Prediction of cognition in Parkinson's disease with a clinical–genetic score: a longitudinal analysis of nine cohorts
Summary Background Cognitive decline is a debilitating manifestation of disease progression in Parkinson's disease. We aimed to develop a clinical–genetic score to predict global cognitive impairment in patients with the disease. Methods In this longitudinal analysis, we built a prediction algo...
Gespeichert in:
| Veröffentlicht in: | Lancet neurology 2017-08, Vol.16 (8), p.620-629 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 629 |
|---|---|
| container_issue | 8 |
| container_start_page | 620 |
| container_title | Lancet neurology |
| container_volume | 16 |
| creator | Liu, Ganqiang, PhD Locascio, Joseph J, PhD Corvol, Jean-Christophe, Prof Boot, Brendon, MBBS Liao, Zhixiang, MS Page, Kara, BS Franco, Daly, BA Burke, Kyle, BS Jansen, Iris E, MS Trisini-Lipsanopoulos, Ana, BS Winder-Rhodes, Sophie, PhD Tanner, Caroline M, Prof Lang, Anthony E, Prof Eberly, Shirley, MS Elbaz, Alexis, Prof Brice, Alexis, Prof Mangone, Graziella, MD Ravina, Bernard, MD Shoulson, Ira, Prof Cormier-Dequaire, Florence, MD Heutink, Peter, Prof van Hilten, Jacobus J, Prof Barker, Roger A, Prof Williams-Gray, Caroline H, PhD Marinus, Johan, PhD Scherzer, Clemens R, Dr |
| description | Summary Background Cognitive decline is a debilitating manifestation of disease progression in Parkinson's disease. We aimed to develop a clinical–genetic score to predict global cognitive impairment in patients with the disease. Methods In this longitudinal analysis, we built a prediction algorithm for global cognitive impairment (defined as Mini Mental State Examination [MMSE] ≤25) using data from nine cohorts of patients with Parkinson's disease from North America and Europe assessed between 1986 and 2016. Candidate predictors of cognitive decline were selected through a backward eliminated Cox's proportional hazards analysis using the Akaike's information criterion. These were used to compute the multivariable predictor on the basis of data from six cohorts included in a discovery population. Independent replication was attained in patients from a further three independent longitudinal cohorts. The predictive score was rebuilt and retested in 10 000 training and test sets randomly generated from the entire study population. Findings 3200 patients with Parkinson's disease who were longitudinally assessed with 27 022 study visits between 1986 and 2016 in nine cohorts from North America and Europe were assessed for eligibility. 235 patients with MMSE ≤25 at baseline and 135 whose first study visit occurred more than 12 years from disease onset were excluded. The discovery population comprised 1350 patients (after further exclusion of 334 with missing covariates) from six longitudinal cohorts with 5165 longitudinal visits over 12·8 years (median 2·8, IQR 1·6–4·6). Age at onset, baseline MMSE, years of education, motor exam score, sex, depression, and β-glucocerebrosidase ( GBA ) mutation status were included in the prediction model. The replication population comprised 1132 patients (further excluding 14 patients with missing covariates) from three longitudinal cohorts with 19 127 follow-up visits over 8·6 years (median 6·5, IQR 4·1–7·2). The cognitive risk score predicted cognitive impairment within 10 years of disease onset with an area under the curve (AUC) of more than 0·85 in both the discovery (95% CI 0·82–0·90) and replication (95% CI 0·78–0·91) populations. Patients scoring in the highest quartile for cognitive risk score had an increased hazard for global cognitive impairment compared with those in the lowest quartile (hazard ratio 18·4 [95% CI 9·4–36·1]). Dementia or disabling cognitive impairment was predicted with an AUC of 0·88 (95% CI 0·79–0· |
