Metabolomics and Lipidomics Analyses Aid Model Classification of Type 2 Diabetes in Non-Human Primates.

Type 2 diabetes (T2D) is a global public health issue characterized by excess weight, abdominal obesity, dyslipidemia, hyperglycemia, and a progressive increase in insulin resistance. Human population studies of T2D development and its effects on systemic metabolism are confounded by many factors that cannot be controlled, complicating the interpretation of results and the identification of early biomarkers. Aged, sedentary, and overweight/obese non-human primates (NHPs) are one of the best animal models to mimic spontaneous T2D development in humans. We sought to identify and distinguish a set of plasma and/or fecal metabolite biomarkers, that have earlier disease onset predictability, and that could be evaluated for their predictability in subsequent T2D studies in human cohorts. In this study, a single plasma and fecal sample was collected from each animal in a colony of 57 healthy and dysmetabolic NHPs and analyzed for metabolomics and lipidomics. The samples were comprehensively analyzed using untargeted and targeted LC/MS/MS. The changes in each animal's disease phenotype were monitored using IVGTT, HbA1c, and other clinical metrics, and correlated with their metabolic profile. The plasma and fecal lipids, as well as bile acid profiles, from Healthy, Dysmetabolic (Dys), and Diabetic (Dia) animals were compared. Following univariate and multivariate analyses, including adjustments for weight, age, and sex, several plasma lipid species were identified to be significantly different between these animal groups. Medium and long-chain plasma phosphatidylcholines (PCs) ranked highest at distinguishing Healthy from Dys animals, whereas plasma triglycerides (TG) primarily distinguished Dia from Dys animals. Random Forest (RF) analysis of fecal bile acids showed a reduction in the secondary bile acid glycoconjugate, GCDCA, in diseased animals (AUC 0.76[0.64, 0.89]). Moreover, metagenomics results revealed several bacterial species, belonging to the genera Roseburia, Ruminococcus, Clostridium, and Streptococcus, to be both significantly enriched in non-healthy animals and associated with secondary bile acid levels. In summary, our results highlight the detection of several elevated circulating plasma PCs and microbial species associated with fecal secondary bile acids in NHP dysmetabolic states. The lipids and metabolites we have identified may help researchers to differentiate individual NHPs more precisely between dysmetabolic and overtly diabetic states. This could help assign animals to study groups that are more likely to respond to potential therapies where a difference in efficacy might be anticipated between early vs. advanced disease.

Glucocerebrosidase activity and lipid levels are related to protein pathologies in Parkinson's disease.

Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are progressive neurodegenerative diseases characterized by the accumulation of misfolded α-synuclein in the form of Lewy pathology. While most cases are sporadic, there are rare genetic mutations that cause disease and more common variants that increase incidence of disease. The most prominent genetic mutations for PD and DLB are in the GBA1 and LRRK2 genes. GBA1 mutations are associated with decreased glucocerebrosidase activity and lysosomal accumulation of its lipid substrates, glucosylceramide and glucosylsphingosine. Previous studies have shown a link between this enzyme and lipids even in sporadic PD. However, it is unclear how the protein pathologies of disease are related to enzyme activity and glycosphingolipid levels. To address this gap in knowledge, we examined quantitative protein pathology, glucocerebrosidase activity and lipid substrates in parallel from 4 regions of 91 brains with no neurological disease, idiopathic, GBA1-linked, or LRRK2-linked PD and DLB. We find that several biomarkers are altered with respect to mutation and progression to dementia. We found mild association of glucocerebrosidase activity with disease, but a strong association of glucosylsphingosine with α-synuclein pathology, irrespective of genetic mutation. This association suggests that Lewy pathology precipitates changes in lipid levels related to progression to dementia.

Comparison of polysomnography in people with Alzheimer's disease and insomnia versus non-demented elderly people with insomnia.

