Evidence map›Paper›PMID 30802256›Full record

ArticlePloS one2019

Evaluating trajectories of episodic memory in normal cognition and mild cognitive impairment: Results from ADNI.

Xiuhua Ding, Richard J Charnigo, Frederick A Schmitt, Richard J Kryscio, Erin L Abner, Alzheimer’s Disease Neuroimaging Initiative

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.5field-weighted citation impact, top 8% of its field
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

13 citing papers in PubMed, 33 citations in OpenAlex.

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  13. [Application of semantic segmentation based on convolutional neural network in medical images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020
    Review
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

6 authors at 2 institutions in 1 country.

Xiuhua DingWestern Kentucky University, Department of Public Health, Bowling Green, Kentucky, United States of America.ORCID 0000-0003-0120-5537
Richard J CharnigoUniversity of Kentucky, Department of Statistics, Lexington, Kentucky, United States of America.
Frederick A SchmittUniversity of Kentucky, Sanders-Brown Center on Aging, Lexington, Kentucky, United States of America.
Richard J KryscioUniversity of Kentucky, Department of Statistics, Lexington, Kentucky, United States of America.
Erin L AbnerUniversity of Kentucky, Department of Biostatistics, Lexington, Kentucky, United States of America.
Alzheimer’s Disease Neuroimaging Initiative
University of Kentucky · USWestern Kentucky University · US

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
University of Kentucky Alzheimer's Disease Core Center - Biomarker CoreP30AG028383 · NIA · UNIVERSITY OF KENTUCKY · PI SCHMITT, FREDERICK · 2006 to 2020
$23.3M
Role of impaired cognitive states & risk factors in conversion to mixed dementiasR01AG038651 · NIA · UNIVERSITY OF KENTUCKY · PI ABNER, ERIN, KRYSCIO, RICHARD J. · 2011 to 2023
$6.1M
NIA NIH HHS P30 AG028383NIA NIH HHS R01 AG038651NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

backgroundMemory assessment is a key factor for the diagnosis of cognitive impairment. However, memory performance over time may be quite heterogeneous within diagnostic groups.

methodTo identify latent trajectories in memory performance and their associated risk factors, we analyzed data from Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who were classified either as cognitively normal or as Mild Cognitive Impairment (MCI) at baseline and were administered the Rey Auditory Verbal Learning test (RAVLT) for up to 9 years. Group-based trajectory modeling on the 30-minute RAVLT delayed recall score was applied separately to the two baseline diagnostic groups.

resultsThere were 219 normal subjects with mean age 75.9 (range from 59.9 to 89.6) and 52.5% male participants, and 372 MCI subjects with mean age 74.8 (range from 55.1 to 89.3) and 63.7% male participants included in the analysis. For normal subjects, six trajectories were identified. Trajectories were classified into three types, determined by the shape, each of which may comprise more than one trajectory: stable (~30% of subjects), curvilinear decline (~ 28%), and linear decline (~ 42%). Notably, none of the normal subjects assigned to the stable stratum progressed to dementia during the study period. In contrast, all trajectories identified for the MCI group tended to decline, although some participants were later re-diagnosed with normal cognition. Age, sex, and education were significantly associated with trajectory membership for both diagnostic groups, while APOE ɛ4 was only significantly associated with trajectories among MCI participants.

conclusionMemory trajectory is a strong indicator of dementia risk. If likely trajectory of memory performance can be identified early, such work may allow clinicians to monitor or predict progression of individual patient cognition. This work also shows the importance of longitudinal cognitive testing and monitoring.

Indexed as

CognitionMemory, EpisodicAgedAged, 80 and overAlzheimer DiseaseCognitive DysfunctionDisease ProgressionFemaleHumansLongitudinal StudiesMaleMemory and Learning TestsMiddle AgedModels, PsychologicalModels, StatisticalPrognosis

Identifiers

PMID30802256
PMCPMC6389289
OpenAlexW2917718778

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.