Impact of the Dopamine System on Long-Term Cognitive Impairment in Parkinson Disease: An Exploratory Study.

Background: Little is known about the impact of the dopamine system on development of cognitive impairment (CI) in Parkinson disease (PD). Objectives: We used data from a multi-site, international, prospective cohort study to explore the impact of dopamine system-related biomarkers on CI in PD. Methods: PD participants were assessed annually from disease onset out to 7 years, and CI determined by applying cut-offs to four measures: (1) Montreal Cognitive Assessment; (2) detailed neuropsychological test battery; (3) Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) cognition score; and (4) site investigator diagnosis of CI (mild cognitive impairment or dementia). The dopamine system was assessed by serial Iodine-123 Ioflupane dopamine transporter (DAT) imaging, genotyping, and levodopa equivalent daily dose (LEDD) recorded at each assessment. Multivariate longitudinal analyses, with adjustment for multiple comparisons, determined the association between dopamine system-related biomarkers and CI, including persistent impairment. Results: Demographic and clinical variables associated with CI were higher age, male sex, lower education, non-White race, higher depression and anxiety scores and higher MDS-UPDRS motor score. For the dopamine system, lower baseline mean striatum dopamine transporter values (P range 0.003-0.005) and higher LEDD over time (P range <0.001-0.01) were significantly associated with increased risk for CI. Conclusions: Our results provide preliminary evidence that alterations in the dopamine system predict development of clinically-relevant, cognitive impairment in Parkinson's disease. If replicated and determined to be causative, they demonstrate that the dopamine system is instrumental to cognitive health status throughout the disease course. TRIAL REGISTRATION: Parkinson's Progression Markers Initiative is registered with ClinicalTrials.gov (NCT01141023).

Metformin use history and genome-wide DNA methylation profile: potential molecular mechanism for aging and longevity.

BACKGROUND: Metformin, a commonly prescribed anti-diabetic medication, has repeatedly been shown to hinder aging in pre-clinical models and to be associated with lower mortality for humans. It is, however, not well understood how metformin can potentially prolong lifespan from a biological standpoint. We hypothesized that metformin's potential mechanism of action for longevity is through its epigenetic modifications. METHODS: To test our hypothesis, we conducted a post-hoc analysis of available genome-wide DNA methylation (DNAm) data obtained from whole blood collected from inpatients with and without a history of metformin use. We assessed the methylation profile of 171 patients (first run) and only among 63 diabetic patients (second run) and compared the DNAm rates between metformin users and nonusers. RESULTS: Enrichment analysis from the Kyoto Encyclopedia of Genes and Genome (KEGG) showed pathways relevant to metformin's mechanism of action, such as longevity, AMPK, and inflammatory pathways. We also identified several pathways related to delirium whose risk factor is aging. Moreover, top hits from the Gene Ontology (GO) included HIF-1α pathways. However, no individual CpG site showed genome-wide statistical significance (p < 5E-08). CONCLUSION: This study may elucidate metformin's potential role in longevity through epigenetic modifications and other possible mechanisms of action.

Sex-Related Longitudinal Change of Motor, Non-Motor, and Biological Features in Early Parkinson's Disease.

BACKGROUND: Investigation of sex-related motor and non-motor differences and biological markers in Parkinson's disease (PD) may improve precision medicine approach. OBJECTIVE: To examine sex-related longitudinal changes in motor and non-motor features and biologic biomarkers in early PD. METHODS: We compared 5-year longitudinal changes in de novo, untreated PD men and women (at baseline N = 423; 65.5%male) of the Parkinson's Progression Markers Initiative (PPMI), assessing motor and non-motor manifestations of disease; and biologic measures in cerebrospinal fluid (CSF) and dopamine transporter deficit on DaTscanTM uptake. RESULTS: Men experienced greater longitudinal decline in self-reported motor (p < 0.001) and non-motor (p = 0.009) aspects of experiences of daily living, such that men had a yearly increase in MDS-UPDRS part II by a multiplicative factor of 1.27 compared to women at 0.7, while men had a yearly increase in MDS-UPDRS part I by a multiplicative factor of 0.98, compared to women at 0.67. Compared to women, men had more longitudinal progression in clinician-assessed motor features in the ON medication state (p = 0.010) and required higher dopaminergic medication dosages over time (p = 0.014). Time to reach specific disease milestones and longitudinal changes in CSF biomarkers and DaTscanTM uptake were not different by sex. CONCLUSION: Men showed higher self-assessed motor and non-motor burden of disease, with possible contributions from suboptimal dopaminergic therapeutic response in men. However, motor features of disease evaluated with clinician-based scales in the OFF medication state, as well as biological biomarkers do not show specific sex-related progression patterns.

Low soluble amyloid-ß 42 is associated with smaller brain volume in Parkinson's disease.

INTRODUCTION: We sought to examine whether levels of soluble alpha-synuclein (α-syn), amyloid-beta (Aß42), phosphorylated tau (p-tau), and total tau (t-tau), as measured in cerebrospinal fluid (CSF), are associated with changes in brain volume in Parkinson's disease. METHODS: We assessed the 4-year change in total brain volume (n = 99) and baseline CSF α-syn, Aß42, p-tau, and t-tau of Parkinson Progression Markers Initiative participants. We used linear mixed models to assess the longitudinal effect of baseline CSF biomarkers on total and regional brain volume and thickness as well as linear regression for cross-sectional analyses at baseline and year 2. All models were adjusted for age and gender; brain volume models also adjusted for baseline intracranial volume. Bonferroni correction was applied. RESULTS: The 4-year change in total brain volume was -21.2 mm3 (95% confidence interval, -26.1, -16.3). There were no significant associations between the 4-year change in total brain volume and baseline levels of any CSF biomarker (all p-values > 0.05). On cross-sectional analyses, CSF Aß42 was linearly associated with total brain volume at baseline (R2 = 0.60, p = 0.0004) and at year 2 (R2 = 0.66, p < 0.0001), with CSF Aß42 < 1100 pg/ml, the threshold for brain amyloid pathology, associated with smaller total brain volume at baseline (p = 0.0010) and at year 2 (p = 0.0002). CSF α-syn was linearly associated with total brain volume at baseline (R2 = 0.58, p = 0.0044) but not at year 2 (R2 = 0.58, p = 0.1342). CONCLUSION: Reduction in soluble Aß42 is associated with lower total brain volume in Parkinson's disease.

Mortality among patients with sepsis associated with a bispectral electroencephalography (BSEEG) score.

We have previously developed a bispectral electroencephalography (BSEEG) device, which was shown to be effective in detecting delirium and predicting patient outcomes. In this study we aimed to apply the BSEEG approach for a sepsis. This was a retrospective cohort study conducted at a single center. Sepsis-positive cases were identified based on retrospective chart review. EEG raw data and calculated BSEEG scores were obtained in the previous studies. The relationship between BSEEG scores and sepsis was analyzed, as well as the relationship among sepsis, BSEEG score, and mortality. Data were analyzed from 628 patients. The BSEEG score from the first encounter (1st BSEEG) showed a significant difference between patients with and without sepsis (p = 0.0062), although AUC was very small indicating that it is not suitable for detection purpose. Sepsis patients with high BSEEG scores showed the highest mortality, and non-sepsis patients with low BSEEG scores showed the lowest mortality. Mortality of non-sepsis patients with high BSEEG scores was as bad as that of sepsis patients with low BSEEG scores. Even adjusting for age, gender, comorbidity, and sepsis status, BSEEG remained a significant predictor of mortality (p = 0.008). These data are demonstrating its usefulness as a potential tool for identification of patients at high risk and management of sepsis.

Bispectral EEG (BSEEG) quantifying neuro-inflammation in mice induced by systemic inflammation: A potential mouse model of delirium.

