Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis
Background: The heterogeneity of gambling disorder (GD) has led to the identification of different subtypes, mostly including phenotypic features, with distinctive implications on the GD severity and treatment outcome. However, clustering analyses based on potential endophenotypic features, such as...
| Autores: | , , , , , , , , , , , , , , |
|---|---|
| Tipo de documento: | artigo |
| Data de publicação: | 2024 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositório: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglês |
| OAI Identifier: | oai:ddd.uab.cat:287017 |
| Acesso em linha: | https://ddd.uab.cat/record/287017 https://dx.doi.org/urn:doi:10.1016/j.comppsych.2023.152435 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Adiponectin Gambling disorder Ghrelin LEAP-2 Leptin Neuropsychology SDG 3 - Good Health and Well-being Transtorno del juego Neuropsicologia |
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Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis |
| title |
Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis |
| spellingShingle |
Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis Baenas Soto, Isabel Maria|||0000-0001-7415-0616 Adiponectin Gambling disorder Ghrelin LEAP-2 Leptin Neuropsychology SDG 3 - Good Health and Well-being Transtorno del juego Neuropsicologia |
| title_short |
Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis |
| title_full |
Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis |
| title_fullStr |
Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis |
| title_full_unstemmed |
Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis |
| title_sort |
Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis |
| dc.creator.none.fl_str_mv |
Baenas Soto, Isabel Maria|||0000-0001-7415-0616 Mora-Maltas, Bernat|||0000-0002-4142-3208 Etxandi Santolaya, Mikel|||0000-0002-5924-8495 Lucas Adell, Ignacio|||0000-0001-9426-5082 Granero, Roser|||0000-0001-6308-3198 Fernández Aranda, Fernando|||0000-0002-2968-9898 Tovar, Sulay|||0000-0001-7813-6520 Solé-Morata, Neus|||0000-0003-0433-0843 Gomez-Peña, Monica|||0000-0001-6194-8266 Moragas, Laura|||0000-0001-5235-7026 Del Pino Gutiérrez, Amparo|||0000-0002-2854-9850 Tapia, Javier|||0000-0001-7938-1480 Dieguez, Carlos|||0000-0002-0919-4337 Goudriaan, Anna E.|||0000-0001-8670-9384 Jiménez Murcia, Susana|||0000-0002-3596-8033 |
| author |
Baenas Soto, Isabel Maria|||0000-0001-7415-0616 |
| author_facet |
Baenas Soto, Isabel Maria|||0000-0001-7415-0616 Mora-Maltas, Bernat|||0000-0002-4142-3208 Etxandi Santolaya, Mikel|||0000-0002-5924-8495 Lucas Adell, Ignacio|||0000-0001-9426-5082 Granero, Roser|||0000-0001-6308-3198 Fernández Aranda, Fernando|||0000-0002-2968-9898 Tovar, Sulay|||0000-0001-7813-6520 Solé-Morata, Neus|||0000-0003-0433-0843 Gomez-Peña, Monica|||0000-0001-6194-8266 Moragas, Laura|||0000-0001-5235-7026 Del Pino Gutiérrez, Amparo|||0000-0002-2854-9850 Tapia, Javier|||0000-0001-7938-1480 Dieguez, Carlos|||0000-0002-0919-4337 Goudriaan, Anna E.|||0000-0001-8670-9384 Jiménez Murcia, Susana|||0000-0002-3596-8033 |
| author_role |
author |
| author2 |
Mora-Maltas, Bernat|||0000-0002-4142-3208 Etxandi Santolaya, Mikel|||0000-0002-5924-8495 Lucas Adell, Ignacio|||0000-0001-9426-5082 Granero, Roser|||0000-0001-6308-3198 Fernández Aranda, Fernando|||0000-0002-2968-9898 Tovar, Sulay|||0000-0001-7813-6520 Solé-Morata, Neus|||0000-0003-0433-0843 Gomez-Peña, Monica|||0000-0001-6194-8266 Moragas, Laura|||0000-0001-5235-7026 Del Pino Gutiérrez, Amparo|||0000-0002-2854-9850 Tapia, Javier|||0000-0001-7938-1480 Dieguez, Carlos|||0000-0002-0919-4337 Goudriaan, Anna E.|||0000-0001-8670-9384 Jiménez Murcia, Susana|||0000-0002-3596-8033 |
| author2_role |
author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
Adiponectin Gambling disorder Ghrelin LEAP-2 Leptin Neuropsychology SDG 3 - Good Health and Well-being Transtorno del juego Neuropsicologia |
| topic |
Adiponectin Gambling disorder Ghrelin LEAP-2 Leptin Neuropsychology SDG 3 - Good Health and Well-being Transtorno del juego Neuropsicologia |
| description |
