Causal association of lifestyle factors, clinical features in the risk of sleep disorders: Based on mendelian randomization analysis DOI Creative Commons
Jingyu Xu, Baojuan Wang,

Wenxing Zhu

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 18, 2024

Abstract Objective: To identify potential genetic risk factors for sleep disorders (SD) and to explore the causal associations between lifestyle clinical features with SD, given limitations of traditional observational studies. Methods: Data from published genome-wide association studies (GWAS) were analyzed, encompassing 34 17 as exposures influencing SD. These analyses yielded information on 51 4 outcomes. Outcomes consisted four categories: non-organic (NSD), narcolepsy, rapid eye movement (REM) behavior (RSBD), obstructive apnea (OSA). All variables exposure outcome derived individuals European ancestry. Two-sample MR analysis was conducted, inverse variance weighted (IVW) primary method evaluating effects. Weighted median estimation (WME), MR-Egger (MRE), simple mode (SM), (WM) used supplementary evaluation methods. Results: (1) The protective causally associated NSD include overall physical activity time (OR: 0.35; 95%CI: 0.13-0.99; P=0.048) fresh fruit intake 0.30; 0.12-0.75; P=0.010). (2) that are related narcolepsy smoking 1.02; 1.01-1.03; P=0.001), alcoholic drinks P=0.013 OR: P<0.001), cereal 1.03; 1.00-1.07; P=0.029), salad/raw vegetable 1.10; 1.03-1.16; P=0.002), TDI 1.05; 1.00-1.10; P=0.036), health rating 1.07; 1.05-1.10; BMI 1.02-1.03; FINS 1.06; 1.03-1.09; P<0.001), TG P<0.001) hypertension 1.08; 1.02-1.15; P=0.011). (3) tea 0.95; 0.93-0.97; non-oily fish 0.91; 0.84-0.99; P=0.021), years schooling 0.97; 0.96-0.99; cognitive performance 0.98; 0.97-0.99; P=0.001), average total household income before tax 0.94; 0.93-0.96; ApoA-I 0.99; 0.99-1.00; HDL 0.98-0.99; P<0.001). (4) OSA 1.20; 1.08-1.34; P=0.001 1.15; 1.06-1.25; alcohol 1.12; 1.01-1.24; P=0.037), coffee 1.25; 1.00-1.56; P=0.046), pork 2.55; 1.37-4.74; P=0.003), 1.56; 1.12-2.19; P=0.009), 2.76; 2.20-3.46; 1.97; 1.85-2.11; WHR 1.30; 1.08-1.55; P=0.004), 1.02-1.11; 2.72; 1.73-4.26; CRP 1.01-1.11; P=0.016). (5) bread 0.63; 0.49-0.83; 0.71; 0.56-0.90; P=0.005), dried 0.64; 0.50-0.83; 0.72; 0.66-0.79; 0.79; 0.71-0.87; 0.78; 0.66-0.93; FPG 0.84; 0.75-0.94; P=0.003), 0.90-0.99; P=0.017) 0.89; 0.85-0.93; (6) After multivariate through adjusting BMI, ApoA-I, TG, still exists. Conclusions: evidence this study suggests among factors: NSD. Smoking drinks, intake, poverty, poor rating, high narcolepsy. Tea non oily education cognition, OSA. Bread Among features: is a factor High FINS, hypertension, FPG, There not enough suggest other relationships meet criteria established.

Language: Английский

Sodium Oxybate: Practical Considerations and Patient Perspectives DOI Creative Commons

Maggie Lavender,

Cécile Martin,

Diana C. Anderson

et al.

CNS Drugs, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Narcolepsy is a rare, chronic sleep disorder with significant impacts on the quality of life people affected by disorder. People narcolepsy (PWN) are diverse patient population evolving symptoms, comorbidities, and perspectives. As PWN have varying needs, clinicians should consider more personalized approach to therapy, including active participation in their care shared decision-making between clinician achieve optimal outcomes. In this review, we discuss various characteristics challenges PWN, present illustrative clinical case scenarios provide proposed framework best address therapy for demystify concerns sodium oxybate.

Language: Английский

Citations

1
In Silico Tuning of Binding Selectivity for New SARS-CoV-2 Main Protease Inhibitors DOI Creative Commons
Feng Wang, Vladislav Yu. Vasilyev

Computer Methods and Programs in Biomedicine, Journal Year: 2025, Volume and Issue: 262, P. 108678 - 108678

Published: Feb. 18, 2025

Language: Английский

Citations

0
A New Dawn in the Management of Narcolepsy DOI Creative Commons

Michael J. Thorpy,

Anne Marie Morse

CNS Drugs, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Language: Английский

Citations

0
Orexin/hypocretin receptor 2 signaling in MCH neurons regulates REM sleep and insulin sensitivity DOI Creative Commons
Shuntaro Izawa, Debora Fuscà, Hong Jiang

et al.

