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Mendeley readers
Attention Score in Context
| Title |
Identification of an Immune-Neuroendocrine Biomarker Panel for Detection of Depression: A Joint Effects Statistical Approach
|
|---|---|
| Published in |
Neuroendocrinology, November 2015
|
| DOI | 10.1159/000442208 |
| Pubmed ID | |
| Authors |
Man K. Chan, Jason D. Cooper, Mariska Bot, Johann Steiner, Brenda W.J.H. Penninx, Sabine Bahn |
| Abstract |
Less than half of depression patients are correctly diagnosed within the primary care setting. Previous proteomic studies have identified numerous immune and neuroendocrine changes in patients. However, few studies have considered the joint effects of biological molecules and their diagnostic potential. Our aim was to develop and validate a diagnostic serum biomarker panel identified through joint effects analysis of multiplex immunoassay profiling data from 1,007 clinical samples. In stage 1, we conducted a meta-analysis of two independent cohorts of 78 first/recent onset drug-naive/drug-free depression patients and 156 controls and applied the 10-fold cross-validation with least absolute shrinkage and selection operator regression to identify an optimal diagnostic prediction model (biomarker panel). In stage 2, we tested the discriminatory performance of this biomarker panel using the naturalistic Netherlands Study of Depression and Anxiety (NESDA) cohort of 468 depression patients and 305 controls. An optimal panel of 33 immune-neuroendocrine biomarkers and gender was selected in the meta-analysis. Testing this biomarker-gender panel using the NESDA cohort resulted in a moderate to good performance to differentiate patients from controls (0.69 < AUC< 0.86), particularly the first-episode patients free of chronic non-psychiatric diseases or medications and following incorporation of sociodemographic covariates (0.76 < AUC < 0.92). Despite the need for additional validation studies, we demonstrated that a blood-based biomarker-sociodemographic panel can detect depression in naturalistic healthcare settings with good discriminatory power. Further refinements of blood biomarker panels aiding in the diagnosis of depression may provide a cost-effective means to increase accuracy of clinical diagnosis within the primary care setting. |
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 % |
|---|---|---|
| United Kingdom | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 64 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 22% |
| Student > Ph. D. Student | 10 | 16% |
| Student > Master | 10 | 16% |
| Student > Bachelor | 5 | 8% |
| Other | 3 | 5% |
| Other | 11 | 17% |
| Unknown | 11 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 13 | 20% |
| Psychology | 12 | 19% |
| Agricultural and Biological Sciences | 6 | 9% |
| Neuroscience | 5 | 8% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Other | 11 | 17% |
| Unknown | 14 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 16 October 2018.
All research outputs
#6,006,225
of 23,989,683 outputs
Outputs from Neuroendocrinology
#169
of 933 outputs
Outputs of similar age
#88,546
of 393,773 outputs
Outputs of similar age from Neuroendocrinology
#4
of 10 outputs
Altmetric has tracked 23,989,683 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 933 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,773 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.