| doi_str_mv | 10.1016/S1474-4422(17)30122-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5761650</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>1_s2_0_S1474442217301229</els_id><sourcerecordid>1919448752</sourcerecordid><originalsourceid>FETCH-LOGICAL-c584t-8f672e378f25f436ded91fdc4e889b145d5334537d2713244cf9f0d115cf4623</originalsourceid><addsrcrecordid>eNqFks9uEzEQxlcIREvhEUArcaA9BDxee-3lUFRVQJEiUYneLdeeTdxu7GLvpsqNd-ANeRK8SRogFy7--81vPOOvKF4CeQsE6nffgAk2YYzSYxAnFQFKJ82j4nB7XPPHuzWlB8WzlG4IocAkPC0OqKxpI0VzWNxfRrTO9C74MrSlCTPv1hvny0sdb51Pwb9JpXUJdcLy3vXzUpemc94Z3f368XOGHntnymRCxPf5rgt-5vrBOq-7UudhlVwa4d55zBnmIfbpefGk1V3CF9v5qLj69PHq_GIy_fr5y_nZdGK4ZP1EtrWgWAnZUt6yqrZoG2itYShlcw2MW15VjFfCUgEVZcy0TUssADctq2l1VJxusHfD9QKtQd9H3am76BY6rlTQTv17491czcJScVFDzUkGnGwA872wi7OpGs8I4zLr5BKy9nibLIbvA6ZeLVwy2HXaYxiSggZAACXNiH29J70JQ8y9WqsaxqTg4-v5RmViSCliu3sBEDW6QK1doMYvViDU2gWqyXGv_q56F_Xw7VnwYSPA3Pqlw6iScehNtkJE0ysb3H9TnO4RHixxiytMf6pRiSqygYwMEGtCU_0GjsrX3g</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1919448752</pqid></control><display><type>article</type><title>Prediction of cognition in Parkinson's disease with a clinical–genetic score: a longitudinal analysis of nine cohorts</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Liu, Ganqiang, PhD ; Locascio, Joseph J, PhD ; Corvol, Jean-Christophe, Prof ; Boot, Brendon, MBBS ; Liao, Zhixiang, MS ; Page, Kara, BS ; Franco, Daly, BA ; Burke, Kyle, BS ; Jansen, Iris E, MS ; Trisini-Lipsanopoulos, Ana, BS ; Winder-Rhodes, Sophie, PhD ; Tanner, Caroline M, Prof ; Lang, Anthony E, Prof ; Eberly, Shirley, MS ; Elbaz, Alexis, Prof ; Brice, Alexis, Prof ; Mangone, Graziella, MD ; Ravina, Bernard, MD ; Shoulson, Ira, Prof ; Cormier-Dequaire, Florence, MD ; Heutink, Peter, Prof ; van Hilten, Jacobus J, Prof ; Barker, Roger A, Prof ; Williams-Gray, Caroline H, PhD ; Marinus, Johan, PhD ; Scherzer, Clemens R, Dr</creator><creatorcontrib>Liu, Ganqiang, PhD ; Locascio, Joseph J, PhD ; Corvol, Jean-Christophe, Prof ; Boot, Brendon, MBBS ; Liao, Zhixiang, MS ; Page, Kara, BS ; Franco, Daly, BA ; Burke, Kyle, BS ; Jansen, Iris E, MS ; Trisini-Lipsanopoulos, Ana, BS ; Winder-Rhodes, Sophie, PhD ; Tanner, Caroline M, Prof ; Lang, Anthony E, Prof ; Eberly, Shirley, MS ; Elbaz, Alexis, Prof ; Brice, Alexis, Prof ; Mangone, Graziella, MD ; Ravina, Bernard, MD ; Shoulson, Ira, Prof ; Cormier-Dequaire, Florence, MD ; Heutink, Peter, Prof ; van Hilten, Jacobus J, Prof ; Barker, Roger A, Prof ; Williams-Gray, Caroline H, PhD ; Marinus, Johan, PhD ; Scherzer, Clemens R, Dr ; PROPARK ; PICNICS ; DIGPD ; PSG ; CamPaIGN ; HBS ; PDBP</creatorcontrib><description>Summary Background Cognitive decline is a debilitating manifestation of disease progression in Parkinson's disease. We aimed to develop a clinical–genetic score to predict global cognitive impairment in patients with the disease. Methods In this longitudinal analysis, we built a prediction algorithm for global cognitive impairment (defined as Mini Mental State Examination [MMSE] ≤25) using data from nine cohorts of patients with Parkinson's disease from North America and Europe assessed between 1986 and 2016. Candidate predictors of cognitive decline were selected through a backward eliminated Cox's proportional hazards analysis using the Akaike's information criterion. These were used to compute the multivariable predictor on the basis of data from six cohorts included in a discovery population. Independent replication was attained in patients from a further three independent longitudinal cohorts. The predictive score was rebuilt and retested in 10 000 training and test sets randomly generated from the entire study population. Findings 3200 patients with Parkinson's disease who were longitudinally assessed with 27 022 study visits between 1986 and 2016 in nine cohorts from North America and Europe were assessed for eligibility. 