Most of the animal studies using inflammation-induced cognitive change have relied on behavioral testing without objective and biologically solid methods to quantify the severity of cognitive disturbances. We have developed a bispectral EEG (BSEEG) method using a novel algorithm in clinical study. This method effectively differentiates between patients with and without delirium, and predict long-term mortality. In the present study, we aimed to apply our bispectral EEG (BSEEG) method, which can detect patients with delirium, to a mouse model of delirium with systemic inflammation induced by lipopolysaccharides (LPS) injection. We recorded EEG after LPS injection using wildtype early adulthood mice (2~3-month-old) and aged mice (18-19-month-old). Animal EEG recordings were converted for power spectral density to calculate BSEEG score using the similar BSEEG algorithm previously developed for our human study. The BSEEG score was relatively stable and slightly high during the day. Alternatively, the BSEEG score was erratic and low in average during the night. LPS injection increased the BSEEG score dose-dependently and diminished the diurnal changes. The mean BSEEG score increased much more in the aged mice group as dosage increased. Our results suggest that BSEEG method can objectively "quantify" level of neuro-Inflammation induced by systemic inflammation (LPS), and that this BSEEG method can be useful as a model of delirium in mice.

Adjusting a subject-specific time of event in longitudinal studies.

Biomedical studies often involve an event that occurs to individuals at different times and has a significant influence on individual trajectories of response variables over time. We propose a statistical model to capture the mean trajectory alteration caused by not only the occurrence of the event but also the subject-specific time of the event. The proposed model provides a post-event mean trajectory smoothly connected with the pre-event mean trajectory by allowing the model parameters associated with the post-event mean trajectory to vary over time of the event. A goodness-of-fit test is considered to investigate how well the proposed model is fit to the data. Hypothesis tests are also developed to assess the influence of the subject-specific time of event on the mean trajectory. Theoretical and simulation studies confirm that the proposed tests choose the correctly specified model consistently and examine the effect of the subject-specific time of event successfully. The proposed model and tests are also illustrated by the analysis of two real-life data from a biomarker study for HIV patients along with their own time of treatment initiation and a body fatness study in girls with different age of menarche.

Clinical and dopamine transporter imaging characteristics of non-manifest LRRK2 and GBA mutation carriers in the Parkinson's Progression Markers Initiative (PPMI): a cross-sectional study.

BACKGROUND: The Parkinson's Progression Markers Initiative (PPMI) is an ongoing observational, longitudinal cohort study of participants with Parkinson's disease, healthy controls, and carriers of the most common Parkinson's disease-related genetic mutations, which aims to define biomarkers of Parkinson's disease diagnosis and progression. All participants are assessed annually with a battery of motor and non-motor scales, 123-I Ioflupane dopamine transporter (DAT) imaging, and biological variables. We aimed to examine whether non-manifesting carriers of LRRK2 and GBA mutations have prodromal features of Parkinson's disease that correlate with reduced DAT binding. METHODS: This cross-sectional analysis is based on assessments done at enrolment in the subset of non-manifesting carriers of LRRK2 and GBA mutations enrolled into the PPMI study from 33 participating sites worldwide. The primary objective was to examine baseline clinical and DAT imaging characteristics in non-manifesting carriers with GBA and LRRK2 mutations compared with healthy controls. DAT deficit was defined as less than 65% of putamen striatal binding ratio expected for the individual's age. We used t tests, χ2 tests, and Fisher's exact tests to compare baseline demographics across groups. An inverse probability weighting method was applied to control for potential confounders such as age and sex. To account for multiple comparisons, we applied a family-wise error rate to each set of analyses. This study is registered with ClinicalTrials.gov, number NCT01141023. FINDINGS: Between Jan 1, 2014, and Jan 1, 2019, the study enrolled 208 LRRK2 (93% G2019S) and 184 GBA (96% N370S) non-manifesting carriers. Both groups were similar with respect to mean age, and about 60% were female. Of the 286 (73%) non-manifesting carriers that had DAT imaging results, 18 (11%) LRRK2 and four (3%) GBA non-manifesting carriers had a DAT deficit. Compared with healthy controls, both LRRK2 and GBA non-manifesting carriers had significantly increased mean scores on the Movement Disorders Society Unified Parkinson's Disease Rating Scale (total score 4·6 [SD 4·4] healthy controls vs 8·4 [7·3] LRRK2 vs 9·5 [9·2] GBA, p<0·0001 for both comparisons) and the Scale for Outcomes for PD - autonomic function (5·8 [3·7] vs 8·1 [5·9] and 8·4 [6·0], p<0·0001 for both comparisons). There was no difference in daytime sleepiness, anxiety, depression, impulsive-compulsive disorders, blood pressure, urate, and rapid eye movement (REM) behaviour disorder scores. Hyposmia was significantly more common only in LRRK2 non-manifesting carriers (69 [36%] of 194 healthy controls vs 114 [55%] of 208 LRRK2 non-manifesting carriers; p=0·0003). Finally, GBA but not LRRK2 non-manifesting carriers showed increased DAT striatal binding ratios compared with healthy controls in the caudate (healthy controls 2·98 [SD 0·63] vs GBA 3·26 [0·63]; p<0·0001), putamen (2·15 [0·56] vs 2·48 [0·52]; p<0·0001), and striatum (2·56 [0·57] vs 2·87 [0·55]; p<0·0001). INTERPRETATION: Our data show evidence of subtle motor and non-motor signs of Parkinson's disease in non-manifesting carriers compared with healthy controls that can precede DAT deficit. Longitudinal data will be essential to confirm these findings and define the trajectory and predictors for development of Parkinson's disease. FUNDING: Michael J Fox Foundation for Parkinson's Research.

