NASH management:New Biomarkers in NAFLD
Manuel Romero-Gómez
Professor of Medicine
UCM Digestive Diseases. Virgen del Rocio University Hospital.
SeLiver group. Institute of Biomedicine of Seville,
University of Seville, Sevilla, Spain.
Barcelona, November 22nd - 23rd, 2019
European Workshop on NASH
Clinical Practice
Conflict of interest
• Scientific advisor to Janssen-Cilag, Intercept, Genfit,NovoNordisk, Medimmune, Gilead, Prosceinto; Kaleido.
• Speaker-Bureau: MSD, Roche, Abbvie, BMS, Gilead,Intercept, Genfit.
• Grants: Abbvie, Gilead e Intercept.
• Manuel Romero-Gómez es co-owner of DeMILI (a non-invasive MR-based method for NASH diagnostic).
What is a biomarker?
A defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions
Types of biomarkers:1. Biochemical biomarkers: Blood/urine/fecal/breath tests2. Imaging biomarkers
BEST (Biomarkers, EndpointS, and other Tools) Resource. FDA/NIH 2016
A. Availability and acceptability
B. Bias of process
C. Cost of tests
D. Diagnostic Accuracy
E. Errors measurement
F. Reliability
Main characteristics of biomarkers
Three aspects of measurement validity: 1) content validity, (makes sense) a biomarker reflects the biological phenomenon
studied, 2) construct validity, (algorithm) in the network of biomarkers or disease
manifestations,3) criterion validity, HOW the biomarker correlates with the specific disease and is
usually measured by sensitivity, specificity, and predictive power.
Susceptibility
Diagnostic
Monitoring
Prognostic
Efficacy
Safety
BEST (Biomarkers, EndpointS, and other Tools) Resource. FDA/NIH 2016
Validation process
Looking for new biomarkers
• Transciptomic
• Genomic
• Epigenetics
• Proteomic
• Microbiome
• Metagenomic
• Lipidomic
• Routine lab
• Metabolomic
Blood Fecal
GenesBreath
Imaging Biomarkers
Biomarkers for susceptibilityB
iom
arke
rs o
f su
scep
tib
ility Genes
PNPLA3
MBOAT TM6SF2 GCKR
miRNAsmiR-34a miR192 miR-200b
miR-122
Metagenome Proteobacteria
The genetic NASH score
NASH=
PNPLA3, TM6SF2 &
MBOAT7
GCKRHSD17B13
rs genotype gene NASH p
rs738409 GG vs. CG/CC PNPLA3 51% vs. 25% P=0.04
rs2645424 GG vs. AG/AA FDFT1 61% vs. 61% P=0.41
rs838145 GG vs. CG/CC FGF21 47% VS. 27% P=0.003
rs58542926 CC vs. CT/TT TM6SF2 30% vs. 32% P=0.29
MicroRNAs 200b & 224
*
*
*
*
p=0.009
p=0.014
p=0.027
p=0.048
Sanyal et al, EASL 2016
N=274 pacientes
AUROC 0,82 (IC95% 0,76-0,87) SE 75%SP 76%VPP 72%VPN 79%
miR-200a miR-34a
HbA1c P3PN
A2M
miRNA as biomarker
Meta-analysis of expression profile studies that have at least one validation study
•NASH vs. Healthy control
•NASH vs. NAFL
•NAFL vs. Healthy control
•NAFLD vs. Healthy control
miRNA-122:605 patients,
FC of 6.73.
miRNA-34a:605 patients,
FC of 4.42.
miRNA-122:202 patients,
FC of 4.31.
miRNA-122:410 patients,
FC of 7.28.
miRNA-192:282 patients,
FC of 4.09.
miRNA-122:336 patients,
FC of 2.81.
miRNA-192:262 patients,
FC of 2.1.
Inverse correlation ? Why?
