ArticleDepression and anxiety2026
A Sleep and Circadian Biomarker-Based Predictive Model for Differentiating Unipolar and Bipolar Depression.
Article in Depression and anxiety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background/Study Objectives: Differentiating unipolar depression (UDD) from bipolar depression (BDD) remains a major clinical challenge with important treatment implications. Sleep-related markers, both subjective (such as hypersomnia in BDD and insomnia in UDD) and objective (actigraphy, polysomnography [PSG]), show promise, yet no study has integrated these approaches into a single predictive model. Methods: Patients with DSM-5-TR-defined UDD or BDD in a depressive episode underwent clinical, questionnaire, actigraphy, and PSG assessments. Discriminating variables were entered into a backward stepwise logistic regression (BSLR) to derive the optimal classification model. Results: The study drew on 159 patients: 43 with BDD (42 actigraphy, 20 PSG) and 116 with UDD (93 actigraphy, 44 PSG). Compared to BDD, patients with UDD reported poorer sleep quality (Pittsburgh sleep quality index [PSQI]), more severe insomnia (insomnia severity index [ISI]), and lower sleep efficiency (SE, actigraphy). Patients with BDD showed longer total rest time per 24 h, lower average activity during the least active 5-h period (L5) and the 10 most active hours (M10), and higher N2% and total NREM sleep (PSG). Six variables were retained in the final BSLR model, explaining 49.4% of the variance. The most discriminative were higher PSQI/ISI and lower actigraphic SE in UDD, versus longer rest time, lower L5 activity, and higher N2% in BDD. The model showed excellent discriminative ability (AUC = 0.926, sensitivity = 0.882, specificity = 0.895, Youden's index = 0.777), with strong predictive values (positive predictive value [PPV] = 88.3%, negative predictive value [NPV] = 89.5%). Conclusion: Integrating subjective, actigraphic, and polysomnographic markers provides a promising, noninvasive approach to differentiate UDD from BDD.
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