Initial severity-dependent longitudinal model with application to a randomized controlled trial of women with depression.

The assessment of initial severity of a disease is arguably one of the most important factors in identifying appropriate therapies. In this paper, we propose an initial severity-dependent longitudinal model to account for the influence of the initial severity of a disease on the posttreatment severity and the efficacy of medical treatments. The proposed model has the flexibility of nonparametric modeling, as it allows coefficients to vary with the initial severity of the disease. It also provides attractive and practical patient-specific interpretation of initial severity-dependent coefficients. As a result, the proposed model enables patient-specific modeling and treatment recommendations consistent with the assessment of the patient's initial severity, and thus, it can be used as a decision support tool for clinicians. A new empirical likelihood approach is employed for efficient estimation and statistical inference about the initial severity-dependent coefficients. In contrast to the literature on marginal regression models, the proposed estimation procedure allows nuisance parameters associated with the working correlation matrix and the error variances to vary smoothly with the initial severity. The effectiveness of the proposed procedure is demonstrated via simulation studies. We further apply the proposed method by analyzing a data set arising from a randomized controlled trial of women with depression and discover an interesting phenomenon; antidepressant medication intervention is effective for patients with moderate or severe depression, whereas psychotherapy intervention using manual-guided cognitive behavior therapy is effective for patients with a severe case of depression.

Statistical inference in a growth curve quantile regression model for longitudinal data.

This article describes a polynomial growth curve quantile regression model that provides a comprehensive assessment about the treatment effects on the changes of the distribution of outcomes over time. The proposed model has the flexibility, as it allows the degree of a polynomial to vary across quantiles. A high degree polynomial model fits the data adequately, yet it is not desirable due to the complexity of the model. We propose the model selection criterion based on an empirical loglikelihood that consistently identifies the optimal degree of a polynomial at each quantile. After the parsimonious model is fitted to the data, the hypothesis test is further developed to evaluate the treatment effects by comparing the growth curves. It is shown that the proposed empirical loglikelihood ratio test statistic follows a chi-square distribution asymptotically under the null hypothesis. Various simulation studies confirm that the proposed test successfully detects the difference between the curves across quantiles. When the empirical loglikelihood is employed, we incorporate the within-subject correlation commonly existing in longitudinal data and gain estimation efficiency of the quantile regression parameters in the growth curve model. The proposed process is illustrated through the analysis of randomized controlled longitudinal depression data.