Basal cell carcinoma genetic susceptibility increases the rate of skin ageing: a Mendelian randomization study

Abstract

BackgroundOnset of basal cell carcinoma (BCC) is connected to skin ageing, but it is unclear whether higher BCC genetic susceptibility drives skin ageing.ObjectivesTo investigate whether loci increasing genetic susceptibility to BCC also drive multiple features of skin ageing, independently of confounding factors, using Mendelian randomization.MethodsA Mendelian randomization study was conducted in older adults from the Leiden Longevity Study (N = 604). A total of 25 BCC loci, selected based on a published genome‐wide association study on BCC (P‐value < 5 × 10−8), were used as genetic instruments for the calculation of a standardized (mean = 0, SD = 1) weighted BCC genetic risk score. Based on facial photographs, we determined perceived age, and skin wrinkling and pigmented spot grading.ResultsA higher BCC genetic risk score was associated with a higher perceived age (adjusted for chronological age and sex) of 0.88 years (95% CI: 0.44, 1.31; P‐value = 7.1e−5), greater wrinkling by 0.14 grades (95% CI: 0.05, 0.23; P‐value = 2.3e−3), and greater pigmented spots by 0.17 grades (95% CI: 0.08, 0.25; P‐value = 1.1e−4). These findings were weakened but still present after exclusion of gene variants in MC1R and IRF4 which have potential pleiotropic effects.ConclusionsMechanisms influenced by genetic loci increasing susceptibility to BCC also drive skin ageing suggesting shared biology and shared targets for interventions.

Exploring the link between gut microbiota and alopecia areata: a two‐sample Mendelian randomization analysis

“…Importantly, in the context of GWAS, a significance threshold of 1e-5 is not conventionally recognized. [25][26][27] Although 1e-5 is a compromise threshold in microbiome-wide association studies (mGWAS), this should be acknowledged due to potential weak instrument bias. Notably, our study set the F-statistic threshold above 10 to mitigate the impact of weak statistical variables, a common practice in gut microbiota research adopting 1e-5 as a significance threshold.…”

“…However, the findings should be interpreted cautiously due to limitations such as the reliance on summarized data, or potential population stratification biases. Importantly, in the context of GWAS, a significance threshold of 1e‐5 is not conventionally recognized 25–27 . Although 1e‐5 is a compromise threshold in microbiome‐wide association studies (mGWAS), this should be acknowledged due to potential weak instrument bias.…”

Biological insights from self-perceived facial aging data of the UKBB participants

“…Skin cancer is one of the top over-represented categories associated with facial aging. Interestingly, a recent independent study on another cohort (Leiden Longevity) found that mechanisms influenced by genetic loci increasing susceptibility to basal cell carcinoma also drive skin ageing suggesting shared biology and shared targets for interventions [35].…”

Assessment of relationships between bullous pemphigoid and neurological diseases: A bidirectional two‐sample Mendelian randomization study

“…The inverse‐variance‐weighted (IVW) model was utilized as the core random‐effects analysis method, which is most extensively used and could offer robust causal evaluations in the context of the absence of directional pleiotropy 16 . After the Bonferroni correction, p < 0.0125 (0.05/4) was identified as significant, while associations with p ≥ 0.0125 and <0.05 were considered as suggesting significance 17 .…”

“…The inverse-variance-weighted (IVW) model was utilized as the core random-effects analysis method, which is most extensively used and could offer robust causal evaluations in the context of the absence of directional pleiotropy. 16 After the Bonferroni correction, p < 0.0125 (0.05/4) was identified as significant, while associations with p ≥ 0.0125 and <0.05 were considered as suggesting significance. 17 Besides, we used odds ratios (ORs) with 95% confidence intervals (CIs) to present the effect estimates, which can be explained as the average variation in the outcome per log-odds increase in the genetically predicted risk of the respective binary exposure.…”