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5. Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

Author

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, Ioannis, Petsalaki, Evangelia, Campbell, Mark, Lelliott, Chris J, Davies, Susan, Orešič, Matej, Hyötyläinen, Tuulia, McGlinchey, Aiden, Mato, Jose M, Millet, Óscar, Dufour, Jean-François, 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, Rasmussen, Daniel Guldager Kring, 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, Mattias, Kechagias, Stergios, 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, Andreas, Trautwein, Christian, Reißing, Johanna, Aithal, Guruprasad P, Francis, Susan, Palaniyappan, Naaventhan, Bradley, Christopher, 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 James, Ross, Trenton, Bernardo, Barbara, Schölch, Corinna, Ertle, Judith, Younes, Ramy, Oldenburger, Anouk, Coxson, Harvey, Ostroff, Rachel, Alexander, Leigh, Biegel, Hannah, Kjær, Mette Skalshøi, Harder, Lea Mørch, Davidsen, Peter, Ellegaard, Jens, Balp, Maria-Magdalena, Brass, Clifford, Jennings, Lori, Martic, Miljen, Löffler, Jürgen, Applegate, Douglas, Shankar, Sudha, Torstenson, Richard, Lindén, Daniel, 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, James, Thakker, Paresh, Derdak, Zoltan, Landgren, Henrik, Lackner, Carolin, Gouw, Annette, Hytiroglou, Prodromos, Mózes, Ferenc E, Lee, Jenny A, Alzoubi, Osama, Staufer, Katharina, Trauner, Michael, Paternostro, Rafael, Stauber, Rudolf E, van Dijk, Anne-Marieke, Mak, Anne Linde, de Saint Loup, Marc, Shima, Toshihide, Gaia, Silvia, Shalimar, Lupșor-Platon, Monica, Wong, Vincent Wai-Sun, Li, Guanlin, Wong, Grace Lai-Hung, Karlas, Thomas, Wiegand, Johannes, Sebastiani, Giada, Tsochatzis, Emmanuel, Liguori, Antonio, Yoneda, Masato, Nakajima, Atsushi, Hagström, Hannes, Akbari, Camilla, Hirooka, Masashi, Chan, Wah-Kheong, Mahadeva, Sanjiv, Rajaram, Ruveena, Zheng, Ming-Hua, George, Jacob, Eslam, Mohammed, Viganò, Mauro, Ridolfo, Sofia, Aithal, Guruprasad Padur, Lee, Dae Ho, Nasr, Patrik, Cassinotto, Christophe, de Lédinghen, Victor, Mendoza, Yuly P, Noureddin, Mazen, Truong, Emily, and Harrison, Stephen A

Published
2023
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16. 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

24. Disturbed lipid and amino acid metabolisms in COVID-19 patients

Author

CDL Staf Research, Circulatory Health, Masoodi, Mojgan, Peschka, Manuela, Schmiedel, Stefan, Haddad, Munif, Frye, Maike, Maas, Coen, Lohse, Ansgar, Huber, Samuel, Kirchhof, Paulus, Nofer, Jerzy-Roch, Renné, Thomas, CDL Staf Research, Circulatory Health, Masoodi, Mojgan, Peschka, Manuela, Schmiedel, Stefan, Haddad, Munif, Frye, Maike, Maas, Coen, Lohse, Ansgar, Huber, Samuel, Kirchhof, Paulus, Nofer, Jerzy-Roch, and Renné, Thomas

Published
2022

27. FRI180. Clinical and genetic factors associated with regression of fibrosis in ACLD after etiological therapy

Author

Mendoza, Yuly, Masoodi, Mojgan, Tsouka, Sofia, Bosch, Jaime, and Berzigotti, Annalisa

Subjects
610 Medicine & health
Abstract

Background and aims: Liver fibrosis is the main determinant of clinical outcomes in advanced chronic liver disease (ACLD) of any etiology. The introduction of effective etiological therapies has shown that liver fibrosis and even cirrhosis is reversible after eliminating the cause of ACLD. However, not all patients experience fibrosis regression, evidencing that other factors modulate this process. In order to better understand the genetic and molecular mechanisms controlling fibrosis regression we analysed the genetic and tran- scriptomic pathways in ACLD with or without regression of fibrosis using high-throughput technologies. Method: We conducted a case-control pilot study of 60 patients with ACLD of different etiology, 30 with histological and/or clinical evidence of regression (Regressors, Re) and 30 without (Non- regressors, NRe), after a minimum of 24 months of etiological therapy. In all patients we performed genotyping (TaqMan assay) of gene variants associated with liver fibrosis (PNPLA3, TM6SF2, SERPINA1, GCKR, TMC4, HSD17B13). Transcriptomic analysis (RNAseq) was done in 30 frozen paired liver biopsies (before and after therapy) of 15 patients. Differentially Expressed Genes (DEGs) analysis were determined using DESeq2 and selected for FDR

