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2. Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression: Diagnostic and mechanistic relevance

Author

Clark, James, Cordell, Heather J., Darlay, Rebecca, Day, Christopher P., Hardy, Tim, Liu, Yang-Lin, Oakley, Fiona, Palmer, Jeremy, Queen, Rachel, Wonders, Kristy, Bossuyt, Patrick M., Holleboom, Adriaan G., Zafarmand, Hadi, Vali, Yasaman, Lee, Jenny, Clement, Karine, Pais, Raluca, Schuppan, Detlef, Allison, Michael, Cuenca, Sergio Rodriguez, Pellegrinelli, Vanessa, Vacca, Michele, Vidal-Puig, Antonio, Hyötyläinen, Tuulia, McGlinchey, Aidan, Orešič, Matej, Sen, Partho, Mato, Jose, Millet, Óscar, Dufour, Jean-Francois, Harrison, Stephen, Neubauer, Stefan, Pavlides, Michael, Mozes, Ferenc, Akhtar, Salma, Banerjee, Rajarshi, Kelly, Matt, Shumbayawonda, Elizabeth, Dennis, Andrea, Erpicum, Charlotte, Romero-Gomez, Manuel, Gallego-Durán, Rocío, Fernández, Isabel, Karsdal, Morten, Leeming, Diana, Fisker, Mette Juul, Erhardtsen, Elisabeth, Rasmussen, Daniel, Qvist, Per, Sinisi, Antonia, Sandt, Estelle, Tonini, Maria Manuela, Parola, Maurizio, Rosso, Chiara, Marra, Fabio, Gastaldelli, Amalia, Francque, Sven, Kechagias, Stergios, Yki-Järvinen, Hannele, Porthan, Kimmo, van Mil, Saskia, Papatheodoridis, George, Cortez-Pinto, Helena, Valenti, Luca, Petta, Salvatore, Miele, Luca, Geier, Andreas, Trautwein, Christian, Hockings, Paul, Newsome, Phil, Wenn, David, Pereira Rodrigues, Cecília Maria, Hanf, Rémy, Chaumat, Pierre, Rosenquist, Christian, Trylesinski, Aldo, Ortiz, Pablo, Duffin, Kevin, Yunis, Carla, Miller, Melissa, Tuthill, Theresa, Ertle, Judith, Younes, Ramy, Alexander, Leigh, Ostroff, Rachel, Kjær, Mette Skalshøi, Mikkelsen, Lars Friis, Brass, Clifford, Jennings, Lori, Balp, Maria-Magdalena, Martic, Miljen, Hanauer, Guido, Shankar, Sudha, Torstenson, Richard, Fournier, Céline, Ehman, Richard, Kalutkiewicz, Michael, Pepin, Kay, Myers, Joel, Shevell, Diane, Ho, Gideon, Landgren, Henrik, Myers, Rob, Doward, Lynda, Whalley, Diane, Twiss, James, Johnson, Katherine, Leary, Peter J., Govaere, Olivier, Barter, Matthew J., Charlton, Sarah H., Cockell, Simon J., Tiniakos, Dina, Zatorska, Michalina, Bedossa, Pierre, Brosnan, M. Julia, Cobbold, Jeremy F., Ekstedt, Mattias, Aithal, Guruprasad P., Clément, Karine, Schattenberg, Jörn M., Boursier, Jerome, Ratziu, Vlad, Bugianesi, Elisabetta, Anstee, Quentin M., and Daly, Ann K.

Published
2022
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3. Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