235 patients with MMSE ≤25 at baseline and 135 whose first study visit occurred more than 12 years from disease onset were excluded. The discovery population comprised 1350 patients (after further exclusion of 334 with missing covariates) from six longitudinal cohorts with 5165 longitudinal visits over 12·8 years (median 2·8, IQR 1·6–4·6). Age at onset, baseline MMSE, years of education, motor exam score, sex, depression, and β-glucocerebrosidase ( GBA ) mutation status were included in the prediction model. The replication population comprised 1132 patients (further excluding 14 patients with missing covariates) from three longitudinal cohorts with 19 127 follow-up visits over 8·6 years (median 6·5, IQR 4·1–7·2). The cognitive risk score predicted cognitive impairment within 10 years of disease onset with an area under the curve (AUC) of more than 0·85 in both the discovery (95% CI 0·82–0·90) and replication (95% CI 0·78–0·91) populations. Patients scoring in the highest quartile for cognitive risk score had an increased hazard for global cognitive impairment compared with those in the lowest quartile (hazard ratio 18·4 [95% CI 9·4–36·1]). Dementia or disabling cognitive impairment was predicted with an AUC of 0·88 (95% CI 0·79–0·94) and a negative predictive value of 0·92 (95% 0·88–0·95) at the predefined cutoff of 0·196. Performance was stable in 10 000 randomly resampled subsets. Interpretation Our predictive algorithm provides a potential test for future cognitive health or impairment in patients with Parkinson's disease. This model could improve trials of cognitive interventions and inform on prognosis. Funding National Institutes of Health, US Department of Defense.</description><identifier>ISSN: 1474-4422</identifier><identifier>EISSN: 1474-4465</identifier><identifier>DOI: 10.1016/S1474-4422(17)30122-9</identifier><identifier>PMID: 28629879</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Age ; Aged ; Aged, 80 and over ; Algorithms ; Biomarkers ; Cardiovascular disease ; Clinical trials ; Cognitive ability ; Cognitive Dysfunction ; Cognitive Dysfunction - diagnosis ; Cognitive Dysfunction - etiology ; Dementia ; Dementia - diagnosis ; Dementia - etiology ; Dementia disorders ; Disease Progression ; Female ; Genes ; Glucosylceramidase ; Health risk assessment ; Humans ; Life Sciences ; Longitudinal Studies ; Male ; Medical prognosis ; Middle Aged ; Motor task performance ; Movement disorders ; Mutation ; Neurodegenerative diseases ; Neurology ; Neurons and Cognition ; Parkinson Disease ; Parkinson Disease - complications ; Parkinson Disease - diagnosis ; Parkinson's disease ; Patients ; Population studies ; Prognosis ; Proportional Hazards Models ; Replication ; Task forces</subject><ispartof>Lancet neurology, 2017-08, Vol.16 (8), p.620-629</ispartof><rights>Elsevier Ltd</rights><rights>2017 Elsevier Ltd</rights><rights>Copyright © 2017 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier Limited Aug 1, 2017</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c584t-8f672e378f25f436ded91fdc4e889b145d5334537d2713244cf9f0d115cf4623</citedby><cites>FETCH-LOGICAL-c584t-8f672e378f25f436ded91fdc4e889b145d5334537d2713244cf9f0d115cf4623</cites><orcidid>0000-0002-0941-3990 ; 0000-0002-2409-9143 ; 0000-0001-6798-4567 ; 0000-0001-7325-0199</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1474442217301229$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28629879$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-04585038$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Ganqiang, PhD</creatorcontrib><creatorcontrib>Locascio, Joseph