BACKGROUND: We used baseline polysomnography (PSG) data obtained during the clinical program development for suvorexant to compare the PSG profiles of people with Alzheimer's disease and insomnia (ADI) versus age-matched elderly individuals with insomnia (EI). METHODS: Sleep laboratory baseline PSG data from participants age 55-80 years from 2 trials in people with insomnia and a trial in people with ADI were included. ADI participants had dementia of mild-to-moderate severity. Diagnostic criteria for insomnia, exclusion for other sleep problems, PSG recording procedures, and endpoint derivations were similar across the trials. All participants underwent a night of in-laboratory PSG prior to the baseline night to allow for screening/adaptation. Participants in the EI and ADI groups were compared with regard to sleep architecture, sleep micro-structure, and quantitative EEG power spectral endpoints. The analysis was performed on a post hoc basis using propensity score matching to compare sleep parameters separately in women and men while accounting for age group and total sleep time. RESULTS: A total of 837 EI and 239 ADI participants were included, with the majority in each population (∼65%) being women. Compared to EI, those with ADI had a lower percentage of time spent in slow wave sleep (and a corresponding higher percentage of time spent in the lighter N1 sleep), a lower number of spindles per minute of N2 sleep, and lower absolute EEG power during NREM sleep, particularly in the lower-frequency bands. Trends for lower REM sleep percentage in ADI did not reach statistical significance. CONCLUSIONS: Our findings in this large data set, in which the influence of sleep problems was effectively subtracted out (since both groups had insomnia), provide strong confirmatory support of results from previous smaller studies in indicating that AD of mild-to-moderate severity is associated with less slow wave sleep, spindles, and lower-frequency EEG power. TRIAL REGISTRATION: ClinicalTrials.gov, numbers NCT01097616, NCT01097629, NCT02750306.

Use of the single-item Patient Global Impression-Severity scale as a self-reported assessment of insomnia severity.

We evaluated a single-item Patient Global Impression-Severity (PGI-S) scale for assessing insomnia severity during the clinical development programme for suvorexant. The analyses used data from two randomised, double-blind, placebo-controlled, 3-month, Phase III clinical trials of suvorexant in patients with Diagnostic and Statistical Manual of Mental Disorders IV criteria insomnia. Patients assessed insomnia severity during the previous week using the PGI-S, a one-item questionnaire containing six response options ranging from 0 (none) to 5 (very severe), at baseline and at Week 2, and Months 1, 2, and 3 after randomisation. The seven-item Insomnia Severity Index (ISI) and other subjective and objective assessments were also completed by patients. PGI-S responses were compared primarily with the ISI using descriptive statistics and correlations. The PGI-S demonstrated favourable measurement characteristics (validity, reliability, responsiveness and sensitivity). PGI-S scores decreased from baseline to Month 3 in a similar pattern to the ISI total score, and the Spearman correlation coefficient between PGI-S and the ISI was .73. An improvement of ≥2 points on the PGI-S defined a treatment responder, based on comparison to the ISI definition of a responder (improvement of ≥6 points). Our present findings suggest that the PGI-S is a simple but valid, reliable, responsive, sensitive, and meaningful patient-reported assessment of insomnia severity. The PGI-S may be particularly useful as a companion outcome to sleep monitoring using wearable sleep devices or smartphones in at-home settings.

How well can a large number of polysomnography sleep measures predict subjective sleep quality in insomnia patients?