Author

Jenny, Lee, Max, Westphal, Yasaman, Vali, Jerome, Boursier, Salvatorre, Petta, Rachel, Ostroff, Leigh, Alexander, Yu, Chen, Celine, Fournier, Andreas, Geier, Sven, Francque, Kristy, Wonder, Dina, Tiniako, Pierre, Bedossa, Mike, Allison, Georgios, Papatheodoridi, Helena, Cortez-Pinto, Raluca, Pai, Jean-Francois, Dufour, Diana Julie, Leeming, Stephen, Harrison, Jeremy, Cobbold, Adriaan G, Holleboom, Hannele, Yki-Järvinen, Javier, Crespo, Mattias, Ekstedt, Guruprasad P, Aithal, Elisabetta, Bugianesi, Manuel, Romero-Gomez, Richard, Torstenson, Morten, Karsdal, Carla, Yuni, Jörn M, Schattenberg, Detlef, Schuppan, Vlad, Ratziu, Clifford, Bra, Kevin, Duffin, Koos, Zwinderman, Michael, Pavlide, Quentin M, Anstee, Patrick M, Bossuyt, Anstee, Quentin M., Daly, Ann K., Govaere, Olivier, Cockell, Simon, Tiniakos, Dina, Bedossa, Pierre, Burt, Alastair, Oakley, Fiona, Cordell, Heather J., Day, Christopher P., Wonders, Kristy, Missier, Paolo, Mcteer, Matthew, Vale, Luke, Oluboyede, Yemi, Breckons, Matt, Bossuyt, Patrick M., Zafarmand, Hadi, Vali, Yasaman, Lee, Jenny, Nieuwdorp, Max, Holleboom, Adriaan G., Verheij, Joanne, Ratziu, Vlad, Clément, Karine, Patino-Navarrete, Rafael, Pais, Raluca, Paradis, Valerie, Schuppan, Detlef, Schattenberg, Jörn M., Surabattula, Rambabu, Myneni, Sudha, Straub, Beate K., Vidal-Puig, Toni, Vacca, Michele, Rodrigues-Cuenca, Sergio, Allison, Mike, Kamzolas, Ioanni, Petsalaki, Evangelia, Campbell, Mark, Lelliott, Chris J., Davies, Susan, Orešič, Matej, Hyötyläinen, Tuulia, Mcglinchey, Aiden, Mato, Jose M., Millet, Óscar, Dufour, Jean-Françoi, Berzigotti, Annalisa, Masoodi, Mojgan, Pavlides, Michael, Harrison, Stephen, Neubauer, Stefan, Cobbold, Jeremy, Mozes, Ferenc, Akhtar, Salma, Olodo-Atitebi, Seliat, Banerjee, Rajarshi, Kelly, Matt, Shumbayawonda, Elizabeth, Dennis, Andrea, Andersson, Anneli, Wigley, Ioan, Romero-Gómez, Manuel, Gómez-González, Emilio, Ampuero, Javier, Castell, Javier, Gallego-Durán, Rocío, Fernández, Isabel, Montero-Vallejo, Rocío, Karsdal, Morten, Guldager Kring Rasmussen, Daniel, Leeming, Diana Julie, Sinisi, Antonia, Musa, Kishwar, Sandt, Estelle, Tonini, Manuela, Bugianesi, Elisabetta, Rosso, Chiara, Armandi, Angelo, Marra, Fabio, Gastaldelli, Amalia, Svegliati, Gianluca, Boursier, Jérôme, Francque, Sven, Vonghia, Luisa, Driessen, Ann, Ekstedt, Mattia, Kechagias, Stergio, Yki-Järvinen, Hannele, Porthan, Kimmo, Arola, Johanna, van Mil, Saskia, Papatheodoridis, George, Cortez-Pinto, Helena, Rodrigues, Cecilia M. P., Valenti, Luca, Pelusi, Serena, Petta, Salvatore, Pennisi, Grazia, Miele, Luca, Geier, Andrea, Trautwein, Christian, Aithal, Guruprasad P., Francis, Susan, Hockings, Paul, Schneider, Moritz, Newsome, Philip, Hübscher, Stefan, Wenn, David, Rosenquist, Christian, Trylesinski, Aldo, Mayo, Rebeca, Alonso, Cristina, Duffin, Kevin, Perfield, James W., Chen, Yu, Yunis, Carla, Tuthill, Theresa, Harrington, Magdalena Alicia, Miller, Melissa, Chen, Yan, Mcleod, Euan Jame, Ross, Trenton, Bernardo, Barbara, Schölch, Corinna, Ertle, Judith, Younes, Ramy, Oldenburger, Anouk, Ostroff, Rachel, Alexander, Leigh, Biegel, Hannah, Skalshøi Kjær, Mette, Mørch Harder, Lea, Davidsen, Peter, Mikkelsen, Lars Frii, Balp, Maria-Magdalena, Brass, Clifford, Jennings, Lori, Martic, Miljen, Löffler, Jürgen, Applegate, Dougla, Shankar, Sudha, Torstenson, Richard, Fournier-Poizat, Céline, Llorca, Anne, Kalutkiewicz, Michael, Pepin, Kay, Ehman, Richard, Horan, Gerald, Ho, Gideon, Tai, Dean, Chng, Elaine, Patterson, Scott D., Billin, Andrew, Doward, Lynda, Twiss, Jame, Thakker, Paresh, Landgren, Henrik, Lackner, Carolin, Gouw, Annette, Hytiroglou, Prodromos, Luca, Miele (ORCID:0000-0003-3464-0068), Jenny, Lee, Max, Westphal, Yasaman, Vali, Jerome, Boursier, Salvatorre, Petta, Rachel, Ostroff, Leigh, Alexander, Yu, Chen, Celine, Fournier, Andreas, Geier, Sven, Francque, Kristy, Wonder, Dina, Tiniako, Pierre, Bedossa, Mike, Allison, Georgios, Papatheodoridi, Helena, Cortez-Pinto, Raluca, Pai, Jean-Francois, Dufour, Diana Julie, Leeming, Stephen, Harrison, Jeremy, Cobbold, Adriaan G, Holleboom, Hannele, Yki-Järvinen, Javier, Crespo, Mattias, Ekstedt, Guruprasad P, Aithal, Elisabetta, Bugianesi, Manuel, Romero-Gomez, Richard, Torstenson, Morten, Karsdal, Carla, Yuni, Jörn M, Schattenberg, Detlef, Schuppan, Vlad, Ratziu, Clifford, Bra, Kevin, Duffin, Koos, Zwinderman, Michael, Pavlide, Quentin M, Anstee, Patrick M, Bossuyt, Anstee, Quentin M., Daly, Ann K., Govaere, Olivier, Cockell, Simon, Tiniakos, Dina, Bedossa, Pierre, Burt, Alastair, Oakley, Fiona, Cordell, Heather J., Day, Christopher P., Wonders, Kristy, Missier, Paolo, Mcteer, Matthew, Vale, Luke, Oluboyede, Yemi, Breckons, Matt, Bossuyt, Patrick M., Zafarmand, Hadi, Vali, Yasaman, Lee, Jenny, Nieuwdorp, Max, Holleboom, Adriaan G., Verheij, Joanne, Ratziu, Vlad, Clément, Karine, Patino-Navarrete, Rafael, Pais, Raluca, Paradis, Valerie, Schuppan, Detlef, Schattenberg, Jörn M., Surabattula, Rambabu, Myneni, Sudha, Straub, Beate K., Vidal-Puig, Toni, Vacca, Michele, Rodrigues-Cuenca, Sergio, Allison, Mike, Kamzolas, Ioanni, Petsalaki, Evangelia, Campbell, Mark, Lelliott, Chris J., Davies, Susan, Orešič, Matej, Hyötyläinen, Tuulia, Mcglinchey, Aiden, Mato, Jose M., Millet, Óscar, Dufour, Jean-Françoi, Berzigotti, Annalisa, Masoodi, Mojgan, Pavlides, Michael, Harrison, Stephen, Neubauer, Stefan, Cobbold, Jeremy, Mozes, Ferenc, Akhtar, Salma, Olodo-Atitebi, Seliat, Banerjee, Rajarshi, Kelly, Matt, Shumbayawonda, Elizabeth, Dennis, Andrea, Andersson, Anneli, Wigley, Ioan, Romero-Gómez, Manuel, Gómez-González, Emilio, Ampuero, Javier, Castell, Javier, Gallego-Durán, Rocío, Fernández, Isabel, Montero-Vallejo, Rocío, Karsdal, Morten, Guldager Kring Rasmussen, Daniel, Leeming, Diana Julie, Sinisi, Antonia, Musa, Kishwar, Sandt, Estelle, Tonini, Manuela, Bugianesi, Elisabetta, Rosso, Chiara, Armandi, Angelo, Marra, Fabio, Gastaldelli, Amalia, Svegliati, Gianluca, Boursier, Jérôme, Francque, Sven, Vonghia, Luisa, Driessen, Ann, Ekstedt, Mattia, Kechagias, Stergio, Yki-Järvinen, Hannele, Porthan, Kimmo, Arola, Johanna, van Mil, Saskia, Papatheodoridis, George, Cortez-Pinto, Helena, Rodrigues, Cecilia M. P., Valenti, Luca, Pelusi, Serena, Petta, Salvatore, Pennisi, Grazia, Miele, Luca, Geier, Andrea, Trautwein, Christian, Aithal, Guruprasad P., Francis, Susan, Hockings, Paul, Schneider, Moritz, Newsome, Philip, Hübscher, Stefan, Wenn, David, Rosenquist, Christian, Trylesinski, Aldo, Mayo, Rebeca, Alonso, Cristina, Duffin, Kevin, Perfield, James W., Chen, Yu, Yunis, Carla, Tuthill, Theresa, Harrington, Magdalena Alicia, Miller, Melissa, Chen, Yan, Mcleod, Euan Jame, Ross, Trenton, Bernardo, Barbara, Schölch, Corinna, Ertle, Judith, Younes, Ramy, Oldenburger, Anouk, Ostroff, Rachel, Alexander, Leigh, Biegel, Hannah, Skalshøi Kjær, Mette, Mørch Harder, Lea, Davidsen, Peter, Mikkelsen, Lars Frii, Balp, Maria-Magdalena, Brass, Clifford, Jennings, Lori, Martic, Miljen, Löffler, Jürgen, Applegate, Dougla, Shankar, Sudha, Torstenson, Richard, Fournier-Poizat, Céline, Llorca, Anne, Kalutkiewicz, Michael, Pepin, Kay, Ehman, Richard, Horan, Gerald, Ho, Gideon, Tai, Dean, Chng, Elaine, Patterson, Scott D., Billin, Andrew, Doward, Lynda, Twiss, Jame, Thakker, Paresh, Landgren, Henrik, Lackner, Carolin, Gouw, Annette, Hytiroglou, Prodromos, and Luca, Miele (ORCID:0000-0003-3464-0068)

