Antioxidant Synergy of the Glutathione Pathway and Incidence of Parkinson\u27s Disease: A Systematic Review

Background: The etiopathogenesis dopaminergic neuron dysfunction of Parkinson\u27s Disease (PD) has not been clearly established. Oxidative stress is a commonly proposed causative mechanism for this dysfunction as variations in antioxidant intake have been observed between older adults with and without PD. The objective of this systematic review was to evaluate the synergistic relationship between antioxidant micronutrients of the glutathione pathway and the incidence of PD in older adults. Methods: This systematic review evaluated the relationship between the intake of vitamins C, D, and E, and selenium, riboflavin and niacin and incidence of PD according to PRISMA guidelines. Four electronic databases (Medline (PubMed), CINAHL, Web of Sciences and SCOPUS) were reviewed, most recently on April 12, 2022. Risk of bias was assessed and reported for each study utilizing respective Joanna Briggs Institute (JBI) Quality Review Checklist for Case Control Studies and Cohort Studies, and Risk of Bias in Systematic Reviews (ROBIS) to assess systematic reviews and meta-analyses. Inclusion criteria were assessment of usual intake of above antioxidants, population of interest was idiopathic PD, and exclusion criteria were assessing population with mean age less than 50 years old, or deemed poor quality of evidence Results: A total of 31 studies were included in the final review after evaluating final inclusion/exclusion criteria. Vitamin D revealed the most prominent relationship with incidence of PD, with 18 of 19 studies identifying a negative relationship between intake and incidence, while results were largely inconclusive for vitamins E and C. There were no studies for selenium, riboflavin or niacin that met criteria for the current study

Association between Self-Reported Prior Nights’ Sleep and Single-Task Gait in Healthy Young Adults: An Exploratory Study Using Machine Learning

Failure to obtain 7-9 hours of sleep has been associated with decreased gait speed in young adults. While Machine Learning (ML) has been used to identify sleep quality in young adults, there are no current studies that have used ML to identify prior night’s sleep in a sample of young adults. PURPOSE: To use ML to identify prior night’s sleep in healthy young adults using single-task walking gait. METHODS: Participants (n=126, age 24.3±4.0yrs; 65% female) completed a survey on their prior night’s sleep and performed a 2-minute walk around a 6m track. Gait data were collected using inertial sensors. Participants were split into 2 groups (\u3c7hs or \u3e9hs: poor sleepers; 7-9hs: good sleepers) and gait characteristics were used to classify participants into each group using ML models via a 10-fold cross validation. A post-hoc ANCOVA was used to assess gait differences. RESULTS: Using Random Forest Classifiers (RFC), top 9 features were extracted. Classification results suggest a 0.79 correlation between gait parameters and prior night’s sleep. The RFC models had a 65.03% mean classification accuracy rate. Top 0.3% of the models had 100% classification accuracy rate. The top 9 features were primarily characteristics that measured variance between lower limb movements. Post-hoc analyses suggest significantly greater variances between lower limb characteristics. CONCLUSION: Good sleepers had more asymmetrical gait patterns (faster gait speed, less trunk motion). Poor sleepers had trouble maintaining gait speed (increased variance in cadence, larger stride lengths, and less time spent in single leg support time). Although the mechanisms of these gait changes are unknown, these findings provide evidence that gait is different for individuals who not receive 7-9 hours of sleep the night before. As evidenced by the high correlation co-efficient of our classification models, gait may be a good way of identifying prior night’s sleep

Examining the Relationship Between Trait Energy and Fatigue and Feelings of Depression in Young Healthy Adults

Depression is usually co-morbid with fatigue. However, we are unaware of studies exploring the relationship between trait energy and fatigue and feelings of depression. Recent evidence suggests that energy and fatigue are two distinct moods. PURPOSE: To examine the association between trait mental and physical energy and fatigue and feelings of depression, within an otherwise healthy young adult cohort. METHODS: Using a cross-sectional design, healthy respondents (n=495) completed a series of self-reported surveys measuring depression, lifestyle factors (sleep, diet, physical activity), and trait mental and physical energy and fatigue. Using a step-wise regression, we controlled for demographics and lifestyle and added trait mental and physical energy and fatigue to the second model. RESULTS: When trait mental and physical energy and fatigue were added to the models, the adjusted R2 increased by 5% (R2 = .112, F(13, 457) = 4.455, p \u3c .001). In our second model, trait mental fatigue was the only significant predictor of depressive mood states (Β = .159, t (457) = 2.512, p = 0.01). CONCLUSION: Young adults, who struggle with high mental fatigue, may also be more likely to report feeling depressed suggesting that fatigue and depression are co-morbid, while low energy and depression are not. Future research should aim to identify epigenetic/genetic factors that influence mental fatigue and how those may be associated with feelings of depression

Association between Self-Reported Prior Night’s Sleep and Single-Task Gait in Healthy, Young Adults: A Study Using Machine Learning

Failure to obtain the recommended 7–9 h of sleep has been associated with injuries in youth and adults. However, most research on the influence of prior night’s sleep and gait has been conducted on older adults and clinical populations. Therefore, the objective of this study was to identify individuals who experience partial sleep deprivation and/or sleep extension the prior night using single task gait. Participants (n = 123, age 24.3 ± 4.0 years; 65% female) agreed to participate in this study. Self-reported sleep duration of the night prior to testing was collected. Gait data was collected with inertial sensors during a 2 min walk test. Group differences (<7 h and >9 h, poor sleepers; 7–9 h, good sleepers) in gait characteristics were assessed using machine learning and a post-hoc ANCOVA. Results indicated a correlation (r = 0.79) between gait parameters and prior night’s sleep. The most accurate machine learning model was a Random Forest Classifier using the top 9 features, which had a mean accuracy of 65.03%. Our findings suggest that good sleepers had more asymmetrical gait patterns and were better at maintaining gait speed than poor sleepers. Further research with larger subject sizes is needed to develop more accurate machine learning models to identify prior night’s sleep using single-task gait

Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning

Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed