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Sleeping Disorders in Healthy Individuals with Different Dietary Patterns and BMI, Questionnaire Assessment.

ABSTRACT: The COVID-19 pandemic and abiding restrictions have affected every life domain. Sleep disturbances are a major health issue that is linked with a higher prevalence of metabolic syndrome, obesity, and psychological burdens. Research of sleeping disorders among vegetarian and non-vegetarian subpopulations is limited. The aim of the study was to assess the prevalence of sleeping disorders during the COVID-19 pandemic among people with different dietary patterns. Using a web-based cross-sectional survey, data were collected from 1987 people. A total of 1956 respondents met all study conditions. The questionnaire consisted of sociodemographic information, assessment of dietary habits, and assessment of the prevalence of insomnia and sleepiness, based on the Athens Insomnia Scale (AIS) and Epworth Sleepiness Scale (ESS). A total of 36.04% (n = 705) respondents declared that they noticed a change in the quality of their sleep during the last year. According to AIS and ESS, non-vegetarians suffered from insomnia or sleepiness more often than vegetarians. Insomnia and sleepiness were also more prevalent among those respondents who declared consumption of fruit and vegetables less often than once a day compared with those who consumed fruit and vegetables daily. Respondents with BMI within the recommended limit (18.5-24.99) suffered from insomnia less often when compared with underweight (BMI < 18.5) or obese (BMI ≥ 25) respondents. Those results may be useful for public health workers and medical professionals in terms of establishing new instruments that help treat sleeping disorders.

SUBMITTER: Piekarska M

PROVIDER: S-EPMC8656560 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundKinarm Standard Tests (KSTs) is a suite of upper limb tasks to assess sensory, motor, and cognitive functions, which produces granular performance data that reflect spatial and temporal aspects of behavior (>100 variables per individual). We have previously used principal component analysis (PCA) to reduce the dimensionality of multivariate data using the Kinarm End-Point Lab (EP). Here, we performed PCA using data from the Kinarm Exoskeleton Lab (EXO), and determined agreement of PCA results across EP and EXO platforms in healthy participants. We additionally examined whether further dimensionality reduction was possible by using PCA across behavioral tasks.MethodsHealthy participants were assessed using the Kinarm EXO (N = 469) and EP (N = 170-200). Four behavioral tasks (six assessments in total) were performed that quantified arm sensory and motor function, including position sense [Arm Position Matching (APM)] and three motor tasks [Visually Guided Reaching (VGR), Object Hit (OH), and Object Hit and Avoid (OHA)]. The number of components to include per task was determined from scree plots and parallel analysis, and rotation type (orthogonal vs. oblique) was decided on a per-task basis. To assess agreement, we compared principal components (PCs) across platforms using distance correlation. We additionally considered inter-task interactions in EXO data by performing PCA across all six behavioral assessments.ResultsBy applying PCA on a per task basis to data collected using the EXO, the number of behavioral parameters were substantially reduced by 58-75% while accounting for 76-87% of the variance. These results compared well to the EP analysis, and we found good-to-excellent agreement values (0.75-0.99) between PCs from the EXO and those from the EP. Finally, we were able to reduce the dimensionality of the EXO data across tasks down to 16 components out of a total of 76 behavioral parameters, which represents a reduction of 79% while accounting for 73% of the total variance.ConclusionPCA of Kinarm robotic assessment appears to capture similar relationships between kinematic features in healthy individuals and is agnostic to the robotic platform used for collection. Further work is needed to investigate the use of PCA-based data reduction for the characterization of neurological deficits in clinical populations.

Project description:BackgroundThe number of tumor suppressor genes for which germline mutations have been linked to cancer risk is steadily increasing. However, while recent reports have linked constitutional normal tissue promoter methylation of BRCA1 and MLH1 to ovarian and colon cancer risk, the role of epigenetic alterations as cancer risk factors remains largely unknown, presenting an important area for future research. Currently, we lack fast and sensitive methods for assessment of promoter methylation status across known tumor suppressor genes.ResultsIn this paper, we present a novel NGS-based approach assessing promoter methylation status across a large panel of defined tumor suppressor genes to base-pair resolution. The method omits the limitations related to commonly used array-approaches. Our panel includes 565 target regions covering the promoters of 283 defined tumor suppressors, selected by pre-specified criteria, and was applied for rapid targeted methylation-specific NGS. The feasibility of the method was assessed by analyzing normal tissue DNA (white blood cells, WBC) samples from 34 healthy postmenopausal women and by performing preliminary assessment of the methylation landscape of tumor suppressors in these individuals. The mean target coverage was 189.6x providing a sensitivity of 0.53%, sufficient for promoter methylation assessment of low-level methylated genes like BRCA1. Within this limited test-set, we detected 206 regions located in the promoters of 149 genes to be differentially methylated (hyper- or hypo-) at > 99% confidence level. Seven target regions in gene promoters (CIITA, RASSF1, CHN1, PDCD1LG2, GSTP1, XPA, and ZNF668) were found to be hyper-methylated in a minority of individuals, with a > 20 percent point difference in mean methylation across the region between individuals. In an exploratory hierarchical clustering analysis, we found that the individuals analyzed may be grouped into two main groups based on their WBC methylation profile across the 283 tumor suppressor gene promoters.ConclusionsMethylation-specific NGS of our tumor suppressor panel, with detailed assessment of differential methylation in healthy individuals, presents a feasible method for identification of novel epigenetic risk factors for cancer.

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Sleeping Disorders in Healthy Individuals with Different Dietary Patterns and BMI, Questionnaire Assessment.

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