Biomarkers of idiosyncratic drug-induced liver injury (DILI) - a systematic review

Figures & data

Figure 1. Study selection flow diagram

Table 1. Summary of included diagnostic and prognostic studies

Study ID (type)	Participants (N)	Controls (N)	Index tests	Reference test	Outcomes	Derivation of DILI threshold & validation
Bell et al. [30] (Diagnostic)	DILIN cohort (n = 74)	Healthy (n = 40)	Mass-spectrometry-based proteomics for serum protein expression	RUCAM and adjudication	Comparison of DILI and control Comparison of DILI patients based on pattern, severity, and causality assessment	The DILIN threshold criteria for DILI No validation cohort
Dragoi et al. [23] (Diagnostic)	Suspected DILI (n = 16)	Healthy (n = 6) Patients with DILI due to another drug (n = 6) Patients with acute liver injury not due to DILI (n = 6)	Proteomic analysis of monocyte-derived hepatocyte-like (MH) cells	RUCAM and adjudication	Identifying specific biomarker for Diclofenac-DILI	Undefined threshold No validation cohort
Ma et al. [32] (Diagnostic)	DILI (n = 38)	Healthy (n = 30)	Serum metabolomic profiling Bile acids	RUCAM	Comparison of DILI and control	ALT > 40 U/L and total bilirubin TBIL > 20 mmol/L No validation cohort
Soga et al. [28] (Diagnostic)	DILI (n = 10)	Healthy (n = 53) Patients with other liver diseases (n = 99)	Serum metabolomic profiling	Unclear	Comparison of patients with different liver diseases and control Developing and validating MLR model from metabolites to discriminate different liver diseases	Undefined threshold Validation cohort: DILI (n = 17) Healthy controls (n = 4) Patients with other liver diseases (n = 54)
Zhang et al. [25] (Diagnostic)	Female patients received Polygonum multiflorum Thunb. (PM) with ALT elevation > 2ULN (n = 6)	Matched female patients received PM without ALT elevation (n = 30)	Serum metabolomic profiling prior exposure to PM	ALT (serum)	Identifying baseline metabolomic profile to predict PM-DILI	Susceptible PM-DILI patients defined as ALT > ULN after PM exposure No validation cohort
Huang et al. [29] (Diagnostic)	PM-induced liver injury (PM-DILI) (n = 13)	Healthy (n = 9) Acute auto-immune hepatitis flare (n = 12) Acute HBV flare (n = 24)	Plasma metabolomic profiling	Unclear	Identifying metabolomic profile associated with PM-DILI Developing decision tree for PM-DILI diagnosis Comparing the metabolomic profile of PM-DILI with other liver injuries (AIH and HBV)	Undefined threshold No validation cohort
Thulin et al. [24] (Diagnostic)	TB/HIV patients received treatment with ALT > 3ULN (n = 38)	TB/HIV patients received treatment without ALT > 3ULN (n = 38)	K18, ccK18 (plasma) miR-122 (plasma) GLDH (plasma) AFP (plasma)	ALT (plasma)	Comparison of sensitivity/specificity of novel biomarkers against ALT	ALT values exceeding three times baseline levels (elevation >30 U/L) at any time point during the study period. No validation cohort