Abstract

Background and aims: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis.

Published
2023

4. Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression: Diagnostic and mechanistic relevance

Author

Johnson, Katherine, Leary, Peter J., Govaere, Olivier, Barter, Matthew J., Charlton, Sarah H., Cockell, Simon J., Tiniakos, Dina, Zatorska, Michalina, Bedossa, Pierre, Brosnan, M. Julia, Cobbold, Jeremy F., Ekstedt, Mattias, Aithal, Guruprasad P., Boursier, Jerome, Ratziu, Vlad, Bugianesi, Elisabetta, Anstee, Quentin M., Daly, Ann K., Clark, James, Cockell, Simon, Cordell, Heather J., Darlay, Rebecca, Day, Christopher P., Hardy, Tim, Liu, Yang-Lin, Oakley, Fiona, Palmer, Jeremy, Queen, Rachel, Wonders, Kristy, Bossuyt, Patrick M., Holleboom, Adriaan G., Zafarmand, Hadi, Vali, Yasaman, Lee, Jenny, Clement, Karine, Pais, Raluca, Schuppan, Detlef, Allison, Michael, Cuenca, Sergio Rodriguez, Pellegrinelli, Vanessa, Vacca, Michele, Vidal-Puig, Antonio, McGlinchey, Aidan, Sen, Partho, Mato, Jose, Dufour, Jean-Francois, Harrison, Stephen, Neubauer, Stefan, Pavlides, Michael, Mozes, Ferenc, Akhtar, Salma, Banerjee, Rajarshi, Kelly, Matt, Shumbayawonda, Elizabeth, Dennis, Andrea, Erpicum, Charlotte, Romero-Gomez, Manuel, Karsdal, Morten, Leeming, Diana, Fisker, Mette Juul, Erhardtsen, Elisabeth, Rasmussen, Daniel, Qvist, Per, Sinisi, Antonia, Sandt, Estelle, Tonini, Maria Manuela, Parola, Maurizio, Rosso, Chiara, Marra, Fabio, Gastaldelli, Amalia, Francque, Sven, Kechagias, Stergios, Porthan, Kimmo, van Mil, Saskia, Papatheodoridis, George, Cortez-Pinto, Helena, Valenti, Luca, Petta, Salvatore, Miele, Luca, Geier, Andreas, Trautwein, Christian, Hockings, Paul, Newsome, Phil, Wenn, David, Chaumat, Pierre, Rosenquist, Christian, Trylesinski, Aldo, Ortiz, Pablo, Duffin, Kevin, Yunis, Carla, Miller, Melissa, Tuthill, Theresa, Ertle, Judith, Younes, Ramy, Alexander, Leigh, Ostroff, Rachel, Mikkelsen, Lars Friis, Brass, Clifford, Jennings, Lori, Balp, Maria-Magdalena, Martic, Miljen, Hanauer, Guido, Shankar, Sudha, Torstenson, Richard, Ehman, Richard, Kalutkiewicz, Michael, Pepin, Kay, Myers, Joel, Shevell, Diane, Ho, Gideon, Landgren, Henrik, Myers, Rob, Doward, Lynda, Whalley, Diane, and Twiss, James

Subjects
Hepatology, Gastroenterology, Internal Medicine, Immunology and Allergy, digestive system diseases
Abstract

Background & Aims: Serum microRNA (miRNA) levels are known to change in non-alcoholic fatty liver disease (NAFLD) and may serve as useful biomarkers. This study aimed to profile miRNAs comprehensively at all NAFLD stages. Methods: We profiled 2,083 serum miRNAs in a discovery cohort (183 cases with NAFLD representing the complete NAFLD spectrum and 10 population controls). miRNA libraries generated by HTG EdgeSeq were sequenced by Illumina NextSeq. Selected serum miRNAs were profiled in 372 additional cases with NAFLD and 15 population controls by quantitative reverse transcriptase PCR. Results: Levels of 275 miRNAs differed between cases and population controls. Fewer differences were seen within individual NAFLD stages, but miR-193a-5p consistently showed increased levels in all comparisons. Relative to NAFL/non-alcoholic steatohepatitis (NASH) with mild fibrosis (stage 0/1), 3 miRNAs (miR-193a-5p, miR-378d, and miR378d) were increased in cases with NASH and clinically significant fibrosis (stages 2–4), 7 (miR193a-5p, miR-378d, miR-378e, miR-320b, miR-320c, miR-320d, and miR-320e) increased in cases with NAFLD activity score (NAS) 5–8 compared with lower NAS, and 3 (miR-193a-5p, miR-378d, and miR-378e) increased but 1 (miR-19b-3p) decreased in steatosis, activity, and fibrosis (SAF) activity score 2–4 compared with lower SAF activity. The significant findings for miR-193a-5p were replicated in the additional cohort with NAFLD. Studies in Hep G2 cells showed that following palmitic acid treatment, miR-193a-5p expression decreased significantly. Gene targets for miR-193a-5p were investigated in liver RNAseq data for a case subgroup (n = 80); liver GPX8 levels correlated positively with serum miR-193a-5p. Conclusions: Serum miR-193a-5p levels correlate strongly with NAFLD activity grade and fibrosis stage. MiR-193a-5p may have a role in the hepatic response to oxidative stress and is a potential clinically tractable circulating biomarker for progressive NAFLD. Lay summary: MicroRNAs (miRNAs) are small pieces of nucleic acid that may turn expression of genes on or off. These molecules can be detected in the blood circulation, and their levels in blood may change in liver disease including non-alcoholic fatty liver disease (NAFLD). To see if we could detect specific miRNA associated with advanced stages of NAFLD, we carried out miRNA sequencing in a group of 183 patients with NAFLD of varying severity together with 10 population controls. We found that a number of miRNAs showed changes, mainly increases, in serum levels but that 1 particular miRNA miR-193a-5p consistently increased. We confirmed this increase in a second group of cases with NAFLD. Measuring this miRNA in a blood sample may be a useful way to determine whether a patient has advanced NAFLD without an invasive liver biopsy.

