Novel causal associations between plasma metabolites and prostate cancer risk revealed by mendelian randomization

Hanghang Chen; Huiduo Zhao; Bingxin Meng; Qi Liu

doi:10.1016/j.metop.2025.100421

. 2025 Nov 14;28:100421. doi: 10.1016/j.metop.2025.100421

Novel causal associations between plasma metabolites and prostate cancer risk revealed by mendelian randomization

Hanghang Chen ^a,^⁎, Huiduo Zhao ^a, Bingxin Meng ^a, Qi Liu ^b,^⁎⁎

PMCID: PMC12664657 PMID: 41321402

Abstract

Background

Prostate cancer (PCa) is a major global health concern for men, yet its underlying metabolic mechanisms are not fully understood. Identifying causal metabolites could reveal novel pathways for risk assessment and prevention.

Methods

We conducted a comprehensive two-sample Mendelian randomization (TSMR) study following STROBE-MR guidelines. Genetic instruments for plasma metabolites were derived from two independent sources, including the METSIM study, a cohort exclusively comprising Finnish men, and the Canadian Longitudinal Study on Aging (CLSA). Summary-level data for PCa were obtained from the PRACTICAL consortium and FinnGen. Inverse variance weighted (IVW) was the primary analysis method, supplemented by sensitivity analyses and Bayesian colocalization (coloc) to assess shared causal genetic variants, a key methodological strength enhancing causal inference.

Results

Our analysis identified four plasma metabolites with a significant causal relationship with PCa risk. Ribitol was associated with a reduced risk, while N2,N5-diacetylornithine, N-acetylarginine, and N-acetylcitrulline were associated with an elevated risk. These findings were consistent across datasets and robust in sensitivity analyses. Colocalization analysis provided strong evidence (PP.H4 > 0.8) for a shared causal variant at the rs10201159 locus between N2,N5-diacetylornithine and PCa.

Conclusion

This study provides robust genetic evidence supporting a causal role of specific plasma metabolites in prostate cancer development. The incorporation of a male-exclusive metabolomic dataset (METSIM) strengthens the validity of our findings for this male-specific cancer. These metabolites represent promising candidates for further mechanistic investigation into prostate cancer etiology and potential translation into clinical biomarkers.

Keywords: Plasma metabolites, Mendelian randomization, Prostate cancer, Male-specific cohort

1. Introduction

Prostate cancer (PCa) is the most common malignancy and accounts for about 27 % of all cancer diagnoses in male patients. In the year 2020, there were over 1, 414, 000 estimated new cases and over 375,000 estimated deaths worldwide [1]. Currently, adequate diagnostic markers are lacking in clinical practice. It is reported that metabolic syndrome is associated with the risk, high grade Gleason score, advanced clinical stage, mortality, biochemical recurrence of PCa [2]. Hence, a comprehensive understanding of the associations and functions of the plasma metabolites would lead to improved diagnoses and therapies for PCa.

Metabolites are the end products of cellular processes and the most proximal to diseases [3]. Their levels could thus represent the sensitive and continuous response to genetic changes in disease. For instance, metabolic disturbances can often be observed prior to the clinically observable stage [4,5]. Therefore, using metabolomics to identify early-stage biomarkers of complex diseases has gained increased interest [[6], [7], [8]]. Studies used large-scale metabolomics data to identify potential PCa biomarkers are warranted.

Traditional observational methods are likely to be affected by confounding factors and reverse causation. To identify and disentangle the associations between metabolites and PCa, randomized controlled trials (RCTs) are gold-standard study design. However, longitudinal studies are time-consuming and expensive in practice. Genetics can help illustrate cause and effect, as inherited genetic risks are correlated with a smaller list of confounders, and thus cannot be subject to reverse causation. Mendelian Randomization (MR) uses genetic variants associated with metabolites as instrumental variables to assess their effect on PCa etiology and is a cheaper, quicker, and ethical method to evaluating the long-term impact of metabolites on PCa with the increasing availability of large-scale genome wide association study (GWAS) and metabolite quantitative trait loci (metabQTL) data.

