Classifying oxidative stress by F-isoprostane levels across human diseases: A meta-analysis.

The notion that oxidative stress plays a role in virtually every human disease and environmental exposure has become ingrained in everyday knowledge. However, mounting evidence regarding the lack of specificity of biomarkers traditionally used as indicators of oxidative stress in human disease and exposures now necessitates re-evaluation. To prioritize these re-evaluations, published literature was comprehensively analyzed in a meta-analysis to quantitatively classify the levels of systemic oxidative damage across human disease and in response to environmental exposures. In this meta-analysis, the F-isoprostane, 8-iso-PGF, was specifically chosen as the representative marker of oxidative damage. To combine published values across measurement methods and specimens, the standardized mean differences (Hedges' g) in 8-iso-PGF levels between affected and control populations were calculated. The meta-analysis resulted in a classification of oxidative damage levels as measured by 8-iso-PGF across 50 human health outcomes and exposures from 242 distinct publications. Relatively small increases in 8-iso-PGF levels (g<0.8) were found in the following conditions: hypertension (g=0.4), metabolic syndrome (g=0.5), asthma (g=0.4), and tobacco smoking (g=0.7). In contrast, large increases in 8-iso-PGF levels were observed in pathologies of the kidney, e.g., chronic renal insufficiency (g=1.9), obstructive sleep apnoea (g=1.1), and pre-eclampsia (g=1.1), as well as respiratory tract disorders, e.g., cystic fibrosis (g=2.3). In conclusion, we have established a quantitative classification for the level of 8-iso-PGF generation in different human pathologies and exposures based on a comprehensive meta-analysis of published data. This analysis provides knowledge on the true involvement of oxidative damage across human health outcomes as well as utilizes past research to prioritize those conditions requiring further scrutiny on the mechanisms of biomarker generation.