Background: The heterogeneity of gambling disorder (GD) has led to the identification of different subtypes, mostly including phenotypic features, with distinctive implications on the GD severity and treatment outcome. However, clustering analyses based on potential endophenotypic features, such as neuropsychological and neuroendocrine factors, are scarce so far. Aims: This study firstly aimed to identify empirical clusters in individuals with GD based on sociodemographic (i.e., age and sex), neuropsychological (i.e., cognitive flexibility, inhibitory control, decision making, working memory, attention, and set-shifting), and neuroendocrine factors regulating energy homeostasis (i.e., leptin, ghrelin, adiponectin, and liver-expressed antimicrobial peptide 2, LEAP-2). The second objective was to compare the profiles between clusters, considering the variables used for the clustering procedure and other different sociodemographic, clinical, and psychological features. Methods: 297 seeking-treatment adult outpatients with GD (93.6% males, mean age of 39.58 years old) were evaluated through a semi-structured clinical interview, self-reported psychometric assessments, and a protocolized neuropsychological battery. Plasma concentrations of neuroendocrine factors were assessed in peripheral blood after an overnight fast. Agglomerative hierarchical clustering was applied using sociodemographic, neuropsychological, and neuroendocrine variables as indicators for the grouping procedure. Comparisons between the empirical groups were performed using Chi-square tests (χ2) for categorical variables, and analysis of variance (ANOVA) for quantitative measures. Results: Three-mutually-exclusive groups were obtained, being neuropsychological features those with the greatest weight in differentiating groups. The largest cluster (Cluster 1, 65.3%) was composed by younger males with strategic and online gambling preferences, scoring higher on self-reported impulsivity traits, but with a lower cognitive impairment. Cluster 2 (18.2%) and 3 (16.5%) were characterized by a significantly higher proportion of females and older patients with non-strategic gambling preferences and a worse neuropsychological performance. Particularly, Cluster 3 had the poorest neuropsychological performance, especially in cognitive flexibility, while Cluster 2 reported the poorest inhibitory control. This latter cluster was also distinguished by a poorer self-reported emotion regulation, the highest prevalence of food addiction, as well as a metabolic profile characterized by the highest mean concentrations of leptin, adiponectin, and LEAP-2. Conclusions: To the best of our knowledge, this is the first study to identify well-differentiated GD clusters using neuropsychological and neuroendocrine features. Our findings reinforce the heterogeneous nature of the disorder and emphasize a role of potential endophenotypic features in GD subtyping. This more comprehensive characterization of GD profiles could contribute to optimize therapeutic interventions based on a medicine of precision. |
| publishDate |
2024 |
| dc.date.none.fl_str_mv |
2 2024-01-01 2024 2024-01-01 |
| dc.type.none.fl_str_mv |
Article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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article |
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https://ddd.uab.cat/record/287017 https://dx.doi.org/urn:doi:10.1016/j.comppsych.2023.152435 |
| url |
https://ddd.uab.cat/record/287017 https://dx.doi.org/urn:doi:10.1016/j.comppsych.2023.152435 |
| dc.language.none.fl_str_mv |
Inglés eng |
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Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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application/pdf |
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reponame:Dipòsit Digital de Documents de la UAB instname:Universitat Autònoma de Barcelona |
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Universitat Autònoma de Barcelona |
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Dipòsit Digital de Documents de la UAB |
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Dipòsit Digital de Documents de la UAB |
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1869409258998595584 |
