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Beckett LA, Saito N, Donohue MC, Harvey DJ. Contributions of the ADNI Biostatistics Core. Alzheimers Dement 2024. [PMID: 39140601 DOI: 10.1002/alz.14159] [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: 04/26/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 08/15/2024]
Abstract
The goal of the Biostatistics Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has been to ensure that sound study designs and statistical methods are used to meet the overall goals of ADNI. We have supported the creation of a well-validated and well-curated longitudinal database of clinical and biomarker information on ADNI participants and helped to make this accessible and usable for researchers. We have developed a statistical methodology for characterizing the trajectories of clinical and biomarker change for ADNI participants across the spectrum from cognitively normal to dementia, including multivariate patterns and evidence for heterogeneity in cognitive aging. We have applied these methods and adapted them to improve clinical trial design. ADNI-4 will offer us a chance to help extend these efforts to a more diverse cohort with an even richer panel of biomarker data to support better knowledge of and treatment for Alzheimer's disease and related dementias. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Biostatistics Core provides study design and analytic support to ADNI investigators. Core members develop and apply novel statistical methodology to work with ADNI data and support clinical trial design. The Core contributes to the standardization, validation, and harmonization of biomarker data. The Core serves as a resource to the wider research community to address questions related to the data and study as a whole.
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Affiliation(s)
- Laurel A Beckett
- Department of Public Health Sciences, University of California, Davis, California, USA
| | - Naomi Saito
- Department of Public Health Sciences, University of California, Davis, California, USA
| | - Michael C Donohue
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, University of California, Davis, California, USA
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Nakagawa S, Kowa H, Takagi Y, Kakei Y, Kagimura T, Sanada S, Nagai Y. Efficacy of a non-pharmaceutical multimodal intervention program in a group setting for patients with mild cognitive impairment: A single-arm interventional study with pre-post and external control analyses. Contemp Clin Trials Commun 2024; 40:101326. [PMID: 39021673 PMCID: PMC11252792 DOI: 10.1016/j.conctc.2024.101326] [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] [Received: 04/03/2024] [Revised: 05/31/2024] [Accepted: 06/13/2024] [Indexed: 07/20/2024] Open
Abstract
Aim This study aimed to evaluate the efficacy of a non-pharmaceutical multimodal intervention program consisting of physical exercise, cognitive stimulation, and health education in a group setting to slow the progression of mild cognitive impairment (MCI). Methods A single-arm interventional study was conducted on 27 patients with MCI. To evaluate the efficacy of the intervention program, a pre-post analysis was performed using EuroQol-5 Dimension (EQ-5D), Mini-Mental State Examination (MMSE), Cognitive Function Instrument (CFI), 5 Cog test, depression, and physical performance before and after the 8-month intervention. Additionally, propensity score and the semi-Bayes analyses were performed to compare the intervention program with standard medical care, using the external control patients' data for MMSE scores. Results Twenty-four patients completed the intervention program. During the study period, although EQ-5D and MMSE scores remained unchanged (mean change 0.02 [95 % confidence interval (CI): -0.004, 0.04], 0.5 [-0.2, 1.3]), CFI and the subcategories of 5Cog (attention and reasoning) improved (mean change -1.23 [-2.24, -0.21], 4.3 [0.9, 7.7], 3.0 [0.4, 5.6]). In the additional analysis comparing changes in MMSE scores, patients who underwent the intervention program had less decline than the external control patients (mean change -1.7 [-2.1, -1.3]) with an observed mean difference of 2.25 [1.46, 3.03], and propensity score-adjusted difference of 2.26 [1.46, 3.05]. The semi-Bayesian approach also suggested that the intervention slowed the progression of MCI. Conclusion A non-pharmaceutical multimodal intervention program could contribute to slowing cognitive decline in patients with MCI.
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Affiliation(s)
- Satoshi Nakagawa
- Division of Translational Science, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
- Translational Research Center for Medical Innovation, Foundation for Biomedical Research and Innovation at Kobe, Kobe, Hyogo, Japan
| | - Hisatomo Kowa
- Division of Cognitive and Psychiatric Rehabilitation, Department of Rehabilitation Science, Kobe University Graduate School of Health Sciences, Kobe, Hyogo, Japan
| | - Yumi Takagi
- Department of Clinical Biostatistics, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Yasumasa Kakei
- Clinical and Translational Research Centre, Kobe University Hospital, Kobe, Hyogo, Japan
| | - Tatsuo Kagimura
- Translational Research Center for Medical Innovation, Foundation for Biomedical Research and Innovation at Kobe, Kobe, Hyogo, Japan
| | - Shoji Sanada
- Division of Translational Science, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
- Clinical and Translational Research Centre, Kobe University Hospital, Kobe, Hyogo, Japan
| | - Yoji Nagai
- Division of Translational Science, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
- Department of Clinical Research Facilitation Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Kyoto, Japan
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3
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Iwata A. How will the emergence of lecanemab change dementia treatment? Geriatr Gerontol Int 2024. [PMID: 39034660 DOI: 10.1111/ggi.14945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 06/25/2024] [Accepted: 07/08/2024] [Indexed: 07/23/2024]
Abstract
The introduction of lecanemab has dramatically changed the field of dementia medicine. Lecanemab, defined as an anti-amyloid-β (Aβ) drug, comprises an antibody against Aβ, a protein structure believed to cause Alzheimer's disease. This drug represents a new direction in dementia treatment. In a phase III study, lecanemab was found to significantly slow cognitive decline, while showing manageable levels of amyloid-related imaging abnormalities, which are side-effects of lecanemab. Furthermore, lecanemab has been shown to effectively reduce Aβ accumulation in patients with early Alzheimer's disease, which might contribute not only to delaying the progression of cognitive decline, but also to improving the quality of life of patients and their families. However, there are conditions for the use of lecanemab, for which the Ministry of Health, Labor and Welfare has issued the Guidelines for Promotion of Optimal Use. These guidelines specify requirements for appropriate patient selection, prescribing physicians and administering medical institutions to ensure safe and effective use. Particular emphasis is placed on the confirmation of amyloid-β accumulation, amyloid-related imaging abnormalities risk management and appropriate handling of side-effects. The clinical use of lecanemab represents an important advancement in the treatment of dementia; however, the understanding and cooperation of healthcare professionals, patients and families are essential to maximize its efficacy and safety. Future issues to be addressed include the sustainability and long-term efficacy of treatment, improvement of clinical symptoms after removal of Aβ and motivation to administer the drug. Although lecanemab offers hope for the treatment of dementia, its use requires careful management. Geriatr Gerontol Int 2024; ••: ••-••.
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Affiliation(s)
- Atsushi Iwata
- Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
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4
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Ichii S, Oba H, Sugimura Y, Yang Y, Shoji M, Ihara K. A Longitudinal Study of CogEvo's Prediction of Cognitive Decline in Older Adults. Healthcare (Basel) 2024; 12:1379. [PMID: 39057523 PMCID: PMC11275605 DOI: 10.3390/healthcare12141379] [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: 04/29/2024] [Revised: 07/03/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
The predictive abilities of computer-based screening devices for early cognitive decline (CD) in older adults have rarely been longitudinally examined. Therefore, this study examined the ability of CogEvo, a short-duration, computer-based cognitive screening device requiring little professional involvement, to predict CD among community-dwelling older adults. We determined whether 119 individuals aged ≥ 65 years living in Japanese rural communities who scored ≥ 24 on the Mini-Mental State Examination (MMSE) at baseline developed CD by annually administering the MMSE to them. CD was defined as an MMSE score of ≤23. At baseline, the overall CogEvo judgment grade, with lower grades indicating better cognitive function, was calculated from the results of various cognitive tasks. Over 2 years, 10 participants developed CD. Participants with grades of 4 had a higher percentage of CD cases than those with grades of ≤3 (p < 0.01). This relationship remained significant after controlling for possible confounders, including the MMSE score at baseline. The sensitivity and specificity of the CogEvo grade cutoff of 4 were 50.0% and 93.6%, respectively. In conclusion, CogEvo may be an efficient tool for identifying individuals at a high risk for dementia. The possibility of missing CD cases should be considered when using CogEvo for screening.
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Affiliation(s)
- Sadanobu Ichii
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan; (S.I.); (Y.S.); (Y.Y.)
| | - Hikaru Oba
- Graduate School of Health Sciences, Hirosaki University, Hirosaki 036-8564, Japan;
| | - Yoshikuni Sugimura
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan; (S.I.); (Y.S.); (Y.Y.)
| | - Yichi Yang
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan; (S.I.); (Y.S.); (Y.Y.)
| | - Mikio Shoji
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi 371-8511, Japan;
| | - Kazushige Ihara
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan; (S.I.); (Y.S.); (Y.Y.)
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Iwatsubo T. Development of disease-modifying therapies against Alzheimer's disease. Psychiatry Clin Neurosci 2024. [PMID: 38842037 DOI: 10.1111/pcn.13681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/04/2024] [Accepted: 04/22/2024] [Indexed: 06/07/2024]
Abstract
To successfully develop disease-modifying therapies (DMT) against Alzheimer's disease (AD), it is important to target the mild stage of the disease, before the pathological changes progress and dementia symptoms are fully manifested. To this end, the AD Neuroimaging Initiative (ADNI), a large-scale observational study, was initiated in the U.S. with the goal of development of DMT that are effective in the early stages of mild cognitive impairment (MCI) by utilizing imaging and biomarkers. In Japan, J-ADNI enrolled and followed up 537 patients, mainly with MCI, and established a platform for evaluation including amyloid PET, and demonstrated a high similarity in the clinical course of amyloid-positive MCI (prodromal AD) in Japan and the U.S. In 2023, the anti-Aβ antibody lecanemab successfully completed a Phase III clinical trial for early AD (prodromal AD + mild AD dementia) and was granted regulatory approval and made available both in the US and Japan. Also, phase III trial of donanemab was completed successful. The J-TRC study was initiated in Japan as a "trial ready cohort (TRC)" consisting of participants who met the eligibility criteria for participation in preclinical and prodromal AD trials. Based on such a platform, the development of DMT for AD will progress more rapidly in the future.
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Affiliation(s)
- Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan
- National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
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Kikuchi M, Miyashita A, Hara N, Kasuga K, Saito Y, Murayama S, Kakita A, Akatsu H, Ozaki K, Niida S, Kuwano R, Iwatsubo T, Nakaya A, Ikeuchi T. Polygenic effects on the risk of Alzheimer's disease in the Japanese population. Alzheimers Res Ther 2024; 16:45. [PMID: 38414085 PMCID: PMC10898021 DOI: 10.1186/s13195-024-01414-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 02/11/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Polygenic effects have been proposed to account for some disease phenotypes; these effects are calculated as a polygenic risk score (PRS). This score is correlated with Alzheimer's disease (AD)-related phenotypes, such as biomarker abnormalities and brain atrophy, and is associated with conversion from mild cognitive impairment (MCI) to AD. However, the AD PRS has been examined mainly in Europeans, and owing to differences in genetic structure and lifestyle, it is unclear whether the same relationships between the PRS and AD-related phenotypes exist in non-European populations. In this study, we calculated and evaluated the AD PRS in Japanese individuals using genome-wide association study (GWAS) statistics from Europeans. METHODS In this study, we calculated the AD PRS in 504 Japanese participants (145 cognitively unimpaired (CU) participants, 220 participants with late mild cognitive impairment (MCI), and 139 patients with mild AD dementia) enrolled in the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) project. In order to evaluate the clinical value of this score, we (1) determined the polygenic effects on AD in the J-ADNI and validated it using two independent cohorts (a Japanese neuropathology (NP) cohort (n = 565) and the North American ADNI (NA-ADNI) cohort (n = 617)), (2) examined the AD-related phenotypes associated with the PRS, and (3) tested whether the PRS helps predict the conversion of MCI to AD. RESULTS The PRS using 131 SNPs had an effect independent of APOE. The PRS differentiated between CU participants and AD patients with an area under the curve (AUC) of 0.755 when combined with the APOE variants. Similar AUC was obtained when PRS calculated by the NP and NA-ADNI cohorts was applied. In MCI patients, the PRS was associated with cerebrospinal fluid phosphorylated-tau levels (β estimate = 0.235, p value = 0.026). MCI with a high PRS showed a significantly increased conversion to AD in APOE ε4 noncarriers with a hazard rate of 2.22. In addition, we also developed a PRS model adjusted for LD and observed similar results. CONCLUSIONS We showed that the AD PRS is useful in the Japanese population, whose genetic structure is different from that of the European population. These findings suggest that the polygenicity of AD is partially common across ethnic differences.
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Affiliation(s)
- Masataka Kikuchi
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Science, The University of Tokyo, 6-2-3 Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan.
- Department of Medical Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan.
| | - Akinori Miyashita
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan
| | - Norikazu Hara
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan
| | - Kensaku Kasuga
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan
| | - Yuko Saito
- Brain Bank for Aging Research (Department of Neuropathology), Tokyo Metropolitan Institute of Geriatrics and Gerontology, Tokyo, Japan
| | - Shigeo Murayama
- Brain Bank for Aging Research (Department of Neuropathology), Tokyo Metropolitan Institute of Geriatrics and Gerontology, Tokyo, Japan
- Brain Bank for Neurodevelopmental, Neurological and Psychiatric Disorders, United Graduate School of Child Development, Osaka University, Osaka, Japan
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research Institute, Niigata University, Niigata, Japan
| | - Hiroyasu Akatsu
- Department of General Medicine & General Internal Medicine, Nagoya City University Graduate School of Medicine, Nagoya, Japan
| | - Kouichi Ozaki
- Medical Genome Center, National Center for Geriatrics and Gerontology, Research Institute, Aichi, Japan
- RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Shumpei Niida
- Core Facility Administration, National Center for Geriatrics and Gerontology, Research Institute, Aichi, Japan
| | - Ryozo Kuwano
- Social Welfare Corporation Asahigawaso, Asahigawaso Research Institute, Okayama, Japan
| | - Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Akihiro Nakaya
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Science, The University of Tokyo, 6-2-3 Kashiwanoha, Kashiwa, Chiba, 277-0882, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, 1-757 Asahimachi, Niigata, 951-8585, Japan.
