Faculty Opinions recommendation of Depression and anxiety as predictors of heart rate variability after myocardial infarction.

Author(s):  
Carmine Pariante ◽  
Valeria Mondelli
2007 ◽  
Vol 38 (3) ◽  
pp. 375-383 ◽  
Author(s):  
E. J. Martens ◽  
I. Nyklíček ◽  
B. M. Szabó ◽  
N. Kupper

BackgroundReduced heart rate variability (HRV) is a prognostic factor for cardiac mortality. Both depression and anxiety have been associated with increased risk for mortality in cardiac patients. Low HRV may act as an intermediary in this association. The present study examined to what extent depression and anxiety differently predict 24-h HRV indices recorded post-myocardial infarction (MI).MethodNinety-three patients were recruited during hospitalization for MI and assessed on self-reported symptoms of depression and anxiety. Two months post-MI, patients were assessed on clinical diagnoses of lifetime depressive and anxiety disorder. Adequate 24-h ambulatory electrocardiography data were obtained from 82 patients on average 78 days post-MI.ResultsIn unadjusted analyses, lifetime diagnoses of major depressive disorder was predictive of lower SDNN [standard deviation of all normal-to-normal (NN) intervals; β=−0.26, p=0.022] and SDANN (standard deviation of all 5-min mean NN intervals; β=0.25, p=0.023), and lifetime anxiety disorder of lower RMSSD (root mean square of successive differences; β=−0.23, p=0.039). Depression and anxiety symptoms did not significantly predict HRV. After adjustment for age, sex, cardiac history and multi-vessel disease, lifetime depressive disorder was no longer predictive of HRV. Lifetime anxiety disorder predicted reduced high-frequency spectral power (β=−0.22, p=0.039) and RMSSD (β=−0.25, p=0.019), even after additional adjustment of anxiety symptoms.ConclusionsClinical anxiety, but not depression, negatively influenced parasympathetic modulation of heart rate in post-MI patients. These findings elucidate the physiological mechanisms underlying anxiety as a risk factor for adverse outcomes, but also raise questions about the potential role of HRV as an intermediary between depression and post-MI prognosis.


Circulation ◽  
1996 ◽  
Vol 93 (7) ◽  
pp. 1388-1395 ◽  
Author(s):  
Narendra Singh ◽  
Dmitry Mironov ◽  
Paul W. Armstrong ◽  
Allan M. Ross ◽  
Anatoly Langer

2021 ◽  
Vol 5 ◽  
pp. 247054702110003
Author(s):  
Megan Chesnut ◽  
Sahar Harati ◽  
Pablo Paredes ◽  
Yasser Khan ◽  
Amir Foudeh ◽  
...  

Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety. We focused on cortisol, heart rate variability and skin conductance that have been well studied in depression and anxiety and implicated in clinical emotional states. A targeted PubMed search was undertaken prioritizing meta-analyses that have linked depression and anxiety to cortisol, heart rate variability and skin conductance. Consistent findings include reduced heart rate variability across depression and anxiety, reduced tonic and phasic skin conductance in depression, and elevated cortisol at different times of day and across the day in depression. We then provide a brief overview of neural circuit disruptions that characterize particular types of depression and anxiety. We also include an illustrative analysis using predictive models to determine how stress markers contribute to specific subgroups of symptoms and how neural circuits add meaningfully to this prediction. For this, we implemented a tree-based multi-class classification model with physiological markers of heart rate variability as predictors and four symptom subtypes, including normative mood, as target variables. We achieved 40% accuracy on the validation set. We then added the neural circuit measures into our predictor set to identify the combination of neural circuit dysfunctions and physiological markers that accurately predict each symptom subtype. Achieving 54% accuracy suggested a strong relationship between those neural-physiological predictors and the mental states that characterize each subtype. Further work to elucidate the complex relationships between physiological markers, neural circuit dysfunction and resulting symptoms would advance our understanding of the pathophysiological pathways underlying depression and anxiety.


1994 ◽  
Vol 28 (8) ◽  
pp. 1273-1276 ◽  
Author(s):  
J. O Valkama ◽  
H. V Huikuri ◽  
K E J. Airaksinen ◽  
M. L Linnaluoto ◽  
J. T Takkunen

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