OBJECTIVE: The determinants of sleep quality (sQUAL) are poorly understood. We evaluated how well a large number of objective polysomnography (PSG) parameters can predict sQUAL in insomnia patients participating in trials of sleep medications or placebo. METHODS: PSG recordings over multiple nights from two clinical drug development programs involving 1158 insomnia patients treated with suvorexant or placebo and 903 insomnia patients treated with gaboxadol or placebo were used post-hoc to analyze univariate and multivariate associations between sQUAL and 98 PSG sleep parameters plus patient's age and gender. Analyses were performed separately for each of the two clinical trial databases. For univariate associations, within-subject correlations were estimated using mixed effect modeling of bi-variate longitudinal data with one variable being a given PSG variable and the other being sQUAL. To evaluate how accurately sQUAL could be predicted by all PSG variables jointly plus patient's age and gender, the Random Forest multivariate technique was used. Random Forest was also used to evaluate the accuracy of sQUAL prediction by subjective sleep measures plus age and gender, and to quantitatively describe the relative importance of each variable for predicting sQUAL. RESULTS: In the univariate analyses, total sleep time (TST) had the largest correlation with sQUAL compared with all other PSG sleep parameters, and the magnitude of the correlation between each PSG sleep architecture parameter and sQUAL generally increased with the strength of their associations with TST. In the multivariate analyses, the overall accuracy of sQUAL prediction, even with the large number of PSG parameters plus patient's age and gender, was moderate (area under the Receiver Operating Characteristic curve (AROC): 71.2-71.8%). Ranking of PSG parameters by their contribution to sQUAL indicated that TST was the most important predictor of sQUAL among all PSG variables. Subjective TST and subjective number of awakenings jointly with patient's age classified sQUAL with higher accuracy (AROC: 78.7-81.7%) than PSG variables plus age and gender. The pattern of findings was consistent across the two clinical trial databases. CONCLUSION: In insomnia patients participating in trials of sleep medications or placebo, PSG variables had a moderate but consistent pattern of association with sQUAL across two separate clinical trial databases. Of the PSG variables evaluated, TST was the best predictor of sQUAL. CLINICAL TRIALS: trial registration at www.clinicaltrials.gov: NCT01097616; NCT01097629; NCT00094627; NCT00094666.

Insight Into Reduction of Wakefulness by Suvorexant in Patients With Insomnia: Analysis of Wake Bouts.

Study Objectives: To examine the duration and frequency of wake bouts underlying the wakefulness-after-sleep-onset (WASO) reduction with suvorexant. Methods: We analyzed polysomnogram recordings from clinical trials involving 1518 insomnia patients receiving suvorexant (40/30, 20/15 mg) or placebo to determine the following: (1) the number of, and time spent in, long or short wake bouts and (2) the association between sleep quality and bout characteristics. We also compared wake and sleep bout characteristics of suvorexant in insomnia patients versus zolpidem in healthy subjects undergoing experimentally induced transient insomnia. Results: Relative to placebo, suvorexant decreased the number and time spent in long wake bouts (>2 minutes) and increased the number and time spent in short wake bouts (≤2 minutes). The time spent in long wake bouts during Night-1 decreased by 32-54 minutes, whereas the time spent in short wake bouts increased by 2-6 minutes. On average, a patient returned to sleep from his or her longest awakening more than twice as fast on suvorexant than placebo. The reduced time spent in long wake bouts resulted in odds ratios of self-reported good or excellent sleep quality ranging from 1.59 to 2.19 versus placebo. The small increase in time spent in short wake bouts had no effect on odds ratios. Findings were more pronounced for the higher (40/30 mg) doses of suvorexant. The wake and sleep bout characteristics of suvorexant differed from zolpidem which equally decreased the number of wake and sleep bouts of all durations during the early part of the night. Conclusion: Suvorexant reduces WASO by reducing long wake bouts. This reduction has a positive effect on sleep quality. Clinical Trials: Trial registration at www.clinicaltrials.gov NCT01097616; NCT01097629.

EEG spectral analysis of NREM sleep in a large sample of patients with insomnia and good sleepers: effects of age, sex and part of the night.