J, PhD</creatorcontrib><creatorcontrib>Corvol, Jean-Christophe, Prof</creatorcontrib><creatorcontrib>Boot, Brendon, MBBS</creatorcontrib><creatorcontrib>Liao, Zhixiang, MS</creatorcontrib><creatorcontrib>Page, Kara, BS</creatorcontrib><creatorcontrib>Franco, Daly, BA</creatorcontrib><creatorcontrib>Burke, Kyle, BS</creatorcontrib><creatorcontrib>Jansen, Iris E, MS</creatorcontrib><creatorcontrib>Trisini-Lipsanopoulos, Ana, BS</creatorcontrib><creatorcontrib>Winder-Rhodes, Sophie, PhD</creatorcontrib><creatorcontrib>Tanner, Caroline M, Prof</creatorcontrib><creatorcontrib>Lang, Anthony E, Prof</creatorcontrib><creatorcontrib>Eberly, Shirley, MS</creatorcontrib><creatorcontrib>Elbaz, Alexis, Prof</creatorcontrib><creatorcontrib>Brice, Alexis, Prof</creatorcontrib><creatorcontrib>Mangone, Graziella, MD</creatorcontrib><creatorcontrib>Ravina, Bernard, MD</creatorcontrib><creatorcontrib>Shoulson, Ira, Prof</creatorcontrib><creatorcontrib>Cormier-Dequaire, Florence, MD</creatorcontrib><creatorcontrib>Heutink, Peter, Prof</creatorcontrib><creatorcontrib>van Hilten, Jacobus J, Prof</creatorcontrib><creatorcontrib>Barker, Roger A, Prof</creatorcontrib><creatorcontrib>Williams-Gray, Caroline H, PhD</creatorcontrib><creatorcontrib>Marinus, Johan, PhD</creatorcontrib><creatorcontrib>Scherzer, Clemens R, Dr</creatorcontrib><creatorcontrib>PROPARK</creatorcontrib><creatorcontrib>PICNICS</creatorcontrib><creatorcontrib>DIGPD</creatorcontrib><creatorcontrib>PSG</creatorcontrib><creatorcontrib>CamPaIGN</creatorcontrib><creatorcontrib>HBS</creatorcontrib><creatorcontrib>PDBP</creatorcontrib><title>Prediction of cognition in Parkinson's disease with a clinical–genetic score: a longitudinal analysis of nine cohorts</title><title>Lancet neurology</title><addtitle>Lancet Neurol</addtitle><description>Summary Background Cognitive decline is a debilitating manifestation of disease progression in Parkinson's disease. We aimed to develop a clinical–genetic score to predict global cognitive impairment in patients with the disease. Methods In this longitudinal analysis, we built a prediction algorithm for global cognitive impairment (defined as Mini Mental State Examination [MMSE] ≤25) using data from nine cohorts of patients with Parkinson's disease from North America and Europe assessed between 1986 and 2016. Candidate predictors of cognitive decline were selected through a backward eliminated Cox's proportional hazards analysis using the Akaike's information criterion. These were used to compute the multivariable predictor on the basis of data from six cohorts included in a discovery population. Independent replication was attained in patients from a further three independent longitudinal cohorts. The predictive score was rebuilt and retested in 10 000 training and test sets randomly generated from the entire study population. Findings 3200 patients with Parkinson's disease who were longitudinally assessed with 27 022 study visits between 1986 and 2016 in nine cohorts from North America and Europe were assessed for eligibility. 235 patients with MMSE ≤25 at baseline and 135 whose first study visit occurred more than 12 years from disease onset were excluded. The discovery population comprised 1350 patients (after further exclusion of 334 with missing covariates) from six longitudinal cohorts with 5165 longitudinal visits over 12·8 years (median 2·8, IQR 1·6–4·6). Age at onset, baseline MMSE, years of education, motor exam score, sex, depression, and β-glucocerebrosidase ( GBA ) mutation status were included in the prediction model. The replication population comprised 1132 patients (further excluding 14 patients with missing covariates) from three longitudinal cohorts with 19 127 follow-up visits over 8·6 years (median 6·5, IQR 4·1–7·2). The cognitive risk score predicted cognitive impairment within 10 years of disease onset with an area under the curve (AUC) of more than 0·85 in both the discovery (95% CI 0·82–0·90) and