Pursuing the holy grail of Precision Medicine based approaches for NASH
Arun J. Sanyal M.B.B.S., M.D.
Professor of Medicine, Physiology and Molecular Pathology
Virginia Commonwealth University School of Medicine
Conflicts of Interest
• Dr. Sanyal is President of Sanyal Biotechnologies
• Stock options for Genfit, Tiziana, Indalo, Durect, Exhalenz, Galmed
• Consultant- Gilead, Intercept*, Allergan*, Lilly, Novo Nordisk, Astra Zeneca-Medimmune*, Novartis, Pfizer, Genentech*, Merck, Bristol Myers*, Boehringer Ingelhiem*, Immuron*, Echosense, GE, OWL*, Birdrock, Tern, Sundise, RedX*, IFMO, Lipocine*, Innovate*, Zydus*, AMRA, Hemoshear,
• Grant support: Bristol Myers, Intercept, Gilead, Allergan, Merck, Echosense, Novartis, Boehringer Ingelhiem
* no financial remuneration in last 24 months
Diet-induced obesity
Arteries(Hypertension,CVD, CAD, PVD)
Heart(HFPEF)
Pancreas(T2DM)
Kidney(CKD)
NAFLD is part of a multi-system disease process
CAD, coronary artery disease; CKD, chronic kidney disease; CVD, cerebrovascular disease; CVS, cardiovascular system; HFPEF, heart failure with preserved ejection fraction; PVD, peripheral vascular disease; T2DM, type 2 diabetes mellitus.
Liver(NAFLD)
Metabolic stressSystemic inflammation and fibrosis
GeneticEpigenetic
Microbiome
LifestyleDiabetes
Age
Alcohol
Ethnicity
NAFLD is a multifactorial disease
Courtesy- Jelena Mann
Personalized management of NAFLD
Assessment of risk
Primary Prevention
Diagnosis and Prognosis
PersonalizedTherapeutic strategy
PersonalizedResponse-guided
therapy
• Family History• GWAS• Microbiome• Behavioral analysis
STRATIFY FOR RISK OFOBESITY, DIABETES,HEART DISEASE, NAFLDAND CIRRHOSIS
Genetics influences the development and progression of NASH
Biochimica et Biophysica Acta 1812 (2011) 1557–1566
Metabo-phenotyping may allow subsegmentation of the population
Individual BCP
𝑓𝑎𝑡
𝑓𝑎𝑡 + 𝑚𝑢𝑠𝑐𝑙𝑒
𝑤𝑒𝑖𝑔ℎ𝑡
𝑚𝑢𝑠𝑐𝑙𝑒
𝐿𝑖𝑣𝑒𝑟 𝑓𝑎𝑡
𝑉𝑖𝑠𝑐𝑒𝑟𝑎𝑙 𝑓𝑎𝑡
𝐴𝑏𝑑𝑜𝑚𝑖𝑛𝑎𝑙 𝑓𝑎𝑡
𝑀𝑢𝑠𝑐𝑙𝑒 𝑓𝑎𝑡
Linge et al. Obesity 2018. Body Composition Profiling in the UK Biobank Imaging Study. https://doi.org/10.1002/oby.22210
The microbiome is everywhere
Role of the Gut Microbiome in Obesity and Diabetes Mellitus
Barlow et al, Nutrition in Clinical Practice, Volume: 30, Issue: 6, Pages: 787-797, First
published: 09 October 2015, DOI: (10.1177/0884533615609896)
How the microbiome impacts NASH development
normal
OTU
NAFLD
• 30 OTU- NAS• 3 OTU- stage 3-4
normal
Transcriptomicprofile
Amino acidmetabolism glycolysis
Carbohydratedigestion
NASH
• 12 pathways altered with NASH
normal
Fecalmetabolome
Spermidinepolyamines
Indolepropionatephenyllactate
Sec Bile Acid SCFA
Oxidant stress
FXR signalingBacterial overgrowth
Gut barrierendotoxin
Endothelial dysfunctionOncogenesisMitochondrial health
Matsumoto et al, Nutrients. 2019 May 27;11(5). , Puri et al Hepatology. 2018 Feb;67(2):534-548, Sanyal et al, AASLD 2019Madeo et al, Science. 2018 Jan 26;359(6374)
Diet-microbial interactions can be used to leverage the gut endocrine functions
TGR5
GLP-1
Release
b
Blood glucose
Insulin
Release
IntestineL Cells
b
Blood glucose
Insulin
Release
α
GLP-1
CDCA > LCA > CA
Kumar et al, BBRC 2012Kumar et al, JBC, 2016
