A Diagnosis of Insomnia Is Associated With Differential Expression of Sleep-Regulating Genes in Military Personnel

Recent evidence and progress for developing precision nursing in symptomatology: A scoping review

AIM

To summarize the omics results of symptomatic research that can help nurses identify intervention targets and design precision interventions for pain, mental health, cognitive impairment, sleep disorder, fatigue, lymphedema, and quality of life, so as to provide a comprehensive summary of help and inspire to precision nursing.

METHODS

CINAHL, PubMed, Web of Science, and ScienceDirect databases were searched. Retrieval time was from January 2012 to December 2021. Symptomatology research applying omics that can be used to guide nurses in designing targeted interventions was included.

RESULTS

Forty-six studies were included in the final review. Symptomatology research that can be integrated with nursing science to develop precision nursing focused on pain, mental health, cognitive impairment, sleep disorder, fatigue, lymphedema, and quality of life. Most studies were related to cognitive impairment (n = 10; 21.74%), pain (n = 9; 19.57%), and mental health (n = 8; 17.39%). Moreover, the included studies involved various omics technologies, such as whole genome, epigenome, transcriptome, proteome, and metabolome.

CONCLUSION

The rapid development of various omic technologies promotes symptomatology research, which can help nurses fully understand the information of patients. Phenotypic characteristics and biomarkers shown in symptomatology research help nurses identify intervention targets and develop individualization interventions, so as to prevent and reduce symptoms and improve the quality of life.

IMPLICATION FOR NURSING AND HEALTH POLICY

This scoping review is the first synthesis of all peer-reviewed literature to summarize and provide important information and references from the omic results of symptomatology studies to develop precision nursing, highlighting the status and development of precision nursing. Nursing education policies should introduce the development and importance of precision nursing. Further research could consider investing more attention in precision nursing. Nursing researchers can carry out some studies applying omics technology to explore more biomarkers, helping guide the formulation of clinical intervention for symptoms.

Role of Oxidative Stress and Inflammation in Insomnia Sleep Disorder and Cardiovascular Diseases: Herbal Antioxidants and Anti-inflammatory Coupled with Insomnia Detection using Machine Learning

Insomnia is well-known as trouble in sleeping and enormously influences human life due to the shortage of sleep. Reactive Oxygen Species (ROS) accrue in neurons during the waking state, and sleep has a defensive role against oxidative damage and dissipates ROS in the brain. In contrast, insomnia is the source of inequity between ROS generation and removal by an endogenous antioxidant defense system. The relationship between insomnia, depression, and anxiety disorders damages the cardiovascular systems' immune mechanisms and functions. Traditionally, polysomnography is used in the diagnosis of insomnia. This technique is complex, with a long time overhead. In this work, we have proposed a novel machine learning-based automatic detection system using the R-R intervals extracted from a single-lead electrocardiograph (ECG). Additionally, we aimed to explore the role of oxidative stress and inflammation in sleeping disorders and cardiovascular diseases, antioxidants' effects, and the psychopharmacological effect of herbal medicine. This work has been carried out in steps, which include collecting the ECG signal for normal and insomnia subjects, analyzing the signal, and finally, automatic classification. We used two approaches, including subjects (normal and insomnia), two sleep stages, i.e., wake and rapid eye movement, and three Machine Learning (ML)-based classifiers to complete the classification. A total number of 3000 ECG segments were collected from 18 subjects. Furthermore, using the theranostics approach, the role of mitochondrial dysfunction causing oxidative stress and inflammatory response in insomnia and cardiovascular diseases was explored. The data from various databases on the mechanism of action of different herbal medicines in insomnia and cardiovascular diseases with antioxidant and antidepressant activities were also retrieved. Random Forest (RF) classifier has shown the highest accuracy (subjects: 87.10% and sleep stage: 88.30%) compared to the Decision Tree (DT) and Support Vector Machine (SVM). The results revealed that the suggested method could perform well in classifying the subjects and sleep stages. Additionally, a random forest machine learning-based classifier could be helpful in the clinical discovery of sleep complications, including insomnia. The evidence retrieved from the databases showed that herbal medicine contains numerous phytochemical bioactives and has multimodal cellular mechanisms of action, viz., antioxidant, anti-inflammatory, vasorelaxant, detoxifier, antidepressant, anxiolytic, and cell-rejuvenator properties. Other herbal medicines have a GABA-A receptor agonist effect. Hence, we recommend that the theranostics approach has potential and can be adopted for future research to improve the quality of life of humans.

