- Email: [email protected]
A new tool to identify patients with Parkinson's disease at increased risk of dementia
Comment
A new tool to identify patients with Parkinson’s disease at increased risk of dementia Many patients with Parkinson’s disease develop dementia; some patients develop dementia within the first 5 years after diagnosis of Parkinson’s disease, whereas others remain free of dementia for more than 10–15 years after diagnosis.1 Identification of individuals at the highest risk of early dementia is important for the development of targeted intervention strategies for the primary prevention of dementia and for enabling future planning for patients, carers, and delivery of true personalised medicine.2 Additionally, identification of patients with increased risk of immediate cognitive decline can reduce the sample size needed for trials of drugs to slow progression to dementia. Previous studies have identified factors associated with increased risk of dementia,1 but an algorithm combining these factors to identify risk for dementia in patients with Parkinson’s disease at an individual level does not exist. Capitalising on the availability of a large dataset from nine established cohorts, Liu and colleagues3 have created an algorithm, based on clinical and genetic features, that was able to accurately identify groups with different risks of dementia and cognitive impairment during 12 years after diagnosis of Parkinson’s disease. The multicentre design allowed a sufficiently large dataset to be separated into a detection cohort and a validation cohort. Other strengths include the long follow-up, the inclusion of population-based cohorts, and thorough statistical analysis. The finding that a score above the predefined cutoff point predicted dementia with high positive (0·877) and negative (0·920) predictive values thus represents an important new finding. In addition to being immediately relevant for patients, carers, and clinicians, the authors estimated that use of the algorithm can reduce the required sample size for clinical trials to prevent dementia by six times, allowing for more cost-efficient trials. However, there are limitations that make the interpretation of the findings somewhat difficult. The retrospective multicentre design means that different definitions of dementia and different cognitive rating scales were used, and patients with different durations of Parkinson’s disease rather than only those with newly diagnosed disease were included. Although 61%
of participants were included within 2 years of disease onset, nearly 40% had a longer duration at inclusion; accordingly, because duration of disease is related to dementia risk this will influence the findings. Together, the clinical–genetic features captured 97% of the variation in cognitive decline. However, the strongest factor was age at onset, which is closely associated with age itself. Given the strong association between age and dementia in the general population, this makes the findings somewhat less interesting. It will be important to explore how the risk score would do without the inclusion of age, for example by analysis of the predictive power of the algorithm in different age groups. Healthy ageing, a key modern influence on lifestyle, could be associated with occurrence of dementia in Parkinson’s disease and is not considered.2 The main objective was prediction of cognition over 10 years. From a patient’s perspective, the risk over a shorter time span, for example 2 years or 5 years, would be even more relevant. However, there is some evidence from time-dependent areas under the curves in the appendix that suggests the findings are less relevant for shorter time periods, but this hypothesis needs to be more thoroughly explored. A major decision made by the authors was which factors to include in the model. The final choice was based on some previous studies but was probably partly determined by the factors available across cohorts and thus is inevitably somewhat arbitrary. Clinical factors that have been shown to predict future cognitive decline but were not included are psychotic symptoms,4 severity of postural instability, and gait-disorders rather than tremor.5 The effect of GBA mutations was only 1·5%; however, genetic factors other than GBA might also increase the risk for dementia, such as the APOE ε4 genotype6 and MAPT on chromosome 17,7 which represent a genetic overlap between Parkinson’s disease and Alzheimer’s disease.8 A polygenic score approach—that is, a single score for each individual, derived from the sum of dementia and cognition-associated risk alleles at hundreds or thousands of loci, weighted by effect size— might be an important avenue for future research but
www.thelancet.com/neurology Published online June 16, 2017 http://dx.doi.org/10.1016/S1474-4422(17)30170-9
Lancet Neurol 2017 Published Online June 16, 2017 http://dx.doi.org/10.1016/ S1474-4422(17)30170-9 See Online/Articles http://dx.doi.org/10.1016/ S1474-4422(17)30122-9
1
Comment
will require much larger discovery and validation sample sizes than those used in this study. Emerging evidence suggests that regional cortical atrophy measured by MRI, and amyloid pathology measured with the CSF marker amyloid β42 or with PET, predict future cognitive decline in Parkinson’s disease.1 Unfortunately, biomarkers were not included in this study and would probably have increased the predictive accuracy. A potential bias not considered is selective attrition due to mortality or to clinical worsening, which are higher in patients with Parkinson’s disease and dementia compared with those without dementia. Thus, some patients might have withdrawn before dementia or substantial cognitive impairment was diagnosed. This effect depends on the duration of the interval between the assessments, which probably varies between the cohorts. In summary, the suggested algorithm represents a substantial new development. Before implemented in clinical practice, this algorithm should be explored in other cohorts with the potential added accuracy from inclusion of further clinical and genetic biomarkers.
