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Identification of an Immune-Neuroendocrine Biomarker Panel for Detection of Depression: A Joint Effects Statistical Approach

Overview of attention for article published in Neuroendocrinology, November 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#46 of 723)
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

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4 tweeters
patent
1 patent

Citations

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7 Dimensions

Readers on

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54 Mendeley
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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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 26%
Student > Ph. D. Student 9 17%
Student > Master 8 15%
Other 5 9%
Student > Bachelor 3 6%
Other 9 17%
Unknown 6 11%
Readers by discipline Count As %
Psychology 11 20%
Medicine and Dentistry 10 19%
Agricultural and Biological Sciences 6 11%
Neuroscience 5 9%
Biochemistry, Genetics and Molecular Biology 5 9%
Other 8 15%
Unknown 9 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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
#3,196,969
of 16,342,693 outputs
Outputs from Neuroendocrinology
#46
of 723 outputs
Outputs of similar age
#73,344
of 347,966 outputs
Outputs of similar age from Neuroendocrinology
#4
of 29 outputs
Altmetric has tracked 16,342,693 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 723 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done particularly well, scoring higher than 93% 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 347,966 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 78% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.