Classifying oxidative stress by F2-isoprostane levels across human diseases: A meta-analysis

Abstract

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 F2-isoprostane, 8-iso-PGF2α, 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-PGF2α levels between affected and control populations were calculated.The meta-analysis resulted in a classification of oxidative damage levels as measured by 8-iso-PGF2α across 50 human health outcomes and exposures from 242 distinct publications. Relatively small increases in 8-iso-PGF2α 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-PGF2α 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-PGF2α 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.

Strategies to decrease oxidative stress biomarker levels in human medical conditions: A meta-analysis on 8-iso-prostaglandin F2α

“…These increases were reported in a prior work by van 't Erve et al [27] and are referred to as the baseline meta-analysis in this publication. The extracted data from each publication can be found in the following Mendeley dataset: 〈http://dx.doi.org/10.17632/6s8k723m7b.1〉.…”

“…Data are presented as percent inhibition ± standard error. Percent inhibition is calculated using Formula (1) and is derived from the difference in Hedges’ g value for the reduction in 8-iso-PGF 2α levels of each strategy and the Hedges’ g for elevated 8-iso-PGF 2α levels for each medical condition as calculated by van 't Erve et al [27]. * denotes an effect statistically significantly different from 0.…”

“…Review articles and strategies for which fewer than 3 unique publications could be found were also excluded from the final analysis. References from van 't Erve et al [27] were used to calculate the g values for the medical condition baselines (populations with the medical conditions compared to a population without any medical conditions).…”

“…Effectiveness was assed as the difference between the fixed-effect model Hedges’ g calculated for each strategy per medical condition (Strategy g ) and the Hedges’ g value calculated for elevated 8-iso-PGF 2α levels between populations with and without the condition ( Baseline g ), as previously calculated by van 't Erve et al [27]. The baseline values from the earlier meta-analysis [27] were taken as 0% inhibition for each medical condition.…”

“…The baseline values from the earlier meta-analysis [27] were taken as 0% inhibition for each medical condition. The effect of each strategy was calculated and converted to a percentage with Formula (1):$\left[\text{100 -}\left(\frac{\text{Baseline g-}\left(\text{Strategy g × -1}\right)}{\text{Baseline g}}\right)\right]\text{× 100}$…”