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. 2024 Jan 1;81(1):101-106.
doi: 10.1001/jamapsychiatry.2023.4096.

Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression

Jakub Tomasik  1 , Scott J Harrison  2 , Nitin Rustogi  1 , Tony Olmert  1 , Giles Barton-Owen  3 , Sung Yeon Sarah Han  1 , Jason D Cooper  1 , Pawel Eljasz  1 , Lynn P Farrag  3 , Lauren V Friend  3 , Emily Bell  3 , Dan Cowell  3 , Sabine Bahn  1   3
Affiliations

Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression

Jakub Tomasik et al. JAMA Psychiatry. .

Abstract

Importance: Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) because of overlapping symptoms and the lack of objective diagnostic tools.

Objective: To identify a reproducible metabolomic biomarker signature in patient dried blood spots (DBSs) that differentiates BD from MDD during depressive episodes and assess its added value when combined with self-reported patient information.

Design, setting, and participants: This diagnostic analysis used samples and data from the Delta study, conducted in the UK between April 27, 2018, and February 6, 2020. The primary objective was to identify BD in patients with a recent (within the past 5 years) diagnosis of MDD and current depressive symptoms (Patient Health Questionnaire-9 score of 5 or more). Participants were recruited online through voluntary response sampling. The analysis was carried out between February 2022 and July 2023.

Main outcomes and measures: Patient data were collected using a purpose-built online questionnaire (n = 635 questions). DBS metabolites (n = 630) were analyzed using a targeted mass spectrometry-based platform. Mood disorder diagnoses were established using the Composite International Diagnostic Interview.

Results: Of 241 patients in the discovery cohort, 170 (70.5%) were female; 67 (27.8%) were subsequently diagnosed with BD and 174 (72.2%) were confirmed as having MDD; and the mean (SD) age was 28.1 (7.1) years. Of 30 participants in the validation cohort, 16 (53%) were female; 9 (30%) were diagnosed with BD and 21 (70%) with MDD; and the mean (SD) age was 25.4 (6.3) years. DBS metabolite levels were assessed in 241 patients with depressive symptoms with a recent diagnosis of MDD, of whom 67 were subsequently diagnosed with BD by the Composite International Diagnostic Interview and 174 were confirmed as having MDD. The identified 17-biomarker panel provided a mean (SD) cross-validated area under the receiver operating characteristic curve (AUROC) of 0.71 (SD, 0.12; P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker. Combining biomarker data with patient-reported information significantly enhanced diagnostic performance of models based on extensive demographic data, PHQ-9 scores, and the outcomes from the Mood Disorder Questionnaire. The identified biomarkers were correlated primarily with lifetime manic symptoms and were validated in a separate group of patients who received a new clinical diagnosis of MDD (n = 21) or BD (n = 9) during the study's 1-year follow-up period, with a mean (SD) AUROC of 0.73 (0.06; P < .001).

Conclusions and relevance: This study provides a proof of concept for developing an accessible biomarker test to facilitate the differential diagnosis of BD and MDD and highlights the potential involvement of ceramides in the pathophysiological mechanisms of mood disorders.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Tomasik reported having a patent pending for dried blood spot biomarkers for bipolar disorder and may benefit financially from patents arising from this work. Mr Olmert reported receiving licensing fees from the University of Cambridge for data produced in this study. Mr Barton-Owen reported being employed by and having unvested options in Psyomics during the conduct of the study. Mr Eljasz reported receiving personal fees from the University of Cambridge during the conduct of the study. Ms Farrag reported having financial interests in Psyomics. Ms Bell reported receiving grants from Innovate UK during the conduct of the study and being a shareholder in Psyomics. Mr Cowell reported receiving grants from Innovate UK during the conduct of the study and being a shareholder in Psyomics. Dr Bahn reported receiving grants from Stanley Medical Research Institute and Psyomics during the conduct of the study; being a founder and shareholder in Psyomics; being Director of Psynova Neurotech outside the submitted work; having a patent pending for dried blood spot biomarkers for bipolar disorder and may benefit financially from patents arising from this work; and receiving payments from the University of Cambridge for licensing of data from the Delta study. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Dried Blood Spot Metabolomic Signature Distinguishing Bipolar Disorder From Major Depressive Disorder in Patients With Current Depressive Symptoms
A, Area under the receiver operating characteristic curve (AUROC) indicating diagnostic performance of the identified biomarker panel in the discovery cohort (67 patients with misdiagnosed BD and 174 with MDD). B, AUROC in the validation cohort (9 patients with BD and 21 with MDD clinically diagnosed with a mood disorder during the 1-year follow-up period). C, Relative importance of individual biomarkers in the classification model. Values represent means obtained from the cross-validated models. GCA indicates glycocholic acid; 3-IAA, indoleacetic acid; 3-IPA, indolepropionic acid; TMAO, trimethylamine N-oxide.
Figure 2.
Figure 2.. Added Diagnostic Value of Biomarkers
A, Comparison of the area under the receiver operating characteristic curve (AUROC) for diagnostic models incorporating patient self-reported end points with and without biomarker data. Error bars indicate SDs. B, Improvement in the standardized net benefit of the diagnostic models after including biomarker data. MDQ indicates Mood Disorder Questionnaire; PHQ-9, Patient Health Questionnaire–9; WEMWBS, Warwick-Edinburgh Mental Well-Being Scale. aP < .05. bP < .001 (corrected resampled t test).
Figure 3.
Figure 3.. Association of Biomarkers With Psychopathology
Mean absolute Pearson correlation coefficients between biomarker measurements and patient self-reported end points, calculated by averaging the correlations with individual items within each symptom category. GCA indicates glycocholic acid; 3-IAA, indoleacetic acid; 3-IPA, indolepropionic acid; TMAO, trimethylamine N-oxide.
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