Heart Rate Variability Changes in Patients With Major Depressive Disorder: Related to Confounding Factors, Not to Symptom Severity?

Heart rate vARiability and physical activity in inpatient treatMent of burnOut and DepressIon (HARMODI): protocol of a cross-sectional study with up to 8-week follow up

INTRODUCTION

Chronic stress can cause an imbalance within the autonomic nervous system, thereby affecting cardiovascular and mental health. Physical activity (PA) may have a positive effect on the autonomic nervous system and stress-related disorders, such as depression and burnout. Heart rate variability (HRV) is a non-invasive marker of the autonomic nervous system. However, limited and inconsistent data exist on the exact relationship between HRV, PA and depression and burnout symptoms. The HARMODI study aims to explore whether HRV is a feasible marker of depression and burnout symptoms and aims to evaluate the role of PA in the treatment of stress-related disorders.

METHODS AND ANALYSES

This is an observational study with a cross-sectional up to 8 week follow-up study design. A total of 153 patients, undergoing psychiatric inpatient treatment with burnout syndrome (Z73) and depressive episode (F32 or F33) or adjustment disorder (F43.2), will be recruited. Data on depression and burnout symptoms, HRV recordings (24-hour, supine, standing and exercise stress test), cognitive function, cardiorespiratory fitness, cardiovascular health, balance and strength will be collected at baseline (T1) and after up to 8 weeks (T2). Continuous data on PA and Ecological Momentary Assessments of exhaustion, mood and tension will be monitored daily throughout inpatient treatment. Multiple regression models, adjusted for potential confounders, will assess the association between HRV as the primary outcome, PA and depression and burnout severity score.

ETHICS AND DISSEMINATION

The protocol has been approved by Swiss Ethics Committee, Cantonal Ethics Committee Zürich. Results of HARMODI will be disseminated through peer-reviewed journals and conference presentations.

TRIAL REGISTRATION NUMBER

NCT05874856.

Association of 20-Year Longitudinal Depressive Symptoms With Left Ventricular Geometry Outcomes in the Coronary Artery Risk Development in Young Adults Study: A Role for Androgens?

OBJECTIVE

Depression is a risk factor for coronary heart disease and left ventricular hypertrophy (LVH) is a potent predictor of coronary heart disease events. Whether depression is associated with LVH has received limited investigation. This study assessed cross-sectional and 20-year longitudinal associations of depressive symptoms with LVH outcomes after accounting for important known confounders.

METHODS

From 5115 participants enrolled in 1985-1986 in the Coronary Artery Risk Development in Young Adults Study, 2533 had serial measures of depressive symptoms and subsequent echocardiography to measure normal LV geometry, concentric remodeling, and LVH. The primary exposure variable was trajectories of the Center for Epidemiologic Studies Depression (CES-D) scale score from 1990-1991 to 2010-2011. Multivariable polytomous logistic regression was used to assess associations of trajectories with a composite LV geometry outcome created using echocardiogram data measured in 2010-2011 and 2015-2016. Sex-specific conflicting results led to exploratory models that examined potential importance of testosterone and sex hormone-binding globulin.

RESULTS

Overall CES-D and Somatic subscale trajectories had significant associations with LVH for female participants only. Odds ratios for the subthreshold (mean CES-D ≈ 14) and stable (mean CES-D ≈ 19) groups were 1.49 (95% confidence interval = 1.05-2.13) and 1.88 (95% confidence interval = 1.16-3.04), respectively. For female participants, sex hormone-binding globulin was inversely associated with LVH, and for male participants, bioavailable testosterone was positively associated with concentric geometry.

CONCLUSIONS

Findings from cross-sectional and longitudinal regression models for female participants, but not male ones, and particularly for Somatic subscale trajectories suggested a plausible link among depression, androgens, and LVH. The role of androgens to the depression-LVH relation requires additional investigation in future studies.

Cardiovascular variations in patients with major depressive disorder versus bipolar disorder

BACKGROUND

Differentiating depression in major depressive disorder and bipolar disorder is challenging in clinical practice. Therefore, reliable biomarkers are urgently needed to differentiate between these diseases. This study's main objective was to assess whether cardiac autonomic function can distinguish patients with unipolar depression (UD), bipolar depression (BD), and bipolar mania (BM).

