TAGS: Skin Care
The Biological Science Research Laboratory of Kao Corporation has found that the degree of skin aging, which varies among individuals, may be assessed by estimating the "biological age" of the skin. This can be done by utilizing expression information of ribonucleic acid (RNA) in skin surface lipids (SSL-RNA), which changes with aging, in combination with machine learning.
Used Machine Learning Model
Recently, the concept of biological age, which indicates the degree of aging and is estimated from the degree of decline in physical functions, has drawn the attention of researchers.
The SSL-RNA monitoring technology reported by Kao in 2019 enables the collection and analysis of SSL-RNA from facial sebum without damaging the skin. Information on biomolecules which change with age, such as RNAs and proteins, plays an important role in estimating biological age. In the study detailed below, Kao investigated whether the biological age of the skin can be estimated using SSL-RNA expression information.
To demonstrate this, Kao constructed and evaluated a machine learning model that predicts age based on the expression levels of 368 RNAs that change with aging using the same study subjects as those in the analysis mentioned above. This evaluation showed a high correlation between the predicted and actual chronological ages. However, even among the same age group, some subjects showed higher or lower predicted age than others. Therefore, the predicted ages suggested biological ages, reflecting the degree of variation in aging among individuals.
Skin Aging Compared with Chronological Age
In this study, ages predicted by the machine learning model were defined as biological ages and were verified to determine whether they were valid as an index of skin aging. In a group of participants in their 40s (n=14), among whom skin aging was expected to vary considerably by individual, participants in the upper 25% of biological age (older biological age group) and those in the lower 25% of biological age (younger biological age group) were included in the analysis to compare skin measurements between the groups.
In the analysis, the older biological age group (n=4) showed more skin roughness at the outer corner of the eyes (larger number of and/or deeper wrinkles), less skin elasticity around the eyes, and more glycated level of the skin around the mouth compared with the younger biological age group.
Based on these findings, it was considered that biological age estimated from SSL-RNA is a valid index of skin aging because individuals whose biological age was relatively older tended to show more aged skin.
Furthermore, the correlations between skin measurements related to skin aging and biological age or chronological age were analyzed in the same age group to verify to what extent biological age can reflect skin aging compared with chronological age.
As a result, it was revealed that biological age significantly correlates with skin measurements while there were no significant correlations between chronological age and skin measurements in the group in their 40s. These findings indicate the possibility that biological age estimated from SSL-RNA by using machine learning more strongly reflected the degree of skin aging.
Kao will further accumulate SSL-RNA data to improve the performance of biological age estimation with the goal of applying it to understand the variations in individual aging and to develop optimal approaches to body and skin care.
Source: Kao