Published
2022

5. Non-invasive tests accurately stratify patients with NAFLD based on their risk of liver-related events

Author

Intercept Pharmaceuticals, Ekstedt, Mattias [0000-0002-5590-8601], Bonacci, Martin [0000-0002-4528-8066], Cure, Sandrine [0000-0002-8296-0771], Kechagias, Stergios [0000-0001-7614-739X], Boursier, Jerome, Hagström, Hannes, Ekstedt, Mattias, Moreau, Clemence, Bonacci, Martin, Cure, Sandrine, Ampuero, Javier, Nasr, Patrik, Tallab, Lilian, Canivet, Clémence M., Kechagias, Stergios, Sánchez-Torrijos, Yolanda, Dincuff, Eloise, Lucena-Valera, Ana, Roux, Marine, Riou, Jeremie, Trylesinski, Aldo, Romero-Gómez, Manuel, Intercept Pharmaceuticals, Ekstedt, Mattias [0000-0002-5590-8601], Bonacci, Martin [0000-0002-4528-8066], Cure, Sandrine [0000-0002-8296-0771], Kechagias, Stergios [0000-0001-7614-739X], Boursier, Jerome, Hagström, Hannes, Ekstedt, Mattias, Moreau, Clemence, Bonacci, Martin, Cure, Sandrine, Ampuero, Javier, Nasr, Patrik, Tallab, Lilian, Canivet, Clémence M., Kechagias, Stergios, Sánchez-Torrijos, Yolanda, Dincuff, Eloise, Lucena-Valera, Ana, Roux, Marine, Riou, Jeremie, Trylesinski, Aldo, and Romero-Gómez, Manuel

Abstract

[Background & Aims] Previous studies on the prognostic significance of non-invasive liver fibrosis tests in non-alcoholic fatty liver disease (NAFLD) lack direct comparison to liver biopsy. We aimed to evaluate the prognostic accuracy of fibrosis-4 (FIB4) and vibration-controlled transient elastography (VCTE), compared to liver biopsy, for the prediction of liver-related events (LREs) in NAFLD., [Methods] A total of 1,057 patients with NAFLD and baseline FIB4 and VCTE were included in a multicenter cohort. Of these patients, 594 also had a baseline liver biopsy. The main study outcome during follow-up was occurrence of LREs, a composite endpoint combining cirrhosis complications and/or hepatocellular carcinoma. Discriminative ability was evaluated using Harrell’s C-index., [Results] FIB4 and VCTE showed good accuracy for the prediction of LREs, with Harrell’s C-indexes >0.80 (0.817 [0.768-0.866] vs. 0.878 [0.835-0.921], respectively, p = 0.059). In the biopsy subgroup, Harrell’s C-indexes of histological fibrosis staging and VCTE were not significantly different (0.932 [0.910-0.955] vs. 0.881 [0.832-0.931], respectively, p = 0.164), while both significantly outperformed FIB4 for the prediction of LREs. FIB4 and VCTE were independent predictors of LREs in the whole study cohort. The stepwise FIB4-VCTE algorithm accurately stratified the risk of LREs: compared to patients with “FIB4 <1.30”, those with “FIB4 ≥1.30 then VCTE <8.0 kPa” had similar risk of LREs (adjusted hazard ratio [aHR] 1.3; 95% CI 0.3–6.8), whereas the risk of LREs significantly increased in patients with “FIB4 ≥1.30 then VCTE 8.0-12.0 kPa” (aHR 3.8; 95% CI 1.3–10.9), and even more for those with “FIB4 ≥1.30 then VCTE >12.0 kPa” (aHR 12.4; 95% CI 5.1–30.2)., [Conclusion] VCTE and FIB4 accurately stratify patients with NAFLD based on their risk of LREs. These non-invasive tests are alternatives to liver biopsy for the identification of patients in need of specialized management.

Published
2022

6. Non-invasive tests accurately stratify patients with NAFLD based on their risk of liver-related events

Author

Boursier, Jerome, Hagstrom, Hannes, Ekstedt, Mattias, Moreau, Clemence, Bonacci, Martin, Cure, Sandrine, Ampuero, Javier, Nasr, Patrik, Tallab, Lilian, Canivet, Clemence M., Kechagias, Stergios, Sanchez, Yolanda, Dincuff, Eloise, Lucena, Ana, Roux, Marine, Riou, Jeremie, Trylesinski, Aldo, Romero-Gomez, Manuel, Boursier, Jerome, Hagstrom, Hannes, Ekstedt, Mattias, Moreau, Clemence, Bonacci, Martin, Cure, Sandrine, Ampuero, Javier, Nasr, Patrik, Tallab, Lilian, Canivet, Clemence M., Kechagias, Stergios, Sanchez, Yolanda, Dincuff, Eloise, Lucena, Ana, Roux, Marine, Riou, Jeremie, Trylesinski, Aldo, and Romero-Gomez, Manuel