Veillonella improves exercise capacity by conversion of lactate to propionate in marathon runners
Schieman et al, Nat Med. 2019 Jun 24. doi: 10.1038/s41591-019-0485-4
Pathophysiological implications
13
Koob, Volkow, 2017
Binge/Intoxication Dopamine Opioid peptides Serotonin GABA Acetylcholine
Withdrawal/Negative Affect CRF Dynorphin Norepinephrine Hypocretin (Orexin) Substance P Dopamine Serotonin NPY Nociceptin Endocannabinoids Oxytocin
Preoccupation/Anticipation Dopamine Glutamate Hypocretin Serotonin CRF
Are subjects with NASH able to change their lifestyle? Data on their stages of change from URICA
Pre- Contem Action Main0
25
50
75Pre-contemplation
Contemplation
Action
Maintenance
contemplation plation tenance
score
s
The precontemplation scores were higher than those for other stagesOf change indicating that most subjects did not perceive that their Lifestyle (diet, exercise) contributed to their NASH
Stewart et al, Liver International 2016
The critical role of behavioral factors in the metabolic syndrome and NAFLD
GENETICS
ECONOMIC
SOCIAL
CULTURAL
POLITICAL
BEHAVIOR• healthy lifestyle choices• Compliance• Motivation• Ability to change behavior
DISEASEPREDISPOSITIONPHENOTYPEPROGRESSION
Potential primary interventions
• Personalized behavioral intervention:• based on stage of change
• based on social support systems
• based on cultural, social and economic considerations
• Diet:• calorie control for all
• specific interventions to modulate microbiome
• Activity and sleep:• shift workers at particular risk and may need special
approaches to avoid bad eating-activity choices
There remains an unmet need for fully integrated models of disease development
Personalized management of NAFLDAssessment of risk
Primary Prevention
Diagnosis and Prognosis
PersonalizedTherapeutic strategy
PersonalizedResponse-guided
therapy
• Non-invasive diagnostic tools• Selection of liver bx candidates• Identification of underlying mechanisms• Selection of best therapy
Some basic concepts in NASH
Disease Onset
cirrhosis
Disease Activity
Stag
e
Liver-related outcomes
SteatohepatitisDisease activity scores(NAS)
Fibrosis
Is there a more precise,Individualized way to evaluate the drivers of disease and targetthem for treatment and use themto assess treatment response?
Disease Predisposition
Genetics
• PNPLA3:• Conserved from potatoes to
humans- TG lipase• Disconnects NASH from insulin
resistance• Accounts for 12-20% of variability
in phenotype
• TM6SF2:• Impairs lipid transport• Related to more advanced disease
HOW IN THE WORLDDOES THIS LEAD TO NASH?
Disease initiators:• Adiposity• Drugs (amiodarone)• TPN• Jejuno-ileal bypass• Genetic disorders• Systemic inflammation
SNPs do not live in a vaccum: learning how SNPs work together in a stressed environment to drive phenotype
NAFL NASH 0 1 2 3
Chen et al, PLoS One. 2013; 8(7): e65982.
Key pathways associated with NASH• eIF recycling• Terpenoid synthesis• Cholesterol synthesis• Steroid biosynthesis
mRNA splicing pathway
DNA methylation signatures reflect aging in patients with nonalcoholic steatohepatitis
Loomba et al, JCI Insight. 2018 Jan 25;3(2).
