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Best Single Biomarkers Predictors for Anxiety, State and Trait From top candidate biomarkers after Steps 1–3 (Discovery, Prioritization, Validation-Bold) (n = 95). Bar graph shows best predictive biomarkers in each group. All markers with * are nominally significant p < 0.05. Table underneath the figures displays the actual number of biomarkers for each group whose ROC AUC p values (A–C) and Cox Odds Ratio p values (D) are at least nominally significant. Some gender and diagnosis groups are missing from the graph as they did not have any significant biomarkers or that the cohort was too small with limited data for the z-scoring by gender-dx. Cross-sectional is based on levels at one visit. Longitudinal is based on levels at multiple visits (integrates levels at most recent visit, maximum levels, slope into most recent visit, and maximum slope). Dividing lines represent the cutoffs for a test performing at chance levels (white), and at the same level as the best biomarkers for all subjects in cross-sectional (gray) and longitudinal (black) based predictions. Biomarkers perform better than chance. Biomarkers performed better when personalized by gender and diagnosis. * nominally significant. ** survived Bonferroni correction for the number of candidate biomarkers tested.
Source publication
Anxiety disorders are increasingly prevalent, affect people’s ability to do things, and decrease quality of life. Due to lack of objective tests, they are underdiagnosed and sub-optimally treated, resulting in adverse life events and/or addictions. We endeavored to discover blood biomarkers for anxiety, using a four-step approach. First, we used a...
Citations
... Promising results have been found for depression and, to a lesser extent, anxiety disorders. [7][8][9][10][11][12][13][14] Several computational models based on blood biomarkers have been proposed for predicting depressive states. For instance, a study involving 897 subjects affected by the Great East Japan Earthquake suggested the potential for categorizing individuals with high levels of depressive symptoms based on their blood plasma metabolite profiles. ...
Background
Anxiety and depression significantly contribute to the overall burden of mental disorders, with depression being one of the leading causes of disability. Despite this, no biochemical test has been implemented for the diagnosis of these mental disorders, while recent studies have highlighted lipids as potential biomarkers.
Methods
Using a streamlined high-throughput lipidome analysis method, direct-infusion mass spectrometry, we evaluated blood plasma lipid levels in 604 individuals from a general urban population and analysed their association with self-reported anxiety and depression symptoms. We also assessed lipidome profiles in 32 patients with clinical depression, matched to 21 healthy controls.
Findings
We found a significant correlation between lipid abundances and the severity of self-reported depression symptoms. Moreover, lipid alterations detected in high scoring volunteers mirrored the lipidome profiles identified in patients with clinical depression included in our study. Based on these findings, we developed a lipid-based predictive model distinguishing individuals reporting severe depressive symptoms from non-depressed subjects with high accuracy.
Interpretation
This study demonstrates the possibility of generalizing lipid alterations from a clinical cohort to the general population and underscores the potential of lipid-based biomarkers in assessing depressive states.
Funding
This study was sponsored by the Moscow Center for Innovative Technologies in Healthcare, №2707-2, №2102-11.
... For cross-sectional analyses, we used biomarker expression levels. For longitudinal analyses, we combined four measures: biomarker expression levels, slope (defined as ratio of levels at current testing visit vs. previous visit, divided by time between visits), maximum levels (at any of the current or past visits), and maximum slope (between any adjacent current or past visits), as described in previous studies [16][17][18]. For decreased biomarkers, we used the minimum rather than the maximum for level calculations. ...
... A two-way unsupervised hierarchical clustering (Fig. S2) was done using subjects in the discovery cohort with high suicidal ideation (HAMD-SI ≥ 2, n = 103). Clustering was done on 4 psychiatric dimensions, using quantitative instruments: stress (SSS4) [21], anxiety (SAS4) [17], mood (SMS7) [16], psychosis (PANSS Positive) [18]. Subjects measures were R. Bhagar et al. ...
... It also has previous genetic evidence [27], and human postmortem brain evidence of being increased in the hippocampus in suicides [28]. INSR has also been shown to be decreased in expression in blood in previous studies we did in stress [25], anxiety [17], depression [16], low memory [26], and hallucinations [26], suggestive of a stress-driven neuropathological component. It is decreased in expression by lithium [29], valproate [30], and antidepressants [31]. ...
