Heritability estimates of individual psychological distress symptoms from genetic variation

Pleiotropy of C1QL proteins across physiological systems and their emerging role in synapse homeostasis

The C1q/TNF superfamily of proteins engages in a pleiotropy of physiological functions associated with various diseases. C1QL proteins demonstrate important protective and regulatory roles in the endocrine, immune, cardiovascular, and nervous systems in both human and rodent studies. Studies in the central nervous system (CNS), adipose, and muscle tissue reveal several C1QL protein and receptor pathways altering multiple cellular responses, including cell fusion, morphology, and adhesion. This review examines C1QL proteins across these systems, summarizing functional and disease associations and highlighting cellular responses based on in vitro and in vivo data, receptor interaction partners, and C1QL-associated protein signaling pathways. We highlight the functions of C1QL proteins in organizing CNS synapses, regulating synapse homeostasis, maintaining excitatory synapses, and mediating signaling and trans-synaptic connections. Yet, while these associations are known, present studies provide insufficient insight into the underlying molecular mechanism of their pleiotropy, including specific protein interactions and functional pathways. Thus, we suggest several areas for more in-depth and interdisciplinary hypothesis testing.

The association between stressful experiences and OCD symptoms in young adults at transdiagnostic risk

BACKGROUND

It is unclear whether there is a specific association between stressful experiences and obsessive-compulsive symptoms or whether this relationship is due to stressful experiences increasing risk for psychopathology generally.

AIMS

The current study examined the association between stressful experiences and obsessive-compulsive symptom dimensions, while adjusting for coexisting psychiatric symptoms and psychological distress in a young adult transdiagnostic at-risk sample.

METHODS

Forty-three participants completed self-report measures assessing obsessive-compulsive symptoms, stressful experiences, and a range of other psychiatric symptoms. Regression models examined the relationship between stressful experiences and different obsessive-compulsive symptoms dimensions (i.e., symmetry, fear of harm, contamination, and unacceptable thoughts), adjusting for the influence of coexisting psychiatric symptoms and psychological distress.

RESULTS

The results showed that there was an association between stressful experiences and obsessive-compulsive symptoms dimension of symmetry. Symptoms of borderline personality disorder were positively associated with the obsessive-compulsive symptom dimensions of symmetry and fear of harm symptoms. Symptoms of psychosis were found to be negatively associated with the obsessive-compulsive symptoms dimension of fear of harm.

CONCLUSIONS

These findings have implications for understanding the psychological mechanisms that underlie symmetry symptoms and highlight the need to study OCS dimensions separately to inform more precise, mechanism-targeted interventions.

Association Between Inflammatory Biomarkers and Mental Health Symptoms in Middle Eastern Refugees in the US

Introduction: Refugees are at increased risk for trauma-related mental health disorders, including anxiety, depression, and post-traumatic stress disorder (PTSD). The underlying biological mechanisms linking trauma to mental disorders need additional study, and the possible pathophysiological role of the immune system is attracting increasing interest. In this study, we investigated whether two well-known pro-inflammatory cytokines (interleukin (IL-8) and IL-6) are associated with mental health symptoms in Middle Eastern refugees displaced to the United States.

Methods: Refugees (n=64, mean age=37.6 years) ages ranged from 21 to 74 years (mean=37.62, SD=11.84) were interviewed one month after arrival in Michigan, United States, using a validated survey in Arabic. Questions covered pre-displacement trauma, current anxiety, depression, and PTSD symptoms. Blood, collected immediately following the interview, was analyzed for the levels of interleukins. Multivariate linear regression was used to determine the association between mental health symptoms and IL-6 and IL-8.

Results: In multivariate modeling, older age (β=0.37; p<0.01) and anxiety (β=0.31; p<0.05) were positively associated with IL-8. Age (β=0.28; p<0.05) and pre-displacement trauma (β=0.40; p<0.05) were positively associated with IL-6. Depression (β=-0.38) was negatively associated with IL-6.

Conclusion/relevance: This study of inflammatory biomarkers suggests the possibility of differential associations between mental health symptoms (anxiety and depression) and pro-inflammatory markers (IL-6 and IL-8). To enhance our ability to prevent and more effectively treat trauma-exposed refugees, we need to better understand the neuroinflammatory mechanisms contributing to mental disorders.

