Genome-Wide Association Study Identifies Genetic Associations with Perceived Age

Beverage consumption and facial skin aging: Evidence from Mendelian randomization analysis

BACKGROUND

Observational studies have linked coffee, alcohol, tea, and sugar-sweetened beverage (SSB) consumption to facial skin aging. However, confounding factors may influence these studies. The present two-sample Mendelian randomization (MR) investigated the potential causal association between beverage consumption and facial skin aging.

METHODS

The single-nucleotide polymorphisms (SNPs) associated with coffee, alcohol, and tea intake were derived from the IEU project. The SSB-associated SNPs were selected from a genome-wide association study (GWAS). Data on facial skin aging were derived from the largest GWAS involving 16 677 European individuals. The inverse variance-weighted (IVW) was the main MR analysis method, supplemented by other methods (MR-Egger, weighted median, simple mode, and weighted mode). The MR-Egger intercept analysis was used for sensitivity analysis. Moreover, we conducted a replication analysis using data from another GWAS dataset on coffee consumption to validate our findings.

RESULTS

Four instrumental variables (IVs) sets were used to examine the causal association between beverage consumption (coffee, alcohol, tea, SSB) and facial skin aging. Our results revealed that genetically predicted higher coffee consumption reduced the risk of facial skin aging (OR: 0.852; 95% CI: 0.753-0.964; p = 0.011, IVW method). The sensitivity analysis confirmed the robustness of the findings, with no evidence of pleiotropy or heterogeneity. The results of replicated MR analysis on coffee consumption were consistent with the initial analysis (OR = 0.997; 95% CI = 0.996-0.999; p = 0.003, IVW method).

CONCLUSIONS

This study manifests that higher coffee consumption is significantly associated with a reduced risk of facial skin aging. These findings can offer novel strategies for identifying the underlying etiology of facial skin aging.

Identification of prospective aging drug targets via Mendelian randomization analysis

Aging represents a multifaceted process culminating in the deterioration of biological functions. Despite the introduction of numerous anti-aging strategies, their therapeutic outcomes have often been less than optimal. Consequently, discovering new targets to mitigate aging effects is of critical importance. We applied Mendelian randomization (MR) to identify potential pharmacological targets against aging, drawing upon summary statistics from both the Decode and FinnGen cohorts, with further validation in an additional cohort. To address potential reverse causality, bidirectional MR analysis with Steiger filtering was utilized. Additionally, Bayesian co-localization and phenotype scanning were implemented to investigate previous associations between genetic variants and traits. Summary-data-based Mendelian randomization (SMR) analysis was conducted to assess the impact of genetic variants on aging via their effects on protein expression. Additionally, mediation analysis was orchestrated to uncover potential intermediaries in these associations. Finally, we probed the systemic implications of drug-target protein expression across diverse indications by MR-PheWas analysis. Utilizing a Bonferroni-corrected threshold, our MR examination identified 10 protein-aging associations. Within this cohort of proteins, MST1, LCT, GMPR2, PSMB4, ECM1, EFEMP1, and ISLR2 appear to exacerbate aging risks, while MAX, B3GNT8, and USP8 may exert protective influences. None of these proteins displayed reverse causality except EFEMP1. Bayesian co-localization inferred shared variants between aging and proteins such as B3GNT8 (rs11670143), ECM1 (rs61819393), and others listed. Mediator analysis pinpointed 1,5-anhydroglucitol as a partial intermediary in the influence LCT exhibits on telomere length. Circulating proteins play a pivotal role in influencing the aging process, making them promising candidates for therapeutic intervention. The implications of these proteins in aging warrant further investigation in future clinical research.

Genetic Analysis of Perceived Youthfulness Reveals Differences in How Men's and Women's Age Is Assessed

Skin aging is a natural process that occurs over time but can be accelerated by sun exposure. Measuring skin age in a large population can provide insight into the extent of skin damage from sun exposure and skin cancer risk. Understanding the genetics of skin aging, within and across sexes (males and females), could improve our understanding of the genetic drivers of both skin aging and skin cancer. We used UK Biobank data to examine the genetic overlap between perceived youthfulness and traits relevant to actinic photoaging. Our GWAS identified 22 genome-wide significant loci for women and 43 for men. The genetic correlation (rg) between perceived youthfulness in men and women was significantly less than unity (rg = 0.75, 95% confidence interval = 0.69-0.80), suggesting a gene-by-sex interaction. In women, perceived youthfulness was modestly correlated with keratinocyte cancer (rg = -0.19) and skin tanning (rg = 0.18). In men, perceived youthfulness was correlated with male-pattern baldness (rg = -0.23). This suggests that the genetic architecture of perceived youthfulness may differ between sexes, with genes influencing skin tanning and skin cancer susceptibility driving the difference in women, whereas genes influencing male-pattern baldness and other puberty-related traits drive the difference in men. We recommend that future genetic analysis of skin aging include a sex-stratified component.

Genetic insights into the gut microbiota and risk of facial skin aging: A Mendelian randomization study

BACKGROUND

A growing number of experimental studies have shown an association between the gut microbiota (GM) and facial skin aging. However, the causal relationship between GM and facial skin aging remains unclear to date.

METHODS

We conducted a two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationship between GM and facial skin aging. MR analysis was mainly performed using the inverse-variance weighting (IVW) method, complemented by the weighted median (MW) method, MR-Egger regression, and weighted mode, and sensitivity analysis was used to test the reliability of MR analysis results.

