The aim of this study was to assess the causal relationships between mineral metabolism disorders, representative of trace elements, and key aging biomarkers: telomere length (TL) and mitochondrial DNA copy number (mtDNA-CN). Utilizing bidirectional Mendelian randomization (MR) analysis in combination with the two-stage least squares (2SLS) method, we explored the causal relationships between mineral metabolism disorders and these aging indicators. Sensitivity analysis can be used to determine the reliability and robustness of the research results. The results confirmed that a positive causal relationship was observed between mineral metabolism disorders and TL (p < 0.05), while the causal relationship with mtDNA-CN was not significant (p > 0.05). Focusing on subgroup analyses of specific minerals, our findings indicated a distinct positive causal relationship between iron metabolism disorders and both TL and mtDNA-CN (p < 0.05). In contrast, disorders in magnesium and phosphorus metabolism did not exhibit significant causal effects on either aging biomarker (p > 0.05). Moreover, reverse MR analysis did not reveal any significant causal effects of TL and mtDNA-CN on mineral metabolism disorders (p > 0.05). The combination of 2SLS with MR analysis further reinforced the positive causal relationship between iron levels and both TL and mtDNA-CN (p < 0.05). Notably, the sensitivity analysis did not indicate significant pleiotropy or heterogeneity within these causal relationships (p > 0.05). These findings highlight the pivotal role of iron metabolism in cellular aging, particularly in regulating TL and sustaining mtDNA-CN, offering new insights into how mineral metabolism disorders influence aging biomarkers. Our research underscores the importance of trace element balance, especially regarding iron intake, in combating the aging process. This provides a potential strategy for slowing aging through the adjustment of trace element intake, laying the groundwork for future research into the relationship between trace elements and healthy aging.

Existing research indicates that different types of meat have varying effects on health and aging, but the specific causal relationships remain unclear. This study aimed to explore the causal relationship between different types of meat intake and aging-related phenotypes. This study employed Mendelian randomization (MR) to select genetic variants associated with meat intake from large genomic databases, ensuring the independence and pleiotropy-free nature of these instrumental variables (IVs), and calculated the F-statistic to evaluate the strength of the IVs. The validity of causal estimates was assessed through sensitivity analyses and various MR methods (MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, and weighted mode), with the MR-Egger regression intercept used to test for pleiotropy bias and Cochran’s Q test employed to evaluate the heterogeneity of the results. The findings reveal a positive causal relationship between meat consumers and DNA methylation PhenoAge acceleration, suggesting that increased meat intake may accelerate the biological aging process. Specifically, lamb intake is found to have a positive causal effect on mitochondrial DNA copy number, while processed meat consumption shows a negative causal effect on telomere length. No significant causal relationships were observed for other types of meat intake. This study highlights the significant impact that processing and cooking methods have on meat’s role in health and aging, enhancing our understanding of how specific types of meat and their preparation affect the aging process, providing a theoretical basis for dietary strategies aimed at delaying aging and enhancing quality of life.

Background/Objectives: The relationship between lipid profiles, telomere length (TL), and cancer risk remains unclear. Methods: This study employed two-sample Mendelian randomization (MR) with mediation analysis to investigate their causal relationships, examining lipid profiles as exposure, TL as mediator, and nine cancer types as outcomes. We conducted our analysis using two-stage least squares (2SLS) regression integrated with inverse variance weighted (IVW) methods to address potential endogeneity and strengthen our causal inference. Results: we found that unfavorable lipid profiles were causally linked to increased TL (p < 0.05). TL showed positive causal associations with lung and hematologic cancers (OR > 1, p < 0.05). Direct associations were observed between total and low-density lipoprotein (LDL) cholesterol and gastric cancer (OR < 1, p < 0.05), and between remnant cholesterol and colorectal cancer (OR > 1, p < 0.05). Mediation analysis revealed TL as a significant mediator in the pathway from lipid profiles to cancer development (p < 0.05). No horizontal pleiotropy was detected. Conclusions: Our findings suggest that lipid metabolism disorders may influence cancer development through telomere regulation, particularly in lung and hematologic cancers. This emphasizes the importance of lipid management in cancer prevention and treatment, especially for these cancer types.