A Pattern-Based Heart Rate Variability Approach in Somatic Symptom Disorder: Evidence from the SOMA.SSD Study
Huesing, P.; Huang, W.-L.; Maehder, K.; Pauls, F.; Nestoriuc, Y.; Loewe, B.; Blankenburg, K.; Schmitz, S.; Hahn, S.; Toussaint, A.
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BackgroundSomatic Symptom Disorder (SSD) is characterised by a complex interplay between physiological regulation and psychological distress. Heart rate variability (HRV) has been proposed as a biomarker reflecting autonomic dysregulation in SSD, but single-parameter approaches have shown limited explanatory power. This study applied a proposed four-pattern HRV classification (normal, low, relatively high sympathetic, relatively high vagal pattern) to investigate its validity and its association with the SSD symptomatology in a German SSD sample. We examined whether these HRV patterns differentiate patients at baseline and predict trajectories of somatic and psychological symptoms over time. MethodsData from 148 German patients with SSD of the SOMA.SSD cohort were analyzed. HRV was measured at baseline via a 5-minute resting electrocardiogram (ECG) and classified into four patterns using age- and sex-adjusted European norms. Psychopathological outcomes (PHQ-15, PHQ-9, SSD-12) were assessed at baseline, 6, and 12 months. Between-group differences were analyzed using rmANOVA and linear mixed-effects models controlling for age and sex. ResultsPatients in the low HRV pattern group showed significantly higher levels of somatic symptom severity, depressive symptoms, and psychological distress compared to those with normal HRV pattern. No significant differences were observed among the high sympathetic and high vagal patterns. Across follow-ups, HRV pattern groups showed stable but distinct symptom levels with higher psychopathology scores in the low HRV pattern group, but without group x time interaction effects. ConclusionsThe four-pattern HRV approach was replicable in a Western SSD sample and identified a subgroup with low HRV and persistently elevated symptom burden. HRV-based pattern classification may serve as a feasible physiological marker for identifying autonomic subtypes in SSD and related disorders.
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