The molecular signature is expected to reflect the point of disease reversal and mechanism of disease reversal
Die
t /
Bas
elin
e
8 wks 22 wks 52 wks
Sterol / Fatty AcidMetabolism
PPARα Signaling
Inflammation
ECM / Fibrosis
Proliferation / Tumor
NAFL NASH F0-2 NASH F3 + HCC
Concordance with humans with NAFL/early NASH- Transcriptome (GSEA, PCR)- Western blots- Immunohistochemistry
GSEA concordance withHumans with cirrhosisNASH HCC
Cazanave et al, Scientific Reports, Dec 2017
25
NASH vs Healthy
SLC1A2
TUBA8
TNIP1
DNAH11
SOD1
EIF3CL
CCS
DYNLT1
SLC29A1
AJUBA
RCC2
CREBZF
SMG5
CENPC
ATG4A
CALM2
CENPF
ERCC6LCENPV
RRP1B
WIPI1
FAF1
CEP57
CENPO
OPTN
KLHDC1
RBM7
DIABLO
TUBG2
TUBGCP4
ATP1B3
PEA15
GRIA3
INVS
MAPRE1
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SYNE2
DNAH5
TCTN2DNALI1
TMEM67
SUN2
B9D1
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NPHP1
FERMT2
LASP1
RAN
CBLL1
CSTB
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RNF187
PHLPP1
IGSF5
SH2D3A
KCND3
TESK2
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KRT18
CFL2NECTIN1
PPIF
LPP
SNX27
MAP3K20
CMYA5
EPHB6
MERTK
LNX2
PDLIM5
ACTR10
KRT8
SENP2
KCNS2
KPNA2
IPO5
LRRTM3
IFT172
LAMC3
CFL1
LCP1
ITPR2
IRF1
CTSB
JUP
GNAT1
TFDP2
CDK14
CYCS
HMGA1
EFCAB6
HIST2H2AC
TOP2A
RASSF4
NRF1
PSMB8
SRSF11
CCNYL1
PSMB9
CCNG1
HIST1H1D
RRAGA
CDC27
ARL3
RBL2
RASSF1
ORC2
LHPP
PSMC3CCNF
PSMD14
GEMIN4
H2AFYPSME1
CBS
PSMC4
CES1
ATP5B
HIST1H4H
KLHL22
DAXX
PSMD1
PSMD10
PSME2
CKS2ATP6V0B
H1F0
PSENEN
CD74
LAMP1
GPHN
PACSIN3
OASL
PARD3
SRC
RRAS HLA-DRB1
CHAD
PPIAITPKC
RIPK2
FGFR3
IRF2
MRASPDGFRB
ITGA4
WNT2
IRF7
HLA-BCOL5A2
ITGA7
TRPV4
HLA-DQA1
OAS1
SMOC1
IP6K2
SYNJ1
MX2
IFITM1
IFI35
MAGI3
ITGA6IL32
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PTK2
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CAPNS1
CCT8
LBR
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PDCD11
EBNA1BP2
AEN
NOP14
FTSJ3
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NSUN6
PWP2
DIEXF
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UBR4
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MPST
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PRDX6
SNRNP70
TAT
PEPD
CNOT4
EIF5A
MAT1A
RNPS1
IMP3
HECTD1
SAP30L
MYO1B
MYO5B
PPP1R3E
UNC13C
EIF3E
AMOTL2
UNC119
RPL22
RPS4Y1
EIF4B
EIF5
FBXL4
SNRPD1
KLHL8
DEK
EED
AHCY
MAP1LC3A
PPP2R1A
BUB1
CASP7NDRG2
CAMK1D
CACNB2
TUBGCP3