Rupprechter et al. [26] (Diagnostic)	TB/HIV patients received treatment with ALT > ULN (50 U/L) (total n = 15): Active tuberculosis (n = 2) Latent TB (n = 2) Nontuberculous mycobacteria infection (n = 1) HIV-TB coinfection (n = 10)	Healthy volunteers (n = 28) Active tuberculosis (n = 42) Latent TB (n = 140) Nontuberculous mycobacteria infection (n = 38) HIV-TB coinfection (n = 231)	miR-122 (serum) K18 (serum)	ALT (serum) RUCAM	Difference of miR-122 and K18 levels in healthy individuals compared to patients with infection Diagnostic accuracy of miR-122 and K18 in detecting ALT elevation in TB treated patients	ALT > 3ULN in the presence of symptoms or > 5ULN in the absence of symptoms. No validation cohort
Church et al. [27] (Diagnostic & Prognostic)	2 SAFE-T cohorts: Swiss DILI (n = 28) Protocol 3A DILI (n = 98) DILIN cohort (n = 141)	Healthy PSTC (n = 81) SAFE-T (n = 192) Treated with known DILI drugs without developing injury Protocol 4 cohort (n = 55) Protocol 5 cohort (n = 92)	AFP (serum) ARG1 (serum) CDH5 (serum) FABP1 (serum) GSTa (serum) K18, ccK18 (serum) MCSFR (plasma) OPN (serum) GLDH (serum) LECT2 (plasma) PON 1 (plasma) SDH (serum) miR-122 (serum)	RUCAM and adjudication	Diagnostic accuracy for DILI detection Prognostic accuracy for unresolved DILI in 6 months Prognostic accuracy for Death/transplantation Developing a prognostic model with MELD score for death/transplantation	SAFE-T cohorts: ALT > 3ULN or ALP > 2ULN, within the last four weeks before the baseline visit DILIN cohort: the DILIN threshold criteria No validation cohort
Steuerwald et al. [31] (Diagnostic & Prognostic)	DILIN cohort (n = 78)	Healthy (n = 40)	Serum cytokines, chemokine and growth factors	RUCAM and adjudication	Comparison of DILI and control Developing a prognostic model to predict death within 6 months	The DILIN threshold criteria No validation cohort
Bonkovsky et al. [35] (Prognostic)	DILIN cohort (n = 127) Acute Liver Failure Study Group (n = 118)	Healthy (n = 63)	Serum cytokines, chemokines, and growth factors	RUCAM and adjudication	Comparison of DILI, acute liver failure due to different etiologies (DILI, auto-immune hepatitis, viral hepatitis, acetaminophen overdose) and control Developing a prognostic model to predict death within 6 months	The DILIN threshold criteria No validation cohort
Russo et al. [36] (Prognostic)	DILIN cohort (n = 78)	Healthy (n = 40)	Serum miRNA	Unclear	Comparison of DILI and control miRNA levels and death within 6 months of DILI onset Developing a prognostic model with albumin to predict death within 6 months	The DILIN threshold criteria No validation cohort
Xie et al. [37] (Prognostic)	DILI (n = 56)	Healthy (n = 34)	Serum metabolites, cytokines, and K18/ccK18	RUCAM	Comparison of severe/non-severe DILI and control Developing model from cytokines/metabolites to predict DILI severity	The DILIN threshold criteria No validation cohort
Peta et al. [34] (Prognostic)	SAFE-T cohort (n = 154 including 29 APAP)	Acute liver injury (n = 22)	Serum apolipoprotein-A1 (Apo-A1), haptoglobin (HAPTO), alpha-2 macroglobulin (A2M) and GGT	RUCAM and adjudication	Prediction of recovery at 12 weeks (ALT < 2ULN, and TBIL < 2ULN. Specific drug signature Risk of fibrosis in DILI	SAFE-T threshold: ALT > 3ULN or ALP > 2ULN, within 4 weeks before the inclusion visit (D0) An increase of at least 2-fold the pre-treatment level to D0 was required when pre-treatment ALT or ALP activity was available and > ULN No validation cohort

