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Aptamer-based search for correlates of plasma and serum water T2: implications for early metabolic dysregulation and metabolic syndrome

Overview of attention for article published in Biomarker Research, September 2018
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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 (#16 of 204)
  • High Attention Score compared to outputs of the same age (83rd percentile)

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17 tweeters
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1 Facebook page

Citations

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5 Dimensions

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17 Mendeley
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Title
Aptamer-based search for correlates of plasma and serum water T2: implications for early metabolic dysregulation and metabolic syndrome
Published in
Biomarker Research, September 2018
DOI 10.1186/s40364-018-0143-x
Pubmed ID
Authors

Vipulkumar Patel, Alok K. Dwivedi, Sneha Deodhar, Ina Mishra, David P. Cistola

Abstract

Metabolic syndrome is a cluster of abnormalities that increases the risk for type 2 diabetes and atherosclerosis. Plasma and serum water T2 from benchtop nuclear magnetic resonance relaxometry are early, global and practical biomarkers for metabolic syndrome and its underlying abnormalities. In a prior study, water T2 was analyzed against ~ 130 strategically selected proteins and metabolites to identify associations with insulin resistance, inflammation and dyslipidemia. In the current study, the analysis was broadened ten-fold using a modified aptamer (SOMAmer) library, enabling an unbiased search for new proteins correlated with water T2 and thus, metabolic health. Water T2 measurements were recorded using fasting plasma and serum from non-diabetic human subjects. In parallel, plasma samples were analyzed using a SOMAscan assay that employed modified DNA aptamers to determine the relative concentrations of 1310 proteins. A multi-step statistical analysis was performed to identify the biomarkers most predictive of water T2. The steps included Spearman rank correlation, followed by principal components analysis with variable clustering, random forests for biomarker selection, and regression trees for biomarker ranking. The multi-step analysis unveiled five new proteins most predictive of water T2: hepatocyte growth factor, receptor tyrosine kinase FLT3, bone sialoprotein 2, glucokinase regulatory protein and endothelial cell-specific molecule 1. Three of the five strongest predictors of water T2 have been previously implicated in cardiometabolic diseases. Hepatocyte growth factor has been associated with incident type 2 diabetes, and endothelial cell specific molecule 1, with atherosclerosis in subjects with diabetes. Glucokinase regulatory protein plays a critical role in hepatic glucose uptake and metabolism and is a drug target for type 2 diabetes. By contrast, receptor tyrosine kinase FLT3 and bone sialoprotein 2 have not been previously associated with metabolic conditions. In addition to the five most predictive biomarkers, the analysis unveiled other strong correlates of water T2 that would not have been identified in a hypothesis-driven biomarker search. The identification of new proteins associated with water T2 demonstrates the value of this approach to biomarker discovery. It provides new insights into the metabolic significance of water T2 and the pathophysiology of metabolic syndrome.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 24%
Other 2 12%
Student > Ph. D. Student 2 12%
Student > Master 1 6%
Researcher 1 6%
Other 1 6%
Unknown 6 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 24%
Chemistry 3 18%
Computer Science 2 12%
Economics, Econometrics and Finance 1 6%
Nursing and Health Professions 1 6%
Other 2 12%
Unknown 4 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 22 April 2020.
All research outputs
#1,922,905
of 18,461,840 outputs
Outputs from Biomarker Research
#16
of 204 outputs
Outputs of similar age
#45,979
of 287,390 outputs
Outputs of similar age from Biomarker Research
#1
of 1 outputs
Altmetric has tracked 18,461,840 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 204 research outputs from this source. They receive a mean Attention Score of 4.1. 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 287,390 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 83% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them