GDF-15, soluble ST2 and Troponin-I: Biomarkers of Subclinical Vascular Disease?

Exploring the Link Between the Serum/Blood Levels of Heavy Metals (Pb, As, Cd, and Cu) and 2 Novel Biomarkers of Cardiovascular Stress (Growth Differentiation Factor 15 and Soluble Suppression of Tumorigenicity 2) in Copper Smelter Workers

OBJECTIVE

Studying the association between the occupational exposure to Pb, As, Cd, and Cu with the serum levels of 2 novel biomarkers of cardiovascular stress; growth differentiation factor 15 and soluble suppression of tumorigenicity 2, in some Egyptian Cu smelter workers.

METHODS

Forty-one exposed workers and 41 administrative controls were clinically evaluated. Serum/blood levels of heavy metals and biomarkers were measured for both groups.

RESULTS

The smelter workers showed significantly elevated levels of heavy metals and biomarkers compared with controls. The elevated serum levels of both biomarkers were significantly and positively correlated with each other, the levels of heavy metals, and the duration of employment of the exposed workers.

CONCLUSIONS

There was a significant association between the levels of heavy metals and both biomarkers among the smelter workers. Further prospective studies should be performed.

Role of GDF-15, YKL-40 and MMP 9 in patients with end-stage kidney disease: focus on sex-specific associations with vascular outcomes and all-cause mortality

BACKGROUND

Sex differences are underappreciated in the current understanding of cardiovascular disease (CVD) in association with chronic kidney disease (CKD). A hallmark of CKD is vascular aging that is characterised, amongst others, by; systemic inflammation, microbiota disbalance, oxidative stress, and vascular calcification-features linked to atherosclerosis/arteriosclerosis development. Thus, it is the necessary to introduce novel biomarkers related to athero-/arteriosclerotic damage for better assessment of vascular ageing in patients CKD. However, little is known about the relationship between uraemia and novel CVD biomarkers, such as growth differentiation factor-15 (GDF-15), cartilage glycoprotein-39 (YKL-40) and matrix metalloproteinase-9 (MMP-9). Therefore, we hypothesise that there are sex-specific relationships between GDF-15, YKL-40, MMP-9 levels in end-stage kidney disease (ESKD) patients in relation to gut microbiota, vascular calcification, inflammation, comorbidities, and all-cause mortality.

METHODS

ESKD patients, males (n = 151) and females (n = 79), not receiving renal replacement therapy were selected from two ongoing prospective ESKD cohorts. GDF-15, YKL-40 and MMP9 were analysed using enzyme-linked immunosorbent assay kits. Biomarker levels were analysed in the context of gut microbiota-derived trimethylamine N-oxide (TMAO), vascular calcification, inflammatory response, oxidative stress, comorbidities, and all-cause mortality.

RESULTS

Increased GDF-15 correlated with higher TMAO in females only, and with higher coronary artery calcification and IL-6. In females, diabetes was associated with elevated GDF-15 and MMP-9, whilst males with diabetes only had elevated GDF-15. No associations were found between biomarkers and CVD comorbidity. Deceased males and females had higher GDF-15 concentrations (p = 0.01 and p < 0.001, respectively), meanwhile only YKL-40 was increased in deceased males (p = 0.02).

CONCLUSIONS

In conclusion, in males GDF-15 and YKL-40 were related to vascular calcification, inflammation, and oxidative stress, whilst in females GDF-15 was related to TMAO. Increased levels of YKL-40 and GDF-15 in males, and only GDF-15 in females, were associated with all-cause mortality. Our findings suggest that sex-specific associations of novel CVD biomarkers have a potential to affect development of cardiovascular complications in patients with ESKD.

Towards frailty biomarkers: Candidates from genes and pathways regulated in aging and age-related diseases

OBJECTIVE

Use of the frailty index to measure an accumulation of deficits has been proven a valuable method for identifying elderly people at risk for increased vulnerability, disease, injury, and mortality. However, complementary molecular frailty biomarkers or ideally biomarker panels have not yet been identified. We conducted a systematic search to identify biomarker candidates for a frailty biomarker panel.

METHODS

Gene expression databases were searched (http://genomics.senescence.info/genes including GenAge, AnAge, LongevityMap, CellAge, DrugAge, Digital Aging Atlas) to identify genes regulated in aging, longevity, and age-related diseases with a focus on secreted factors or molecules detectable in body fluids as potential frailty biomarkers. Factors broadly expressed, related to several "hallmark of aging" pathways as well as used or predicted as biomarkers in other disease settings, particularly age-related pathologies, were identified. This set of biomarkers was further expanded according to the expertise and experience of the authors. In the next step, biomarkers were assigned to six "hallmark of aging" pathways, namely (1) inflammation, (2) mitochondria and apoptosis, (3) calcium homeostasis, (4) fibrosis, (5) NMJ (neuromuscular junction) and neurons, (6) cytoskeleton and hormones, or (7) other principles and an extensive literature search was performed for each candidate to explore their potential and priority as frailty biomarkers.

RESULTS

A total of 44 markers were evaluated in the seven categories listed above, and 19 were awarded a high priority score, 22 identified as medium priority and three were low priority. In each category high and medium priority markers were identified.

CONCLUSION

Biomarker panels for frailty would be of high value and better than single markers. Based on our search we would propose a core panel of frailty biomarkers consisting of (1) CXCL10 (C-X-C motif chemokine ligand 10), IL-6 (interleukin 6), CX3CL1 (C-X3-C motif chemokine ligand 1), (2) GDF15 (growth differentiation factor 15), FNDC5 (fibronectin type III domain containing 5), vimentin (VIM), (3) regucalcin (RGN/SMP30), calreticulin, (4) PLAU (plasminogen activator, urokinase), AGT (angiotensinogen), (5) BDNF (brain derived neurotrophic factor), progranulin (PGRN), (6) α-klotho (KL), FGF23 (fibroblast growth factor 23), FGF21, leptin (LEP), (7) miRNA (micro Ribonucleic acid) panel (to be further defined), AHCY (adenosylhomocysteinase) and KRT18 (keratin 18). An expanded panel would also include (1) pentraxin (PTX3), sVCAM/ICAM (soluble vascular cell adhesion molecule 1/Intercellular adhesion molecule 1), defensin α, (2) APP (amyloid beta precursor protein), LDH (lactate dehydrogenase), (3) S100B (S100 calcium binding protein B), (4) TGFβ (transforming growth factor beta), PAI-1 (plasminogen activator inhibitor 1), TGM2 (transglutaminase 2), (5) sRAGE (soluble receptor for advanced glycosylation end products), HMGB1 (high mobility group box 1), C3/C1Q (complement factor 3/1Q), ST2 (Interleukin 1 receptor like 1), agrin (AGRN), (6) IGF-1 (insulin-like growth factor 1), resistin (RETN), adiponectin (ADIPOQ), ghrelin (GHRL), growth hormone (GH), (7) microparticle panel (to be further defined), GpnmB (glycoprotein nonmetastatic melanoma protein B) and lactoferrin (LTF). We believe that these predicted panels need to be experimentally explored in animal models and frail cohorts in order to ascertain their diagnostic, prognostic and therapeutic potential.