Role of novel biomarkers in kidney transplantation

Table 1 Summary of novel biomarker studies of chemokines associated with immunologic outcomes

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Rabant et al[11], 2015	244	Urine	uCXCL9, uCXCL10	Rejection	CXCL9/10a correlated with ti+mvi (i+t; g + ptc) CXCL10: Cra diagnosed TCMR and ABMR (AUC > 0.75); CXCL10: Cr + DSAa improved the diagnosis of ABMR (AUC = 0.83)
Hricik et al[12], 2015	21	Urine	CXCL9	Rejection	uCXCL9 predicts AR by a median of 15 d before clinical detection
Faddoul et al[13], 2018	184	Urine and plasma	IFN-γ ELISpot; CXCL9	ACR	CXCL9 predictive of ACR; IFN-γ predictive of 1 year ↓eGFR; neither predicted 5-yr outcomes
Xu et al[14], 2018	87	Plasma	Circulating fractalkine, IFN-γ and IP-10	AR	Fractalkine on day 0, IP-10 at +7 and IFN-γ on +7 had the highest AUC (0.866) for predicting AR in 1 mo (sensitivity 86.8%; specificity 89.8%)
Tefik et al[15], 2019	65 (9 rejection, 56 stable)	Plasma	IL-2, IL-8	Rejection	IL-2b and IL-8c predict AR; IL-2b and IL-8d levels correlated with ↓ 3 mo eGFR in the AR group
de Holanda et al[16], 2018	73	Plasma	sCD30	Rejection; Graft survival	Plasma CD30 at +7, +14 associated w AR (P = 0.036). No difference in 5 yr graft survival

^aP < 0.001 vs histology.

^bP < 0.05 vs non-rejection group.

^cP < 0.02 vs non-rejection group.

^dP <0.01 vs non-rejection group.u: Urinary; C-X-C: C-terminal amino acid sequence Cystine-X-Cystine; CXCL9: C-X-C motif chemokine ligand nine; CXCL10: C-X-C motif chemokine ligand ten; ti: Total inflammation; mvi: Microvascular inflammation; i: Interstitial inflammation; t: Tubulitis; g: Glomerulitis; ptc: Peritubular capillaritis; Cr: Creatinine; TCMR: T cell-mediated rejection: ABMR: Antibody-mediated rejection; AUC: Area under the curve; DSA: Donor specific antibodies; AR: Acute rejection; ACR: Acute cellular rejection; IFN-γ: Interferon gamma; eGFR: Estimated glomerular filtration rate; IP-10: Interferon gamma-induced protein ten; IL-2: Interleukin-2; IL-8: Interleukin-8; CD30: Cluster of differentiation thirty; sCD30: Soluble cluster of differentiation 30.

Table 2 Summary of micro-ribonucleic acid-related novel biomarker studies associated with immunologic outcomes

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Matz et al[17], 2016	160	Plasma	miR-15B, miR-103A, miR-106A	TCMR	miR-15Ba,b, miR-103Aa,b and miR-106Aa,b discriminated patients with stable graft function from patients with TCMR and UTI
Matz et al[18], 2018	111	Plasma	miR-223-3p; miR-424-3p; miR-145-5p; miR-15b-5p	ABMR, TCMR, IFTA	miR-223-3p, miR-424-3p and miR-145-5p distinguished TCMR and ABMR from stable graft function; mir-145-5P decreased in IFTA (AUC 0.891) compared to stable graft function
Millán et al[19], 2017	80	Urine	miR-142-3p, miR-210-3p and miR-155-5p, CXCL10	Rejection	↑miR-142-3p, ↑miR-155-5p, ↑CXCL10 + ↓miR-210-3p (AUC = 0.875) and CXCL10 (AUC = 0.865) discriminate rejectors and nonrejectors (sensitivity 85%, 84% and specificity 86% and 80% respectively)
Halloran et al[20], 2017	519	Allograft biopsy	Molecular Microscope® Diagnostic System (MMDx™)/microRNA	TCMR, ABMR	Agreement between MMDx™ and histology = 77% for TCMR, 77% for ABMR, and 76% for no rejection with blinding to histology, HLA. MMDx™c agreed with clinical judgment (87%) more than histology (80%)
Ledeganck et al[21], 2019	11 studies	Allograft biopsy	microRNA	TCMR, ABMR, cABMR	↑miR-142, miR-155, miR-223 and ↓miR-125, miR-30, miR-204 predict TCMR, ABMR, cABMR
Lorenzen et al[23], 2015	93	Urine	lcRNA; RP11-354P17.15-001 (L328)	TCMR	RP11-354P17.15-001d (L328) was associated with acute TCMR (AUC = 0.76) sensitivity 49%, specificity 95%; L328 can detect subclinical TCMR

^aP < 0.001 for TCMR vs controls.

^bP < 0.001 for UTI vs controls.

^cP < 0.005 vs histology.