METHODS

We recruited 791 patients with mood disorders, including 191 with UD, 286 with BD, and 314 with BM, who had been drug free for at least 2 weeks. Cardiovascular status was measured using heart rate variability (HRV) and pulse wave velocity (PWV) indicators via finger photoplethysmography during a 5-min rest period.

RESULTS

Patients with BD showed lower HRV but higher heart rates than those with UD and BM. The PWV indicators were lower in the UD group than in the bipolar disorder group. The covariates of age, sex, and body mass index affected the cardiovascular characteristics. After adjusting for covariates, the HRV and PWV variations among the three groups remained significant. Comparisons between the UD and BD groups showed that the variable with the largest effect size was the frequency-domain indices of HRV, very low and high frequency, followed by heart rate. The area under the receiver operating characteristic curve (AUC) for each cardiovascular variable ranged from 0.661 to 0.714. The High-frequency index reached the highest AUC.

LIMITATIONS

Cross-sectional design and the magnitude of heterogeneity across participants with mood disorders limited our findings.

CONCLUSION

Patients with BD, but not BM, had a greater extent of cardiac imbalance than those with UD. Thus, HRV may serve as a psychophysiological biomarker for the differential diagnosis of UD and BD.

A rationale for considering heart/brain axis control in neuropsychiatric disease

Neuropsychiatric diseases (NPD) represent a significant global disease burden necessitating innovative approaches to pathogenic understanding, biomarker identification and therapeutic strategy. Emerging evidence implicates heart/brain axis malfunction in NPD etiology, particularly via the autonomic nervous system (ANS) and brain central autonomic network (CAN) interaction. This heart/brain inter-relationship harbors potentially novel NPD diagnosis and treatment avenues. Nevertheless, the lack of multidisciplinary clinical approaches as well as a limited appreciation of molecular underpinnings has stymied progress. Large-scale preclinical multi-systemic functional data can therefore provide supplementary insight into CAN and ANS interaction. We here present an overview of the heart/brain axis in NPD and establish a unique rationale for utilizing a preclinical cardiovascular disease risk gene set to glean insights into heart/brain axis control in NPD. With a top-down approach focusing on genes influencing electrocardiogram ANS function, we combined hierarchical clustering of corresponding regional CAN expression data and functional enrichment analysis to reveal known and novel molecular insights into CAN and NPD. Through 'support vector machine' inquiries for classification and literature validation, we further pinpointed the top 32 genes highly expressed in CAN brain structures altering both heart rate/heart rate variability (HRV) and behavior. Our observations underscore the potential of HRV/hyperactivity behavior as endophenotypes for multimodal disease biomarker identification to index aberrant executive brain functioning with relevance for NPD. This work heralds the potential of large-scale preclinical functional genetic data for understanding CAN/ANS control and introduces a stepwise design leveraging preclinical data to unearth novel heart/brain axis control genes in NPD.

Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices

To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

BACKGROUND

Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful.

OBJECTIVE

We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common.

METHODS

We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma.

RESULTS

A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes.

CONCLUSIONS

Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.

Electrophysiological correlates of mindfulness in patients with major depressive disorder

Objectives

Mindfulness-based interventions (MBI) can reduce both stress and depressive symptoms. However, the impact of mindfulness on stress level in depressed subjects remains unclear. This study aims to assess electrophysiological correlates of mindfulness in patients with major depressive disorder (MDD) at baseline, under stress exposure, and in relaxation following stress exposure.

Methods

Perceived mindfulness was assessed with the Freiburger Mindfulness Inventory (FMI) in 89 inpatients (mean age 51) with MDD [mean Beck Depression Inventory (BDI) 30]. Electrophysiological parameters [resting heart rate (RHR), heart rate variability (HRV), respiration rate, skin conductance, and skin temperature] were recorded at 5-min baseline, 1-min stress exposure, and 5-min self-induced relaxation.

Results

Freiburger Mindfulness Inventory was strongly inversely correlated with symptom severity measured by BDI (r = –0.53, p < 0.001). No correlations between FM score and electrophysiological parameters in any of the three conditions (baseline, stress exposure, relaxed state) could be found. The factor openness was associated with higher VLF (very low frequency of HRV) in the baseline condition. However, this correlation was no more significant after regression analysis when corrected for respiratory rate, age, and sex.

Conclusion

Autonomous nervous reactivity in depression was not associated with perceived mindfulness as measured by FMI score and presented electrophysiological parameters, despite the strong inverse correlation between state mindfulness and symptom severity.

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.