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Example of a potential report for physicians Using the panel of the top biomarkers for anxiety from Table 1 (n = 19). This subject (Phchp328) was previously described by us in a suicidality biomarker study, as high risk for suicide, and died by suicide a year after completing our study. No information was provided to the patient’s clinicians by us at that time due to anonymity and privacy rules in research studies. The raw expression values of the 19 biomarkers for the high and low anxiety groups were Z-scored by gender and diagnosis. We calculated as thresholds the average expression value for a biomarker in the high anxiety group SAS-4 ≥ 60, and in the low anxiety group SAS-4 ≤ 40. The first average should be higher than the second average in increased biomarkers, and the reverse is true for decreased biomarkers. 15 out of 19 biomarkers were thus concordant. We also calculated as thresholds the average expression value for a biomarker in the first-year hospitalizations group, and in the not hospitalized in first-year group. We did the same thing for all future hospitalizations, and no future hospitalizations. The first average should be higher than the second average in increased biomarkers, and the reverse is true for decreased biomarkers. 18 out of 19 biomarkers were thus concordant for first year, and for all future. The Z-scored expression value of each increased in expression biomarker was compared to the average value for the biomarker in the high anxiety group SAS-4 ≥ 60, and the average value of the low anxiety group SAS-4 ≤ 40, resulting in scores of 1 if above high anxiety, 0 if below low anxiety, and 0.5 if it was in between. The reverse was done for decreased in expression biomarkers. The digitized biomarkers were then added into a polygenic risk score and normalized for the number of biomarkers in the panel, resulting in a percentile score. We did the same thing for first-year hospitalizations, and all future hospitalizations, generating a combined score for chronic anxiety risk. The digitized biomarkers were also used for matching with existing psychiatric medications and alternative treatments (nutraceuticals and others). We used our large datasets and literature databases to match biomarkers to medications that had effects on gene expression opposite to their expression in high anxiety. Each medication matched to a biomarker got the biomarker score of 1, 0.5, or 0. The scores for the medications were added, normalized for the number of biomarkers that were 1 or 0.5 in that patient, resulting in a percentile match.
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
... K. Roseberry et al.'s [31] study aimed to identify blood biomarkers that can predict treatment response and severity in various anxiety disorders. An important finding was the function of NTRK3 (Neurotrophic Receptor Tyrosine Kinase 3), in the regulation of the HPA axis through neurotrophins like BDNF. ...
... Behavioral improvements with ramelteon were highly significant (p < 0.0001), suggesting its potential for treating PTSD and anxiety disorders. Further supporting serotonin's role in anxiety, K. Roseberry et al. [31] identified SLC6A4 (serotonin transporter gene) in their research on blood biomarkers for anxiety and relevant treatment response. ...
... In terms of genetics, K. Roseberry et al. [31] found that GAD1 (encoding glutamate decarboxylase 1, an enzyme critical for the synthesis of GABA) modestly predicted clinically severe anxiety in all patients in the independent test cohort. GABRA1 was also correlated with the anxiolytic mechanism of Suanzaoren Decoction (SZRT) [52]. ...
Anxiety disorders are among the most common psychiatric conditions that significantly impair one’s quality of life and place a significant burden on healthcare systems. Conventional treatments have certain restraints, such as potential side effects and limited efficacy. Τhe underlying pathophysiological mechanisms of anxiety are not fully understood. A comprehensive literature search was performed in MEDLINE and Scopus databases for original English-language articles published between January 2014 and December 2024. Study selection, data extraction, and screening were independently carried out by multiple investigators using predefined criteria. Our review aimed to help better comprehend the molecular basis of anxiety, focusing on the hypothalamic–pituitary–adrenal (HPA) axis, serotonergic signaling, and gamma-aminobutyric acid (GABA) neurotransmission. In addition, we addressed the role of epigenetics and pharmacogenomics in personalized treatment. Although novel anxiety treatments are promising, they are predominantly preclinical and highly heterogeneous, which poses a challenge to achieving reliable therapeutic efficacy. Our findings could potentially contribute to the development of new therapeutic interventions. Further research is warranted, especially in human subjects, with an aim to combine genetic and epigenetic profiles to refine treatment approaches and develop innovative therapeutics.
... 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.
... To identify biomarkers for neuropsychiatric disorders, (Roseberry, 2023) focused on blood samples, as brain biopsies and cerebrospinal fluid collection are challenging. The authors studied gene expression changes in immune cells within whole-blood samples. ...
The chapter addresses the persistent concerns surrounding the detecting and early intervention of anxiety and mood disorders. These mental health conditions have become increasingly prevalent, affecting individuals across various ages and socio-economic backgrounds. However, despite the growing awareness of their impact, challenges persist in timely diagnosis, leading to delayed treatment and aggravated conditions. By examining the continuum from clinical settings to algorithmic analyses, the chapter strives to elucidate how intelligent solutions, fueled by datasets, artificial intelligence (AI), machine learning (ML), and deep learning (DL), can enhance the accuracy, efficiency, and accessibility of diagnosis. The chapter's primary concern revolves around leveraging the power of science and technology to revolutionize the diagnostic landscape. It aims to unravel the transformative potential of transitioning from conventional clinical assessments to data-driven algorithms.
... 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.