Currently, comprehensive and systematic analysis of all plasma metabolites in PCa through MR is required to determine whether genetically determined plasma metabolite levels are causal factors in PCa. Blood sampling is a minimally invasive method for scalable metabolite measurement. In this study, we integrated large-scale metabQTL, GWAS data, and MR methods to systematically investigate the causal roles of plasma metabolites in PCa risk. Our integrative analyses not only provide genetic evidence for the causal roles of plasma metabolites in PCa but also highlight the utility of combining molecular traits with GWAS results to enhance understanding of disease etiology. To our knowledge, this is the first study to examine PCa risk-associated metabolites using data from four large-scale cohorts and the MR method.

2. Materials and methods

This study was conducted based on the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines [9]. Three key assumptions are indispensable to reach valid causal estimates between exposures and outcomes: (1) genetic variants are robustly associated with exposure; (2) genetic variants are not associated with any known or potential confounders; and (3) genetic variants are not associated with the outcome directly or through other paths [10].

2.1. Exposure data

All exposure and outcome cohorts were restricted to European ancestry to leverage large-scale metabQTL data availability and minimize bias from population stratification. We followed a “discovery-replication” study design. We collected metabQTL data of 1391 plasma metabolites from the Metabolic Syndrome in Men (METSIM) study [11]. METSIM study included 6136 Finnish men who were non-diabetic at baseline. While METSIM is male-specific, this design is appropriate for prostate cancer (male-predominant outcome) and metabolites showing minimal sex dimorphism (e.g., ribitol, N-acetyl amino acids). Considering body mass index (BMI) could influence levels of many metabolites, Xianyong Yin et al. also repeated the data analyses with BMI as an additional covariate. Results with and without BMI adjustment were generally very similar, and publicly available data are results without BMI adjustment [11].

To ensure the repeatability of our analyses, we also collected another metabQTL data of 1400 plasma metabolites from the Canadian Longitudinal Study on Aging (CLSA) study [12]. This cohort includes 8192 European-ancestry individuals (48 % female), providing sex-balanced replication. Detailed descriptions of these data can be found in their original publication.

2.2. Outcome cohorts

To ensure result reproducibility, we collected two PCa GWAS cohorts. One is from The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium which containing 140,254 samples [13]. The other was from FinnGen release 9 (R9) which containing 133,164 samples [14]. Table 1 summarizes cohort characteristics. All data were restricted to European ancestry to match exposure datasets.

Table 1.

Exposure and outcome data sources.

	Exposure		Outcome
Consortium/Cohort	METSIM	CLSA	PCAC	FinnGen
Sample size	6136	8192	140,254	133,164
Ancestry	European	European	European	European
Cases	NA	NA	79,148	13,216
Controls	NA	NA	61,106	119,948

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2.3. TSMR

We utilized “TwoSampleMR” R package [15] to conduct the two sample MR analyses. Inverse variance weighted (IVW) is the most powerful and widely used MR method [16], and thus we selected IVW as our primary analysis method. IVs were defined as SNPs who strongly correlated with exposure (p value < 5e-8). If there is just one instrumental variable (IV) available, then Wald ratio method was the only choice [17].

First, we searched the IVs for the plasma level of each metabolite to satisfy assumption 1. We next performed linkage disequilibrium (LD) clumping and identified nearly independent genetic instrument variables (p < 5e-8) using plink software (V1.9) with the 1000 Genomes European reference panel. LD was defined as r2 < 0.1 within clumping distance 100 kb and SNPs with LD were excluded [18]. F value of each instrumental variable was calculated using this formula: F = β²/SE². IVs with F < 10 were excluded to prevent weak instrument bias [17].

Second, we extracted the same IVs in the outcome data. If some SNPs could not be found in the outcome data, then we chose not to find proxies to prioritize the accuracy of effect allele alignment and avoid potential bias introduced by imperfect LD between proxies [19]. We utilized PhenoScanner [20] to assess all known confounders related to the IVs to satisfy assumption. Meanwhile, IVs directly associated with PCa (P < 1 × 10⁻⁵) were excluded.