replication (95% CI 0·78–0·91) populations. Patients scoring in the highest quartile for cognitive risk score had an increased hazard for global cognitive impairment compared with those in the lowest quartile (hazard ratio 18·4 [95% CI 9·4–36·1]). Dementia or disabling cognitive impairment was predicted with an AUC of 0·88 (95% CI 0·79–0·94) and a negative predictive value of 0·92 (95% 0·88–0·95) at the predefined cutoff of 0·196. Performance was stable in 10 000 randomly resampled subsets. Interpretation Our predictive algorithm provides a potential test for future cognitive health or impairment in patients with Parkinson's disease. This model could improve trials of cognitive interventions and inform on prognosis. Funding National Institutes of Health, US Department of Defense.</description><subject>Age</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Biomarkers</subject><subject>Cardiovascular disease</subject><subject>Clinical trials</subject><subject>Cognitive ability</subject><subject>Cognitive Dysfunction</subject><subject>Cognitive Dysfunction - diagnosis</subject><subject>Cognitive Dysfunction - etiology</subject><subject>Dementia</subject><subject>Dementia - diagnosis</subject><subject>Dementia - etiology</subject><subject>Dementia disorders</subject><subject>Disease Progression</subject><subject>Female</subject><subject>Genes</subject><subject>Glucosylceramidase</subject><subject>Health risk assessment</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Longitudinal Studies</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Middle Aged</subject><subject>Motor task performance</subject><subject>Movement disorders</subject><subject>Mutation</subject><subject>Neurodegenerative diseases</subject><subject>Neurology</subject><subject>Neurons and Cognition</subject><subject>Parkinson Disease</subject><subject>Parkinson Disease - complications</subject><subject>Parkinson Disease - diagnosis</subject><subject>Parkinson's disease</subject><subject>Patients</subject><subject>Population studies</subject><subject>Prognosis</subject><subject>Proportional Hazards Models</subject><subject>Replication</subject><subject>Task forces</subject><issn>1474-4422</issn><issn>1474-4465</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFks9uEzEQxlcIREvhEUArcaA9BDxee-3lUFRVQJEiUYneLdeeTdxu7GLvpsqNd-ANeRK8SRogFy7--81vPOOvKF4CeQsE6nffgAk2YYzSYxAnFQFKJ82j4nB7XPPHuzWlB8WzlG4IocAkPC0OqKxpI0VzWNxfRrTO9C74MrSlCTPv1hvny0sdb51Pwb9JpXUJdcLy3vXzUpemc94Z3f368XOGHntnymRCxPf5rgt-5vrBOq-7UudhlVwa4d55zBnmIfbpefGk1V3CF9v5qLj69PHq_GIy_fr5y_nZdGK4ZP1EtrWgWAnZUt6yqrZoG2itYShlcw2MW15VjFfCUgEVZcy0TUssADctq2l1VJxusHfD9QKtQd9H3am76BY6rlTQTv17491czcJScVFDzUkGnGwA872wi7OpGs8I4zLr5BKy9nibLIbvA6ZeLVwy2HXaYxiSggZAACXNiH29J70JQ8y9WqsaxqTg4-v5RmViSCliu3sBEDW6QK1doMYvViDU2gWqyXGv_q56F_Xw7VnwYSPA3Pqlw6iScehNtkJE0ysb3H9TnO4RHixxiytMf6pRiSqygYwMEGtCU_0GjsrX3g</recordid><startdate>20170801</startdate><enddate>20170801</enddate><creator>Liu, Ganqiang, PhD</creator><creator>Locascio, Joseph J, PhD</creator><creator>Corvol, Jean-Christophe, Prof</creator><creator>Boot, Brendon, MBBS</creator><creator>Liao, Zhixiang, MS</creator><creator>Page, Kara, BS</creator><creator>Franco, Daly, BA</creator><creator>Burke, Kyle, BS</creator><creator>Jansen, Iris E, MS</creator><creator>Trisini-Lipsanopoulos, Ana, BS</creator><creator>Winder-Rhodes, Sophie, PhD</creator><creator>Tanner, Caroline M, Prof</creator><creator>Lang, Anthony E, Prof</creator><creator>Eberly, Shirley, MS</creator><creator>Elbaz, Alexis, Prof</creator><creator>Brice, Alexis, Prof</creator><creator>Mangone, Graziella, MD</creator><creator>Ravina, Bernard, MD</creator><creator>Shoulson, Ira, Prof</creator><creator>Cormier-Dequaire, Florence, MD</creator><creator>Heutink, Peter, Prof</creator><creator>van Hilten, Jacobus J, Prof</creator><creator>Barker, Roger A, Prof</creator><creator>Williams-Gray, Caroline H, PhD</creator><creator>Marinus, Johan, PhD</creator><creator>Scherzer, Clemens R, Dr</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><general>Elsevier</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0TZ</scope><scope>3V.