CAMK2D
CREB3
PPP2CA
DDIT4
STRN3
ZYX
RPS6KA2RPS6KA1
ACTR1A
SIK1
PRKX
CNGA1
CDH15
AKAP9
DUSP5
LRRC7
RICTORMBP
CDH2PRKCE
FOXO3
SGK1SGK2
MAPK13CREB3L3
SPEN
PTPRO
EIF2B4
MAPK6
YWHAZ
RHEB
SGK3
MEF2A
YWHAH
DEPTOR
PTPA
CDK1 CALM1
STK3
PMF1
TBL1Y
UTRN CELF1
CNPY3
GABRE
PI4K2A IMPA1
INPP4A
FMR1
SCYL2
DTNA
CAPG
NMT2
SNAP25
COPZ2
SEC24C CLTC
NECAP2
BLNK
FZD4
SEC23A
RASAL2
MAGI1
FZD5
HLA-DRA
BRD7
TLR7
SH3KBP1
PKD2
CD72
CMTM3
SH3D19
DNAJC6
ARF3
CAPZB
WNT11
CLDN14
CCDC88A
TNFRSF14
TAGLN2
NECTIN3
RGS16
AP2S1
TTC21B
KCTD5
FXR1
WDR19
IFT81
IFT46
KCTD2
SGCB
VPS41
SUCO
GOSR2
DMD VAMP8
HIVEP1
CLDN12
CPEB3
YKT6
MET CYFIP2
COL3A1
IFI30
SH3PXD2A
PIK3R5
TCF7L2
ITPK1
PTCH1
TIAM1
RAC2
ARHGEF4
CD151
CTSD
ITCH
ARPC5
ACTA2
ARRDC3
TMEM173
ARRDC4
TMED9
IGSF8
MAVS
RNF125
INPP5B
ELMO2
ACTR2
ARHGAP31
ABI3
NUDT4
TNIK
RAPGEF6
IP6K3
DSG1
CD82
STAP2
MYL6CXCL9
RGS14CCL28
NEU4
FPR3
C3AR1
ACKR3
RHOBTB2
S100A4
TSPAN3
AGFG1
NEU1
RAP2A
GRK3
AKTIP
WHRN
DFNB59
CDH23
EHD4
MUC3A
ST3GAL1
ST6GALNAC6
SETBP1
SYBU
OGN
LRIG3
SLC17A5
POLR1D
BSN
INHBC
GPR182
INHBE
NOD2
MYO1C
CTSA
ARF6
FCGR2A
NME1
HLA-DPB1
GRTP1 MYH10
UBTF
ERC1
ACVR2B
ST3GAL6
CHD4
FUT2
ZFHX4
LSP1
RBMS1
RAMP1
OR13D1
MYOM1
CXCL10
FBXO8
PROC
PPARA
NFE2L2
NR5A2
LPL
NFKBIZ
GAPDH
LGALS1
SLC25A20
LHX9
XPA
TMTC1
CARHSP1
SCD5
CYP4A11
PDK2
ABCB4
GGT1
SCAND1
PLIN2PKM
PGAM1
ALDH5A1
GCLM
GSS
RNF111
PC
DCAF11
CITED2
NONORAD54L2PYGB
HK3
HPRT1
CSACSL4
PRODH
ENPP1
G6PC3
GRHPR
ACLY
ADSSL1
CPS1
RTN3
SLC28A2
AGL
RTN1
ASS1
ASPASCN9A XYLB
ENO3
DPP3
AHR
ME1
LGALS3BP
PCK1
AKR1B10
GCK
CNDP2
SLC23A2
NADKSLC23A1 FTCD
UCKL1
GCLC
GATM
GYS2
ALDH18A1
CPSF6
AMY2B
HOGA1
DAO
PRDM10
FN1
JUN
SOCS2
IL2RB
PRKCD
CALR
TRPM8
PRKCA
SNAI2
ADRB3
IGFALS
PTPN3
AGPAT2
FGB
PAPPA2
HTR7
FGG
LEPR
CBLC
NCF2
GNA12
THBS2
EPHA1
GPC4
ERBB2
IRS2
PLA2G6
MPLKIPFAT1
MTOR
SERPINF2
KLF15
MED13MED27
RORA
CEBPD
MED13L FABP4MED29
MACROD1
MTHFD1
CBFA2T3
POU2F1
ZFP1
PLIN1
DCAF6
GNMT
APH1B
PER3
ATP6V0D1
SKIL
RBBP6
FBXL3
FBXL7
FBXO2
TAP1
ZC3H6
UBIAD1
FBXO4
CCNI
PAWR
AR
FOXO1
ATF3
INSR
GCAT
SARDH
AGXT
ATP6V1A
FOLH1
ATP6V1E1
CDK6
PPARGC1A
NCOR1
CDKN1A
SNAPC4
HIST1H1B
GADD45A
TFPI
MPZL1
LARP1B
RALGAPA1
SIRPA
PABPC1L
LSM1
LSM5
KLHL13
YY2
HNRNPL
CEBPA
SHBG
SERPINC1
SLC16A7
ALOX5
F5
NFKB1
SIRT1
LTB4R2
CCR5
LPAR6PTGFR
TXLNG
RFX5
MMP15
PLD2
IFNGR2
GRK2
SORBS1
COL1A2
LYZ REN
MMP9
RAC3
KALRN
LEF1
DEPDC7
GNB1 CIITA
RHOG
RHOQ
MYH11
ARHGAP5GMIP
GHR
THY1
TMSB4X
HRG SERPINE1
