Associations between wearable device-measured sleep variability and cognition among older adults

Healthy sleep habits are protective against adverse health outcomes, but it is unclear how strongly sleep intraindividual variability is associated with cognitive function among older adults. In this study we aimed to examine how accelerometer-derived intraindividual variability in sleep duration, efficiency, onset timing, and offset timing is associated with cognition using cross-sectional data from the 2011-2014 waves of the National Health and Nutrition Examination Survey (NHANES). Cognition was assessed by creating a composite measure derived by summing z-scores from the Digit Symbol Substitution Test (DSST), Consortium to Establish a Registry for Alzheimers Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). A final cohort of 2508 older adults aged 60+ with at least three days of accelerometer wear time who completed all three cognitive tests were included in this study. After centering all sleep intraindividual variability metrics and adjusting for demographic factors, the presence of diabetes, depressive symptoms, and measures of functional independence, we found that increased intraindividual variability in sleep onset timing was associated with worse cognition (Beta, -0.12; 95% CI, -0.19 to -0.05), as was increased intraindividual variability in sleep efficiency (Beta, -0.12; 95% CI, -0.20 to -0.05), and increased intraindividual variability in sleep duration (Beta, -0.10; 95% CI, -0.17 to -0.03). This study suggests that sleep guidance aimed at preserving cognition among older adults could be revised to include a focus on sleep consistency regarding onset timing, quality, and duration.Comment: Typo corrected in the abstrac

Using wearable device-based machine learning models to autonomously identify older adults with poor cognition

Conducting cognitive tests is time-consuming for patients and clinicians. Wearable device-based prediction models allow for continuous health monitoring under normal living conditions and could offer an alternative to identifying older adults with cognitive impairments for early interventions. In this study, we first derived novel wearable-based features related to circadian rhythms, ambient light exposure, physical activity levels, sleep, and signal processing. Then, we quantified the ability of wearable-based machine-learning models to predict poor cognition based on outcomes from the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimers Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). We found that the wearable-based models had significantly higher AUCs when predicting all three cognitive outcomes compared to benchmark models containing age, sex, education, marital status, household income, diabetic status, depression symptoms, and functional independence scores. In addition to uncovering previously unidentified wearable-based features that are predictive of poor cognition such as the standard deviation of the midpoints of each persons most active 10-hour periods and least active 5-hour periods, our paper provides proof-of-concept that wearable-based machine learning models can be used to autonomously screen older adults for possible cognitive impairments. Such models offer cost-effective alternatives to conducting initial screenings manually in clinical settings

Genome-wide association meta-analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus

Systemic lupus erythematosus (SLE; OMIM 152700) is a genetically complex autoimmune disease. Genome-wide association studies (GWASs) have identified more than 50 loci as robustly associated with the disease in single ancestries, but genome-wide transancestral studies have not been conducted. We combined three GWAS data sets from Chinese (1,659 cases and 3,398 controls) and European (4,036 cases and 6,959 controls) populations. A meta-analysis of these studies showed that over half of the published SLE genetic associations are present in both populations. A replication study in Chinese (3,043 cases and 5,074 controls) and European (2,643 cases and 9,032 controls) subjects found ten previously unreported SLE loci. Our study provides further evidence that the majority of genetic risk polymorphisms for SLE are contained within the same regions across both populations. Furthermore, a comparison of risk allele frequencies and genetic risk scores suggested that the increased prevalence of SLE in non-Europeans (including Asians) has a genetic basis

Associations between wearable device-measured sleep variability and cognition among older adults

Importance: Healthy sleep habits are protective against adverse health outcomes, but it is unclear how strongly sleep intraindividual variability is associated with cognitive function among older adults. Objective: To examine how intraindividual variability in sleep duration, efficiency, onset timing, and offset timing is associated with cognition among older adults in the United States. Design: Cross-sectional Setting: 2011-2014 waves of the National Health and Nutrition Examination Survey (NHANES) Participants: Older adults aged 60+ with valid accelerometer and cognitive test data Exposures: Accelerometer-derived variability in sleep duration, efficiency, onset timing, and offset timing. Average metrics were also considered for comparison purposes. Main Outcome and Measures: A composite cognitive measure derived by summing z-scores from the Digit Symbol Substitution Test (DSST), Consortium to Establish a Registry for Alzheimer&rsquo;s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Results: A final cohort of 2508 older adults aged 60+ with at least three days of accelerometer wear time who completed all three cognitive tests in the NHANES 2011-2014 waves were included in this study. After adjusting for demographic factors, the presence of diabetes, depressive symptoms, and measures of functional independence, we found that increased intraindividual variability in sleep onset timing was associated with worse cognition (&beta;, -0.12; 95% CI, -0.19 to -0.05), as was increased intraindividual variability in sleep efficiency (&beta;, -0.12; 95% CI, -0.20 to -0.05), and increased intraindividual variability in sleep duration (&beta;, -0.10; 95% CI, -0.17 to -0.03). Conclusion and Relevance: This study found that greater intraindividual variability in sleep duration, efficiency, and onset timing were significantly associated with worse cognition among older adults. Sleep variability metrics can be useful targets for interventions seeking to decrease the risk of cognitive impairments.</p

