Presented by:Nagi Abdalla엠네기Metabolomics Discloses Potential Biomarkers fortheNoninvasive Diagnosis of Idiopathic PortalHy...
Contents Introduction / Idiopathic Portal Hypertension Methods Results Discussion & Conclusion
INTRODUCTION
Idiopathic Portal Hypertension (Noncirrhotic Portal Fibrosis, Hepatoportal Sclerosis) Etiology: (unknown) Recurrent inf...
Study Aim The need for a less invasive diagnostic method Liver biopsy might not be helpful to diagnose IPH Importance o...
METHODS
Patients Diagnosis criteria: Signs of portal hypertension Exclusion of cirrhosis Exclusion of hepatic venous thrombosi...
 Blood samples: were collected in citrate-containing tubesand centrifuged then stored at -80C 99 samples were collected...
 Data processing: LC-MS data processing: Noise reduction  identify relevantpeak intensities  normalization to other pe...
 Multivariate data analysis: Missing variables were not considered t-test P value corrected by multiple comparisons and...
 Model validation was done by: using training (2/3 of data) and test sets (1/3 of data) to predict classmembership and c...
RESULTS
Main clinical characteristics of thepatients included in the studyVariables IPH, n=33 Cirrhosis, n=33Healthyvolunteers, n=...
Metabolites analysis: PLS-DA(HPI vs. CH)
Metabolites analysis: PLS-DA(HPI vs. HV)
DISCUSSION
 The PLS-DA models show a clear differentiation of IPHvs. cirrhotic patients & IPH vs. healthy controls basedon a subset ...
Study limitations Patient number: however, as IPH is a rare condition, a sample over 30patients could be considered adeq...
CONCLUSION The results from this study disclose a subset ofputative biomarkers of IPH patients with IPH could be identif...
Thank you for listening감사합니다!
metabolomics discloses potential biomarkers for the noninvasive diagnosis of idiopathic portal hypertension
metabolomics discloses potential biomarkers for the noninvasive diagnosis of idiopathic portal hypertension
metabolomics discloses potential biomarkers for the noninvasive diagnosis of idiopathic portal hypertension
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metabolomics discloses potential biomarkers for the noninvasive diagnosis of idiopathic portal hypertension

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Susana Seijo , MD
1
, Juan Jose Lozano , PhD
2,6
, Cristina Alonso , PhD
3
, Enric Reverter , MD
1,6
, Rosa Miquel , MD
4
, Juan G. Abraldes , MD
1,6
,
Maria Luz Martinez-Chantar , PhD
5,6
, Angeles Garcia-Criado , MD
7
, Annalisa Berzigotti , MD, PhD
1,7
, Azucena Castro , PhD
3
,
Jose M. Mato , PhD
5,6
, Jaume Bosch , MD, PhD, FRCP
1,6
and Juan Carlos Garcia-Pagan , MD, PhD

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  • Types of portal hypertension: cirrhotic and non-cirrhotic…..
  • 1962Vitamin A toxicity, methotrexate and 6-mercaptopurineHarmanci, O. and Y. Bayraktar (2007). "Clinical characteristics of idiopathic portal hypertension." World J Gastroenterol 13(13): 1906-1911.familial aggregation of IPH and a high frequency of HLA-DR3 have been observed among Indian patientsHLA-DR3 is associated with early-age onset myasthenia gravis, Hashimoto's thyroiditis (along with DR5), primary sclerosing cholangitis,[2] and opportunistic infections in AIDS,[3] but lowered risk for cancers
  • VIP score, the Variable Importance in the Projection,
  • HAART = highly active antiretroviral therapy
  • metabolomics discloses potential biomarkers for the noninvasive diagnosis of idiopathic portal hypertension