Abbreviations: Multiple logistic regression models (MLR); Polygonum multiflorum Thunb (PM); total cytokeratin 18 (K18); caspase cleaved cytokeratin 18 (ccK18); microRNA-122 (miR-122); glutamate dehydrogenase (GLDH); alpha fetoprotein (AFP); arginase 1 (ARG1); cadherin 5 (CDH5); liver fatty acid binding protein (L-FABP); glutathione-S-transferase α (GSTα); macrophage colony stimulating factor receptor (M-CSF-R); osteopontin (OPN); leukocyte cell-derived chemotaxin 2 (LECT2); paraoxonase 1 (PON1, normalized to prothrombin protein); sorbitol dehydrogenase (SDH); apolipoprotein-A1 (ApoA1); haptoglobin (HAPTO); alpha-2 macroglobulin (A2M); gamma-glutamyl transpeptidase (GGT).

Table 2. Summary of performance characteristics of diagnostic biomarkers

Study ID	Biomarker/model	Reported measure	Traditional biomarker	Reported measure
Bell et al [30].	Priority 1 proteins	AUROC, Percentage correctly classified as DILI (accuracy)		AUROC, Percentage correctly classified as DILI (accuracy)
	Apolipoprotein E	0.97, 89%	ALT	0.99, 73%
	+ inter-alpha-trypsin inhibitor (heavy chain H3, isoform 1)	0.98, 91%	AST	0.99, 67%
	+ gelsolin	0.99, 92%	ALP	0.96, 68%
	+ complement C7	0.99, 93%	TBL	0.94, 77%
	+serum amyloid P	0.99, 95%	ALT+AST+ ALP+ TBL	0.99, 81%
	+ age	0.99, 96%
Ma et al. [32]	Bile acids	AUROC	Traditional biomarkers	AUROC
	GCA	0.978	ALT	0.97
	TCA	0.985	AST	0.97
	TUDCA	0.909	GGT	0.97
	GCDCA	0.954	ALP	0.85
	GCDCS	0.946	TBL	0.91
	TDCA	0.976
	DCA	0.77
	LCA	0.66
	CDCA	0.67
Soga et al [28].	C-glutamyl dipeptides	AUROC (95% CI)
	MLR using (ALT, γ-Glu-Citrulline) in the training cohort	0.817 (0.639–0.995)
	MLR using (ALT, γ-Glu-Citrulline) in the validation cohort	0.849 (0.763–0.934)
Zhang et al. [25]	Serum metabolites	AUROC (95% CI)
	PE 22:6	0.939 (0.822 −1.0)
	Crotonyl-CoA	0.933 (0.764 −1.0)
	Indole-5,6-quinone	0.917 (0.789– 0.989)
	2E-tetradecenoyl-CoA	0.911 (0.789 −1.0)
	Phenyllactic acid	0.906 (0.767 −0.983)
	Phosphoribosyl-ATP	0.900 (0.767 −0.978)
Huang et al. [29]	Decision tree classification model	AUROC, accuracy
	P-cresol sulfate/phenylalanine ratio followed by inosine/bilirubin ratio	0.931, 89.8%
Rupprechter et al. [26]	Candidate biomarkers	AUROC (95% CI)
	miR-122	0.93 (0.88–0.98)
	K18	0.80 (0.72–0.87)
Church et al [27].	Candidate biomarkers	AUROC (95% CI)	Traditional biomarkers	AUROC (95% CI)
	K18	0.947 (0.928–0.966)	ALT	0.99 (0.984–0.996)
	FABP1	0.916 (0.890–0.941)	AST	0.975 (0.963–0.987)
	ccK18	0.911 (0.887–0.935)	ALP	0.902 (0.873–0.930)
	GLDH	0.907 (0.870–0.945)	TBL	0.857 (0.821–0.892)
	MCSFR	0.854 (0.822–0.887)
	miR-122	0.831 (0.779–0.883)
	AFP	0.826 (0.793–0.859)
	GSTα	0.827 (0.792–0.862)
	SDH	0.819 (0.763–0.876)
	OPN	0.758 (0.718–0.799)
	CDH5	0.658 (0.614–0.701)
	PON1	0.612 (0.542–0.682)
	ARG1	0.564 (0.519–0.609)
	LECT2	0.519 (0.450–0.588)

Abbreviation: GCA: glycocholic acid, TCA: taurocholic acid, TUDCA: tauroursodeoxycholic acid, GCDCA: glycochenodeoxycholic acid, TDCA: taurodeoxycholic acid, TBL: total bilirubin, DCA: deoxycholic acid, LCA: lithocholic acid, CDCA: chenodeoxycholic acid, total cytokeratin 18 (K18); liver fatty acid-binding protein (L-FABP); caspase cleaved cytokeratin 18 (ccK18); glutamate dehydrogenase (GLDH); macrophage colony-stimulating factor receptor (M-CSF-R); microRNA-122 (miR-122); alpha-fetoprotein (AFP); glutathione-S-transferase α (GSTα); sorbitol dehydrogenase (SDH); osteopontin (OPN); cadherin 5 (CDH5); (PON1, normalized to prothrombin protein); arginase 1 (ARG1); leukocyte cell-derived chemotaxin 2 (LECT2).