Using wearable device-based machine learning models to autonomously identify older adults with poor cognition

Conducting cognitive tests is time-consuming for patients and clinicians. Wearable device-based prediction models allow for continuous health monitoring under normal living conditions and could offer an alternative to identifying older adults with cognitive impairments for early interventions. In this study, we first derived novel wearable-based features related to circadian rhythms, ambient light exposure, physical activity levels, sleep, and signal processing. Then, we quantified the ability of wearable-based machine-learning models to predict poor cognition based on outcomes from the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer&rsquo;s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). We found that the wearable-based models had significantly higher AUCs when predicting all three cognitive outcomes compared to benchmark models containing age, sex, education, marital status, household income, diabetic status, depression symptoms, and functional independence scores. In addition to uncovering previously unidentified wearable-based features that are predictive of poor cognition such as the standard deviation of the midpoints of each person&rsquo;s most active 10-hour periods and least active 5-hour periods, our paper provides proof-of-concept that wearable-based machine learning models can be used to autonomously screen older adults for possible cognitive impairments. Such models offer cost-effective alternatives to conducting initial screenings manually in clinical settings.</p

Identifying the most predictive risk factors for future cognitive impairment among elderly Chinese

Importance: In China,&nbsp;the&nbsp;societal burden of&nbsp;cognitive&nbsp;impairments&nbsp;continues to increase as&nbsp;the&nbsp;country ages, but our knowledge remains limited regarding how to accurately&nbsp;predict&nbsp;future&nbsp;cognitive&nbsp;impairment&nbsp;at&nbsp;the&nbsp;individual level&nbsp;for&nbsp;preventative interventions.&nbsp;Identifying&nbsp;the&nbsp;most&nbsp;predictive&nbsp;risk&nbsp;factors&nbsp;and socioeconomic groups where&nbsp;predictions&nbsp;are less accurate would provide a foundation&nbsp;for&nbsp;developing targeted&nbsp;prediction&nbsp;models that can&nbsp;identify&nbsp;elderly&nbsp;at high&nbsp;risks&nbsp;of&nbsp;future&nbsp;cognitive&nbsp;impairments. Objectives: To quantify how well demographics, instrumental activities of daily living, activities of daily living,&nbsp;cognitive&nbsp;tests, social&nbsp;factors, psychological&nbsp;factors, diet, exercise and sleep, chronic diseases, and three recently published&nbsp;prediction&nbsp;models&nbsp;predict&nbsp;future&nbsp;cognitive&nbsp;impairments&nbsp;in&nbsp;the&nbsp;general&nbsp;Chinese&nbsp;population and&nbsp;among&nbsp;male, female, rural, urban, educated, and uneducated&nbsp;elderly. Design:&nbsp;The&nbsp;Chinese&nbsp;Longitudinal Healthy Longevity Survey (CLHLS) is a prospective cohort study of&nbsp;elderly&nbsp;Chinese&nbsp;from 23 provinces. Individual information from&nbsp;the&nbsp;2011 CLHLS survey was used to&nbsp;predict&nbsp;if participants would become&nbsp;cognitively&nbsp;impaired&nbsp;by follow-up in 2014. Setting: Population-based. Participants: 4047 CLHLS participants 60 years of age or older without&nbsp;cognitive&nbsp;impairments&nbsp;at baseline were included. Main Outcome:&nbsp;Cognitive&nbsp;impairment&nbsp;was&nbsp;identified&nbsp;through&nbsp;the&nbsp;Chinese&nbsp;language version of&nbsp;the&nbsp;Mini Mental State Examination (MMSE).&nbsp;Predictive&nbsp;ability was quantified using&nbsp;the&nbsp;AUC, sensitivity, and specificity across 20 repeats of 10-fold cross validation where&nbsp;the&nbsp;target variable was an indicator of&nbsp;cognitive&nbsp;impairment&nbsp;3 years from&nbsp;the&nbsp;baseline survey. Results: A total of 337 (8.3%)&nbsp;elderly&nbsp;Chinese&nbsp;became&nbsp;cognitively&nbsp;impaired&nbsp;by&nbsp;the&nbsp;follow up survey.&nbsp;The&nbsp;risk&nbsp;factor&nbsp;groups with&nbsp;the&nbsp;most&nbsp;predictive&nbsp;ability in&nbsp;the&nbsp;general population were demographics (AUC, 0.78, 95% CI, 0.77-0.78),&nbsp;cognitive&nbsp;tests (AUC, 0.72, 95% CI, 0.72-0.73), and instrumental activities of daily living (AUC, 0.71, 95% CI, 0.70-0.71). Demographics,&nbsp;cognitive&nbsp;tests, instrumental activities of daily living, and all three re-created&nbsp;prediction&nbsp;models had significantly higher AUCs when making&nbsp;predictions&nbsp;among&nbsp;women compared to men and&nbsp;among&nbsp;the&nbsp;uneducated compared to&nbsp;the&nbsp;educated. Dietary&nbsp;factors, which have yet to be included in&nbsp;prediction&nbsp;models in China, had more&nbsp;predictive&nbsp;power (AUC, 0.59, 95% CI, 0.58-0.60) than activities of daily living (AUC, 0.57, 95% CI, 0.56-0.57), psychological&nbsp;factors&nbsp;(AUC, 0.58, 95% CI, 0.57-0.59), and chronic diseases (AUC, 0.53, 95% CI, 0.52-0.53). Conclusion and relevance: This study suggests that demographics,&nbsp;cognitive&nbsp;tests, and instrumental activities of daily living are&nbsp;the&nbsp;most&nbsp;useful&nbsp;risk&nbsp;factors&nbsp;for&nbsp;predicting&nbsp;future&nbsp;cognitive&nbsp;impairment&nbsp;among&nbsp;elderly&nbsp;Chinese. However,&nbsp;the&nbsp;most&nbsp;useful&nbsp;risk&nbsp;factors&nbsp;and existing models have lower&nbsp;predictive&nbsp;power&nbsp;among&nbsp;male, urban, and educated&nbsp;elderly&nbsp;Chinese. More efforts are needed to ensure that equally accurate&nbsp;risk&nbsp;assessments can be conducted across different socioeconomic groups in China.</p

