L M Pardo1, M A Hamer1, F Liu2,3, P Velthuis1, M Kayser2, D A Gunn4, T Nijsten1. 1. Department of Dermatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands. 2. Department ofGenetic Identification, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands. 3. Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, China. 4. Unilever R&D, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LQ, U.K.
Abstract
BACKGROUND: The underlying phenotypic correlations between wrinkles, pigmented spots (PS), telangiectasia and other related facial-ageing subphenotypes are not well understood. OBJECTIVES: To analyse the underlying phenotypic correlation structure between seven features for facial ageing: global wrinkling, perceived age (PA), Griffiths photodamage grading, PS, telangiectasia, actinic keratosis (AK) and keratinocyte cancer (KC). METHODS: This was a cross-sectional study. Facial photographs and a full-body skin examination were used. We used principal component analysis (PCA) to derive principal components (PCs) of common variation between the features. We performed multivariable linear regressions between age, sex, body mass index, smoking and ultraviolet radiation exposure and the PC scores derived from PCA. We also tested the association between the main PC scores and 140 single-nucleotide polymorphisms (SNPs) previously associated with skin-ageing phenotypes. RESULTS: We analysed data from 1790 individuals with complete data on seven features of skin ageing. Three main PCs explained 73% of the total variance of the ageing phenotypes: a hypertrophic/wrinkling component (PC1: global wrinkling, PA and Griffiths grading), an atrophic/skin colour component (PC2: PS and telangiectasia) and a cancerous component (PC3: AK and KC). The associations between lifestyle and host factors differed per PC. The strength of SNP associations also differed per component with the most SNP associations found with the atrophic component [e.g. the IRF4 SNP (rs12203592); P-value = 1·84 × 10-22 ]. CONCLUSIONS: Using a hypothesis-free approach, we identified three major underlying phenotypes associated with extrinsic ageing. Associations between determinants for skin ageing differed in magnitude and direction per component. What's already known about this topic? Facial ageing is a complex phenotype consisting of different features including wrinkles, pigmented changes, telangiectasia and cancerous-related growths; it is not clear how these phenotypes are related to each other and to other phenotypes. A few studies have described two main clinical phenotypes for photoageing, namely hypertrophic ageing and atrophic ageing, which have been based solely on the clinical assessment of photoageing characteristics. What does this study add? We are the first to use epidemiology data to identify three main components associated with photoageing, namely a hypertrophic component (global wrinkling; perceived age; Griffiths grading) and atrophic component (pigmented spots; telangiectasia) and a cancer component (actinic keratosis; keratinocyte cancer). Association analysis showed different effects and direction of environmental determinants and genetic associations with the three components, with the most significant gene variants associations found for the atrophic component.
BACKGROUND: The underlying phenotypic correlations between wrinkles, pigmented spots (PS), telangiectasia and other related facial-ageing subphenotypes are not well understood. OBJECTIVES: To analyse the underlying phenotypic correlation structure between seven features for facial ageing: global wrinkling, perceived age (PA), Griffiths photodamage grading, PS, telangiectasia, actinic keratosis (AK) and keratinocyte cancer (KC). METHODS: This was a cross-sectional study. Facial photographs and a full-body skin examination were used. We used principal component analysis (PCA) to derive principal components (PCs) of common variation between the features. We performed multivariable linear regressions between age, sex, body mass index, smoking and ultraviolet radiation exposure and the PC scores derived from PCA. We also tested the association between the main PC scores and 140 single-nucleotide polymorphisms (SNPs) previously associated with skin-ageing phenotypes. RESULTS: We analysed data from 1790 individuals with complete data on seven features of skin ageing. Three main PCs explained 73% of the total variance of the ageing phenotypes: a hypertrophic/wrinkling component (PC1: global wrinkling, PA and Griffiths grading), an atrophic/skin colour component (PC2: PS and telangiectasia) and a cancerous component (PC3: AK and KC). The associations between lifestyle and host factors differed per PC. The strength of SNP associations also differed per component with the most SNP associations found with the atrophic component [e.g. the IRF4 SNP (rs12203592); P-value = 1·84 × 10-22 ]. CONCLUSIONS: Using a hypothesis-free approach, we identified three major underlying phenotypes associated with extrinsic ageing. Associations between determinants for skin ageing differed in magnitude and direction per component. What's already known about this topic? Facial ageing is a complex phenotype consisting of different features including wrinkles, pigmented changes, telangiectasia and cancerous-related growths; it is not clear how these phenotypes are related to each other and to other phenotypes. A few studies have described two main clinical phenotypes for photoageing, namely hypertrophic ageing and atrophic ageing, which have been based solely on the clinical assessment of photoageing characteristics. What does this study add? We are the first to use epidemiology data to identify three main components associated with photoageing, namely a hypertrophic component (global wrinkling; perceived age; Griffiths grading) and atrophic component (pigmented spots; telangiectasia) and a cancer component (actinic keratosis; keratinocyte cancer). Association analysis showed different effects and direction of environmental determinants and genetic associations with the three components, with the most significant gene variants associations found for the atrophic component.
Authors: Rainer Voegeli; Rotraut Schoop; Elodie Prestat-Marquis; Anthony V Rawlings; Todd K Shackelford; Bernhard Fink Journal: Int J Cosmet Sci Date: 2021-08-08 Impact factor: 2.416
Authors: Abigail K Langton; Zara Ali; Mark Hann; Jean Ayer; Rachel E B Watson; Christopher E M Griffiths Journal: Acta Derm Venereol Date: 2020-12-09 Impact factor: 3.875
Authors: M Arfan Ikram; Guy Brusselle; Mohsen Ghanbari; André Goedegebure; M Kamran Ikram; Maryam Kavousi; Brenda C T Kieboom; Caroline C W Klaver; Robert J de Knegt; Annemarie I Luik; Tamar E C Nijsten; Robin P Peeters; Frank J A van Rooij; Bruno H Stricker; André G Uitterlinden; Meike W Vernooij; Trudy Voortman Journal: Eur J Epidemiol Date: 2020-05-04 Impact factor: 8.082