Is Blood Testing for Unipolar and Bipolar Depression Coming?

By Grant H Brenner

In medicine, diagnosis and evaluation are of critical importance for treatment planning and outcomes. Biological understanding of general medical conditions is further along than for psychiatric diagnosis, at least in part because of the complexity of the brain and the need for more sophisticated tools to understand how neuropsychiatric processes relate to mind and behavior, and what this means for diagnosis and care.

Psychiatric diagnosis is based on careful review of past history and current presentation of symptoms. Psychiatric disorders are defined by a constellation of possible symptoms, the requirement to meet a significant subset of those symptoms with defined time frames, the requirement that there is significant dysfunction or suffering associated with the conditions, and that it is not better accounted for by something else¹.

Having clinically-relevant tests would represent a huge advance in psychiatry. Candidates for testing range from a variety of biochemical or genetic markers (e.g., conventional blood testing), to using personalized medicine to track changes in behavior characteristic of illness (e.g., changes in day-to-day habits such as suddenly spending much more time at home, which can happen with depression and anxiety, or becoming much more active with idiosyncratic traveling, which could be associated with mania), to using machine learning to look for patterns in functional and structural brain imaging reflective of different "biotypes" of depression, to looking for brain activity indicative of suicidal intent.

Unipolar Versus Bipolar Depression

Depression is an area where diagnosis is a challenge negatively impacting treatment. While there are many different types of depression, a major distinction is between unipolar depression and bipolar depression. As study authors Salvetat and colleagues note in their Translational Psychiatry (2022) paper on using RNA blood markers, the diagnosis of bipolar disorder is often significantly delayed—people with bipolar often are first diagnosed with major depression (a form of unipolar depression), in up to 21% of patients.

This happens for several reasons: first, the primary symptoms of depression overlap completely for unipolar and bipolar; second, because unless someone has had a clear manic episode (at least a week of serious, problematic symptoms from increased energy), the elevated mood and behavioral changes with less severe symptoms (“hypomanic”) may not be identified on history-taking because either they are more subtle, or did not appear problematic because they were part of a phase of feeling happy.

In other cases, people may not yet have had manic symptoms, presenting only with depression at first but with undetected bipolar risk. In still other cases, people with bipolar disorder may first have been diagnosed with similar-appearing conditions, including ADHD, substance use and anxiety disorders. Furthermore, people may have more than one diagnosis, and they often exacerbate one another. Finally, unipolar depression is far more common, affecting 10% of men and 20% of women, while bipolar disorder affects only 1% of the population, and rendering unipolar depression a more likely diagnosis. Both are associated with serious problems including increased risk of suicide, increased risk for future medical problems and substance use disorders, and decreased well-being and life satisfaction in personal and professional spheres.

The Rich Diversity of RNA

RNA (ribonucleic acid, as contrasted with DNA or deoxyribonucleic acid) markers are an attractive target for diagnostic test development, Salvetat and colleagues note. RNA is written from the DNA genetic code, a process called “transcription.” After transcription, RNA is modified in a variety of ways for a variety of purposes, often but not always coding for a protein via "messenger" or mRNA.

Beyond mRNA, modifications called “epitranscriptonomic mechanisms” are associated with depression and other disorders in several studies. They include chemical changes like methylation, in which a methyl group is added to the RNA molecule as a way of telling the body what to do with it; microRNAs, small pieces of RNA which modify protein synthesis by interacting with longer mRNAs; and RNA editing, with substitutions in the basic sequence of RNA building blocks, called “bases” (there are four of them—adenine, uracil, cytosine, and guanine).

RNA changes affect protein synthesis and cellular messaging, ultimately causing changes in how brain cells function, for example by changing how ions (e.g. calcium) cross cell walls, inflammatory cascades and immune function, RNA stability and shape, and neurotransmitter signaling.

In this study, researchers looked for specific RNA edit types in depressed patients versus people without depression (“healthy controls”) to find previously unidentified gene profiles characteristic of one or the other. If such differences are found, they could be used to develop clinically-applicable testing.

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Over 400 participants were recruited for this study, including 267 depressed patients from an outpatient emergency psychiatric setting, and 143 healthy controls. Symptoms of depression and mania were rated using standardized instruments alongside expert clinical assessment. Blood samples were collected and analyzed using machine learning to differentiate unipolar and bipolar depression based on ratings and clinical evaluation, as a function of RNA edit patterns.

Findings

Researchers identified a large number of possible edits, with type broad types representing over 70% of the variants. They then looked at differences in RNA edits (or “editions”) for correlations between bipolar and unipolar depression, narrowing the list of candidate biomarkers at each step of analysis to arrive at eight targets.

Then, working in reverse, the editions were tested as a candidate diagnostic marker to see how well they predicted depression type, to arrive at a final set of six RNA genetic targets which meaningfully improved diagnosis with good sensitivity and specificity of 90.9 and 84.6%, respectively. Sensitivity is how well a positive test shows the presence of disease ("true positive"), and the specificity of how well a negative test identifies being disease-free ("true negative").

Other than a preliminary association with an interferon RNA (associated with immune function), most of the RNA editions identified were in untranslated regions, leaving future studies to determine whether these variations have a functional impact or if they are non-functional edits which for statistical reasons travel (e.g., are nearby on the same gene and so tend to be found together) with biologically-significant genes.

Implications

Genetic testing presents a variety of ethical questions, including the basic one of whether to test or not. Some will prefer not to get genetic information because of how it may change the way they feel about themselves and their future, while others will want to have actionable information available. When testing can guide treatment and outcome, people are more likely to opt in than when the knowledge isn’t accompanied by clearly effective interventions.

For the 1 in 5 patients diagnosed with unipolar depression who turn out to have bipolar disorder, early detection and treatment would likely prevent considerable suffering and dysfunction—though research would be required to determine whether interventions (such as therapy, medications, or behavioral changes) can prevent or blunt illness, or whether close monitoring alone would be appropriate. Likewise, determining a lower probability of bipolar would allow a surer diagnosis and treatment of unipolar depression².

While not ready for “prime time,” this study shows promise for a clinically useful blood test that can help clinicians tell apart unipolar and bipolar depression, allowing patients to receive an accurate diagnosis and targeted treatment much earlier. Ongoing research is needed to reproduce and expand the preliminary analysis from this study to see if it carries through for broader application. RNA edition analysis holds promise for diagnostic testing of other conditions.