^dP <0.001 vs controls. miR: Mature form of microribonucleic acid; RNA: Ribonucleic acid; TCMR: T cell-mediated rejection; HLA: Human leukocyte antigen; UTI: Urinary tract infection; ABMR: Antibody-mediated rejection; IFTA: Interstitial fibrosis tubular atrophy; AUC: Area under the curve; C-X-C: C-terminal amino acid sequence Cystine-X-Cystine; CXCL10: C-X-C motif chemokine ligand ten; MMDx™: Molecular Microscope^® Diagnostic System; cABMR: Chronic antibody-mediated rejection; lcRNA: Long noncoding RNAs.

Table 3 Summary of leukocyte subclass related biomarkers associated with immunologic outcomes

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Luque et al[25], 2019	175	Plasma	donor reactive memory B cells (mBC)	ABMR	For-cause bx: mBC in 100% ABMR/DSA+ and most cABMR, +/- DSA [24/30 (80%) and 21/29 (72.4%)]. Protocol bx: mBC > dnDSA was observed at 6 and 24 mo (8.8% vs 7.7% and 15.5% vs 11.1%) and identified pts with ongoing subABMR (AUC = 0.917, 0.809)
Gorbacheva et al[26], 2016		Plasma	mCD4	Rejection	Murine models with sensitized mCD4 T cells had SCr > 1 mg/dL (1.7 ± 0.6 mg/dL by 6–8 d post-transplant) and developed graft failure. At rejection, these recipients had DSA and ↑ frequencies of donor–reactive T cells producing IFN-γ compared with controls
Yazdani et al[27], 2019	95	Plasma	NK gene expression model -> NK cells	Rejection	NK cells predict ABMRavs no rejection (AUC = 0.98); ABMRbvs TCMR (AUC = 0.91) as well as histology: 22/24 biopsies with mvi (g + ptc) had ↑ NK levels (AUC = 0.89) Moreover, activated NK cells had the best predictive capability of graft failure at 1-2 yr (AUC = 0.74). NK cell infiltrationd predicted graft failure independent of histology
Cortes-Cerisuelo et al[28], 2017	23	Plasma	CD28+CD4+	Rejection	CD28+CD4+ T cell frequency is associated with rejection on belatacept based IS

^aP < 0.001 vs controls.

^bP < 0.001 vs TCMR. ^cP < 0.0001 vs biopsies w/o mvi.

^dP < 0.05 vs controls. mBC: Donor reactive memory B-cells; ABMR: Antibody-mediated rejection; DSA: Donor specific antibodies; cABMR: Chronic antibody-mediated rejection; bx: Biopsy; dnDSA: De novo donor specific antibodies; pts: Patients; subABMR: Subclinical ABMR; AUC: Area under the curve; mCD4: Memory cluster of differentiation four; SCr: Serum creatinine; IFN-γ: Interferon gamma; mvi: Microvascular inflammation; NK: Natural killer; TCMR: T cell-mediated rejection; CD28+CD4+: Cluster of differentiation twenty eight and cluster of differentiation four; IS: Immunosuppression.

Table 4 Summary of gene expression related biomarkers associated with immunologic outcomes

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Friedewald et al[10], 2019	308	Plasma	Blood based biomarker/gene expression profile	Subclinical acute rejection	GEP AR biomarker predicted sc-AR (sensitivity 64%, specificity 87%, PPV = 61%, NPV = 88%)
Zhang et al[29], 2019	113	Plasma	TREx	Rejection at 3 mo, Graft failure	TREx predicts sc-AR at 3 mo in 113 KTRs (AUC = 0.830; NPV = 0.98, PPV = 0.79)
Crespo et al[30], 2017	75	Plasma	kSORT™ + ELISpot	Subclinical rejection	kSORT™ + ELISpot predict sc-ARa, sc-TCMRa and sc-ABMRa (AUC > 0.85)
First et al[32], 2019	192; 45	Plasma	TruGraf® GEP	Surveillance of patients with stable allograft function	In 87.5% of the cases, investigators’ clinical decisions were influenced by TruGraf® results. In 45 patients TruGraf® supported 87% of clinical decisions with 93% of investigators stating they would use TruGraf® in subsequent patient care
Sigdel et al[33], 2019	150 KTRs (43 stable, 45 AR, 19 borderline AR, 43 BKVN)	Urine	Common rejection module (11 genes)	Rejection	10/11 genes were elevated in AR when compared to stable graft function. Psmb9 and CXCL10 could classify AR versus stable graft function as accurately as the 11-gene model (sensitivity = 93.6%, specificity = 97.6%); uCRM score differentiate AR from stable graft function (AUC = 0.9886)

^aP < 0.001 vs controls. GEP: Gene expression profile; AR: Acute rejection; sc-AR: Subclinical acute rejection; PPV: Positive predictive value; NPV: Negative predictive value; TRex: Targeted expression assay; KTRs: Kidney transplant recipients; kSORT™: Kidney Solid Organ Response Test; ELISpot: Enzyme-linked immune absorbent spot; sc-TCMR: Subclinical T cell-mediated rejection; sc-ABMR: Subclinical antibody-mediated rejection; BKVN: BK virus nephropathy; Psmb9: Proteasome 20S Subunit Beta 9; C-X-C: C-terminal amino acid sequence Cystine-X-Cystine; CXCL10: C-X-C motif chemokine ligand ten; uCRM: Urinary common rejection module.