DA has received research support or honoraria from Astra-Zeneca, H Lundbeck, Novartis Pharmaceuticals, and GE Health, and is a paid consultant for H Lundbeck, Eisai, and Axovant. DA is a Royal Society Wolfson Research Merit Award Holder and thanks the Wolfson Foundation and the Royal Society for their support. This Comment represents independent research [part] funded by the National Institute for Health Research (NIHR) Biomedical Research Centre, Maudsley NHS Foundation Trust, and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. BC and KRC declare no competing interests. 1 2 3
4 5 6 7 8
Aarsland D, Creese B, Politis M, et al. Cognitive decline in Parkinson disease. Nat Rev Neurol 2017; 13: 217–31. Titova N, Chaudhuri KR. Personalized medicine for Parkinson’s disease: time to be precise. Mov Disord (in press). Liu G, Locascio JJ, Corvol C, et al. Assessment of a clinical-genetic score for the prediction of cognition in Parkinson’s disease: a longitudinal analysis of nine cohorts. Lancet Neurol 2017; published online June 16. http://dx.doi. org/10.1016/S1474-4422(17)30122-9. Ffytche DH, Creese B, Politis M, et al. The psychosis spectrum in Parkinson disease. Nat Rev Neurol 2017; 13: 81–95. Evans JR, Mason SL, Williams-Gray CH, et al. The natural history of treated Parkinson’s disease in an incident, community based cohort. J Neurol Neurosurgery Psychiatry 2011; 82: 1112–18. Williams-Gray CH, Goris A, Saiki M, et al. Apolipoprotein E genotype as a risk factor for susceptibility to and dementia in Parkinson’s disease. J Neurol 2009; 256: 493–98. Williams-Gray CH, Evans JR, Goris A, et al. The distinct cognitive syndromes of Parkinson’s disease: 5 year follow-up of the CamPaIGN cohort. Brain 2009; 132: 2958–69. Desikan RS, Schork AJ, Wang Y, et al. Genetic overlap between Alzheimer’s disease and Parkinson’s disease at the MAPT locus. Mol Psychiatry 2015; 20: 1588–95.
*Dag Aarsland, Byron Creese, K Ray Chaudhuri Institute of Psychiatry, Psychology and Neuroscience, Department of Old Age Psychiatry Stavanger University Hospital, Stavanger, Norway (DA); and Department of Basic and Clinical Neuroscience, The Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, UK (BC, KRC) [email protected]
2
www.thelancet.com/neurology Published online June 16, 2017 http://dx.doi.org/10.1016/S1474-4422(17)30170-9
A new tool to identify patients with Parkinson’s disease at increased risk of dementia Many patients with Parkinson’s disease develop dementia; some patients develop dementia within the first 5 years after diagnosis of Parkinson’s disease, whereas others remain free of dementia for more than 10–15 years after diagnosis.1 Identification of individuals at the highest risk of early dementia is important for the development of targeted intervention strategies for the primary prevention of dementia and for enabling future planning for patients, carers, and delivery of true personalised medicine.2 Additionally, identification of patients with increased risk of immediate cognitive decline can reduce the sample size needed for trials of drugs to slow progression to dementia. Previous studies have identified factors associated with increased risk of dementia,1 but an algorithm combining these factors to identify risk for dementia in patients with Parkinson’s disease at an individual level does not exist. Capitalising on the availability of a large dataset from nine established cohorts, Liu and colleagues3 have created an algorithm, based on clinical and genetic features, that was able to accurately identify groups with different risks of dementia and cognitive impairment during 12 years after diagnosis of Parkinson’s disease. The multicentre design allowed a sufficiently large dataset to be separated into a detection cohort and a validation cohort. Other strengths include the long follow-up, the inclusion of population-based cohorts, and thorough statistical analysis. The finding that a score above the predefined cutoff point predicted dementia with high positive (0·877) and negative (0·920) predictive values thus represents an important new finding. In addition to being immediately relevant for patients, carers, and clinicians, the authors estimated that use of the algorithm can reduce the required sample size for clinical trials to prevent dementia by six times, allowing for more cost-efficient trials. However, there are limitations that make the interpretation of the findings somewhat difficult. The retrospective multicentre design means that different definitions of dementia and different cognitive rating scales were used, and patients with different durations of Parkinson’s disease rather than only those with newly diagnosed disease were included. Although 61%