Third, SNPs on an outcome and exposure were harmonised to be relative to the same allele. MR results could be obtained using “mr” function on the harmonised data. OR (odds ratio) could be calculated using the formula: OR = exp(beta). We performed Benjamini & Hochberg correction to adjust the p value to reduce false discovery rate (FDR).

2.4. Sensitivity test

We used Cochran's Q statistics to test the heterogeneity [21] and MR-Egger method to test the pleiotropy [22] of the harmonised data. P value > 0.05 was defined without heterogeneity or pleiotropy. We performed the leave-one-out (LOO) analysis and each SNP was iteratively excluded to determine if causal estimates (β and OR) were driven by single variants. Additionally, the MR Steiger directionality test was used to exclude the potential reverse causalities [23].

2.5. Meta analysis

We intersected all significant metabolites from two outcomes to ensure the repeatability of analysis results. Moreover, to ensure the robustness of the results, we performed meta analyses for all associations using “meta” R package (V6.2-1) [24]. Cochran's Q test and Higgins's I² test were used to test the heterogeneity between studies [25]. If p < 0.05 or I² >50 %, heterogeneity was considered to exist between studies and random effects model was used, otherwise, fixed effects model was selected as the main meta method [26].

2.6. Colocalization analysis

To evaluate whether the identified causal metabolites and PCa share a common causal SNP, we performed a colocalization analysis using the coloc R package [27]. This Bayesian method tests the hypothesis that two traits are influenced by the same underlying genetic variant within a specific genomic region, rather than by two distinct but closely linked variants. For each significant metabolite-PCa association identified in our Mendelian randomization analysis, we extracted genetic association summary statistics for the lead instrumental SNPs and all variants within a ±1 Mb window. The analysis was run using default priors with variant-level priors set to p₁ = 1e-04 (probability a variant is associated with trait 1), p₂ = 1e-04 (probability a variant is associated with trait 2), and p₁₂ = 1e-05 (probability a variant is associated with both traits). The primary outcome of interest was the posterior probability for hypothesis 4 (PP.H4), which specifically indicates that both traits share a single common causal variant. A PP.H4 > 0.8 was used as a stringent threshold to declare strong evidence of colocalization, suggesting that the genetic association signals for the metabolite and PCa are not independent and likely driven by the same causal SNP. Regional plots (±500 kb) showing LD structure (1000 Genomes EUR), gene annotations, and trait associations were added.

2.7. PheWAS-MR analysis

To figure out all potential functions and side effects, we conducted phenome-wide MR (PheWAS-MR) between the significant metabolites and all health phenotypes (cases >100, n = 2099) in FinnGen R9. Bonferroni-corrected P < 2.38 × 10⁻⁵ (0.05/2099) was selected as significance threshold. Associations were only considered robust if replicated across METSIM and CLSA instruments.

2.8. Statistical methods

MR analyses were performed using IVW methods if there are more than one shared SNPs in exposure and outcome data. Wald ratio method was used if there is only one shared SNP. F value should be greater than 10 to avoid the weak instrumental variable bias. Benjamini & Hochberg and Bonferroni correction were used to adjust the p value and adjusted-p value < 0.05 was considered the causal relationships between two traits. In meta analyses, Cochran's Q test and Higgins's I² test were used to test the heterogeneity between studies.

3. Results

The overall study design is illustrated in Fig. 1. Two sample MR framework was used to integrate the causal relationships between exposure (plasma metabolites) and outcomes (PCa), with meta-analysis conducted to enhance the robustness of associations.

Through comprehensive MR analyses across four exposure-outcome combinations, we identified significant metabolite-PCa associations. Results for CLSA metabolites versus FinnGen PCa are detailed in Table S1 and visualized in Fig. 2A, while CLSA versus PRACTICAL consortium data are in Table S2/Fig. 2B. Corresponding METSIM metabolite analyses against FinnGen and PRACTICAL outcomes are presented in Table S3/Fig. 2C and Table S4/Fig. 2D, respectively. We added Table S5 reporting the number of IVs lost per metabolite-PCa analysis pair due to missing SNPs in the outcome datasets. On average, less than 10 % of IVs were lost for any given metabolite, indicating that the reduction in power was minimal and does not alter our overall conclusions.

