“Man is not so much concerned with real problems as with his imaginary anxieties about real problems.”
— Epictetus
A newly developed blood test detects specific biomarkers associated with anxiety, allowing a person to predict their risk of developing the disorder. In addition, this tool also helps monitor the severity of symptoms in individuals who already have anxiety, providing more accurate monitoring.
Anxiety is a state of increased reactivity to events that are perceived as challenging, threatening, or harmful. In psychiatric patients, this condition can be aggravated by a life trajectory marked by adversity.
Therefore, these patients represent an ideal study sample for the identification of blood biomarkers that can be used in a generalized and transdiagnostic way, that is, applicable to different mental disorders, including anxiety.
Due to the lack of objective tests available to diagnose and monitor anxiety, the condition is often underdiagnosed and inadequately treated. This can lead to adverse events throughout life, such as hospitalizations, as well as the risk of developing addictions and even suicide.
Identifying specific biomarkers would eliminate the subjectivity associated with current assessments, providing a better estimate of the risk of developing the disorder, in addition to helping to guide more effective treatments. A biomarker is a substance or characteristic in the body that can be measured to indicate the presence or progression of a disease or the response to a treatment.
A study from Indiana University, published in Molecular Psychiatry, sought to identify these biomarkers through a systematic, multi-step approach.
First, the researchers conducted a longitudinal study with psychiatric patients, observing changes in gene expression in the blood during phases of low and high anxiety, measured by a scale developed by the team, the Simplified Anxiety Scale (SAS-4). This scale is similar to the Simplified Mood Scale (SMS-7), which had already been published previously.
In a second step, the researchers used the Convergent Functional Genomics (CFG) approach, which integrates previous data from studies in humans and animal models, to prioritize the most relevant biomarkers.
They then validated the top biomarkers in an independent cohort of patients with severe anxiety, identifying 95 candidate biomarkers. These biomarkers were then tested to predict anxiety severity and the risk of clinical worsening (such as hospitalizations). Using follow-up data from patients’ medical records and blood samples, the researchers were able to identify the biomarkers that were most effective at predicting anxiety worsening.
The biomarkers with the best overall evidence were GAD1, ERCC6L2, NTRK3, ADRA2A, FZD10, GRK4, SLC6A4, ATP1B2, NRG1, CLIC6, EFNA5, GPX7, SLC6A2, and TMEM138.
GAD1 (Glutamate Decarboxylase 1), the top overall biomarker for anxiety in this study, synthesizes gamma-aminobutyric acid (GABA) from glutamate. Abnormalities in the GABA neurotransmitter system have been observed in individuals with mood and anxiety disorders.
In addition, GAD1 has previous genetic evidence in anxiety and panic disorders. Its expression in the blood is increased in cases of high anxiety, with outcomes being slightly better in women. It also predicts future hospitalizations with anxiety in everyone.
Another important biomarker identified was ERCC6L2, whose expression was reduced by 68% in states of high anxiety. In men, especially those with bipolar disorder, ERCC6L2 expression was reduced by 72% across the board. Furthermore, in men with depression, gene expression was highly predictive of clinical anxiety (100%).
The biomarker SLC6A2 showed increased expression in states of high anxiety, being 63% in men and 76% in women. This gene encodes the norepinephrine transporter, and the discovery reinforces the importance of medications that inhibit this transporter in the treatment of anxiety disorders.
The relevance of the SLC6A4 gene, associated with the serotonin transporter, which is already a target of medications such as selective serotonin reuptake inhibitors (SSRIs), was also detected.
Several cutting-edge individual biomarkers are known to be modulated by drugs in current clinical use to treat affective disorders and suicide, such as lithium (GAD1, ATP1B2, NRG1), the nutraceutical omega-3 fatty acids (GAD1, CLIC6, EFNA5, SLC6A4), and antidepressants (ADRA2A, FZD10, GPX7, SLC6A2, SLC6A4, TMEM138). This has potential utility in pharmacogenomic approaches that match anxious and suicidal patients with the right drugs and monitor treatment response.
The researchers also performed a detailed analysis of the interacting protein networks associated with these biomarkers. For example, the HTR2A gene was identified as part of a network that includes GAD1, GABBR1, and the serotonin transporter SLC6A4.
These genes are targets of traditional treatments for anxiety, such as SSRIs and benzodiazepines. Other networks involve genes related to neural connectivity and activity, suggesting different mechanisms of action that could be explored therapeutically.
Finally, the researchers investigated whether these biomarkers could be modulated by existing drugs, identifying potential candidates for repurposing in the treatment of anxiety.
Overall, based on the number of biomarkers modulated in expression away from anxiety, valproate had the best evidence of broad efficacy in anxiety disorders, followed by omega-3 fatty acids.
Another alternative treatment that was a top match was EEG gamma-band frequency, which is increased by meditation and other mindfulness practices. Lithium and fluoxetine were next, and benzodiazepines were a lesser match than that.
Omega-3 fatty acids and meditation may be a widely deployable preventive treatment with minimal side effects, including in women who are or may become pregnant.
Only one of the top biomarkers, DYNLL2, has evidence of being modulated by benzodiazepines away from high anxiety; the others do not, which is interesting and clinically useful because it brings to light other non-addictive choices.
Bioinformatic analyses of the high anxiety biomarker panel revealed potential new therapies, including the hormone estradiol, the 5-HT2A antagonist pirenperone, the opioid agonist loperamide, and the antiarrhythmic disopyramide. The estrogen receptor ESR1 was also a key genetic discovery in the study.
This work represents a significant advance in understanding the biological mechanisms underlying anxiety and offers avenues for personalized treatments that could increase the efficacy of medications and reduce their side effects, improving patient well-being.
READ MORE:
Towards precision medicine for anxiety disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs
Roseberry, K., Le-Niculescu, H., Levey, D.F. et al.
Mol Psychiatry 28, 2894–2912 (2023).
Abstract:
Anxiety disorders are increasingly prevalent, affect people’s ability to do things and decrease their quality of life. Due to a 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 longitudinal within-subject design in individuals with psychiatric disorders to discover blood gene expression changes between self-reported low anxiety and high anxiety states. Second, we prioritized the list of candidate biomarkers with a Convergent Functional Genomics approach using other evidence in the field. Third, we validated our top biomarkers from discovery and prioritization in an independent cohort of psychiatric subjects with clinically severe anxiety. Fourth, we tested these candidate biomarkers for clinical utility, i.e. ability to predict anxiety severity state, and future clinical worsening (hospitalizations with anxiety as a contributory cause), in another independent cohort of psychiatric subjects. We showed increased accuracy of individual biomarkers with a personalized approach, by gender and diagnosis, particularly in women. The biomarkers with the best overall evidence were GAD1, NTRK3, ADRA2A, FZD10, GRK4, and SLC6A4. Finally, we identified which of our biomarkers are targets of existing drugs (such as valproate, omega-3 fatty acids, fluoxetine, lithium, sertraline, benzodiazepines, and ketamine), and thus can be used to match patients to medications and measure response to treatment. We also used our biomarker gene expression signature to identify drugs that could be repurposed for treating anxiety, such as estradiol, pirenperone, loperamide, and disopyramide. Given the detrimental impact of untreated anxiety, the current lack of objective measures to guide treatment, and the addiction potential of existing benzodiazepines-based anxiety medications, there is an urgent need for more precise and personalized approaches like the one we developed.
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