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1. Evaluation of the performance of Dutch Lipid Clinic Network score in an Italian FH population: The LIPIGEN study

Author

Arca, Marcello, Averna, Maurizio, Bertolini, Stefano, Calandra, Sebastiano, Catapano, Alberico Luigi, Tarugi, Patrizia, Pellegatta, Fabio, Angelico, Francesco, Bartuli, Andrea, Biasucci, Giacomo, Biolo, Gianni, Bonanni, Luca, Bonomo, Katia, Borghi, Claudio, Bossi, Antonio Carlo, Branchi, Adriana, Carubbi, Francesca, Cipollone, Francesco, Citroni, Nadia, Federici, Massimo, Ferri, Claudio, Fiorenza, Anna Maria, Giaccari, Andrea, Giorgino, Francesco, Guardamagna, Ornella, Iannuzzi, Arcangelo, Iughetti, Lorenzo, Lupattelli, Graziana, Lupi, Alessandro, Mandraffino, Giuseppe, Marcucci, Rossella, Maroni, Lorenzo, Miccoli, Roberto, Mombelli, Giuliana, Muntoni, Sandro, Pecchioli, Valerio, Pederiva, Cristina, Pipolo, Antonio, Pisciotta, Livia, Pujia, Arturo, Purrello, Francesco, Repetti, Elena, Rubba, Paolo, Sabbà, Carlo, Sampietro, Tiziana, Sarzani, Riccardo, Tagliabue, Milena Paola, Trenti, Chiara, Vigna, Giovanni Battista, Werba, Josè Pablo, Zambon, Sabina, Zenti, Maria Grazia, Minicocci, Ilenia, Noto, Davide, Fortunato, Giuliana, Banderali, Giuseppe, Benso, Andrea, Bigolin, Paola, Bonora, Enzo, Bruzzi, Patrizia, Bucci, Marco, Buonuomo, Paola Sabrina, Capra, Maria Elena, Cardolini, Iris, Cefalù, Baldassarre, Cervelli, Nazzareno, Chiariello, Giuseppe, Cocci, Guido, Colombo, Emanuela, Cremonini, Anna Laura, D'Addato, Sergio, D'Erasmo, Laura, Dal Pino, Beatrice, De Sanctis, Luisa, De Vita, Emanuele, Del Ben, Maria, Di Costanzo, Alessia, Di Taranto, Maria Donata, Fasano, Tommaso, Gentile, Luigi, Gentile, Marco, Ghirardello, Omar, Grigore, Liliana, Lussu, Milena, Meregalli, Giancarla, Moffa, Simona, Montalcini, Tiziana, Morgia, Valeria, Nascimbeni, Fabio, Pasta, Andrea, Pavanello, Chiara, Saitta, Antonino, Scicali, Roberto, Siepi, Donatella, Spagnolli, Walter, Spina, Rossella, Sticchi, Elena, Suppressa, Patrizia, Vigo, Lorenzo, Vinci, Pierandrea, Casula, Manuela, Manzato, Enzo, Olmastroni, Elena, Tragni, Elena, Zampoleri, Veronica, and Pirillo, Angela

Published
2018
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3. Familial hypercholesterolemia: The Italian Atherosclerosis Society Network (LIPIGEN)

Author

Averna, Maurizio, Cefalã¹, Angelo B., Casula, Manuela, Noto, Davide, Arca, Marcello, Bertolini, Stefano, Calandra, Sebastiano, Catapano, Alberico L., Tarugi, Patrizia, Catapano, Alberico Luigi, Pellegatta, Fabio, Angelico, Francesco, Bartuli, Andrea, Biasucci, Giacomo, Biolo, Gianni, Bonanni, Luca, Bonomo, Katia, Borghi, Claudio, Bossi, Antonio Carlo, Branchi, Adriana, Carubbi, Francesca, Cipollone, Francesco, Citroni, Nadia, Federici, Massimo, Ferri, Claudio, Fiorenza, Anna Maria, Giaccari, Andrea, Giorgino, Francesco, Guardamagna, Ornella, Iannuzzi, Arcangelo, Iughetti, Lorenzo, Lupattelli, Graziana, Mandraffino, Giuseppe, Marcucci, Rossella, Mombelli, Giuliana, Muntoni, Sandro, Pecchioli, Valerio, Pederiva, Cristina, Pipolo, Antonio, Pisciotta, Livia, Pujia, Arturo, Purrello, Francesco, Repetti, Elena, Rubba, Paolo, SabbÃ&nbsp, Carlo, Sampietro, Tiziana, Sarzani, Riccardo, Tagliabue, Milena Paola, Trenti, Chiara, Vigna, Giovanni Battista, Werba, Josà Pablo, Zambon, Sabina, Zenti, Maria Grazia, Montali, Anna, Fortunato, Giuliana, Grigore, Liliana, Del Ben, Maria, Maranghi, Marianna, Barbagallo, Carlo M., Buonuomo, Paola Sabrina, Capra, Maria Elena, Vinci, Pierandrea, D'Addato, Sergio, Galbiati, Stella, Nascimbeni, Fabio, Bucci, Marco, Spagnoli, Walter, Cardolini, Iris, Cervelli, Nazzareno, Emanuela, Colombo, Vinsin, A. Sun, Laviola, Luigi, Bello, Francesca, Chiariello, Giuseppe, Predieri, Barbara, Siepi, Donatella, Saitta, Antonino, Giusti, Betti, Pavanello, Chiara, Lussu, Milena, Prati, Lucia, Banderali, Giuseppe, Balleari, Giulia, Montalcini, Tiziana, Scicali, Roberto, Gentile, Luigi, Gentile, Marco, Suppressa, Patrizia, Sbrana, Francesco, Cocci, Guido, Benso, Andrea, Negri, Emanuele Alberto, Ghirardello, Omar, Lorenzo, Vigo, Zambon, Alberto, Enzo, Bonora, Minicocci, Ilenia, Spina, Rossella, Orlando, Camilla, Di Taranto, Maria Donata, Chiodo, Lorenzo, Garlaschelli, Katia, Manzato, Enzo, Tragni, Elena, Averna, M., Cefalu', A., Casula, M., Noto, D., Arca, M., Bertolini, S., Calandra, S., Catapano, A., Tarugi, P., Pellegatta, F., Angelico, F., Bartuli, A., Biasucci, G., Biolo, G., Bonanni, L., Bonomo, K., Borghi, C., Bossi, A., Branchi, A., Carubbi, F., Cipollone, F., Citroni, N., Federici, M., Ferri, C., Fiorenza, A., Giaccari, A., Giorgino, F., Guardamagna, O., Iannuzzi, A., Iughetti, L., Lupattelli, G., Mandraffino, G., Marcucci, R., Mombelli, G., Muntoni, S., Pecchioli, V., Pederiva, C., Pipolo, A., Pisciotta, L., Pujia, A., Purrello, F., Repetti, E., Rubba, P., Sabbã , C., Sampietro, T., Sarzani, R., Tagliabue, M., Trenti, C., Vigna, G., Werba, J., Zambon, S., Zenti, M., Montali, A., Fortunato, G., Grigore, L., Del Ben, M., Maranghi, M., Cefalã¹, A., Barbagallo, C., Buonuomo, P., Capra, M., Vinci, P., D'Addato, S., Galbiati, S., Nascimbeni, F., Bucci, M., Spagnoli, W., Cardolini, I., Cervelli, N., Emanuela, C., Vinsin, A., Laviola, L., Bello, F., Chiariello, G., Predieri, B., Siepi, D., Saitta, A., Giusti, B., Pavanello, C., Lussu, M., Prati, L., Banderali, G., Balleari, G., Montalcini, T., Scicali, R., Gentile, L., Gentile, M., Suppressa, P., Sbrana, F., Cocci, G., Benso, A., Negri, E., Ghirardello, O., Lorenzo, V., Zambon, A., Enzo, B., Minicocci, I., Spina, R., Orlando, C., Di Taranto, M., Chiodo, L., Garlaschelli, K., Manzato, E., Tragni, E., Averna, Maurizio, Cefalã¹, Angelo B., Casula, Manuela, Noto, Davide, Arca, Marcello, Bertolini, Stefano, Calandra, Sebastiano, Catapano, Alberico L., Tarugi, Patrizia, Catapano, Alberico Luigi, Pellegatta, Fabio, Angelico, Francesco, Bartuli, Andrea, Biasucci, Giacomo, Biolo, Gianni, Bonanni, Luca, Bonomo, Katia, Borghi, Claudio, Bossi, Antonio Carlo, Branchi, Adriana, Carubbi, Francesca, Cipollone, Francesco, Citroni, Nadia, Federici, Massimo, Ferri, Claudio, Fiorenza, Anna Maria, Giaccari, Andrea, Giorgino, Francesco, Guardamagna, Ornella, Iannuzzi, Arcangelo, Iughetti, Lorenzo, Lupattelli, Graziana, Mandraffino, Giuseppe, Marcucci, Rossella, Mombelli, Giuliana, Muntoni, Sandro, Pecchioli, Valerio, Pederiva, Cristina, Pipolo, Antonio, Pisciotta, Livia, Pujia, Arturo, Purrello, Francesco, Repetti, Elena, Rubba, Paolo, Sabbã , Carlo, Sampietro, Tiziana, Sarzani, Riccardo, Tagliabue, Milena Paola, Trenti, Chiara, Vigna, Giovanni Battista, Werba, Josã Pablo, Zambon, Sabina, Zenti, Maria Grazia, Montali, Anna, Fortunato, Giuliana, Grigore, Liliana, DEL BELLO, Francesca, Maranghi, Marianna, Barbagallo, Carlo M., Buonuomo, Paola Sabrina, Capra, Maria Elena, Vinci, Pierandrea, D'Addato, Sergio, Galbiati, Stella, Nascimbeni, Fabio, Bucci, Marco, Spagnoli, Walter, Cardolini, Iri, Cervelli, Nazzareno, Emanuela, Colombo, Vinsin, A. Sun, Laviola, Luigi, Bello, Francesca, Chiariello, Giuseppe, Predieri, Barbara, Siepi, Donatella, Saitta, Antonino, Giusti, Betti, Pavanello, Chiara, Lussu, Milena, Prati, Lucia, Banderali, Giuseppe, Balleari, Giulia, Montalcini, Tiziana, Scicali, Roberto, Gentile, Luigi, Gentile, Marco, Suppressa, Patrizia, Sbrana, Francesco, Cocci, Guido, Benso, Andrea, Negri, Emanuele Alberto, Ghirardello, Omar, Lorenzo, Vigo, Zambon, Alberto, Enzo, Bonora, Minicocci, Ilenia, Spina, Rossella, Orlando, Camilla, Di Taranto, Maria Donata, Chiodo, Lorenzo, Garlaschelli, Katia, Manzato, Enzo, Tragni, Elena, Cefalù, Angelo B., Sabbà, Carlo, Werba, Josè Pablo, Del Ben, Maria, and Colombo, Emanuela

