Evolution of Heart Rate Variability and Heart Rate Turbulence in Patients with Depressive Illness Treated with Selective Serotonin Reuptake Inhibitors

Diminazene aceturate attenuates systemic inflammation via microbiota gut-5-HT brain-spleen sympathetic axis in male mice

The sympathetic arm of the inflammatory reflex is the efferent pathway through which the central nervous system (CNS) can control peripheral immune responses. Diminazene aceturate (DIZE) is an antiparasitic drug that has been reported to exert protective effects on various experimental models of inflammation. However, the pathways by which DIZE promotes a protective immunomodulatory effects still need to be well established, and no studies demonstrate the capacity of DIZE to modulate a neural reflex to control inflammation. C57BL/6 male mice received intraperitoneal administration of DIZE (2 mg/Kg) followed by lipopolysaccharide (LPS, 5 mg/Kg, i.p.). Endotoxemic animals showed hyperresponsiveness to inflammatory signals, while those treated with DIZE promoted the activation of the inflammatory reflex to attenuate the inflammatory response during endotoxemia. The unilateral cervical vagotomy did not affect the anti-inflammatory effect of DIZE in the spleen and serum. At the same time, splenic denervation attenuated tumor necrosis factor (TNF) synthesis in the spleen and serum. Using broad-spectrum antibiotics for two weeks showed that LPS modulated the microbiota to induce a pro-inflammatory profile in the intestine and reduced the serum concentration of tryptophan and serotonin (5-HT), while DIZE restored serum tryptophan and increased the hypothalamic 5-HT levels. Furthermore, the treatment with 4-Chloro-DL-phenylalanine (pcpa, an inhibitor of 5-HT synthesis) abolished the anti-inflammatory effects of the DIZE in the spleen. Our results indicate that DIZE promotes microbiota modulation to increase central 5-HT levels and activates the efferent sympathetic arm of the inflammatory reflex to control splenic TNF production in endotoxemic mice.

The Contribution of Genetic Testing in Optimizing Therapy for Patients with Recurrent Depressive Disorder

(1) Background: The aim of this study was to analyze the impact of pharmacogenetic-guided antidepressant therapy on the 12-month evolution of the intensity of depressive symptoms in patients with recurrent depressive disorder (RDD) in comparison to a control group of depressive subjects who were treated conventionally. (2) Methods: This prospective longitudinal study was conducted between 2019 and 2022, and the patients were evaluated by employing the Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A) and the Clinical Global Impressions Scale: Severity and Improvement. We followed them up at 1, 3, 6, and 12 months. (3) Results: Of the 76 patients with RDD, 37 were tested genetically (Group A) and 39 were not (Group B). Although the patients from Group A had statistically significantly more severe MDD at baseline than those from Group B (p < 0.001), by adjusting their therapy according to the genetic testing, they had a progressive and more substantial reduction in the severity of RDD symptoms [F = 74.334; η2 = 0.674; p < 0.001], indicating a substantial association with the results provided by the genetic testing (67.4%). (4) Conclusions: In patients with RDD and a poor response to antidepressant therapy, pharmacogenetic testing allows for treatment adjustment, resulting in a constant and superior reduction in the intensity of depression and anxiety symptoms.

Integrating Genome-wide information and Wearable Device Data to Explore the Link of Anxiety and Antidepressants with Heart Rate Variability

Background

Anxiety disorders are associated with decreased heart rate variability (HRV), but the underlying mechanisms remain elusive.

Methods

We selected individuals with whole-genome sequencing, Fitbit, and electronic health record data (N=920; 61,333 data points) from the All of Us Research Program. Anxiety PRS were derived with PRS-CS after meta-analyzing anxiety genome-wide association studies from three major cohorts-UK Biobank, FinnGen, and the Million Veterans Program (N Total =364,550). The standard deviation of average RR intervals (SDANN) was calculated using five-minute average RR intervals over full 24-hour heart rate measurements. Antidepressant exposure was defined as an active antidepressant prescription at the time of the HRV measurement in the EHR. The associations of daily SDANN measurements with the anxiety PRS, antidepressant classes, and antidepressant substances were tested. Participants with lifetime diagnoses of cardiovascular disorders, diabetes mellitus, and major depression were excluded in sensitivity analyses. One-sample Mendelian randomization (MR) was employed to assess potential causal effect of anxiety on SDANN.

Results

Anxiety PRS was independently associated with reduced SDANN (beta=-0.08; p=0.003). Of the eight antidepressant medications and four classes tested, venlafaxine (beta=-0.12, p=0.002) and bupropion (beta=-0.071, p=0.01), tricyclic antidepressants (beta=-0.177, p=0.0008), selective serotonin reuptake inhibitors (beta=-0.069; p=0.0008) and serotonin and norepinephrine reuptake inhibitors (beta=-0.16; p=2×10 -6 ) were associated with decreased SDANN. One-sample MR indicated an inverse effect of anxiety on SDANN (beta=-2.22, p=0.03).