</scope><scope>7RV</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8C2</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>7X8</scope><scope>1XC</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0941-3990</orcidid><orcidid>https://orcid.org/0000-0002-2409-9143</orcidid><orcidid>https://orcid.org/0000-0001-6798-4567</orcidid><orcidid>https://orcid.org/0000-0001-7325-0199</orcidid></search><sort><creationdate>20170801</creationdate><title>Prediction of cognition in Parkinson's disease with a clinical–genetic score: a longitudinal analysis of nine cohorts</title><author>Liu, Ganqiang, PhD ; Locascio, Joseph J, PhD ; Corvol, Jean-Christophe, Prof ; Boot, Brendon, MBBS ; Liao, Zhixiang, MS ; Page, Kara, BS ; Franco, Daly, BA ; Burke, Kyle, BS ; Jansen, Iris E, MS ; Trisini-Lipsanopoulos, Ana, BS ; Winder-Rhodes, Sophie, PhD ; Tanner, Caroline M, Prof ; Lang, Anthony E, Prof ; Eberly, Shirley, MS ; Elbaz, Alexis, Prof ; Brice, Alexis, Prof ; Mangone, Graziella, MD ; Ravina, Bernard, MD ; Shoulson, Ira, Prof ; Cormier-Dequaire, Florence, MD ; Heutink, Peter, Prof ; van Hilten, Jacobus J, Prof ; Barker, Roger A, Prof ; Williams-Gray, Caroline H, PhD ; Marinus, Johan, PhD ; Scherzer, Clemens R, Dr</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c584t-8f672e378f25f436ded91fdc4e889b145d5334537d2713244cf9f0d115cf4623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Age</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Biomarkers</topic><topic>Cardiovascular disease</topic><topic>Clinical trials</topic><topic>Cognitive ability</topic><topic>Cognitive Dysfunction</topic><topic>Cognitive Dysfunction - diagnosis</topic><topic>Cognitive Dysfunction - etiology</topic><topic>Dementia</topic><topic>Dementia - diagnosis</topic><topic>Dementia - etiology</topic><topic>Dementia disorders</topic><topic>Disease Progression</topic><topic>Female</topic><topic>Genes</topic><topic>Glucosylceramidase</topic><topic>Health risk assessment</topic><topic>Humans</topic><topic>Life Sciences</topic><topic>Longitudinal Studies</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Middle Aged</topic><topic>Motor task performance</topic><topic>Movement disorders</topic><topic>Mutation</topic><topic>Neurodegenerative diseases</topic><topic>Neurology</topic><topic>Neurons and Cognition</topic><topic>Parkinson Disease</topic><topic>Parkinson Disease - complications</topic><topic>Parkinson Disease - diagnosis</topic><topic>Parkinson's disease</topic><topic>Patients</topic><topic>Population studies</topic><topic>Prognosis</topic><topic>Proportional Hazards Models</topic><topic>Replication</topic><topic>Task forces</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Ganqiang, PhD</creatorcontrib><creatorcontrib>Locascio, Joseph J, PhD</creatorcontrib><creatorcontrib>Corvol, Jean-Christophe, Prof</creatorcontrib><creatorcontrib>Boot, Brendon, MBBS</creatorcontrib><creatorcontrib>Liao, Zhixiang, MS</creatorcontrib><creatorcontrib>Page, Kara, BS</creatorcontrib><creatorcontrib>Franco, Daly, BA</creatorcontrib><creatorcontrib>Burke, Kyle, BS</creatorcontrib><creatorcontrib>Jansen, Iris E, MS</creatorcontrib><creatorcontrib>Trisini-Lipsanopoulos, Ana, BS</creatorcontrib><creatorcontrib>Winder-Rhodes, Sophie, PhD</creatorcontrib><creatorcontrib>Tanner, Caroline M, Prof</creatorcontrib><creatorcontrib>Lang, Anthony