Subjects
0301 basic medicine, Candidate gene, Genetic testing, Settore MED/09 - Medicina Interna, Databases, Factual, DNA Mutational Analysis, Disease, Familial hypercholesterolemia, 030204 cardiovascular system & hematology, 0302 clinical medicine, Dyslipidemias, National network, Internal Medicine, Cardiology and Cardiovascular Medicine, Risk Factors, Prospective Studies, Program Development, Prospective cohort study, medicine.diagnostic_test, General Medicine, Prognosis, Cholesterol, Phenotype, Italy, Genetic Markers, medicine.medical_specialty, MEDLINE, Hyperlipoproteinemia Type II, 03 medical and health sciences, Databases, Internal medicine, medicine, Humans, Genetic Predisposition to Disease, Factual, Retrospective Studies, business.industry, Settore MED/13 - ENDOCRINOLOGIA, Retrospective cohort study, medicine.disease, Atherosclerosis, Mutation, 030104 developmental biology, Endocrinology, Dyslipidemia, Genetic marker, business
Abstract

Background and aims: Primary dyslipidemias are a heterogeneous group of disorders characterized by abnormal levels of circulating lipoproteins. Among them, familial hypercholesterolemia is the most common lipid disorder that predisposes for premature cardiovascular disease. We set up an Italian nationwide network aimed at facilitating the clinical and genetic diagnosis of genetic dyslipidemias named LIPIGEN (LIpid TransPort Disorders Italian GEnetic Network). Methods: Observational, multicenter, retrospective and prospective study involving about 40 Italian clinical centers. Genetic testing of the appropriate candidate genes at one of six molecular diagnostic laboratories serving as nationwide DNA diagnostic centers. Results and conclusions: From 2012 to October 2016, available biochemical and clinical information of 3480 subjects with familial hypercholesterolemia identified according to the Dutch Lipid Clinic Network (DLCN) score were included in the database and genetic analysis was performed in 97.8% of subjects, with a mutation detection rate of 92.0% in patients with DLCN score >= 6. The establishment of the LIPIGEN network will have important effects on clinical management and it will improve the overall identification and treatment of primary dyslipidemias in Italy. (C) 2017 The Authors. Published by Elsevier Ireland Ltd.