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7
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Sato K, Niimi Y, Ihara R, Suzuki K, Iwata A, Iwatsubo T. Simplifying Alzheimer's Disease Monitoring: A Novel Machine-Learning Approach to Estimate the Clinical Dementia Rating Sum of Box Changes Using the Mini-Mental State Examination and Functional Activities Questionnaire. J Alzheimers Dis 2024; 99:953-963. [PMID: 38759009 DOI: 10.3233/jad-231426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2024]
Abstract
Background Primary outcome measure in the clinical trials of disease modifying therapy (DMT) drugs for Alzheimer's disease (AD) has often been evaluated by Clinical Dementia Rating sum of boxes (CDRSB). However, CDR testing requires specialized training and 30-50 minutes to complete, not being suitable for daily clinical practice. Objective Herein, we proposed a machine-learning method to estimate CDRSB changes using simpler cognitive/functional batteries (Mini-Mental State Examination [MMSE] and Functional Activities Questionnaire [FAQ]), to replace CDR testing. Methods Baseline data from 944 ADNI and 171 J-ADNI amyloid-positive participants were used to build machine-learning models predicting annualized CDRSB changes between visits, based on MMSE and FAQ scores. Prediction performance was evaluated with mean absolute error (MAE) and R2 comparing predicted to actual rmDeltaCDRSB/rmDeltayear. We further assessed whether decline in cognitive function surpassing particular thresholds could be identified using the predicted rmDeltaCDRSB/rmDeltayear. RESULTS The models achieved the minimum required prediction errors (MAE < 1.0) and satisfactory prediction accuracy (R2>0.5) for mild cognitive impairment (MCI) patients for changes in CDRSB over periods of 18 months or longer. Predictions of annualized CDRSB progression>0.5, >1.0, or >1.5 demonstrated a consistent performance (i.e., Matthews correlation coefficient>0.5). These results were largely replicated in the J-ADNI case predictions. CONCLUSIONS Our method effectively predicted MCI patient deterioration in the CDRSB based solely on MMSE and FAQ scores. It may aid routine practice for disease-modifying therapy drug efficacy evaluation, without necessitating CDR testing at every visit.
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Affiliation(s)
- Kenichiro Sato
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Tokyo, Japan
| | - Yoshiki Niimi
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Tokyo, Japan
- Department of Healthcare Economics and Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryoko Ihara
- Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Kazushi Suzuki
- Division of Neurology, Internal Medicine, National Defense Medical College, Saitama, Japan
| | - Atsushi Iwata
- Department of Neurology, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan
| | - Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Tokyo, Japan
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Saito S, Suzuki K, Ohtani R, Maki T, Kowa H, Tachibana H, Washida K, Kawabata N, Mizuno T, Kanki R, Sudoh S, Kitaguchi H, Shindo K, Shindo A, Oka N, Yamamoto K, Yasuno F, Kakuta C, Kakuta R, Yamamoto Y, Hattori Y, Takahashi Y, Nakaoku Y, Tonomura S, Oishi N, Aso T, Taguchi A, Kagimura T, Kojima S, Taketsuna M, Tomimoto H, Takahashi R, Fukuyama H, Nagatsuka K, Yamamoto H, Fukushima M, Ihara M. Efficacy and Safety of Cilostazol in Mild Cognitive Impairment: A Randomized Clinical Trial. JAMA Netw Open 2023; 6:e2344938. [PMID: 38048134 PMCID: PMC10696485 DOI: 10.1001/jamanetworkopen.2023.44938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 10/15/2023] [Indexed: 12/05/2023] Open
Abstract
Importance Recent evidence indicates the efficacy of β-amyloid immunotherapy for the treatment of Alzheimer disease, highlighting the need to promote β-amyloid removal from the brain. Cilostazol, a selective type 3 phosphodiesterase inhibitor, promotes such clearance by facilitating intramural periarterial drainage. Objective To determine the safety and efficacy of cilostazol in mild cognitive impairment. Design, Setting, and Participants The COMCID trial (A Trial of Cilostazol for Prevention of Conversion from Mild Cognitive Impairment to Dementia) was an investigator-initiated, double-blind, phase 2 randomized clinical trial. Adult participants were registered between May 25, 2015, and March 31, 2018, and received placebo or cilostazol for up to 96 weeks. Participants were treated in the National Cerebral and Cardiovascular Center and 14 other regional core hospitals in Japan. Patients with mild cognitive impairment with Mini-Mental State Examination (MMSE) scores of 22 to 28 points (on a scale of 0 to 30, with lower scores indicating greater cognitive impairment) and Clinical Dementia Rating scores of 0.5 points (on a scale of 0, 0.5, 1, 2, and 3, with higher scores indicating more severe dementia) were enrolled. The data were analyzed from May 1, 2020, to December 1, 2020. Interventions The participants were treated with placebo, 1 tablet twice daily, or cilostazol, 50 mg twice daily, for up to 96 weeks. Main Outcomes and Measures The primary end point was the change in the total MMSE score from baseline to the final observation. Safety analyses included all adverse events. Results The full analysis set included 159 patients (66 [41.5%] male; mean [SD] age, 75.6 [5.2] years) who received placebo or cilostazol at least once. There was no statistically significant difference between the placebo and cilostazol groups for the primary outcome. The least-squares mean (SE) changes in the MMSE scores among patients receiving placebo were -0.1 (0.3) at the 24-week visit, -0.8 (0.3) at 48 weeks, -1.2 (0.4) at 72 weeks, and -1.3 (0.4) at 96 weeks. Among those receiving cilostazol, the least-squares mean (SE) changes in MMSE scores were -0.6 (0.3) at 24 weeks, -1.0 (0.3) at 48 weeks, -1.1 (0.4) at 72 weeks, and -1.8 (0.4) at 96 weeks. Two patients (2.5%) in the placebo group and 3 patients (3.8%) in the cilostazol group withdrew owing to adverse effects. There was 1 case of subdural hematoma in the cilostazol group, which may have been related to the cilostazol treatment; the patient was successfully treated surgically. Conclusions and Relevance In this randomized clinical trial, cilostazol was well tolerated, although it did not prevent cognitive decline. The efficacy of cilostazol should be tested in future trials. Trial Registration ClinicalTrials.gov Identifier: NCT02491268.
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Affiliation(s)
- Satoshi Saito
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Keisuke Suzuki
- Innovation Center for Translational Research, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Ryo Ohtani
- Department of Neurology, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Takakuni Maki
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hisatomo Kowa
- Division of Neurology, Kobe University Hospital, Kobe, Japan
| | | | - Kazuo Washida
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Japan
| | | | - Toshiki Mizuno
- Department of Neurology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Rie Kanki
- Department of Neurology, Osaka City General Hospital, Osaka, Japan
| | - Shinji Sudoh
- Department of Neurology, National Hospital Organization, Utano National Hospital, Kyoto, Japan
| | - Hiroshi Kitaguchi
- Department of Neurology, Kurashiki Central Hospital, Kurashiki, Japan
| | - Katsuro Shindo
- Department of Neurology, Kurashiki Central Hospital, Kurashiki, Japan
| | - Akihiro Shindo
- Department of Neurology, Graduate School of Medicine, Mie University, Tsu, Japan
| | - Nobuyuki Oka
- Department of Neurology, National Hospital Organization Minami Kyoto Hospital, Joyo, Japan
| | - Keiichi Yamamoto
- Internal Medicine and Neurology, Nara Midori Clinic, Nara, Japan
| | - Fumihiko Yasuno
- Department of Psychiatry, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Chikage Kakuta
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Ryosuke Kakuta
- Department of Data Science, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yumi Yamamoto
- Department of Molecular Innovation in Lipidemiology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yorito Hattori
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yukako Takahashi
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yuriko Nakaoku
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shuichi Tonomura
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Oishi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshihiko Aso
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Akihiko Taguchi
- Department of Regenerative Medicine Research, Institute of Biomedical Research and Innovation, Kobe, Japan
| | - Tatsuo Kagimura
- Translational Research Center for Medical Innovation, Foundation for Biomedical Research and Innovation at Kobe, Kobe, Japan
| | - Shinsuke Kojima
- Translational Research Center for Medical Innovation, Foundation for Biomedical Research and Innovation at Kobe, Kobe, Japan
| | - Masanori Taketsuna
- Translational Research Center for Medical Innovation, Foundation for Biomedical Research and Innovation at Kobe, Kobe, Japan
| | - Hidekazu Tomimoto
- Department of Neurology, Graduate School of Medicine, Mie University, Tsu, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hidenao Fukuyama
- Research and Educational Unit of Leaders for Integrated Medical System, Kyoto University, Kyoto, Japan
| | - Kazuyuki Nagatsuka
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Haruko Yamamoto
- Department of Data Science, National Cerebral and Cardiovascular Center, Suita, Japan
| | | | - Masafumi Ihara
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Japan
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Borchert RJ, Azevedo T, Badhwar A, Bernal J, Betts M, Bruffaerts R, Burkhart MC, Dewachter I, Gellersen HM, Low A, Lourida I, Machado L, Madan CR, Malpetti M, Mejia J, Michopoulou S, Muñoz-Neira C, Pepys J, Peres M, Phillips V, Ramanan S, Tamburin S, Tantiangco HM, Thakur L, Tomassini A, Vipin A, Tang E, Newby D, Ranson JM, Llewellyn DJ, Veldsman M, Rittman T. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement 2023; 19:5885-5904. [PMID: 37563912 DOI: 10.1002/alz.13412] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 05/18/2023] [Accepted: 06/02/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
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Affiliation(s)
- Robin J Borchert
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Tiago Azevedo
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - AmanPreet Badhwar
- Department of Pharmacology and Physiology, University of Montreal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Jose Bernal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Matthew Betts
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, University of Magdeburg, Magdeburg, Germany
| | - Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | | | - Ilse Dewachter
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Audrey Low
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Luiza Machado
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Jhony Mejia
- Department of Biomedical Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Sofia Michopoulou
- Imaging Physics, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Carlos Muñoz-Neira
- Research into Memory, Brain sciences and dementia Group (ReMemBr Group), Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Artificial Intelligence & Computational Neuroscience Group (AICN Group), Sheffield Institute for Translational Neuroscience (SITraN), Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Jack Pepys
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Marion Peres
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Siddharth Ramanan
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, UK
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alessandro Tomassini
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Eugene Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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10
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Kang SH, Kang M, Han JH, Lee ES, Lee KJ, Chung SJ, Suh SI, Koh SB, Eo JS, Kim CK, Oh K. Independent effect of Aβ burden on cognitive impairment in patients with small subcortical infarction. Alzheimers Res Ther 2023; 15:178. [PMID: 37838715 PMCID: PMC10576878 DOI: 10.1186/s13195-023-01307-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/17/2023] [Indexed: 10/16/2023]
Abstract
BACKGROUND The effect of amyloid-β (Aβ) on cognitive impairment in patients with small subcortical infarction remains controversial, although a growing body of evidence shows a substantial overlap between Alzheimer's disease (AD) and subcortical ischemic vascular dementia, another form of cerebral small vessel disease (cSVD). Therefore, we investigated the relationships between Aβ positivity and the development of post-stroke cognitive impairment (PSCI) in patients with small subcortical infarction. METHODS We prospectively recruited 37 patients aged ≥ 50 years, with first-ever small subcortical infarction, who underwent amyloid positron emission tomography, 3 months after stroke at Korea University Guro Hospital. We also enrolled CU participants matched for age and sex with stroke patients for comparison of Aβ positivity. Patients were followed up at 3 and 12 months after the stroke to assess cognitive decline. Logistic and linear mixed-effect regression analyses were performed to identify the effect of Aβ positivity on PSCI development and long-term cognitive trajectories. RESULTS At 3 months after stroke, 12/37 (32.4%) patients developed PSCI, and 11/37 (29.7%) patients had Aβ deposition. Aβ positivity (odds ratio [OR] = 72.2, p = 0.024) was predictive of PSCI development regardless of cSVD burden. Aβ positivity (β = 0.846, p = 0.014) was also associated with poor cognitive trajectory, assessed by the Clinical Dementia Rating-Sum of Box, for 1 year after stroke. CONCLUSIONS Our findings highlight that Aβ positivity is an important predictor for PSCI development and cognitive decline over 1 year. Furthermore, our results provide evidence that anti-AD medications may be a strategy for preventing cognitive decline in patients with small subcortical infarctions.
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Affiliation(s)
- Sung Hoon Kang
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea
| | - Minwoong Kang
- Department of Biomedical Research Center, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Jung Hoon Han
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea
| | - Eun Seong Lee
- Department of Nuclear Medicine, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea
| | - Keon-Joo Lee
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea
| | - Su Jin Chung
- Department of Neurology, Myongji Hospital, Hanyang University College of Medicine, Goyang, South Korea
| | - Sang-Il Suh
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea
| | - Jae Seon Eo
- Department of Nuclear Medicine, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea.
| | - Chi Kyung Kim
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea.
| | - Kyungmi Oh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, 148 Gurodong-ro, Guro-gu, Seoul, 08308, South Korea
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11
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Bhagavatula S, Cabeen R, Harris NG, Gröhn O, Wright DK, Garner R, Bennett A, Alba C, Martinez A, Ndode-Ekane XE, Andrade P, Paananen T, Ciszek R, Immonen R, Manninen E, Puhakka N, Tohka J, Heiskanen M, Ali I, Shultz SR, Casillas-Espinosa PM, Yamakawa GR, Jones NC, Hudson MR, Silva JC, Braine EL, Brady RD, Santana-Gomez CE, Smith GD, Staba R, O'Brien TJ, Pitkänen A, Duncan D. Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies. Epilepsy Res 2023; 195:107201. [PMID: 37562146 PMCID: PMC10528111 DOI: 10.1016/j.eplepsyres.2023.107201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/04/2023] [Accepted: 07/31/2023] [Indexed: 08/12/2023]
Abstract
Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.
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Affiliation(s)
- Sweta Bhagavatula
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Ryan Cabeen
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Neil G Harris
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Olli Gröhn
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - David K Wright
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Rachael Garner
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Alexis Bennett
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Celina Alba
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Aubrey Martinez
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Pedro Andrade
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Tomi Paananen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Robert Ciszek
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Riikka Immonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Eppu Manninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Noora Puhakka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mette Heiskanen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Idrish Ali
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Sandy R Shultz
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Pablo M Casillas-Espinosa
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Glenn R Yamakawa
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Nigel C Jones
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Matthew R Hudson
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Juliana C Silva
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Emma L Braine
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Rhys D Brady
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Cesar E Santana-Gomez
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Gregory D Smith
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Terence J O'Brien
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Asla Pitkänen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dominique Duncan
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
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12
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Masuda H, Mori M, Hirano S, Uzawa A, Uchida T, Muto M, Ohtani R, Aoki R, Hirano Y, Kuwabara S. Higher longitudinal brain white matter atrophy rate in aquaporin-4 IgG-positive NMOSD compared with healthy controls. Sci Rep 2023; 13:12631. [PMID: 37537208 PMCID: PMC10400628 DOI: 10.1038/s41598-023-38893-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 07/17/2023] [Indexed: 08/05/2023] Open
Abstract
We aimed to compare longitudinal brain atrophy in patients with neuromyelitis optica spectrum disorder (NMOSD) with healthy controls (HCs). The atrophy rate in patients with anti-aquaporin-4 antibody-positive NMOSD (AQP4 + NMOSD) was compared with age-sex-matched HCs recruited from the Japanese Alzheimer's Disease Neuroimaging Initiative study and another study performed at Chiba University. Twenty-nine patients with AQP4 + NMOSD and 29 HCs were enrolled in the study. The time between magnetic resonance imaging (MRI) scans was longer in the AQP4 + NMOSD group compared with the HCs (median; 3.2 vs. 2.9 years, P = 0.009). The annualized normalized white matter volume (NWV) atrophy rate was higher in the AQP4 + NMOSD group compared with the HCs (median; 0.37 vs. - 0.14, P = 0.018). The maximum spinal cord lesion length negatively correlated with NWV at baseline MRI in patients with AQP4 + NMOSD (Spearman's rho = - 0.41, P = 0.027). The annualized NWV atrophy rate negatively correlated with the time between initiation of persistent prednisolone usage and baseline MRI in patients with AQP4 + NMOSD (Spearman's rho = - 0.43, P = 0.019). Patients with AQP4 + NMOSD had a greater annualized NWV atrophy rate than HCs. Suppressing disease activity may prevent brain atrophy in patients with AQP4 + NMOSD.