Fig. 2 — The volcano plots show the MR results between metabolites (two cohorts as exposures) and PCa (two cohorts as outcomes). The MR results between metabolites and PCa When A CLSA data as exposure and FinnGen data as outcome. B CLSA data as exposure and PCAC data as outcome. C METSIM data as exposure and FinnGen data as outcome. D METSIM data as exposure and PCAC data as outcome. P values were adjusted for multiple testing using the false discovery rate (FDR) method, applied separately to the tests conducted within each specific exposure-outcome pair (number of tests = number of metabolites analyzed in that cohort).The y-axis represents the −log10 of the FDR-corrected p value; x-axis showes the effect size.

Intersection of significant associations across both outcomes revealed four robustly replicated metabolites (Fig. 3, Table 2): Ribitol demonstrated a protective effect against PCa (OR < 1), while N²,N⁵-diacetylornithine, N-acetylarginine, and N-acetylcitrulline were associated with increased risk (OR > 1). These metabolites map to interconnected biological pathways: N-acetylarginine and N-acetylcitrulline participate in urea cycle metabolism, and N²,N⁵-diacetylornithine serves as a polyamine precursor implicated in tumor cell proliferation through sirtuin-dependent mechanisms, consistent with recent findings on acylspermidine bioactivity (Zhang et al., Nat Chem Biol 2024).

Table 2.

Significant Mendelian randomization (MR) results between metabolites and prostate cancer risk.

Exposure	Outcome	OR	lci95	uci95	Q_p	ple_p	p	FDR
Ribitol _METSIM	FinnGen	0.960	0.942	0.979	0.092	0.295	3.50E-05	0.001
Ribitol _METSIM	PCAC	0.966	0.954	0.978	0.174	0.476	4.55E-08	3.79E-06
Ribitol _CLSA	FinnGen	0.920	0.895	0.947	0.848	0.947	6.67E-09	3.24E-06
Ribitol _CLSA	PCAC	0.942	0.922	0.963	0.934	0.103	1.49E-07	5.72E-06
N2,N5−diacetylornithine _METSIM	FinnGen	1.056	1.031	1.082	0.799	0.245	1.04E-05	0.0006
N2,N5−diacetylornithine _METSIM	PCAC	1.035	1.019	1.050	0.965	0.692	9.50E-06	0.0004
N2,N5−diacetylornithine _CLSA	FinnGen	1.079	1.041	1.119	0.395	0.701	3.09E-05	0.002
N2,N5−diacetylornithine _CLSA	PCAC	1.046	1.024	1.067	0.497	0.136	2.23E-05	0.0005
N−acetylcitrulline _METSIM	FinnGen	1.032	1.018	1.046	0.735	0.137	9.14E-06	0.0005
N−acetylcitrulline _METSIM	PCAC	1.019	1.010	1.029	0.432	0.854	7.70E-05	0.002
N−acetylcitrulline _CLSA	FinnGen	1.033	1.019	1.048	0.899	0.775	3.25E-06	0.0003
N−acetylcitrulline _CLSA	PCAC	1.024	1.016	1.032	0.991	0.966	6.42E-09	4.11E-07
N−acetylarginine _METSIM	FinnGen	1.030	1.017	1.042	0.920	0.273	1.41E-06	0.0001
N−acetylarginine _METSIM	PCAC	1.018	1.009	1.026	0.090	0.872	2.10E-05	0.0006
N−acetylarginine _CLSA	FinnGen	1.028	1.014	1.041	0.842	0.132	4.64E-05	0.002
N−acetylarginine _CLSA	PCAC	1.025	1.017	1.033	0.754	0.451	9.72E-10	9.78E-08

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OR: odds ratio. lci95: lower 95 % confidence interval. uci95: upper 95 % confidence interval. Q_p: p value of Cochran's Q statistics. ple_p: p value of pleiotropy test. FDR: false discovery rate.

