute of Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, Texas, USA, Department of Psychiatry and Behavioral Health, University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - Leigh A. Johnson
- Institute of Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, Texas, USA, Department of Internal Medicine, University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - Melissa Edwards
- Department of Psychology, University of North Texas, Denton, Texas, USA
| | - Robert C. Barber
- Institute of Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, Texas, USA, Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - Rebecca Cunningham
- Institute of Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, Texas, USA, Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - Meharvan Singh
- Institute of Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, Texas, USA, Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, Texas, USA
| | - Sid E. O'Bryant
- Institute of Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, Texas, USA, Department of Internal Medicine, University of North Texas Health Science Center, Fort Worth, Texas, USA
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