| Title |
Discriminating poststroke depression from stroke by nuclear magnetic resonance spectroscopy-based metabonomic analysis
|
|---|---|
| Published in |
Neuropsychiatric Disease and Treatment, August 2016
|
| DOI | 10.2147/ndt.s110613 |
| Pubmed ID | |
| Authors |
Jianqi Xiao, Jie Zhang, Dan Sun, Lin Wang, Lijun Yu, Hongjing Wu, Dan Wang, Xuerong Qiu |
| Abstract |
Poststroke depression (PSD), the most common psychiatric disease that stroke survivors face, is estimated to affect ~30% of poststroke patients. However, there are still no objective methods to diagnose PSD. In this study, to explore the differential metabolites in the urine of PSD subjects and to identify a potential biomarker panel for PSD diagnosis, the nuclear magnetic resonance-based metabonomic method was applied. Ten differential metabolites responsible for discriminating PSD subjects from healthy control (HC) and stroke subjects were found, and five of these metabolites were identified as potential biomarkers (lactate, α-hydroxybutyrate, phenylalanine, formate, and arabinitol). The panel consisting of these five metabolites provided excellent performance in discriminating PSD subjects from HC and stroke subjects, achieving an area under the receiver operating characteristic curve of 0.946 in the training set (43 HC, 45 stroke, and 62 PSD subjects). Moreover, this panel could classify the blinded samples from the test set (31 HC, 33 stroke, and 32 PSD subjects) with an area under the curve of 0.946. These results laid a foundation for the future development of urine-based objective methods for PSD diagnosis and investigation of PSD pathogenesis. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 5 | 20% |
| Other | 4 | 16% |
| Student > Ph. D. Student | 2 | 8% |
| Librarian | 1 | 4% |
| Student > Doctoral Student | 1 | 4% |
| Other | 4 | 16% |
| Unknown | 8 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 12% |
| Psychology | 3 | 12% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Computer Science | 2 | 8% |
| Engineering | 2 | 8% |
| Other | 6 | 24% |
| Unknown | 7 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 August 2016.
All research outputs
#16,721,717
of 25,374,647 outputs
Outputs from Neuropsychiatric Disease and Treatment
#1,719
of 3,132 outputs
Outputs of similar age
#242,201
of 381,036 outputs
Outputs of similar age from Neuropsychiatric Disease and Treatment
#70
of 102 outputs
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