Redox-related biomarkers in human cardiovascular disease - classical footprints and beyond

Abstract

Global epidemiological studies show that chronic non-communicable diseases such as atherosclerosis and metabolic disorders represent the leading cause of premature mortality and morbidity. Cardiovascular disease such as ischemic heart disease is a major contributor to the global burden of disease and the socioeconomic health costs. Clinical and epidemiological data show an association of typical oxidative stress markers such as lipid peroxidation products, 3-nitrotyrosine or oxidized DNA/RNA bases with all major cardiovascular diseases. This supports the concept that the formation of reactive oxygen and nitrogen species by various sources (NADPH oxidases, xanthine oxidase and mitochondrial respiratory chain) represents a hallmark of the leading cardiovascular comorbidities such as hyperlipidemia, hypertension and diabetes. These reactive oxygen and nitrogen species can lead to oxidative damage but also adverse redox signaling at the level of kinases, calcium handling, inflammation, epigenetic control, circadian clock and proteasomal system. The in vivo footprints of these adverse processes (redox biomarkers) are discussed in the present review with focus on their clinical relevance, whereas the details of their mechanisms of formation and technical aspects of their detection are only briefly mentioned. The major categories of redox biomarkers are summarized and explained on the basis of suitable examples. Also the potential prognostic value of redox biomarkers is critically discussed to understand what kind of information they can provide but also what they cannot achieve.

Keywords: Cardiovascular disease; Comorbidities; Oxidative stress; Redox biomarker.

Graphical abstract

Fig. 1

Concept of redox biomarkers for comorbidities in myocardial infarction (MI). Identification of specific redox biomarkers for different comorbidities (e.g. hypertension, hyperlipidemia or diabetes) in major cardiovascular events (e.g. MI) would significantly advance redox diagnostic approaches. Currently most redox biomarkers belong to the category of „additive accumulation” that could at least be used as prognostic tools to identify patients with multiple comorbidities representing the group of highest cardiovascular risk. Heart cartoon from Servier Medical Art by Servier, licensed under a Creative Commons Attribution 3.0 Unported License.

Fig. 2

Redox pathways associated with putative biomarkers of oxidative stress [10,43]. The processes that lead to oxidative modifications of proteins, lipids and nucleotides are highly complex. Enzymes, such as XO, NOX and NOS, as well as organelles such as mitochondria, can produce ROS and RNS. ROS/RNS from primary sources can trigger secondary ROS sources (e.g. conversion of XDH to XO or uncoupling of eNOS) [31]. ROS can serve as substrates for other enzymes to generate additional types of ROS, such as the generation of HOCl from H₂O₂ by MPO. Importantly, some ROS sources are solely producing detrimental ROS (filled red color, e.g. NOX1/2/5), some have physiological functions but generate ROS as a side product or in the presence of certain substrates (dashed red line, e.g. LPO, Mito) or even produce mostly beneficial ROS (filled red color with red line, e.g. NOX4). Cellular systems and enzymes, including the SOD, GSH/GPx and Trx/Prx system, counterbalance the production of ROS (filled green color). In addition, increased levels of ROS activate the transcription factor Nrf2 via Keap1 to transcribe genes such as HO-1 that are involved in counteracting these ROS (filled green color). Oxidative stress affects cGMP signaling through its effects on nitric oxide (^•NO) production, scavenging and on the ^•NO producing enzyme eNOS as well as the ^•NO receptor sGC (by oxidative heme depletion to the apo-sGC) and its target kinase PKG. In principle, all of these mentioned ROS producing, detoxifying and oxidatively modified enzymes and systems represent redox biomarkers as their expression or activity will provide information on the redox balance. ROS and RNS can be directly detected using specific probes (e.g. by chemiluminescence, fluorescence based HPLC or EPR-based methods) [46,57]. ROS and RNS also leave footprints in vivo in the form of classical oxidative stress markers such as oxidized amino acids in proteins or oxidized nucleotides in DNA/RNA as well as free or protein-bound reactive aldehydes from glucotoxicity and lipid peroxidation (filled grey color) [43]. In addition, functional readout of signaling pathways (e.g. redox-regulated kinases and calcium-handling [35,36], epigenetic [[37], [38], [39]] and lipidome profiles [36], P-VASP as the endpoint of the NO/cGMP signaling cascade and novel activity markers such as sNox2-dp or apo-sGC; all filled purple color) can significantly add to our understanding and identification of redox imbalances. For the sake of clarity, we could not show and discuss all existing ROS/RNS sources, species and targets in the present scheme, but focused on the most important ones according to the cited references. Abbreviations: AGE, advanced glycation end products; cGMP, cyclic guanosine monophosphate; EPR, electron paramagnetic resonance; GSH, glutathione; GPx, GSH peroxidase; GR, GSH reductase; H₂O₂, hydrogen peroxide; HO-1, heme oxygenase-1; HOCl, hypochlorous acid; LPO, lipoxygenases; MAO, monoamine oxidase; MPO, myeloperoxidase; NOS, nitric oxide synthase; NOX, NADPH oxidase; ONOO⁻, peroxynitrite; PKG, protein kinase G; Prx, peroxiredoxin; P-VASP, phospho(Ser239)-vasodilator-stimulated phosphoprotein; RNS, reactive nitrogen species; ROS, reactive oxygen species; sGC, soluble guanylate cyclase; sNox2-dp, soluble Nox2-derived peptide; SOD, superoxide dismutase; Trx, thioredoxin; TR, Trx reductase; u-eNOS, uncoupled eNOS; XDH, xanthine dehydrogenase; XO, xanthine oxidase. Reused from Ref. [43] and updated from Refs. [10,57,161] with permission under the terms of the Creative Commons Attribution Noncommercial License. Copyright © [43]; Published by Mary Ann Liebert, Inc. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