Progress in Detection of Insomnia Sleep Disorder: A Comprehensive Review

Lack of adequate sleep is a major source of many harmful diseases related to heart, brain, psychological changes, high blood pressure, diabetes, weight gain, etc. 40 to 50% of the world's population is suffering from poor or inadequate sleep. Insomnia is a sleep disorder in which an individual complaint of difficulties in starting/continuing sleep at least four weeks regularly. It is estimated that 70% of heart diseases are generated during insomnia sleep disorder. The main objective of this study is to determine all work conducted on insomnia detection and to make a database. We used two procedures including network visualization techniques on two databases including PubMed and Web of Science to complete this study. We found 169 and 36 previous publications of insomnia detection in the PubMed and the Web of Science databases, respectively. We analyzed 10 datasets, 2 databases, 21 genes, and 23 publications with 30105 subjects of insomnia detection. This work has revealed the future way and gap so far directed on insomnia detection and has also tried to provide objectives for the future work to be proficient in a scientific and significant manner.

Whole blood transcriptome analysis using RNA sequencing in individuals with insomnia disorder and good sleepers: a pilot study

BACKGROUND

Insomnia is a highly prevalent condition that is associated with negative health outcomes, yet little is known about the underlying molecular mechanisms.

METHOD

RNA sequencing was conducted using blood samples from 15 individuals with primary insomnia and 15 age- and gender-matched good sleeper controls. The RNA library was sequenced with 150 base pair paired-ends on the Illumina NovaSeq-6000 platform. Alignment was performed using human reference genome hg38. Differential gene expression analysis was performed using DESeq2 following alignment, using log fold change ±0.50, and had a false discovery rate p-value <0.05. Pathway analysis was performed using Ingenuity Pathway Analysis.

RESULTS

We found 288 differentially expressed genes in insomnia patients when compared to controls. Upregulated genes included LINC02224 (Long Intergenic Non-Protein Coding RNA 2224), DUX4L9 (Double Homeobox 4 Like 9), and TUSC3 (Tumor Suppressor Candidate 3) and down regulated genes included CTXN2 (Cortexin 2), CSMD1 (CUB And Sushi Multiple Domains 1), and SLC12A1 (Solute Carrier Family 12 Member 1). Ingenuity® Pathway Analysis (IPA) revealed 3 associated networks (score>40) with genes and hubs related to inflammation (nuclear factor-kB), oxidative stress (Mitochondrial complex 1) and ubiquitination.

CONCLUSION

Differentially expressed genes in this analysis are functionally associated with inflammation and immune response, mitochondrial and metabolic processes. Further research into the transcriptomic changes in insomnia is needed to understand related pathways to the disorder and provide new avenues for diagnostics and therapeutics.

Collaborative Framework to Advance Symptom Science: An Intramural Perspective

PURPOSE

To describe the collaborative framework used by National Institute of Nursing Research (NINR) investigators to advance symptom science and to provide a research exemplar.

MODEL

The National Institutes of Health (NIH) Symptom Science Model (SSM) was developed to guide symptom science researchers to understand the molecular underpinnings of symptoms using innovative "omics" approaches. The process begins with a review of the literature to understand the state of the science of the symptoms of interest and is followed by cross-sectional, case-controlled, or longitudinal studies to identify potential biological correlates of these symptoms. The final steps include validation of these potential symptom biomarkers using multidisciplinary, collaborative, preclinical experiments, and proof-of-concept clinical trials.

RESEARCH EXEMPLAR

Using the NIH SSM as a guide, the identification of biologic correlates of symptoms using omics and bioinformatic strategies depends on determining the distinct phenotype of the symptoms of interest. The identified biologic correlates of these symptoms are then validated for their functional relevance using in vitro and ex vivo experiments, or through proof-of-concept clinical trials. NINR investigators observed that activation of specific receptors in neural networks can trigger inflammation-related fatigue development and predispose patients to develop chronicity of symptoms. Specifically targeting these neural receptors can reduce symptom intensity.

