Epigenetic and biological age acceleration in children with atopic dermatitis.

Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease resulting from the complex interplay of genetic and environmental factors, meriting exploration using temporally dynamic biomarkers. DNA methylation-based algorithms have been trained to accurately estimate biological age, and deviation of predicted age from true age (epigenetic age acceleration) has been implicated in several inflammatory diseases, including asthma. Objective: We sought to determine the role of epigenetic and biological aging, telomere length, and epigenetically inferred abundance of 7 inflammatory biomarkers in AD. Methods: We performed DNA methylation-based analyses in a pediatric AD cohort (n = 24, mean ± standard deviation [SD] age 2.56 ± 0.28 years) and age-matched healthy subjects (n = 24, age 2.09 [0.15] years) derived from blood using 5 validated algorithms that assess epigenetic age (Horvath, Skin&Blood) and biological age (PhenoAge, GrimAge), telomere length (TelomereLength), and inflammatory biomarker levels. Results: Epigenetic and biological age, but not telomere length, were accelerated in AD patients for 4 algorithms: Horvath (+0.88 years; 95% confidence interval [CI], 0.33 to 1.4; P = 2.3 × 10-3), Skin&Blood (+0.95 years; 95% CI, 0.67 to 1.2; P = 1.8 × 10-8), PhenoAge (+8.2 years; 95% CI, 3.4 to 13.0; P = 1.3 × 10-3), and GrimAge (+1.8 years 95% CI, 0.22 to 3.3; P = .026). Moreover, patients had increased levels of ß2 microglobulin (+47,584.4 ng/mL; P = .029), plasminogen activation inhibitor 1 (+3,432.9 ng/mL; P = 1.1 × 10-5), and cystatin C (+31,691 ng/mL; P = 4.0 × 10-5), while levels of tissue inhibitor metalloproteinase 1 (-370.7 ng/mL; P = 7.5 × 10-4) were decreased compared to healthy subjects. Conclusion: DNA methylation changes associated with epigenetic and biological aging, and inflammatory proteins appear early in life in pediatric AD and may be relevant clinical biomarkers of pathophysiology.

Signatures of epigenetic, biological and mitotic age acceleration and telomere shortening are associated with arsenic-induced skin lesions.

Chronic arsenic exposure is a global health hazard significantly associated with the development of deleterious cutaneous changes and increased keratinocyte cancer risk. Although arsenic exposure is associated with broad-scale cellular and molecular changes, gaps exist in understanding how these changes impact the skin and facilitate malignant transformation. Recently developed epigenetic "clocks" can accurately predict chronological, biological and mitotic age, as well as telomere length, on the basis of tissue DNA methylation state. Deviations of predicted from expected age (epigenetic age dysregulation) have been associated with numerous complex diseases, increased all-cause mortality and higher cancer risk. We investigated the ability of these algorithms to detect molecular changes associated with chronic arsenic exposure in the context of associated skin lesions. To accomplish this, we utilized a multi-algorithmic approach incorporating seven "clocks" (Horvath, Skin&Blood, PhenoAge, PCPhenoAge, GrimAge, DNAmTL and epiTOC2) to analyze peripheral blood of pediatric and adult cohorts of arsenic-exposed (n = 84) and arsenic-naïve (n = 33) individuals, among whom n = 18 were affected by skin lesions. Arsenic-exposed adults with skin lesions exhibited accelerated epigenetic (Skin&Blood: + 7.0 years [95% CI 3.7; 10.2], q = 6.8 × 10-4), biological (PhenoAge: + 5.8 years [95% CI 0.7; 11.0], q = 7.4 × 10-2, p = 2.8 × 10-2) and mitotic age (epiTOC2: + 19.7 annual cell divisions [95% CI 1.8; 37.7], q = 7.4 × 10-2, p = 3.2 × 10-2) compared to healthy arsenic-naïve individuals; and accelerated epigenetic age (Skin&Blood: + 2.8 years [95% CI 0.2; 5.3], q = 2.4 × 10-1, p = 3.4 × 10-2) compared to lesion-free arsenic-exposed individuals. Moreover, lesion-free exposed adults exhibited accelerated Skin&Blood age (+ 4.2 [95% CI 1.3; 7.1], q = 3.8 × 10-2) compared to their arsenic-naïve counterparts. Compared to the pediatric group, arsenic-exposed adults exhibited accelerated epigenetic (+ 3.1 to 4.4 years (95% CI 1.2; 6.4], q = 2.4 × 10-4-3.1 × 10-3), biological (+ 7.4 to 7.8 years [95% CI 3.0; 12.1] q = 1.6 × 10-3-2.8 × 10-3) and mitotic age (+ 50.0 annual cell divisions [95% CI 15.6; 84.5], q = 7.8 × 10-3), as well as shortened telomere length (- 0.23 kilobases [95% CI - 0.13; - 0.33], q = 2.4 × 10-4), across all seven algorithms. We demonstrate that lifetime arsenic exposure and presence of arsenic-associated skin lesions are associated with accelerated epigenetic, biological and mitotic age, and shortened telomere length, reflecting altered immune signaling and genomic regulation. Our findings highlight the usefulness of DNA methylation-based algorithms in identifying deleterious molecular changes associated with chronic exposure to the heavy metal, serving as potential prognosticators of arsenic-induced cutaneous malignancy.

Gene-Environment Analyses in a UK Biobank Skin Cancer Cohort Identifies Important SNPs in DNA Repair Genes That May Help Prognosticate Disease Risk.

BACKGROUND: Despite well-established relationships between sun exposure and skin cancer pathogenesis/progression, specific gene-environment interactions in at-risk individuals remain poorly-understood. METHODS: We leveraged a UK Biobank cohort of basal cell carcinoma (BCC, n = 17,221), cutaneous squamous cell carcinoma (cSCC, n = 2,331), melanoma in situ (M-is, n = 1,158), invasive melanoma (M-inv, n = 3,798), and healthy controls (n = 448,164) to quantify the synergistic involvement of genetic and environmental factors influencing disease risk. We surveyed 8,798 SNPs from 190 DNA repair genes, and 11 demographic/behavioral risk factors. RESULTS: Clinical analysis identified darker skin (RR = 0.01-0.65) and hair (RR = 0.27-0.63) colors as protective factors. Eleven SNPs were significantly associated with BCC, three of which were also associated with M-inv. Gene-environment analysis yielded 201 SNP-environment interactions across 90 genes (FDR-adjusted q < 0.05). SNPs from the FANCA gene showed interactions with at least one clinical factor in all cancer groups, of which three (rs9926296, rs3743860, rs2376883) showed interaction with nearly every factor in BCC and M-inv. CONCLUSIONS: We identified novel risk factors for keratinocyte carcinomas and melanoma, highlighted the prognostic value of several FANCA alleles among individuals with a history of sunlamp use and childhood sunburns, and demonstrated the importance of combining genetic and clinical data in disease risk stratification. IMPACT: This study revealed genome-wide associations with important implications for understanding skin cancer risk in the context of the rapidly-evolving field of precision medicine. Major individual factors (including sex, hair and skin color, and sun protection use) were significant mediators for all skin cancers, interacting with >200 SNPs across four skin cancer types.