Suicidality remains a clear and present danger in society in general, and for mental health patients in particular. Lack of widespread use of objective and/or quantitative information has hampered treatment and prevention efforts. Suicidality is a spectrum of severity from vague thoughts that life is not worth living, to ideation, plans, attempts, and completion. Blood biomarkers that track suicidality risk provide a window into the biology of suicidality, as well as could help with assessment and treatment. Previous studies by us were positive. Here we describe new studies we conducted transdiagnostically in psychiatric patients, starting with the whole genome, to expand the identification, prioritization, validation and testing of blood gene expression biomarkers for suicidality, using a multiple independent cohorts design. We found new as well as previously known biomarkers that were predictive of high suicidality states, and of future psychiatric hospitalizations related to them, using cross-sectional and longitudinal approaches. The overall top increased in expression biomarker was SLC6A4, the serotonin transporter. The top decreased biomarker was TINF2, a gene whose mutations result in very short telomeres. The top biological pathways were related to apoptosis. The top upstream regulator was prednisolone. Taken together, our data supports the possibility that biologically, suicidality is an extreme stress-driven form of active aging/death. Consistent with that, the top subtypes of suicidality identified by us just based on clinical measures had high stress and high anxiety. Top therapeutic matches overall were lithium, clozapine and ketamine, with lithium stronger in females and clozapine stronger in males. Drug repurposing bioinformatic analyses identified the potential of renin-angiotensin system modulators and of cyclooxygenase inhibitors. Additionally, we show how patient reports for doctors would look based on blood biomarkers testing, personalized by gender. We also integrated with the blood biomarker testing social determinants and psychological measures (CFI-S, suicidal ideation), showing synergy. Lastly, we compared that to machine learning approaches, to optimize predictive ability and identify key features. We propose that our findings and comprehensive approach can have transformative clinical utility.
... Therefore, future research on anxiety symptoms could be based on medical diagnoses rather than self-report alone. Medical diagnoses provide a more objective and detailed assessment of anxiety status by combining clinical observations, medical records, and, in some cases, psychometric tests administered by professionals (65). Finally, it is important to note that the inability to generalize the findings to a larger population, due to the type of sampling used and the number of participants involved, is an obvious limitation in the current study. ...
Background
The link between physical and mental health and screen time in adolescents has been the subject of scientific scrutiny in recent years. However, there are few studies that have evaluated the association between social network addiction (SNA) and metabolic risk in this population.
Objective
This study determined the association between SNA and anxiety symptoms with the risk of metabolic syndrome (MetS) in adolescents.
Methods
A cross-sectional study was conducted in Peruvian adolescents aged 12 to 18 years, who completed a Social Network Addiction Questionnaire and the Generalized Anxiety Disorder 2-item scale (GAD-2), between September and November 2022. A total of 903 participants were included in the study using a non-probability convenience sample. Sociodemographic and anthropometric data were also collected. Binary logistic regression was used to explore the association between SNA and anxiety symptoms with MetS in a cross-sectional analysis.
Results
Males were more likely to have MetS than females (OR = 1.133, p = 0.028). Participants who were 16 years of age or older and those with excess body weight were 2.166, p = 0.013 and 19.414, p < 0.001 times more likely to have MetS, respectively. Additionally, SNA (OR = 1.517, p = 0.016) and the presence of anxiety symptoms (OR = 2.596, p < 0.001) were associated with MetS.
Conclusion
Our findings suggest associations between SNA, anxiety symptoms, and MetS among youth. However, more studies are needed to better understand this association and to deepen the possible clinical and public health implications.
... We used a systematic discovery, prioritization, validation, and testing approach, as we have done over the years for other disorders [5,[28][29][30][31][32]. For discovery, we used a hard to accomplish but powerful within-subject design, with an N of 25 subjects with 65 visits for hallucinations, and 31 subjects with 95 visits for delusions. ...
Psychosis occurs inside the brain, but may have external manifestations (peripheral molecular biomarkers, behaviors) that can be objectively and quantitatively measured. Blood biomarkers that track core psychotic manifestations such as hallucinations and delusions could provide a window into the biology of psychosis, as well as help with diagnosis and treatment. We endeavored to identify objective blood gene expression biomarkers for hallucinations and delusions, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We were successful in identifying biomarkers that were predictive of high hallucinations and of high delusions states, and of future psychiatric hospitalizations related to them, more so when personalized by gender and diagnosis. Top biomarkers for hallucinations that survived discovery, prioritization, validation and testing include PPP3CB, DLG1, ENPP2, ZEB2, and RTN4. Top biomarkers for delusions include AUTS2, MACROD2, NR4A2, PDE4D, PDP1, and RORA. The top biological pathways uncovered by our work are glutamatergic synapse for hallucinations, as well as Rap1 signaling for delusions. Some of the biomarkers are targets of existing drugs, of potential utility in pharmacogenomics approaches (matching patients to medications, monitoring response to treatment). The top biomarkers gene expression signatures through bioinformatic analyses suggested a prioritization of existing medications such as clozapine and risperidone, as well as of lithium, fluoxetine, valproate, and the nutraceuticals omega-3 fatty acids and magnesium. Finally, we provide an example of how a personalized laboratory report for doctors would look. Overall, our work provides advances for the improved diagnosis and treatment for schizophrenia and other psychotic disorders.
