{"id":26453,"date":"2025-10-24T15:29:56","date_gmt":"2025-10-24T13:29:56","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&p=26453"},"modified":"2025-10-24T15:33:48","modified_gmt":"2025-10-24T13:33:48","slug":"artificial-intelligence-predicts-gba1-mutated-status-in-parkinsons-disease-patients","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/artificial-intelligence-predicts-gba1-mutated-status-in-parkinsons-disease-patients\/","title":{"rendered":"Artificial Intelligence Predicts GBA1 Mutated Status in Parkinson’s Disease Patients"},"content":{"rendered":"\n

GBA1 mutations represent a major genetic risk factor for Parkinson\u2019s Disease (PD), contributing to a significant proportion of cases depending on population characteristics and age at onset. This study investigated whether Artificial Intelligence (AI) <\/strong>could be harnessed to predict GBA1 mutation status in PD patients, aiming to develop a machine learning model capable of providing an accurate pre-test estimate based on key clinical and demographic indicators. To this end, a cohort of 58 GBA1-PD patients was matched with 58 non-mutated PD patients, and 124 features were recorded for each participant. Using a Leave-One-Out cross-validation<\/strong> approach and SHapley Additive exPlanations (SHAP)<\/strong> to quantify feature contributions, XGBoost emerged as the most effective algorithm for this supervised classification task. The resulting model relied on five primary clinical variables\u2014including family history, cognitive scores (MDS-UPDRS 1.1), and motor function measures (MDS-UPDRS 3.8a, 3.8b, and rigidity subscores)\u2014and achieved 73% overall accuracy<\/strong>, rising to 94%<\/strong> in patients with high SHAP confidence. These findings demonstrate the potential of AI to support targeted genetic screening in PD<\/strong>, particularly in settings with limited clinical resources. Limitations include the relatively small sample size and absence of external validation, highlighting the need for further research in larger, independent cohorts to refine and validate the predictive model.<\/p>\n\n\n\n

<\/div>\n\n\n\n

Artificial Intelligence Predicts\u00a0GBA1\u00a0Mutated Status in Parkinson’s Disease Patients<\/strong><\/p>\n\n\n\n

Autors<\/strong>: Giulia Di Rauso MD,\u00a0Alessandro Ghibellini MSc,\u00a0Sara Grisanti PhD,\u00a0Valentina Fioravanti MD PhD,\u00a0Edoardo Monfrini MD PhD,\u00a0Giulia Toschi PhD,\u00a0Giacomo Portaro MD,\u00a0Giacomo Argenziano MD,\u00a0Ruggero Bacchin MD,\u00a0Jessica Rossi MD,\u00a0Rossella Sabadini MD,\u00a0Valeria Ferrari MD,\u00a0Andrea Melpignano MD,\u00a0Francesca Pacillo MD,\u00a0Maria Scarano MD,\u00a0Anna Groppi MD,\u00a0Luca Gherardini MSc<\/strong>,\u00a0Chiara Lucchi PhD,\u00a0Giuseppe Biagini MD, PhD,\u00a0Sara Montepietra MD,\u00a0Maria Chiara Malaguti MD PhD,\u00a0Isabella Campanini PhD,\u00a0Andrea Merlo PhD,\u00a0Andrea Castellucci MD,\u00a0Angelo Ghidini MD,\u00a0Alessandro Fraternali MD,\u00a0Annibale Versari MD,\u00a0Augusto Scaglioni MD,\u00a0Jefri J. Paul PhD,\u00a0Luciano Bononi Eng,\u00a0Maurizio Gabbrielli Eng,\u00a0Alessio Di Fonzo MD PhD,\u00a0Peter Bauer MD,\u00a0Francesco Cavallieri MD PhD,\u00a0Franco Valzania MD<\/p>\n\n\n\n

DOI<\/strong>: 10.1002\/mdc3.70334<\/p>\n\n\n\n

<\/div>\n\n\n\n\t\n \n \n\t\t\t\n\n\t\t\t\t\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n \n \n","protected":false},"excerpt":{"rendered":"