Published
2017

4. Spectrum of mutations in Italian patients with familial hypercholesterolemia: New results from the LIPIGEN study

Author

Pirillo, Angela, Garlaschelli, Katia, Arca, Marcello, Averna, Maurizio, Bertolini, Stefano, Calandra, Sebastiano, Tarugi, Patrizia, Catapano, Alberico L., Catapano, Alberico Luigi, Pellegatta, Fabio, Angelico, Francesco, Bartuli, Andrea, Biasucci, Giacomo, Biolo, Gianni, Bonanni, Luca, Bonomo, Katia, Borghi, Claudio, Bossi, Antonio Carlo, Branchi, Adriana, Carubbi, Francesca, Cipollone, Francesco, Citroni, Nadia, Federici, Massimo, Ferri, Claudio, Fiorenza, Anna Maria, Giaccari, Andrea, Giorgino, Francesco, Guardamagna, Ornella, Iannuzzi, Arcangelo, Iughetti, Lorenzo, Lupattelli, Graziana, Mandraffino, Giuseppe, Marcucci, Rossella, Mombelli, Giuliana, Muntoni, Sandro, Pecchioli, Valerio, Pederiva, Cristina, Pipolo, Antonio, Pisciotta, Livia, Pujia, Arturo, Purrello, Francesco, Repetti, Elena, Rubba, Paolo, SabbÃ&nbsp, Carlo, Sampietro, Tiziana, Sarzani, Riccardo, Tagliabue, Milena Paola, Trenti, Chiara, Vigna, Giovanni Battista, Werba, Josà Pablo, Zambon, Sabina, Zenti, Maria Grazia, Montali, Anna, Noto, Davide, Fortunato, Giuliana, Grigore, Liliana, Del Ben, Maria, Maranghi, Marianna, Cefalã¹, A. Baldassarre, Buonuomo, Paola Sabrina, Capra, Maria Elena, Vinci, Pierandrea, D'Addato, Sergio, Galbiati, Stella, Nascimbeni, Fabio, Bucci, Marco, Spagnoli, Walter, Cardolini, Iris, Cervelli, Nazzareno, Emanuela, Colombo, Sun, Vinsin A., Laviola, Luigi, Bello, Francesca, Chiariello, Giuseppe, Predieri, Barbara, Siepi, Donatella, Saitta, Antonino, Giusti, Betti, Pavanello, Chiara, Lussu, Milena, Prati, Lucia, Banderali, Giuseppe, Balleari, Giulia, Montalcini, Tiziana, Scicali, Roberto, Gentile, Luigi, Gentile, Marco, Suppressa, Patrizia, Sbrana, Francesco, Cocci, Guido, Benso, Andrea, Negri, Emanuele Alberto, Ghirardello, Omar, Lorenzo, Vigo, Zambon, Alberto, Enzo, Bonora, Minicocci, Ilenia, Spina, Rossella, Orlando, Camilla, Di Taranto, Maria Donata, Casula, Manuela, Chiodo, Lorenzo, Manzato, Enzo, Tragni, Elena, Pirillo, Angela, Garlaschelli, Katia, Arca, Marcello, Averna, Maurizio, Bertolini, Stefano, Calandra, Sebastiano, Tarugi, Patrizia, Catapano, Alberico L., Catapano, Alberico Luigi, Pellegatta, Fabio, Angelico, Francesco, Bartuli, Andrea, Biasucci, Giacomo, Biolo, Gianni, Bonanni, Luca, Bonomo, Katia, Borghi, Claudio, Bossi, Antonio Carlo, Branchi, Adriana, Carubbi, Francesca, Cipollone, Francesco, Citroni, Nadia, Federici, Massimo, Ferri, Claudio, Fiorenza, Anna Maria, Giaccari, Andrea, Giorgino, Francesco, Guardamagna, Ornella, Iannuzzi, Arcangelo, Iughetti, Lorenzo, Lupattelli, Graziana, Mandraffino, Giuseppe, Marcucci, Rossella, Mombelli, Giuliana, Muntoni, Sandro, Pecchioli, Valerio, Pederiva, Cristina, Pipolo, Antonio, Pisciotta, Livia, Pujia, Arturo, Purrello, Francesco, Repetti, Elena, Rubba, Paolo, Sabbã , Carlo, Sampietro, Tiziana, Sarzani, Riccardo, Tagliabue, Milena Paola, Trenti, Chiara, Vigna, Giovanni Battista, Werba, Josã Pablo, Zambon, Sabina, Zenti, Maria Grazia, Montali, Anna, Noto, Davide, Fortunato, Giuliana, Grigore, Liliana, DEL BELLO, Francesca, Maranghi, Marianna, Cefalã¹, A. Baldassarre, Buonuomo, Paola Sabrina, Capra, Maria Elena, Vinci, Pierandrea, D'Addato, Sergio, Galbiati, Stella, Nascimbeni, Fabio, Bucci, Marco, Spagnoli, Walter, Cardolini, Iri, Cervelli, Nazzareno, Emanuela, Colombo, Sun, Vinsin A., Laviola, Luigi, Bello, Francesca, Chiariello, Giuseppe, Predieri, Barbara, Siepi, Donatella, Saitta, Antonino, Giusti, Betti, Pavanello, Chiara, Lussu, Milena, Prati, Lucia, Banderali, Giuseppe, Balleari, Giulia, Montalcini, Tiziana, Scicali, Roberto, Gentile, Luigi, Gentile, Marco, Suppressa, Patrizia, Sbrana, Francesco, Cocci, Guido, Benso, Andrea, Negri, Emanuele Alberto, Ghirardello, Omar, Lorenzo, Vigo, Zambon, Alberto, Enzo, Bonora, Minicocci, Ilenia, Spina, Rossella, Orlando, Camilla, Di Taranto, Maria Donata, Casula, Manuela, Chiodo, Lorenzo, Manzato, Enzo, Tragni, Elena, Sabbà, Carlo, Werba, Josè Pablo, Del Ben, Maria, Cefalù, A. Baldassarre, DI BELLO, Francesca, Pirillo, A., Garlaschelli, K., Arca, M., Averna, M., Bertolini, S., Calandra, S., Tarugi, P., Catapano, A., Pellegatta, F., Angelico, F., Bartuli, A., Biasucci, G., Biolo, G., Bonanni, L., Bonomo, K., Borghi, C., Bossi, A., Branchi, A., Carubbi, F., Cipollone, F., Citroni, N., Federici, M., Ferri, C., Fiorenza, A., Giaccari, A., Giorgino, F., Guardamagna, O., Iannuzzi, A., Iughetti, L., Lupattelli, G., Mandraffino, G., Marcucci, R., Mombelli, G., Muntoni, S., Pecchioli, V., Pederiva, C., Pipolo, A., Pisciotta, L., Pujia, A., Purrello, F., Repetti, E., Rubba, P., Sabbã , C., Sampietro, T., Sarzani, R., Tagliabue, M., Trenti, C., Vigna, G., Werba, J., Zambon, S., Zenti, M., Montali, A., Noto, D., Fortunato, G., Grigore, L., Del Ben, M., Maranghi, M., Cefalu', A., Buonuomo, P., Capra, M., Vinci, P., D'Addato, S., Galbiati, S., Nascimbeni, F., Bucci, M., Spagnoli, W., Cardolini, I., Cervelli, N., Emanuela, C., Sun, V., Laviola, L., Bello, F., Chiariello, G., Predieri, B., Siepi, D., Saitta, A., Giusti, B., Pavanello, C., Lussu, M., Prati, L., Banderali, G., Balleari, G., Montalcini, T., Scicali, R., Gentile, L., Gentile, M., Suppressa, P., Sbrana, F., Cocci, G., Benso, A., Negri, E., Ghirardello, O., Lorenzo, V., Zambon, A., Enzo, B., Minicocci, I., Spina, R., Orlando, C., Di Taranto, M., Casula, M., Chiodo, L., Manzato, E., Tragni, E., and Colombo, Emanuela

Subjects
0301 basic medicine, Apolipoprotein E, Candidate gene, Settore MED/09 - Medicina Interna, Databases, Factual, Apolipoprotein B, DNA Mutational Analysis, Familial hypercholesterolemia, 030204 cardiovascular system & hematology, Compound heterozygosity, PCSK9, 0302 clinical medicine, Risk Factors, Receptors, Genetics, Homozygote, Autosomal dominant trait, Pathogenic variants, General Medicine, Prognosis, APOB, LDLR, Cholesterol, Phenotype, Italy, Autosomal Recessive Hypercholesterolemia, Apolipoprotein B-100, lipids (amino acids, peptides, and proteins), Proprotein Convertase 9, Cardiology and Cardiovascular Medicine, Preliminary Data, Genetic Markers, Familial hypercholesterolemiaLDLRPCSK9APOBPathogenic variants, Heterozygote, Biology, Pathogenic variant, LDL, Hyperlipoproteinemia Type II, 03 medical and health sciences, Databases, medicine, Internal Medicine, Humans, Genetic Predisposition to Disease, Factual, Settore MED/13 - ENDOCRINOLOGIA, medicine.disease, Atherosclerosis, 030104 developmental biology, Receptors, LDL, Mutation, biology.protein
Abstract

Background Familial hypercholesterolemia (FH) is an autosomal dominant disease characterized by elevated plasma levels of LDL-cholesterol that confers an increased risk of premature atherosclerotic cardiovascular disease. Early identification and treatment of FH patients can improve prognosis and reduce the burden of cardiovascular mortality. Aim of this study was to perform the mutational analysis of FH patients identified through a collaboration of 20 Lipid Clinics in Italy (LIPIGEN Study). Methods We recruited 1592 individuals with a clinical diagnosis of definite or probable FH according to the Dutch Lipid Clinic Network criteria. We performed a parallel sequencing of the major candidate genes for monogenic hypercholesterolemia (LDLR, APOB, PCSK9, APOE, LDLRAP1, STAP1). Results A total of 213 variants were detected in 1076 subjects. About 90% of them had a pathogenic or likely pathogenic variants. More than 94% of patients carried pathogenic variants in LDLR gene, 27 of which were novel. Pathogenic variants in APOB and PCSK9 were exceedingly rare. We found 4 true homozygotes and 5 putative compound heterozygotes for pathogenic variants in LDLR gene, as well as 5 double heterozygotes for LDLR/APOB pathogenic variants. Two patients were homozygous for pathogenic variants in LDLRAP1 gene resulting in autosomal recessive hypercholesterolemia. One patient was found to be heterozygous for the ApoE variant p.(Leu167del), known to confer an FH phenotype. Conclusions This study shows the molecular characteristics of the FH patients identified in Italy over the last two years. Full phenotypic characterization of these patients and cascade screening of family members is now in progress.