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Affiliation(s)
- Hiroki Masuda
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan.
| | - Masahiro Mori
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Shigeki Hirano
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Akiyuki Uzawa
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Tomohiko Uchida
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Mayumi Muto
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
- Department of Neurology, Chiba Rosai Hospital, 2-16, Tatsumidai-Higashi, Ichihara, 290-0003, Japan
| | - Ryohei Ohtani
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
- Department of Neurology, Kimitsu Chuo Hospital, 1010, Sakurai, Kisarazu-Shi, Chiba, 292-8535, Japan
| | - Reiji Aoki
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Yoshiyuki Hirano
- Research Center for Child Mental Development, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Satoshi Kuwabara
- Department of Neurology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
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13
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Igarashi A, Azuma MK, Zhang Q, Ye W, Sardesai A, Folse H, Chavan A, Tomita K, Tahami Monfared AA. Predicting the Societal Value of Lecanemab in Early Alzheimer's Disease in Japan: A Patient-Level Simulation. Neurol Ther 2023; 12:1133-1157. [PMID: 37188886 PMCID: PMC10310671 DOI: 10.1007/s40120-023-00492-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 04/28/2023] [Indexed: 05/17/2023] Open
Abstract
INTRODUCTION Alzheimer's disease (AD), a neurodegenerative disorder that progresses from mild cognitive impairment (MCI) to dementia, is responsible for significant burden on caregivers and healthcare systems. In this study, data from the large phase III CLARITY AD trial were used to estimate the societal value of lecanemab plus standard of care (SoC) versus SoC alone against a range of willingness-to-pay (WTP) thresholds from a healthcare and societal perspective in Japan. METHODS A disease simulation model was used to evaluate the impact of lecanemab on disease progression in early AD based on data from the phase III CLARITY AD trial and published literature. The model used a series of predictive risk equations based on clinical and biomarker data from the Alzheimer's Disease Neuroimaging Initiative and Assessment of Health Economics in Alzheimer's Disease II study. The model predicted key patient outcomes, including life years (LYs), quality-adjusted life years (QALYs), and total healthcare and informal costs of patients and caregivers. RESULTS Over a lifetime horizon, patients treated with lecanemab plus SoC gained an additional 0.73 LYs compared with SoC alone (8.50 years vs. 7.77 years). Lecanemab, with an average treatment duration of 3.68 years, was found to be associated with a 0.91 increase in patient QALYs and a total increase of 0.96 when accounting for caregiver utility. The estimated value of lecanemab varied according to the WTP thresholds (JPY 5-15 million per QALY gained) and the perspective employed. From the narrow healthcare payer's perspective, it ranged from JPY 1,331,305 to JPY 3,939,399. From the broader healthcare payer's perspective, it ranged from JPY 1,636,827 to JPY 4,249,702, while from the societal perspective, it ranged from JPY 1,938,740 to JPY 4,675,818. CONCLUSION The use of lecanemab plus SoC would improve health and humanistic outcomes with reduced economic burden for patients and caregivers with early AD in Japan.
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Affiliation(s)
- Ataru Igarashi
- Department of Health Economics and Outcomes Research, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
- Department of Public Health, Yokohama City University School of Medicine, Kanagawa, Japan
| | - Mie Kasai Azuma
- Medical Headquarter, Clinical Planning and Development, Eisai Co., Ltd., Tokyo, Japan
| | - Quanwu Zhang
- Global Alzheimer's Disease and Brain Health, Eisai Inc., 200 Metro Blvd., Nutley, NJ, 07110, USA
| | - Weicheng Ye
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Aditya Sardesai
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Henri Folse
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | - Ameya Chavan
- Evidence Synthesis, Modeling and Communication, Evidera Inc., Bethesda, MD, 20814, USA
| | | | - Amir Abbas Tahami Monfared
- Global Alzheimer's Disease and Brain Health, Eisai Inc., 200 Metro Blvd., Nutley, NJ, 07110, USA.
- Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
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14
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Maheux E, Koval I, Ortholand J, Birkenbihl C, Archetti D, Bouteloup V, Epelbaum S, Dufouil C, Hofmann-Apitius M, Durrleman S. Forecasting individual progression trajectories in Alzheimer's disease. Nat Commun 2023; 14:761. [PMID: 36765056 PMCID: PMC9918533 DOI: 10.1038/s41467-022-35712-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 12/19/2022] [Indexed: 02/12/2023] Open
Abstract
The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.
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Affiliation(s)
- Etienne Maheux
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Igor Koval
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Juliette Ortholand
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Colin Birkenbihl
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Damiano Archetti
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Vincent Bouteloup
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Stéphane Epelbaum
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), center of excellence of neurodegenerative diseases (CoEN), department of Neurology, DMU Neurosciences, Paris, France
| | - Carole Dufouil
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Martin Hofmann-Apitius
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Stanley Durrleman
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France.
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Shimoda M, Kaneko K, Nakagawa T, Kawano N, Otsuka R, Ota A, Naito H, Matsunaga M, Ichino N, Yamada H, Chiang C, Hirakawa Y, Tamakoshi K, Aoyama A, Yatsuya H. Relationship Between Fasting Blood Glucose Levels in Middle Age and Cognitive Function in Later Life: The Aichi Workers' Cohort Study. J Epidemiol 2023; 33:76-81. [PMID: 34024876 PMCID: PMC9794446 DOI: 10.2188/jea.je20210128] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND There is limited evidence regarding the relationship between Diabetes mellitus (DM) in middle age and mild cognitive impairment after a follow-up. Therefore, we investigated the relationship between fasting blood glucose (FBG) levels in middle age and cognitive function assessed using the Japanese version of the Montreal Cognitive Assessment (MoCA-J) in later life, following over 15 years of follow-up in the Aichi Workers' Cohort Study in Japan. METHODS Participants were 253 former local government employees aged 60-79 years in 2018 who participated in a baseline survey conducted in 2002. Using baseline FBG levels and self-reported history, participants were classified into the normal, impaired fasting glucose (IFG) and, and DM groups. Total MoCA-J score ranges from 0 to 30, and cognitive impairment was defined as MoCA-J score ≤25 in this study. A general linear model was used to estimate the mean MoCA-J scores in the FBG groups, adjusted for age, sex, educational year, smoking status, alcohol consumption, physical activity, body mass index, systolic blood pressure, total cholesterol, and estimated glomerular filtration rate. RESULTS The mean MoCA-J score in the total population was 25.0, and the prevalence of MoCA-J score ≤25 was 49.0%. Multivariable-adjusted total MoCA-J scores were 25.2, 24.8, and 23.4 in the normal, IFG, and DM groups, respectively. The odds ratio of MoCA-J score ≤25 in the DM group was 3.29. CONCLUSION FBG level in middle age was negatively associated with total MoCA-J scores assessed later in life, independent of confounding variables.
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Affiliation(s)
- Masako Shimoda
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kayo Kaneko
- Department of Occupational and Environmental Health, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
| | | | | | - Rei Otsuka
- National Center for Geriatrics and Gerontology, Aichi, Japan
| | - Atsuhiko Ota
- Department of Public Health, Fujita Health University School of Medicine, Aichi, Japan
| | - Hisao Naito
- Department of Public Health, Fujita Health University School of Medicine, Aichi, Japan
| | - Masaaki Matsunaga
- Department of Public Health, Fujita Health University School of Medicine, Aichi, Japan
| | - Naohiro Ichino
- Department of Public Health, Fujita Health University School of Medicine, Aichi, Japan
| | - Hiroya Yamada
- Department of Public Health, Fujita Health University School of Medicine, Aichi, Japan
| | - Chifa Chiang
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yoshihisa Hirakawa
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Koji Tamakoshi
- Department of Nursing, Nagoya University School of Health Sciences, Nagoya, Japan
| | - Atsuko Aoyama
- Nagoya University of Arts and Sciences, Aichi, Japan
| | - Hiroshi Yatsuya
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Nagoya, Japan,Department of Public Health, Fujita Health University School of Medicine, Aichi, Japan
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16
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Ikeuchi T, Kanda M, Kitamura H, Morikawa F, Toru S, Nishimura C, Kasuga K, Tokutake T, Takahashi T, Kuroha Y, Miyazawa N, Tanaka S, Utsumi K, Ono K, Yano S, Hamano T, Naruse S, Yajima R, Kawashima N, Kaneko C, Tachibana H, Yano Y, Kato Y, Toue S, Jinzu H, Kitamura A, Yokoyama Y, Kaneko E, Yamakado M, Nagao K. Decreased circulating branched-chain amino acids are associated with development of Alzheimer's disease in elderly individuals with mild cognitive impairment. Front Nutr 2022; 9:1040476. [PMID: 36590218 PMCID: PMC9794986 DOI: 10.3389/fnut.2022.1040476] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
Background Nutritional epidemiology has shown that inadequate dietary protein intake is associated with poor brain function in the elderly population. The plasma free amino acid (PFAA) profile reflects nutritional status and may have the potential to predict future changes in cognitive function. Here, we report the results of a 2-year interim analysis of a 3-year longitudinal study following mild cognitive impairment (MCI) participants. Method In a multicenter prospective cohort design, MCI participants were recruited, and fasting plasma samples were collected. Based on clinical assessment of cognitive function up to 2 years after blood collection, MCI participants were divided into two groups: remained with MCI or reverted to cognitively normal ("MCI-stable," N = 87) and converted to Alzheimer's disease (AD) ("AD-convert," N = 68). The baseline PFAA profile was compared between the two groups. Stratified analysis based on apolipoprotein E ε4 (APOE ε4) allele possession was also conducted. Results Plasma concentrations of all nine essential amino acids (EAAs) were lower in the AD-convert group. Among EAAs, three branched-chain amino acids (BCAAs), valine, leucine and isoleucine, and histidine (His) exhibited significant differences even in the logistic regression model adjusted for potential confounding factors such as age, sex, body mass index (BMI), and APOE ε4 possession (p < 0.05). In the stratified analysis, differences in plasma concentrations of these four EAAs were more pronounced in the APOE ε4-negative group. Conclusion The PFAA profile, especially decreases in BCAAs and His, is associated with development of AD in MCI participants, and the difference was larger in the APOE ε4-negative population, suggesting that the PFAA profile is an independent risk indicator for AD development. Measuring the PFAA profile may have importance in assessing the risk of AD conversion in the MCI population, possibly reflecting nutritional status. Clinical trial registration [https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000025322], identifier [UMIN000021965].
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Affiliation(s)
- Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan,Takeshi Ikeuchi,
| | - Mayuka Kanda
- Research Institute for Bioscience Products and Fine Chemicals, Ajinomoto Co., Inc., Kawasaki, Japan
| | - Hitomi Kitamura
- Research Institute for Bioscience Products and Fine Chemicals, Ajinomoto Co., Inc., Kawasaki, Japan
| | - Fumiyoshi Morikawa
- Department of Psychiatry, Asahikawa Keisenkai Hospital, Asahikawa, Japan
| | - Shuta Toru
- Department of Neurology, Nitobe Memorial Nakano General Hospital, Tokyo, Japan
| | | | - Kensaku Kasuga
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Takayoshi Tokutake
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan,Department of Neurology, Brain Research Institute, Niigata University, Niigata, Japan
| | | | - Yasuko Kuroha
- Department of Neurology, Nishiniigata Chuo Hospital, Niigata, Japan
| | - Nobuhiko Miyazawa
- Department of Neurosurgery, Kofu Neurosurgical Hospital, Kofu, Japan
| | | | - Kumiko Utsumi
- Department of Psychiatry, Sunagawa City Medical Center, Sunagawa, Japan
| | - Kenjiro Ono
- Division of Neurology, Department of Medicine, Showa University School of Medicine, Tokyo, Japan
| | - Satoshi Yano
- Division of Neurology, Department of Medicine, Showa University School of Medicine, Tokyo, Japan
| | - Tadanori Hamano
- Faculty of Medical Sciences, Second Department of Internal Medicine, University of Fukui, Fukui, Japan
| | - Satoshi Naruse
- Department of Neurology, Midori Hospital, Niigata, Japan
| | - Ryuji Yajima
- Department of Neurology, Midori Hospital, Niigata, Japan
| | | | | | | | - Yuki Yano
- Research Institute for Bioscience Products and Fine Chemicals, Ajinomoto Co., Inc., Kawasaki, Japan
| | - Yumiko Kato
- Research Institute for Bioscience Products and Fine Chemicals, Ajinomoto Co., Inc., Kawasaki, Japan
| | - Sakino Toue
- Research Institute for Bioscience Products and Fine Chemicals, Ajinomoto Co., Inc., Kawasaki, Japan
| | - Hiroko Jinzu
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Japan
| | - Akihiko Kitamura
- Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Yuri Yokoyama
- Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Eiji Kaneko
- Institute of Education, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Kenji Nagao
- Research Institute for Bioscience Products and Fine Chemicals, Ajinomoto Co., Inc., Kawasaki, Japan,*Correspondence: Kenji Nagao,
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Pölsterl S, Wachinger C. Identification of causal effects of neuroanatomy on cognitive decline requires modeling unobserved confounders. Alzheimers Dement 2022; 19:1994-2005. [PMID: 36419215 DOI: 10.1002/alz.12825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Carrying out a randomized controlled trial to estimate the causal effects of regional brain atrophy due to Alzheimer's disease (AD) is impossible. Instead, we must estimate causal effects from observational data. However, this generally requires knowing and having recorded all confounders, which is often unrealistic. METHODS We provide an approach that leverages the dependencies among multiple neuroanatomical measures to estimate causal effects from observational neuroimaging data without the need to know and record all confounders. RESULTS Our analyses of N = 732 $N=732$ subjects from the Alzheimer's Disease Neuroimaging Initiative demonstrate that using our approach results in biologically meaningful conclusions, whereas ignoring unobserved confounding yields results that conflict with established knowledge on cognitive decline due to AD. DISCUSSION The findings provide evidence that the impact of unobserved confounding can be substantial. To ensure trustworthy scientific insights, future AD research can account for unobserved confounding via the proposed approach.
