Correlates of poor sleep based upon wrist actigraphy data in bipolar disorder

Preliminary Evidence for the Sequentially Mediated Effect of Racism-Related Stress on Pain Sensitivity Through Sleep Disturbance and Corticolimbic Opioid Receptor Function

Sleep disturbance predicts worse pain outcomes. Because sleep disturbance inequitably impacts Black adults - with racism as the upstream cause - understanding how racism-related stress impacts pain through sleep might help minimize racialized pain inequities. This preliminary study examined sequential mediation of the effect of racism-related stress on experimental pain through sleep disturbance and corticolimbic μOR function in pain-free non-Hispanic Black (NHB) and White (NHW) adults. Participants completed questionnaires, actigraphy, positron emission tomography, and sensory testing. We reproduced findings showing greater sleep disturbance and pain sensitivity among NHB participants; greater sleep disturbance (r = .35) and lower pain tolerance (r=-.37) were significantly associated with greater racism-related stress. In a sequential mediation model, the total effect of racism-related stress on pain tolerance (β=-.38, P = .005) weakened after adding sleep disturbance and ventromedial prefrontal cortex (vmPFC) μOR binding potential (BPND) as mediators (β = -.18, P = .16). The indirect effect was statistically significant [point estimate = -.003, (-.007, -.0003). Findings showed a potential sequentially mediated effect of racism-related stress on pain sensitivity through sleep disturbance and vmPFC μOR BPND. As policy efforts are enacted to eliminate the upstream cause of systemic racism, these results cautiously suggest that sleep interventions within racism-based trauma informed therapy might help prevent downstream effects on pain. PERSPECTIVE: This preliminary study identified the effect of racism-related stress on pain through sleep disturbance and mu-opioid receptor binding potential in the ventromedial prefrontal cortex. Findings cautiously support the application of sleep interventions within racism-based trauma-informed therapy to prevent pain inequities as policy changes function to eliminate all levels of racism.

Digital phenotype of mood disorders: A conceptual and critical review

BACKGROUND

Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care.

OBJECTIVE

The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders.

METHODS

We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence.

RESULTS

Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).

CONCLUSION

The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.

Development and validation of a nomogram based on lymphocyte subsets to distinguish bipolar depression from major depressive disorder

OBJECTIVE

Bipolar depression (BD) and major depressive disorder (MDD) are both common affective disorders. The common depression episodes make it difficult to distinguish between them, even for experienced clinicians. Failure to properly diagnose them in a timely manner leads to inappropriate treatment strategies. Therefore, it is important to distinguish between BD and MDD. The aim of this study was to develop and validate a nomogram model that distinguishes BD from MDD based on the characteristics of lymphocyte subsets.

MATERIALS AND METHODS

A prospective cross-sectional study was performed. Blood samples were obtained from participants who met the inclusion criteria. The least absolute shrinkage and selection operator (LASSO) regression model was used for factor selection. A differential diagnosis nomogram for BD and MDD was developed using multivariable logistic regression and the area under the curve (AUC) with 95% confidence interval (CI) was calculated, as well as the internal validation using a bootstrap algorithm with 1,000 repetitions. Calibration curve and decision curve analysis (DCA) were used to evaluate the calibration and clinical utility of the nomogram, respectively.

RESULTS

A total of 166 participants who were diagnosed with BD (83 cases) or MDD (83 cases), as well as 101 healthy controls (HCs) between June 2018 and January 2022 were enrolled in this study. CD19⁺ B cells, CD3⁺ T cells, CD3^-CD16/56⁺ NK cells, and total lymphocyte counts were strong predictors of the diagnosis of BD and MDD and were included in the differential diagnosis nomogram. The AUC of the nomogram and internal validation were 0.922 (95%; CI, 0.879-0.965), and 0.911 (95% CI, 0.838-0.844), respectively. The calibration curve used to discriminate BD from MDD showed optimal agreement between the nomogram and the actual diagnosis. The results of DCA showed that the net clinical benefit was significant.

CONCLUSION

This is an easy-to-use, repeatable, and economical nomogram for differential diagnosis that can help clinicians in the individual diagnosis of BD and MDD patients, reduce the risk of misdiagnosis, facilitate the formulation of appropriate treatment strategies and intervention plans.