Conclusions

Anxiety and antidepressants are independently associated with decreased HRV, and anxiety appears to exert a causal effect on HRV. Our observational findings provide novel insights into the impact of anxiety on HRV.

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.

Evaluating anxiety in elective coronary angiography study: rationale, design, and study methodology

AIMS

Prevalence of anxiety disorder in coronary artery disease reaches up to 15% and about half of patients with coronary artery disease have anxiety or depression comorbidity. Prevalence of anxiety in patients undergoing percutaneous coronary intervention ranges 24-72%. Depression can often overlap with anxiety symptoms and the evaluation of anxiety in elective coronary angiography study (ANGST) aims to determine the prevalence of anxiety by excluding patients with comorbid depressive symptoms. ANGST also aims to determine how anxiety correlates with psychological parameters (personality traits, coping strategies) and with outcome of elective coronary angiography (ECA).

METHODS

We will conduct a prospective single-center cross-section study in patients undergoing ECA. Anxiety will be evaluated at four time points using self-rating questionnaires: 14 days prior to ECA; 2-4 h before ECA; 24 h after ECA, but prior to discharge; and 4-6 weeks after discharge. The primary outcome of ANGST is the burden of anxiety experienced by patients without depressive symptoms and a correlation of anxiety with ECA outcome.

CONCLUSION

Our study aims to provide evidence on which personality traits and coping strategies affect the levels of anxiety. We will also determine psychometric properties of the two questionnaires used in our study. The results will have implications for improvement of interventions designed to recognize anxiety and will offer future research of psychological and/or pharmacological interventions to reduce the burden of anxiety.

Assessment of the Impact of Alcohol Consumption Patterns on Heart Rate Variability by Machine Learning in Healthy Young Adults

Background and Objectives: Autonomic nervous system (ANS) dysfunction is present in early stages of alcohol abuse and increases the likelihood of cardiovascular events. Given the nonlinear pattern of dynamic interaction between sympathetic nervous system (SNS) and para sympathetic nervous system (PNS) and the complex relationship with lifestyle factors, machine learning (ML) algorithms are best suited for analyzing alcohol impact over heart rate variability (HRV), because they allow the analysis of complex interactions between multiple variables. This study aimed to characterize autonomic nervous system dysfunction by analysis of HRV correlated with cardiovascular risk factors in young individuals by using machine learning. Materials and Methods: Total of 142 young adults (28.4 ± 4.34 years) agreed to participate in the study. Alcohol intake and drinking patterns were assessed by the AUDIT (Alcohol Use Disorders Identification Test) questionnaire and the YAI (Yearly Alcohol Intake) index. A short 5-min HRV evaluation was performed. Post-hoc analysis and machine learning algorithms were used to assess the impact of alcohol intake on HRV. Results: Binge drinkers presented slight modification in the frequency domain. Heavy drinkers had significantly lower time-domain values: standard deviation of RR intervals (SDNN) and root mean square of the successive differences (RMSSD), compared to casual and binge drinkers. High frequency (HF) values were significantly lower in heavy drinkers (p = 0.002). The higher low-to-high frequency ratio (LF/HF) that we found in heavy drinkers was interpreted as parasympathetic inhibition. Gradient boosting machine learner regression showed that age and alcohol consumption had the biggest scaled impact on the analyzed HRV parameters, followed by smoking, anxiety, depression, and body mass index. Gender and physical activity had the lowest impact on HRV. Conclusions: In healthy young adults, high alcohol intake has a negative impact on HRV in both time and frequency-domains. In parameters like HRV, where a multitude of risk factors can influence measurements, artificial intelligence algorithms seem to be a viable alternative for correct assessment.

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

Background

The aim of this study was to assess the electrophysiological and other influencing factors correlating with symptom severity in patients with major depressive disorder (MDD) under three different conditions: baseline, stress exposure, and relaxation following stress exposure.

Methods

Symptom severity was assessed using the Beck Depression Inventory (BDI-II) in 89 inpatients (37 women; mean age 51 years) with MDD. Resting heart rate (RHR), heart rate variability (HRV), respiration rate (RR), skin conductance (SC), and skin temperature (ST) were recorded at baseline for 300 s, under stress exposure for 60 s, and under self-induced relaxation for 300 s. Age, nicotine consumption, body mass index, and blood pressure were evaluated as influencing factors.

Results

The BDI-II mean score was 29.7 points. Disease severity correlated positively with SC elevation under stress exposure and with a higher RR in the relaxed state, but no association was found between HRV and symptom severity. Age and higher blood pressure were both associated with lower HRV and higher RHR.

Conclusion

The results indicate that, in patients with MDD, changes in the autonomic nervous system (ANS) are complex; and the assessment of ANS reactivity to stressors is useful. Elevated blood pressure might be underdiagnosed, although it is already relevant in patients with MDD in their early 50s.