Use of the Tissue Common Rejection Module Score in Kidney Transplant as an Objective Measure of Allograft Inflammation

Multi-omics Approach in Kidney Transplant: Lessons Learned from COVID-19 Pandemic

Purpose of Review

Multi-omics approach has advanced our knowledge on transplantation-associated clinical outcomes, such as acute rejection and infection, and emerging omics data are becoming available in kidney transplant and COVID-19. Herein, we discuss updated findings of multi-omics data on kidney transplant outcomes, as well as COVID-19 and kidney transplant.

Recent Findings

Transcriptomics, proteomics, and metabolomics revealed various inflammation pathways associated with kidney transplantation-related outcomes and COVID-19. Although multi-omics data on kidney transplant and COVID-19 is limited, activation of innate immune pathways and suppression of adaptive immune pathways were observed in the active phase of COVID-19 in kidney transplant recipients.

Summary

Multi-omics analysis has led us to a deeper exploration and a more comprehensive understanding of key biological pathways in complex clinical settings, such as kidney transplantation and COVID-19. Future multi-omics analysis leveraging multi-center biobank collaborative will further advance our knowledge on the precise immunological responses to allograft and emerging pathogens.

Improving liver transplant outcomes with transplant-omics and network biology

PURPOSE OF REVIEW

Molecular omics data is increasingly ubiquitous throughout medicine. In organ transplantation, recent large-scale research efforts are generating the 'transplant-ome' - the entire set of molecular omics data, including the genome, transcriptome, proteome, and metabolome. Importantly, early studies in anesthesiology have demonstrated how perioperative interventions alter molecular profiles in various patient populations. The next step for anesthesiologists and intensivists will be to tailor perioperative care to the transplant-ome of individual liver transplant patients.

RECENT FINDINGS

In liver transplant patients, elements of the transplant-ome predict complications and point to novel interventions. Importantly, molecular profiles of both the donor organ and recipient contribute to this risk, and interventions like normothermic machine perfusion influence these profiles. As we can now measure various omics molecules simultaneously, we can begin to understand how these molecules interact to form molecular networks and emerging technologies offer noninvasive and continuous ways to measure these networks throughout the perioperative period. Molecules that regulate these networks are likely mediators of complications and actionable clinical targets throughout the perioperative period.

SUMMARY

The transplant-ome can be used to tailor perioperative care to the individual liver transplant patient. Monitoring molecular networks continuously and noninvasively would provide new opportunities to optimize perioperative management.

Role of Systemic Inflammatory Markers in Pediatric Kidney Transplantation

BACKGROUND

Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII = N × P/L), and neutrophil percentage-albumin ratio (NPAR) have become accepted markers of inflammation in recent years. These indices are used as indicators of disease activity, mortality, and morbidity in many diseases. This study evaluated the relationship between inflammatory indices and graft function in pediatric kidney transplant recipients.

METHODS

Medical records of pediatric patients who underwent kidney transplantation at Ege University between 1995 and 2020 were reviewed retrospectively. Demographic, clinical, and laboratory data were recorded during the third month, first year, and fifth year of transplantation and at the last visit.

RESULTS

The median age of the 119 patients (60 boys/59 girls) at the time of transplantation was 154 months, and the median follow-up period was 101 months. According to Spearman correlation analysis, patients' final creatinine levels were positively correlated with NLR (r = 0.319), PLR (r = 0.219), SII (r = 0.214), and NPAR (r = 0.347) of the last visit; final estimate glomerular filtration rate levels were negatively correlated with NLR (P = .010, r = -0.250) and NPAR (P = .004, r = -0.277). The median NPAR of the patients with chronic allograft dysfunction at the last visit was found to be statistically significantly higher than without (P = .032).

CONCLUSION

NLR, PLR, SII, and NPAR values are correlated with creatinine levels after 5 years of kidney transplantation. The NPAR and final creatinine levels had the highest correlation coefficient among these inflammatory markers. These results suggest that inflammatory markers, especially NPAR, may be a candidate to be an indicator of ongoing inflammation in the graft.