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The Mayo Clinic Study of Aging: Design and Sampling, Participation, Baseline Measures and Sample Characteristics
Open accessAbstract
Background: The objective of this study was to establish a prospective population-based cohort to investigate the prevalence, incidence and risk factors for mild cognitive impairment (MCI) and dementia. Methods: The Olmsted County, Minn., population, aged 70–89 years on October 1, 2004, was enumerated using the Rochester Epidemiology Project. Eligible subjects were randomly selected and invited to participate. Participants underwent a comprehensive in-person evaluation including the Clinical Dementia Rating Scale, a neurological evaluation and neuropsychological testing. A consensus diagnosis of normal cognition, MCI or dementia was made by a panel using previously published criteria. A subsample of subjects was studied via telephone interview. Results: Four hundred and two subjects with dementia were identified from a detailed review of their medical records but were not contacted. At baseline, we successfully evaluated 703 women aged 70–79 years, 769 women aged 80–89 years, 730 men aged 70–79 years and 517 men aged 80–89 years (total n = 2,719). Among the participants, 2,050 subjects were evaluated in person and 669 via telephone. Conclusions: Strengths of the study are that the subjects were randomly selected from a defined population, the majority of the subjects were examined in person, and MCI was defined using published criteria. Here, we report the design and sampling, participation, baseline measures and sample characteristics.
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Self-citationAssociation of Hospitalization with Long‐Term Cognitive Trajectories in Older Adults
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Self-citationLongitudinal atrophy in prodromal dementia with Lewy bodies points to cholinergic degeneration
Kantarci, K., Nedelska, Z., Chen, Q., Senjem, M. L., Schwarz, C. G., Gunter, J. L., Przybelski, S. A., Lesnick, T. G., Kremers, W. K., Fields, J. A., et al. (2022) · Brain Communications · doi:10.1093/braincomms/fcac013 · Cited by 39
Self-citationRetinal examination for the diagnosis of Alzheimer's disease
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Dang, C., Harrington, K. D., Lim, Y. Y., Ames, D., Hassenstab, J., Laws, S. M., Yassi, N., Hickey, M., Rainey-Smith, S., Robertson, J., et al. (2018) · Journal of Alzheimer’s Disease · doi:10.3233/jad-180507 · Cited by 31
White Matter Degeneration Pathways Associated With Tau Deposition in Alzheimer Disease
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Self-citationBarnard, L., Botha, H., Corriveau-Lecavalier, N., Graff-Radford, J., Dicks, E., Gogineni, V., Zhang, G., Burkett, B. J., Johnson, D. R., Huls, S. J., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000213831 · Cited by 25
Self-citationGenetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis
Ramanan, V. K., Gebre, R. K., Graff-Radford, J., Hofrenning, E., Algeciras-Schimnich, A., Figdore, D. J., Lowe, V. J., Mielke, M. M., Knopman, D. S., Ross, O. A., et al. (2023) · Brain · doi:10.1093/brain/awad196 · Cited by 24
Self-citationNODDI in gray matter is a sensitive marker of aging and early AD changes
Yu, X., Przybelski, S. A., Reid, R. I., Lesnick, T. G., Raghavan, S., Graff‐Radford, J., Lowe, V. J., Kantarci, K., Knopman, D. S., Petersen, R. C., et al. (2024) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12627 · Cited by 23
Self-citationRoberts, R. O., Kang, Y. N., Hu, C., Moser, C. D., Wang, S., Moore, M. J., Graham, R. P., Lai, J.-P., Petersen, R. C., & Roberts, L. R. (2017) · Journal of Alzheimer's Disease Reports · doi:10.3233/adr-170028 · Cited by 19
Self-citationBrain mechanical properties predict longitudinal cognitive change in aging and Alzheimer’s disease
Pavuluri, K., Huston, J., Ehman, R. L., Manduca, A., Vemuri, P., Jack, C. R., Senjem, M. L., & Murphy, M. C. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.01.001 · Cited by 18
Fu, S., Vassilaki, M., Ibrahim, O. A., Petersen, R. C., Pagali, S., St Sauver, J., Moon, S., Wang, L., Fan, J. W., Liu, H., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.958539 · Cited by 15
Self-citationFedFSA: Hybrid and federated framework for functional status ascertainment across institutions
Fu, S., Jia, H., Vassilaki, M., Keloth, V. K., Dang, Y., Zhou, Y., Garg, M., Petersen, R. C., St Sauver, J., Moon, S., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104623 · Cited by 17
Self-citationCleven, L., Syrjanen, J. A., Geda, Y. E., Christenson, L. R., Petersen, R. C., Vassilaki, M., Woll, A., & Krell-Roesch, J. (2023) · BMC Public Health · doi:10.1186/s12889-023-15119-7 · Cited by 16
Self-citation
Metadata
- Authors
- Roberts, R. O., Geda, Y. E., Knopman, D. S., Cha, R. H., Pankratz, V. S., Boeve, B. F., Ivnik, R. J., Tangalos, E. G., Petersen, R. C., & Rocca, W. A.
- Venue
- Neuroepidemiology
- Publisher
- S. Karger AG
- Published
- 2008
- Volume
- 30
- Issue
- 1
- Pages
- 58-69
- ISSN
- 0251-5350 (ISSN-L)
- 1423-0208
- OpenAlex
- W2020780341
- Cited by
- 793
- References
- 8
- FWCI
- 12.54
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
