Analysis of Multi-Part Phenotypic Changes in Skin to Characterize the Trajectory of Skin Aging in Chinese Women

Clin Cosmet Investig Dermatol. 2022 Apr 13:15:631-642. doi: 10.2147/CCID.S349401. eCollection 2022.

Abstract

Purpose: As the human body's largest organ exposed to the external environment, the skin suffers from internal and external aging factors, leading to wrinkles, loss of elasticity, sagging, and rough appearance. However, little is known of the characteristics of skin aging of different body parts in Chinese women. Here, we study the signs of extrinsic skin aging in different body parts to identify the knowledge map of manifestations of aging in Chinese women.

Patients and methods: Wrinkle and texture phenotypes and collagen samples from the face, neck, hands, and arms of 326 Chinese women were collected. The correlations between phenotypes and ages and the differences in phenotypes by age were evaluated.

Results: The wrinkle and texture phenotypes around the eyes and mouth and of the hands were strongly correlated with age. Ages 32 and 58 showed the largest number of differentially changed aging phenotypes. The number of aging phenotypes increased sharply between the ages of 24 and 30, suggesting that the skin was undergoing rapid aging. Eye aging was the most rapidly changing phenotype between 19 and 30 years old. Wrinkles at the corner of the eyes showed a significant difference in the older group, suggesting an early onset and long-term effects.

Conclusion: This is the first study to be performed on the characteristics of skin aging among Chinese women that takes account of multiple areas of the body. It was found that 24 years old was the time point at which the skin begins to age in Chinese women. This provides important clues for aging-related research and personalized skin care.

Keywords: Chinese women; aging; phenotypes; skin; wrinkles.

Grants and funding

This work was supported by the industry Support Foundation of Huangpu District, Shanghai, Big Data Construction and Application of Artificial Intelligence Skin Diagnosis and Treatment (XK2020007).