Prognostic value of classical oxidative stress markers for cardiovascular risk. (A) Association of markers of lipid peroxidation (isoprostane [8-iso-PGF2α] and malondialdehyde [MDA]) [75,83,85], nitro-oxidative stress (3-nitrotyrosine [3-NT]) [96], oxidative mitochondrial DNA damage (8-hydroxy-deoxyguanosine [8-OHdG]) [120] and derivatives of reactive oxygen metabolites (d-ROMs) [125] with cardiovascular risk in the comparison of healthy subjects with cardiovascular disease patients. (B) Associations of d-ROM levels and total thiol levels (TTL) with cardiovascular disease-specific mortality (adjusted for age and sex). d-ROMs groups [Carratelli Units]: reference, <340; T1, 341–400; T2, 401–500; T3, >500. Graph was generated from tabular data in Ref. [124] with permission and reused from Ref. [48]. Copyright © 2015, Schöttker et al. * indicates significant differences (p < 0.05) versus reference groups.

Fig. 4

Additivity of comorbidities on redox biomarkers levels in patients with cardiovascular disease. (A) Urinary levels of 8-iso-PGF_2α showed additive accumulation with risk factors for heart disease (comorbidities such as obesity, diabetes mellitus, hypercholesterolemia, hypertension, and smoking). Median, interquartile range, outliers and extremes of urinary excretion are presented; p-value was calculated for the trend. Reused from Ref. [77] with permission. Copyright © 2004, Wolters Kluwer Health. (B) Levels of sNox2‐dp and urinary isoprostanes in patients with hypertension (HT) and chronic obstructive pulmonary disease (COPD) and/or atrial fibrillation (AF) on top. Data are presented as mean ± SD (n = 33–49). *, p < 0.05 vs. HT; #, p < 0.05 vs. COPD; §, p < 0.05 vs. AF. Graphs were generated from data in Ref. [174] with permission. Copyright © 2019, Mary Ann Liebert Inc.

Fig. 5

Odds ratio (OR) or association of redox biomarkers with health risks or complications. Grey boxes represent classical oxidative stress markers. Purple boxes represent advanced or experimental redox biomarkers. Numbers in square brackets represent the 95% confidence interval. Abbreviations: ADMA, asymmetric dimethylarginine; CV, cardiovascular; CAD, coronary artery disease; CVD, cardiovascular disease; FMD, flow-mediated dilation; IHD, ischemic heart disease; MACE, major cardiovascular events; oxLDL, oxidized low-density lipoprotein. All alphabetical cross references in this figure (^{a, b, c …}) are linked to literature reference numbers in the figure legend. The respective references are as follows: ^a [75], ^b [83], ^c [124], ^d [184], ^e [85], ^f [96], ^g [125], ^h [88], ⁱ [120], ^j [77], ^k [86], ^l [98], ^m [126], ⁿ [185], ^o [87], ^p [89], ^q [90], ^r [108], ^s [110], ^t [111], ^u [114], ^v [140], ^w [141], ^x [145,[148], [149], [150], [151], [152]], ^y [160], ^z [[239], [240], [241]], ^& [187]. Open access source for body image can be found at Pixabay (https://pixabay.com/de/photos/anatomie-frau-mensch-k%C3%B6rper-haut-254120/). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)