E, Prof</creatorcontrib><creatorcontrib>Eberly, Shirley, MS</creatorcontrib><creatorcontrib>Elbaz, Alexis, Prof</creatorcontrib><creatorcontrib>Brice, Alexis, Prof</creatorcontrib><creatorcontrib>Mangone, Graziella, MD</creatorcontrib><creatorcontrib>Ravina, Bernard, MD</creatorcontrib><creatorcontrib>Shoulson, Ira, Prof</creatorcontrib><creatorcontrib>Cormier-Dequaire, Florence, MD</creatorcontrib><creatorcontrib>Heutink, Peter, Prof</creatorcontrib><creatorcontrib>van Hilten, Jacobus J, Prof</creatorcontrib><creatorcontrib>Barker, Roger A, Prof</creatorcontrib><creatorcontrib>Williams-Gray, Caroline H, PhD</creatorcontrib><creatorcontrib>Marinus, Johan, PhD</creatorcontrib><creatorcontrib>Scherzer, Clemens R, Dr</creatorcontrib><creatorcontrib>PROPARK</creatorcontrib><creatorcontrib>PICNICS</creatorcontrib><creatorcontrib>DIGPD</creatorcontrib><creatorcontrib>PSG</creatorcontrib><creatorcontrib>CamPaIGN</creatorcontrib><creatorcontrib>HBS</creatorcontrib><creatorcontrib>PDBP</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Pharma and Biotech Premium PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Lancet Titles</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Psychology</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Lancet neurology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Ganqiang, PhD</au><au>Locascio, Joseph J, PhD</au><au>Corvol, Jean-Christophe, Prof</au><au>Boot, Brendon, MBBS</au><au>Liao, Zhixiang, MS</au><au>Page, Kara, BS</au><au>Franco, Daly, BA</au><au>Burke, Kyle, BS</au><au>Jansen, Iris E, MS</au><au>Trisini-Lipsanopoulos, Ana, BS</au><au>Winder-Rhodes, Sophie, PhD</au><au>Tanner, Caroline M, Prof</au><au>Lang, Anthony E, Prof</au><au>Eberly, Shirley, MS</au><au>Elbaz, Alexis, Prof</au><au>Brice, Alexis, Prof</au><au>Mangone, Graziella, MD</au><au>Ravina, Bernard, MD</au><au>Shoulson, Ira, Prof</au><au>Cormier-Dequaire, Florence, MD</au><au>Heutink, Peter, Prof</au><au>van Hilten, Jacobus J, Prof</au><au>Barker, Roger A, Prof</au><au>Williams-Gray, Caroline H, PhD</au><au>Marinus, Johan, PhD</au><au>Scherzer, Clemens R, Dr</au><aucorp>PROPARK</aucorp><aucorp>PICNICS</aucorp><aucorp>DIGPD</aucorp><aucorp>PSG</aucorp><aucorp>CamPaIGN</aucorp><aucorp>HBS</aucorp><aucorp>PDBP</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of cognition in Parkinson's disease with a clinical–genetic score: a longitudinal analysis of nine cohorts</atitle><jtitle>Lancet neurology</jtitle><addtitle>Lancet Neurol</addtitle><date>2017-08-01</date><risdate>2017</risdate><volume>16</volume><issue>8</issue><spage>620</spage><epage>629</epage><pages>620-629</pages><issn>1474-4422</issn><eissn>1474-4465</eissn><abstract>Summary Background Cognitive decline is a debilitating manifestation of disease progression in Parkinson's disease. We aimed to develop a clinical–genetic score to predict global cognitive impairment in patients with the disease. Methods In this longitudinal analysis, we built a prediction algorithm for global cognitive impairment (defined as Mini Mental State Examination [MMSE] ≤25) using data from nine cohorts of patients with Parkinson's disease from North America and Europe assessed between 1986 and 2016. Candidate predictors of cognitive decline were selected through a backward eliminated Cox's proportional hazards analysis using the Akaike's information criterion. These were used to compute the multivariable predictor on the basis of data from six cohorts included in a discovery population. Independent replication was attained in patients from a further three independent longitudinal cohorts. The predictive score was rebuilt and retested in 10 000 training and test sets randomly generated from the entire study population. Findings 3200 patients with Parkinson's disease who were longitudinally assessed with 27 022 study visits between 1986 and 2016 in nine cohorts from North America and Europe were assessed for eligibility. 