Published
2017

5. Evaluation of the performance of Dutch Lipid Clinic Network score in an Italian FH population: The LIPIGEN study

Author

Casula, Manuela, primary, Olmastroni, Elena, additional, Pirillo, Angela, additional, Catapano, Alberico Luigi, additional, Arca, Marcello, additional, Averna, Maurizio, additional, Bertolini, Stefano, additional, Calandra, Sebastiano, additional, Tarugi, Patrizia, additional, Pellegatta, Fabio, additional, Angelico, Francesco, additional, Bartuli, Andrea, additional, Biasucci, Giacomo, additional, Biolo, Gianni, additional, Bonanni, Luca, additional, Bonomo, Katia, additional, Borghi, Claudio, additional, Bossi, Antonio Carlo, additional, Branchi, Adriana, additional, Carubbi, Francesca, additional, Cipollone, Francesco, additional, Citroni, Nadia, additional, Federici, Massimo, additional, Ferri, Claudio, additional, Fiorenza, Anna Maria, additional, Giaccari, Andrea, additional, Giorgino, Francesco, additional, Guardamagna, Ornella, additional, Iannuzzi, Arcangelo, additional, Iughetti, Lorenzo, additional, Lupattelli, Graziana, additional, Lupi, Alessandro, additional, Mandraffino, Giuseppe, additional, Marcucci, Rossella, additional, Maroni, Lorenzo, additional, Miccoli, Roberto, additional, Mombelli, Giuliana, additional, Muntoni, Sandro, additional, Pecchioli, Valerio, additional, Pederiva, Cristina, additional, Pipolo, Antonio, additional, Pisciotta, Livia, additional, Pujia, Arturo, additional, Purrello, Francesco, additional, Repetti, Elena, additional, Rubba, Paolo, additional, Sabbà, Carlo, additional, Sampietro, Tiziana, additional, Sarzani, Riccardo, additional, Tagliabue, Milena Paola, additional, Trenti, Chiara, additional, Vigna, Giovanni Battista, additional, Werba, Josè Pablo, additional, Zambon, Sabina, additional, Zenti, Maria Grazia, additional, Minicocci, Ilenia, additional, Noto, Davide, additional, Fortunato, Giuliana, additional, Banderali, Giuseppe, additional, Benso, Andrea, additional, Bigolin, Paola, additional, Bonora, Enzo, additional, Bruzzi, Patrizia, additional, Bucci, Marco, additional, Buonuomo, Paola Sabrina, additional, Capra, Maria Elena, additional, Cardolini, Iris, additional, Cefalù, Baldassarre, additional, Cervelli, Nazzareno, additional, Chiariello, Giuseppe, additional, Cocci, Guido, additional, Colombo, Emanuela, additional, Cremonini, Anna Laura, additional, D'Addato, Sergio, additional, D'Erasmo, Laura, additional, Dal Pino, Beatrice, additional, De Sanctis, Luisa, additional, De Vita, Emanuele, additional, Del Ben, Maria, additional, Di Costanzo, Alessia, additional, Di Taranto, Maria Donata, additional, Fasano, Tommaso, additional, Gentile, Luigi, additional, Gentile, Marco, additional, Ghirardello, Omar, additional, Grigore, Liliana, additional, Lussu, Milena, additional, Meregalli, Giancarla, additional, Moffa, Simona, additional, Montalcini, Tiziana, additional, Morgia, Valeria, additional, Nascimbeni, Fabio, additional, Pasta, Andrea, additional, Pavanello, Chiara, additional, Saitta, Antonino, additional, Scicali, Roberto, additional, Siepi, Donatella, additional, Spagnolli, Walter, additional, Spina, Rossella, additional, Sticchi, Elena, additional, Suppressa, Patrizia, additional, Vigo, Lorenzo, additional, Vinci, Pierandrea, additional, Casula, Manuela, additional, Manzato, Enzo, additional, Tragni, Elena, additional, and Zampoleri, Veronica, additional

Published
2018
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7. Metabolomic profile of systemic sclerosis patients

Author

Murgia, Federica, primary, Svegliati, Silvia, additional, Poddighe, Simone, additional, Lussu, Milena, additional, Manzin, Aldo, additional, Spadoni, Tatiana, additional, Fischetti, Colomba, additional, Gabrielli, Armando, additional, and Atzori, Luigi, additional

Published
2018
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9. Familial hypercholesterolemia: The Italian Atherosclerosis Society Network (LIPIGEN)

Author

Averna, Maurizio, primary, Cefalù, Angelo B., additional, Casula, Manuela, additional, Noto, Davide, additional, Arca, Marcello, additional, Bertolini, Stefano, additional, Calandra, Sebastiano, additional, Catapano, Alberico L., additional, Tarugi, Patrizia, additional, Averna, Maurizio, additional, Catapano, Alberico Luigi, additional, Pellegatta, Fabio, additional, Angelico, Francesco, additional, Bartuli, Andrea, additional, Biasucci, Giacomo, additional, Biolo, Gianni, additional, Bonanni, Luca, additional, Bonomo, Katia, additional, Borghi, Claudio, additional, Bossi, Antonio Carlo, additional, Branchi, Adriana, additional, Carubbi, Francesca, additional, Cipollone, Francesco, additional, Citroni, Nadia, additional, Federici, Massimo, additional, Ferri, Claudio, additional, Fiorenza, Anna Maria, additional, Giaccari, Andrea, additional, Giorgino, Francesco, additional, Guardamagna, Ornella, additional, Iannuzzi, Arcangelo, additional, Iughetti, Lorenzo, additional, Lupattelli, Graziana, additional, Mandraffino, Giuseppe, additional, Marcucci, Rossella, additional, Mombelli, Giuliana, additional, Muntoni, Sandro, additional, Pecchioli, Valerio, additional, Pederiva, Cristina, additional, Pipolo, Antonio, additional, Pisciotta, Livia, additional, Pujia, Arturo, additional, Purrello, Francesco, additional, Repetti, Elena, additional, Rubba, Paolo, additional, Sabbà, Carlo, additional, Sampietro, Tiziana, additional, Sarzani, Riccardo, additional, Tagliabue, Milena Paola, additional, Trenti, Chiara, additional, Vigna, Giovanni Battista, additional, Werba, Josè Pablo, additional, Zambon, Sabina, additional, Zenti, Maria Grazia, additional, Montali, Anna, additional, Fortunato, Giuliana, additional, Grigore, Liliana, additional, Del Ben, Maria, additional, Maranghi, Marianna, additional, Barbagallo, Carlo M., additional, Buonuomo, Paola Sabrina, additional, Capra, Maria Elena, additional, Vinci, Pierandrea, additional, D'Addato, Sergio, additional, Galbiati, Stella, additional, Nascimbeni, Fabio, additional, Bucci, Marco, additional, Spagnoli, Walter, additional, Cardolini, Iris, additional, Cervelli, Nazzareno, additional, Emanuela, Colombo, additional, Vinsin, A. Sun, additional, Laviola, Luigi, additional, Bello, Francesca, additional, Chiariello, Giuseppe, additional, Predieri, Barbara, additional, Siepi, Donatella, additional, Saitta, Antonino, additional, Giusti, Betti, additional, Pavanello, Chiara, additional, Lussu, Milena, additional, Prati, Lucia, additional, Banderali, Giuseppe, additional, Balleari, Giulia, additional, Montalcini, Tiziana, additional, Scicali, Roberto, additional, Gentile, Luigi, additional, Gentile, Marco, additional, Suppressa, Patrizia, additional, Sbrana, Francesco, additional, Cocci, Guido, additional, Benso, Andrea, additional, Negri, Emanuele Alberto, additional, Ghirardello, Omar, additional, Lorenzo, Vigo, additional, Zambon, Alberto, additional, Enzo, Bonora, additional, Minicocci, Ilenia, additional, Spina, Rossella, additional, Orlando, Camilla, additional, Di Taranto, Maria Donata, additional, Chiodo, Lorenzo, additional, Garlaschelli, Katia, additional, Manzato, Enzo, additional, and Tragni, Elena, additional

