Evidence map›Paper›PMID 27811210›Full record

ArticleClinical chemistry2017

A Novel Protein Glycan-Derived Inflammation Biomarker Independently Predicts Cardiovascular Disease and Modifies the Association of HDL Subclasses with Mortality.

Robert W McGarrah, Jacob P Kelly, Damian M Craig, Carol Haynes, Ryan C Jessee, Kim M Huffman, William E Kraus, Svati H Shah

Abstract read
In one paragraph

Article in Clinical chemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Trial
  7. Trial
  8. Trial
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Robert W McGarrahDivision of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC; robert.mcgarrah@dm.duke.edu.
Jacob P KellyDivision of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC.
Damian M CraigDuke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC.
Carol HaynesDuke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC.
Ryan C JesseeDivision of Rheumatology, Department of Medicine, Duke University School of Medicine, Durham, NC.
Kim M HuffmanDuke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC.
William E KrausDivision of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC.
Svati H ShahDivision of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC.

Funding

MULTIDISCIPLINARY HEART &VASCULAR DISEASEST32HL007101 · NHLBI · DUKE UNIVERSITY · PI FREEDMAN, NEIL J. · 1985 to 2020
$9.8M
NHLBI NIH HHS T32 HL007101
6 · The paper itself

Abstract

backgroundEvidence suggests that systemic inflammation may adversely impact HDL function. In this study we sought to evaluate the independent and incremental predictive performance of GlycA-a novel serum inflammatory biomarker that is an aggregate measure of enzymatically glycosylated acute phase proteins-and HDL subclasses on adverse events in a retrospective observational study of a secondary prevention population and to understand a priori defined potential interactions between GlycA and HDL subclasses.

methodsGlycA and HDL subclasses were measured using proton nuclear magnetic resonance spectroscopy in 7617 individuals in the CATHGEN (CATHeterization GENetics) cardiac catheterization biorepository.

resultsGlycA was associated with presence [odds ratio (OR) 1.07 (1.02-1.13), P = 0.01] and extent [OR 1.08 (1.03, 1.12) P < 0.0005] of coronary artery disease and with all-cause mortality [hazard ratio (HR) 1.34 (1.29-1.39), P < 0.0001], cardiovascular mortality [1.37 (1.30-1.45), P < 0.0001] and noncardiovascular mortality [1.46 (1.39-1.54) P < 0.0001] in models adjusted for 10 cardiovascular risk factors. GlycA and smaller HDL subclasses had independent but opposite effects on mortality risk prediction, with smaller HDL subclasses being protective [HR 0.69 (0.66-0.72), P < 0.0001]. There was an interaction between GlycA and smaller HDL subclasses-increasing GlycA concentrations attenuated the inverse association of smaller HDL subclasses with mortality. Adding GlycA and smaller HDL subclasses into the GRACE (Global Registry of Acute Coronary Events) and Framingham Heart Study Risk Scores improved mortality risk prediction, discrimination and reclassification.

conclusionsThese findings highlight the interaction of systemic inflammation and HDL with clinical outcomes and may increase precision for clinical risk assessment in secondary prevention populations.

Indexed as

BiomarkersCardiovascular DiseasesCholesterol, HDLFemaleHumansInflammationLipoproteinsMaleMiddle AgedNuclear Magnetic Resonance, BiomolecularPolysaccharidesSurvival RateBiomarkersCholesterol, HDLLipoproteinsPolysaccharides

Identifiers

PMID27811210
PMCPMC5429588

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.