Table 5 Summary of donor-derived cell-free deoxyribonucleic acid biomarkers associated with immunologic outcomes

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Oellerich et al[35], 2019	189	Plasma	dd-cfDNA	Rejection	In pts with BPR, dd-cfDNA(cp/mL) was 3.3x and dd-cfDNA(%) 2.0x higher (82 cp/mL; 0.57%) than in stable pts w/o rejection (25 cp/mL; 0.29%). dd-cfDNA abs number > dd-cfDNA % (AUC = 0.73). OR = 7.31 for dd-cfDNA (cp/mL)
Stites et al[36], 2020	79 KTRs with TCMR 1A/borderline rejection	Plasma	dd-cfDNA	eGFR, dnDSA, Future rejection	↑dd-cfDNA predict adverse outcomes: Among patients with ↑dd-cfDNAa, eGFR ↓ by 8.5% vs 0% in ↓dd-cfDNA pts. dnDSA seen in 40% (17/42) vs2.7%b and future or persistent rejection occurred in 9 of 42 ptsa (21.4% vs 0%)
Bloom et al[38], 2017	102	Plasma	dd-cfDNA	Rejection	Distinguished any rejection from non-rejection along with ABMR from non-ABMR
Huang et al[40], 2019	63	Plasma	dd-cfDNA	ABMR	dd-cfDNA discriminated ABMRc [median 1.35%; interquartile range (IQR): 1.10%-1.90%] from no rejection (median 0.38%, IQR: 0.26%-1.10%). dd-cfDNA did not distinguish TCMR from no rejection
Whitlam et al[41], 2019	61	Plasma	dd-cfDNA	aABMR cABMR	gd-cfDNA and fraction were predictive of aAMR (AUC = 0.92, 0.85) and composite dx of ABMR (AUC = 0.91, 0.89). gd-cfDNA w/ modest sensitivity (0.90; 0.85) and specificity (0.88, 0.79) for aAMR and ABMR

^aP < 0.005 vs low level dd-cfDNA pts.

^bP < 0.0001 vs low level dd-cfDNA pts.

^cP < 0.001 vs no rejection. dd-cfDNA: Donor derived-cell free deoxyribonucleic acid; Abs: Absolute; BPR: Biopsy proven rejection; AUC: Area under the curve; OR: Odds ratio; KTRs: Kidney transplant recipients; TCMR: T cell-mediated rejection; eGFR: Estimated glomerular filtration rate; dnDSA: De novo donor specific antibodies; ABMR: Antibody-mediated rejection; cABMR: Chronic antibody-mediated rejection; IQR: Interquartile range; dx: Diagnosis; aAMR: Acute antibody-mediated rejection; aABMR: Acute antibody mediated rejection; gd-cfDNA: Graft-derived cell-free DNA; Pts: Patients.

Table 6 Summary of biomarkers associated with graft quality

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Parikh et al[46], 2016	671	Perfusate	NGAL, L-FABP	6 mo eGFR	Each doubling of perfusate NGAL and L-FABP were independently associated with ↓6-month eGFR (1.7mL/min per 1.73m2; 1.48mL/min per 1.73m2 )
Moser et al[47], 2017	41	Perfusate	MMP-2, LDH, NGAL	Biomarker levels	MMP-2a,b, LDHa,b, and NGALa,b were found in highest perfusate concentrations in DCD kidneys, followed by DBD and living donor allografts
Hamaoui et al[48], 2017	10	Perfusate	Perfusate lactate	Perfusion	10 DCD porcine kidneys perfused via HMP with modified AL solutionc had significantly ↓ perfusion lactate levels (3.1 vs 4.1 mmol/L) during reperfusion than those in UW solution
van Smaalen et al[49], 2017	390	Perfusate	Extracellular histone concentration	1 yr graft survival	(extracellular histone) was associated w/ 1 year graft failure (HR = 1.386) 1 year graft survival was ↑ for the ↓ extracellular histone groupd (83% vs 71%) , maintained up to 5 yearse (76% vs 65%)
Weissenbacher et al[50], 2019	11	Perfusate	NGAL, KIM-1	Kidney quality	↑ perfusate NGAL level was found in the lowest quality kidney. In the perfused kidneys w/o urine recirculation, NGAL and KIM-1 ↓ over time. Small sample size; NGAL/ KIM-1 not predictive of kidney quality
Hosgood et al[53], 2017	56	Urine	NGAL, endothelin-1	Kidney quality per EVKP score	↑ levels of NGAL and ET-1 were associated with ↑ EVKP scoref (P < 0.05)

^aP < 0.0001 vs Donation after brain death kidneys.

^bP < 0.0001 vs living donor kidneys.

^cP < 0.05 vs Deceased cardiac death donor kidneys perfused with University of Wisconsin solution.

^dP < 0.01 vs increased extracellular histone group.

^eP < 0.05 vs increased extracellular histone group.

