Literature DB >> 24509229

Lipidomics: potential role in risk prediction and therapeutic monitoring for diabetes and cardiovascular disease.

Peter J Meikle1, Gerard Wong2, Christopher K Barlow2, Bronwyn A Kingwell2.   

Abstract

Lipidomics has developed rapidly over the past decade to the point where clinical application may soon be possible. Developments including high throughput technologies enable the simultaneous quantification of several hundred lipid species, thereby providing a global assessment of lipid metabolism. Given the key role of lipids in the pathophysiology of diabetes and cardiovascular disease, lipidomics has the potential to: i) Significantly improve prediction of future disease risk, ii) Inform on mechanisms of disease pathogenesis, iii) Identify patient groups responsive to particular therapies and iv) More closely monitor response to therapy. Lipidomic analyses of both whole plasma and lipoprotein subfractions are integral to the current initiative to understand the relationships between lipoprotein composition and function and how these are affected by disease and treatment. This approach will not only aid in appropriate targeting of existing lipid lowering therapies such as statins and fibrates, but will be important in unravelling the controversies surrounding HDL-based therapies which have failed in clinical trials to date. The ultimate utility of lipidomics to clinical practice will depend firstly on the ability of risk prediction models incorporating lipidomic parameters to significantly improve upon conventional clinical risk markers in predicting future disease risk. Secondly, for widespread application, lipidomic-based measurements must be practical and accessible through standard pathology laboratories. This review will cover developments in lipidomics including methodology, bioinformatics/statistics, insights into disease pathophysiology, the effect of therapeutic interventions, the role of large clinical outcome trials in validating lipidomic approaches to patient management and potential applications in clinical practice.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bioinformatics; Cardiovascular disease; Diabetes; Lipid metabolism; Risk assessment; Therapeutic monitoring

Mesh:

Year:  2014        PMID: 24509229     DOI: 10.1016/j.pharmthera.2014.02.001

Source DB:  PubMed          Journal:  Pharmacol Ther        ISSN: 0163-7258            Impact factor:   12.310


  50 in total

1.  Lipid Profiling of In Vitro Cell Models of Adipogenic Differentiation: Relationships With Mouse Adipose Tissues.

Authors:  Lucy Liaw; Igor Prudovsky; Robert A Koza; Rea V Anunciado-Koza; Matthew E Siviski; Volkhard Lindner; Robert E Friesel; Clifford J Rosen; Paul R S Baker; Brigitte Simons; Calvin P H Vary
Journal:  J Cell Biochem       Date:  2016-03-16       Impact factor: 4.429

2.  Minimal variation of the plasma lipidome after delayed processing of neonatal cord blood.

Authors:  John M Wentworth; Naiara G Bediaga; Megan A S Penno; Esther Bandala-Sanchez; Komal N Kanojia; Konstantinos A Kouremenos; Jennifer J Couper; Leonard C Harrison
Journal:  Metabolomics       Date:  2018-09-25       Impact factor: 4.290

3.  Sexual dimorphism in the fetal cardiac response to maternal nutrient restriction.

Authors:  Sribalasubashini Muralimanoharan; Cun Li; Ernesto S Nakayasu; Cameron P Casey; Thomas O Metz; Peter W Nathanielsz; Alina Maloyan
Journal:  J Mol Cell Cardiol       Date:  2017-06-19       Impact factor: 5.000

4.  The prediction of type 2 diabetes in women with previous gestational diabetes mellitus using lipidomics.

Authors:  Martha Lappas; Piyushkumar A Mundra; Gerard Wong; Kevin Huynh; Debra Jinks; Harry M Georgiou; Michael Permezel; Peter J Meikle
Journal:  Diabetologia       Date:  2015-04-17       Impact factor: 10.122

5.  Arachidonic acid-derived signaling lipids and functions in impaired healing.

Authors:  Sandeep Dhall; Dayanjan Shanaka Wijesinghe; Zubair A Karim; Anthony Castro; Hari Priya Vemana; Fadi T Khasawneh; Charles E Chalfant; Manuela Martins-Green
Journal:  Wound Repair Regen       Date:  2015-09-14       Impact factor: 3.617

Review 6.  Sphingolipids and phospholipids in insulin resistance and related metabolic disorders.

Authors:  Peter J Meikle; Scott A Summers
Journal:  Nat Rev Endocrinol       Date:  2016-10-21       Impact factor: 43.330

Review 7.  Lipidomics in the Study of Hypertension in Metabolic Syndrome.

Authors:  Hemant Kulkarni; Manju Mamtani; John Blangero; Joanne E Curran
Journal:  Curr Hypertens Rep       Date:  2017-01       Impact factor: 5.369

Review 8.  Lipids: biomarkers of healthy aging.

Authors:  I Almeida; S Magalhães; A Nunes
Journal:  Biogerontology       Date:  2021-04-10       Impact factor: 4.277

9.  Urinary Lipidomics: evidence for multiple sources and sexual dimorphism in healthy individuals.

Authors:  J Graessler; C S Mehnert; K-M Schulte; S Bergmann; S Strauss; T D Bornstein; J Licinio; M-L Wong; A L Birkenfeld; S R Bornstein
Journal:  Pharmacogenomics J       Date:  2017-06-13       Impact factor: 3.550

Review 10.  Lipidomics: Techniques, Applications, and Outcomes Related to Biomedical Sciences.

Authors:  Kui Yang; Xianlin Han
Journal:  Trends Biochem Sci       Date:  2016-09-20       Impact factor: 13.807

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