Abstract

Background & Aims: Previous studies on the prognostic significance of non-invasive liver fibrosis tests in non-alcoholic fatty liver disease (NAFLD) lack direct comparison to liver biopsy. We aimed to evaluate the prognostic accuracy of fibrosis-4 (FIB4) and vibration-controlled transient elastography (VCTE), compared to liver biopsy, for the prediction of liver-related events (LREs) in NAFLD. Methods: A total of 1,057 patients with NAFLD and baseline FIB4 and VCTE were included in a multicenter cohort. Of these patients, 594 also had a baseline liver biopsy. The main study outcome during follow-up was occurrence of LREs, a composite endpoint combining cirrhosis complications and/or hepatocellular carcinoma. Discriminative ability was evaluated using Harrells C-index. Results: FIB4 and VCTE showed good accuracy for the prediction of LREs, with Harrells C-indexes >0.80 (0.817 [0.768-0.866] vs. 0.878 [0.835-0.921], respectively, p = 0.059). In the biopsy subgroup, Harrells C-indexes of histological fibrosis staging and VCTE were not significantly different (0.932 [0.910-0.955] vs. 0.881 [0.832-0.931], respectively, p = 0.164), while both significantly outperformed FIB4 for the prediction of LREs. FIB4 and VCTE were independent predictors of LREs in the whole study cohort. The stepwise FIB4-VCTE algorithm accurately stratified the risk of LREs: compared to patients with "FIB4 <1.30", those with "FIB4 >- 1.30 then VCTE <8.0 kPa" had similar risk of LREs (adjusted hazard ratio [aHR] 1.3; 95% CI 0.3-6.8), whereas the risk of LREs significantly increased in patients with "FIB4 >1.30 then VCTE 8.0-12.0 kPa" (aHR 3.8; 95% CI 1.3-10.9), and even more for those with "FIB4 >-1.30 then VCTE >12.0 kPa" (aHR 12.4; 95% CI 5.1- 30.2). Conclusion: VCTE and FIB4 accurately stratify patients with NAFLD based on their risk of LREs. These non-invasive tests are alternatives to liver biopsy for the identification of patients in need of, Funding Agencies|Intercept Pharmaceuticals

Published
2022
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8. Efficacy of telbivudine with conditional tenofovir intensification in patients with chronic hepatitis B: results from the 2-year roadmap strategy

Author

Piratvisuth, Teerha, primary, Komolmit, Piyawat, additional, Chan, Henry, additional, Tanwandee, Tawesak, additional, Sukeepaisarnjaroen, Wattana, additional, Pessoa, Mário, additional, Fassio, Eduardo, additional, Ono, Suzane, additional, Bessone, Fernando, additional, Daruich, Jorge, additional, Zeuzem, Stefan, additional, Manns, Michael, additional, Uddin, Alkaz, additional, Dong, Yuhong, additional, and Trylesinski, Aldo, additional

Published
2016
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10. Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression: Diagnostic and mechanistic relevance