^fP < 0.05 vs EVKP group A. NGAL: Neutrophil gelatinase-associated lipocalin; L-FABP: Liver fatty acid binding protein; eGFR: Estimated glomerular filtration rate; MMP-2: Matrix metalloproteinase-2 LDH: Lactate dehydrogenase; DBD: Donation after brain death; DCD: Deceased cardiac death donor; HMP: Hypothermic machine perfusion; AL: Adenosine lidocaine; UW: University of Wisconsin; HR: Hazard ratio; KIM-1: Kidney injury molecule-1; EVKP: Ex vivo normothermic kidney perfusion; ET-1: Endothelin-1.

Table 7 Summary of biomarkers associated with delayed graft function

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Parikh et al[46], 2016	671	Perfusate	NGAL, IL-18, L-FABP	DGF	Base (NGAL) was significantly ↑ in allografts with DGFa. This was also observed in post values of IL-18a and base/post perfusate L-FABP levelsb. These biomarkers did not significantly correlate with DGF development on multivariate adjustment
van Smaalen et al[49], 2017	390	Perfusate	Extracellular histone concentration	DGF	Extracellular histone concentration was significantly ↑ in the DGF group (median 0.70 µg/mL (IQR 0.4 to 0.98) compared to grafts that functioned immediatelyc (median, 0.42 (IQR 0.07 to 0.78). Interestingly there was no significant difference in extracellular histone concentration in grafts with primary non-function vs DGF
van Balkom et al[57], 2017	40	Perfusate	Leptin, GM-CSF, periostin, plasminogen activator inhibitor-1, osteopontin	DGF	5 perfusate proteins/158 tested predicted DGF. Leptin and GM-CSF -> most predictive. Validation with 40 kidneys found that leptin, GM-CSF + BMI predict DGF (AUC = 0.89 (95%CI: 0.74 to 1.00), which performed better than KDRI and DGF risk calculator (AUC 0.55, 0.59)
Roest et al[58], 2019	48	Perfusate	microRNA mir-505-3p	DGF	In 8 DCD and DBD donors, ↑ levels of perfusate microRNA mir-505-3p correlated with DGFb (OR 1.12). This was confirmed via validation of 40 allografts, of which 20 developed DGFb. Interestingly, this predictive capability held true solely for DCD allograftsc
Truche et al[59], 2019	41	Urine and Plasma	uNGAL, uNAG, LDH, UCr	DGF	DGF -UNGAL, UNAG AUC 1, 0.96 (0.84-1.0) , urinary tubular injury biomarker-to-creatinine ratio, and LDH AUC = 1 and 0.92 (95%CI: 0.73 to 1.0)
Pianta et al[60], 2015	81	Urine	Urinary clusterin, IL-18, KIM-1, NGAL	DGF	Urinary clusterin predicted DGF at 4 h (AUC = 0.72 (95%CI: 0.57 to 0.97), as did IL-18 , KIM-1 and NGAL; eGFR at 90 d was inversely correlated with urinary clusterin at 12 hb (Pearson r = −0.26, and 7 db (Pearson r = −0.25)
Reese et al[61], 2016	1304	Urine	Microalbumin, NGAL, KIM-1, IL-18, L-FABP	AKI, DGF, 6-month eGFR	Microalbumin, NGAL, KIM-1, IL-18, L-FABP from deceased donors at procurement; predictive of AKI; NGAL associated with DGF (RR = 1.21 (95%CI: 1.02 to 1.43), NGAL and L-FABP associated with lower 6 mo eGFR
Nielsen et al[62], 2019	225	Plasma and urine	pNGAL, uNGAL uL-FABP, urine cystatin C, urine YLK-40	DGF, 1 yr mGFR/eGFR	pNGAL 1 d after tx -> associated with DGF. Did not correlate to 12-mo eGFR; no relation w L-FABP, cystatin C, and YLK-40
Koo et al[63], 2016	94	Urine	Microalbumin, NGAL, KIM-1, IL-18, L-FABP	DGF, 1 yr graft function	NGAL predicts AKI; NGAL + L-FABP predicts DGF (AUC 0.758, 0.704); NGAL + L-FABP + Cr better than DGF calculator and KDPI. L-FABP predictive of 1 yr graft functionb
Li et al[64], 2019	1036	Urine and plasma	uNGAL, pNGAL	DGF	Composite AUC for 24 hours uNGAL was 0.91 (95%CI: 0.89 to 0.94) and the overall DOR for 24 hours uNGAL was 24.17; sensitivity 0.88, specificity 0.81. The composite AUC for 24 hours pNGAL was 0.95 (95%CI: 0.93 to 0.97) with an overall DOR for 24 hours pNGAL = 43.11 with sensitivity 0.91 and specificity 0.86
Bank et al[65], 2019	74 (DCD KTRs)	Urine	Urinary TIMP-2	DGF	TIMP-2/mOsm on day-1 and day-10 identified patients with DGF (AUC = 0.91) and prolonged DGF (AUC = 0.80); Consecutive TIMP-2/mOsm values showed a ↓ in TIMP-2/mOsm before an ↑estimated glomerular filtration rate, predicting resolution of fDGF
McGuinness et al[66], 2016	94		hsa-miR-217; hsa-miR-125b	DGF	miRNA + donor age + type donation predicted DGF in 83% of cases (61% sensitivity, 91% specificity)
Ledeganck et al[21], 2019	11 studies	Allograft biopsy	microRNA	DGF	Upregulation of miR-21-3P and miR-182-5p associated with DGF

^aP < 0.005 vs non-DGF allografts.