Published
2022
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28. Exploring metabolic space of advanced chronic liver disease regression

Author

Mendoza, Yuly, Tsouka, Sofia, Bosch, Jaime, Berzigotti, Annalisa, and Masoodi, Mojgan

Subjects
Hepatology, 610 Medicine & health
Abstract

Background and aims: Liver fibrosis is the main determinant of clinical outcomes in advanced chronic liver disease (ACLD) of any etiology. Regression of bridging fibrosis and cirrhosis can take place after effective etiological treatment, but not in all cases, suggesting that factors other than the etiology play a role in the modulation of this important outcome. In patients achieving regression of ACLD, liver function markedly improves, suggesting that several metabolic pathways are influenced by regression of fibrosis. However, the metabolic pathways associated with fibrosis regression in ACLD have not been characterized. We hypothesize that thorough assessment of liver metabolism could provide evidence-based data for better characterization of cirrhosis patients with regression following effective etiological treatment, thus providing a rational basis for personalized interventions. Method: We performed a case-control pilot study of 60 patients with ACLD of different etiologies, 30 with histological and/or clinical evidence of regression of ACLD (Regressors) and 30 without any improvement (Non-regressors) after a minimum of 24 months of successful etiological therapy. We used the combination of metabolic modelling and metabolic profiling to define metabolic pathways and associated signature that differentiate Regressors from Non-regres- sors. We have developed a transcriptomics-driven metabolic model- ling approach to assess the regression of ACLD. We further investigated the associated metabolic signature using mass spec- trometry-based metabolomics. Results: Although Regressors and Non-regressors showed similar profiles at baseline, our observation is pointing to fatty acid β- oxidation and arachidonic acid metabolism (associated to inflamma- tion and oxidative stress) as key metabolic pathways differentiating Regressors from Non-regressors after therapy, suggesting that lipid metabolism plays a central role in the modulation of the extracellular matrix in ACLD. In addition, Using Least Absolute Shrinkage and Selection Operator (LASSO) analyses, the Regressor phenotype was predicted with 80% accuracy based on the levels of identified lipid markers after etiologic therapy.

Published
2022
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30. Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

Author

Mózes, Ferenc E, Lee, Jenny A, Vali, Yasaman, Alzoubi, Osama, Staufer, Katharina, Trauner, Michael, Paternostro, Rafael, Stauber, Rudolf E, Holleboom, Adriaan G, van Dijk, Anne-Marieke, Mak, Anne Linde, Boursier, Jérôme, de Saint Loup, Marc, Shima, Toshihide, Bugianesi, Elisabetta, Gaia, Silvia, Armandi, Angelo, Shalimar, Lupșor-Platon, Monica, Wong, Vincent Wai-Sun, Li, Guanlin, Wong, Grace Lai-Hung, Cobbold, Jeremy, Karlas, Thomas, Wiegand, Johannes, Sebastiani, Giada, Tsochatzis, Emmanuel, Liguori, Antonio, Yoneda, Masato, Nakajima, Atsushi, Hagström, Hannes, Akbari, Camilla, Hirooka, Masashi, Chan, Wah-Kheong, Mahadeva, Sanjiv, Rajaram, Ruveena, Zheng, Ming-Hua, George, Jacob, Eslam, Mohammed, Petta, Salvatore, Pennisi, Grazia, Viganò, Mauro, Ridolfo, Sofia, Aithal, Guruprasad Padur, Palaniyappan, Naaventhan, Lee, Dae Ho, Ekstedt, Mattias, Nasr, Patrik, Cassinotto, Christophe, de Lédinghen, Victor, Berzigotti, Annalisa, Mendoza, Yuly P, Noureddin, Mazen, Truong, Emily, Fournier-Poizat, Céline, Geier, Andreas, Martic, Miljen, Tuthill, Theresa, Anstee, Quentin M, Harrison, Stephen A, Bossuyt, Patrick M, Pavlides, Michael, 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, Ioannis, Petsalaki, Evangelia, Campbell, Mark, Lelliott, Chris J, Davies, Susan, Orešič, Matej, Hyötyläinen, Tuulia, McGlinchey, Aiden, Mato, Jose M, Millet, Óscar, Dufour, Jean-François, 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, Rasmussen, Daniel Guldager Kring, 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, Mattias, Kechagias, Stergios, 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, Andreas, Trautwein, Christian, Reißing, Johanna, Aithal, Guruprasad P, Francis, Susan, Palaniyappan, Naaventhan, Bradley, Christopher, 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 James, Ross, Trenton, Bernardo, Barbara, Schölch, Corinna, Ertle, Judith, Younes, Ramy, Oldenburger, Anouk, Coxson, Harvey, Ostroff, Rachel, Alexander, Leigh, Biegel, Hannah, Kjær, Mette Skalshøi, Harder, Lea Mørch, Davidsen, Peter, Ellegaard, Jens, Balp, Maria-Magdalena, Brass, Clifford, Jennings, Lori, Martic, Miljen, Löffler, Jürgen, Applegate, Douglas, Shankar, Sudha, Torstenson, Richard, Lindén, Daniel, 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, James, Thakker, Paresh, Derdak, Zoltan, Landgren, Henrik, Lackner, Carolin, Gouw, Annette, and Hytiroglou, Prodromos