LncRNA ADAMTS9-AS1 knockdown restricts cell proliferation and EMT in non-small cell lung cancer

A recent bioinformatics analysis identified long non‐coding RNA antisense 1 ADAMTS9-AS1 as an independent prognostic marker in several tumors, including prostate cancer and bladder cancer. Nevertheless, the prognostic value and functional role of ADAMTS9-AS1 in non-small cell lung cancer (NSCLC) remain elusive. Here, we first found that the expression of ADAMTS9-AS1 was significantly upregulated in NSCLC tissues compared with adjacent normal tissues using quantitative real time PCR analysis. Clinically, we observed that ADAMTS9-AS1 expression was associated with TNM stage, lymph node metastasis and poor prognosis in NSCLC patients. By performing lossof-function assay in A549 and 95D cells, our in vitro experiments further showed that knockdown of ADAMTS9-AS1 remarkedly suppressed cell proliferation, caused cell cycle G0/G1 arrest and apoptosis, and inhibited cell migration and invasion in NSCLC cells using CCK-8, colony formation, flow cytometry and transwell assays. Moreover, we found that ADAMTS9-AS1 knockdown downregulated the expression of CDK4, N-cadherin, Vimentin, but upregulated the expression of Bad and E-cadherin. In summary, our results revealed that ADAMTS9-AS1 may serve as a potential therapeutic target for the treatment of patients with NSCL

Intracerebroventricular administration of CYX-6, a potent μ-opioid receptor agonist, a δ- and κ-opioid receptor antagonist and a biased ligand at μ, δ & κ-opioid receptors, evokes antinociception with minimal constipation and respiratory depression in rats in contrast to morphine

Mu opioid receptor (MOPr) agonists are thought to produce analgesia via modulation of G-protein-coupled intracellular signalling pathways whereas the β-arrestin2 pathway is proposed to mediate opioid-related adverse effects. Here, we report the antinociception, constipation and respiratory depressant profile of CYX-6, a potent MOPr agonist that is also a delta and a kappa opioid receptor (DOPr/KOPr) antagonist and that lacks β-arrestin2 recruitment at each of the MOPr, DOPr and the KOPr. In anaesthetised male Sprague Dawley rats, an intracerebroventricular (i.c.v.) guide cannula was stereotaxically implanted. After 5–7 days post-surgical recovery, rats received a single i.c.v. bolus dose of CYX-6 (3–30 nmol), morphine (100 nmol) or vehicle. Antinociception was assessed using the warm water tail flick test (52.5 ± 0.5 °C). Constipation was assessed using the charcoal meal gut motility test and the castor oil-induced diarrhoea test. Respiratory depression was measured by whole-body plethysmography in awake, freely moving animals, upon exposure to a hypercapnic gas mixture (8% CO, 21% O and 71% N). The intrinsic pharmacology of CYX-6 given by the i.c.v. route in rats showed that it produced dose-dependent antinociception. It also produced respiratory stimulation rather than depression and it had a minimal effect on intestinal motility in contrast to the positive control, morphine. CYX-6 is an endomorphin-2 analogue that dissociates antinociception from constipation and respiratory depression in rats. Our findings provide useful insight to inform the discovery and development of novel opioid analgesics with a superior tolerability profile compared with morphine