AUTHOR=Gu Ki-Nam , Leem Sangseob , Kim Hanji , Shin Joong-Gon , Seo Jung Yeon , Hwang Sunghwan , Jeong Eui Taek , Kim Yunkwan , Kang Nae Gyu 
  
TITLE=Periorbital skin index as a biomarker for biological aging and health status
  
JOURNAL=Frontiers in Aging
  
VOLUME=Volume 7 - 2026
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2026.1715245
  
DOI=10.3389/fragi.2026.1715245
  
ISSN=2673-6217
  
ABSTRACT=BackgroundThe periorbital skin area is particularly susceptible to aging compared to other facial regions due to its unique anatomical features and frequent muscle movements. This leads to early development of wrinkles and discoloration, which affect one’s appearance. Because of these characteristics, the eye-region skin serves as a representative indicator reflecting both skin aging and overall health status.ObjectivesThis study aims to develop and validate a straightforward, non-invasive method to evaluate changes in the eye-region skin as reliable markers of aging and overall physiological condition.MethodsWe analyzed facial images from 2,515 Korean women aged 20–69 and evaluated various periorbital features, including wrinkles, morphological characteristics, and pigmented spots, using skin measurement devices and computational image analysis techniques. To assess skin aging and health status, we developed age prediction models based on different combinations of these periorbital features for each individual. Subsequently, Principal Component Analysis (PCA) was performed to summarize disease history variables for each participant, and the correlation between the first principal component (PC1) and periorbital skin age was evaluated using Pearson correlation analysis.ResultsPeriorbital skin features showed significant associations with chronological age. We developed nine distinct age prediction models by combining different subsets of these features, each producing a unique aging score. Among them, seven models demonstrated strong correlations with actual age (r > 0.7), confirming their predictive reliability. These individual model outputs were collectively considered as unified aging markers representing periorbital skin age. To evaluate clinical relevance, we analyzed the association between periorbital skin age derived from the model incorporating all skin features and disease history. Periorbital skin age showed significant associations with five out of seven diseases individually, as well as with the PC1 summarizing all disease histories collectively.ConclusionThis study establishes ‘periorbital skin age’ as a non-invasive biomarker that effectively reflects both the progression of skin aging and underlying medical conditions. Our findings highlight the potential utility of eye-region skin assessment in clinical and health monitoring settings, offering a practical tool for evaluating physiological aging and disease risk.