The Prognostic Value of Derivatives-Reactive Oxygen Metabolites (d-ROMs) for Cardiovascular Disease Events and Mortality: A Review.
Filippo PigazzaniDavide GorniKenneth A DyarMatteo PedrelliGwen KennedyGabriele CostantinoAgostino BrunoIsla MackenzieThomas M MacDonaldUwe J F TietgeJacob GeorgePublished in: Antioxidants (Basel, Switzerland) (2022)
Oxidative stress participates in the development and exacerbation of cardiovascular diseases (CVD). The ability to promptly quantify an imbalance in an individual reductive-oxidative (RedOx) state could improve cardiovascular risk assessment and management. Derivatives-reactive oxygen metabolites (d-ROMs) are an emerging biomarker of oxidative stress quantifiable in minutes through standard biochemical analysers or by a bedside point-of-care test. The current review evaluates available data on the prognostic value of d-ROMs for CVD events and mortality in individuals with known and unknown CVD. Outcome studies involving small and large cohorts were analysed and hazard ratio, risk ratio, odds ratio, and mean differences were used as measures of effect. High d-ROM plasma levels were found to be an independent predictor of CVD events and mortality. Risk begins increasing at d-ROM levels higher than 340 UCARR and rises considerably above 400 UCARR. Conversely, low d-ROM plasma levels are a good negative predictor for CVD events in patients with coronary artery disease and heart failure. Moreover, combining d-ROMs with other relevant biomarkers routinely used in clinical practice might support a more precise cardiovascular risk assessment. We conclude that d-ROMs represent an emerging oxidative-stress-related biomarker with the potential for better risk stratification both in primary and secondary cardiovascular prevention.
Keyphrases
- oxidative stress
- cardiovascular disease
- risk assessment
- heart failure
- cardiovascular events
- human health
- dna damage
- clinical practice
- diabetic rats
- ms ms
- chronic obstructive pulmonary disease
- induced apoptosis
- heavy metals
- type diabetes
- risk factors
- electronic health record
- machine learning
- coronary artery disease
- big data
- heat shock
- signaling pathway
- intensive care unit
- climate change