Evidence map›Paper›PMID 33269100›Full record

ArticleAging and disease2020

Alteration of Metabolic Profile and Potential Biomarkers in the Plasma of Alzheimer's Disease.

Yaping Shao, Yang Ouyang, Tianbai Li, Xinyao Liu, Xiaojiao Xu, Song Li, Guowang Xu, Weidong Le

Open access · goldAbstract read
In one paragraph

Article in Aging and disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 2 pooled it
3.1field-weighted citation impact, top 7% 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

45 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.

  1. Pooled it
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  9. Efficacy ofJMIR research protocols · 2026
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  15. Metabolic dysregulation in Alzheimer's disease: A brain metabolomics approach.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
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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 at 3 institutions in 2 countries.

Yaping Shao1Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Yang Ouyang3CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
Tianbai Li1Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Xinyao Liu1Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Xiaojiao Xu1Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Song Li1Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Guowang Xu3CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
Weidong Le1Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
Dalian Medical University · CNChinese Academy of Sciences · CNFirst Affiliated Hospital of Dalian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The expending of elderly population worldwide has resulted in a dramatic rise in the incidence of chronic diseases such as Alzheimer's disease (AD). Inadequate understanding of the mechanisms underlying AD has hampered the development of efficient tools for definitive diagnosis and curative interventions. Previous studies have attempted to discover reliable biomarkers of AD, but these biomarkers can only be measured through invasive (neuropathological markers in cerebrospinal fluid) or expensive (positron emission tomography scanning or magnetic resonance imaging) techniques. Metabolomics is a high-throughput technology that can detect and catalog large numbers of small metabolites and may be a useful tool for characterization of AD and identification of biomarkers. In this study, we used ultra-performance liquid chromatography-mass spectrometry based untargeted metabolomics to measure the concentrations of plasma metabolites in a cohort of subjects with AD (n=44) and cognitively normal controls (Ctrl, n=94). The AD group showed marked reductions in levels of polyunsaturated fatty acids, acyl-carnitines, degradation products of tryptophan, and elevated levels of bile acids compared to the Ctrl group. We then validated the results using an independent cohort that included subjects with AD (n=30), mild cognitive impairment (MCI, n=13), healthy controls (n=43), and non-AD neurological disease controls (NDC, n=31). We identified five metabolites comprising cholic acid, chenodeoxycholic acid, allocholic acid, indolelactic acid, and tryptophan that were able to distinguish patients with AD from both Ctrl and NDC with satisfactory sensitivity and specificity. The concentrations of these metabolites were significantly correlated with disease severity. Our results also suggested that altered bile acid profiles in AD and MCI might indicate early risk for the development of AD. These findings may allow for development of new approaches for diagnosis of AD and may provide novel insights into AD pathogenesis.

Indexed as

Alzheimer’s diseasebiomarkermetabolic pathway alterationmetabolomicsplasma

Identifiers

PMID33269100
PMCPMC7673846
OpenAlexW3110512485

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LicenceCC BY
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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.