Evidence map›Paper›PMID 42149314›Full record

ArticleGeroScience2026

Development of 5-year risk prediction models for incident dementia and mortality in a community-dwelling older Japanese population: The Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD).

Xiangyin Meng, Takanori Honda, Tomoyuki Ohara, Yoshihiko Furuta, Mao Shibata, Jun Hata, Yasuyuki Taki, Tatsuya Mikami, Tetsuya Maeda, Kenjiro Ono and 6 more

Abstract read
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In one paragraph

Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Xiangyin MengDepartment of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Takanori HondaDepartment of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Tomoyuki OharaDepartment of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Yoshihiko FurutaDepartment of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Mao ShibataDepartment of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Jun HataDepartment of Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Yasuyuki TakiDepartment of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.
Tatsuya MikamiDepartment of Preemptive Medicine, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan.
Tetsuya MaedaDivision of Neurology and Gerontology, Department of Internal Medicine, School of Medicine, Iwate Medical University, Morioka, Iwate, Japan.
Kenjiro OnoDepartment of Neurology, Kanazawa University Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan.
Masaru MimuraCenter for Preventive Medicine, Keio University School of Medicine, Tokyo, Japan.
Ritsuko HanajimaDivision of Neurology, Department of Brain and Neurosciences, Faculty of Medicine, Tottori University, Yonago, Japan.
Jun-Ichi IgaDepartment of Neuropsychiatry, Ehime University Graduate School of Medicine, Ehime University, Ehime, Japan.
Minoru TakebayashiDepartment of Psychiatry and Neuroscience, Center for Metabolic Regulation of Healthy Aging, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan.
Toshiharu NinomiyaDepartment of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. ninomiya.toshiharu.734@m.kyushu-u.ac.jp.ORCID http://orcid.org/0000-0003-1345-9032
On the behalf of the JPSC-AD study group

Funding

Japan Agency for Medical Research and Development dk0207025Japan Agency for Medical Research and Development dk0207053
6 · The paper itself

Abstract

Improving cognitively healthy survival is important for achieving healthy aging. Therefore, it would be valuable to estimate the future risk of either incident dementia or death in community-dwelling older adults. This study aimed to develop a set of risk prediction models for either incident dementia or death that can be applied according to data availability across diverse clinical settings, using longitudinal data from community-dwelling older Japanese adults. A total of 8,334 participants aged ≥65 years were prospectively followed up from 2016 to 2023. Logistic regression was used to develop multivariable prediction models. The developed models were translated into simplified scoring systems based on the β coefficients. The discrimination abilities of the models were assessed by C-statistics, and the calibration was assessed by calibration plots. During the follow-up period, 1,151 participants developed either dementia or death. In the multivariable model using potential predictors commonly available in the primary care setting, age, male sex, formal education ≤9 years, diabetes mellitus, use of lipid-lowering agents, leanness, history of stroke, history of heart disease, history of respiratory disease, history of cancer, current smoking, no regular exercise habit, and low frequency of social interactions were selected as predictors. Adding cognitive status, depressive symptoms, apolipoprotein E-ε4 carrier status, and brain MRI markers to the model further enhanced its predictive performance. The developed models and simplified scores showed good discrimination and calibration. Risk stratification with our models may be useful for assessing the risks of incident dementia and death and, consequently, for prolonging cognitively healthy survival.

Indexed as

DementiaPublic HealthRisk predictionSurvival

Identifiers

PMID42149314

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.