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Affiliation(s)
- Sebastian Pölsterl
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Christian Wachinger
- The Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany.,Technical University of Munich, School of Medicine, Department of Radiology, Munich, Germany
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18
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Kowa H, Uchimura M, Ohashi A, Hiroe M, Ono R. Self Assessment Memory Scale (SAMS), a new simple method for evaluating memory function. Front Aging Neurosci 2022; 14:1024497. [DOI: 10.3389/fnagi.2022.1024497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/25/2022] [Indexed: 11/23/2022] Open
Abstract
We have developed a new method for easy self-assessment of changes in memory recall impairment, which can be used during the very early stages of dementia. An 8-picture recall and a 16-word regression were assessed, respectively, and the index was calculated by adding up the ratio of correct responses to both tests. A total of 85 subjects including 12 MCI, 8 AD, and 65 older persons with normal cognitive function were evaluated, and the correlation with the WMS-R Logical Memory II score was examined. The results showed that there was a statistically significant correlation between the 8-picture recall (R = 0.872, p < 0.0001) and the index (R = 0.857, p < 0.0001), respectively, with the Logical Memory score. We have named this index as Self Assessment Memory Scale (SAMS), and are now developing a digital tool to enable easy and self-administered evaluation of recall.
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19
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Kandiah N, Choi SH, Hu CJ, Ishii K, Kasuga K, Mok VC. Current and Future Trends in Biomarkers for the Early Detection of Alzheimer's Disease in Asia: Expert Opinion. J Alzheimers Dis Rep 2022; 6:699-710. [PMID: 36606209 PMCID: PMC9741748 DOI: 10.3233/adr-220059] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/23/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) poses a substantial healthcare burden in the rapidly aging Asian population. Early diagnosis of AD, by means of biomarkers, can lead to interventions that might alter the course of the disease. The amyloid, tau, and neurodegeneration (AT[N]) framework, which classifies biomarkers by their core pathophysiological features, is a biomarker measure of amyloid plaques and neurofibrillary tangles. Our current AD biomarker armamentarium, comprising neuroimaging biomarkers and cerebrospinal fluid biomarkers, while clinically useful, may be invasive and expensive and hence not readily available to patients. Several studies have also investigated the use of blood-based measures of established core markers for detection of AD, such as amyloid-β and phosphorylated tau. Furthermore, novel non-invasive peripheral biomarkers and digital biomarkers could potentially expand access to early AD diagnosis to patients in Asia. Despite the multiplicity of established and potential biomarkers in AD, a regional framework for their optimal use to guide early AD diagnosis remains lacking. A group of experts from five regions in Asia gathered at a meeting in March 2021 to review the current evidence on biomarkers in AD diagnosis and discuss best practice around their use, with the goal of developing practical guidance that can be implemented easily by clinicians in Asia to support the early diagnosis of AD. This article summarizes recent key evidence on AD biomarkers and consolidates the experts' insights into the current and future use of these biomarkers for the screening and early diagnosis of AD in Asia.
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Affiliation(s)
- Nagaendran Kandiah
- Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore,Correspondence to: Nagaendran Kandiah, Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore 308232. Tel.: +65 6592 2653; Fax: +65 6339 2889; E-mail: ; ORCID: 0000-0001-9244-4298
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Republic of Korea
| | - Chaur-Jong Hu
- Department of Neurology, Dementia Center, Shuang Ho Hospital, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Kenji Ishii
- Team for Neuroimaging Research, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Kensaku Kasuga
- Department of Molecular Genetics, Center for Bioresources, Brain Research Institute, Niigata University, Niigata, Japan
| | - Vincent C.T. Mok
- Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China,Li Ka Shing Institute of Health Sciences, Gerald Choa Neuroscience Institute, Lui Che Woo Institute of Innovative Medicine, Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong, China
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20
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Kasuga K, Kikuchi M, Tsukie T, Suzuki K, Ihara R, Iwata A, Hara N, Miyashita A, Kuwano R, Iwatsubo T, Ikeuchi T. Different AT(N) profiles and clinical progression classified by two different N markers using total tau and neurofilament light chain in cerebrospinal fluid. BMJ Neurol Open 2022; 4:e000321. [PMID: 36046332 PMCID: PMC9379489 DOI: 10.1136/bmjno-2022-000321] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 06/29/2022] [Indexed: 12/12/2022] Open
Abstract
Background The AT(N) classification was proposed for categorising individuals according to biomarkers. However, AT(N) profiles may vary depending on the markers chosen and the target population. Methods We stratified 177 individuals who participated in the Japanese Alzheimer's Disease Neuroimaging Initiative by AT(N) classification according to cerebrospinal fluid (CSF) biomarkers. We compared the frequency of AT(N) profiles between the classification using total tau and neurofilament light chain (NfL) as N markers (AT(N)tau and AT(N)NfL). Baseline characteristics, and longitudinal biological and clinical changes were examined between AT(N) profiles. Results We found that 9% of cognitively unimpaired subjects, 49% of subjects with mild cognitive impairment, and 61% of patients with Alzheimer's disease (AD) dementia had the biological AD profile (ie, A+T+) in the cohort. The frequency of AT(N) profiles substantially differed between the AT(N)tau and AT(N)NfL classifications. When we used t-tau as the N marker (AT(N)tau), those who had T- were more frequently assigned to (N)-, whereas those who had T+were more frequently assigned to (N)+ than when we used NfL as the N marker (AT(N)NfL). During a follow-up, the AD continuum group progressed clinically and biologically compared with the normal biomarker group in both the AT(N)tau and AT(N)NfL classifications. More frequent conversion to dementia was observed in the non-AD pathological change group in the AT(N)tau classification, but not in the AT(N)NfL classification. Conclusions AT(N)tau and AT(N)NfL in CSF may capture different aspects of neurodegeneration and provide a different prognostic value. The AT(N) classification aids in understanding the AD continuum biology in various populations.
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Affiliation(s)
- Kensaku Kasuga
- Molecular Genetics, Niigata University Brain Research Institute, Niigata, Japan
| | - Masataka Kikuchi
- Genome Informatics, Graduate School of Medicine, Osaka University, Osaka, Japan.,Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
| | - Tamao Tsukie
- Molecular Genetics, Niigata University Brain Research Institute, Niigata, Japan
| | - Kazushi Suzuki
- Neurology, National Defense Medical College, Tokorozawa, Japan
| | - Ryoko Ihara
- Neurology, Tokyo Metropolitan Geriatric Medical Center Hospital, Tokyo, Japan
| | - Atsushi Iwata
- Neurology, Tokyo Metropolitan Geriatric Medical Center Hospital, Tokyo, Japan
| | - Norikazu Hara
- Molecular Genetics, Niigata University Brain Research Institute, Niigata, Japan
| | - Akinori Miyashita
- Molecular Genetics, Niigata University Brain Research Institute, Niigata, Japan
| | | | - Takeshi Iwatsubo
- Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takeshi Ikeuchi
- Molecular Genetics, Niigata University Brain Research Institute, Niigata, Japan
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21
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Yamamoto Y, Hirano J, Ueda R, Yoshitake H, Yamagishi M, Kimura M, Kamiya K, Shino M, Mimura M, Yamagata B. White matter alterations in the dorsal attention network contribute to a high risk of unsafe driving in healthy older people. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2022; 1:e45. [PMID: 38868688 PMCID: PMC11114439 DOI: 10.1002/pcn5.45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 08/21/2022] [Indexed: 06/14/2024]
Abstract
Aim Healthy older drivers may be at high risk of fatal traffic accidents. Our recent study showed that volumetric alterations in gray matter in the brain regions within the dorsal attention network (DAN) were strongly related to the risk of unsafe driving in healthy older people. However, the relationship between white matter (WM) structural connectivity and driving ability in healthy older people is still unclear. Methods We used diffusion tensor imaging to examine the association between microstructural alterations in the DAN and the risk of unsafe driving among healthy older people. We enrolled 32 healthy older individuals aged over 65 years and screened unsafe drivers using an on-road driving test. We then determined the pattern of WM aberrations in unsafe drivers using tract-based spatial statistics. Results The analysis demonstrated that unsafe drivers had significantly higher axial diffusivity values in nine WM clusters compared with safe drivers. These results were primarily observed bilaterally in the dorsal superior longitudinal fasciculus, which is involved in the DAN. Furthermore, correlation analyses showed that higher axial diffusivity values in the superior longitudinal fasciculus were associated with lower Trail Making Test A scores within unsafe drivers. This result suggests that functionally, WM microstructural alterations in the DAN are associated with attention problems, which may contribute to the risk of unsafe driving among healthy older people. Conclusion Our findings may elucidate the neurobiological mechanisms underlying the increased risk of unsafe driving in healthy older people, potentially facilitating the development of new interventions to prevent fatal accidents.
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Affiliation(s)
- Yasuharu Yamamoto
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Jinichi Hirano
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Ryo Ueda
- Office of Radiation TechnologyKeio University HospitalTokyoJapan
| | - Hiroshi Yoshitake
- Department of Human and Engineered Environmental StudiesThe University of TokyoTokyoJapan
| | - Mika Yamagishi
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Mariko Kimura
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
- Graduate School of PsychologyRissho UniversityTokyoJapan
| | - Kei Kamiya
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Motoki Shino
- Department of Human and Engineered Environmental StudiesThe University of TokyoTokyoJapan
| | - Masaru Mimura
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
| | - Bun Yamagata
- Department of NeuropsychiatryKeio University School of MedicineTokyoJapan
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22
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Park C, Jang JW, Joo G, Kim Y, Kim S, Byeon G, Park SW, Kasani PH, Yum S, Pyun JM, Park YH, Lim JS, Youn YC, Choi HS, Park C, Im H, Kim S. Predicting progression to dementia with “comprehensive visual rating scale” and machine learning algorithms. Front Neurol 2022; 13:906257. [PMID: 36071894 PMCID: PMC9443667 DOI: 10.3389/fneur.2022.906257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/14/2022] [Indexed: 11/17/2022] Open
Abstract
Background and Objective Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. Methods We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. Results Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701–0.711) were used than when clinical data and cortical thickness (accuracy 0.650–0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. Conclusions Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.
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Affiliation(s)
- Chaeyoon Park
- Department of Convergence Security, Kangwon National University, Chuncheon, South Korea
| | - Jae-Won Jang
- Department of Convergence Security, Kangwon National University, Chuncheon, South Korea
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | - Gihun Joo
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
| | - Seongheon Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
| | - Gihwan Byeon
- Department of Psychiatry, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, South Korea
| | - Sang Won Park
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | | | - Sujin Yum
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
| | - Jung-Min Pyun
- Department of Neurology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Young Ho Park
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Hyun-Soo Choi
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
| | - Chihyun Park
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
| | - Hyeonseung Im
- Department of Convergence Security, Kangwon National University, Chuncheon, South Korea
- Interdisciplinary Graduate Program in Medical Bigdata Convergence, Chuncheon, South Korea
- Department of Computer Science and Engineering, Kangwon National University, Chuncheon, South Korea
- *Correspondence: Hyeonseung Im
| | - SangYun Kim
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Neurology, Seoul National University College of Medicine, Seoul, South Korea
- SangYun Kim
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23
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Poulakis K, Pereira JB, Muehlboeck JS, Wahlund LO, Smedby Ö, Volpe G, Masters CL, Ames D, Niimi Y, Iwatsubo T, Ferreira D, Westman E. Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer's disease. Nat Commun 2022; 13:4566. [PMID: 35931678 PMCID: PMC9355993 DOI: 10.1038/s41467-022-32202-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/18/2022] [Indexed: 11/08/2022] Open
Abstract
Understanding Alzheimer's disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.
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Affiliation(s)
- Konstantinos Poulakis
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| | - Joana B Pereira
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmo, Sweden
| | - J-Sebastian Muehlboeck
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Lars-Olof Wahlund
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Örjan Smedby
- Department of Biomedical Engineering and Health Systems (MTH), KTH Royal Institute of Technology, Stockholm, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - David Ames
- Academic Unit for Psychiatry of Old Age, St George's Hospital, University of Melbourne, Melbourne, Victoria, Australia
- National Ageing Research Institute, Parkville, Victoria, Australia
| | - Yoshiki Niimi
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Tokyo, Japan
| | - Takeshi Iwatsubo
- Unit for Early and Exploratory Clinical Development, The University of Tokyo Hospital, Tokyo, Japan
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Eric Westman
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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Kishikawa Y, Miyabara H, Uchinoura M, Yamaguchi Y, Nishimura S, Shibata S, Shibata H, Owada H. Use of the Tokyo Cognitive Assessment for mild cognitive impairment to characterize elderly people that use day care services in Japan. J Phys Ther Sci 2022; 34:577-583. [PMID: 35937627 PMCID: PMC9345756 DOI: 10.1589/jpts.34.577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/18/2022] [Indexed: 11/25/2022] Open
Abstract
[Purpose] This study compared the motor skills and cognitive functions of elderly
participants who required support with those who did not require support. We aimed to
evaluate the characteristics of impairment in sub-items of cognitive function in patients
who needed support to predict future clinical issues. [Participants and Methods] We
surveyed 31 participants requiring support under the day care service insurance system for
which they attended day care service centers in Japan (rehabilitation users) and 10
healthy participants who attended a university for lifelong learning (healthy elders).
Data on personal attributes of the participants were collected, and the Cardio-Ankle
Vascular Index and motor and cognitive functions were assessed. [Results] Although the
participants undergoing rehabilitation were, on average, 6 years older than the healthy
elders, we found no significant differences between the two groups in closed-eye,
one-legged standing, grip strength, or quadriceps muscle strength. In terms of the Tokyo
Cognitive Assessment for mild cognitive impairment, we found no significant differences
between those undergoing rehabilitation and healthy elders in clock drawing performance,
serial 7 task performance, or orientation; however, there were significant differences in
erase character, copy of triangular pyramid, composition, read of digits, go/no-go, word
recall, story reproduction, ToCA total score. [Conclusion] We believe that it is
imperative for day care service centers to conduct programs that maintain cognitive
function in addition to programs for improvement of physical function.
