Can metabolic biomarkers help distinguish between depressive episodes of bipolar disorder and major depressive disorder?
In a recent study published in JAMA Psychiatry, researchers investigate whether reproducible signatures of metabolomic biomarkers could be obtained from dried blood spot samples to distinguish between depressive episodes related to major depressive disorder and bipolar disorder.
Study: Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression. Image Credit: Khosro / Shutterstock.com
One of the major challenges in diagnosing mental health disorders is the overlapping and wide range of symptoms. Bipolar disorder, for example, is often misdiagnosed initially due to overlapping symptoms with other mental health disorders such as major depressive disorder.
The tendency of patients to seek help only during depressive episodes, as opposed to manic episodes, combined with the subjective assessment of self-reported symptoms during psychiatric evaluations, make the proper diagnosis of bipolar disorder more challenging.
Biomarkers are becoming a reliable and objective method for diagnosing physical ailments and various mood disorders. Validated metabolomic biomarkers may help distinguish symptoms like depressive episodes, which often present similarly in cases of major depressive disorder and bipolar disorder.
However, identifying biomarkers for diagnosing bipolar disorder continues to be challenging due to the confounding effects of medications, patients with widely differing symptom polarities, and the paucity of independent validation cohorts.
About the study
In the present study, researchers address the challenges and limitations of effectively diagnosing bipolar disorder by identifying and validating signatures for metabolomic biomarkers from dried blood spot samples. These samples were obtained from adult patients between the ages of 18 and 45 who presented indications of mild depressive symptoms on the patient health questionnaire completed during participant recruitment. The study excluded patients who were pregnant, breastfeeding, or exhibited suicidal tendencies.
The participants were required to complete an online questionnaire comprising 635 adaptive questions on mood disorders and mental well-being. The questionnaire consisted of various categories such as demographic data, psychiatric history, depressive symptoms, manic symptoms, personality traits, and comorbid psychiatric symptoms. Information on prescribed medications was also collected.
The World Health Organization (WHO) World Mental Health Composite International Diagnostic Interview was used to establish diagnoses of mood disorders. Follow-up visits were conducted at six and 12 months for any alterations in diagnoses.
Individuals diagnosed with major depressive disorders in the previous five years and whose diagnoses had been either retained as major depressive disorder or altered to bipolar disorder after the diagnostic interview during follow-up were part of the discovery cohort.
The validation cohort consisted of participants who exhibited mild depressive symptoms during the onset of the study and were diagnosed with either major depressive disorder or bipolar disorder by a professional during the one-year follow-up. A dried blood spot sample collection kit was mailed to all study participants to obtain fasting blood samples.
A metabolic platform based on targeted mas-spectrometry was used to analyze 630 metabolites to identify the biomarkers. The Pearson correlation index was calculated to correlate biomarkers with symptoms.
Incorporating metabolic biomarkers in the diagnosis process and self-reported symptoms appears to improve the effective diagnosis of bipolar disorder. Moreover, this approach allowed the researchers to distinguish between depressive episodes related to major depressive disorder and bipolar disorder.
The additional information provided by the biomarkers was particularly useful in cases where the data on psychiatric symptoms were insufficient, or the diagnosis was at an intermediate threshold. The correlation analyses found specific metabolic biomarkers that were consistently correlated to manic symptoms. Sphingolipids like ceramides were also found to be involved in the pathological mechanisms of mood disorders.
Metabolite levels were assessed from dried blood spot samples from a total of 241 patients, 67 of whom were subsequently diagnosed with bipolar disorder through the diagnostic interview. The 17-biomarker panel showed ceramides to be the stronger biomarker. Furthermore, the validation cohort confirmed the correlation between the identified biomarkers and lifetime manic symptoms.
The study findings provide a proof of concept for the evaluation and identification of biomarkers to improve mood disorder diagnoses. More specifically, ceramide levels consistently correlated with manic symptoms, thus indicating that the use of biomarkers, along with patient-reported symptoms, could improve mental health disorder diagnoses.
- Tomasik, J., Scott J. H., Rustogi, N., et al. (2023). Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2023.4096
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Tags: Biomarker, Bipolar Disorder, Blood, Breastfeeding, Depression, Depressive Disorder, Diagnostic, Fasting, Major Depressive Disorder, Mental Health, Metabolite, Metabolites, Mood Disorder, Psychiatry, Spectrometry
Dr. Chinta Sidharthan
Chinta Sidharthan is a writer based in Bangalore, India. Her academic background is in evolutionary biology and genetics, and she has extensive experience in scientific research, teaching, science writing, and herpetology. Chinta holds a Ph.D. in evolutionary biology from the Indian Institute of Science and is passionate about science education, writing, animals, wildlife, and conservation. For her doctoral research, she explored the origins and diversification of blindsnakes in India, as a part of which she did extensive fieldwork in the jungles of southern India. She has received the Canadian Governor General’s bronze medal and Bangalore University gold medal for academic excellence and published her research in high-impact journals.