GCGR
GNAS
MAPKAPK2
F2RL1
SMAD7
ITGAM
SDC1
IGFBP1DUSP1
IGF1
NANOG
PRKAG2CTGF
PLAUIGF2
ALB
MMRN1
EPAS1
ITIH4DGKD
IGFBP2
ANGPTL3
HSPA6
FADS2PTGS1
ABCB11
NAMPT
UBE2A
SLCO1A2
ANXA5
COTL1
UBE3A
ABCG2
DNAJC25
ABCC2
MRPS26
DNAJC19
DLEU1
TIMM17A
ANGPTL8
HSPA4LHSPA1A
ABCA8
DNAJC12
NAPEPLDDPYSL2
CHDH
BCAP31
MSN
FUT4
ITGAE
TFE3
ERVW-1
ACVR1C
MUL1
CASP1
UBE2N
PELI2
PELI1
IKZF1
MFN2
TSC22D1
NIPA1
DEFB1
PRKN
NFYB
SERPINF1
ICAM3
KYNU
AADAT
CSAD ELOVL5
HAAO
DLAT
ITLN1
POR
EEF1A2
HNMT
GALNT2
DLD
AOX1
HSD17B2
GSTM1
ACMSD
NFE2L3
ACAT1
ABCC5
GLYCTKAACS
BPGM
PDE11A
ALDH3A1
MCEE
ALDH6A1
DHRS9
AKR1A1
PCCA
ACADSB
MCC
MCCC1
GCDH
GPT2
AMDHD1
HAL
ALPL
AMMECR1
KDM8
GPT
ACSM5
ABAT
PPT1
SLC38A7
FADS1
CYP2C19
AKR1B1 MAFK
CYP1A1MAOA
MUT
GALM
Immune function
IGF regulation &
platelet activation
Phospholipids in phagocytosis, TP53
regulates metabolic genes, FGFR
signaling
FA, TAG metabolism
Intermediate,
glucose metabolism
Hoang et al, MS under review 2019
Critical networks that drive histological activity (NAFLD ACTIVITY SCORES)
PPI= protein-protein interactome
Hubs= central components of communitiesHoang et al, 2019, MS under review
Top networks related to severity of fibrosis
GPCRs
Hoang et al, 2019, MS under review
Top pathways linked to severity of histological activity and fibrosis stage
NAFLD activity score Fibrosis stage
1 Intrinsic pathway for Apoptosis Ephrin-signaling
2 Fc epsilon receptor signaling CDK Chk1/2
3 Regulation of PTEN TP53
4 Transcription of TP53 RHO GTPases
5 Vesicle-mediated transport EphB-forward signaling
6 TNFR2-noncanonical NFkB G2 cell cycle
7 T cell receptor signaling Fc gamma receptor signaling
8 Membrane trafficking Signaling by MET
9 Programmed cell death Chondroitin metabolism
10 MHC class II antigen processing TP53 mediated transcription
Based on Gene Set Variance Analysis (GSVA)
Development of a gene-NAS (gNAS) score that is associated with histological NAS
Hoang et al, AASLD 2018
Development of a gene-Fibrosis (g-fib) score
Hoang et al, AASLD 2018
Molecular subtypes with distinct reactomeclusters not distinguishable by histological NAS
Cluster 1: immune regulation, cluster 2: cell proliferation, cluster 3: ECM
Sample clusters with distinct reactome pathway clusters linked to fibrosis stage
Subtypes of NASH based on methionine metabolome
Mayo et al, Hepatol Commun. 2018 Jul; 2(7): 807–820.