Liu CH et al. J Hepatol 2018
Potential Biomarkers from microbiome
Bio
mar
kers
ofr
om
. icr
ob
iom
e
Dysbiosis
Lower Bacterioidetes Higher Clostridum Coccoides
Higher Bacteroides Lower Prevotella
Increased Bacteroides & Ruminococcus
Lower Prevotella
Increase in Proteobacteria
SCFA Propionate Butyrate
LPS/Ethanol HiAlcKpn
BCAA
Bile acids Reduction of secondary bile acids
Choline / trimethylamine
MICROBIOME & NAFLD
13
Stages of liver damage: Role of ECM
• *HA is both produced in and cleared by the liver
ECMs drive fibrosis and are involved in repairLiver becomes progressively damaged and less
elastic as fibrosis exceeds fibrinolysis
ECM
Proteins
ECMs include: Collagens – Laminin – Fibronectin – Proteoglycans – Glycoproteins – HA – PIIINP - TIMP-1
Procollagen III aminoterminalpeptide
(PIIINP)
Tissue inhibitor ofmetalloproteinase 1 (TIMP-1)
Hyaluronic acid (HA)
ELF Score*† = 2.278 + 0.851 ln (CHA) + 0.751 ln (CPIIINP) + 0.394 ln (CTIMP-1)
14
Fibrosis assessment : The ELF scoring system
ELF has been validated against biopsy-proven fibrosis in across multiple forms of CLD 7.7 9.8 11.3†
Guillaume et al, Alimentary Pharmacol Ther 2019
Usefulness in clinical practice
https://www.hepamet-fibrosis-score.eu
NAFLD fibrosis score = -1.675 + 0.037 × age (year) + 0.094 × BMI (kg/m2) + 1.13 × IFG/diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio - 0.013 × platelet count (×109/L) - 0.66 × albumin (g/dL)
AST(U/L) x Edad(años)
Plaq(miles) x ALT (U/L)√
http://nafldscore.com/
https://www.hepatitisc.uw.edu/page/clinical-calculators/fib-4
Detection and referral: fNITs
Hepamet Fibrosis Score
FIB-4
NAFLD Fibrosis Score
Alw
ays
op
tfo
rb
iop
sy
Op
tfo
rb
iop
syo
nly
ifab
solu
tece
rtai
nty
of
dis
ease
Ampuero et al. CGH 2019
Cost-effectiveness of screening and referral across NITs.N
AFL
D s
cree
nin
gin
pat
ien
tsat
ris
ko
f fi
bro
sis
SOC 850/850 x 570,78€ 485.163 €
Combined
Score116/850 (13.5%)
NITs
FIB4 > 1.30 337/850 (40%)
NFS > -1.455 322/850 (38%)
HFS > 0.12 149/850 (17.5%)
(OR:0,35,
IC95%:0,28-0,44)
98.745€
[95.324-102.165]NFS
(OR:0,32,
IC95%:0,26-0,40)
107.307€
[103.753 – 110.860]FIB4
Combined Score HFS & FIB4 HFS & NFS NFS & FIB4 HFS NFS FIB4
Cost x Unit 0,7011 (DM)1,9534
0,7011 (DM)1,9534
0,7011 (DM)1,9534
0,7011 0,6032 (DM)1,855
0,7011 0,6313
Limitations of liver biopsy as gold standard:a) Diagnostic criteria for steatohepatitis
NAScore Steatosis Ballooning Inflammation
0 < 5% No No
1 5%-33% Few <2 foci
2 33%-66% prominent 2-4 foci
3 >66% > 4 foci
NASH diagnosis Yes No
SAF
Bedossa P et al. Hepatology 2012;56:1751
S3A2F1 S1A2F4
b) Overlap between inflammatory activity and fibrosis stage
Limitations of liver biopsy as gold standard:c) Sampling variability
Courtesy- Dr. David KleinerN=51 NAFLD (2 samples of liver biopsy)
Diagnostic accuracy of 2nd biopsy:NASH: 0.81 (0.65-0.90)F3-F4: 0.87 (0.7-0.95)Ballooning: 0.66 (0.57-0.73) Ratziu V et al. Gastro 2005
NPV NASH: 74%>1 Fibrosis stage: 41%Bridging fibrosis in just 1 biopsy 35%
Evidence of NAFLD progression from steatosis to fibrosing-steatohepatitis…N=108 mean follow-up 6.6 years
44%SIMPLE (BLAND)
STEATOSIS
STEATO-HEPATITIS7%
12/27
6/75 McPherson S et al. J Hepatol 2015
d) progression over the time
Simple steatosis
BorderlineNASH
DefiniteNASH
F0-F1 F2-F4
Overall survival free of liver transplantation
Outcomes NASHF0-F2
NASHF3-F4
Overall mortality 1.41 3.28
CVD mortality 1.38 4.36
HCC 15.7 16.9
Angulo, et al. Gastroenterology 2015; Ekstedt, et al. Hepatology 2015; Younossi, et al 2016, in press.
