Fractal analysis of heart rate variability (specifically DFA-α2) can identify subclinical depressive symptoms in healthy subjects, highlighting a potential autonomic mechanism linking depression to cardiovascular risk.
ABSTRACT Introduction Psychopathology, and in particular depression, is a cardiovascular risk factor independent from co-occurring pathology. This link is traced back to the mind-heart-body connection, whose underlying mechanisms are, to date, not completely known. To study psychopathology in relation to the heart, it is necessary to observe the autonomic nervous system, which mediates among the parts of that connection. Its gold standard of evaluation is the study of heart rate variability (HRV). Objective To assess whether any association exists between the HRV parameters and sub-threshold depressive symptoms in a sample of healthy subjects Methods Two short-term HRV recordings (5 min - supine and sitting) were analysed in 77 healthy subjects. Here we adopted a three-fold approach to evaluate HRV: a set of scores belonging to the time domain (SDNN, pNN50, RMSSD); to the frequency domain (high, low, and very low frequencies) and a set of ‘nonlinear’ parameters. The PHQ-9 scale was used to detect depressive symptoms. Results Depressive symptoms were associated only with a parameter from the non-linear approach and specifically the long-term fluctuations of fractal dimensions (DFA-α2). This association remained significant even after controlling for age, gender, BMI, arterial hypertension, anti-hypertensive drugs, dyslipidaemia, and smoking habit. Moreover, the DFA-α2 was not affected by the baroreflex (postural change), unlike other autonomic markers. Conclusion In conclusion, fractal analysis of HRV (DFA-α2) allows to predict depressive symptoms below diagnostic threshold in healthy subjects regardless of their health status. DFA-α2 may be then considered as an imprint of subclinical depression on the heart rhythm.
Mandarano et al. (Thu,) studied this question.