IntroductionSkin changes are among the most visible signs of aging. Skin properties such as hydration, elasticity, and antioxidant capacity play a key role in the skin aging process. Skin aging is a complex process influenced by heritable and environmental factors. Recent studies on twins have revealed that up to 60% of the skin aging variation between individuals can be attributed to genetic factors, while the remaining 40% is due to non-genetic factors. Recent advances in genomics and bioinformatics approaches have led to the association of certain single nucleotide polymorphisms (SNPs) to skin properties. Our aim was to classify individuals based on an ensemble of multiple polymorphisms associated with certain properties of the skin for providing personalized skin care and anti-aging therapies.Methods and resultsWe identified the key proteins and SNPs associated with certain properties of the skin that contribute to skin aging. We selected a set of 13 SNPs in gene coding for these proteins which are potentially associated with skin aging. Finally, we classified a sample of 120 female volunteers into ten clusters exhibiting different skin properties according to their genotypic signature.ConclusionThis is the first study that describes the actual frequency of genetic polymorphisms and their distribution in clusters involved in skin aging in a Caucasian population. Individuals can be divided into genetic clusters defined by genotypic variables. These genotypic variables are linked with polymorphisms in one or more genes associated with certain properties of the skin that contribute to a person’s perceived age. Therefore, by using this classification, it is possible to characterize human skin care and anti-aging needs on the basis of an individual’s genetic signature, thus opening the door to personalized treatments addressed at specific populations. This is part of an ongoing effort towards personalized anti-aging therapies combining genetic signatures with environmental and life style evaluations.

BackgroundPerceived age has been defined as the age that a person is visually estimated to be on the basis of physical appearance. In a society where a youthful appearance are an object of desire for consumers, and a source of commercial profit for cosmetic companies, this concept has a prominent role. In addition, perceived age is also an indicator of overall health status in elderly people, since old-looking people tend to show higher rates of morbidity and mortality. However, there is a lack of objective methods for quantifying perceived age.MethodsIn order to satisfy the need of objective approaches for estimating perceived age, a novel algorithm was created. The novel algorithm uses supervised mathematical learning techniques and error retropropagation for the creation of an artificial neural network able to learn biophysical and clinically assessed parameters of subjects. The algorithm provides a consistent estimation of an individual’s perceived age, taking into account a defined set of facial skin phenotypic traits, such as wrinkles and roughness, number of wrinkles, depth of wrinkles, and pigmentation. A nonintervention, epidemiological cross-sectional study of cases and controls was conducted in 120 female volunteers for the diagnosis of perceived age using this novel algorithm. Data collection was performed by clinical assessment of an expert panel and biophysical assessment using the ANTERA 3D® device.Results and discussionEmploying phenotype data as variables and expert assignments as objective data, the algorithm was found to correctly classify the samples with an accuracy of 92.04%. Therefore, we have developed a method for determining the perceived age of a subject in a standardized, consistent manner. Further application of this algorithm is thus a promising approach for the testing and validation of cosmetic treatments and aesthetic surgery, and it also could be used as a screening method for general health status in the population.