Objective To assess the relevance of quantitative EEG (qEEG) measures as outcomes of disease severity and progression in Parkinson disease (PD). Methods Main databases were systematically searched (January 2018) for studies of sufficient methodologic quality that examined correlations between clinical symptoms of idiopathic PD and cortical (surface) qEEG metrics. Results Thirty-six out of 605 identified studied were included. Results were classified into 4 domains: cognition (23 studies), motor function (13 studies), responsiveness to interventions (7 studies), and other (10 studies). In cross-sectional studies, EEG slowing correlated with global cognitive impairment and with diffuse deterioration in other domains. In longitudinal studies, decreased dominant frequency and increased θ power, reflecting EEG slowing, were biomarkers of cognitive deterioration at an individual level. Results on motor dysfunction and treatment yielded contrasting findings. Studies on functional connectivity at an individual level and longitudinal studies on other domains or on connectivity measures were lacking. Conclusion qEEG measures reflecting EEG slowing, particularly decreased dominant frequency and increased θ power, correlate with cognitive impairment and predict future cognitive deterioration. qEEG could provide reliable and widely available biomarkers for nonmotor disease severity and progression in PD, potentially promoting early diagnosis of nonmotor symptoms and an objective monitoring of progression. More studies are needed to clarify the role of functional connectivity and network analyses.

Objective: Differences in disease progression in Parkinson disease (PD) have variously been attributed to 2 motor subtypes: tremor-dominant (TD) and postural instability and gait difficulty (PIGD)–dominant (PG). We evaluated the role of these phenotypic variants in severity and progression of nondopaminergic manifestations of PD and motor complications. Methods: Linear mixed models were applied to data from the Profiling Parkinson9s disease (PROPARK) cohort (n = 396) to evaluate the effect of motor subtype on severity and progression of cognitive impairment (Scales for Outcomes in Parkinson9s disease [SCOPA]–Cognition [SCOPA-COG]), depression (Hospital Anxiety and Depression Scale [HADS]), autonomic dysfunction (SCOPA–Autonomic [SCOPA-AUT]), excessive daytime sleepiness, psychotic symptoms (SCOPA–Psychiatric Complications [SCOPA-PC]), and motor complications. In first analyses, subtype as determined by the commonly used ratio of tremor over PIGD score was entered as a factor, whereas in second analyses separate tremor and PIGD scores were used. Results were verified in an independent cohort (Estudio Longitudinal de Pacientes con Enfermedad de Parkinson [ELEP]; n = 365). Results: The first analyses showed that PG subtype patients had worse SCOPA-COG, HADS, SCOPA-AUT, SCOPA-PC, and motor complications scores, and exhibited faster progression on the SCOPA-COG. The second analyses showed that only higher PIGD scores were associated with worse scores for these variables; tremor score was not associated with severity or progression of any symptom. Analyses in the independent cohort yielded similar results. Conclusions: In contrast to PIGD, which consistently was associated with greater severity of nondopaminergic symptoms, there was no evidence of a benign effect of tremor. Our findings do not support the use of the TD subtype as a prognostic trait in PD. The results showed that severity of PIGD is a useful indicator of severity and prognosis in PD by itself.

BACKGROUND Quality of life (QoL) in patients with acromegaly is reduced irrespective of disease-state. The contributions of multifactorial determinants of QoL in several disease stages are presently not well known. OBJECTIVE To systematically review predictors of QoL in acromegalic patients. METHODS Main databases were systematically searched using predefined search terms for potentially relevant articles up to January 2017. Inclusion criteria included: separate acromegaly cohort, non-hereditary acromegaly, QoL as study parameter with clearly described method of measurement and quantitative results, N≥10 patients, article in English and adult patients only. Studies were included using the PRISMA flow diagram. RESULTS We identified 1162 studies; 51 studies met the inclusion criteria: 31 cross-sectional observational studies (mean AcroQoL score 62.7 (range 46.6 – 87.0, n=1597)), 9 had a longitudinal component (mean baseline AcroQoL score 61.4 (range 54.3 – 69.0, n=386)) and 15 were intervention-studies (mean baseline AcroQoL score 58.6 (range 52.2 – 75.3, n=521)). Disease-activity reflected by biochemical control measures yielded mixed, and therefore inconclusive results with respect to their effect on QoL. Addition of pegvisomant to somatostatin analogs and start of lanreotide autogel resulted in improvement in QoL. Data from intervention-studies on other treatment modalities were too limited to draw conclusions on the effects of these modalities on QoL. Interestingly, higher BMI and greater degree of depression showed consistently negative associations with QoL. Hypopituitarism was not significantly correlated with QoL in acromegaly. CONCLUSION At present, there is insufficient published data to support that biochemical control, or treatment of acromegaly in general, is associated with improved QoL. Studies with somatostatin receptor ligand treatment, i.e. particularly lanreotide autogel and pegvisomant have shown improved QoL, but consensus on the correlation with biochemical control is missing. Longitudinal studies investigating predictors in treatment-naive patients and their follow-up after therapeutic interventions are lacking, but are urgently needed. Other factors, i.e. depression and obesity were identified from cross-sectional cohort studies as consistent factors associated with poor QoL. Perhaps treatment strategies of acromegaly patients should not only focus on normalizing biochemical markers, but emphasize improvement of QoL by alternative interventions such as psychosocial or weight lowering interventions.