End Treatment Response Biomarkers for vitamin E (results from PIVENS)
Metabolites* Vit.E responder vsVit.E non-responder
Fold change P-value†
Gamma-
glutamylleucine
0.82 0.02
Gamma-
glutamylvaline
0.80 0.03
Sphingosine 0.64 0.02
* ANCOVA model adjusting for baseline level of metabolites † Tukey’s post test for pairwise comparisons
Cheng et al, PlosONE September, 2012
Monocyte-derived hepatocyte like cells provide personalized assessment of drug-induced toxicity risk
Benesic et al, Clinical Gastroenterology and Hepatology 2018;16:1488–1494
Personalized management of NAFLDAssessment of risk
Primary Prevention
Diagnosis and Prognosis
PersonalizedTherapeutic strategy
PersonalizedResponse-guided
therapy
• Noninvasive assessment of outcomes• Selection of second line Rx• Optimization of outcomes by
combining behavioral, dietary and drug Rx
So where do we go from here?
New data bring new questions
• Do static analyses predict dynamic changes in disease state?
• How to leverage this to understand spontaneous waxing and waning of disease?
• Will molecular subtype targeted therapy enhance efficacy rates?
• Can we make a NASH-chip so that we can identify which pathway is relevant for which patient?
3-D in vitro culture systems with flow may be a tool in Precision Medicine
Hemodynamic Flow
Interstitial Flow
Transport
Inflow
HSTx NASH
System
Human Biopsies (Puri et al. (2007))
NAFL NASH
Log2 FC p-value Log2 FC
p-
value Log2 FC p-value
TOTAL 1.1 <0.01 1.6 <0.05 1.5 <0.05
SFA 1.1 <0.01 1.7 <0.05 1.4 <0.05
MUFA 2.0 <0.01 2.5 n.s. 2.1 <0.05
PUFA -0.1 0.70 0.5 <0.05 0.9 <0.05
n-6/n-3 ratio 0.5 <0.01 0.7 <0.05 0.5 <0.05
FFA 0.5 0.06 0.1 n.s. 0.1 n.s.
DAG 2.1 <0.01 1.4 <0.05 0.8 <0.05
TAG 2.4 <0.01 3.2 <0.05 2.9 <0.05
CE 0.6 0.01 0.2 n.s. 0.0 n.s.
PC -0.3 0.13 -0.4 <0.05 -0.3 n.s.
PE -0.4 0.08 -0.3 <0.05 0.0 n.s.
SM 0.2 0.37 -0.2 n.s. 0.4 n.s.
Dash et al, American Journal of Physiology, 2013
Hemoshear (HSTX) system
FFA + insulin + TNF
Feaver et al, JCI Insight. 2016 Dec 8;1(20):e90954
40
Exploring Combination Therapies
As an example, introducing multiple drugs in search of synergies opens the door
for potential combination therapies.
Pathway Response
-0,75
-0,50
-0,25
0,00
0,25
0,50
0,75
1,00
1,25
1,50
1,75
2,00
FXR Signaling
Cholesterol Biosynthesis
Gluconeogenesis
TGFB Signaling
Collagen Formation
Inflammation
NASH
HEALTHY
OCA
LY2109761
Hypothetical patient stratification
Impaired satiety mechanisms
Impaired thermogenesis
Periph adipogenesis/lipolysis
Adipose inflammation
Augmented DNL
Impaired TG formation
Inappropriate TG lipolysis
Active lipotoxic lipid synthesis
Liver inflammatory pathways
Impaired wound response
Augmented fibrogenesis
Pathway category: Pt A Pt B Pt CLikelihood of
contributing to NASH
phenotype
Low
High
PNPLA3?→
“lean NASH”
Courtesy- Brent Tetri
Stratification → targeted treatment“Personalized medicine”
Impaired satiety mechanisms
Impaired thermogenesis
Periph adipogenesis/lipolysis
Adipose inflammation
Augmented DNL
Impaired TG formation
Inappropriate TG lipolysis
Active lipotoxic lipid synthesis
Liver inflammatory pathways
Impaired wound response
Augmented fibrogenesis
Pathway category: Pt A Pt B Pt C
PNPLA3?→
“lean NASH”
ACC1, SCD inhib.
JNK inhib?
PPARγ ligands
PGC1α activators
Central modulation
?
CYP4 inhibitors
iPLA2 inhibitors
Hedgehog inhib
Galectin inhib?
DGAT2 activators
Courtesy- Brent Tetri
Huang Dee: Nai-Ching (2600 BC, First Medical Text)
Superior doctors prevent the diseaseMediocre doctors treat the disease before evident
Inferior doctors treat the full-blown disease
Everest View, Kala Patthar, Nepal
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