of participants were included within 2 years of disease onset, nearly 40% had a longer duration at inclusion; accordingly, because duration of disease is related to dementia risk this will influence the findings. Together, the clinical–genetic features captured 97% of the variation in cognitive decline. However, the strongest factor was age at onset, which is closely associated with age itself. Given the strong association between age and dementia in the general population, this makes the findings somewhat less interesting. It will be important to explore how the risk score would do without the inclusion of age, for example by analysis of the predictive power of the algorithm in different age groups. Healthy ageing, a key modern influence on lifestyle, could be associated with occurrence of dementia in Parkinson’s disease and is not considered.2 The main objective was prediction of cognition over 10 years. From a patient’s perspective, the risk over a shorter time span, for example 2 years or 5 years, would be even more relevant. However, there is some evidence from time-dependent areas under the curves in the appendix that suggests the findings are less relevant for shorter time periods, but this hypothesis needs to be more thoroughly explored. A major decision made by the authors was which factors to include in the model. The final choice was based on some previous studies but was probably partly determined by the factors available across cohorts and thus is inevitably somewhat arbitrary. Clinical factors that have been shown to predict future cognitive decline but were not included are psychotic symptoms,4 severity of postural instability, and gait-disorders rather than tremor.5 The effect of GBA mutations was only 1·5%; however, genetic factors other than GBA might also increase the risk for dementia, such as the APOE ε4 genotype6 and MAPT on chromosome 17,7 which represent a genetic overlap between Parkinson’s disease and Alzheimer’s disease.8 A polygenic score approach—that is, a single score for each individual, derived from the sum of dementia and cognition-associated risk alleles at hundreds or thousands of loci, weighted by effect size— might be an important avenue for future research but
www.thelancet.com/neurology Published online June 16, 2017 http://dx.doi.org/10.1016/S1474-4422(17)30170-9
Lancet Neurol 2017 Published Online June 16, 2017 http://dx.doi.org/10.1016/ S1474-4422(17)30170-9 See Online/Articles http://dx.doi.org/10.1016/ S1474-4422(17)30122-9
1
Comment
will require much larger discovery and validation sample sizes than those used in this study. Emerging evidence suggests that regional cortical atrophy measured by MRI, and amyloid pathology measured with the CSF marker amyloid β42 or with PET, predict future cognitive decline in Parkinson’s disease.1 Unfortunately, biomarkers were not included in this study and would probably have increased the predictive accuracy. A potential bias not considered is selective attrition due to mortality or to clinical worsening, which are higher in patients with Parkinson’s disease and dementia compared with those without dementia. Thus, some patients might have withdrawn before dementia or substantial cognitive impairment was diagnosed. This effect depends on the duration of the interval between the assessments, which probably varies between the cohorts. In summary, the suggested algorithm represents a substantial new development. Before implemented in clinical practice, this algorithm should be explored in other cohorts with the potential added accuracy from inclusion of further clinical and genetic biomarkers.
DA has received research support or honoraria from Astra-Zeneca, H Lundbeck, Novartis Pharmaceuticals, and GE Health, and is a paid consultant for H Lundbeck, Eisai, and Axovant. DA is a Royal Society Wolfson Research Merit Award Holder and thanks the Wolfson Foundation and the Royal Society for their support. This Comment represents independent research [part] funded by the National Institute for Health Research (NIHR) Biomedical Research Centre, Maudsley NHS Foundation Trust, and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. BC and KRC declare no competing interests. 1 2 3
4 5 6 7 8
Aarsland D, Creese B, Politis M, et al. Cognitive decline in Parkinson disease. Nat Rev Neurol 2017; 13: 217–31. Titova N, Chaudhuri KR. Personalized medicine for Parkinson’s disease: time to be precise. Mov Disord (in press). Liu G, Locascio JJ, Corvol C, et al. Assessment of a clinical-genetic score for the prediction of cognition in Parkinson’s disease: a longitudinal analysis of nine cohorts. Lancet Neurol 2017; published online June 16. http://dx.doi. org/10.1016/S1474-4422(17)30122-9. Ffytche DH, Creese B, Politis M, et al. The psychosis spectrum in Parkinson disease. Nat Rev Neurol 2017; 13: 81–95. Evans JR, Mason SL, Williams-Gray CH, et al. The natural history of treated Parkinson’s disease in an incident, community based cohort. J Neurol Neurosurgery Psychiatry 2011; 82: 1112–18. Williams-Gray CH, Goris A, Saiki M, et al. Apolipoprotein E genotype as a risk factor for susceptibility to and dementia in Parkinson’s disease. J Neurol 2009; 256: 493–98. Williams-Gray CH, Evans JR, Goris A, et al. The distinct cognitive syndromes of Parkinson’s disease: 5 year follow-up of the CamPaIGN cohort. Brain 2009; 132: 2958–69. Desikan RS, Schork AJ, Wang Y, et al. Genetic overlap between Alzheimer’s disease and Parkinson’s disease at the MAPT locus. Mol Psychiatry 2015; 20: 1588–95.
*Dag Aarsland, Byron Creese, K Ray Chaudhuri Institute of Psychiatry, Psychology and Neuroscience, Department of Old Age Psychiatry Stavanger University Hospital, Stavanger, Norway (DA); and Department of Basic and Clinical Neuroscience, The Maurice Wohl Clinical Neuroscience Institute, King’s College London, London, UK (BC, KRC) [email protected]
2
www.thelancet.com/neurology Published online June 16, 2017 http://dx.doi.org/10.1016/S1474-4422(17)30170-9