^bP < 0.05 vs non-DGF allografts.

^cP < 0.001 vs immediately functioning grafts. NGAL: Neutrophil gelatinase-associated lipocalin; IL-18: Interleukin eighteen; L-FABP: Liver fatty acid binding protein; DGF: Delayed graft function; IQR: Interquartile range; GM-CSF: Granulocyte-macrophage colony-stimulating factor; BMI: Body mass index; AUC: Area under the curve; KDRI: Kidney donor risk index; RNA: Ribonucleic acid; mir: Pre-microRNA; DCD: Deceased cardiac death donor; DBD: Deceased brain death donor; OR: Odds ratio; u: Urinary; uNGAL: Urinary neutrophil gelatinase-associated lipocalin; uNAG: Urinary N-acetyl-β-glucosaminidase; LDH: Lactate dehydrogenase; UCr: Urine creatinine; KIM-1: Kidney injury molecule-1; CI: Confidence interval; eGFR: Estimated glomerular filtration rate; RR: Relative risk; pNGAL: Plasma neutrophil gelatinase-associated lipocalin; YLK-40: Chitinase-3-like protein mGFR: Measured glomerular filtration rate; KDPI: Kidney donor profile index; DOR: Diagnostic odds ratio; TIMP-2: Tissue inhibitor of metalloproteinases 2; mOsm: Milliosmoles; fDGF: Functional delayed graft function; hsa: Homo sapiens; miR: Mature form of microRNA.

Table 8 Summary of biomarkers associated with cardiovascular events and cardiovascular mortality

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Foster et al[68], 2017	508	Urine and plasma	Cystatin C, B2M, Cr	CV events, Mortality, Kidney failure	HR eGFRcys and HR eGFRB2M < 30 vs 60+ were 2.02a (95%CI: 1.09 to 3.76) and 2.56b (95%CI: 1.35 to 4.88) for CV events; 3.92c (95%CI: 2.11 to 7.31) and 4.09b (95%CI: 2.21 to 7.54) for mortality; and 9.49c (95%CI: 4.28 to 21.00) and 15.53b (95%CI 6.99 to 34.51) for kidney failure
Bansal et al[69], 2016	1027	Urine	uNGAL, uKIM-1, IL-18, L-FABP, UCr	CV events, Graft failure, mortality	Each ↑ log in uNGAL/Cr associated with a 24% ↑ risk of CV events (aHR = 1.24 (95%CI: 1.06 to 1.45), graft failure (1.40; 1.16 to 1.68), and risk of death (1.44; 1.26 to 1.65). uKIM-1/Cr and IL-18/Cr associated with higher risk of death (1.29; 1.03 to 1.61 and 1.25; 1.04 to 1.49 per log increase)
Park et al[70], 2017	1184 (300 CVD, 371 death, 513 random sub-cohort)	Urine	urine alpha 1 microglobulin [A1M], monocyte chemoattractant protein-1 [MCP-1], procollagen type I [PINP] and type III [PIIINP] N-terminal amino peptide)	CV events, Death	↑uA1M (HR per doubling of biomarker = 1.40 (95%CI: 1.21 to 1.62), MCP-1 [HR 1.18 (1.03 to 1.36)], and PINP [HR = 1.13 (1.03 to 1.23)]were associated with CVD events and death (HR per doubling α1m = 1.51 (95%CI: 1.32 to 1.72); MCP-1 = 1.31 (1.13 to 1.51); PINP = 1.11 (1.03 to 1.20)
Devine et al[71], 2020	367	Plasma	ST2	CV events, CV mortality, All-cause mortality	↑ ST2 was associated with CV events (aHR = 1.31 (95% CI: 1.00 to 1.73); significantly for CV mortalityd (aHR = 1.61; (95%CI: 1.07 to 2.41; P = 0.022), The addition of ST2, to risk prediction models for CV mortality/events failed to improve their predictive accuracy
Yepes- Calderón et al[72], 2020	604	Plasma	Malondialdehyde	CV mortality	During a follow-up period, 110 KTRs died, with 40% CV death. MDA was significantly associated with the risk for CV mortality. The association between MDA concentration and the risk for CV mortality was stronger in KTRs with ↓ eGFR [HR 2.09 (95%CI: 1.45-3.00) per 1-SD increment]

^aP < 0.05 vs eGFR_cys > 60.

^bP < 0.005 vs eGFR_B2M > 60.

^cP < 0.005 vs eGFR_cys > 60.

