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Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs
Age at onset is one of the most important clinical features of schizophrenia that could indicate greater genetic loadings. Neurological soft signs (NSS) are considered as a potential endophenotype for schizophrenia. However, the association between NSS and different age-onset schizophrenia still rem...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6781040/ https://www.ncbi.nlm.nih.gov/pubmed/31514416 http://dx.doi.org/10.3390/jcm8091443 |
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author | Chen, Bao-Yu Tsai, I-Ning Lin, Jin-Jia Lu, Ming-Kun Tan, Hung-Pin Jang, Fong-Lin Gan, Shu-Ting Lin, Sheng-Hsiang |
author_facet | Chen, Bao-Yu Tsai, I-Ning Lin, Jin-Jia Lu, Ming-Kun Tan, Hung-Pin Jang, Fong-Lin Gan, Shu-Ting Lin, Sheng-Hsiang |
author_sort | Chen, Bao-Yu |
collection | PubMed |
description | Age at onset is one of the most important clinical features of schizophrenia that could indicate greater genetic loadings. Neurological soft signs (NSS) are considered as a potential endophenotype for schizophrenia. However, the association between NSS and different age-onset schizophrenia still remains unclear. We aimed to compare risk model in patients with early-onset schizophrenia (EOS) and adult-onset schizophrenia (AOS) with NSS. This study included 262 schizophrenia patients, 177 unaffected first-degree relatives and 243 healthy controls. We estimated the discriminant abilities of NSS models for early-onset schizophrenia (onset age < 20) and adult-onset schizophrenia (onset age ≥ 20) using three data mining methods: artificial neural networks (ANN), decision trees (DT) and logistic regression (LR). We then assessed the magnitude of NSS performance in EOS and AOS families. For the four NSS subscales, the NSS performance were greater in EOS and AOS families compared with healthy individuals. More interestingly, there were significant differences found between patients’ families and control group in the four subscales of NSS. These findings support the potential for neurodevelopmental markers to be used as schizophrenia vulnerability indicators. The NSS models had higher discriminant abilities for EOS than for AOS. NSS were more accurate in distinguishing EOS patients from healthy controls compared to AOS patients. Our results support the neurodevelopmental hypothesis that EOS has poorer performance of NSS than AOS. Hence, poorer NSS performance may be imply trait-related NSS feature in EOS. |
format | Online Article Text |
id | pubmed-6781040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67810402019-10-30 Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs Chen, Bao-Yu Tsai, I-Ning Lin, Jin-Jia Lu, Ming-Kun Tan, Hung-Pin Jang, Fong-Lin Gan, Shu-Ting Lin, Sheng-Hsiang J Clin Med Article Age at onset is one of the most important clinical features of schizophrenia that could indicate greater genetic loadings. Neurological soft signs (NSS) are considered as a potential endophenotype for schizophrenia. However, the association between NSS and different age-onset schizophrenia still remains unclear. We aimed to compare risk model in patients with early-onset schizophrenia (EOS) and adult-onset schizophrenia (AOS) with NSS. This study included 262 schizophrenia patients, 177 unaffected first-degree relatives and 243 healthy controls. We estimated the discriminant abilities of NSS models for early-onset schizophrenia (onset age < 20) and adult-onset schizophrenia (onset age ≥ 20) using three data mining methods: artificial neural networks (ANN), decision trees (DT) and logistic regression (LR). We then assessed the magnitude of NSS performance in EOS and AOS families. For the four NSS subscales, the NSS performance were greater in EOS and AOS families compared with healthy individuals. More interestingly, there were significant differences found between patients’ families and control group in the four subscales of NSS. These findings support the potential for neurodevelopmental markers to be used as schizophrenia vulnerability indicators. The NSS models had higher discriminant abilities for EOS than for AOS. NSS were more accurate in distinguishing EOS patients from healthy controls compared to AOS patients. Our results support the neurodevelopmental hypothesis that EOS has poorer performance of NSS than AOS. Hence, poorer NSS performance may be imply trait-related NSS feature in EOS. MDPI 2019-09-11 /pmc/articles/PMC6781040/ /pubmed/31514416 http://dx.doi.org/10.3390/jcm8091443 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Bao-Yu Tsai, I-Ning Lin, Jin-Jia Lu, Ming-Kun Tan, Hung-Pin Jang, Fong-Lin Gan, Shu-Ting Lin, Sheng-Hsiang Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs |
title | Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs |
title_full | Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs |
title_fullStr | Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs |
title_full_unstemmed | Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs |
title_short | Risk Model Assessment in Early-Onset and Adult-Onset Schizophrenia Using Neurological Soft Signs |
title_sort | risk model assessment in early-onset and adult-onset schizophrenia using neurological soft signs |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6781040/ https://www.ncbi.nlm.nih.gov/pubmed/31514416 http://dx.doi.org/10.3390/jcm8091443 |
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