To begin to resolve conflicts among current competing taxonomies of child and adolescent psychopathology, the authors developed an interview covering the symptoms of anxiety, depression, inattention, and disruptive behavior used in the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, 1994), the International Statistical Classification of Diseases and Related Health Problems (ICD-10; World Health Organization, 1992), and several implicit taxonomies. This interview will be used in the future to compare the internal and external validity of alternative taxonomies. To provide an informative framework for future hypothesis-testing studies, the authors used principal factor analysis to induce new testable hypotheses regarding the structure of this item pool in a representative sample of 1,358 children and adolescents ranging in age from 4 to 17 years. The resulting hypotheses differed from the DSM-IV, particularly in suggesting that some anxiety symptoms are part of the same syndrome as depression, whereas separation anxiety, fears, and compulsions constitute a separate anxiety dimension.

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Background Accurate diagnosis and early detection of complex disease has the potential to be of enormous benefit to clinical trialists, patients, and researchers alike. We sought to create a non-invasive, low-cost, and accurate classification model for diagnosing Parkinson’s disease risk to serve as a basis for future disease prediction studies in prospective longitudinal cohorts. Methods We developed a simple disease classifying model within 367 patients with Parkinson’s disease and phenotypically typical imaging data and 165 controls without neurological disease of the Parkinson’s Progression Marker Initiative (PPMI) study. Olfactory function, genetic risk, family history of PD, age and gender were algorithmically selected as significant contributors to our classifying model. This model was developed using the PPMI study then tested in 825 patients with Parkinson’s disease and 261 controls from five independent studies with varying recruitment strategies and designs including the Parkinson’s Disease Biomarkers Program (PDBP), Parkinson’s Associated Risk Study (PARS), 23andMe, Longitudinal and Biomarker Study in PD (LABS-PD), and Morris K. Udall Parkinson’s Disease Research Center of Excellence (Penn-Udall). Findings Our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0.923 (95% CI = 0.900 – 0.946) with high sensitivity (0.834, 95% CI = 0.711 – 0.883) and specificity (0.903, 95% CI = 0.824 – 0.946) in PPMI at its optimal AUC threshold (0.655). The model is also well-calibrated with all Hosmer-Lemeshow simulations suggesting that when parsed into random subgroups, the actual data mirrors that of the larger expected data, demonstrating that our model is robust and fits well. Likewise external validation shows excellent classification of PD with AUCs of 0.894 in PDBP, 0.998 in PARS, 0.955 in 23andMe, 0.929 in LABS-PD, and 0.939 in Penn-Udall. Additionally, when our model classifies SWEDD as PD, they convert within one year to typical PD more than would be expected by chance, with 4 out of 17 classified as PD converting to PD during brief follow-up; while SWEDD not classified as PD showed one conversion to PD out of 38 participants (test of proportions, p-value = 0.003). Interpretation This model may serve as a basis for future investigations into the classification, prediction and treatment of Parkinson’s disease, particularly those planning on attempting to identify prodromal or preclinical etiologically typical PD cases in prospective cohorts for efficient interventional and biomarker studies. Funding Please see the acknowledgements and funding section at the end of the manuscript.

Objective: To report the rates and predictors of progression from normal cognition to either mild cognitive impairment (MCI) or dementia using standardized neuropsychological methods.Methods: A prospective cohort of patients diagnosed with Parkinson disease (PD) and baseline normal cognition was assessed for cognitive decline, performance, and function for a minimum of 2 years, and up to 6. A panel of movement disorders experts classified patients as having normal cognition, MCI, or dementia, with 55/68 (80.9%) of eligible patients seen at year 6. Kaplan-Meier curves and Cox proportional hazard models were used to examine cognitive decline and its predictors. Results:We enrolled 141 patients, who averaged 68.8 years of age, 63% men, who had PD on average for 5 years. The cumulative incidence of cognitive impairment was 8.5% at year 1, increasing to 47.4% by year 6. All incident MCI cases had progressed to dementia by year 5. In a multivariate analysis, predictors of future decline were male sex (p 5 0.02), higher Unified Parkinson's Disease Rating Scale motor score (p # 0.001), and worse global cognitive score (p , 0.001).Conclusions: Approximately half of patients with PD with normal cognition at baseline develop cognitive impairment within 6 years and all new MCI cases progress to dementia within 5 years. Our results show that the transition from normal cognition to cognitive impairment, including dementia, occurs frequently and quickly. Certain clinical and cognitive variables may be useful in predicting progression to cognitive impairment in PD. Nonmotor symptoms are common in Parkinson disease (PD), 1 including mild cognitive impairment (MCI) and dementia (PDD).2 Up to 80% of patients with PD develop dementia long-term, 3 and 20%-30% of patients with PD without dementia meet criteria for MCI. 4 Both PD-MCI and PDD impact negatively on patient quality of life, cost of care, and caregiver burden. 5,6 Longitudinal reports on patients with early PD-MCI show that more than 25% will develop dementia within 3 years, 7 and MCI at disease onset increases risk for development of dementia. 8Another study reported that up to 50% of patients with early PD developed cognitive decline within 5 years, 9 although the sample size was relatively small and lack of cognitive impairment at baseline was defined by Mini-Mental State Examination score only.Structural MRI, and plasma and CSF biomarkers, are associated with cognitive functioning and predict future cognitive decline in PD.1 However, many biomarkers are invasive, costly, and done mainly at academic centers conducting research. Demographic and clinical factors such as age, 10 motor subtypes, 11 and early visuospatial, language, and fluency deficits 12,13 have also been shown to predict future cognitive decline. However, to our knowledge, no research has focused on those patients defined as having normal cognition (NC) at baseline, which allows for examination of the course of cognitive decline from its clinical onset.