Table 3. Summary of performance characteristics of prognostic biomarkers

Study ID	Biomarker/model	Outcome endpoint	Sensitivity	Specificity	PPV	NPV	AUROC
Church et al [27].	International Normalized Ratio (INR, traditional biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.920 (0.864–0.977)
	Total bilirubin (traditional biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.821 (0.733–0.909)
	Aspartate aminotransferase (AST, traditional biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.700 (0.587–0.814)
	Alanine aminotransferase (ALT, traditional biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.606 (0.433–0.780)
	Alkaline phosphatase (ALP, traditional biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.597 (0.433–0.760)
	Osteopontin (OPN, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.858 (0.759–0.957)
	Cytokeratin 18 (K18, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.832 (0.737–0.927)
	Macrophage colony-stimulating factor receptor (MCSFR, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.775 (0.654–0.896)
	Caspase cleaved cytokeratin 18 (ccK18, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.778 (0.676–0.881)
	Fatty acid binding protein-1 (FABP1, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.721 (0.608–0.833)
	Alpha-fetoprotein (AFP, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.687 (0.566–0.809)
	Cadherin-5 (CDH-5, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.623 (0.498–0.748)
	Arginase-1 (ARG1, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.588 (0.436–0.741)
	Glutathione S-transferase alpha (GST-α, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.536 (0.359–0.713)
	Apoptotic index (AI, candidate biomarker)	Death/transplantation at 6 months	NA	NA	NA	NA	0.761 (0.627–0.895)
	MELD score value 20–29 + K18 ≥7.98 and MCSFR ≥6.94 MELD score value 20–29 + K18 <7.98 or MCSFR <6.94	Death/transplantation at 6 months	0.933	0.899	0.438 ^†	0.991 ^†	0.895 ^†
Steuerwald et al. [31]	Serum albumin ≤2.8 g/dL/>2.8 g/dL	Death at 6 months	1.0	0.60 (0.48–0.72)	0.26 (0.11–0.40)	1.0	NA
	IL-9, IL-17, PDGF-bb, RANTES All below the median/at least one above the median	Death at 6 months	0.80 (0.55–1.0)	0.94 (0.89–1.0)	0.67 (0.40–0.67)	0.97 (0.93–1.0)
	All four immune analytes below the median and albumin ≤2.8 g/dL/at least one immune analyte above the median or albumin >2.8 g/dL	Death at 6 months	0.78 (0.51–1.0)	0.99 (0.96–1.0)	0.88 (0.65–1.0)	0.97 (0.93–1.0)
Bonkovsky et al. [35]	RANTES below the median (11,349 pg/mL) and serum albumin ≤2.8 g/dL^‡	Death at 6 months	0.39 (0.30–0.47)	0.91 (0.86–0.96)	0.41 (0.33–0.50)	0.90 (0.85–0.95)	0.65 (0.53–0.77) *
Russo et al. [36]	miRNA-122 <7.89 RFU and serum albumin ≤2.8 g/dL	Death at 6 months	1.0	0.81	0.38	1.0	NA
Xie et al. [37]	Model including 31 metabolites and five cytokines (PDGF-bb, IP-10, IL-1Rα, MIP-1b, and TNF-α)	Severe DILI	NA	NA	NA	NA	0.983
Peta et al [34].	Apolipoprotein A1	Recovery at 12 weeks	0.94 ^†	0.08 ^†	0.69 ^†	0.38 ^†	0.663 (0.536–0.760)
	Haptoglobin	Recovery at 12 weeks	1.0^†	0^†	0.69^†	NA ^†	0.619 (0.496–0.718)
	ActiTest components (excluding ALT and total bilirubin, used as reference), i.e. apolipoprotein A1, haptoglobin, GGT, A2M, plus age and gender	Recovery at 12 weeks	0.85^†	0.31^†	0.73^†	0.48^†	0.723 (0.610–0.806)

Abbreviations: PPV: positive predictive value; NPV: negative predictive value; AUROC: area under the ROC curve; NA: data not available; MELD: Model for End-Stage Liver Disease; IL-9: interleukin 9; IL-17: interleukin 17; PDGF: Platelet-derived growth factor; RANTES: Regulated upon Activation, Normal T Cell Expressed and Presumably Secreted, also known as chemokine ligand 5 (CCL5); RFU: relative fluorescence units; IP-10: interferon gamma-induced protein 10; IL-1Rα: interleukin 1 receptor alpha; MIP-1b: macrophage inflammatory protein 1 beta; TNF-α: tumor necrosis factor-alpha; GGT: gamma-glutamyl transferase; A2M: alpha-2 macroglobulin. Severe DILI was defined as per DILIN study (Elevations in serum ALT and/or ALP levels, and the total serum bilirubin level is ≥2.5 mg/dL + at least one of the following: hepatic failure (INR ≥1.5, ascites, or encephalopathy) or other organ failure believed to be due to a DILI event (i.e. renal, or pulmonary). Recovery was defined as an ALT <2 ULN and BILI <2 ULN attained between 8 and 12 weeks.

NA: not reported.

^†These values were not reported in the original studies and were calculated based on other reported measures.

^‡Only including 127 acute DILI cases from the DILIN cohort.

Table 4. Risk of bias summary for diagnostic studies: review authors’ judgments about each domain for each included study

Study ID	Patient selection	Index test	Reference standard	Flow and timing
Bell 2012 [30]	Unclear	High	Low	High
Dragoi 2018 [23]	Low	High	Low	Unclear
Ma 2019 [32]	Low	High	Low	Unclear
Soga 2011 [28]	Unclear	High	Unclear	Unclear
Zhang 2020 [25]	Low	High	High	Low
Huang 2020 [29]	Unclear	High	Unclear	Unclear
Thulin 2014 [24]	Low	High	High	Low
Rupprechter 2020 [26]	Low	Low	High	Low
Church 2019 [27]	Unclear	Low	Low	Low
Steuerwald 2013 [31]	Low	Low	Low	Unclear

