Metabolomic profiling was carried out on 53 post-mortem brain samples from subjects diagnosed with schizophrenia, depression, bipolar disorder (SDB), diabetes, and controls. Chromatography on a ZICpHILIC column was used with detection by Orbitrap mass spectrometry. Data extraction was carried out with m/z Mine 2.14 with metabolite searching against an in-house database. There was no clear discrimination between the controls and the SDB samples on the basis of a principal components analysis (PCA) model of 755 identified or putatively identified metabolites. Orthogonal partial least square discriminant analysis (OPLSDA) produced clear separation between 17 of the controls and 19 of the SDB samples (R2CUM 0.976, Q2 0.671, p-value of the cross-validated ANOVA score 0.0024). The most important metabolites producing discrimination were the lipophilic amino acids leucine/isoleucine, proline, methionine, phenylalanine, and tyrosine; the neurotransmitters GABA and NAAG and sugar metabolites sorbitol, gluconic acid, xylitol, ribitol, arabinotol, and erythritol. Eight samples from diabetic brains were analysed, six of which grouped with the SDB samples without compromising the model (R2 CUM 0.850, Q2 CUM 0.534, p-value for cross-validated ANOVA score 0.00087). There appears on the basis of this small sample set to be some commonality between metabolic perturbations resulting from diabetes and from SDB.
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http://dx.doi.org/10.1016/j.csbj.2016.02.003 | DOI Listing |
Alzheimers Dement
December 2024
University of California, San Francisco, San Francisco, CA, USA
Background: Growing evidence reports an association between sleep‐disordered breathing (SDB) and cognitive impairment, including mild cognitive impairment (MCI) and dementia. However, there is limited research on the link between cognitive impairment and in‐home measures of SDB and how this association may differ by race, ethnicity, and sex.
Method: We studied 822 individuals who were enrolled in the community‐based Health and Aging Brain Study‐Health Disparities (HABS‐HD)‐Dormir study.
PLoS One
January 2025
Department of Psychology, Concordia University, Montréal, Quebec, Canada.
In cognitive science, the sensation of "groove" has been defined as the pleasurable urge to move to music. When listeners rate rhythmic stimuli on derived pleasure and urge to move, ratings on these dimensions are highly correlated. However, recent behavioural and brain imaging work has shown that these two components may be separable.
View Article and Find Full Text PDFNeurourol Urodyn
January 2025
Behavioral Science Institute, Radboud University, Nijmegen, The Netherlands.
Aims: Sleep disordered breathing (SDB), lower urinary tract dysfunction (LUTD), and enuresis (NE) are common in children and adolescents and have serious consequences, especially on social and emotional development. Even though much is known about the association between SDB and NE among adults, the number of articles in children and adolescents is limited. Therefore, the aim of the present scoping review was to map out the current knowledge about SDB and LUTD in children and adolescents.
View Article and Find Full Text PDFAm J Vet Res
December 2024
Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY.
Objective: To retrospectively describe the management of sleep-disordered breathing (SDB) via permanent (crico)tracheostomy (PT).
Methods: The sample was 3 client-owned dogs. Each of the dogs had variable clinical signs related to their SDB with all having severely affected quality of sleep and experiencing multiple apneic episodes a night in the study period from January 1, 2019, to December 31, 2023.
JACC Clin Electrophysiol
November 2024
Centre for Heart Rhythm Disorders, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia. Electronic address:
Background: Sleep-disordered breathing (SDB) is common in patients with atrial fibrillation (AF) and negatively impacts treatment outcomes. Optimal tools for AF patient selection for SDB testing are lacking.
Objectives: This study sought to develop and validate a prediction tool to detect patients who have AF with moderate-to-severe SDB.
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