e) Histological features related to outcomes
Fibrosis
Portal Inflammation/Ballooning
NASH
Statistical approach to diagnostic accuracy➢ Sensitivity, specificity, positive and negative predictive values (PPV, NPV)➢ Likelihood ratio: LR+ = sensitivity / (1-specificity) &
LR- = (1-sensitivity) / specificity➢ The area under the ROC curve (AUC)
▪ Obuchowski’s correction▪ Leave-one-out cross-validation (LOOCV) (also called the Jackknife).
Multiple rounds of cross-validation are performed using different partitions, validation results are averaged over the rounds.
➢ Youden's index: (sensitivity + specificity) – 1➢ Diagnostic odds ratio (DOR): DOR = (TP/FN)/(FP/TN).➢ Diagnostic accuracy: (TP+TN)/(TP+TN+FP+FN).➢ Decision tree >> Net Benefit >> Decision curve analysis
Poynard et al. BMC Gastroenterology 2008Majumdar et al. Hepatology 2019
*N: number of human patients with biopsy
Barr J, et al. J Prot Res 2010 & 2012
J. Crespo, et al. Journal of Hepatology, Vol. 64, Issue 2, S478
Mayo R, Crespo J, Martinez-Arranz I, et al. Hepatology Comm., 2018
Bril F, et al. Diabetes, Obesity and Metabolism 2018;20(7):1702-9
development
22
The OWLiver Test has been reported to be a good test for the diagnosis of NAFLD and NASH, based on the triglyceride profile and Body Mass Index (BMI)
Mayo R, et al. Hepatology Communications 2018;2(7):807-20.
NAFLD: Non-alcoholic Fatty Liver DiseaseNAFL: Non-alcoholic Fatty LiverNASH: Non-Alcoholic SteatohepatitisNL: Normal liver (= No NAFLD)
23
OWLivercohort (n=830)
NAFLD (n=701)
Controls(n=129)
90% F0,F1,F2
Caucasian BMI ≥ 25Controlled DM2 or not
diabetics
Discovery Trial (n= 467)
Blind Validation EU (n= 294)
Blind validation US (n = 69)
24
OWLiver DM2 (n=616)
Esteatosis (n=263)
NASH (n=353)
F0,F1,
F2,F3BMI ≥ 25
ControlledDM2 or not
diabetics
Discovery Trial (n= 616)
Blind Validation (n= 65)
ImagingBiomarkers
Ultrasono-graphy
Fibroscan
CAP
SSI Shear-wave
MRI
PDFF Fibro-MRI
NASH-MRI
LMS MRE
Imaging Biomarkers
Method TE SW CAP PDFF LMS DeMILI MRE
Biomarker kPa kPa dB/m %FF LIF (cT1) NASH-MRI kPa
Ultrasonography as first imaging biomarker
LIVER ULTRASONOGRAPHY:• Hyper-echogenicity• Far gain attenuation• Blurred border with gallbladder• Blurred border to vessels
Ultrasonography limitations:
Not able to segregate steatohepatitis fromsteatosis.