Published
2017
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10. Spectrum of mutations in Italian patients with familial hypercholesterolemia: New results from the LIPIGEN study

Author

Pirillo, Angela, primary, Garlaschelli, Katia, additional, Arca, Marcello, additional, Averna, Maurizio, additional, Bertolini, Stefano, additional, Calandra, Sebastiano, additional, Tarugi, Patrizia, additional, Catapano, Alberico L., additional, Catapano, Alberico Luigi, additional, Pellegatta, Fabio, additional, Angelico, Francesco, additional, Bartuli, Andrea, additional, Biasucci, Giacomo, additional, Biolo, Gianni, additional, Bonanni, Luca, additional, Bonomo, Katia, additional, Borghi, Claudio, additional, Bossi, Antonio Carlo, additional, Branchi, Adriana, additional, Carubbi, Francesca, additional, Cipollone, Francesco, additional, Citroni, Nadia, additional, Federici, Massimo, additional, Ferri, Claudio, additional, Fiorenza, Anna Maria, additional, Giaccari, Andrea, additional, Giorgino, Francesco, additional, Guardamagna, Ornella, additional, Iannuzzi, Arcangelo, additional, Iughetti, Lorenzo, additional, Lupattelli, Graziana, additional, Mandraffino, Giuseppe, additional, Marcucci, Rossella, additional, Mombelli, Giuliana, additional, Muntoni, Sandro, additional, Pecchioli, Valerio, additional, Pederiva, Cristina, additional, Pipolo, Antonio, additional, Pisciotta, Livia, additional, Pujia, Arturo, additional, Purrello, Francesco, additional, Repetti, Elena, additional, Rubba, Paolo, additional, Sabbà, Carlo, additional, Sampietro, Tiziana, additional, Sarzani, Riccardo, additional, Tagliabue, Milena Paola, additional, Trenti, Chiara, additional, Vigna, Giovanni Battista, additional, Werba, Josè Pablo, additional, Zambon, Sabina, additional, Zenti, Maria Grazia, additional, Montali, Anna, additional, Noto, Davide, additional, Fortunato, Giuliana, additional, Grigore, Liliana, additional, Del Ben, Maria, additional, Maranghi, Marianna, additional, Cefalù, A. Baldassarre, additional, Buonuomo, Paola Sabrina, additional, Capra, Maria Elena, additional, Vinci, Pierandrea, additional, D'Addato, Sergio, additional, Galbiati, Stella, additional, Nascimbeni, Fabio, additional, Bucci, Marco, additional, Spagnoli, Walter, additional, Cardolini, Iris, additional, Cervelli, Nazzareno, additional, Emanuela, Colombo, additional, Sun, Vinsin A., additional, Laviola, Luigi, additional, Bello, Francesca, additional, Chiariello, Giuseppe, additional, Predieri, Barbara, additional, Siepi, Donatella, additional, Saitta, Antonino, additional, Giusti, Betti, additional, Pavanello, Chiara, additional, Lussu, Milena, additional, Prati, Lucia, additional, Banderali, Giuseppe, additional, Balleari, Giulia, additional, Montalcini, Tiziana, additional, Scicali, Roberto, additional, Gentile, Luigi, additional, Gentile, Marco, additional, Suppressa, Patrizia, additional, Sbrana, Francesco, additional, Cocci, Guido, additional, Benso, Andrea, additional, Negri, Emanuele Alberto, additional, Ghirardello, Omar, additional, Lorenzo, Vigo, additional, Zambon, Alberto, additional, Enzo, Bonora, additional, Minicocci, Ilenia, additional, Spina, Rossella, additional, Orlando, Camilla, additional, Di Taranto, Maria Donata, additional, Casula, Manuela, additional, Chiodo, Lorenzo, additional, Manzato, Enzo, additional, and Tragni, Elena, additional

Published
2017
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14. Is the body composition development in premature infants associated with a distinctive nuclear magnetic resonance metabolomic profiling of urine?

Author

Morniroli, Daniela, Dessì, Angelica, Giannì, Maria Lorella, Roggero, Paola, Noto, Antonio, Atzori, Luigi, Lussu, Milena, Fanos, Vassilios, and Mosca, Fabio

Subjects
DEVELOPMENT of premature infants, BODY composition, NUCLEAR magnetic resonance, PREMATURE infants, METABOLOMICS, URINE
Abstract

Objective: Preterm infants' body composition at term-corrected age differs from that of term infants but appears to be similar at the age of 3 months. The aim of this study was to compare the metabolomic pattern of preterm infants at term and at 3 months with that of term infants and to determine its association with body composition development. Method: We designed a pilot study. Growth and body composition were evaluated by an air displacement plethysmography system in 13 preterm infants and seven term newborns at term and at 3 months of corrected age. Urine samples were collected at the same time points and analysed by nuclear magnetic resonance. Results: At term-corrected age, preterm infants showed a higher fat mass percentage compared with that of term newborns, whereas at 3 months of corrected age, the body composition parameters were similar between the groups. At the first time point, nuclear magnetic resonance analysis showed a urinary increase in choline/phosphocholine, betaine and glucose in preterm infants. At the second time point, the preterm group exhibited a urinary increase in choline/phosphocholine and a decrease in betaine. Conclusions: The increased urinary excretion of choline, a betaine precursor, could reflect a potential altered metabolism in preterm infants. [ABSTRACT FROM AUTHOR]

Published
2019
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15. Impact of Early Postnatal Nutrition on the NMR Urinary Metabolic Profile of Infant

Author

Cesare Marincola, Flaminia, primary, Corbu, Sara, additional, Lussu, Milena, additional, Noto, Antonio, additional, Dessì, Angelica, additional, Longo, Stefania, additional, Civardi, Elisa, additional, Garofoli, Francesca, additional, Grenci, Beatrice, additional, Mongini, Elisa, additional, Budelli, Andrea, additional, Grinzato, Alessia, additional, Fasano, Francesca, additional, Fanos, Vassilios, additional, and Stronati, Mauro, additional

Published
2016
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17. Discovery of biochemical biomarkers for aggression: A role for metabolomics in psychiatry

Author

Hagenbeek, Fiona A., primary, Kluft, Cornelis, additional, Hankemeier, Thomas, additional, Bartels, Meike, additional, Draisma, Harmen H. M., additional, Middeldorp, Christel M., additional, Berger, Ruud, additional, Noto, Antonio, additional, Lussu, Milena, additional, Pool, René, additional, Fanos, Vassilios, additional, and Boomsma, Dorret I., additional

Published
2016
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19. 1H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI).

Author

Lussu, Milena, Camboni, Tania, Piras, Cristina, Serra, Corrado, Del Carratore, Francesco, Griffin, Julian, Atzori, Luigi, and Manzin, Aldo

Subjects
*NUCLEAR magnetic resonance spectroscopy, *METABOLOMICS, *ESCHERICHIA coli, *URINARY tract infections, *URINARY tract infection treatment, *PATIENTS
Abstract

Background: Urinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis. Methods: Urine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The 1H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve. Results: Orthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R2Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity. Conclusions: Acetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations. [ABSTRACT FROM AUTHOR]

Published
2017
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20. The urinary 1H-NMR metabolomics profile of an italian autistic children population and their unaffected siblings.