Abstract

Histologically assessed liver fibrosis stage has prognostic significance in patients with non-alcoholic fatty liver disease (NAFLD) and is accepted as a surrogate endpoint in clinical trials for non-cirrhotic NAFLD. Our aim was to compare the prognostic performance of non-invasive tests with liver histology in patients with NAFLD.

Published
2023
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31. Disruption of the circadian clock component BMAL1 elicits an endocrine adaption impacting on insulin sensitivity and liver disease

Author

Jouffe, Céline, primary, Weger, Benjamin D., additional, Martin, Eva, additional, Atger, Florian, additional, Weger, Meltem, additional, Gobet, Cédric, additional, Ramnath, Divya, additional, Charpagne, Aline, additional, Morin-Rivron, Delphine, additional, Powell, Elizabeth E., additional, Sweet, Matthew J., additional, Masoodi, Mojgan, additional, Uhlenhaut, N. Henriette, additional, and Gachon, Frédéric, additional

Published
2022
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36. Additional file 3 of An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer’s disease

Author

Clark, Christopher, Dayon, Loïc, Masoodi, Mojgan, Bowman, Gene L., and Popp, Julius

Abstract

Additional file 3: Additional Tables, including coarse-grain categories used for pathway enrichment (Table S1), relationship between selected CSF molecules and latent factors (Table S2), correlations between selected proteins and lipids with CDR-SoB and MMSE scores (Tables S3) and CSF proteins presenting an association with cognitive impairment (Table S4).

Published
2021
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37. Distinct lipid profiles predict improved glycemic control in obese, nondiabetic patients after a low-caloric diet intervention: the Diet, Obesity and Genes randomized trial

Author

Valsesia, Armand, Saris, Wim HM, Astrup, Arne, Hager, Jörg, Masoodi, Mojgan, Valsesia, Armand, Saris, Wim HM, Astrup, Arne, Hager, Jörg, and Masoodi, Mojgan

Abstract

Background: An aim of weight loss is to reduce the risk of type 2 diabetes (T2D) in obese subjects. However, the relation with long-term glycemic improvement remains unknown. Objective: We evaluated the changes in lipid composition during weight loss and their association with long-term glycemic improvement. Design: We investigated the plasma lipidome of 383 obese, nondiabetic patients within a randomized, controlled dietary intervention in 8 European countries at baseline, after an 8-wk low-caloric diet (LCD) (800-1000 kcal/d), and after 6 mo of weight maintenance. Results: After weight loss, a lipid signature identified 2 groups of patients who were comparable at baseline but who differed in their capacities to lose weight and improve glycemic control. Six months after the LCD, one group had significant glycemic improvement [homeostasis model assessment of insulin resistance (HOMA-IR) mean change: −0.92; 95% CI: −1.17, −0.67)]. The other group showed no improvement in glycemic control (HOMA-IR mean change: −0.26; 95% CI: −0.64, 0.13). These differences were sustained for ≥1 y after the LCD. The same conclusions were obtained with other endpoints (Matsuda index and fasting insulin and glucose concentrations). Significant differences between the 2 groups were shown in leptin gene expression in adipose tissue biopsies. Significant differences were also observed in weight-related endpoints (body mass index, weight, and fat mass). The lipid signature allowed prediction of which subjects would be considered to be insulin resistant after 6 mo of weight maintenance [validation's receiver operating characteristic (ROC) area under the curve (AUC): 71%; 95% CI: 62%, 81%]. This model outperformed a clinical data-only model (validation's ROC AUC: 61%; 95% CI: 50%, 71%; P = 0.01). Conclusions: In this study, we report a lipid signature of LCD success (for weight and glycemic outcome) in obese, nondiabetic patients. Lipid changes during an 8-wk LCD allowed us to predict insulin