^dP < 0.05 vs low ST2 group. B2M: Beta-2-microglobulin; Cr: Creatinine; CV: Cardiovascular; HR: Hazard ratio; eGFR: Estimated glomerular filtration rate; eGFR_cys: Estimated glomerular filtration rate based on cysteine; eGFR_B2M: Estimated glomerular filtration rate based on beta-2-microglobulin; uNGAL: Urinary neutrophil gelatinase-associated lipocalin; KIM-1: Kidney injury molecule 1; IL-18: Interleukin eighteen; L-FABP: Liver fatty acid binding protein; UCr: Urine creatinine; aHR: Adjusted hazard ratio; A1M: Alpha 1 microglobulin; MCP-1: Monocyte chemoattractant protein-1; PINP: Procollagen type I intact N-terminal peptide; PIIINP: Procollagen type III intact N-terminal peptide; ST2: Cardiac biomarker; MDA: Malondialdehyde; SD: Standard deviation.

Table 9 Summary of biomarkers associated with infectious outcomes

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Fernández-Ruiz et al[73], 2017	100	Plasma	sCD30	Bacterial infection	sCD30 correlates to bacterial infection at baselinea and 1 moa, 3 moa, and 6 moa after KT. Patients with sCD30 ≥ 13.5 ng/mL had lower 12-mo bacterial infection-free survivalb (35.0% vs 80.0%) Baseline sCD30 levels ≥ 13.5 ng/mL is a risk factor for infectionc (HR: 4.65; 2.05-10.53)
Sadeghi et al[74], 2016	70	Plasma	IL-23	CMV infection	Patients with post-KT CMV disease (n = 13; 150 ± 106 d post-KT range 41–363 d) had higher pre-KT IL-23d (8.6 ± 4.4 vs 8.0 ± 17) and IL-23/Cr ratiosd than patients w/o CMV disease post-KT (n = 57). Pre-KT IL-23 plasma level of > 7 pg/mL is a risk factor for post-KT CMV infection/reactivation and symptomatic infectione (RR = 4.50, 95%CI: 1.23 to 16.52) ROC curve analysis post-KT CMV disease showed a sensitivity of 69% and a specificity of 67%
Leone et al[75], 2019	189	Plasma	94ins/delE37delATTG NFKB1 polymorphism	CMV infection	65% of CMV infections occurred in ins/ins group. Survival free from CMV was 54.7% for ins/ins group and 79.4% for del carriers one-year post-KT. A multivariate regression for del carriers showed a ↓ risk of CMV infectionf and recurrence for ins/ins KTRsg (HR = 0.224, 0.307)
Kim et al[77], 2017	385	Urine	Urine microRNA bkv-miR-B1-5p and bkv-miR-B1-3p	BKVN	↑ bkv-miR-B1-5p and bkv-miR-B1-3p in KTRs w biopsy proven BKVN distinguished them from disease free recipients (AUC = 0.989, 0.985). Only 13 KTRs with BKVN
Abend et al[78], 2017	116	Plasma	Donor BK virus antibody, recipient BK virus antibody	Post-transplant BK viremia	Donor BK virus antibody seropositivity correlated to post-transplant BK viremiah (OR = 5.0; 95%CI: 1.9-12.7). The authors did not examine for BKVN however
Ho et al[79], 2018	107	Urine	CXCL10	BKVN	↑CXCL10 correlated with t+ii (uCXCL10/creatinine, 1.23 ng/mmol vs 0.46 ng/mmol; AUC = 0.69) and mvi, specifically ptci (uCXCL10/creatinine, 1.72 ng/mmol vs 0.46 ng/mmol; AUC = 0.69) compared to normal histology. Urinary CXCL10i corresponded with BKV, but not CMV viremia. These urine CXCL10 findings were confirmed in the independent validation set

^aP < 0.05 vs kidney transplant recipients without bacterial infection.

^bP < 0.0001 vs kidney transplant recipients with sCD30 < 13.5 ng/mL.

^cP < 0.001 vs kidney transplant recipients with sCD30 < 13.5 ng/mL.

^dP < 0.05 vs kidney transplant recipients w/o CMV disease.

^eP < 0.05 vs kidney transplant recipients with pre-Tx IL 23 < 7 pg/mL.

^fP < 0.005 vs ins/ins carriers.

^gP < 0.05 vs del carriers.

^hP < 0.0001 vs seronegative BK virus antibody donors.

ⁱP < 0.05 vs low CXCL10 KTRs. sCD30: Soluble cluster of differentiation 30; KT: Kidney transplant; HR: Hazard ratio; IL-23: Interleukin twenty three; CMV: Cytomegalovirus; RR: Relative risk; Cr: Creatinine; ROC: Receiver operating characteristic; ins: Insertion; del: Deletion; NFKB1: Nuclear Factor kappa-light-chain-enhancer of activated B cells; KTRs: Kidney transplant recipients; bkv: BK viral; RNA: Ribonucleic acid; miR: Mature form of micro RNA; BKVN: BK virus nephropathy; AUC: Area under the curve; OR: Odds ratio; CI: Confidence interval; C-X-C: C-terminal amino acid sequence Cystine-X-Cystine; CXCL10: C-X-C motif chemokine ligand ten; t: Tubulitis; i: Interstitial inflammation; mvi: Microvascular inflammation; ptc: Peritubular capillaritis.