Table 5. Risk of bias summary for prognostic studies: review authors’ judgments about each domain for each included study

Study ID	Study participation	Study attrition	Prognostic factor measurement	Outcome measurement	Study confounding	Statistical analysis and reporting
Church 2019 [27]	Low	Low	Moderate	Low	Moderate	Low
Russo 2017 [36]	Low	Moderate	Moderate	Low	Moderate	Low
Peta 2017 [34]	Moderate	Low	Moderate	Moderate	Low	Low
Xie 2019 [37]	Low	Low	Moderate	Low	Low	Low
Steuerwald 2013 [31]	Low	Moderate	Moderate	Low	Moderate	Low
Bonkovsky 2019 [35]	Moderate	Low	Moderate	Low	Moderate	Moderate

Bell LN, Vuppalanchi R, Watkins PB, et al. Serum proteomic profiling in patients with drug-induced liver injury. Aliment Pharmacol Ther. 2012:600–612. DOI:https://doi.org/10.1111/j.1365-2036.2011.04982.x.

 Google Scholar

Dragoi D, Benesic A, Pichler G, et al. Proteomics analysis of monocyte-derived hepatocyte-like cells identifies integrin beta 3 as a specific biomarker for drug-induced liver injury by diclofenac. Front Pharmacol. 2018;9:699.

 PubMed Web of Science ®Google Scholar

Ma Z, Wang X, Yin P, et al. Serum metabolome and targeted bile acid profiling reveals potential novel biomarkers for drug-induced liver injury. Medicine (Baltimore). 2019 Aug;98(31):e16717.

 PubMed Web of Science ®Google Scholar

Soga T, Sugimoto M, Honma M, et al. Serum metabolomics reveals gamma-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. J Hepatol. 2011 Oct;55(4):896–905.

 PubMed Web of Science ®Google Scholar

Zhang L, Niu M, Wei AW, et al. Risk profiling using metabolomic characteristics for susceptible individuals of drug-induced liver injury caused by Polygonum multiflorum. Arch Toxicol. 2020 Jan;94(1):245–256.

 PubMed Web of Science ®Google Scholar

Huang Y, Zhao X, Zhang ZT, et al. Metabolomics profiling and diagnosis biomarkers searching for drug-induced liver injury implicated to Polygonum multiflorum: a cross-sectional cohort study. Front Med (Lausanne). 2020;7:592434.

 PubMed Web of Science ®Google Scholar

Thulin P, Nordahl G, Gry M, et al. Keratin-18 and microRNA-122 complement alanine aminotransferase as novel safety biomarkers for drug-induced liver injury in two human cohorts. Liver Int. 2014 Mar;34(3):367–378.

 PubMed Web of Science ®Google Scholar

Rupprechter SAE, Sloan DJ, Oosthuyzen W, et al. MicroRNA-122 and cytokeratin-18 have potential as a biomarkers of drug-induced liver injury in European and African patients on treatment for mycobacterial infection. Br J Clin Pharmacol. 2021 Jan 11;87(8):3206–3217.

 PubMed Web of Science ®Google Scholar

Church RJ, Kullak-Ublick GA, Aubrecht J, et al. Candidate biomarkers for the diagnosis and prognosis of drug-induced liver injury: an international collaborative effort. Hepatology. 2019;69(2): 760–773.

 PubMed Web of Science ®Google Scholar

Steuerwald NM, Foureau DM, Norton HJ, et al. Profiles of serum cytokines in acute drug-induced liver injury and their prognostic significance. PLoS One. 2013;8(12):e81974.

 PubMed Web of Science ®Google Scholar

Bonkovsky HL, Barnhart HX, Foureau DM, et al. Cytokine profiles in acute liver injury-results from the US drug-induced liver injury network (DILIN) and the acute liver failure study group. PLoS One. 2018;13(10):e0206389.

 PubMed Web of Science ®Google Scholar

Russo MW, Steuerwald N, Norton HJ, et al. Profiles of miRNAs in serum in severe acute drug induced liver injury and their prognostic significance. Liver Int. 2017 May;37(5):757–764.

 PubMed Web of Science ®Google Scholar

Xie Z, Chen E, Ouyang X, et al. Metabolomics and cytokine analysis for identification of severe drug-induced liver injury. J Proteome Res. 2019 Jun 7;18(6):2514–2524.

 PubMed Web of Science ®Google Scholar

Peta V, Tse C, Perazzo H, et al. Serum apolipoprotein A1 and haptoglobin, in patients with suspected drug-induced liver injury (Dili) as biomarkers of recovery. PLoS One. 2017;12(12):e0189436.

 PubMed Web of Science ®Google Scholar