235 patients with MMSE ≤25 at baseline and 135 whose first study visit occurred more than 12 years from disease onset were excluded. The discovery population comprised 1350 patients (after further exclusion of 334 with missing covariates) from six longitudinal cohorts with 5165 longitudinal visits over 12·8 years (median 2·8, IQR 1·6–4·6). Age at onset, baseline MMSE, years of education, motor exam score, sex, depression, and β-glucocerebrosidase ( GBA ) mutation status were included in the prediction model. The replication population comprised 1132 patients (further excluding 14 patients with missing covariates) from three longitudinal cohorts with 19 127 follow-up visits over 8·6 years (median 6·5, IQR 4·1–7·2). The cognitive risk score predicted cognitive impairment within 10 years of disease onset with an area under the curve (AUC) of more than 0·85 in both the discovery (95% CI 0·82–0·90) and replication (95% CI 0·78–0·91) populations. Patients scoring in the highest quartile for cognitive risk score had an increased hazard for global cognitive impairment compared with those in the lowest quartile (hazard ratio 18·4 [95% CI 9·4–36·1]). Dementia or disabling cognitive impairment was predicted with an AUC of 0·88 (95% CI 0·79–0·94) and a negative predictive value of 0·92 (95% 0·88–0·95) at the predefined cutoff of 0·196. Performance was stable in 10 000 randomly resampled subsets. Interpretation Our predictive algorithm provides a potential test for future cognitive health or impairment in patients with Parkinson's disease. This model could improve trials of cognitive interventions and inform on prognosis. Funding National Institutes of Health, US Department of Defense.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>28629879</pmid><doi>10.1016/S1474-4422(17)30122-9</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-0941-3990</orcidid><orcidid>https://orcid.org/0000-0002-2409-9143</orcidid><orcidid>https://orcid.org/0000-0001-6798-4567</orcidid><orcidid>https://orcid.org/0000-0001-7325-0199</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1474-4422 |
| ispartof | Lancet neurology, 2017-08, Vol.16 (8), p.620-629 |
| issn | 1474-4422 1474-4465 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5761650 |
| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Age Aged Aged, 80 and over Algorithms Biomarkers Cardiovascular disease Clinical trials Cognitive ability Cognitive Dysfunction Cognitive Dysfunction - diagnosis Cognitive Dysfunction - etiology Dementia Dementia - diagnosis Dementia - etiology Dementia disorders Disease Progression Female Genes Glucosylceramidase Health risk assessment Humans Life Sciences Longitudinal Studies Male Medical prognosis Middle Aged Motor task performance Movement disorders Mutation Neurodegenerative diseases Neurology Neurons and Cognition Parkinson Disease Parkinson Disease - complications Parkinson Disease - diagnosis Parkinson's disease Patients Population studies Prognosis Proportional Hazards Models Replication Task forces |
| title | Prediction of cognition in Parkinson's disease with a clinical–genetic score: a longitudinal analysis of nine cohorts |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T14%3A24%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prediction%20of%20cognition%20in%20Parkinson's%20disease%20with%20a%20clinical%E2%80%93genetic%20score:%20a%20longitudinal%20analysis%20of%20nine%20cohorts&rft.jtitle=Lancet%20neurology&rft.au=Liu,%20Ganqiang,%20PhD&rft.aucorp=PROPARK&rft.date=2017-08-01&rft.volume=16&rft.issue=8&rft.spage=620&rft.epage=629&rft.pages=620-629&rft.issn=1474-4422&rft.eissn=1474-4465&rft_id=info:doi/10.1016/S1474-4422(17)30122-9&rft_dat=%3Cproquest_pubme%3E1919448752%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1919448752&rft_id=info:pmid/28629879&rft_els_id=1_s2_0_S1474442217301229&rfr_iscdi=true |