UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000051776 |
|---|---|
| Receipt number | R000059089 |
| Scientific Title | A Retrospective Study to Explore Early Screening Indicators for AD pathology |
| Date of disclosure of the study information | 2023/08/31 |
| Last modified on | 2025/03/31 11:56:02 |
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Basic information
Public title
A Retrospective Study to Explore Early Screening Indicators for AD pathology
Acronym
A Retrospective Study to Explore Early Screening Indicators for AD pathology
Scientific Title
A Retrospective Study to Explore Early Screening Indicators for AD pathology
Scientific Title:Acronym
A Retrospective Study to Explore Early Screening Indicators for AD pathology
Region
| Japan |
Condition
Condition
Mild Cognitive impairment
Healthy adult volunteers
Classification by specialty
| Neurology | Adult |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
To identify combinations of background factors and clinical indicators useful for prescreening Amyloid-beta accumulation in the brain of patients with MCI due to AD/preclinical AD, based on subject background (age, gender, comorbidities, medical history, lifestyle, educational history, etc.) and clinical laboratory data (general blood test values, etc.) accessible to primary care doctors.
Basic objectives2
Others
Basic objectives -Others
Biomarker
Trial characteristics_1
Exploratory
Trial characteristics_2
Developmental phase
Not applicable
Assessment
Primary outcomes
To construct a prediction model for the presence or absence of Amyloid-beta accumulation in the brain using machine learning based on clinical information (variables) collected retrospectively, and to evaluate its prediction accuracy.
Key secondary outcomes
Base
Study type
Observational
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| Not applicable |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
Subjects of this study are participants in Usuki Cohort (the cohort of local elderly residents in Usuki City, Oita Prefecture) and those who visited Oita University Hospital, and they meet all the following inclusion criteria.
1. MCI who fulfilled the diagnostic criteria of Petersen (CDR 0.5), or healthy adult volunteers who did not fulfill the diagnostic criteria of Petersen (CDR 0).
2. Subjects undergone amyloid PET ([11C]PiB: Pittsburgh compound-B) testing.
Key exclusion criteria
Subjects of this study are participants in Usuki Cohort (the cohort of local elderly residents in Usuki City, Oita Prefecture) and those who visited Oita University Hospital, and they meet all the following inclusion criteria.
1. Subjects with some missing data.
2. Subjects who have stated their willingness to participate in the research.
3. Any other subjects who are judged to be ineligible by the principal investigator or sub-investigator.
Target sample size
200
Research contact person
Name of lead principal investigator
| 1st name | Noriyuki |
| Middle name | |
| Last name | Kimura |
Organization
Oita University Faculty of Medicine
Division name
Department of Neurology
Zip code
879-5593
Address
1-1 Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
TEL
097-586-5814
noriyuki@oita-u.ac.jp
Public contact
Name of contact person
| 1st name | Noriyuki |
| Middle name | |
| Last name | Kimura |
Organization
Oita University Faculty of Medicine
Division name
Department of Neurology
Zip code
879-5593
Address
1-1 Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
TEL
097-586-5814
Homepage URL
noriyuki@oita-u.ac.jp
Sponsor or person
Institute
Oita University
Institute
Department
Personal name
Funding Source
Organization
Eisai Co., Ltd.
Organization
Division
Category of Funding Organization
Profit organization
Nationality of Funding Organization
Other related organizations
Co-sponsor
Eisai Co., Ltd.
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Oita University Faculty of Medicine Ethics Committee
Address
1-1 Idaigaoka, Hasama, Yufu, Oita 879-5593, Japan
Tel
097-586-6380
rinrikenkyu@oita-u.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
大分大学医学部神経内科学講座（大分県）
エーザイ株式会社（東京都）
Other administrative information
Date of disclosure of the study information
| 2023 | Year | 08 | Month | 31 | Day |
Related information
URL releasing protocol
Publication of results
Published
Result
URL related to results and publications
https://alzres.biomedcentral.com/articles/10.1186/s13195-024-01650-1
Number of participants that the trial has enrolled
260
Results
The classification performance using L2-regularized logistic regression showed that Model 1 (demographic characteristics, MMSE subscores) and Model 2 (demographic characteristics, blood test results) had similar performance (ROC AUC [SD], 0.70 [0.01]). However, Model 3 (demographic characteristics, blood test results, MMSE subscores) demonstrated improved performance (ROC AUC [SD], 0.73 [0.01]). The most important variables were MMSE subscores for delayed recall and orientation place, age, TSH, and MCV.