Table 10 Summary of biomarkers associated with post-transplant malignancy

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Hope et al[83], 2015	82 (56 KTRs +malignancy, 26 KTRs - malignancy)	Plasma	LDH; IFN-γ; ELISpot	Post- transplant malignancy	Low NK cell function -> HR 2.1 (0.97-5.00) metastatic Ca, Ca-related death, septic death
Pontrelli et al[84], 2019	156: 93 KTRs, 34 controls + malignancy, 29 healthy subjects	Plasma	IL-27	Post-transplant malignancy	IL-27 plasma levels were able to discriminate patients with post-transplant neoplasia with a specificity of 80% and a sensitivity of 81%
Garnier et al[85], 2019	89	Plasma	CD4+CD45RC	Post-transplant malignancy	KTRs with a low CD4+CD45RChigh population (< 51.9%) carried a 3.7 fold risk of cancera (HR = 3.71 (95%CI: 1.24 to 11.1), CD4+CD45high population having a 20-fold higher risk of rejectionb (HR = 21.7 (95%CI: 2.67-176.2)

^aP < 0.05 vs kidney transplant recipients with a high CD4+CD45R population.

^bP < 0.001 vs Kidney transplant recipients with a low CD4+CD45R population. KTR: Kidney transplant recipient; LDH: Lactate dehydrogenase; IFN-γ: Interferon gamma; ELISpot: Enzyme-linked immunosorbent spot assay; NK: Natural killer; HR: Hazard ratio; Ca: Cancer; IL-27: Interleukin twenty seven; CD4+CD45RC: CD45RC – cluster of differentiation four + forty five isoform with alternative mRNA splicing of exon 6; CI: Confidence interval.

Table 11 Summary of biomarkers associated with post-transplant diabetes mellitus

Ref.	n	Sample	Biomarkers	Outcome	Study conclusion
Heldal et al[87], 2018	852	Plasma	20 biomarkers	PTDM	6/20 biomarkers associated with PTDM; significant include soluble TNF type 1a Pentraxin 3a macrophage migration inhibitory factora and endothelial protein C receptorb
Yepes-Calderón et al[88], 2019	516	Plasma	Malondialdehyde	PTDM	MDA was inversely associated with PTDM, independent of immunosuppressive therapy, transplant-specific covariates, lifestyle, inflammation, and metabolism parametersa (HR, 0.55; 95%CI, 0.36-0.83 per 1-SD increase)

^aP < 0.05 vs kidney transplant recipients without Post transplant diabetes mellitus.

^bP < 0.005 vs kidney transplant recipients without Post transplant diabetes mellitus. PTDM: Post transplant diabetes mellitus; TNF: Tumor necrosis factor; MDA: Malondialdehyde; HR: Hazard ratio; SD: Standard deviation; CI: Confidence interval.

Table 12 Summary of biomarkers associated with graft survival and/or patient survival