CONCLUSIONS

Through using the NIH SSM as a guide, NINR investigators quickly generate data and discoveries that significantly advance symptom science by simultaneously collaborating with multiple experts and core laboratories to identify more correlates and validate their functional relevance in order to further understand the biological underpinnings of the symptoms of interest.

CLINICAL RELEVANCE

The collaborative framework used by NINR investigators as guided by the NIH SSM identifies functionally relevant clinical markers that can explain the biological underpinnings of symptoms and can be targeted to optimize symptom management.

Genetic Pathways to Insomnia

This review summarizes current research on the genetics of insomnia, as genetic contributions are thought to be important for insomnia etiology. We begin by providing an overview of genetic methods (both quantitative and measured gene), followed by a discussion of the insomnia genetics literature with regard to each of the following common methodologies: twin and family studies, candidate gene studies, and genome-wide association studies (GWAS). Next, we summarize the most recent gene identification efforts (primarily GWAS results) and propose several potential mechanisms through which identified genes may contribute to the disorder. Finally, we discuss new genetic approaches and how these may prove useful for insomnia, proposing an agenda for future insomnia genetics research.

National Institutes of Health Symptom Science Model sheds light on patient symptoms

Since the establishment of the nursing profession, identifying and alleviating the subjective symptoms experienced by patients has been at the core of nursing practice. In supporting the scientific foundation for clinical practice, nursing science has maintained a consistent commitment to prevent, manage, and eliminate symptoms. Scientists from the intramural research program at the National Institute of Nursing Research (NINR), a component of the National Institutes of Health, developed a National Institutes of Health Symptom Science Model (NIH-SSM) to guide symptom science research programs engaged in the use of emerging "omic" methods such as the genotyping of symptom phenotypes. The NIH-SSM was developed based on the NINR intramural research program's success in designing and implementing methods for examining identified symptoms or symptom clusters. The NIH-SSM identifies the research process of characterizing symptom phenotypes, identifying and testing biomarkers, and ultimately developing clinical interventions in cancer-related fatigue, gastrointestinal disorders, and traumatic brain injuries. The purpose of this article was to demonstrate how scientists can apply the NIH-SSM, leading the broader scientific community in advancing personalized and precise clinical interventions.

Profile of suvorexant in the management of insomnia

Suvorexant, approved in late 2014 in the United States and Japan for the treatment of insomnia characterized by difficulty achieving and/or maintaining sleep, is a dual orexin receptor antagonist and the first drug in its class to reach the market. Its development followed from the 1998 discovery of orexins (also called hypocretins), excitatory neuropeptides originating from neurons in the hypothalamus involved in regulation of sleep and wake, feeding behavior and energy regulation, motor activity, and reward-seeking behavior. Suvorexant objectively improves sleep, shortening the time to achieve persistent sleep and reducing wake after sleep onset, although at approved doses (≤20 mg) the benefit was subjectively assessed as modest. Its half-life of 12 hours is relatively long for a modern hypnotic; however, at approved doses (≤20 mg) next-day sedation and driving impairment were much less apparent than at higher doses. Suvorexant is metabolized by the hepatic CYP3A system and should be avoided in combination with strong CYP3A inhibitors. Drug levels are higher in women and obese people; hence, dosing should be conservative in obese women. Administration with food delays drug absorption and is not advised. No dose adjustment is needed for advanced age, renal impairment, or mild-to-moderate hepatic impairment. Suvorexant in contraindicated in narcolepsy and has not been studied in children. In alignment with the changes begun in 2013 in the labeling of other hypnotics, the United States Food and Drug Administration advises that the lowest dose effective to treat symptoms be used and that patients be advised of the possibility of next-day impairment in function, including driving. Infrequent but notable side effects included abnormal dreams, sleep paralysis, and suicidal ideation that were dose-related and reported to be mild. Given its mechanism of action, cataplexy and rapid eye movement (REM) sleep behavior disorder could potentially occur in some patients taking this medication.