    1. 1. Presented by:Nagi Abdalla엠네기Metabolomics Discloses Potential Biomarkers fortheNoninvasive Diagnosis of Idiopathic PortalHypertension
    2. 2. Contents Introduction / Idiopathic Portal Hypertension Methods Results Discussion & Conclusion
    3. 3. INTRODUCTION
    4. 4. Idiopathic Portal Hypertension (Noncirrhotic Portal Fibrosis, Hepatoportal Sclerosis) Etiology: (unknown) Recurrent infections Altered immune response Genetic predisposition (HLA-DR3 ) Hypercoagulability HIV infection Diagnosis : based on the presence of unequivocal portalhypertension and a definite histological exclusion ofcirrhosis and any other specific disorder that is able to
    5. 5. Study Aim The need for a less invasive diagnostic method Liver biopsy might not be helpful to diagnose IPH Importance of metabolomics in clinical research.. Aim of the study:“to discover a noninvasive metabolomic profile inplasma allowing differentiating IPH from healthyindividuals and from patients with CH”
    6. 6. METHODS
    7. 7. Patients Diagnosis criteria: Signs of portal hypertension Exclusion of cirrhosis Exclusion of hepatic venous thrombosis Other exclusions: Patients with other conditions such as thrombosis,hepatocellular CA, liver biopsy with <6 complete portaltracts HIV patients were only included if IPH diagnosis wasunequivocal “only patients with unequivocal IPH were included inthe study”
    8. 8.  Blood samples: were collected in citrate-containing tubesand centrifuged then stored at -80C 99 samples were collected Ethical statement: informed consent was given to allparticipants Experimental procedures:(A global metabolite profiling UPLC-MS methodology) LC-MS system: UPLC-(TOF)MS Source: ESI @150◦C Column: 1 mm i.d. × 100 mm Acquity 1.7 μm C8 BEH column(Waters) M.P: A:0.05%FA B: CAN (0.05%FA) gradient flow
    9. 9.  Data processing: LC-MS data processing: Noise reduction  identify relevantpeak intensities  normalization to other peaks in the sample inter-assay normalization to reference sample followinglinear regression method. Pairwise univariate data analysis was performed in IPH vs.CH samples and IPH vs. HVs, to eliminate biomarkers that donot discriminate between groups
    10. 10.  Multivariate data analysis: Missing variables were not considered t-test P value corrected by multiple comparisons and VIPscore (estimates the importance of each variable in theprojection PLS model) “VIP ≥1” Results are 202 (IPH-CH) and 57 (IPH-HV) significantmarkers (P<0.05) Markers with higher VIP (2.2/2.1) were selected to build aPLS-DA model to discriminate IPH from CH and IPH from HV. Markers selection is based on strong parameters: (1-0.7) of both: R2 (goodness of fit) Q2 (goodness of prediction)
    11. 11.  Model validation was done by: using training (2/3 of data) and test sets (1/3 of data) to predict classmembership and class discrimination [X100 “random” times] corresponding random sampling cross-validated AUC measures weredetermined for each set as (mean±SD) to check sensitivity andspecificity Heatmaps were created to represent the selected models A hierarchical clustering algorithm was performed on bothvariables and samples.
    12. 12. RESULTS
    13. 13. Main clinical characteristics of thepatients included in the studyVariables IPH, n=33 Cirrhosis, n=33Healthyvolunteers, n=33Age at time of blood sample (years) 42±16**59±8 39±10Gender (male), n (%) 21 (64) 27 (82) 19 (58)Signs of portal hypertension, n (%)Varices 28 (85) 25 (76) —Variceal bleeding 13 (39) 8 (24) —Ascites 10 (30) 11 (33) —Hepatic encephalopathy 0 2 (6) —Laboratory dataHematocrit (%) 39±5.6#39±6.4 41±3.2Platelet count (× 109/l) 114±92##99±36.9 265±49.5Creatinine (mg/dl) 0.9±0.2 0.86±0.3 0.89±0.2AST (UI/l) 36±15*,##67±44 19±4.7ALT (UI/l) 41±29*,#66±49 20±10.2Albumin (g/l) 41±5.3**37±3.9 43±3.1Bilirubin (mg/dl) 1.3±1.2#1.1±0.4 0.7±0.3Prothrombin ratio (%) 78±13##79±11 93±7.9Child-Pugh class, n (%)A 27 (82) 27 (82) —B 6 (18) 6 (18) —C 0 0 —
    14. 14. Metabolites analysis: PLS-DA(HPI vs. CH)
    15. 15. Metabolites analysis: PLS-DA(HPI vs. HV)
    16. 16. DISCUSSION
    17. 17.  The PLS-DA models show a clear differentiation of IPHvs. cirrhotic patients & IPH vs. healthy controls basedon a subset of 28 & 31 metabolites respectively, with anexcellent predictive power (based on R2 and Q2 values)& AUC. The cross-validation showed an excellent performanceof both models with a good sensibility, specificity, andAUC in the training and testing sets. In this study: sub analysis of the metabolomic profile ofIPH patients was unable to cluster patients into differentIPH groups and the author suggested to study largerpopulation of patients Thus this study supports the use of metabolomicprofiling to diagnose the disease rather than identifyingthe etiology• Some of the detected metabolites may reflect some ofthe drugs that patients are taking. However, it seems
    18. 18. Study limitations Patient number: however, as IPH is a rare condition, a sample over 30patients could be considered adequate lack of an external validation set: since this is a pilot study; such external validationstudies will be more appropriate at a later step, when thespecific metabolites included in the models could beidentified with new technologies. However, the existence of metabolites discriminatingIPH from CH and HV opens the interesting possibilitythat the identification of these specific metabolitesmay disclose some keys for a better understanding ofthe pathogenesis of IPH
    19. 19. CONCLUSION The results from this study disclose a subset ofputative biomarkers of IPH patients with IPH could be identified based ontheir metabolic profile, obviating the need forinvasive investigations and facilitating the correctdiagnosis of this uncommon disease.
    20. 20. Thank you for listening감사합니다!

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