Liver hyper-ecogenicity do not correlate withhepatic injury
Brilliant liver requires differential diagnosis
Steatosis detected by ultrasonography whenhigher than 12.5%
shear-wave elastography
Individual patient data metaanalysis CAP detecting steatosis
Karlas et al. J Hepatol 2017;66:1022-1030Romero-Gómez M, Cortez-Pinto H. J Hepatol 2017
AUROC
S0 vs. S1-S3 0.82 (0.81-0.84)
S0-S1 vs. S2-S3 0.87 (0.85-0.88)
S0-S1-S2 vs. S3 0.88 (0.86-0.91)
Transient Elastography CAP (dB/m)
N=2735
NAFLD (n=537); HepC (n=997); HepB (n=1003); Others (n=198)
F0: 304 (11%); F1: 970 (36%); F2: 725 (27%); F3:334 (12%; F4: 350 (13%)Etiology – Diabetes – BMI
J Hepatol 2015
Castera L. Hepatology 2010; Boursier J Hepatol 2016
Interpretation of kPa according to metabolic derangement of the liver
TE: 10.0 kPaHFS: 0.24
• High sensitivity and specificity (Gold standard method
for hepatocyte steatosis)
• Reproducible
• Whole liver analysis
• Not time-consuming
• Expensive
Chemical shift T2 vs T2 FAT SAT
In our patient: PDFF 40%
PDFF = Pdfat / (PDfat+PDwater)
Multi-echo Chemical-Shift-Encoded
MR (MECSE-MR) sequences
Redor SB et al. J Magn Reson Imaging 2011
Idilman IS et al. Acta Radiol 2016
Proton Density Fat Fraction(PDFF)
T1ρ detects early liver fibrosis as collagen content correlates with T1ρ. Mean liver T1ρ values in cirrhosis are significantly higher than those in healthy subjects.T1ρ maps are reproducible and accurate.Iron-corrected T1 (IR-SS-FP sequence) correlates with the Ishak degree of fibrosis (F0–F6, rs = 0.68, p <0.0001, 95% CI 0.54–0.78) with significant differences between all groups.
Rauscher I et al. Evaluation of T1ρ as a potential MR biomarker for liver cirrhosis: comparison of healthy control subjects and patients with liver cirrhosis. Eur J Radiol. 2014;83:900-4
Banerjee R et al. Multiparametric MR for the non-invasive diagnosis of liver disease. J Hepatol. 2014;60: 69.Jiang J et al. An experimental study on the assessment of rabbit hepatic fibrosis by using MR T1ρ imaging. Magn Reson Imaging.
2016;34(3):308.
Liver MultiScan: T1ρ Mapping
Healthy Control
Cirrhosis Child-Pugh A
Confounding factors are steatosis (need for fat suppression), inflammation and iron (need for T2* correction).
T1 mapping: 949 ms
T2 BH STIR DYNAMIC
DEMILI: NASH-MRI & FIBRO-MRI
Gallego-Durán et al. 2016
2.3.2 Fibrosis Quantification with MRE (Magnetic Resonance Elastography)
Petitclerc L et al. J Magn Reson Imaging 2017
• Uses different type of sequences: Gradient-Recall Eco (GRE), Spin Echo (SE) or Echo-planar imaging (EPI).
• Hepatic elasticity is measured in kPa (Range 2.05-2.44 kPa for normal livers).
• RME scale ranges between 0 and 8kPa and is not equivalent to the one used in echography. 1 kPa in ERM corresponds to approximately 3 kPa in echography.
Singh et al. Clin Gastroenterol Hepatol 2015;e-446:440-451
F0 F1 F2 F3 F4
NAFLD <2.5 2.5 3.4 4.8 6.7
10.0
8.0
6.0
4.0
2.0
0.0
Stage 2
40.0
30.0
20.0
10.0
0.0
Control
Non-invasive evaluation of fibrosis in NASH
n=142 (NAFLD)
BMI: 28.1±4.63
Male: 81/61
Age: 57+14y
F0: 14; F1: 51; F2: 32; F3: 34; F4: 11
NAS: 1‒2: 6/15; 3‒4: 32/51; 5‒8: 30/5/3/0
NASH: NO: 34; YES: 108
Ballooning: NO: 32; Few: 96; Many: 14
