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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".