Cell Reports, Journal Year: 2025, Volume and Issue: 44(2), P. 115277 - 115277

Published: Feb. 1, 2025

Highlights•Ox2R is expressed in distinct molecular sub-populations of MCH neurons•Orexin B elicits excitatory and inhibitory responses neurons•Ox2R inactivation neurons impairs REM sleep female mice•Ox2R insulin sensitivity miceSummaryOrexin/hypocretin receptor type 2 (Ox2R), which widely the brain, receives orexin signals modulates metabolism. Ox2R selective agonists are currently under clinical trials for narcolepsy treatment. Here, we focused on expression function melanin-concentrating hormone (MCH) neurons, have opposite roles to metabolism regulation. Ox2R-expressing showed heterogeneity RNA expression, application brain slices induced both neuron populations. reduced transitions from non-rapid eye movement (NREM) impaired with excessive feeding after a fasting period mice. In conclusion, mediates vivo, regulate mice.Graphical abstract

Language: Английский

Citations

0
Integrating Drug Target Information in Deep Learning Models to Predict the Risk of Adverse Events in Patients with Comorbid Post-Traumatic Stress Disorder and Alcohol Use Disorder DOI Creative Commons

Oshin Miranda,

Xiguang Qi, M. Daniel Brannock

et al.

Biomedicines, Journal Year: 2024, Volume and Issue: 12(12), P. 2772 - 2772

Published: Dec. 5, 2024

Background/Objectives: Comorbid post-traumatic stress disorder (PTSD) and alcohol use (AUD) patients are at a significantly higher risk of adverse outcomes, including opioid disorder, depression, suicidal behaviors, death, yet limited treatment options exist for this population. This study aimed to build on previous research by incorporating drug target information into novel deep learning model, T-DeepBiomarker, predict outcomes identify potential therapeutic medications. Methods: We utilized electronic medical record (EMR) data from the University Pittsburgh Medical Center (UPMC), analyzing 5565 PTSD + AUD patients. T-DeepBiomarker was developed integrating multimodal data, lab results, information, comorbidities, neighborhood-level social determinants health (SDoH), individual-level SDoH (e.g., psychotherapy veteran status). The model trained events, within three months following any clinical encounter. Candidate medications targeting significant proteins were identified through literature reviews. Results: achieved high predictive performance with an AUROC 0.94 in Several medications, OnabotulinumtoxinA, Dronabinol, Acamprosate, Celecoxib, Exenatide, Melatonin, Semaglutide, as potentially reducing events proteins. Conclusions: demonstrates accuracy predicting highlights candidate drugs effects. These findings advance pharmacotherapy high-risk population that warrant further investigation.

Language: Английский

Citations

1
Causal association of lifestyle factors, clinical features in the risk of sleep disorders: Based on mendelian randomization analysis DOI Creative Commons
Jingyu Xu, Baojuan Wang,

Wenxing Zhu

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 18, 2024

Abstract Objective: To identify potential genetic risk factors for sleep disorders (SD) and to explore the causal associations between lifestyle clinical features with SD, given limitations of traditional observational studies. Methods: Data from published genome-wide association studies (GWAS) were analyzed, encompassing 34 17 as exposures influencing SD. These analyses yielded information on 51 4 outcomes. Outcomes consisted four categories: non-organic (NSD), narcolepsy, rapid eye movement (REM) behavior (RSBD), obstructive apnea (OSA). All variables exposure outcome derived individuals European ancestry. Two-sample MR analysis was conducted, inverse variance weighted (IVW) primary method evaluating effects. Weighted median estimation (WME), MR-Egger (MRE), simple mode (SM), (WM) used supplementary evaluation methods. Results: (1) The protective causally associated NSD include overall physical activity time (OR: 0.35; 95%CI: 0.13-0.99; P=0.048) fresh fruit intake 0.30; 0.12-0.75; P=0.010). (2) that are related narcolepsy smoking 1.02; 1.01-1.03; P=0.001), alcoholic drinks P=0.013 OR: P<0.001), cereal 1.03; 1.00-1.07; P=0.029), salad/raw vegetable 1.10; 1.03-1.16; P=0.002), TDI 1.05; 1.00-1.10; P=0.036), health rating 1.07; 1.05-1.10; BMI 1.02-1.03; FINS 1.06; 1.03-1.09; P<0.001), TG P<0.001) hypertension 1.08; 1.02-1.15; P=0.011). (3) tea 0.95; 0.93-0.97; non-oily fish 0.91; 0.84-0.99; P=0.021), years schooling 0.97; 0.96-0.99; cognitive performance 0.98; 0.97-0.99; P=0.001), average total household income before tax 0.94; 0.93-0.96; ApoA-I 0.99; 0.99-1.00; HDL 0.98-0.99; P<0.001). (4) OSA 1.20; 1.08-1.34; P=0.001 1.15; 1.06-1.25; alcohol 1.12; 1.01-1.24; P=0.037), coffee 1.25; 1.00-1.56; P=0.046), pork 2.55; 1.37-4.74; P=0.003), 1.56; 1.12-2.19; P=0.009), 2.76; 2.20-3.46; 1.97; 1.85-2.11; WHR 1.30; 1.08-1.55; P=0.004), 1.02-1.11; 2.72; 1.73-4.26; CRP 1.01-1.11; P=0.016). (5) bread 0.63; 0.49-0.83; 0.71; 0.56-0.90; P=0.005), dried 0.64; 0.50-0.83; 0.72; 0.66-0.79; 0.79; 0.71-0.87; 0.78; 0.66-0.93; FPG 0.84; 0.75-0.94; P=0.003), 0.90-0.99; P=0.017) 0.89; 0.85-0.93; (6) After multivariate through adjusting BMI, ApoA-I, TG, still exists. Conclusions: evidence this study suggests among factors: NSD. Smoking drinks, intake, poverty, poor rating, high narcolepsy. Tea non oily education cognition, OSA. Bread Among features: is a factor High FINS, hypertension, FPG, There not enough suggest other relationships meet criteria established.

Language: Английский

Citations

0