Shared genetic architectures of subjective well-being in East Asian and European ancestry populations

Subjective well-being (SWB) has been explored in European ancestral populations; however, whether the SWB genetic architecture is shared across populations remains unclear. We conducted a cross-population genome-wide association study for SWB using samples from Korean (n = 110,919) and European (n = 563,176) ancestries. Five ancestry-specific loci and twelve cross-ancestry significant genomic loci were identified. One novel locus (rs12298541 near HMGA2) associated with SWB was also identified through the European meta-analysis. Significant cross-ancestry genetic correlation for SWB between samples was observed. Polygenic risk analysis in an independent Korean cohort (n = 22,455) demonstrated transferability between populations. Significant correlations between SWB and major depressive disorder, and significant enrichment of central nervous system-related polymorphisms heritability in both ancestry populations were found. Hence, large-scale cross-ancestry genome-wide association studies can advance our understanding of SWB genetic architecture and mental health.

Genome-Wide Interaction Study of Late-Onset Asthma With Seven Environmental Factors Using a Structured Linear Mixed Model in Europeans

Asthma is among the most common chronic diseases worldwide, creating a substantial healthcare burden. In late-onset asthma, there are wide global differences in asthma prevalence and low genetic heritability. It has been suggested as evidence for genetic susceptibility to asthma triggered by exposure to multiple environmental factors. Very few genome-wide interaction studies have identified gene-environment (G×E) interaction loci for asthma in adults. We evaluated genetic loci for late-onset asthma showing G×E interactions with multiple environmental factors, including alcohol intake, body mass index, insomnia, physical activity, mental status, sedentary behavior, and socioeconomic status. In gene-by-single environment interactions, we found no genome-wide significant single-nucleotide polymorphisms. However, in the gene-by-multi-environment interaction study, we identified three novel and genome-wide significant single-nucleotide polymorphisms: rs117996675, rs345749, and rs17704680. Bayes factor analysis suggested that for rs117996675 and rs17704680, body mass index is the most relevant environmental factor; for rs345749, insomnia and alcohol intake frequency are the most relevant factors in the G×E interactions of late-onset asthma. Functional annotations implicate the role of these three novel loci in regulating the immune system. In addition, the annotation for rs117996675 supports the body mass index as the most relevant environmental factor, as evidenced by the Bayes factor value. Our findings help to understand the role of the immune system in asthma and the role of environmental factors in late-onset asthma through G×E interactions. Ultimately, the enhanced understanding of asthma would contribute to better precision treatment depending on personal genetic and environmental information.

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning

BACKGROUND

Fatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis.

METHODS

Tongue and Face Diagnosis Analysis-1 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue.

RESULTS

The results showed that all the four classifiers over "Tongue & Pulse" joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, and with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively.

CONCLUSION

This study proposed and validated an innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.

Prioritization of human well-being spectrum related GWAS-SNVs using ENCODE-based web-tools predict interplay between PSMC3, ITIH4, and SERPINC1 genes in modulating well-being

Several traits related to positive and negative affect show a high genetic as well as phenotypic correlation with well-being in humans, and are therefore collectively termed as "Well-being spectrum". Genome-Wide Association studies (GWA studies) on "well-being measurement" have led to identification of several genomic variants (Single Nucleotide Variants - SNVs), but very little has been explained with respect to their functionality and mode of alteration of well-being. Utilizing a pool of 1258 GWA studies based SNVs on "well-being measurement", we prioritized the SNVs and tried to annotate well-being related functionality through several bioinformatic tools to predict whether a protein sequence variation affects protein function, as well as experimentally validated datasets available in ENCODE based web-tools namely rSNPBase, RegulomeDB, Haploreg, along with GTEx Portal and STRING based protein interaction networks. Prioritization yielded three key SNVs; rs3781627-A, rs13072536-T and 5877-C potentially regulating three genes, PSMC3, ITIH4 and SERPINC1, respectively. Interestingly, the genes showed well clustered protein-protein interaction (maximum combined confidence score >0.4) with other well-being candidate genes, namely TNF and CRP genes suggesting their important role in modulation of well-being. PSMC3 and ITIH4 genes are also involved in driving acute phase responses signifying a probable cross-talk between well-being and psychoneuroimmunological system. To best of our knowledge this study is the first of its kind where the well-being associated GWA studies-SNVs were prioritized and functionally annotated, majorly based on functional data available in public domain, which revealed PSMC3, ITIH4 and SERPINC1 genes as probable candidates in regulation of well-being spectrum.

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population

Background

Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively.

Methods

In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data.

Results

Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 (P < 0.05), on the contrast, correlation analysis of tongue and pulse in the sub-health fatigue group showed no statistical significance.

Conclusions

The complex network technology was suitable for correlation analysis of symptoms and indexes in fatigue population, and tongue and pulse data had a certain diagnostic contribution to the classification of fatigue population.