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Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasisBaenas Soto, Isabel Maria|||0000-0001-7415-0616Mora-Maltas, Bernat|||0000-0002-4142-3208Etxandi Santolaya, Mikel|||0000-0002-5924-8495Lucas Adell, Ignacio|||0000-0001-9426-5082Granero, Roser|||0000-0001-6308-3198Fernández Aranda, Fernando|||0000-0002-2968-9898Tovar, Sulay|||0000-0001-7813-6520Solé-Morata, Neus|||0000-0003-0433-0843Gomez-Peña, Monica|||0000-0001-6194-8266Moragas, Laura|||0000-0001-5235-7026Del Pino Gutiérrez, Amparo|||0000-0002-2854-9850Tapia, Javier|||0000-0001-7938-1480Dieguez, Carlos|||0000-0002-0919-4337Goudriaan, Anna E.|||0000-0001-8670-9384Jiménez Murcia, Susana|||0000-0002-3596-8033AdiponectinGambling disorderGhrelinLEAP-2LeptinNeuropsychologySDG 3 - Good Health and Well-beingTranstorno del juegoNeuropsicologiaBackground: The heterogeneity of gambling disorder (GD) has led to the identification of different subtypes, mostly including phenotypic features, with distinctive implications on the GD severity and treatment outcome. However, clustering analyses based on potential endophenotypic features, such as neuropsychological and neuroendocrine factors, are scarce so far. Aims: This study firstly aimed to identify empirical clusters in individuals with GD based on sociodemographic (i.e., age and sex), neuropsychological (i.e., cognitive flexibility, inhibitory control, decision making, working memory, attention, and set-shifting), and neuroendocrine factors regulating energy homeostasis (i.e., leptin, ghrelin, adiponectin, and liver-expressed antimicrobial peptide 2, LEAP-2). The second objective was to compare the profiles between clusters, considering the variables used for the clustering procedure and other different sociodemographic, clinical, and psychological features. Methods: 297 seeking-treatment adult outpatients with GD (93.6% males, mean age of 39.58 years old) were evaluated through a semi-structured clinical interview, self-reported psychometric assessments, and a protocolized neuropsychological battery. Plasma concentrations of neuroendocrine factors were assessed in peripheral blood after an overnight fast. Agglomerative hierarchical clustering was applied using sociodemographic, neuropsychological, and neuroendocrine variables as indicators for the grouping procedure. Comparisons between the empirical groups were performed using Chi-square tests (χ2) for categorical variables, and analysis of variance (ANOVA) for quantitative measures. Results: Three-mutually-exclusive groups were obtained, being neuropsychological features those with the greatest weight in differentiating groups. The largest cluster (Cluster 1, 65.3%) was composed by younger males with strategic and online gambling preferences, scoring higher on self-reported impulsivity traits, but with a lower cognitive impairment. Cluster 2 (18.2%) and 3 (16.5%) were characterized by a significantly higher proportion of females and older patients with non-strategic gambling preferences and a worse neuropsychological performance. Particularly, Cluster 3 had the poorest neuropsychological performance, especially in cognitive flexibility, while Cluster 2 reported the poorest inhibitory control. This latter cluster was also distinguished by a poorer self-reported emotion regulation, the highest prevalence of food addiction, as well as a metabolic profile characterized by the highest mean concentrations of leptin, adiponectin, and LEAP-2. Conclusions: To the best of our knowledge, this is the first study to identify well-differentiated GD clusters using neuropsychological and neuroendocrine features. Our findings reinforce the heterogeneous nature of the disorder and emphasize a role of potential endophenotypic features in GD subtyping. This more comprehensive characterization of GD profiles could contribute to optimize therapeutic interventions based on a medicine of precision. 22024-01-0120242024-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/287017https://dx.doi.org/urn:doi:10.1016/j.comppsych.2023.152435reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2870172026-06-06T12:50:31Z |
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15.300719 |