Author

Lussu, Milena, Noto, Antonio, Masili, Alice, Rinaldi, Andrea C., Dessì, Angelica, De Angelis, Maria, De Giacomo, Andrea, Fanos, Vassilios, Atzori, Luigi, and Francavilla, Ruggiero

Abstract

Autism spectrum disorders (ASD) make a dishomogeneous group of psychiatric diseases having either genetic and environmental components, including changes of the microbiota. The rate of diagnosis, based on a series of psychological tests and observed behavior, dramatically increased in the past few decades. Currently, no biological markers are available and the pathogenesis is not defined. The purpose of this study was to evaluate the potential use of 1H-NMR metabolomics to analyze the global biochemical signature of ASD patients ( n = 21) and controls ( n = 21), these being siblings of autistic patients. A multivariate model has been used to extrapolate the variables of importance. The discriminating urinary metabolites were identified; in particular, significantly increased levels of hippurate, glycine, creatine, tryptophan, and d-threitol and decreased concentrations of glutamate, creatinine, lactate, valine, betaine, and taurine were observed in ASD patients. Based on the identified discriminant metabolites, the attention was focused on two possible mechanisms that could be involved in ASD: oxidative stress conditions and gut microflora modifications. In conclusion, nuclear magnetic resonance-based metabolomics analysis of the urine seems to have the potential for the identification of a metabolic fingerprint of ASD phenotypes and appears to be suitable for further investigation of the disease mechanisms. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1058-1066. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Published
2017
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21. Urinary metabolomics of pregnant women at term: a combined GC/MS and NMR approach

Author

Caboni, Pierluigi, primary, Meloni, Alessandra, additional, Lussu, Milena, additional, Carta, Emanuela, additional, Barberini, Luigi, additional, Noto, Antonio, additional, Deiana, Sara Francesca, additional, Mereu, Rossella, additional, Ragusa, Antonio, additional, Paoletti, Anna Maria, additional, Melis, Gian Benedetto, additional, Fanos, Vassilios, additional, and Atzori, Luigi, additional

Published
2014
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22. Urinary metabolomics of bronchopulmonary dysplasia (BPD): preliminary data at birth suggest it is a congenital disease

Author

Fanos, Vassilios, primary, Cristina Pintus, Maria, additional, Lussu, Milena, additional, Atzori, Luigi, additional, Noto, Antonio, additional, Stronati, Mauro, additional, Guimaraes, Hercilia, additional, Marcialis, Maria Antonietta, additional, Rocha, Gustavo, additional, Moretti, Corrado, additional, Papoff, Paola, additional, Lacerenza, Serafina, additional, Puddu, Silvia, additional, Giuffrè, Mario, additional, Serraino, Francesca, additional, Mussap, Michele, additional, and Corsello, Giovanni, additional

Published
2014
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23. Urinary 1H-NMR and GC-MS metabolomics predicts early and late onset neonatal sepsis

Author

Fanos, Vassilios, primary, Caboni, Pierluigi, additional, Corsello, Giovanni, additional, Stronati, Mauro, additional, Gazzolo, Diego, additional, Noto, Antonio, additional, Lussu, Milena, additional, Dessì, Angelica, additional, Giuffrè, Mario, additional, Lacerenza, Serafina, additional, Serraino, Francesca, additional, Garofoli, Francesca, additional, Serpero, Laura Domenica, additional, Liori, Barbara, additional, Carboni, Roberta, additional, and Atzori, Luigi, additional

Published
2014
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29. Metabolomics in newborns with intrauterine growth retardation (IUGR): urine reveals markers of metabolic syndrome

Author

Dessì, Angelica, primary, Atzori, Luigi, additional, Noto, Antonio, additional, Adriaan Visser, Gerard Hille, additional, Gazzolo, Diego, additional, Zanardo, Vincenzo, additional, Barberini, Luigi, additional, Puddu, Melania, additional, Ottonello, Giovanni, additional, Atzei, Alessandra, additional, Magistris, Anna De, additional, Lussu, Milena, additional, Murgia, Federica, additional, and Fanos, Vassilios, additional

Published
2011
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30. A metabolomic approach in an experimental model of hypoxia-reoxygenation in newborn piglets: urine predicts outcome

Author

Atzori, Luigi, primary, Xanthos, Theodoros, additional, Barberini, Luigi, additional, Antonucci, Roberto, additional, Murgia, Federica, additional, Lussu, Milena, additional, Aroni, Filippia, additional, Varsami, Marianna, additional, Papalois, Apostolos, additional, Lai, Adolfo, additional, D'Aloja, Ernesto, additional, Iacovidou, Nicoletta, additional, and Fanos, Vassilios, additional

Published
2010
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31. 1H NMR metabolite fingerprinting as a new tool for body fluid identification in forensic science.

Author

Scano, Paola, Locci, Emanuela, Noto, Antonio, Navarra, Gabriele, Murgia, Federica, Lussu, Milena, Barberini, Luigi, Atzori, Luigi, De Giorgio, Fabio, Rosa, Maria Francesca, and d'Aloja, Ernesto

Subjects
BODY fluids, FORENSIC sciences, NUCLEAR magnetic resonance spectroscopy, METABOLITES, URINE
Abstract

In this feasibility study, we propose, for the first time, 1H NMR spectroscopy coupled with mathematical strategies as a valid tool for body fluid (BF) trace identification in forensic science. In order to assess the ability of this approach to identify traces composed either by a single or by two different BFs, samples of blood, urine, saliva, and semen were collected from different donors, and binary mixtures were prepared. 1H NMR analyses were carried out for all samples. Spectral data of the whole set were firstly submitted to unsupervised principal component analysis (PCA); it showed that samples of the same BF cluster well on the basis of their characterizing molecular components and that mixtures exhibit intermediate characteristics among BF typologies. Furthermore, samples were divided into a training set and a test set. An average NMR spectral profile for each typology of BF was obtained from the training set and validated as representative of each BF class. Finally, a fitting procedure, based on a system of linear equations with the four obtained average spectral profiles, was applied to the test set and the mixture samples; it showed that BFs can be unambiguously identified, even as components of a mixture. The successful use of this mathematical procedure has the advantage, in forensics, of overcoming bias due to the analyst's personal judgment. We therefore propose this combined approach as a valid, fast, and non-destructive tool for addressing the challenges in the identification of composite traces in forensics. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Published
2013
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32. Evaluation of the performance of Dutch Lipid Clinic Network score in an Italian FH population: The LIPIGEN study