Author

Raluca Pais, Rachel Ostroff, Stephen Harrison, Lars Friis Mikkelsen, Elisabeth Erhardtsen, Sudha Shankar, Kimmo Porthan, Jérôme Boursier, Antonia Sinisi, Michael Kalutkiewicz, Sven Francque, Miljen Martic, Vanessa Pellegrinelli, Phil N. Newsome, Guido Hanauer, Hannele Yki-Järvinen, Rebecca Darlay, Joel Myers, Carla Yunis, Salvatore Petta, Mette Skalshøi Kjær, Pablo Ortiz, Ann K. Daly, James H. Clark, Dina Tiniakos, Yasaman Vali, Hadi Zafarmand, Matej Orešič, Maurizio Parola, Estelle Sandt, Lori L. Jennings, Matt Kelly, Tuulia Hyötyläinen, Detlef Schuppan, Céline Fournier, Chiara Rosso, Diane E. Shevell, Maria Manuela Tonini, Paul Hockings, Aidan McGlinchey, Salma Akhtar, Mette Juul Fisker, Morten A. Karsdal, Diane Whalley, Melissa R. Miller, Aldo Trylesinski, Mattias Ekstedt, Stefan Neubauer, Jeremy M. Palmer, Partho Sen, Michael Pavlides, Per Qvist, Isabel Fernández, Luca Miele, Fabio Marra, Stergios Kechagias, Richard Torstenson, Katherine Johnson, Jean-François Dufour, Elisabetta Bugianesi, M. Julia Brosnan, George V. Papatheodoridis, Kay M. Pepin, Daniel Guldager Kring Rasmussen, Henrik Landgren, Rachel Queen, Simon Cockell, Michael Allison, Patrick M.M. Bossuyt, Rocío Gallego-Durán, Christian Rosenquist, Leigh Alexander, Elizabeth Shumbayawonda, Michele Vacca, Antonio Vidal-Puig, David Wenn, Rémy Hanf, Oscar Millet, Michalina Zatorska, R. Myers, José M. Mato, Jenny Lee, Theresa Tuthill, James Twiss, Ramy Younes, Peter Leary, Lynda Doward, Kristy Wonders, Guruprasad P. Aithal, Sarah Charlton, Vlad Ratziu, Cecília M. P. Rodrigues, Christian Trautwein, Helena Cortez-Pinto, Gideon Ho, Matt J. Barter, Judith Ertle, Jörn M. Schattenberg, Maria-Magdalena Balp, Yang-Lin Liu, Clifford A. Brass, Olivier Govaere, Amalia Gastaldelli, Sergio Rodriguez Cuenca, Pierre Chaumat, Fiona Oakley, Luca Valenti, Simon J. Cockell, Saskia W.C. van Mil, Ferenc E. Mózes, Andreas Geier, Timothy Hardy, Pierre Bedossa, Andrea Dennis, Richard L. Ehman, Charlotte Erpicum, Karine Clément, Jeremy F. L. Cobbold, Christopher P. Day, Rajarshi Banerjee, Manuel Romero-Gómez, Quentin M. Anstee, Adriaan G. Holleboom, Heather J. Cordell, Kevin L. Duffin, Diana Julie Leeming, Epidemiology and Data Science, APH - Methodology, APH - Personalized Medicine, Vascular Medicine, ACS - Diabetes & metabolism, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, APH - Aging & Later Life, ARD - Amsterdam Reproduction and Development, Graduate School, Investigators, LITMUS Consortium, Johnson K., Leary P.J., Govaere O., Barter M.J., Charlton S.H., Cockell S.J., Tiniakos D., Zatorska M., Bedossa P., Brosnan M.J., Cobbold J.F., Ekstedt M., Aithal G.P., Clement K., Schattenberg J.M., Boursier J., Ratziu V., Bugianesi E., Anstee Q.M., Daly A.K., Clark J., Cordell H.J., Darlay R., Day C.P., Hardy T., Liu Y.-L., Oakley F., Palmer J., Queen R., Wonders K., Bossuyt P.M., Holleboom A.G., Zafarmand H., Vali Y., Lee J., Pais R., Schuppan D., Allison M., Cuenca S.R., Pellegrinelli V., Vacca M., Vidal-Puig A., Hyotylainen T., McGlinchey A., Oresic M., Sen P., Mato J., Millet O., Dufour J.-F., Harrison S., Neubauer S., Pavlides M., Mozes F., Akhtar S., Banerjee R., Kelly M., Shumbayawonda E., Dennis A., Erpicum C., Romero-Gomez M., Gallego-Duran R., Fernandez I., Karsdal M., Leeming D., Fisker M.J., Erhardtsen E., Rasmussen D., Qvist P., Sinisi A., Sandt E., Tonini M.M., Parola M., Rosso C., Marra F., Gastaldelli A., Francque S., Kechagias S., Yki-Jarvinen H., Porthan K., van Mil S., Papatheodoridis G., Cortez-Pinto H., Valenti L., Petta S., Miele L., Geier A., Trautwein C., Hockings P., Newsome P., Wenn D., Pereira Rodrigues C.M., Hanf R., Chaumat P., Rosenquist C., Trylesinski A., Ortiz P., Duffin K., Yunis C., Miller M., Tuthill T., Ertle J., Younes R., Alexander L., Ostroff R., Kjaer M.S., Mikkelsen L.F., Brass C., Jennings L., Balp M.-M., Martic M., Hanauer G., Shankar S., Torstenson R., Fournier C., Ehman R., Kalutkiewicz M., Pepin K., Myers J., Shevell D., Ho G., Landgren H., Myers R., Doward L., Whalley D., Twiss J., Miller, Melissa, Tuthill, Theresa, Ertle, Judith, Younes, Ramy, Alexander, Leigh, Ostroff, Rachel, Kjær, Mette Skalshøi, Mikkelsen, Lars Friis, Brass, Clifford, Jennings, Lori, Balp, Maria-Magdalena, Martic, Miljen, Hanauer, Guido, Shankar, Sudha, Torstenson, Richard, Fournier, Céline, Ehman, Richard, Kalutkiewicz, Michael, Pepin, Kay, Myers, Joel, Shevell, Diane, Ho, Gideon, Landgren, Henrik, Myers, Rob, Doward, Lynda, Whalley, Diane, Twiss, James, Clark, James, Cordell, Heather J., Darlay, Rebecca, Day, Christopher P., Hardy, Tim, Liu, Yang-Lin, Oakley, Fiona, Palmer, Jeremy, Queen, Rachel, Wonders, Kristy, Bossuyt, Patrick M., Holleboom, Adriaan G., Zafarmand, Hadi, Vali, Yasaman, Lee, Jenny, Clement, Karine, Pais, Raluca, Schuppan, Detlef, Allison, Michael, Cuenca, Sergio Rodriguez, Pellegrinelli, Vanessa, Vacca, Michele, Vidal-Puig, Antonio, Hyötyläinen, Tuulia, McGlinchey, Aidan, Orešič, Matej, Sen, Partho, Mato, Jose, Millet, Óscar, Dufour, Jean-Francois, Harrison, Stephen, Neubauer, Stefan, Pavlides, Michael, Mozes, Ferenc, Akhtar, Salma, Banerjee, Rajarshi, Kelly, Matt, Shumbayawonda, Elizabeth, Dennis, Andrea, Erpicum, Charlotte, Romero-Gomez, Manuel, Gallego-Durán, Rocío, Fernández, Isabel, Karsdal, Morten, Leeming, Diana, Fisker, Mette Juul, Erhardtsen, Elisabeth, Rasmussen, Daniel, Qvist, Per, Sinisi, Antonia, Sandt, Estelle, Tonini, Maria Manuela, Parola, Maurizio, Rosso, Chiara, Marra, Fabio, Gastaldelli, Amalia, Francque, Sven, Kechagias, Stergios, Yki-Järvinen, Hannele, Porthan, Kimmo, van Mil, Saskia, Papatheodoridis, George, Cortez-Pinto, Helena, Valenti, Luca, Petta, Salvatore, Miele, Luca, Geier, Andreas, Trautwein, Christian, Hockings, Paul, Newsome, Phil, Wenn, David, Pereira Rodrigues, Cecília Maria, Hanf, Rémy, Chaumat, Pierre, Rosenquist, Christian, Trylesinski, Aldo, Ortiz, Pablo, Duffin, Kevin, and Yunis, Carla