... Research into clinical, (epi)genetic, proteomic, metabolomic, microbiome, physiological and neuroimaging biomarkers as predictors of treatment resistance in anxiety disorders, allowing for a more personalized and precise care in this field, was welcomed by the panel (see Table 1, statement 12). However, the very limited currently available evidence was acknowledged [92][93][94][95] . ...
... Some studies of limited quality and highly heterogeneous in design suggest a number of potential risk factors -such as high expressed emotions within the family, higher severity and longer duration of the disorder, earlier age of onset, or presence of comorbid conditions -which however have not been consistently replicated 13,19,81,82 . In a similar vein, the identification of reliable and valid biomarkers indicating an increased risk of treatment resistance would be helpful to inform algorithms for individually tailoring an intensified treatment for those patients 22,23,25,93,94,115 . ...
Anxiety disorders are very prevalent and often persistent mental disorders, with a considerable rate of treatment resistance which requires regulatory clinical trials of innovative therapeutic interventions. However, an explicit definition of treatment‐resistant anxiety disorders (TR‐AD) informing such trials is currently lacking. We used a Delphi method‐based consensus approach to provide internationally agreed, consistent and clinically useful operational criteria for TR‐AD in adults. Following a summary of the current state of knowledge based on international guidelines and an available systematic review, a survey of free‐text responses to a 29‐item questionnaire on relevant aspects of TR‐AD, and an online consensus meeting, a panel of 36 multidisciplinary international experts and stakeholders voted anonymously on written statements in three survey rounds. Consensus was defined as ≥75% of the panel agreeing with a statement. The panel agreed on a set of 14 recommendations for the definition of TR‐AD, providing detailed operational criteria for resistance to pharmacological and/or psychotherapeutic treatment, as well as a potential staging model. The panel also evaluated further aspects regarding epidemiological subgroups, comorbidities and biographical factors, the terminology of TR‐AD vs. “difficult‐to‐treat” anxiety disorders, preferences and attitudes of persons with these disorders, and future research directions. This Delphi method‐based consensus on operational criteria for TR‐AD is expected to serve as a systematic, consistent and practical clinical guideline to aid in designing future mechanistic studies and facilitate clinical trials for regulatory purposes. This effort could ultimately lead to the development of more effective evidence‐based stepped‐care treatment algorithms for patients with anxiety disorders.
... [84][85][86] Roseberry et al, suggest that Valproate could provide a possible therapeutic route for anxiety disorders. [87] This is consistent with VPA, prescribed for the treatment of bipolar disorder, as a possible therapeutic approach to trauma, particularly for irritability associated with trauma. [88] The restriction of these analyses to white British individuals only, reduces the representativeness of the results. ...
Physical trauma is often associated with psychological trauma and is a risk factor for the development of major depressive disorder (MDD). We derived a traumatic physical injury phenotype in the Generation Scotland cohort and showed that it was significantly associated with a diagnosis of recurrent major depression and measures of related symptoms, particularly disorganised thought. Blood-based methylome-wide analyses of traumatic injury were performed in groups of individuals with or without diagnoses of MDD. Nominally significant differences in DNA methylation were identified at 40,003 CpG sites in the effect size of the association of DNA methylation in individuals with and without MDD. Individuals with recurrent MDD showed a significant higher levels of DNA methylation at CpG sites associated with the first exon and lower levels associated with exon boundaries. This may suggest alterations in gene expression and splicing in individuals with recurrent MDD compared to controls. Analyses at the level of CpG site, genes and gene ontologies implicated dysregulation in neuronal development, e.g. MATN2 and ZEB2, and processes related to synaptic plasticity, including dendrite development, excitatory synapse and GABAergic signalling. Analyses of brain-expression patterns highlight the limbic lobe and supraoptic nuclei, brain regions associated with fear memory encoding. The results suggest that traumatic injury is associated with patterns of DNA methylation but that the direction of these associations differ in individuals with MDD compared to controls, highlighting the need for novel analysis approaches.
Anxiety is a widespread psychological disorder affecting both humans and animals. It is a typical stress reaction; however, its longer persistence can cause severe health disorders affecting the day-to-day life activities of individuals. An intriguing facet of the anxiety-related disorder can be addressed better by investigating the role of neurotransmitters in regulating emotions, provoking anxiety, analyzing the cross-talks between neurotransmitters, and, most importantly, identifying the biomarkers of the anxiety. Recent years have witnessed the potential role of the gut microbiota in human health and disorders, including anxiety. Animal models are commonly used to study anxiety disorder as they offer a simpler and more controlled environment than humans. Ultimately, developing new strategies for diagnosing and treating anxiety is of paramount interest to medical scientists. Altogether, this review article shall highlight the intricate mechanisms of anxiety while emphasizing the emerging role of gut microbiota in regulating metabolic pathways through various interaction networks in the host. In addition, the review will foster information about the therapeutic interventions of the anxiety and related disorder.
Graphical abstract