Author

Casula, M. a., B, Be, Olmastroni, E. a., Pirillo, A. c., Catapano, D, A. L. b., D, Jemail, Author, Lipigen, Group, Averna, M. g., Bertolini, S. h., Calandra, S. i., Tarugi, P. i., Pellegatta, F. k., Angelico, F. f., Bartuli, A. l., Biasucci, G. m., Biolo, G. n., Bonanni, L. o., Bonomo, K. p., Borghi, C. q., Bossi, A. C. r., Branchi, A. s., Carubbi, F. t., Cipollone, F. u., Citroni, N. v., Federici, M. w., Ferri, C. x., Fiorenza, A. M. y., Giaccari, A. z., Giorgino, Aa, F., Guardamagna, Ab, O., Iannuzzi, Ac, A., Iughetti, Ad, L., Lupattelli, Ae, G., Lupi, Af, A., Mandraffino, Giuseppe, Ag, G., Marcucci, Ah, R., Maroni, Ai, L., Miccoli, Aj, R., Mombelli, Ak, G., Muntoni, Al, S., Pecchioli, Am, V., Pederiva, An, C., Pipolo, Ao, A., Pisciotta, Ap, L., Pujia, Aq, A., Purrello, Ar, F., Repetti, As, E., Rubba, At, P., Sabbà, Au, C., Sampietro, Av, T., Sarzani, Aw, R., Tagliabue, M. P., Ax, Trenti, Ay, C., Vigna, G. B., Az, Werba, J. P., Ba, Zambon, Bb, S., Zenti, M. G., Bc, Minicocci, I. f., Noto, D. g., Fortunato, Bd, G., Banderali, An, G., Benso, Ax, A., Bigolin, Bb, P., Bonora, Bc, E., Bruzzi, Ad, P., Bucci, M. u., Buonuomo, P. S. l., Capra, M. E. m., Cardolini, I. w., Cefalù, B. g., Cervelli, N. x., Chiariello, Ac, G., Cocci, Aw, G., Colombo, E. y., Cremonini, A. L., Ap, D'Addato, S. q., D'Erasmo, L. f., Dal, Pino, Av, B., Sanctis, De, Ab, L., Vita, De, Ao, E., Del, Ben, M. f., Costanzo, Di, A. f., Taranto, Di, M. D., Bd, Fasano, Ay, T., Gentile, As, L., At, M., Ghirardello, Az, O., Grigore, L. k., Lussu, Al, M., Meregalli, G. r., Moffa, S. z., Montalcini, Aq, T., Morgia, Nascimbeni, F. t., Pasta, A. h., Pavanello, Ak, C., Saitta, Antonino, Ag, A., Scicali, Ar, R., Siepi, Ae, D., Spagnolli, W. v., Spina, R. g., Sticchi, Ah, E., Suppressa, Au, P., Vigo, Ba, L., Vinci, P. n., Manzato, Bf, E., Tragni, Be, E., Zampoleri, Bg, V., Casula, Manuela, Olmastroni, Elena, Pirillo, Angela, Catapano, Alberico Luigi, Arca, Marcello, Averna, Maurizio, Bertolini, Stefano, Calandra, Sebastiano, Tarugi, Patrizia, Pellegatta, Fabio, Angelico, Francesco, Bartuli, Andrea, Biasucci, Giacomo, Biolo, Gianni, Bonanni, Luca, Bonomo, Katia, Borghi, Claudio, Bossi, Antonio Carlo, Branchi, Adriana, Carubbi, Francesca, Cipollone, Francesco, Citroni, Nadia, Federici, Massimo, Ferri, Claudio, Fiorenza, Anna Maria, Giaccari, Andrea, Giorgino, Francesco, Guardamagna, Ornella, Iannuzzi, Arcangelo, Iughetti, Lorenzo, Lupattelli, Graziana, Lupi, Alessandro, Mandraffino, Giuseppe, Marcucci, Rossella, Maroni, Lorenzo, Miccoli, Roberto, Mombelli, Giuliana, Muntoni, Sandro, Pecchioli, Valerio, Pederiva, Cristina, Pipolo, Antonio, Pisciotta, Livia, Pujia, Arturo, Purrello, Francesco, Repetti, Elena, Rubba, Paolo, Sabbà, Carlo, Sampietro, Tiziana, Sarzani, Riccardo, Tagliabue, Milena Paola, Trenti, Chiara, Vigna, Giovanni Battista, Werba, Josè Pablo, Zambon, Sabina, Zenti, Maria Grazia, Minicocci, Ilenia, Noto, Davide, Fortunato, Giuliana, Banderali, Giuseppe, Benso, Andrea, Bigolin, Paola, Bonora, Enzo, Bruzzi, Patrizia, Bucci, Marco, Buonuomo, Paola Sabrina, Capra, Maria Elena, Cardolini, Iri, Cefalù, Baldassarre, Cervelli, Nazzareno, Chiariello, Giuseppe, Cocci, Guido, Colombo, Emanuela, Cremonini, Anna Laura, D'Addato, Sergio, D'Erasmo, Laura, Dal Pino, Beatrice, De Sanctis, Luisa, De Vita, Emanuele, Del Ben, Maria, Di Costanzo, Alessia, Di Taranto, Maria Donata, Fasano, Tommaso, Gentile, Luigi, Gentile, Marco, Ghirardello, Omar, Grigore, Liliana, Lussu, Milena, Meregalli, Giancarla, Moffa, Simona, Montalcini, Tiziana, Morgia, Valeria, Nascimbeni, Fabio, Pasta, Andrea, Pavanello, Chiara, Saitta, Antonino, Scicali, Roberto, Siepi, Donatella, Spagnolli, Walter, Spina, Rossella, Sticchi, Elena, Suppressa, Patrizia, Vigo, Lorenzo, Vinci, Pierandrea, Manzato, Enzo, Tragni, Elena, Zampoleri, Veronica, D'addato, Sergio, D'erasmo, Laura, Casula, M, Olmastroni, E, Pirillo, A, Catapano, A, Averna, M, Noto, D, Cefalu, B, and Spina, R

Subjects
Male, Settore MED/09 - Medicina Interna, Genetic testing, Predictive Value of Test, Familial hypercholesterolemia, 030204 cardiovascular system & hematology, Decision Support Technique, 0302 clinical medicine, Retrospective Studie, Risk Factors, Cardiovascular Disease, Genetic Marker, Prospective Studies, 030212 general & internal medicine, Age of Onset, Prospective cohort study, education.field_of_study, medicine.diagnostic_test, Middle Aged, Dutch Lipid Clinic Network score, Adult, Biomarkers, Cardiovascular Diseases, Cholesterol, LDL, Female, Genetic Markers, Genetic Predisposition to Disease, Genetic Testing, Humans, Hyperlipoproteinemia Type II, Italy, Phenotype, Predictive Value of Tests, Reproducibility of Results, Retrospective Studies, Risk Assessment, Decision Support Techniques, Mutation, 3. Good health, Cholesterol, Cardiology and Cardiovascular Medicine, Human, medicine.medical_specialty, Population, Reproducibility of Result, Physical examination, LDL, 03 medical and health sciences, Internal medicine, medicine, First-degree relatives, education, business.industry, Risk Factor, Settore MED/13 - ENDOCRINOLOGIA, Biomarker, medicine.disease, Missing data, Dutch Lipid Clinic Network score, Familial hypercholesterolemia, Genetic testing, Prospective Studie, Age of onset, business
Abstract

Background and aims Familial hypercholesterolemia (FH) is an inherited disorder characterized by high levels of blood cholesterol from birth and premature coronary heart disease. Thus, the identification of FH patients is crucial to prevent or delay the onset of cardiovascular events, and the availability of a tool helping with the diagnosis in the setting of general medicine is essential to improve FH patient identification. Methods This study evaluated the performance of the Dutch Lipid Clinic Network (DLCN) score in FH patients enrolled in the LIPIGEN study, an Italian integrated network aimed at improving the identification of patients with genetic dyslipidaemias, including FH. Results The DLCN score was applied on a sample of 1377 adults (mean age 42.9 ± 14.2 years) with genetic diagnosis of FH, resulting in 28.5% of the sample classified as probable FH and 37.9% as classified definite FH. Among these subjects, 43.4% had at least one missing data out of 8, and about 10.0% had 4 missing data or more. When analyzed based on the type of missing data, a higher percentage of subjects with at least 1 missing data in the clinical history or physical examination was classified as possible FH (DLCN score 3–5). We also found that using real or estimated pre-treatment LDL-C levels may significantly modify the DLCN score. Conclusions Although the DLCN score is a useful tool for physicians in the diagnosis of FH, it may be limited by the complexity to retrieve all the essential information, suggesting a crucial role of the clinical judgement in the identification of FH subjects.

Published
2018

33. Urinary 1H-NMR and GC-MS metabolomics predicts early and late onset neonatal sepsis.

Author

Fanos, Vassilios, Caboni, Pierluigi, Corsello, Giovanni, Stronati, Mauro, Gazzolo, Diego, Noto, Antonio, Lussu, Milena, DessÃÊ, Angelica, GiuffrÃN, Mario, Lacerenza, Serafina, Serraino, Francesca, Garofoli, Francesca, Serpero, Laura Domenica, Liori, Barbara, Carboni, Roberta, and Atzori, Luigi

Subjects
*METABOLOMICS, *GAS chromatography/Mass spectrometry (GC-MS), *SEPSIS, *NEONATAL death, *MYCOSES, *NEONATAL infections
Abstract

ABSTRACT: The purpose of this article is to study one of the most significant causes of neonatal morbidity and mortality: neonatal sepsis. This pathology is due to a bacterial or fungal infection acquired during the perinatal period. Neonatal sepsis has been categorized into two groups: early onset if it occurs within 3–6 days and late onset after 4–7 days. Due to the not-specific clinical signs, along with the inaccuracy of available biomarkers, the diagnosis is still a major challenge. In this regard, the use of a combined approach based on both nuclear magnetic resonance (1H-NMR) and gas-chromatography-mass spectrometry (GC-MS) techniques, coupled with a multivariate statistical analysis, may help to uncover features of the disease that are still hidden. The objective of our study was to evaluate the capability of the metabolomics approach to identify a potential metabolic profile related to the neonatal septic condition. The study population included 25 neonates (15 males and 10 females): 9 (6 males and 3 females) patients had a diagnosis of sepsis and 16 were healthy controls (9 males and 7 females). This study showed a unique metabolic profile of the patients affected by sepsis compared to non-affected ones with a statistically significant difference between the two groups (p = 0.05). [Copyright &y& Elsevier]

Published
2014
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34. Urinary 1 H-NMR Metabolomics in the First Week of Life Can Anticipate BPD Diagnosis.

