We study the interaction between balance function and anxiety in humans via a virtual reality based experimental setup, fundamentally designed to examine postural control and neurological feedback during balance-demanding height and perturbation shifts, while in quiet stance. In this work, sensory organization balance test (SOT) was utilized to quantify the balance function in subjects, and the salient features of the recorded electrocardiogram (EKG), electromyogram (EMG) and electroencephalography (EEG) signals described the subject's physiological anxiety-related responses. Also, patient-reported outcomes measurement information system (PROMIS) trait anxiety questionnaire was adopted to indicate the self-assessed trait anxiety scores by the subjects. Our statistical analysis indicates that subjects with higher SOT scores encounter a lower heart rate variability during baseline recording relative to the experimental trials and lower co-contraction index of the ankle dorsiflexion (during the experimental trials), suggesting a lower anxiety state. A similar trend was further observed via the self-reported PROMIS questionnaire responses, estimating anxiety experienced in daily life. Additionally, we utilize machine learning to describe the predictability of balance function states via the relative \alpha-band power from the frontal channels of the recorded EEG signals as descriptive features characterizing anxiety. The discussed trends analytically demonstrate the potential for adopting balance function to anticipate anxiety, especially in stressful environments involving postural threats.