Previous studies of the differences between patients with insomnia and good sleepers with regard to quantitative electroencephalographic measures have mostly utilized small samples and consequently had limited ability to account for potentially important confounding factors of age, sex and part of the night. We conducted a power spectral analysis using a large database of sleep electroencephalographic recordings to evaluate differences between patients with insomnia (N = 803) and good sleepers (N = 811), while simultaneously accounting for these factors and their interaction. Comparisons of power as a function of age and part of the night were made between cohorts (patients with insomnia versus good sleepers) by sex. Absolute power in the delta, theta and sigma bands declined with age for both females and males. Females had significantly greater power than males at all ages, and for each band, cohort and part of the night. These sex differences were much greater than differences between patients with insomnia and good sleepers. Compared with good sleepers, patients with insomnia under age 40-45 years had reduced delta band power during Part 1 of the night. Females with insomnia over age 45 years had increased delta and theta band power in Parts 2 and 3 of the night, and males with insomnia under age 40 years had reduced theta power in Part 1. Females with insomnia had increased beta2 power in all parts of the night, and males with insomnia had reduced alpha power during all parts of the night. Relative power (the proportion that an individual frequency band contributes to the total power) decreased in the delta band and increased in all other bands with age for both cohorts, sexes and all parts of the night. This analysis provides a unique resource for quantitative information on the differences in power spectra between patients with insomnia and good sleepers accounting for age, sex and part of the night.

Muscle artifacts in multichannel EEG: characteristics and reduction.

OBJECTIVE: To study the characteristics of unintentional muscle activities in clinical EEG, and to develop a high-throughput method to reduce them for better revealing drug or biological effects on EEG. METHODS: Two clinical EEG datasets are involved. Pure muscle signals are extracted from EEG using Independent Component Analysis (ICA) for studying their characteristics. A high-throughput method called ICA-SR is introduced based on a new feature named Spectral Ratio (SR). RESULTS: The spectral and temporal characteristics of the muscle artifacts are illustrated using representative muscle signals. The spatial characteristics are presented at both the group- and the subject-level, and are consistent under three different electrode reference methodologies. Objectively compared with an existing method, ICA-SR is shown to reduce more artifacts, while introduce less distortion to EEG. Its effectiveness is further demonstrated in real clinical EEG with the help of a CO(2)-inhalation EEG recording session. CONCLUSION: The characteristics of unintentional muscle activities align with the reported characteristics of controlled muscle activities. Artifact spatial characteristics can be EEG equipment dependent. The ICA-SR method can effectively and efficiently process clinical EEG. SIGNIFICANCE: Armed with advanced signal processing algorithms, this study expands our knowledge of muscle activities in EEG from muscle-controlled experiments to general clinical trials. The ICA-SR method provides an urgently needed solution with validated performance for efficiently processing large volumes of clinical EEG.

High-throughput ocular artifact reduction in multichannel electroencephalography (EEG) using component subspace projection.

After a review of the ocular artifact reduction literature, a high-throughput method designed to reduce the ocular artifacts in multichannel continuous EEG recordings acquired at clinical EEG laboratories worldwide is proposed. The proposed method belongs to the category of component-based methods, and does not rely on any electrooculography (EOG) signals. Based on a concept that all ocular artifact components exist in a signal component subspace, the method can uniformly handle all types of ocular artifacts, including eye-blinks, saccades, and other eye movements, by automatically identifying ocular components from decomposed signal components. This study also proposes an improved strategy to objectively and quantitatively evaluate artifact reduction methods. The evaluation strategy uses real EEG signals to synthesize realistic simulated datasets with different amounts of ocular artifacts. The simulated datasets enable us to objectively demonstrate that the proposed method outperforms some existing methods when no high-quality EOG signals are available. Moreover, the results of the simulated datasets improve our understanding of the involved signal decomposition algorithms, and provide us with insights into the inconsistency regarding the performance of different methods in the literature. The proposed method was also applied to two independent clinical EEG datasets involving 28 volunteers and over 1000 EEG recordings. This effort further confirms that the proposed method can effectively reduce ocular artifacts in large clinical EEG datasets in a high-throughput fashion.