Cargando…
Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1)
Over the course of the disease, about 80% of Parkinson’s disease patients will develop cognitive impairment. However, predictive factors associated with cognitive decline are still under investigation. Here, we investigated which clinically available markers are predictive of cognitive impairment in...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6942604/ https://www.ncbi.nlm.nih.gov/pubmed/31853652 http://dx.doi.org/10.1007/s00702-019-02125-6 |
_version_ | 1783484740127424512 |
---|---|
author | Wilson, Heather Pagano, Gennaro Yousaf, Tayyabah Polychronis, Sotirios De Micco, Rosa Giordano, Beniamino Niccolini, Flavia Politis, Marios |
author_facet | Wilson, Heather Pagano, Gennaro Yousaf, Tayyabah Polychronis, Sotirios De Micco, Rosa Giordano, Beniamino Niccolini, Flavia Politis, Marios |
author_sort | Wilson, Heather |
collection | PubMed |
description | Over the course of the disease, about 80% of Parkinson’s disease patients will develop cognitive impairment. However, predictive factors associated with cognitive decline are still under investigation. Here, we investigated which clinically available markers are predictive of cognitive impairment in a cohort of early drug-naïve Parkinson’s disease patients. 294 drug-naïve Parkinson’s disease patients, who were cognitively normal at baseline, were recruited from the Parkinson’s Progression Markers Initiative. At 36-month follow-up, patients were diagnosed with cognitive impairment according to two levels: Level 1 diagnosis was defined as MoCA < 26 and Level 2 diagnosis was defined as MoCA < 26, alongside an impaired score on at least two neuropsychological tests. Predictive variables with a validated cut-off were divided into normal or abnormal measures, whilst others were divided into normal or abnormal measures based on the decile with the highest power of prediction. At 3 years’ follow-up, 122/294 Parkinson’s disease (41.5%) patients had cognitive decline. We found that age at Parkinson’s disease onset, MDS-UPDRS Part-III, Hopkin’s Learning Verbal Test-Revised Recall, Semantic Fluency Test and Symbol Digit Modalities Test were all predictors of cognitive decline. Specifically, age at Parkinson’s disease onset, Semantic Fluency Test and symbol Digit Modalities Test were predictors of cognitive decline defined by Level 2. The combination of three abnormal tests, identified as the most significant predictors of cognitive decline, gave a 63.6–86.7% risk of developing cognitive impairment defined by Level 2 and Level 1 criteria, respectively, at 36-month follow-up. Our findings show that these clinically available measures encompass the ability to identify drug-naïve Parkinson’s disease patients with the highest risk of developing cognitive impairment at the earliest stages. Therefore, by implementing this in a clinical setting, we can better monitor and manage patients who are at risk of cognitive decline. |
format | Online Article Text |
id | pubmed-6942604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer Vienna |
record_format | MEDLINE/PubMed |
spelling | pubmed-69426042020-01-16 Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1) Wilson, Heather Pagano, Gennaro Yousaf, Tayyabah Polychronis, Sotirios De Micco, Rosa Giordano, Beniamino Niccolini, Flavia Politis, Marios J Neural Transm (Vienna) Neurology and Preclinical Neurological Studies - Original Article Over the course of the disease, about 80% of Parkinson’s disease patients will develop cognitive impairment. However, predictive factors associated with cognitive decline are still under investigation. Here, we investigated which clinically available markers are predictive of cognitive impairment in a cohort of early drug-naïve Parkinson’s disease patients. 294 drug-naïve Parkinson’s disease patients, who were cognitively normal at baseline, were recruited from the Parkinson’s Progression Markers Initiative. At 36-month follow-up, patients were diagnosed with cognitive impairment according to two levels: Level 1 diagnosis was defined as MoCA < 26 and Level 2 diagnosis was defined as MoCA < 26, alongside an impaired score on at least two neuropsychological tests. Predictive variables with a validated cut-off were divided into normal or abnormal measures, whilst others were divided into normal or abnormal measures based on the decile with the highest power of prediction. At 3 years’ follow-up, 122/294 Parkinson’s disease (41.5%) patients had cognitive decline. We found that age at Parkinson’s disease onset, MDS-UPDRS Part-III, Hopkin’s Learning Verbal Test-Revised Recall, Semantic Fluency Test and Symbol Digit Modalities Test were all predictors of cognitive decline. Specifically, age at Parkinson’s disease onset, Semantic Fluency Test and symbol Digit Modalities Test were predictors of cognitive decline defined by Level 2. The combination of three abnormal tests, identified as the most significant predictors of cognitive decline, gave a 63.6–86.7% risk of developing cognitive impairment defined by Level 2 and Level 1 criteria, respectively, at 36-month follow-up. Our findings show that these clinically available measures encompass the ability to identify drug-naïve Parkinson’s disease patients with the highest risk of developing cognitive impairment at the earliest stages. Therefore, by implementing this in a clinical setting, we can better monitor and manage patients who are at risk of cognitive decline. Springer Vienna 2019-12-18 2020 /pmc/articles/PMC6942604/ /pubmed/31853652 http://dx.doi.org/10.1007/s00702-019-02125-6 Text en © The Author(s) 2019 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Neurology and Preclinical Neurological Studies - Original Article Wilson, Heather Pagano, Gennaro Yousaf, Tayyabah Polychronis, Sotirios De Micco, Rosa Giordano, Beniamino Niccolini, Flavia Politis, Marios Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1) |
title | Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1) |
title_full | Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1) |
title_fullStr | Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1) |
title_full_unstemmed | Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1) |
title_short | Predict cognitive decline with clinical markers in Parkinson’s disease (PRECODE-1) |
title_sort | predict cognitive decline with clinical markers in parkinson’s disease (precode-1) |
topic | Neurology and Preclinical Neurological Studies - Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6942604/ https://www.ncbi.nlm.nih.gov/pubmed/31853652 http://dx.doi.org/10.1007/s00702-019-02125-6 |
work_keys_str_mv | AT wilsonheather predictcognitivedeclinewithclinicalmarkersinparkinsonsdiseaseprecode1 AT paganogennaro predictcognitivedeclinewithclinicalmarkersinparkinsonsdiseaseprecode1 AT yousaftayyabah predictcognitivedeclinewithclinicalmarkersinparkinsonsdiseaseprecode1 AT polychronissotirios predictcognitivedeclinewithclinicalmarkersinparkinsonsdiseaseprecode1 AT demiccorosa predictcognitivedeclinewithclinicalmarkersinparkinsonsdiseaseprecode1 AT giordanobeniamino predictcognitivedeclinewithclinicalmarkersinparkinsonsdiseaseprecode1 AT niccoliniflavia predictcognitivedeclinewithclinicalmarkersinparkinsonsdiseaseprecode1 AT politismarios predictcognitivedeclinewithclinicalmarkersinparkinsonsdiseaseprecode1 |