Peripheral body temperature rhythm is associated with suicide risk in major depressive disorder: a case-control study

Transitional photoperiod induces a mania-like behavior in male mice

This study aimed to investigate the behavioral responses and circadian rhythms of mice to both rapid and gradual increases in photoperiod, mimicking the transition from winter to summer, which is associated with a heightened prevalence of hospitalizations for mania and suicidal behavior. Behavioral tests were performed in C57BL/6 male mice exposed to a transitional photoperiod, from short to long durations. To determine if circadian rhythms are affected, we measured spontaneous locomotor activity and body temperature. Mice exhibited heightened exploratory and risk-taking behaviors compared with equatorial and static long (16:8 h of light-dark cycle for several days) groups. These behaviors were prevented by lithium. Spontaneous locomotor activity and body temperature rhythms persisted and were effectively synchronized; however, the relative amplitude of activity and interdaily stability were diminished. Additionally, the animals displayed increased activity during the light phase. Photoperiodic transition modulates behavior and circadian rhythms, mirroring certain features observed in bipolar disorder patients. This study introduces an animal model for investigating mania-like behavior induced by photoperiodic changes, offering potential insights for suicide prevention strategies and the management of mood disorders.

Scoping review update on somatic symptom disorder that includes additional Chinese data

Somatic symptom disorder (SSD) is a new diagnosis introduced into the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which is expected to solve the diagnostic difficulties of patients with medically unexplained symptoms. Based on the previous work, this review aims to comprehensively synthesise updated evidence related to SSD from recent years in English publications and, more extensively, from data published in Chinese language journals. The scoping review update was based on an earlier scoping review and included Chinese language publication data from China National Knowledge Internet (CNKI), WANFANG and WEIPU between January 2013 and May 2022 and data from PubMed, PsycINFO, and Cochrane Library between June 2020 and May 2022. Initially, 2 984 articles were identified, of which 63 full texts were included for analysis. In China, SSD is mainly applied in scientific research, but it also shows good predictive validity and clinical application potential. The mean frequency of SSD was 4.5% in the general population, 25.2% in the primary care population and 33.5% in diverse specialised care settings. Biological factors, such as brain region changes and heart rate variability, are associated with the onset of SSD. Psychological impairment related to somatic symptoms is the best predictor of prognosis. While adolescent SSD was significantly associated with family function, SSD overall is associated with an increased dysfunction of cognition and emotion, decreased quality of life, and high comorbidity with anxiety and depressive disorders. Further research is needed on suicide risk and cultural and gender-related issues. Updating the data of Chinese language studies, our research enriches the evidence-based findings related to the topics addressed in the text sections of the SSD chapter of DSM-5. However, research gaps remain about SSD reliability, population-based prevalence, suicide risk, and cultural and gender-related issues.

Peripheral body temperature rhythm as a marker of the severity of depression symptoms in fibromyalgia

BACKGROUND

Circadian rhythm alterations have been reported in fibromyalgia (FM) and depression. Peripheral body temperature (PBT) is a reliable measure of the circadian system, so we compared the PBT rhythm between persons with FM and controls. We evaluated PBT correlation with depression symptoms and pain severity in women with FM.

METHODS

We included 101 women aged 30-65 with FM diagnosis (FM group, n = 83) and controls (n = 18). Twenty-four-hour PBT was assessed by actigraphy. For the analysis, in the FM group, the PBT measurement was divided into four periods: morning (6 a.m.-noon), afternoon (noon-6 p.m.), evening (6 p.m.-midnight), and night (midnight-6 a.m.). According to their scores on the Hamilton Depression Rating Scale (HDRS), participants were classified as having mild or moderate to severe depression symptoms.

RESULTS

There was no difference in PBT between FM and controls. Subjects with FM and moderate to severe depression symptoms showed a higher PBT (p = .003) during the evening period (p = .004). The analysis of PBT rhythm revealed an interaction between time and group according to mild or moderate to severe depression symptoms (χ2 (3) = 12.79, p < .005). The pain severity was positively correlated with PBT (ß=0.22, [CI 95%, 0.07-0.37], p = .003).

CONCLUSIONS

PBT rhythm was not a sensitive measure for discriminating persons with FM from controls. In FM, PBT is related to the severity of depression symptoms and pain intensity.

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.