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Affiliation(s)
- Yuki Kishikawa
- Department of Rehabilitation Sciences, Faculty of Rehabilitation Sciences, Nishikyushu University: 4490-9 Osaki, Kanzaki, Saga 842-8585, Japan
| | - Hiroya Miyabara
- Department of Rehabilitation Sciences, Faculty of Rehabilitation Sciences, Nishikyushu University: 4490-9 Osaki, Kanzaki, Saga 842-8585, Japan
| | | | - Yuji Yamaguchi
- Department of Sports Health and Welfare, Faculty of Health and Social Welfare Sciences, Nishikyushu University, Japan
| | | | | | | | - Hiromi Owada
- Department of Rehabilitation, Division of Physical Therapy, Sendai Seiyo Gakuin College, Japan
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25
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Sakurai K, Nihashi T, Kimura Y, Iwata K, Ikenuma H, Arahata Y, Okamura N, Yanai K, Akagi A, Ito K, Kato T, Nakamura A, Group MS. Age-related increase of monoamine oxidase B in amyloid-negative cognitively unimpaired elderly subjects. Ann Nucl Med 2022; 36:777-784. [PMID: 35781672 DOI: 10.1007/s12149-022-01760-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 06/02/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Monoamine oxidase B (MAO-B) is highly abundant in reactive astrocytes and upregulated in neuroinflammatory processes. However, the age-related change of MAO-B in amyloid-negative cognitively unimpaired elderly subjects has not yet been sufficiently evaluated on positron emission tomography (PET). 18F-THK5351 is a radiotracer with high affinity to MAO-B, which may potentially serve as an imaging biomarker for detecting neuroinflammation. The purpose of this study was to investigate the age-related topographic change of 18F-THK5351 PET in amyloid-negative cognitively unimpaired elderly subjects. METHODS The age-related change of 18F-THK5351 retention was evaluated on the visual analysis, voxel and region of interest (ROI)-based analyses using Statistical Parametric Mapping and PETSurfer tool of FreeSurfer in 31 amyloid-negative cognitively unimpaired elderly subjects. RESULTS On visual inspection, elderly groups showed the spread of 18F-THK5351 accumulation from the medial to inferolateral temporal and basal frontal lobes, and cingulate gyrus. Additionally, voxel- and ROI-based analysis demonstrated the correlation between 18F-THK5351 accumulation and participants' age, especially in the inferior temporal lobes. CONCLUSIONS This study demonstrated age-dependent increase of 18F-THK5351 retention in amyloid-negative cognitively unimpaired subjects, which suggests an increase in MAO-B positive reactive astrocytes with aging.
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Affiliation(s)
- Keita Sakurai
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan
| | - Takashi Nihashi
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan
| | - Yasuyuki Kimura
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan
| | - Kaori Iwata
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan
| | - Hiroshi Ikenuma
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan
| | - Yutaka Arahata
- Department of Neurology, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Nobuyuki Okamura
- Division of Pharmacology, Faculty of Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kazuhiko Yanai
- Department of Pharmacology, Tohoku University School of Medicine, Sendai, Japan
| | - Akio Akagi
- Department of Neuropathology, Institute for Medical Science of Aging, Aichi Medical University, Nagakute, Japan
| | - Kengo Ito
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan.,Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan
| | - Takashi Kato
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan. .,Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan.
| | - Akinori Nakamura
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, Aichi, 474-8511, Japan.,Department of Biomarker Research, National Center for Geriatrics and Gerontology, Obu, Japan
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26
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Clinical correlations of cerebrospinal fluid biomarkers including neuron-glia 2 and neurofilament light chain in patients with multiple system atrophy. Parkinsonism Relat Disord 2022; 102:30-35. [DOI: 10.1016/j.parkreldis.2022.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/14/2022] [Accepted: 07/14/2022] [Indexed: 11/19/2022]
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27
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Hirakawa A, Sato H, Hanazawa R, Suzuki K. Estimating the longitudinal trajectory of cognitive function measurement using short-term data with different disease stages: Application in Alzheimer's disease. Stat Med 2022; 41:4200-4214. [PMID: 35749990 DOI: 10.1002/sim.9504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 11/05/2022]
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by a gradual decline in cognitive function over a few decades. The Mini-Mental State Examination (MMSE) is a widely used measure for evaluating global cognitive functioning. Characterizing the longitudinal trajectory of the MMSE in the population of interest is important to detect AD onset for preventive intervention. In this study, we formulate a new class of longitudinal trajectory modeling for MMSE from short-term individual data based on an ordinary differential equation. The proposed method models the relationship between individual decline speed of MMSE and the average MMSE using the fractional polynomial function model and subsequently estimates the longitudinal trajectory of MMSE by solving the ordinary differential equation for the estimated model. The appropriate model for trajectory estimation is selected based on the proposed criterion for quantifying the goodness of trajectory fit. The accuracy of the trajectory estimation of the proposed method was demonstrated via simulation studies. The proposed method was successfully applied to MMSE data from the Japanese Alzheimer's Disease Neuroimaging Initiative study.
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Affiliation(s)
- Akihiro Hirakawa
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Hiroyuki Sato
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Ryoichi Hanazawa
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Keisuke Suzuki
- Innovation Center for Translational Research, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
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28
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Wang C, Li Y, Tsuboshita Y, Sakurai T, Goto T, Yamaguchi H, Yamashita Y, Sekiguchi A, Tachimori H. A high-generalizability machine learning framework for predicting the progression of Alzheimer's disease using limited data. NPJ Digit Med 2022; 5:43. [PMID: 35414651 PMCID: PMC9005545 DOI: 10.1038/s41746-022-00577-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/11/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.
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Affiliation(s)
- Caihua Wang
- Imaging Technology Center, FUJIFILM Corporation, Kanagawa, Japan.
| | - Yuanzhong Li
- Imaging Technology Center, FUJIFILM Corporation, Kanagawa, Japan.
| | | | - Takuya Sakurai
- Imaging Technology Center, FUJIFILM Corporation, Kanagawa, Japan
| | - Tsubasa Goto
- Imaging Technology Center, FUJIFILM Corporation, Kanagawa, Japan
| | - Hiroyuki Yamaguchi
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan.,Department of Psychiatry, Yokohama City University School of Medicine, Yokohama, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Atsushi Sekiguchi
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hisateru Tachimori
- Department of Clinical Epidemiology, Translational Medical Center, National Center of Neurology and Psychiatry, Tokyo, Japan.,Endowed Course for Health System Innovation, Keio University School of Medicine, Tokyo, Japan
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29
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Kajimoto Y, Kameda M, Kambara A, Kuroda K, Tsuji S, Nikaido Y, Saura R, Wanibuchi M. Impact of Early Intervention for Idiopathic Normal Pressure Hydrocephalus on Long-Term Prognosis in Prodromal Phase. Front Neurol 2022; 13:866352. [PMID: 35481276 PMCID: PMC9035988 DOI: 10.3389/fneur.2022.866352] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 02/24/2022] [Indexed: 11/23/2022] Open
Abstract
Objectives Because the progression of idiopathic normal pressure hydrocephalus (iNPH) is partially irreversible, we hypothesized that early intervention would markedly improve its prognosis. To test this hypothesis, we retrospectively investigated the long-term prognosis of patients with early intervention in the prodromal phase of iNPH. Methods We defined the prodromal phase of iNPH as a 3m Timed Up and Go (TUG) of 13.5 s or less and a Mini-Mental State Examination (MMSE) of 24 or more. Of the 83 iNPH patients who underwent shunt surgery at Osaka Medical and Pharmaceutical University Hospital over 3 years from January 2015, 12 prodromal phase cases (73.3 ± 6.2 years, 10 males and 2 females) were included in the study. The iNPH grading scale (INPHGS), MMSE, Frontal Assessment Battery (FAB), intermittent gait disturbance (IGD), social participation status, and development of comorbidities were evaluated over 4 years. Results Preoperative MMSE was 27.2 ± 1.5, FAB was 14.1 ± 1.8, TUG was 10.7 ± 1.4 s, and total iNPHGS was 2.8 ± 1.4. At 1, 2, 3, and 4 years postoperatively, total INPHGS improved to 0.8, 0.9, 1.5, and 1.7, respectively, and remained significantly better than preoperatively except at 4 years postoperatively. The MMSE improved slightly to 27.5 after 1 year and then declined by 0.35 per year. After 4 years, the mean MMSE was 26.1, and only one patient had an MMSE below 23. FAB improved to 15.2 after 1 year and then declined slowly at 0.85/year. Ten patients (83%) maintained a high capacity for social participation postoperatively. The preoperative tendency to fall and IGD in 9 (75%) and 8 (67%) patients, respectively, completely disappeared postoperatively, resulting in improved mobility. Shunt malfunction associated with four weight fluctuations and one catheter rupture caused temporary worsening of symptoms, which were recovered by valve re-setting and catheter revision, respectively. Conclusion Early intervention in the prodromal phase of iNPH patients maintained good cognitive and mobility function and social participation ability in the long term. The maintenance of long-term cognitive function suggests its preventive effect on dementia. To realize early intervention for iNPH, it is desirable to establish an early diagnosis system for iNPH.
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Affiliation(s)
- Yoshinaga Kajimoto
- Department of Neurosurgery, Osaka Medical and Pharmaceutical University, Takatsuki, Japan
- *Correspondence: Yoshinaga Kajimoto
| | - Masahiro Kameda
- Department of Neurosurgery, Osaka Medical and Pharmaceutical University, Takatsuki, Japan
| | - Akihiro Kambara
- Department of Neurosurgery, Osaka Medical and Pharmaceutical University, Takatsuki, Japan
| | - Kenji Kuroda
- Clinical Department of Rehabilitation, Osaka Medical and Pharmaceutical University, Takatsuki, Japan
| | - Shohei Tsuji
- Clinical Department of Rehabilitation, Osaka Medical and Pharmaceutical University, Takatsuki, Japan
| | - Yasutaka Nikaido
- Clinical Department of Rehabilitation, Osaka Medical and Pharmaceutical University, Takatsuki, Japan
| | - Ryuichi Saura
- Department of Physical and Rehabilitation Medicine, Osaka Medical and Pharmaceutical University, Takatsuki, Japan
| | - Masahiko Wanibuchi
- Department of Neurosurgery, Osaka Medical and Pharmaceutical University, Takatsuki, Japan
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30
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Wagatsuma K, Miwa K, Kamitaka Y, Koike E, Yamao T, Yoshii T, Kobayashi R, Nezu S, Sugamata Y, Miyaji N, Imabayashi E, Ishibashi K, Toyohara J, Ishii K. Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [ 18 F]FDG and [ 11 C]PiB. Med Phys 2022; 49:2995-3005. [PMID: 35246870 DOI: 10.1002/mp.15593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 02/22/2022] [Accepted: 02/27/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal-to-noise ratio on images and more accurate quantitation than ordered subset-expectation maximization (OSEM). The reconstruction parameter (β) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain PET images acquired using 18 F-fluoro-2-deoxy-D-glucose ([18 F]FDG) and 11 C-labeled Pittsburg compound B ([11 C]PiB). METHODS Images generated from Hoffman 3D brain and cylindrical phantoms were acquired using a Discovery PET/CT 710 and reconstructed using OSEM + time-of-flight (TOF) under clinical conditions and BPL + TOF (β = 20-1,000). Contrast was calculated from images generated by the Hoffman 3D brain phantom, and noise and uniformity were calculated from those generated by the cylindrical phantom. Five cognitively healthy controls and five patients with Alzheimer disease were assessed using [18 F]FDG and [11 C]PiB PET to validate the findings from the phantom study. The β values were restricted by the findings of the phantom study, then one certified nuclear medicine physician and two certified nuclear medicine technologists visually determined optimal β values by scoring the quality parameters of image contrast, image noise, cerebellar stability, and overall image quality of PET images from 1 (poor) to 5 (excellent). RESULTS The contrast in BPL satisfied the Japanese Society of Nuclear Medicine (JSNM) criterion of ≥ 55% and exceeded that of OSEM at ranges of β = 20-450 and 20-600 for [18 F]FDG and [11 C]PiB, respectively. The image noise in BPL satisfied the JSNM criterion of ≤ 15% and was below that in OSEM when β = 150-1000 and 400-1,000 for [18 F]FDG and [11 C]PiB, respectively. The phantom study restricted the ranges of β values to 100-300 and 300-500 for [18 F]FDG and [11 C]PiB, respectively. The BPL scores for grey-white matter contrast and image noise, exceeded those of OSEM in [18 F]FDG and [11 C]PiB images regardless of β values. Visual evaluation confirmed that the optimal β values were 200 and 450 for [18 F]FDG and [11 C]PiB, respectively. CONCLUSIONS The BPL achieved better image contrast and less image noise than OSEM, while maintaining quantitative SUVR due to full convergence, more rigorous noise control and edge preservation. The optimal β values for [18 F]FDG and [11 C]PiB brain PET were apparently 200 and 450, respectively. The present study provides useful information about how to determine optimal β values in BPL for brain PET imaging. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Kei Wagatsuma
- School of Allied Health Sciences, Kitasato University, 1-15-1 Kitazato, Minami-ku, Sagamihara, Kanagawa, 252-0373, Japan.,Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Kenta Miwa
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Fukushima, Fukushima, 960-1295, Japan
| | - Yuto Kamitaka
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Emiya Koike
- Department of Radiology, Fukushima Medical University Hospital, 1 Hikariga-oka, Fukushima City, 960-1295, Japan
| | - Tensho Yamao
- Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, Fukushima, Fukushima, 960-1295, Japan
| | - Tokiya Yoshii
- Department of Radiology, Fukushima Medical University Hospital, 1 Hikariga-oka, Fukushima City, 960-1295, Japan
| | - Rinya Kobayashi
- Department of Radiology, Tokai University Hospital, 143 Shimokasuya, Isehara-shi, Kanagawa, 259-1193, Japan
| | - Shogo Nezu
- School of Health Science, International University of Health and Welfare, 2600-1 Kitakanemaru, Ohtawara, 324-8501, Japan
| | - Yuta Sugamata
- School of Health Science, International University of Health and Welfare, 2600-1 Kitakanemaru, Ohtawara, 324-8501, Japan
| | - Noriaki Miyaji
- Department of Nuclear Medicine, Cancer Institute Hospital of Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo, 135-8550, Japan
| | - Etsuko Imabayashi
- Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, Quantum Life and Medical Science Directorate, National Institutes for Quantum Science and Technology, 4-9-1 Anagawa, Inage-ku, Chiba, 263-8555, Japan
| | - Kenji Ishibashi
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Jun Toyohara
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Kenji Ishii
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
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Kim J, Jung SH, Choe YS, Kim S, Kim B, Kim HR, Son SJ, Hong CH, Na DL, Kim HJ, Cho SJ, Won HH, Seo SW. Ethnic differences in the frequency of β-amyloid deposition in cognitively normal individuals. Neurobiol Aging 2022; 114:27-37. [DOI: 10.1016/j.neurobiolaging.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 10/18/2022]
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Kondo T, Hara N, Koyama S, Yada Y, Tsukita K, Nagahashi A, Ikeuchi T, Ishii K, Asada T, Arai T, Yamada R, Inoue H. Dissection of the polygenic architecture of neuronal Aβ production using a large sample of individual iPSC lines derived from Alzheimer's disease patients. NATURE AGING 2022; 2:125-139. [PMID: 37117761 DOI: 10.1038/s43587-021-00158-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/23/2021] [Indexed: 04/30/2023]
Abstract
Genome-wide association studies have demonstrated that polygenic risks shape Alzheimer's disease (AD). To elucidate the polygenic architecture of AD phenotypes at a cellular level, we established induced pluripotent stem cells from 102 patients with AD, differentiated them into cortical neurons and conducted a genome-wide analysis of the neuronal production of amyloid β (Aβ). Using such a cellular dissection of polygenicity (CDiP) approach, we identified 24 significant genome-wide loci associated with alterations in Aβ production, including some loci not previously associated with AD, and confirmed the influence of some of the corresponding genes on Aβ levels by the use of small interfering RNA. CDiP genotype sets improved the predictions of amyloid positivity in the brains and cerebrospinal fluid of patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Secondary analyses of exome sequencing data from the Japanese ADNI and the ADNI cohorts focused on the 24 CDiP-derived loci associated with alterations in Aβ led to the identification of rare AD variants in KCNMA1.
