Kunin, AnatolijAnatolijKuninSargheini, NafisehNafisehSargheiniBirkenbihl, ColinColinBirkenbihlMoiseeva, NataliaNataliaMoiseevaFröhlich, HolgerHolgerFröhlichGolubnitschaja, OlgaOlgaGolubnitschaja2022-03-062022-03-062020https://publica.fraunhofer.de/handle/publica/26489710.1007/s13167-020-00229-8Verbal communication is one of the most sophisticated human motor skills reflecting both-the mental and physical health of an individual. Voice parameters and quality changes are usually secondary towards functional and/or structural laryngological alterations under specific systemic processes, syndrome and pathologies. These include but are not restricted to dry mouth and Sicca syndromes, body dehydration, hormonal alterations linked to pubertal, menopausal, and andropausal status, respiratory disorders, gastrointestinal reflux, autoimmune diseases, endocrinologic disorders, underweight versus overweight and obesity, and diabetes mellitus. On the other hand, it is well-established that stress overload is a significant risk factor of cascading pathologies, including but not restricted to neurodegenerative and psychiatric disorders, diabetes mellitus, cardiovascular disease, stroke, and cancers. Our current study revealed voice perturbations under the stress overload as a potentially useful biomarker to identify individuals in suboptimal health conditions who might be strongly predisposed to associated pathologies. Contextually, extended surveys applied in the population might be useful to identify, for example, persons at high risk for respiratory complications under pandemic conditions such as COVID-19. Symptoms of dry mouth syndrome, disturbed microcirculation, altered sense regulation, shifted circadian rhythm, and low BMI were positively associated with voice perturbations under the stress overload. Their functional interrelationships and relevance for cascading associated pathologies are presented in the article. Automated analysis of voice recordings via artificial intelligence (AI) has a potential to derive digital biomarkers. Further, predictive machine learning models should be developed that allows for detecting a suboptimal health condition based on voice recordings, ideally in an automated manner using derived digital biomarkers. Follow-up stratification and monitoring of individuals in suboptimal health conditions are recommended using disease-specific cell-free nucleic acids (ccfDNA, ctDNA, mtDNA, miRNA) combined with metabolic patterns detected in body fluids. Application of the cost-effective targeted prevention within the phase of reversible health damage is recommended based on the individualised patient profiling.enpredictive preventive personalised medicinevoice perturbationsuboptimal healthsurvey stressprimary vascular dysregulationVasospasmbiomarker patternIndividualised patient profilephenotypingflammer syndromebody mass indexunderweightdry mouth syndromeHyposalivationXerostomiaSicca syndromehigh altitude sicknessTinnitussense regulationpain sensitivityexercise-induced hypoalgesiamicrocirculationthirstcircadian rhythmOtorhinolaryngologoical disordersdisease predispositionmachine learning modelsassociationrisk factorsrespiratory complicationspopulation screeningpandemicrisk assessmentlifestyle interventionHealthcareartificial intelligence (AI)003610005006518journal article