A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline. A fully automated ML pipeline was applied to 112 PD patients, taking EEG time-series as input and predicted class-labels as output. The most extreme cognitive scores were selected for class differentiation, i.e. best vs. worst cognitive performance (n = 20 per group). 16,674 features were extracted per patient; feature-selection was performed using a Boruta algorithm. A random forest classifier was modelled; 10-fold cross-validation with Bayesian optimization was performed to ensure generalizability. The predicted class-probabilities of the entire cohort were compared to actual cognitive performance. Both groups were differentiated with a mean accuracy of 0.92; using only occipital peak frequency yielded an accuracy of 0.67. Class-probabilities and actual cognitive performance were negatively linearly correlated (β = -0.23 (95% confidence interval (-0.29, -0.18))). Particularly high accuracies were achieved using a compound of automatically extracted EEG biomarkers to classify PD patients according to cognition, rather than a single spectral EEG feature. Automated EEG assessment may have utility for cognitive profiling of PD patients during the DBS screening.

The response to levodopa (LR) is important for managing Parkinson’s Disease and is measured with clinical scales prior to (OFF) and after (ON) levodopa. The aim of this study was to ascertain whether an ambulatory wearable device could predict the LR from the response to the first morning dose. The ON and OFF scores were sorted into six categories of severity so that separating Parkinson’s Kinetigraph (PKG) features corresponding to the ON and OFF scores became a multi-class classification problem according to whether they fell below or above the threshold for each class. Candidate features were extracted from the PKG data and matched to the class labels. Several linear and non-linear candidate statistical models were examined and compared to classify the six categories of severity. The resulting model predicted a clinically significant LR with an area under the receiver operator curve of 0.92. This study shows that ambulatory data could be used to identify a clinically significant response to levodopa. This study has also identified practical steps that would enhance the reliability of this test in future studies.

IINTRODUCTION. Remission criteria of acromegaly are based on biochemical variables, i.e. normalization of increased hormone levels. However, the established reduction in Quality of Life (QoL) is suggested to be independent of biochemical control. The aim of this study was to test which aspects predict Qol best in acromegaly. METHODS/Design. This is a prospective cohort study in 80 acromegalic patients, with a cross-sectional and longitudinal part. The main outcome measure was health-related quality of life (QoL), measured by a generic and a disease-specific questionnaire (the SF-36 and AcroQol). Main predictors were age, gender, biochemical control, disease characteristics, treatment modalities and psychopathology. RESULTS. Our cohort of 80 acromegalics had a mean age 54.7 ± 12.3 years with an average disease duration of 10.8 ± 10.0 years. Ratio macro-/microadenoma was 54/26. In adjusted mixed method models, we found that psychopathology significantly predicts QoL in acromegaly (in models including the variables age, gender, disease duration, tumor size, basal hormone levels, relevant treatment modalities and relevant comorbidities), with a higher degree of psychopathology indicating a lower QoL (depression vs. AcroQoL: B=-1.175, p<0.001, depression vs. SF36: B=-1.648, p<0.001, anxiety vs. AcroQoL: B=-0.399, p<0.001, anxiety vs. SF36: B=-0.661, p<0.001). The explained variances demonstrate superiority of psychopathology over biochemical control and other variables in predicting QoL in our models. DISCUSSION. Superiority of psychopathology over biochemical control calls for a more extensive approach regarding diagnosing depression and anxiety in pituitary adenomas to improve QoL. Depressive symptoms and anxiety are modifiable factors that might provide valuable targets for possible future treatment interventions.