Subjects
SCORING SYSTEM, CPM, counts per million, AUROC, area under the receiver operating characteristic, RC799-869, AST, aspartate aminotransferase, MicroRNA, Non-alcoholic fatty liver disease, Biomarker, Sequencing, TGF-β, transforming growth factor-beta, Gastroenterology, STEATOHEPATITIS, Liver disease, 0302 clinical medicine, Fibrosis, miRNA, microRNA, logFC, log2 fold change, FIBROSIS, Immunology and Allergy, 0303 health sciences, education.field_of_study, NAS, NAFLD activity score, medicine.diagnostic_test, Fatty liver, GTEx, Genotype-Tissue Expression, Diseases of the digestive system. Gastroenterology, 3. Good health, Real-time polymerase chain reaction, Biomarker, MicroRNA, Non-alcoholic fatty liver disease, Sequencing, Liver biopsy, ACID, Biomarker (medicine), 030211 gastroenterology & hepatology, Life Sciences & Biomedicine, Research Article, EXPRESSION, medicine.medical_specialty, NAFLD, non-alcoholic fatty liver disease, NASH, non-alcoholic steatohepatitis, Population, Gastroenterology and Hepatology, SAF, steatosis–activity–fibrosis, VALIDATION, ER, endoplasmic reticulum, 03 medical and health sciences, cDNA, complementary DNA, Internal medicine, ALT, alanine aminotransferase, Gastroenterologi, Internal Medicine, medicine, NAFL, non-alcoholic fatty liver, ALGORITHM, FIB-4, fibrosis-4, education, 030304 developmental biology, PCA, principal component analysis, Science & Technology, Gastroenterology & Hepatology, Hepatology, business.industry, FC, fold change, medicine.disease, digestive system diseases, FLIP, fatty liver inhibition of progression, Ct, cycle threshold, Steatosis, qPCR, quantitative PCR, business
Abstract

Background & Aims Serum microRNA (miRNA) levels are known to change in non-alcoholic fatty liver disease (NAFLD) and may serve as useful biomarkers. This study aimed to profile miRNAs comprehensively at all NAFLD stages. Methods We profiled 2,083 serum miRNAs in a discovery cohort (183 cases with NAFLD representing the complete NAFLD spectrum and 10 population controls). miRNA libraries generated by HTG EdgeSeq were sequenced by Illumina NextSeq. Selected serum miRNAs were profiled in 372 additional cases with NAFLD and 15 population controls by quantitative reverse transcriptase PCR. Results Levels of 275 miRNAs differed between cases and population controls. Fewer differences were seen within individual NAFLD stages, but miR-193a-5p consistently showed increased levels in all comparisons. Relative to NAFL/non-alcoholic steatohepatitis (NASH) with mild fibrosis (stage 0/1), 3 miRNAs (miR-193a-5p, miR-378d, and miR378d) were increased in cases with NASH and clinically significant fibrosis (stages 2–4), 7 (miR193a-5p, miR-378d, miR-378e, miR-320b, miR-320c, miR-320d, and miR-320e) increased in cases with NAFLD activity score (NAS) 5–8 compared with lower NAS, and 3 (miR-193a-5p, miR-378d, and miR-378e) increased but 1 (miR-19b-3p) decreased in steatosis, activity, and fibrosis (SAF) activity score 2–4 compared with lower SAF activity. The significant findings for miR-193a-5p were replicated in the additional cohort with NAFLD. Studies in Hep G2 cells showed that following palmitic acid treatment, miR-193a-5p expression decreased significantly. Gene targets for miR-193a-5p were investigated in liver RNAseq data for a case subgroup (n = 80); liver GPX8 levels correlated positively with serum miR-193a-5p. Conclusions Serum miR-193a-5p levels correlate strongly with NAFLD activity grade and fibrosis stage. MiR-193a-5p may have a role in the hepatic response to oxidative stress and is a potential clinically tractable circulating biomarker for progressive NAFLD. Lay summary MicroRNAs (miRNAs) are small pieces of nucleic acid that may turn expression of genes on or off. These molecules can be detected in the blood circulation, and their levels in blood may change in liver disease including non-alcoholic fatty liver disease (NAFLD). To see if we could detect specific miRNA associated with advanced stages of NAFLD, we carried out miRNA sequencing in a group of 183 patients with NAFLD of varying severity together with 10 population controls. We found that a number of miRNAs showed changes, mainly increases, in serum levels but that 1 particular miRNA miR-193a-5p consistently increased. We confirmed this increase in a second group of cases with NAFLD. Measuring this miRNA in a blood sample may be a useful way to determine whether a patient has advanced NAFLD without an invasive liver biopsy., Graphical abstract, Highlights • Serum miRNA was sequenced in 183 NAFLD cases of varying severity and 10 population controls. • Plasma levels of miR-193a-5p were significantly increased in patients with advanced fibrosis, high NAS scores, or high SAF scores. • Other miRNAs including miR378d and miR378e were also significantly increased in certain comparisons. • The findings for miR-193a-5p were replicated in a cohort of 372 additional NAFLD cases.

Published
2022

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