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Abstract Background Previous studies demonstrated a positive relationship between birthweight and breast cancer; however, inconsistent, sometimes even controversial, observations also emerged, and the nature of such relationship remains unknown. Methods Using summary statistics of birthweight and breast cancer, we assessed the fetal/maternal-specific genetic correlation between them via LDSC and prioritized fetal/maternal-specific pleiotropic genes through MAIUP. Relying on summary statistics we conducted Mendelian randomization (MR) to evaluate the fetal/maternal-specific origin of causal relationship between birthweight, age of menarche, age at menopause and breast cancer. Results With summary statistics we identified a positive genetic correlation between fetal-specific birthweight and breast cancer (r g = 0.123 and P = 0.013) as well as a negative but insignificant correlation between maternal-specific birthweight and breast cancer (r g = − 0.068, P = 0.206); and detected 84 pleiotropic genes shared by fetal-specific birthweight and breast cancer, 49 shared by maternal-specific birthweight and breast cancer. We also revealed fetal-specific birthweight indirectly influenced breast cancer risk in adulthood via the path of age of menarche or age at menopause in terms of MR-based mediation analysis. Conclusion This study reveals that shared genetic foundation and causal mediation commonly drive the connection between the two traits, and that fetal/maternal-specific birthweight plays substantially distinct roles in such relationship. However, our work offers little supportive evidence for the fetal origins hypothesis of breast cancer originating in utero.

Background: Neurodegenerative diseases (NDDs) are the leading cause of disability worldwide while their metabolic pathogenesis is unclear. Genome-wide association studies (GWASs) offer an unprecedented opportunity to untangle the relationship between metabolites and NDDs.Methods: By leveraging two-sample Mendelian randomization (MR) approaches and relying on GWASs summary statistics, we here explore the causal association between 486 metabolites and five NDDs including Alzheimer’s Disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson’s disease (PD), and multiple sclerosis (MS). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. We also performed linkage disequilibrium score regression (LDSC) and colocalization analyses to distinguish causal metabolite-NDD associations from genetic correlation and LD confounding of shared causal genetic variants. Finally, a metabolic pathway analysis was further conducted to identify potential metabolite pathways.Results: We detected 164 metabolites which were suggestively associated with the risk of NDDs. Particularly, 2-methoxyacetaminophen sulfate substantially affected ALS (OR = 0.971, 95%CIs: 0.961 ∼ 0.982, FDR = 1.04E-4) and FTD (OR = 0.924, 95%CIs: 0.885 ∼ 0.964, FDR = 0.048), and X-11529 (OR = 1.604, 95%CIs: 1.250 ∼ 2.059, FDR = 0.048) and X-13429 (OR = 2.284, 95%CIs: 1.457 ∼ 3.581, FDR = 0.048) significantly impacted FTD. These associations were further confirmed by the weighted median and maximum likelihood methods, with MR-PRESSO and the MR-Egger regression removing the possibility of pleiotropy. We also observed that ALS or FTD can alter the metabolite levels, including ALS and FTD on 2-methoxyacetaminophen sulfate. The LDSC and colocalization analyses showed that none of the identified associations could be driven by genetic correlation or confounding by LD with common causal loci. Multiple metabolic pathways were found to be involved in NDDs, such as “urea cycle” (P = 0.036), “arginine biosynthesis” (P = 0.004) on AD and “phenylalanine, tyrosine and tryptophan biosynthesis” (P = 0.046) on ALS.Conclusion: our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases.

Cardiovascular diseases (CVDs) remain the main cause of morbidity and mortality worldwide. The pathological mechanism and underlying biological processes of these diseases with metabolites remain unclear. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal effect of metabolites on these diseases by making full use of the latest GWAS summary statistics for 486 metabolites and six major CVDs. Extensive sensitivity analyses were implemented to validate our MR results. We also conducted linkage disequilibrium score regression (LDSC) and colocalization analysis to investigate whether MR findings were driven by genetic similarity or hybridization between LD and disease-associated gene loci. We identified a total of 310 suggestive associations across all metabolites and CVDs, and finally obtained four significant associations, including bradykinin, des-arg(9) (odds ratio [OR] = 1.160, 95% confidence intervals [CIs]: 1.080–1.246, false discovery rate [FDR] = 0.022) on ischemic stroke, N-acetylglycine (OR = 0.946, 95%CIs: 0.920–0.973, FDR = 0.023), X-09026 (OR = 0.845, 95%CIs: 0.779–0.916, FDR = 0.021) and X-14473 (OR = 0.938, 95%CIs = 0.907–0.971, FDR = 0.040) on hypertension. Sensitivity analyses showed that these causal associations were robust, the LDSC and colocalization analyses demonstrated that the identified associations were unlikely confused by LD. Moreover, we identified 15 important metabolic pathways might be involved in the pathogenesis of CVDs. Overall, our work identifies several metabolites that have a causal relationship with CVDs, and improves our understanding of the pathogenesis and treatment strategies for these diseases.