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Affiliation(s)
- Takayuki Kondo
- Medical-risk Avoidance based on iPS Cells Team, RIKEN Center for Advanced Intelligence Project (AIP), Kyoto, Japan
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
- iPSC-based Drug Discovery and Development Team, RIKEN BioResource Research Center (BRC), Kyoto, Japan
| | - Norikazu Hara
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Satoshi Koyama
- Unit of Statistical Genetics, Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuichiro Yada
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
- iPSC-based Drug Discovery and Development Team, RIKEN BioResource Research Center (BRC), Kyoto, Japan
| | - Kayoko Tsukita
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
- iPSC-based Drug Discovery and Development Team, RIKEN BioResource Research Center (BRC), Kyoto, Japan
| | - Ayako Nagahashi
- Medical-risk Avoidance based on iPS Cells Team, RIKEN Center for Advanced Intelligence Project (AIP), Kyoto, Japan
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Kenji Ishii
- Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Takashi Asada
- Department of Psychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Tetsuaki Arai
- Department of Psychiatry, Division of Clinical Medicine, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Ryo Yamada
- Unit of Statistical Genetics, Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Haruhisa Inoue
- Medical-risk Avoidance based on iPS Cells Team, RIKEN Center for Advanced Intelligence Project (AIP), Kyoto, Japan.
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.
- iPSC-based Drug Discovery and Development Team, RIKEN BioResource Research Center (BRC), Kyoto, Japan.
- Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Japan.
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Development of a Novel Nutrition-Related Multivariate Biomarker for Mild Cognitive Impairment Based on the Plasma Free Amino Acid Profile. Nutrients 2022; 14:nu14030637. [PMID: 35276996 PMCID: PMC8840028 DOI: 10.3390/nu14030637] [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] [Received: 12/27/2021] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 11/16/2022] Open
Abstract
Nutritional epidemiology has shown the importance of protein intake for maintaining brain function in the elderly population. Mild cognitive impairment (MCI) may be associated with malnutrition, especially protein intake. We explored blood-based biomarkers linking protein nutritional status with MCI in a multicenter study. In total, 219 individuals with MCI (79.5 ± 5.7 year) from 10 institutions and 220 individuals who were cognitively normal (CN, 76.3 ± 6.6 year) in four different cities in Japan were recruited. They were divided into the training (120 MCI and 120 CN) and validation (99 MCI and 100 CN) groups. A model involving concentrations of PFAAs and albumin to discriminate MCI from CN individuals was constructed by multivariate logistic regression analysis in the training dataset, and the performance was evaluated in the validation dataset. The concentrations of some essential amino acids and albumin were significantly lower in MCI group than CN group. An index incorporating albumin and PFAA discriminated MCI from CN participants with the AUC of 0.705 (95% CI: 0.632–0.778), and the sensitivities at specificities of 90% and 60% were 25.3% and 76.8%, respectively. No significant association with BMI or APOE status was observed. This cross-sectional study suggests that the biomarker changes in MCI group may be associated with protein nutrition.
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Birkenbihl C, Salimi Y, Fröhlich H. Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data-driven disease modeling. Alzheimers Dement 2022; 18:251-261. [PMID: 34109729 DOI: 10.1002/alz.12387] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/19/2021] [Accepted: 04/25/2021] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Given study-specific inclusion and exclusion criteria, Alzheimer's disease (AD) cohort studies effectively sample from different statistical distributions. This heterogeneity can propagate into cohort-specific signals and subsequently bias data-driven investigations of disease progression patterns. METHODS We built multi-state models for six independent AD cohort datasets to statistically compare disease progression patterns across them. Additionally, we propose a novel method for clustering cohorts with regard to their progression signals. RESULTS We identified significant differences in progression patterns across cohorts. Models trained on cohort data learned cohort-specific effects that bias their estimations. We demonstrated how six cohorts relate to each other regarding their disease progression. DISCUSSION Heterogeneity in cohort datasets impedes the reproducibility of data-driven results and validation of progression models generated on single cohorts. To ensure robust scientific insights, it is advisable to externally validate results in independent cohort datasets. The proposed clustering assesses the comparability of cohorts in an unbiased, data-driven manner.
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Affiliation(s)
- Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Weber CJ, Carrillo MC, Jagust W, Jack CR, Shaw LM, Trojanowski JQ, Saykin AJ, Beckett LA, Sur C, Rao NP, Mendez PC, Black SE, Li K, Iwatsubo T, Chang C, Sosa AL, Rowe CC, Perrin RJ, Morris JC, Healan AM, Hall SE, Weiner MW. The Worldwide Alzheimer's Disease Neuroimaging Initiative: ADNI-3 updates and global perspectives. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12226. [PMID: 35005206 PMCID: PMC8719344 DOI: 10.1002/trc2.12226] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 11/05/2021] [Indexed: 11/06/2022]
Abstract
The Worldwide Alzheimer's Disease Neuroimaging Initiative (WW-ADNI) is a collaborative effort to investigate imaging and biofluid markers that can inform Alzheimer's disease treatment trials. It is a public-private partnership that spans North America, Argentina, Australia, Canada, China, Japan, Korea, Mexico, and Taiwan. In 2004, ADNI researchers began a naturalistic, longitudinal study that continues today around the globe. Through several successive phases (ADNI-1, ADNI-GO, ADNI-2, and ADNI-3), the study has fueled amyloid and tau phenotyping and refined neuroimaging methodologies. WW-ADNI researchers have successfully standardized analyses and openly share data without embargo, providing a rich data set for other investigators. On August 26, 2020, the Alzheimer's Association convened WW-ADNI researchers who shared updates from ADNI-3 and their vision for ADNI-4.
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Affiliation(s)
| | | | - William Jagust
- School of Public Health and Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | | | - Leslie M. Shaw
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory MedicinePerelman School of MedicineInstitute on AgingPerelman School of MedicineAlzheimer's Disease Core Center, Perelman School of MedicineUdall Parkinson's Research CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterDepartment of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Cyrille Sur
- Merck Research LaboratoriesMerckKenilworthNew JerseyUSA
| | - Naren P. Rao
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBengaluruKarnatakaIndia
| | | | - Sandra E. Black
- Department of Medicine (Neurology)Hurvitz Brain Sciences ProgramCanadian Partnership for Stroke Recovery, and LC Campbell Cognitive Neurology Research UnitHurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreUniversity of TorontoTorontoCanada
| | - Kuncheng Li
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Takeshi Iwatsubo
- Department of NeuropathologyGraduate School of MedicineThe University of TokyoTokyoJapan
| | - Chiung‐Chih Chang
- Department of General Neurology and Institute for Translational Research in BiomedicineKaohsiung Chang Gung Memorial HospitalChang Gung University College of MedicineKaohsiungTaiwan
| | - Ana Luisa Sosa
- National Institute of Neurology and Neurosurgery of MexicoMexico CityMexico
| | - Christopher C. Rowe
- Department of Molecular Imaging and TherapyAustin Health and Florey Department of Neuroscience and Mental HealthUniversity of MelbourneMelbourneVictoriaAustralia
| | - Richard J. Perrin
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of Pathology and ImmunologyDepartment of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
| | - John C. Morris
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesDepartment of RadiologyDepartment of MedicineDepartment of PsychiatryDepartment of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
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Shiino A, Shirakashi Y, Ishida M, Tanigaki K. Machine learning of brain structural biomarkers for Alzheimer's disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12246. [PMID: 34692983 PMCID: PMC8515359 DOI: 10.1002/dad2.12246] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 06/11/2021] [Indexed: 12/30/2022]
Abstract
INTRODUCTION We developed machine learning (ML) designed to analyze structural brain magnetic resonance imaging (MRI), and trained it on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In this study, we verified its utility in the Japanese population. METHODS A total of 535 participants were enrolled from the Japanese ADNI database, including 148 AD, 152 normal, and 235 mild cognitive impairment (MCI). Probability of AD was expressed as AD likelihood scores (ADLS). RESULTS The accuracy of AD diagnosis was 88.0% to 91.2%. The accuracy of predicting the disease progression in non-dementia participants over a 3-year observation was 76.0% to 79.3%. More than 90% of the participants with low ADLS did not progress to AD within 3 years. In the amyloid positron emission tomography (PET)-positive MCI, the hazard ratio of progression was 2.39 with low ADLS, and 5.77 with high ADLS. When high ADLS was defined as N+ and Pittsburgh compound B (PiB) PET positivity was defined as A+, the time to disease progression for 50% of MCI participants was 23.7 months in A+N+, whereas it was 52.3 months in A+N-. CONCLUSION These results support the feasibility of our ML for the diagnosis of AD and prediction of the disease progression.
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Affiliation(s)
- Akihiko Shiino
- Molecular Neuroscience Research CenterShiga University of Medical ScienceShigaJapan
| | - Yoshitomo Shirakashi
- Molecular Neuroscience Research CenterShiga University of Medical ScienceShigaJapan
| | - Manabu Ishida
- Department of NeurologyShimane UniversityShimaneJapan
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Nakahata N, Nakamura T, Kawarabayashi T, Seino Y, Ichii S, Ikeda Y, Amari M, Takatama M, Murashita K, Ihara K, Itoh K, Nakaji S, Shoji M. Age-Related Cognitive Decline and Prevalence of Mild Cognitive Impairment in the Iwaki Health Promotion Project. J Alzheimers Dis 2021; 84:1233-1245. [PMID: 34633321 DOI: 10.3233/jad-210699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The Iwaki Health Promotion Project (IHPP) is a community-based study for the prevention of lifestyle-related diseases and improvement of quality of life. OBJECTIVE Between 2014 and 2017, a total of 4,442 Iwaki town residents from 19 to 93 years of age participated in annual surveys to clarify the natural course of age-related cognitive decline and mild cognitive impairment (MCI). METHODS Modified OLD and SED-11Q questionnaires, MMSE, Logical Memory II, educational history, and APOE genotypes were examined at the first screening. MCI and dementia were diagnosed at the second examination by detailed neurological examination, CDR, and MRI, and followed for 3 years. Spline regression analyses based on a linear mixed model was adopted for statistical analysis. RESULTS MMSE scores declined with age from 55 to 64 years. There was also interaction between levels of education and ages. At the second examination, 56 MCI and 5 dementia patients were identified. None of the MCI cases progressed to dementia during the 3 years. During follow-up examinations, 13 cases showed improved MMSE scores (0.95 point/year), 5 remained stable, and 7 deteriorated (-0.83 point/year). Five cases showed improved CDR-SOB scores (-0.28 point/year), 9 remained stable, and 6 deteriorated (0.3 point/year). CONCLUSION IHPP revealed that age- and education-related cognitive decline began and advanced from 55 years of age. The prevalence of MCI and dementia was estimated to be 5.9%in the Iwaki town cohort over 60 yeas of age. About 30%of MCI cases showed progression of cognitive decline.
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Affiliation(s)
- Naoko Nakahata
- Department of Rehabilitation Sciences, Division of Speech-Language-Hearing Therapy, School of Health Sciences, Hirosaki University of Health and Welfare, Hirosaki, Aomori, Japan.,Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Takumi Nakamura
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.,Department of Neurology, Gunma University Hospital, Maebashi, Japan
| | - Takeshi Kawarabayashi
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.,Department of Neurology, Gunma University Hospital, Maebashi, Japan.,Department of Neurology, Dementia Research Center, Geriatrics Research Institute and Hospital, Maebashi, Japan
| | - Yusuke Seino
- Department of Neurology, Hirosaki National Hospital, Hirosaki, Japan
| | - Sadanobu Ichii
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Yoshio Ikeda
- Department of Neurology, Gunma University Hospital, Maebashi, Japan
| | - Masakuni Amari
- Department of Neurology, Dementia Research Center, Geriatrics Research Institute and Hospital, Maebashi, Japan
| | - Masamitsu Takatama
- Department of Neurology, Dementia Research Center, Geriatrics Research Institute and Hospital, Maebashi, Japan
| | - Koichi Murashita
- Center of Innovation Research Initiatives Organization, Hirosaki University, Hirosaki, Aomori, Japan
| | - Kazunari Ihara
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Ken Itoh
- Department of Stress Response Science, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Shigeyuki Nakaji
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
| | - Mikio Shoji
- Department of Social Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.,Department of Neurology, Gunma University Hospital, Maebashi, Japan.,Department of Neurology, Dementia Research Center, Geriatrics Research Institute and Hospital, Maebashi, Japan
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Nan H, Kim YJ, Tsuchiya M, Fukao T, Hara N, Hagihara A, Nishioka K, Hattori N, Hara N, Ikeuchi T, Ohtsuka T, Takiyama Y. A Novel Heterozygous Missense Variant in the CIAO1 Gene in a Family with Alzheimer's Disease: The Val67Ile Variant Promotes the Interaction of CIAO1 and Amyloid-β Protein Precursor. J Alzheimers Dis 2021; 84:599-605. [PMID: 34569959 PMCID: PMC8673532 DOI: 10.3233/jad-210706] [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
Familial dementia is a rare inherited disease involving progressive impairment of memory, thinking, and behavior. We report a novel heterozygous pathogenic variant (c.199G > A, p.Val67Ile) in the CIAO1 gene that appears to be co-segregated with Alzheimer’s disease in a Japanese family. Biochemical analysis of CIAO1 protein revealed that the variant increases the interaction of CIAO1 with immature amyloid-β protein precursor (AβPP), but not mature or soluble AβPP, indicating plausible CIAO1 involvement in AβPP processing. Our study indicates that a heterozygous variant in the CIAO1 gene may be closely related to autosomal dominant familial dementia.