Imajo K et al. Gastroenterology 2016;150:626
TE Failure rate: 10% (15/142)
12.0
10.0
8.0
6.0
4.0
2.0
Fibrosis
(kP
a)
Stage 0 Stage 2 Stage 2 Stage 3 Stage 4
MR elastographyKruskal-Wallis tests
P<0.001
Control
Fibrosis
(kP
a)
Stage 0 Stage 2 Stage 3 Stage 4
Transient elastographyKruskal-Wallis tests
P<0.001
1-specificity
Sen
siti
vity
0.4 0.6 0.8 1.00.20
MR elastography10.0
8.0
6.0
4.0
2.0
0.0
1-specificity
Sen
siti
vity
0.4 0.6 0.8 1.00.20
Transient elastography
Stage ≥1Stage ≥2Stage ≥3Stage ≥4
MRE (n=142) TE (n=127)
Fibrosisstage
Cut-off level, kPa
AUROC 95% CI Se Sp PPV NPVCut-off level,
kPaAUROC 95% CI Se Sp PPV NPV
≥1 2.5 0.80 0.71‒0.89 75.0 85.7 99.0 84.6 7.0 0.78 0.70‒0.87 61.7 100.0 100.0 86.6
≥2 3.4 0.89 0.85‒0.94 87.3 85.0 88.4 83.6 11.0 0.82 0.74‒0.89 65.2 88.7 88.2 66.2
≥3 4.8 0.89 0.83‒0.95 74.5 86.9 74.5 81.0 11.4 0.88 0.79‒0.97 85.7 83.8 75.0 91.9
≥4 6.7 0.97 0.94‒1.00 90.9 94.5 58.8 99.2 14.0 0.92 0.86‒0.98 100.0 75.9 73.0 100.0
Stage ≥1Stage ≥2Stage ≥3Stage ≥4
Diagnostic accuracy of imaging modalities in detecting fibrosis, steatosis and NASHMax. of Youden's Index
Prev
(%)
Modality AUC
[95% CI]
Cutoff Sens
(%)
Spec
(%)
Fibrosis
F ≥2 12%
FS-LSM 0.81 [0.73;0.89] 6.05 85% 64%
MRE-LSM 0.83 [0.73;0.92] 2.70 65% 86%
LMS-LIF 0.66 [0.54;0.79] 2.0625 85% 46%
F ≥3 4%
FS-LSM 0.94 [0.88;1.00] 8.4 100% 84%
MRE-LSM 0.96 [0.89;1.00] 3.4 83% 100%
LMS-LIF 0.62 [0.32;0.91] 2.6 50% 77%
Steatosis
G ≥1 76%FS-CAP 0.8 [0.73;0.88] 336.50 51% 97%
LMS-PDFF 0.93 [0.88;0.98] 4.85 90% 82%
G ≥2 39%FS-CAP 0.82 [0.75;0.88] 293.5 94% 57%
LMS-PDFF 0.96 [0.93;0.99] 9.8 92% 90%
G ≥3 16%FS-CAP 0.76 [0.68;0.85] 306.5 92% 51%
LMS-PDFF 0.94 [0.89;0.98] 14.0 88% 89%
NASH
36%FS-LSM 0.61 [0.52;0.70] 5.25 71% 52%
MRE-LSM 0.58 [0.48;0.68] 2.65 34% 87%
LMS-LIF 0.71 [0.63;0.79] 2.6875 40% 91%
Prospective Prevalence Study of Adult NAFLD/NASH Utilizing Multi-Modality Imaging Compared with Liver Biopsy
N=160
Harrison et al. EASL2015
T1 mapping
STIR
• Inflammation
mDixon-Quant
• Iron overload
• Fat infiltration
Colestasis
Stasis
Breathing
• False positive
• Errors
Stiffness: 3.42 kPa
Diagnostic algorithm using imaging biomarkers in clinical practice
NA
SH-F
IBR
OSI
S
FIBROSIS
HFS < 0,12
TE < 8,9 kPaNo fibrosis
Gray zone MRE
HFS > 0,47
TE > 15,4 kPa
Advanced
fibrosis
NASH
CAP
PDFFDeMILI
(NASH-MRI)
Romero-Gómez, LITMUS project proposal 2018
“The stronger the gold standard the greater the biomarker”
Limitation Method
Availability MRE
Bias of process Multiparametric MR
Cost of tests MR methods/Paid NITs
Errors measurement Routine methods
Reliability Blood tests
Acceptability Liver biopsy
Non-monitor end-point Breath test & LIF
“Biomarker for NASH remained elusive”
Kinner S et al. Dig Dis Sci 2016;61:1337
Take home messages
• The stronger the gold standard the better the biomarker.
• Biomarkers for NASH detection remains the most controversial, probably due to the weaknesses of the histological feature.
• When developing a biomarker we need to keep in mind all the characteristics from A to E.
• Development of biomarkers able to detect NASH and fibrosis stage and to predict NASH resolution and fibrosis regression is mandatory.
@SeLiver_group