RESULTS

Eleven GM taxa associated with facial skin aging were identified by IVW method analysis, Family Victivallaceae (p = 0.010), Genus Eubacterium coprostanoligenes group (p = 0.038), and Genus Parasutterella (p = 0.011) were negatively associated with facial skin aging, while Phylum Verrucomicrobia (p = 0.034), Family Lactobacillaceae (p = 0.017) and its subgroups Genus Lactobacillus (p = 0.038), Genus Parabacteroides (p = 0.040), Genus Eggerthella (p = 0.049), Genus Family XIII UCG001 (p = 0.036), Genus Phascolarctobacterium (p = 0.027), and Genus Ruminococcaceae UCG005 (p = 0.012) were positively associated with facial skin aging. At Class and Order levels, we did not find a causal relationship between GM and facial skin aging. Results of sensitivity analyses did not show evidence of pleiotropy and heterogeneity.

CONCLUSION

Our findings confirm the causal relationship between GM and facial skin aging, providing a new perspective on delaying facial aging.

Epigenetics insights from perceived facial aging

Facial aging is the most visible manifestation of aging. People desire to look younger than others of the same chronological age. Hence, perceived age is often used as a visible marker of aging, while biological age, often estimated by methylation markers, is used as an objective measure of age. Multiple epigenetics-based clocks have been developed for accurate estimation of general biological age and the age of specific organs, including the skin. However, it is not clear whether the epigenetic biomarkers (CpGs) used in these clocks are drivers of aging processes or consequences of aging. In this proof-of-concept study, we integrate data from GWAS on perceived facial aging and EWAS on CpGs measured in blood. By running EW Mendelian randomization, we identify hundreds of putative CpGs that are potentially causal to perceived facial aging with similar numbers of damaging markers that causally drive or accelerate facial aging and protective methylation markers that causally slow down or protect from aging. We further demonstrate that while candidate causal CpGs have little overlap with known epigenetics-based clocks, they affect genes or proteins with known functions in skin aging, such as skin pigmentation, elastin, and collagen levels. Overall, our results suggest that blood methylation markers reflect facial aging processes, and thus can be used to quantify skin aging and develop anti-aging solutions that target the root causes of aging.

Relationships between circulating metabolites and facial skin aging: a Mendelian randomization study

BACKGROUND

Blood metabolites are important to various aspects of our health. However, currently, there is little evidence about the role of circulating metabolites in the process of skin aging.

OBJECTIVES

To examine the potential effects of circulating metabolites on the process of skin aging.

METHOD

In the primary analyses, we applied several MR methods to study the associations between 249 metabolites and facial skin aging risk. In the secondary analyses, we replicated the analyses with another array of datasets including 123 metabolites. MR Bayesian model averaging (MR-BMA) method was further used to prioritize the metabolites for the identification of predominant metabolites that are associated with skin aging.

RESULTS

In the primary analyses, only the unsaturation degree of fatty acids was found significantly associated with skin aging with the IVW method after multiple testing (odds ratio = 1.084, 95% confidence interval = 1.049-1.120, p = 1.737 × 10^-06). Additionally, 11 out of 17 unsaturation-related biomarkers showed a significant or suggestively significant causal effect [p < 0.05 and > 2 × 10^-4 (0.05/249 metabolites)]. In the secondary analyses, seven metabolic biomarkers were found significantly associated with skin aging [p < 4 × 10^-4 (0.05/123)], while six of them were related to the unsaturation degree. MR-BMA method validated that the unsaturation degree of fatty acids plays a dominant role in facial skin aging.

CONCLUSIONS

Our study used systemic MR analyses and provided a comprehensive atlas for the associations between circulating metabolites and the risk of facial skin aging. Genetically proxied unsaturation degree of fatty acids was highlighted as a dominant factor correlated with the risk of facial skin aging.

Perceived Age in Patients Exposed to Distinct UV Indexes: A Systematic Review

Photodamage is caused by chronic sun exposure and ultraviolet radiation and presents as wrinkles, sagging, and pigmented spots. An increase in the ultraviolet index can increase a person's perceived age by worsening skin photodamage. However, since the ultraviolet index varies considerably between geographical regions, perceived age might vary substantially among them. This review aims to describe the differences in chronological and perceived age in regions of the world with different ultraviolet indexes. A literature search of three databases was conducted for studies analyzing perceived age and its relationship to sun exposure. Ultraviolet indexes from the included studies were retrieved from the National Weather Service and the Tropospheric Emission Monitoring Internet Service. Out of 104 studies, seven fulfilled the inclusion criteria. Overall, 3,352 patients were evaluated for perceived age. All studies found that patients with the highest daily sun exposures had the highest perceived ages for their chronological age (p < 0.05). People with high sun exposure behaviors living in regions with high ultraviolet indexes will look significantly older than same-aged peers living in lower ultraviolet index regions.

Mendelian randomization of genetically independent aging phenotypes identifies LPA and VCAM1 as biological targets for human aging

Length and quality of life are important to us all, yet identification of promising drug targets for human aging using genetics has had limited success. In the present study, we combine six European-ancestry genome-wide association studies of human aging traits-healthspan, father and mother lifespan, exceptional longevity, frailty index and self-rated health-in a principal component framework that maximizes their shared genetic architecture. The first principal component (aging-GIP1) captures both length of life and indices of mental and physical wellbeing. We identify 27 genomic regions associated with aging-GIP1, and provide additional, independent evidence for an effect on human aging for loci near HTT and MAML3 using a study of Finnish and Japanese survival. Using proteome-wide, two-sample, Mendelian randomization and colocalization, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) and vascular cell adhesion molecule 1 on aging-GIP1. Together, our results demonstrate that combining multiple aging traits using genetic principal components enhances the power to detect biological targets for human aging.