Predicting Chronological Age via the Skin Volatile Profile

Area of Science:

• Volatilomics
• Biochemistry
• Dermatology

Background:

• Skin volatile emissions provide noninvasive insights into metabolic activity and the skin microbiome.
• Previous small studies suggest correlations between specific volatile compounds and aging.
• Understanding age-related changes in skin volatiles is crucial for developing new health diagnostics.

Purpose of the Study:

• To investigate the correlation between skin volatile compounds and chronological age in a healthy cohort.
• To identify specific volatile compounds that change significantly with age.
• To assess the predictive power of skin volatiles for age estimation.

Main Methods:

• Collected and analyzed skin volatile emissions from 60 healthy participants using headspace-solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS).
• Identified 18 suitable volatile compounds and classified them based on gender influences.
• Employed multiple linear regression (MLR) analysis to model age using significant volatiles and gender.

Main Results:

• Six volatile compounds (endogenous and exogenous) showed significant changes in abundance with participant age (p < 0.1).
• Skin volatile profiles demonstrated a strong predictive ability for age, explaining 68% of the variance.
• The predictive power of skin volatiles for age surpassed that of other biochemical measures like skin surface pH and water content.

Conclusions:

• Skin volatile emissions are significantly impacted by aging, reflecting both endogenous and exogenous compound origins.
• Skin volatiles offer a robust, noninvasive biomarker for age prediction, outperforming traditional biochemical markers.
• This research supports the development of volatilomics-based diagnostics for assessing the healthy "baseline" skin volatile profile.