Ref.	n	Sample	Biomarkers	Dysfunction	Study conclusion
de Holanda et al[16], 2018	73	Plasma	sCD30	Rejection; Graft survival	sCD30 at +7, +14 associated with ARa. No difference in 5 yr graft survival
Koo et al[63], 2016	94	Urine	microalbumin, NGAL, KIM-1, IL-18, L-FABP	DGF, slow graft function , 1 yr graft function	NGAL predicts AKI; NGAL + L-FABP predicts DGF, slow graft function (AUC 0.758, 0.704); NGAL + L-FABP + Cr better than DGF calculator and KDPI. L-FABP predictive of 1 yr graft functionb
Foster et al[68], 2017	508	Urine and plasma	Cystatin C, B2M, Cr	CV events, Mortality, Kidney failure	HR eGFRcys and HR eGFRB2M < 30 vs 60+ were 2.02c (1.09-3.76) and 2.56d (1.35-4.88) for CV events; 3.92e (2.11-7.31) and 4.09d (2.21-7.54) for mortality; and 9.49e (4.28-21.00) and 15.53d (6.99-34.51) for kidney failure
Bansal et al[69], 2016	1027	Urine	uNGAL, KIM-1, IL-18, L-FABP, Ucr	CV events, Graft failure, mortality	Each ↑ log in uNGAL/Cr associated with a 24% ↑ risk of CV events (aHR 1.24; 1.06 to 1.45), graft failure (1.40; 1.16 to 1.68), and risk of death (1.44; 1.26 to 1.65). uKIM-1/Cr and IL-18/Cr associated with higher risk of death (1.29; 1.03 to 1.61 and 1.25;1.04 to 1.49 per log increase)
O’Connell et al[89], 2016	204	Biopsy	Gene set of 13 genes	IFTA, Graft loss at 2/3 yr	Gene set prediction > clinicopathologic variables (AUC 0.967 > AUC 0.706, AUC 0.806) for IFTA; predicted graft loss at 2 and 3 years (AUC 0.842, 0.844), validated in 2 public datasets
Ix et al[90], 2017	748	Urine	Urine A1M, MCP-1, procollagen type III and type I amino-terminal amino pro-peptide	Graft failure	In adjusted models, ↑ concentrations of urine A1M (HR per doubling, 1.73; 1.43-2.08) and MCP-1 (HR per doubling, 1.60; 1.32-1.93) were associated with allograft failure. With the adjustment, urine A1M (HR per doubling, 1.76; 95%CI: 1.27-2.44)] and MCP-1 levels (HR per doubling, 1.49; 95%CI: 1.17-1.89) remained associated with allograft failure
Heylen et al[92], 2018	154	Biopsy	DNA methylation	1-yr graft function	↑ methylation risk scoref at transplant predicted chronic injury at 1 yr (OR 45; 98 to 499; P < 0.001; AUC 0.919) vs standard baseline clinical risk factors, including age, donor criteria, donor last SCr, CIT, anastomosis time, HLA mismatches (combined AUC 0.743) sensitivity, specificity, and PPV, NPV values of MRS-based ROC curves were 90%, 90%, 95%, and 82%
Park et al[70], 2017	1184 (300 CVD, 371 death, 513 random sub-cohort)	Urine	Urine A1M MCP-1, PINP and PIIINP	CV events, Mortality	In adjusted models, higher urine AlM (HR per doubling of biomarker = 1.40 (95%CI: 1.21 to 1.62), MCP-1 [HR = 1.18 (1.03 to 1.36)], and PINP [HR = 1.13 (95%CI: 1.03 to 1.23) were associated with CVD events. These three markers were also associated with death (HR per doubling A1M = 1.51 (95%CI: 1.32 to 1.72); MCP-1 = 1.31 (1.13 to 1.51); PINP = 1.11 (95%CI: 1.03 to 1.20)
Smedbråten et al[91], 2017	382	Plasma	CL-L1, CL-K1	CV mortality, Graft survival, Patient survival	↑CL-L1 (≥ 376 ng/mL) and ↑CL-K1 (≥ 304 ng/mL) levels at transplantation were associated with mortality in multivariate Cox analysesg [HR = 1.50 (95%CI: 1.09 to 2.07) and HR = 1.43 (95%CI: 1.02 to 1.99)] ↑CL-K1 levels were associated with CV mortality. No association between measured biomarkers and death-censored graft loss was found
San Segundo et al[93], 2019	133	Plasma	Abs number peripheral blood Treg cells	Death-censored graft survival	↑ Treg cells 1 yr post-KTh showed better DCGL (5-yr survival, 92.5% vs 81.4%). 1-yr Treg cellsh showed a ROC AUC of 63.1% (95%CI: 52.9 to 73.2) for predicting DCGL. After multivariate Cox regression analysis, an ↑ number of peripheral blood Treg cellsh was protective factor for DCGL (HR = 0.961 (95%CI: 0.924 to 0.998), irrespective of 1-yr proteinuria and renal function

^aP < 0.05 vs grafts without rejection.

^bP < 0.05 vs immediate function grafts.

^cP < 0.05 vs eGFR_cys > 60.

^dP < 0.005 vs eGFR_B2M > 60.

^eP < 0.005 vs eGFR_cys >60.

^fP < 0.005 vs low methylation risk score at transplant.

^gP < 0.05 vs KTRs with collectin levels below cutoff.

^hP < 0.05 vs KTRs with absolute number of peripheral blood Treg cells below threshold. sCD30: Soluble cluster of differentiation thirty; AR: Acute rejection; uNGAL: Urinary neutrophil gelatinase-associated lipocalin; KIM-1: Kidney injury molecule 1; IL-18: Interleukin eighteen; L-FABP: Liver fatty acid binding protein; DGF: Delayed graft function; AKI: Acute kidney injury; AUC: Area under the curve; KDPI: Kidney donor profile index; B2M: Beta-2-microglobulin; Cr: Creatinine; CV: Cardiovascular; HR: Hazard ratio; Abs: Absolute; eGFR: Estimated glomerular filtration rate; eGFR_cys: Estimated glomerular filtration rate based on cysteine; eGFR_B2M : Estimated glomerular filtration rate based on beta-2-microglobulin; u: Urine; UCr: Urine creatinine; aHR: Adjusted hazard ratio; IFTA: Interstitial fibrosis tubular atrophy; A1M: Alpha 1 microglobulin; MCP-1: Monocyte chemoattractant protein-1; PINP: Procollagen type I intact N-terminal peptide; PIIINP: Procollagen type III intact N-terminal peptide; DNA: Deoxyribonucleic acid; OR: Odds ratio; SCr: Serum creatinine; CIT: Cold ischemia time; HLA: Human leukocyte antigen; PPV: Positive predictive value; NPV: Negative predictive value; MRS: Methylation risk score; CI: Confidence interval; CL-L1: Collectin liver-1: CL-K1-collectin kidney-1; Treg: Regulatory T cells; KT: Kidney transplant; DCGL: 95% eath censored graft loss; ROC: Receiver operating characteristic.