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Affiliation(s)
- Haitian Nan
- Department of Neurology, Graduate School of Medical Sciences, University of Yamanashi, Yamanashi, Japan
| | - Yeon-Jeong Kim
- Department of Biochemistry, Graduate School of Medical Sciences, University of Yamanashi, Yamanashi, Japan
| | - Mai Tsuchiya
- Department of Neurology, Graduate School of Medical Sciences, University of Yamanashi, Yamanashi, Japan
| | - Toko Fukao
- Department of Neurology, Graduate School of Medical Sciences, University of Yamanashi, Yamanashi, Japan
| | - Noriko Hara
- Department of Internal Medicine, Minobusan Hospital, Yamanashi, Japan
| | - Atsushi Hagihara
- Department of Internal Medicine, Minobusan Hospital, Yamanashi, Japan
| | - Kenya Nishioka
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Norikazu Hara
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Niigata, Japan
| | - Toshihisa Ohtsuka
- Department of Biochemistry, Graduate School of Medical Sciences, University of Yamanashi, Yamanashi, Japan
| | - Yoshihisa Takiyama
- Department of Neurology, Graduate School of Medical Sciences, University of Yamanashi, Yamanashi, Japan
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Murata S, Ono R, Yasuda H, Tanemura R, Kido Y, Kowa H. Effect of a Combined Exercise and Cognitive Activity Intervention on Cognitive Function in Community-dwelling Older Adults: A Pilot Randomized Controlled Trial. Phys Ther Res 2021; 24:112-119. [PMID: 34532206 DOI: 10.1298/ptr.e10057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 11/20/2020] [Indexed: 11/23/2022]
Abstract
OBJECTIVE The purpose of this study is to investigate the effect of an intervention combining exercise and cognitive activity on cognitive function in healthy older adults. METHODS This pilot randomized controlled trial recruited 33 eligible, healthy communitydwelling older adults (mean age, 77.1 years old; women, 51.5%), who were divided into intervention and waitlist control groups. The intervention group was engaged weekly in a group activity comprising exercise and discussions of homework, which included reading aloud, simple arithmetic, and simple activities, like spotting differences, for cognitive stimulation. They were also required to complete cognitive activity homework twice a week. The waitlist control group received no intervention. The main outcomes were cognitive function assessed using the Mini-Mental State Examination, delayed recall score on the Logical Memory IIA of the Wechsler Memory Scale Revised, Trail Making Test, and digit symbol substitution test. RESULTS According to the results, Mini-Mental State Examination scores were maintained in the intervention group but declined in the control group [Mean change in outcomes in control group (95% confidence interval): -1.68 (-2.89 to -0.48)]. Additional mean change in outcomes in intervention group were found [1.68 (0.02 to 3.35)]. CONCLUSIONS Interventions combining exercise and cognitive activity can be helpful for preserving cognitive function in healthy older adults.
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Affiliation(s)
- Shunsuke Murata
- Department of Public Health, Kobe University, Graduate School of Health Sciences, Japan.,Japan Society for the Promotion of Science, Japan.,Department of Preventive Medicine and Epidemiology, National Cerebral and Cardiovascular Center Research Institute, Japan
| | - Rei Ono
- Department of Public Health, Kobe University, Graduate School of Health Sciences, Japan
| | - Hisafumi Yasuda
- Department of Public Health, Kobe University, Graduate School of Health Sciences, Japan
| | - Rumi Tanemura
- Department of Rehabilitation Science, Kobe University, Graduate School of Health Science, Japan
| | - Yoshiaki Kido
- Department of Biophysics, Kobe University, Graduate School of Health Science, Japan
| | - Hisatomo Kowa
- Department of Rehabilitation Science, Kobe University, Graduate School of Health Science, Japan
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Mérida I, Jung J, Bouvard S, Le Bars D, Lancelot S, Lavenne F, Bouillot C, Redouté J, Hammers A, Costes N. CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [ 18F]FDG PET, T1 and FLAIR MRI, and CT images available for research. EJNMMI Res 2021; 11:91. [PMID: 34529159 PMCID: PMC8446124 DOI: 10.1186/s13550-021-00830-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 08/15/2021] [Indexed: 01/05/2023] Open
Abstract
We present a database of cerebral PET FDG and anatomical MRI for 37 normal adult human subjects (CERMEP-IDB-MRXFDG). Thirty-nine participants underwent static [18F]FDG PET/CT and MRI, resulting in [18F]FDG PET, T1 MPRAGE MRI, FLAIR MRI, and CT images. Two participants were excluded after visual quality control. We describe the acquisition parameters, the image processing pipeline and provide participants' individual demographics (mean age 38 ± 11.5 years, range 23-65, 20 women). Volumetric analysis of the 37 T1 MRIs showed results in line with the literature. A leave-one-out assessment of the 37 FDG images using Statistical Parametric Mapping (SPM) yielded a low number of false positives after exclusion of artefacts. The database is stored in three different formats, following the BIDS common specification: (1) DICOM (data not processed), (2) NIFTI (multimodal images coregistered to PET subject space), (3) NIFTI normalized (images normalized to MNI space). Bona fide researchers can request access to the database via a short form.
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Affiliation(s)
- Inés Mérida
- CERMEP-Imagerie du Vivant, Lyon, France.
- CHU de Lyon HCL - GH Est, 59 Boulevard Pinel., 69677, Bron Cedex, France.
| | - Julien Jung
- INSERM U1028/CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
- Hospices Civils de Lyon, University Hospitals, Lyon, France
| | - Sandrine Bouvard
- Université Claude Bernard Lyon 1, Lyon Neuroscience Research Center, INSERM, CNRS, Lyon, France
| | - Didier Le Bars
- CERMEP-Imagerie du Vivant, Lyon, France
- Hospices Civils de Lyon, University Hospitals, Lyon, France
| | - Sophie Lancelot
- CERMEP-Imagerie du Vivant, Lyon, France
- INSERM U1028/CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
- Hospices Civils de Lyon, University Hospitals, Lyon, France
| | | | | | | | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, Kings' College London, King's College London and Guy's and St Thomas' PET Centre, London, UK
- Neurodis Foundation, Lyon, France
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Cui C, Higashiyama A, Lopresti BJ, Ihara M, Aizenstein HJ, Watanabe M, Chang Y, Kakuta C, Yu Z, Mathis CA, Kokubo Y, Fukuda T, Villemagne VL, Klunk WE, Lopez OL, Kuller LH, Miyamoto Y, Sekikawa A. Comparing Pathological Risk Factors for Dementia between Cognitively Normal Japanese and Americans. Brain Sci 2021; 11:1180. [PMID: 34573201 PMCID: PMC8469296 DOI: 10.3390/brainsci11091180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/28/2021] [Accepted: 08/30/2021] [Indexed: 11/16/2022] Open
Abstract
The Alzheimer's Disease Neuroimaging Initiative showed that Japanese had significantly lower brain Aβ burden than Americans among a cognitively normal population. This cross-sectional study aimed to compare vascular disease burden, Aβ burden, and neurodegeneration between cognitively normal elderly Japanese and Americans. Japanese and American participants were matched for age (±4-year-old), sex, and Apolipoprotein E (APOE) genotype. Brain vascular disease burden and brain Aβ burden were measured using white matter lesions (WMLs) and 11C-labeled Pittsburgh Compound B (PiB) retention, respectively. Neurodegeneration was measured using hippocampal volumes and cortical thickness. A total of 95 Japanese and 95 Americans were recruited (50.5% men, mean age = 82). Compared to Americans, Japanese participants had larger WMLs, and a similar global Aβ standardized uptake value ratio (SUVR), cortical thickness and hippocampal volumes. Japanese had significantly lower regional Aβ SUVR in the anterior ventral striatum, posterior cingulate cortex, and precuneus. Cognitively normal elderly Japanese and Americans had different profiles regarding vascular disease and Aβ burden. This suggests that multiple risk factors are likely to be involved in the development of dementia. Additionally, Japanese might have a lower risk of dementia due to lower Aβ burden than Americans. Longitudinal follow-up of these cohorts is warranted to ascertain the predictive accuracy of these findings.
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Affiliation(s)
- Chendi Cui
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA; (C.C.); (L.H.K.)
| | - Aya Higashiyama
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita 564-8565, Japan; (A.H.); (M.W.); (Y.K.); (Y.M.)
- Department of Hygiene, Wakayama Medical University, Wakayama 641-0011, Japan
| | - Brian J. Lopresti
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA; (B.J.L.); (Z.Y.); (C.A.M.)
| | - Masafumi Ihara
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita 564-8565, Japan; (M.I.); (C.K.)
| | - Howard J. Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (H.J.A.); (V.L.V.); (W.E.K.)
| | - Makoto Watanabe
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita 564-8565, Japan; (A.H.); (M.W.); (Y.K.); (Y.M.)
| | - Yuefang Chang
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA;
| | - Chikage Kakuta
- Department of Neurology, National Cerebral and Cardiovascular Center, Suita 564-8565, Japan; (M.I.); (C.K.)
| | - Zheming Yu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA; (B.J.L.); (Z.Y.); (C.A.M.)
| | - Chester A. Mathis
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA; (B.J.L.); (Z.Y.); (C.A.M.)
| | - Yoshihiro Kokubo
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita 564-8565, Japan; (A.H.); (M.W.); (Y.K.); (Y.M.)
| | - Tetsuya Fukuda
- Department of Radiology, National Cerebral and Cardiovascular Center, Suita 564-8565, Japan;
| | - Victor L. Villemagne
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (H.J.A.); (V.L.V.); (W.E.K.)
| | - William E. Klunk
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (H.J.A.); (V.L.V.); (W.E.K.)
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA;
| | - Oscar L. Lopez
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA;
| | - Lewis H. Kuller
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA; (C.C.); (L.H.K.)
| | - Yoshihiro Miyamoto
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita 564-8565, Japan; (A.H.); (M.W.); (Y.K.); (Y.M.)
- Open Innovation Center, National Cerebral and Cardiovascular Center, Suita 564-8565, Japan
| | - Akira Sekikawa
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA; (C.C.); (L.H.K.)
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Otani T, Otsuka H, Matsushita K, Otomi Y, Kunikane Y, Azane S, Amano M, Harada M, Miyoshi H. Effect of different examination conditions on image quality and quantitative value of amyloid positron emission tomography using 18F-flutemetamol. Ann Nucl Med 2021; 35:1004-1014. [PMID: 34046870 DOI: 10.1007/s12149-021-01634-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/20/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE The recommended start time for 18F-flutemetamol amyloid positron emission tomography (PET) examination is 60-120 min after 18F-flutemetamol injection, while an acquisition time of 10-30 min is generally recommended. We aimed to elucidate the effects of different examination conditions on image quality, diagnostic ability, and quantitative value of amyloid PET using 18F-flutemetamol. METHODS We acquired data on a Discovery PET/computed tomography 710 scanner using Hoffman brain and pillar phantoms with 20 MBq of 18F for 30 min. The images were reconstructed into 10-, 20-, and 30-min periods. The ordered subset-expectation maximization algorithm was used for image reconstruction, which uses a 2- or 4-mm Gaussian filter and a combination of iteration and subset numbers. The percentage contrast and coefficient of variation (CV; as the image noise) were used as physical evaluation indices for reconstructed images, and images with superior contrast and low image noise were selected for clinical evaluation. The imaging data of 15 symptomatic patients (n = 7 and n = 8 for positive and negative diagnoses of Alzheimer's disease, respectively) were reconstructed under the phantom study conditions. Radiographers visually evaluated and ranked the clinical images based on the overall contrast and image noise, and nuclear medicine specialists diagnosed Alzheimer's disease. We compared the standardized uptake value ratio (SUVR) obtained with different acquisition conditions. RESULTS The basic study using the phantom revealed high convergence of contrast and image noise in five patterns of acquisition time and filter strengths. Regarding visual evaluation, the use of a 2-mm Gaussian filter caused difficulties in diagnosis because the brain parenchymal accumulation was mottled with high image noise. Differences in image quality and diagnostic ability due to different examination times were not significant. Differences in the SUVR were not significant in patients with a negative Alzheimer's disease diagnosis; in patients with a positive diagnosis, the SUVR showed significant fluctuation depending on the acquisition conditions. CONCLUSION The differences in image quality and diagnostic performance due to the differences in 10-min acquisition time were not significant; however, of note, SUVR showed significant fluctuation depending on the acquisition conditions in patients diagnosed with Alzheimer's disease.
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Affiliation(s)
- Tamaki Otani
- Advance Radiation Research, Education, and Management Center, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.
| | - Hideki Otsuka
- Department of Medical Imaging/Nuclear Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Kou Matsushita
- Faculty of Health Science, Tokushima University Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yoichi Otomi
- Department of Radiology and Radiation Oncology, Tokushima University Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yamato Kunikane
- Department of Radiology, Tokushima University Hospital, 2-50-1 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Shota Azane
- Department of Radiology, Tokushima University Hospital, 2-50-1 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Masafumi Amano
- Department of Radiology, Tokushima University Hospital, 2-50-1 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Masafumi Harada
- Department of Radiology and Radiation Oncology, Tokushima University Graduate School of Biomedical Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hirokazu Miyoshi
- Advance Radiation Research, Education, and Management Center, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
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Hampel H, Shaw LM, Aisen P, Chen C, Lleó A, Iwatsubo T, Iwata A, Yamada M, Ikeuchi T, Jia J, Wang H, Teunissen CE, Peskind E, Blennow K, Cummings J, Vergallo A. State-of-the-art of lumbar puncture and its place in the journey of patients with Alzheimer's disease. Alzheimers Dement 2021; 18:159-177. [PMID: 34043269 PMCID: PMC8626532 DOI: 10.1002/alz.12372] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/24/2021] [Accepted: 04/12/2021] [Indexed: 01/01/2023]
Abstract
Recent advances in developing disease‐modifying therapies (DMT) for Alzheimer's disease (AD), and the recognition that AD pathophysiology emerges decades before clinical symptoms, necessitate a paradigm shift of health‐care systems toward biomarker‐guided early detection, diagnosis, and therapeutic decision‐making. Appropriate incorporation of cerebrospinal fluid biomarker analysis in clinical practice is an essential step toward system readiness for accommodating the demand of AD diagnosis and proper use of DMTs—once they become available. However, the use of lumbar puncture (LP) in individuals with suspected neurodegenerative diseases such as AD is inconsistent, and the perception of its utility and safety differs considerably among medical specialties as well as among regions and countries. This review describes the state‐of‐the‐art evidence concerning the safety profile of LP in older adults, discusses the risk factors for LP‐associated adverse events, and provides recommendations and an outlook for optimized use and global implementation of LP in individuals with suspected AD.