Article in journal: Movement Disorders Clinical Practice, 2025<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[14],"class_list":["post-26453","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-intelligence"],"yoast_head":"\nArtificial Intelligence Predicts GBA1 Mutated Status in Parkinson's Disease Patients - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/artificial-intelligence-predicts-gba1-mutated-status-in-parkinsons-disease-patients\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial Intelligence Predicts GBA1 Mutated Status in Parkinson's Disease Patients\" \/>\n<meta property=\"og:description\" content=\"Article in journal: Movement Disorders Clinical Practice, 2025\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/artificial-intelligence-predicts-gba1-mutated-status-in-parkinsons-disease-patients\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2025-10-24T13:33:48+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/artificial-intelligence-predicts-gba1-mutated-status-in-parkinsons-disease-patients\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/artificial-intelligence-predicts-gba1-mutated-status-in-parkinsons-disease-patients\\\/\",\"name\":\"Artificial Intelligence Predicts GBA1 Mutated Status in Parkinson's Disease Patients - 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This study investigated whether A<strong>rtificial Intelligence (AI) <\/strong>could be harnessed to predict GBA1 mutation status in PD patients, aiming to develop a machine learning model capable of providing an accurate pre-test estimate based on key clinical and demographic indicators. To this end, a cohort of 58 GBA1-PD patients was matched with 58 non-mutated PD patients, and 124 features were recorded for each participant. Using a <strong>Leave-One-Out cross-validation<\/strong> approach and <strong>SHapley Additive exPlanations (SHAP)<\/strong> to quantify feature contributions, XGBoost emerged as the most effective algorithm for this supervised classification task. The resulting model relied on five primary clinical variables\u2014including family history, cognitive scores (MDS-UPDRS 1.1), and motor function measures (MDS-UPDRS 3.8a, 3.8b, and rigidity subscores)\u2014and achieved <strong>73% overall accuracy<\/strong>, rising to <strong>94%<\/strong> in patients with high SHAP confidence. These findings demonstrate the potential of AI to support <strong>targeted genetic screening in PD<\/strong>, particularly in settings with limited clinical resources. Limitations include the relatively small sample size and absence of external validation, highlighting the need for further research in larger, independent cohorts to refine and validate the predictive model.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">GBA1 mutations represent a major genetic risk factor for Parkinson\u2019s Disease (PD), contributing to a significant proportion of cases depending on population characteristics and age at onset. This study investigated whether A<strong>rtificial Intelligence (AI) <\/strong>could be harnessed to predict GBA1 mutation status in PD patients, aiming to develop a machine learning model capable of providing an accurate pre-test estimate based on key clinical and demographic indicators. To this end, a cohort of 58 GBA1-PD patients was matched with 58 non-mutated PD patients, and 124 features were recorded for each participant. Using a <strong>Leave-One-Out cross-validation<\/strong> approach and <strong>SHapley Additive exPlanations (SHAP)<\/strong> to quantify feature contributions, XGBoost emerged as the most effective algorithm for this supervised classification task. The resulting model relied on five primary clinical variables\u2014including family history, cognitive scores (MDS-UPDRS 1.1), and motor function measures (MDS-UPDRS 3.8a, 3.8b, and rigidity subscores)\u2014and achieved <strong>73% overall accuracy<\/strong>, rising to <strong>94%<\/strong> in patients with high SHAP confidence. These findings demonstrate the potential of AI to support <strong>targeted genetic screening in PD<\/strong>, particularly in settings with limited clinical resources. Limitations include the relatively small sample size and absence of external validation, highlighting the need for further research in larger, independent cohorts to refine and validate the predictive model.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-Xjft0R","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-oCKehL","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Artificial Intelligence Predicts\u00a0GBA1\u00a0Mutated Status in Parkinson's Disease Patients<\/strong><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Artificial Intelligence Predicts\u00a0GBA1\u00a0Mutated Status in Parkinson's Disease Patients<\/strong><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-oCKehL","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: Giulia Di Rauso MD,\u00a0Alessandro Ghibellini MSc,\u00a0Sara Grisanti PhD,\u00a0Valentina Fioravanti MD PhD,\u00a0Edoardo Monfrini MD PhD,\u00a0Giulia Toschi PhD,\u00a0Giacomo Portaro MD,\u00a0Giacomo Argenziano MD,\u00a0Ruggero Bacchin MD,\u00a0Jessica Rossi MD,\u00a0Rossella Sabadini MD,\u00a0Valeria Ferrari MD,\u00a0Andrea Melpignano MD,\u00a0Francesca Pacillo MD,\u00a0Maria Scarano MD,\u00a0Anna Groppi MD,\u00a0<strong>Luca Gherardini MSc<\/strong>,\u00a0Chiara Lucchi PhD,\u00a0Giuseppe Biagini MD, PhD,\u00a0Sara Montepietra MD,\u00a0Maria Chiara Malaguti MD PhD,\u00a0Isabella Campanini PhD,\u00a0Andrea Merlo PhD,\u00a0Andrea Castellucci MD,\u00a0Angelo Ghidini MD,\u00a0Alessandro Fraternali MD,\u00a0Annibale Versari MD,\u00a0Augusto Scaglioni MD,\u00a0Jefri J. Paul PhD,\u00a0Luciano Bononi Eng,\u00a0Maurizio Gabbrielli Eng,\u00a0Alessio Di Fonzo MD PhD,\u00a0Peter Bauer MD,\u00a0Francesco Cavallieri MD PhD,\u00a0Franco Valzania MD<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: Giulia Di Rauso MD,\u00a0Alessandro Ghibellini MSc,\u00a0Sara Grisanti PhD,\u00a0Valentina Fioravanti MD PhD,\u00a0Edoardo Monfrini MD PhD,\u00a0Giulia Toschi PhD,\u00a0Giacomo Portaro MD,\u00a0Giacomo Argenziano MD,\u00a0Ruggero Bacchin MD,\u00a0Jessica Rossi MD,\u00a0Rossella Sabadini MD,\u00a0Valeria Ferrari MD,\u00a0Andrea Melpignano MD,\u00a0Francesca Pacillo MD,\u00a0Maria Scarano MD,\u00a0Anna Groppi MD,\u00a0<strong>Luca Gherardini MSc<\/strong>,\u00a0Chiara Lucchi PhD,\u00a0Giuseppe Biagini MD, PhD,\u00a0Sara Montepietra MD,\u00a0Maria Chiara Malaguti MD PhD,\u00a0Isabella Campanini PhD,\u00a0Andrea Merlo PhD,\u00a0Andrea Castellucci MD,\u00a0Angelo Ghidini MD,\u00a0Alessandro Fraternali MD,\u00a0Annibale Versari MD,\u00a0Augusto Scaglioni MD,\u00a0Jefri J. Paul PhD,\u00a0Luciano Bononi Eng,\u00a0Maurizio Gabbrielli Eng,\u00a0Alessio Di Fonzo MD PhD,\u00a0Peter Bauer MD,\u00a0Francesco Cavallieri MD PhD,\u00a0Franco Valzania MD<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-oCKehL","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1002\/mdc3.70334<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1002\/mdc3.70334<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-Xjft0R","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/movementdisorders.onlinelibrary.wiley.com\/doi\/10.1002\/mdc3.70334","button_style":"primary","target":"_self","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/26453","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":4,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/26453\/revisions"}],"predecessor-version":[{"id":26462,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/26453\/revisions\/26462"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=26453"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=26453"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=26453"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}