Published
2021

39. Obesity in mice with adipocyte-specific deletion of clock component Arntl

Author

Paschos, Georgios K., Ibrahim, Salam, Song, Wen-Liang, Kunieda, Takeshige, Grant, Gregory, Reyes, Teresa M., Bradfield, Christopher A., Vaughan, Cheryl H., Eiden, Michael, Masoodi, Mojgan, Griffin, Julian L., Wang, Fenfen, Lawson, John A., and FitzGerald, Garret A.

Subjects
Bioenergetics -- Physiological aspects -- Genetic aspects -- Research, Energy metabolism -- Physiological aspects -- Genetic aspects -- Research, Neuropeptides -- Physiological aspects -- Genetic aspects -- Research, Fat cells -- Physiological aspects -- Genetic aspects -- Research, Biological sciences, Health
Abstract

Adipocytes store excess energy in the form of triglycerides and signal the levels of stored energy to the brain. Here we show that adipocyte-specific deletion of Arntl (also known as Bmall), a gene encoding a core molecular clock component, results in obesity in mice with a shift in the diurnal rhythm of food intake, a result that is not seen when the gene is disrupted in hepatocytes or pancreatic islets. Changes in the expression of hypothalamic neuropeptides that regulate appetite are consistent with feedback from the adipocyte to the central nervous system to time feeding behavior. Ablation of the adipocyte clock is associated with a reduced number of polyunsaturated fatty acids in adipocyte triglycerides. This difference between mutant and wild-type mice is reflected in the circulating concentrations of polyunsaturated fatty acids and nonesterified polyunsaturated fatty acids in hypothalamic neurons that regulate food intake. Thus, this study reveals a role for the adipocyte clock in the temporal organization of energy regulation, highlights timing as a modulator of the adipocyte-hypothalamic axis and shows the impact of timing of food intake on body weight., Several components of energy homeostasis, including locomotor activity, feeding, thermogenesis, glucose and lipid metabolism, are subject to circadian regulation that synchronizes energy intake, storage and utilization with changes in the [...]

Published
2012
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40. Comprehensive lipidomics analysis of bioactive lipids in complex regulatory networks

Author

Masoodi, Mojgan, Eiden, Michael, Koulman, Albert, Spaner, David, and Volmer, Dietrich A.

Subjects
Lipids -- Chemical properties, Biological products -- Chemical properties, Biological products -- Composition, Extraction (Chemistry) -- Methods, Chemistry
Abstract

In the present work we describe the development of an analytical technique for simultaneous profiling of over 100 biochemically related lipid mediators in biological samples. A multistep procedure was implemented to extract eicosanoids and other bioactive lipids from the biological matrix, chromatographically separate them using fast reversed-phase liquid chromatography, tentatively identify new candidate eicosanoids through a matching process of retention times, isotope distribution patterns, and high-resolution orbitrap MS/MS fragmentation patterns, and subsequently quantify tentative candidates by means of analytical reference standards. Key new aspects of this profiling technique included the classification of bioactive lipids into 12 groups according to their calculated exact masses and the development of optimized liquid chromatographic conditions for these groups to achieve sufficient separation of the numerous isobaric and isomeric species, many of which exhibited virtually identical collision-induced dissociation behavior. Importantly, no analytical standards were required at this screening stage of the assay, and tentative identifications were achieved by matching results to selected reference species from each of the groups. The analytical figures of merit for the orbitrap assay such as linear dynamic range, limit of detection, limit of quantitation, and precision demonstrated that the performance of the assay was very similar to that of a quadrupole linear ion trap assay, which was used for validation purposes. The method allowed us to examine eicosanoid profiles within the signaling cascade in chronic lymphocytic leukemia (CLL) cells under basal conditions and following arachidonic acid stimulation. The preliminary screening based on high-resolution tandem mass spectrometry data along with isotope pattern and retention time matching revealed the presence of 15 bioactive lipids, belonging to a range of prostaglandin, leukotriene, and hydroxy and epoxy fatty acid lipid mediators produced by CLL cells. 10.1021/ac1015563

Published
2010

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