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Affiliation(s)
- Harald Hampel
- Eisai Inc., Neurology Business Group, Woodcliff Lake, New Jersey, USA
| | - Leslie M Shaw
- Perelman School of Medicine, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Paul Aisen
- USC Alzheimer's Therapeutic Research Institute, San Diego, California, USA
| | - Christopher Chen
- Memory Aging and Cognition Centre, Departments of Pharmacology and Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Alberto Lleó
- Sant Pau Memory Unit, Hospital de la Santa Creu i Sant Pau-Biomedical Research Institute Sant Pau-Universitat Autònoma de Barcelona, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Takeshi Iwatsubo
- Department of Neuropathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Atsushi Iwata
- Tokyo Metropolitan Geriatric Hospital, 35-2 Sakaecho, Itabashi-ku, Tokyo, Japan
| | - Masahito Yamada
- Department of Neurology and Neurobiology of Aging, Kanazawa University Graduate School of Medical Science, Kanazawa, Japan
| | - Takeshi Ikeuchi
- Department of Molecular Genetics, Brain Research Institute, Niigata University, Asahimachi, Niigata, Japan
| | - Jianping Jia
- Innovation Center for Neurological Disorders, Department of Neurology, Xuan Wu Hospital, Capital Medical University, Beijing, China
| | - Huali Wang
- Dementia Care and Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing Dementia Key Lab, National Clinical Research Center for Mental Disorders, Beijing, China
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Elaine Peskind
- VA Northwest Mental Illness Research, Education and Clinical Center, VA Puget Sound Health Care System, Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, Washington, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, Nevada, USA
| | - Andrea Vergallo
- Eisai Inc., Neurology Business Group, Woodcliff Lake, New Jersey, USA
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Robert C, Wilson CS, Lipton RB, Arreto CD. Evolution of the Research Literature and the Scientific Community of Alzheimer's Disease from 1983-2017: A 35-Year Survey. J Alzheimers Dis 2021; 75:1105-1134. [PMID: 32390624 DOI: 10.3233/jad-191281] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
This study surveys the development of Alzheimer's disease (AD) in the research literature, the scientific community, and the journals containing AD papers over a 35-year period. Research papers on AD published from 1983 to 2017 in journals indexed in the Web of Science were analyzed in seven five-year periods. The number of AD papers increased from 1,095 in 1983-1987 to 50,532 by 2013-2017 and in the same time period, the number of participating countries went from 27 to 152. The US was the most prolific country throughout, followed by several European countries, Canada, Australia, and Japan. Asian countries have emerged and by 2013-2017, China surpassed all but the US in productivity. Countries in Latin America and Africa have also contributed to AD research. Additionally, several new non-governmental institutions (e.g., ADNI, ADI) have emerged and now play a key role in the fight against AD. Likewise the AD scientific publishing universe evolved in various aspects: an increase in number of journals containing AD papers (227 journals in 1983-1987 to 3,257 in 2013-2017); appearance of several AD-focused journals, e.g., Alzheimer's & Dementia, Journal of Alzheimer's Disease; and the development of special issues dedicated to AD. Our paper complements the numerous extant papers on theoretical and clinical aspects of AD and provides a description of the research landscape of the countries and journals contributing papers related to AD.
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Affiliation(s)
- Claude Robert
- Université Paris Descartes, Paris, France.,Gliaxone, Saint Germain Sous Doue, France
| | - Concepción S Wilson
- Formerly at: School of Information Systems, Technology and Management, University of New South Wales, UNSW Sydney, Australia
| | - Richard B Lipton
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Charles-Daniel Arreto
- Gliaxone, Saint Germain Sous Doue, France.,Université Paris Descartes, Faculté de Chirurgie Dentaire, Hôpital Bretonneau, HUPNVS, AP-HP, Paris, France
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46
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Sato K, Ihara R, Suzuki K, Niimi Y, Toda T, Jimenez‐Maggiora G, Langford O, Donohue MC, Raman R, Aisen PS, Sperling RA, Iwata A, Iwatsubo T. Predicting amyloid risk by machine learning algorithms based on the A4 screen data: Application to the Japanese Trial-Ready Cohort study. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12135. [PMID: 33778148 PMCID: PMC7988864 DOI: 10.1002/trc2.12135] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/01/2020] [Accepted: 12/08/2020] [Indexed: 12/29/2022]
Abstract
BACKGROUND Selecting cognitively normal elderly individuals with higher risk of brain amyloid deposition is critical to the success of prevention trials for Alzheimer's disease (AD). METHODS Based on the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease study data, we built machine-learning models and applied them to our ongoing Japanese Trial-Ready Cohort (J-TRC) webstudy participants registered within the first 9 months (n = 3081) of launch to predict standard uptake value ratio (SUVr) of amyloid positron emission tomography. RESULTS Age, family history, online Cognitive Function Instrument and CogState scores were important predictors. In a subgroup of J-TRC webstudy participants with known amyloid status (n = 37), the predicted SUVr corresponded well with the self-reported amyloid test results (area under the curve = 0.806 [0.619-0.992]). DISCUSSION Our algorithms may be usable for automatic prioritization of candidate participants with higher amyloid risks to be preferentially recruited from the J-TRC webstudy to in-person study, maximizing efficiency for the identification of preclinical AD participants.
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Affiliation(s)
- Kenichiro Sato
- Department of NeurologyGraduate School of MedicineThe University of TokyoTokyoJapan
| | - Ryoko Ihara
- Department of NeuropathologyGraduate School of MedicineThe University of TokyoTokyoJapan
- Unit for Early and Exploratory Clinical DevelopmentThe University of Tokyo HospitalTokyoJapan
| | - Kazushi Suzuki
- Unit for Early and Exploratory Clinical DevelopmentThe University of Tokyo HospitalTokyoJapan
| | - Yoshiki Niimi
- Unit for Early and Exploratory Clinical DevelopmentThe University of Tokyo HospitalTokyoJapan
| | - Tatsushi Toda
- Department of NeurologyGraduate School of MedicineThe University of TokyoTokyoJapan
| | - Gustavo Jimenez‐Maggiora
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Oliver Langford
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Michael C. Donohue
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Rema Raman
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | | | - Atsushi Iwata
- Department of NeurologyGraduate School of MedicineThe University of TokyoTokyoJapan
- Department of NeurologyTokyo Metropolitan Geriatric Medical Center HospitalTokyoJapan
| | - Takeshi Iwatsubo
- Department of NeuropathologyGraduate School of MedicineThe University of TokyoTokyoJapan
- Unit for Early and Exploratory Clinical DevelopmentThe University of Tokyo HospitalTokyoJapan
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47
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Koike S, Tanaka SC, Okada T, Aso T, Yamashita A, Yamashita O, Asano M, Maikusa N, Morita K, Okada N, Fukunaga M, Uematsu A, Togo H, Miyazaki A, Murata K, Urushibata Y, Autio J, Ose T, Yoshimoto J, Araki T, Glasser MF, Van Essen DC, Maruyama M, Sadato N, Kawato M, Kasai K, Okamoto Y, Hanakawa T, Hayashi T. Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan. Neuroimage Clin 2021; 30:102600. [PMID: 33741307 PMCID: PMC8209465 DOI: 10.1016/j.nicl.2021.102600] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 01/31/2021] [Accepted: 02/12/2021] [Indexed: 01/03/2023]
Abstract
Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (BMB-HBM, FY2018 ~ FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. A high-quality scanning protocol, Harmonization Protocol (HARP), was established for five high-quality 3 T scanners to obtain multimodal brain images including T1 and T2-weighted, resting-state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, suggesting sensitivity to subject-specific connectome. A total of 75 TS and more than two thousand patients with various psychiatric and neurological disorders are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a prospective, multi-level harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.
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Affiliation(s)
- Shinsuke Koike
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Meguro-ku, Tokyo 153-8902, Japan; University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Meguro-ku, Tokyo 153-8902, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan
| | - Tomohisa Okada
- Human Brain Research Center, Kyoto University, Kyoto 606-8507, Japan
| | - Toshihiko Aso
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Hyogo 650-0047, Japan
| | - Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan
| | - Michiko Asano
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Meguro-ku, Tokyo 153-8902, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Meguro-ku, Tokyo 153-8902, Japan; Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira-shi, Tokyo 187-8551, Japan
| | - Kentaro Morita
- Department of Rehabilitation, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Naohiro Okada
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Meguro-ku, Tokyo 153-8902, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki 444-8585, Japan
| | - Akiko Uematsu
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Meguro-ku, Tokyo 153-8902, Japan
| | - Hiroki Togo
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira-shi, Tokyo 187-8551, Japan
| | - Atsushi Miyazaki
- Tamagawa University Brain Science Institute, 6-1-1 Tamagawagakuen, Machida, Tokyo 194-8610, Japan
| | | | | | - Joonas Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Hyogo 650-0047, Japan
| | - Takayuki Ose
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Hyogo 650-0047, Japan
| | - Junichiro Yoshimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan
| | - Toshiyuki Araki
- Department of Peripheral Nervous System Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8551, Japan
| | - Matthew F Glasser
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO USA; Department of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO USA
| | - Megumi Maruyama
- Research Enhancement Strategy Office, National Institute for Physiological Sciences, Okazaki 444-8585, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki 444-8585, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan; Center for Advanced Intelligence Project, RIKEN, Tokyo 103-0027, Japan
| | - Kiyoto Kasai
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Meguro-ku, Tokyo 153-8902, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima 734-8551, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira-shi, Tokyo 187-8551, Japan; Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto 606-8303, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Hyogo 650-0047, Japan.
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Yamamoto Y, Yamagata B, Hirano J, Ueda R, Yoshitake H, Negishi K, Yamagishi M, Kimura M, Kamiya K, Shino M, Mimura M. Regional Gray Matter Volume Identifies High Risk of Unsafe Driving in Healthy Older People. Front Aging Neurosci 2020; 12:592979. [PMID: 33343333 PMCID: PMC7744700 DOI: 10.3389/fnagi.2020.592979] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 11/09/2020] [Indexed: 12/02/2022] Open
Abstract
In developed countries, the number of traffic accidents caused by older drivers is increasing. Approximately half of the older drivers who cause fatal accidents are cognitively normal. Thus, it is important to identify older drivers who are cognitively normal but at high risk of causing fatal traffic accidents. However, no standardized method for assessing the driving ability of older drivers has been established. We aimed to establish an objective assessment of driving ability and to clarify the neural basis of unsafe driving in healthy older people. We enrolled 32 healthy older individuals aged over 65 years and classified unsafe drivers using an on-road driving test. We then utilized a machine learning approach to distinguish unsafe drivers from safe drivers based on clinical features and gray matter volume data. Twenty-one participants were classified as safe drivers and 11 participants as unsafe drivers. A linear support vector machine classifier successfully distinguished unsafe drivers from safe drivers with 87.5% accuracy (sensitivity of 63.6% and specificity of 100%). Five parameters (age and gray matter volume in four cortical regions, including the left superior part of the precentral sulcus, the left sulcus intermedius primus [of Jensen], the right orbital part of the inferior frontal gyrus, and the right superior frontal sulcus), were consistently selected as features for the final classification model. Our findings indicate that the cortical regions implicated in voluntary orienting of attention, decision making, and working memory may constitute the essential neural basis of driving behavior.
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Affiliation(s)
- Yasuharu Yamamoto
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Bun Yamagata
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Jinichi Hirano
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Ryo Ueda
- Office of Radiation Technology, Keio University Hospital, Tokyo, Japan
| | - Hiroshi Yoshitake
- Department of Human and Engineered Environmental Studies, The University of Tokyo, Tokyo, Japan
| | - Kazuno Negishi
- Department of Ophthalmology, Keio University School of Medicine, Tokyo, Japan
| | - Mika Yamagishi
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Mariko Kimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.,Graduate School of Psychology, Rissho University, Tokyo, Japan
| | - Kei Kamiya
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Motoki Shino
- Department of Human and Engineered Environmental Studies, The University of Tokyo, Tokyo, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
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49
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Birkenbihl C, Salimi Y, Domingo‐Fernándéz D, Lovestone S, Fröhlich H, Hofmann‐Apitius M. Evaluating the Alzheimer's disease data landscape. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2020; 6:e12102. [PMID: 33344750 PMCID: PMC7744022 DOI: 10.1002/trc2.12102] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 09/21/2020] [Indexed: 01/08/2023]
Abstract
INTRODUCTION Numerous studies have collected Alzheimer's disease (AD) cohort data sets. To achieve reproducible, robust results in data-driven approaches, an evaluation of the present data landscape is vital. METHODS Previous efforts relied exclusively on metadata and literature. Here, we evaluate the data landscape by directly investigating nine patient-level data sets generated in major clinical cohort studies. RESULTS The investigated cohorts differ in key characteristics, such as demographics and distributions of AD biomarkers. Analyzing the ethnoracial diversity revealed a strong bias toward White/Caucasian individuals. We described and compared the measured data modalities. Finally, the available longitudinal data for important AD biomarkers was evaluated. All results are explorable through our web application ADataViewer (https://adata.scai.fraunhofer.de). DISCUSSION Our evaluation exposed critical limitations in the AD data landscape that impede comparative approaches across multiple data sets. Comparison of our results to those gained by metadata-based approaches highlights that thorough investigation of real patient-level data is imperative to assess a data landscape.
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Affiliation(s)
- Colin Birkenbihl
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
| | - Yasamin Salimi
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
| | - Daniel Domingo‐Fernándéz
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
| | | | | | - Holger Fröhlich
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
| | - Martin Hofmann‐Apitius
- Department of BioinformaticsFraunhofer Institute for Algorithms and Scientific Computing (SCAI)Sankt AugustinGermany
- Bioinformatics GroupRheinische Friedrich‐Wilhelms‐Universität BonnBonnGermany
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Kudoh C, Hori T, Yasaki S, Ubagai R, Tabira T. Effects of Ferulic Acid and Angelica archangelica Extract (Feru-guard ®) on Mild Cognitive Impairment: A Multicenter, Randomized, Double-Blind, Placebo-Controlled Prospective Trial. J Alzheimers Dis Rep 2020; 4:393-398. [PMID: 33163900 PMCID: PMC7592837 DOI: 10.3233/adr-200211] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
We conducted a multicenter, randomized, double-blind, placebo-controlled prospective trial examining a supplement containing ferulic acid and Angelica archangelica extract (Feru-guard ®) for mild cognitive impairment (MCI). In the intention-to-treat population, Mini-Mental State Examination (MMSE) scores were significantly better at 24 weeks (p = 0.041) in the active group. In the per protocol population, MMSE was significantly better in the active group at 24 weeks (p = 0.008), and mixed effect models for repeated measures (MMRM) showed significant difference (p = 0.016). ADAS-Jcog was significantly better at 24 (p = 0.035) and 48 weeks (p = 0.015) in the active group, and MMRM was significant (p = 0.031). Thus, Feru-guard ® may be useful for MCI.
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Affiliation(s)
- Chiaki Kudoh
- Kudoh Clinic for Neurosurgery & Neurology, Ota-ku, Tokyo, Japan
| | - Tomokatsu Hori
- Moriyama Neurological Center Hospital, Edogawa-ku, Tokyo, Japan
| | - Shunji Yasaki
- Department of Neurology, Shin-Yurigaoka General Hospital, Furusawa, Aso-ku, Kawasaki, Japan
| | - Ryu Ubagai
- Moriyama Neurological Center Hospital, Edogawa-ku, Tokyo, Japan
| | - Takeshi Tabira
- Kudoh Clinic for Neurosurgery & Neurology, Ota-ku, Tokyo, Japan.,Department of Diagnosis, Prevention and Treatment of Dementia, Graduate School of Medicine, Juntendo University, Bunkyo-ku, Tokyo, Japan
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