Evidence map›Paper›PMID 42246933›Full record

ArticleCNS neuroscience & therapeutics2026

Multimodal Integration of Gait Dysfunction, Amyloid PET, and Plasma Biomarkers for Differentiating Etiological Subtypes in Mild Cognitive Impairment.

Jiaonan Wu, Fang Tang, Xinyi Lv, Yiwei Wang, Feng Gao, Yong Shen, Zhaozhao Cheng, Jiong Shi

Abstract read
In one paragraph

Article in CNS neuroscience & therapeutics, 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
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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

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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

8 authors.

Jiaonan WuDepartment of Neurology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Fang TangDepartment of Neurology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Xinyi LvDepartment of Neurology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Yiwei WangGraduate School, Bengbu Medical University, Bengbu, China.
Feng GaoDepartment of Neurology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Yong ShenDepartment of Neurology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Zhaozhao ChengDepartment of Neurology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Jiong ShiDepartment of Neurology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.ORCID 0000-0001-7694-1366

Funding

Anhui Provincial Clinical Medical Research and Translation Special Fund 202304295107020051Anhui Provincial Key R&D Programs 202304295107020056Fundamental Research Funds for the Central Universities YD9100002033Natural Science Foundation of Anhui Province 2308085QH265Natural Science Research Projects in Anhui Province Universities 2023AH053410the Strategic Priority Research Program of the Chinese Academy of Sciences XDB39000000
6 · The paper itself

Abstract

objectiveTo investigate gait characteristics and plasma biomarkers in individuals with mild cognitive impairment (MCI) stratified by amyloid-β (Aβ) positivity on positron emission tomography (PET), and to evaluate the predictive value for Alzheimer's disease (AD)-related MCI.

methodsA total of 168 participants were enrolled, including 50 amyloid PET-negative MCI (MCI-), 51 amyloid PET-positive MCI (MCI+), and 61 cognitively normal (CN) individuals. Gait assessments were conducted using a multi-sensor motion analysis system during dual-task paradigms (cognitive load superimposed on locomotion). Plasma samples were analyzed for neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and phosphorylated tau at threonine 217 (p-tau217) via ultra-sensitive immunoassays.

resultsGait analyses identified 125 features, with 69 distinguishing MCI+ from CN and 36 differentiating MCI+ from MCI-. Receiver operating characteristic (ROC) analyses showed that dual-task gait under the countdown and animal-naming conditions (gait-countdown [GCD] and gait-animal naming [GAN]) discriminated MCI+ from CN with an AUC of 0.850. The combined models integrating GCD and GAN with plasma GFAP or p-tau217 yielded AUCs of 0.919 and 0.951, respectively. Similarly, GCD&GAN features demonstrated an AUC of 0.862 for distinguishing MCI+ from MCI-, with GFAP (AUC = 0.933) and p-tau217 (AUC = 0.986) enhancing predictive performance.

conclusionThis study provides evidence that dual-task gait assessments, when combined with plasma biomarkers such as GFAP and p-tau217, may improve the discriminatory power for identifying AD-related MCI. The integration of biomechanical and molecular markers holds promise for advancing early detection strategies and therapeutic monitoring in AD.

Indexed as

Amyloid beta-PeptidesCognitive DysfunctionGait Disorders, NeurologicPositron-Emission TomographyAgedBiomarkersDual-Task TestsFemaleGlial Fibrillary Acidic ProteinHumansMaleNeurofilament Proteinstau ProteinsAmyloid beta-PeptidesBiomarkersGlial Fibrillary Acidic Proteinneurofilament protein LNeurofilament Proteinstau Proteinsamyloid PETdual‐task gaitgait analysismild cognitive impairmentplasma biomarkers

Identifiers

PMID42246933
PMCPMC13239215

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Registered trials

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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.