Results date posted
| 2025 | Year | 03 | Month | 31 | Day |
Results Delayed
Results Delay Reason
Date of the first journal publication of results
| 2025 | Year | 01 | Month | 21 | Day |
Baseline Characteristics
In total, 38.5% (101/262) of participant records were amyloid-beta positive, 79.8% (209/262) had MCI (CDR = 0.5), and 20.2% (53/262) were cognitively normal (CDR = 0). The mean (SD) age was 73.8 (7.8) years, 51.9% (136/262) were male, and the mean (SD) MMSE score was 26.3 (2.4).
Participant flow
Of the 855 patients in the USUKI cohort and the 230 patients who visited Oita University Hospital, 703 and 71, respectively, were excluded because they did not have amyloid PiB-PET data, and 8 and 13, respectively, because they had a CDR > 0.5; additionally, 1 case in the USUKI cohort was excluded because of an MMSE score < 20, indicating dementia, and 4 in Oita University Hospital because they did not have CDR data. No participants were excluded because of a large number of missing or abnormal values. The participant with the most missing values had 11 of 34 variables missing (11 MMSE subscores). Prior to analysis, it was determined that exclusion of this participant because of the high number of missing values was not necessary. Note that out of 285, two participants overlapped across the two cohorts, and therefore the dataset screened for eligibility included 283 unique participants. The overlapping records for the two participants were treated as separate records because the PiB-PET scan dates were approximately 4 years apart between the two cohorts. Overall, 285 records of participants with MCI or normal cognitive function were screened for eligibility and 262 (260 unique participants) were included in the analysis set; 22 were excluded because there was a > 365-day period between their PiB-PET and the collection of data related to other variables, and one patient in the USUKI cohort was excluded because of missing blood test records.
Adverse events
Outcome measures
The primary endpoint was the classification performance of the presence or absence of intracerebral amyloid-beta accumulation using five different machine learning models.
･ Model 0: demographic characteristics (age, sex, body mass index, years of education)
･ Model 1: Model 0 plus all MMSE subscores
･ Model 2: Model 0 plus blood test results (excluding ApoE4 phenotype) and the other demographic characteristics (medical history, current alcohol consumption, smoking status)
･ Model 3: Model 2 plus all MMSE subscores
･ Model 4: Model 3 plus ApoE4 phenotype
The true label for positive amyloid-beta accumulation, or the positive presence of intracerebral amyloid-beta accumulation, was defined as a mean PiB standardized uptake value ratio (SUVR) of 1.2 or higher.
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Completed
Date of protocol fixation
| 2023 | Year | 05 | Month | 12 | Day |
Date of IRB
| 2023 | Year | 06 | Month | 22 | Day |
Anticipated trial start date
| 2023 | Year | 09 | Month | 01 | Day |
Last follow-up date
| 2024 | Year | 04 | Month | 30 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Retrospective observational study
Condition:
(1) Healthy adult volunteers
(2) Patients with MCI due to AD/preclinical AD
Data:
(A) Usuki cohort data from October 1, 2015 to November 30, 2017 which met the eligibility criteria.
(B) Data from Oita University Hospital: Patients were seen between September 1, 2012 and November 30, 2017 and met the eligibility criteria.
Method: To construct a prediction model for the presence or absence of Amyloid-beta accumulation in the brain using machine learning based on clinical information (variables) collected retrospectively, and to evaluate its prediction accuracy.
Management information
Registered date
| 2023 | Year | 08 | Month | 01 | Day |
Last modified on
| 2025 | Year | 03 | Month | 31 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000059089
