Kao calculates biological age using sebum RNA information that changes with aging

Kao found that it is possible to calculate the biological age by combining the expression information of sebum RNA, which fluctuates with aging, and machine learning, and to estimate the degree of progression of skin aging that varies from person to person.

The results of this research will be presented at the "21st Annual Meeting of the Japanese Society of Anti-Aging Medicine" (June 25-27) and the "86th SCCJ (Japan Cosmetic Engineers Association) Research Discussion Meeting" (July 15). He made a presentation and received the best presentation award at the SCCJ research debate.

Even if the calendar age is the same, the degree of aging progress and the appearance of symptoms vary from person to person, but in recent years, the idea of ​​biological age, which estimates the degree of aging progress from the degree of deterioration of body function, has attracted attention.The company focused on "sebum RNA monitoring technology" that collects and analyzes sebum RNA from facial sebum without damaging the skin, and this time, estimates the biological age of the skin using sebum RNA expression information. I examined whether it could be done.

The company has confirmed that the expression pattern of sebum RNA changes with aging, and this time, we targeted 113 women aged 20 to 59 years and 368 types that are strongly related to the calendar age. Enrichment analysis was performed on RNA.

As a result, it was clarified that sebum RNA whose expression pattern changes with aging contains many functions related to aging such as inflammation, cell death, cell aging and epidermal differentiation. ..

From these results, we thought that it would be possible to estimate the degree of aging of the human body using sebum RNA, and in the same subject, we used the expression levels of 368 types of sebum RNA, which fluctuate with age, to determine the calendar age. We constructed and verified a machine learning model to predict.

As a result, a high correlation was found between the predicted value by sebum RNA and the calendar age.

However, on the other hand, there were some people with high and low prediction values ​​in the same age group, so it is highly possible that the prediction values ​​indicate the biological age that reflects the difference in the degree of aging of each individual.

Subsequently, the age calculated from the machine learning model was defined as the biological age, and it was evaluated whether it was valid as an index of skin aging.

Among those in their 40s (14 people), who are expected to have large individual differences in skin aging, those with relatively high biological age (25%) and those with low biological age (25%) are selected and closely related to skin aging. We examined the difference in skin measurement values ​​related to.

As a result, it was confirmed that the skin surface of the outer corners of the eyes was rough, the elasticity of the skin around the eyes was low, and the saccharification of the skin around the mouth was advanced in the people (4 people) who were relatively older in biological age. did it.

This indicates that people with a relatively high biological age tend to have aging skin, and the biological age based on the sebum RNA created this time is a valid index for skin aging. Was shown.

Furthermore, in order to verify how much this biological age reflects the aging state of the skin compared to the calendar age, skin measurements related to skin aging in the same age and the biological age, Correlation analysis with calendar age was performed.

As a result, it was clarified that in the 40s, the calendar age was not significantly correlated with the skin measurement value, while the biological age was significantly correlated with the skin measurement value.

This indicates that the biological age calculated from sebum RNA using machine learning may more strongly reflect the degree of skin aging.

Although calendar age is irreversible, biological age may be changed to a better state by reviewing environmental factors, so in the future, by accumulating sebaceous RNA data, the estimation accuracy of biological age will be accurate. Aiming to understand the progress of aging of each individual and apply it to the approach to the body and skin according to it