Prolonging effects of Valeriana fauriei root extract on pentobarbital-induced sleep in caffeine-induced insomnia model mice and the pharmacokinetics of its active ingredients under conditions of glycerol fatty acid ester as emulsifiers

Ota, Misato; Maki, Yasuhito; Xu, Ling; Makino, Toshiaki

doi:10.1016/j.jep.2022.115625

Cited by 4 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This suggests that VF may have a greater effect on sleep onset than VO. However, because KGD, the main component of VF, does not shorten sleep latency [8], the mechanism for improving sleep onset requires further investigation.…”

Section: Discussionmentioning

confidence: 99%

“…Meanwhile, VF (5 g as VF/kg, p.o.) significantly shortened sleep latency in a concentration‐dependent manner in the insomnia mouse model [8], whereas a high dosage of VO (12.5 or 37.5 g as VO/kg, p.o., the doses converted assuming an extraction efficiency of 8%) exhibited shortened sleep latency in a sleep‐disturbed rat model [19]. This suggests that VF may have a greater effect on sleep onset than VO.…”

Section: Discussionmentioning

confidence: 99%

“…The KGD content was used as a quality criterion because KGD has been shown to contribute to almost all of the sedative effects of VF [7,8]. KGD in two VF capsules was quantified to 20.3 ± 1.2 (n = 5) mg by the following method, which is a modification of the procedure described in previous reports [7,11].…”

Section: Preparation Of Vf Extract and Soft Capsulesmentioning

confidence: 99%

“…significantly shortened sleep latency and prolonged pentobarbital‐induced sleep in mice with caffeine‐induced insomnia, partially mediated via the GABAergic nervous system. In addition, we showed that KGD was distributed in the brain of mice following oral administration [7, 8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of capsules containing Valeriana fauriei root extract for sleep improvement: A randomized, double‐blind, placebo‐controlled, cross‐over comparative trial

Ota

Oiwa

Maki

et al. 2023

Traditional & Kampo Medicine

Self Cite

View full text Add to dashboard Cite

AimThis clinical trial aimed to clarify the sleep‐ameliorating effects of capsules containing Valeriana fauriei root (VF) extract and their adverse effects.MethodsThe trial was a randomized, double‐blind, placebo‐controlled, cross‐over comparative clinical trial involving participants with an Athens Insomnia Scale (AIS) of 6 or higher, selected by prescreening questionnaires. After randomization, the participants took VF (2 g as VF/day) or placebo capsules for two weeks and were asked to complete the Obstructive Sleep Apnea (OSA) sleep questionnaire daily to assess their sleep status and any changes in their physical condition. On the last day of each term, the participants reported their sleep improvement experience.ResultsNo significant differences were found between the VF and placebo groups on the OSA sleep questionnaire. Significantly, more participants in the VF‐treated group experienced improvements in sleep onset and quality than those in the placebo group. The sleep onset improved particularly in those with early awakening, whereas the sleep quality particularly improved in those with midnight awakening. Although a few participants experienced physical, gastrointestinal and psychological changes when taking VF capsules, there was no significant difference from the placebo group, and no serious adverse events occurred.ConclusionWe clinically determined that VF, a folk medicine, improves sleep onset and sleep quality in people with suspected insomnia without serious adverse events at the dosage of 2 g VF/day.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Preparation Of Vf Extract and Soft Capsulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of capsules containing Valeriana fauriei root extract for sleep improvement: A randomized, double‐blind, placebo‐controlled, cross‐over comparative trial

Ota

Oiwa

Maki

et al. 2023

Traditional & Kampo Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is epidemiological evidence for the relationship between increased age and increased sleep disorders, older age has been associated with increased sleep latency, decreased time spent in rapid eye movement (REM) sleep and stage-3 sleep, and increased frequency of waking up during the night PLOS ONE [56][57][58][59][60][61][62]. Furthermore, increased caffeine usage has been found to be linked with difficulty falling asleep, decreased time falling asleep, and decreased quality of sleep [63]. Additionally, increased alcohol is associated with sleep disorders, leading to decreased sleep latency and potential physiologic need for alcohol as a depressant to allow for sleep in multiple patients [64][65][66].…”

Section: Plos Onementioning

confidence: 99%

Use of machine learning to identify risk factors for insomnia

Huang

2023

PLoS ONE

View full text Add to dashboard Cite

Importance Sleep is critical to a person’s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. Objective The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. Design, setting, and participants A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Methods A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Results Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Conclusion Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

show abstract

Safety evaluation of continuous administration of a high dose of Valeriana fauriei root extract in mice and in humans through an open‐label study

Ota,

Oiwa,

Maki

et al. 2023

Traditional & Kampo Medicine

Self Cite

View full text Add to dashboard Cite

AimThis study aimed to clarify the safety of Valeriana fauriei root (VF) ethanol extract through in vivo and clinical trials.MethodsMice were divided into VF and control groups. They were given VF (4 g VF/kg/day) or the vehicle mixed in the powdered food for four weeks. Food consumption and body weight changes were evaluated, and blood biochemistry and organ weights were assessed on the last day. In addition, an open‐label study was conducted on healthy subjects who took VF capsules (10 g VF/body/day) for four weeks and were asked to report any changes in their physical condition. Physical, hematologic, blood biochemical examination and urinalysis were performed before, two weeks, and four weeks into the study.ResultsFood consumption and weight gain in mice did not differ between the VF and control groups, except for experimental error, and there were no particular differences in the weights of the brain, lung, heart, liver, spleen, and kidney. In mice, VF administration resulted in small but significant decreases in albumin in males and females, and total cholesterol and high‐density lipoprotein cholesterol in females only. These levels were unchanged, but daytime sleepiness was observed in humans.ConclusionSince there was little change in the experimental values at continuous high doses of VF at 10 g/day, it can be concluded that approximately one‐fifth of this dose (2 g VF/day) is less harmful. However, daytime sleepiness should be considered.

show abstract

Prolonging effects of Valeriana fauriei root extract on pentobarbital-induced sleep in caffeine-induced insomnia model mice and the pharmacokinetics of its active ingredients under conditions of glycerol fatty acid ester as emulsifiers

Cited by 4 publications

References 24 publications

Evaluation of capsules containing Valeriana fauriei root extract for sleep improvement: A randomized, double‐blind, placebo‐controlled, cross‐over comparative trial

Evaluation of capsules containing Valeriana fauriei root extract for sleep improvement: A randomized, double‐blind, placebo‐controlled, cross‐over comparative trial

Use of machine learning to identify risk factors for insomnia

Safety evaluation of continuous administration of a high dose of Valeriana fauriei root extract in mice and in humans through an open‐label study

Contact Info

Product

Resources

About