A Sleep and Circadian Biomarker-Based Predictive Model for Differentiating Unipolar and Bipolar Depression

18/09/2026

AI Summary

Integrated subjective, actigraphic, and polysomnographic model accurately differentiates UDD from BDD (AUC 0.926, sensitivity 88.2%, specificity 89.5%).
Unipolar depression showed worse subjective sleep (higher PSQI and ISI) and lower actigraphic sleep efficiency.
Bipolar depression featured longer total rest time, lower L5 daytime activity, and increased N2 percentage on polysomnography.

TL;DR (AI Generated)

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.

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).

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%).

Basic summary

Depress Anxiety. 2026 Sep 15;2026:5556144. doi: 10.1155/da/5556144. eCollection 2026.

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.

PMID:42751051 | PMC:PMC13579109 | DOI:10.1155/da/5556144

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