Author

Pintus MC, Lussu M, Dessì A, Pintus R, Noto A, Masile V, Marcialis MA, Puddu M, Fanos V, and Atzori L

Subjects
Biomarkers metabolism, Bronchopulmonary Dysplasia metabolism, Female, Humans, Infant, Newborn, Male, Multivariate Analysis, Bronchopulmonary Dysplasia diagnosis, Bronchopulmonary Dysplasia diagnostic imaging, Metabolomics methods, Proton Magnetic Resonance Spectroscopy methods
Abstract

Despite the advancements in medical knowledge and technology, the etiopathogenesis of bronchopulmonary dysplasia (BPD) is not yet fully understood although oxidative stress seems to play a role, leading to a very demanding management of these patients by the neonatologist. In this context, metabolomics can be useful in understanding, diagnosing, and treating this illness since it is one of the newest omics science that analyzes the metabolome of an individual through the investigation of biological fluids such as urine and blood. In this study, 18 patients admitted to the Neonatal Intensive Care Unit of the Cagliari University Hospital were enrolled. Among them, 11 patients represented the control group and 7 patients subsequently developed BPD. A sample of urine was collected from each patient at 7 days of life and analyzed through 1 H-NMR coupled with multivariate statistical analysis. The discriminant metabolites between the 2 groups noted were alanine, betaine, trimethylamine-N-oxide, lactate, and glycine. Utilizing metabolomics, it was possible to detect the urinary metabolomics fingerprint of neonates in the first week of life who subsequently developed BPD. Future studies are needed to confirm these promising results suggesting a possible role of microbiota and oxidative stress, and to apply this technology in clinical practice.

Published
2018
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35. 1 H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI).

Author

Lussu M, Camboni T, Piras C, Serra C, Del Carratore F, Griffin J, Atzori L, and Manzin A

Subjects
Acetates analysis, Adult, Aged, Aged, 80 and over, Bacteria pathogenicity, Bacteriuria urine, Biomarkers, Escherichia coli Infections microbiology, Female, Fungi pathogenicity, Humans, Male, Methylamines analysis, Middle Aged, Multivariate Analysis, ROC Curve, Reference Values, Sensitivity and Specificity, Time Factors, Urinary Tract microbiology, Urinary Tract Infections microbiology, Escherichia coli pathogenicity, Escherichia coli Infections diagnosis, Metabolomics methods, Proton Magnetic Resonance Spectroscopy methods, Urinary Tract Infections diagnosis
Abstract

Background: Urinary tract infection (UTI) is one of the most common diagnoses in girls and women, and to a lesser extent in boys and men younger than 50 years. Escherichia coli, followed by Klebsiella spp. and Proteus spp., cause 75-90% of all infections. Infection of the urinary tract is identified by growth of a significant number of a single species in the urine, in the presence of symptoms. Urinary culture is an accurate diagnostic method but takes several hours or days to be carried out. Metabolomics analysis aims to identify biomarkers that are capable of speeding up diagnosis., Methods: Urine samples from 51 patients with a prior diagnosis of Escherichia coli-associated UTI, from 21 patients with UTI caused by other pathogens (bacteria and fungi), and from 61 healthy controls were analyzed. The 1 H-NMR spectra were acquired and processed. Multivariate statistical models were applied and their performance was validated using permutation test and ROC curve., Results: Orthogonal Partial Least Squares-discriminant Analysis (OPLS-DA) showed good separation (R 2 Y = 0.76, Q2=0.45, p < 0.001) between UTI caused by Escherichia coli and healthy controls. Acetate and trimethylamine were identified as discriminant metabolites. The concentrations of both metabolites were calculated and used to build the ROC curves. The discriminant metabolites identified were also evaluated in urine samples from patients with other pathogens infections to test their specificity., Conclusions: Acetate and trimethylamine were identified as optimal candidates for biomarkers for UTI diagnosis. The conclusions support the possibility of a fast diagnostic test for Escherichia coli-associated UTI using acetate and trimethylamine concentrations.

Published
2017
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36. The urinary 1 H-NMR metabolomics profile of an italian autistic children population and their unaffected siblings.

Author

Lussu M, Noto A, Masili A, Rinaldi AC, Dessì A, De Angelis M, De Giacomo A, Fanos V, Atzori L, and Francavilla R

Subjects
Adolescent, Biomarkers urine, Child, Child, Preschool, Female, Humans, Hydrogen, Italy, Male, Autism Spectrum Disorder urine, Magnetic Resonance Spectroscopy methods, Metabolomics methods, Siblings
Abstract

Autism spectrum disorders (ASD) make a dishomogeneous group of psychiatric diseases having either genetic and environmental components, including changes of the microbiota. The rate of diagnosis, based on a series of psychological tests and observed behavior, dramatically increased in the past few decades. Currently, no biological markers are available and the pathogenesis is not defined. The purpose of this study was to evaluate the potential use of 1 H-NMR metabolomics to analyze the global biochemical signature of ASD patients (n = 21) and controls (n = 21), these being siblings of autistic patients. A multivariate model has been used to extrapolate the variables of importance. The discriminating urinary metabolites were identified; in particular, significantly increased levels of hippurate, glycine, creatine, tryptophan, and d-threitol and decreased concentrations of glutamate, creatinine, lactate, valine, betaine, and taurine were observed in ASD patients. Based on the identified discriminant metabolites, the attention was focused on two possible mechanisms that could be involved in ASD: oxidative stress conditions and gut microflora modifications. In conclusion, nuclear magnetic resonance-based metabolomics analysis of the urine seems to have the potential for the identification of a metabolic fingerprint of ASD phenotypes and appears to be suitable for further investigation of the disease mechanisms. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1058-1066. © 2017 International Society for Autism Research, Wiley Periodicals, Inc., (© 2017 International Society for Autism Research, Wiley Periodicals, Inc.)

Published
2017
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37. 1H NMR metabolite fingerprinting as a new tool for body fluid identification in forensic science.

Author

Scano P, Locci E, Noto A, Navarra G, Murgia F, Lussu M, Barberini L, Atzori L, De Giorgio F, Rosa MF, and d'Aloja E

Subjects
Adult, Female, Humans, Male, Middle Aged, Principal Component Analysis, Protons, Reference Values, Body Fluids chemistry, Body Fluids metabolism, Forensic Sciences instrumentation, Nuclear Magnetic Resonance, Biomolecular
Abstract

In this feasibility study, we propose, for the first time, (1)H NMR spectroscopy coupled with mathematical strategies as a valid tool for body fluid (BF) trace identification in forensic science. In order to assess the ability of this approach to identify traces composed either by a single or by two different BFs, samples of blood, urine, saliva, and semen were collected from different donors, and binary mixtures were prepared. (1)H NMR analyses were carried out for all samples. Spectral data of the whole set were firstly submitted to unsupervised principal component analysis (PCA); it showed that samples of the same BF cluster well on the basis of their characterizing molecular components and that mixtures exhibit intermediate characteristics among BF typologies. Furthermore, samples were divided into a training set and a test set. An average NMR spectral profile for each typology of BF was obtained from the training set and validated as representative of each BF class. Finally, a fitting procedure, based on a system of linear equations with the four obtained average spectral profiles, was applied to the test set and the mixture samples; it showed that BFs can be unambiguously identified, even as components of a mixture. The successful use of this mathematical procedure has the advantage, in forensics, of overcoming bias due to the analyst's personal judgment. We therefore propose this combined approach as a valid, fast, and non-destructive tool for addressing the challenges in the identification of composite traces in forensics., (Copyright © 2013 John Wiley & Sons, Ltd.)

Published
2013
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