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Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts

Overview of attention for article published in Lipids in Health and Disease, April 2016
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Title
Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts
Published in
Lipids in Health and Disease, April 2016
DOI 10.1186/s12944-016-0234-3
Pubmed ID
Authors

Manju Mamtani, Hemant Kulkarni, Gerard Wong, Jacquelyn M. Weir, Christopher K. Barlow, Thomas D. Dyer, Laura Almasy, Michael C. Mahaney, Anthony G. Comuzzie, David C. Glahn, Dianna J. Magliano, Paul Zimmet, Jonathan Shaw, Sarah Williams-Blangero, Ravindranath Duggirala, John Blangero, Peter J. Meikle, Joanne E. Curran

Abstract

Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening. Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia - the AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D. The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76 %. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention. Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 91 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 22%
Researcher 18 20%
Professor 10 11%
Other 7 8%
Student > Master 6 7%
Other 12 13%
Unknown 18 20%
Readers by discipline Count As %
Medicine and Dentistry 21 23%
Agricultural and Biological Sciences 14 15%
Biochemistry, Genetics and Molecular Biology 12 13%
Nursing and Health Professions 4 4%
Chemistry 4 4%
Other 12 13%
Unknown 24 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 25 December 2016.
All research outputs
#18,450,346
of 22,860,626 outputs
Outputs from Lipids in Health and Disease
#985
of 1,452 outputs
Outputs of similar age
#219,947
of 300,360 outputs
Outputs of similar age from Lipids in Health and Disease
#22
of 34 outputs
Altmetric has tracked 22,860,626 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,452 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.