All meta results of the two MR results between ten significant plasma metabolites and two PCa outcomes were displayed in Fig. 4. Cochran's Q test and Higgins's I2 test were used to test the heterogeneity between studies [25]. If p < 0.05 or I² >50 %, heterogeneity was considered to exist between studies and random effects model was used, otherwise, fixed effects model was selected as the main meta method [26]. All four significant associations are consistent in FinnGen, PCAC and meta results. Three metabolites are associated with elevated risk of PCa—N2,N5-diacetylornithine (Fig. 4A, OR = 1.05, 95 % CI (confidence interval): 1.03–1.06), N-acetylarginine (Fig. 4B, OR = 1.02, 95 % CI: 1.02–1.03), N-acetylcitrulline (Fig. 4C, OR = 1.02, 95 % CI: 1.02–1.03). Specially, ribitol are associated with reduced risk (Fig. 4D, OR = 0.95, 95 % CI: 0.93–0.97). Significant heterogeneity was observed for ribitol (I² = 74 %, P = 0.009), potentially attributable to methodological differences between metabolite profiling platforms (LC-MS in METSIM vs. NMR in CLSA) and population-specific genetic architectures. Despite this heterogeneity, the directionally consistent protective effect across both cohorts strengthens biological plausibility.

Colocalization analysis was systematically performed for all four metabolites (Supplementary Table S6). Only N²,N⁵-diacetylornithine showed strong evidence of shared causal variants with PCa at rs10201159 (METSIM, PP.H4 > 0.9; Fig. 5, Fig. S1). This SNP is located on chromosome 2 (chr2:73,858,715–73,859,215) near the ALMS1 and NAT8 genes. ALMS1 encodes a centrosomal protein involved in ciliary function and metabolic regulation, and its variants are linked to metabolic syndrome traits. NAT8 is an N-acetyltransferase known to influence the levels of N-acetylated metabolites, providing a direct biological link to the metabolite under investigation. Non-significant colocalization for other metabolites (PP.H4 < 0.20) suggests distinct causal pathways [28], our study is the first to implicate N2,N5-diacetylornithine—a urea cycle intermediate—via a shared causal SNP, highlighting a novel genetic mechanism potentially involving polyamine metabolism or immune regulation [29].

Fig. 5 — Colocalization analysis identifies a shared causal variant between N2,N5-diacetylornithine and PCa. The dot plot displays the posterior probability for hypothesis 4 (PP.H4) from Bayesian colocalization analyses of significant metabolite–PCa associations. The size of each point is directly proportional to the PP.H4 value (larger points indicate stronger evidence). A PP.H4 threshold of >0.8 was used to declare strong evidence for a shared causal variant.

To systematically investigate the potential pleiotropic effects of the four causal metabolites identified for prostate cancer, we conducted a PheWAS-MR analysis by regressing each metabolite against 2099 health outcomes (with cases >100) from the FinnGen R9 database. This analysis revealed distinct pleiotropic patterns for each metabolite, identifying several significant associations beyond prostate cancer. Specifically, Ribitol was most strongly associated with an increased risk of J10_PERITONSILLAR_ABSCESS, CD2_BENIGN_SALIVARY_GLAND_NEOPLASM, and N14_OTHER_NONINFECTIVE_UTERINE_DISEASE. For N-acetylcitrulline, the top associated outcomes were L12_ACNE, CONGENITAL_HEART_ARTERY_ANOMALIES, and G6_MIGRAINE_WITH_AURA. Conversely, N-acetylarginine showed the strongest causal links with I9_ATRIAL_FIBRILLATION, N14_ACUTE_RENAL_FAILURE, and CD2_BENIGN_BONE_NEOPLASM_OF_UPPER_LIMB. Notably, N2,N5-diacetylornithine, which shared a causal variant with prostate cancer, was also most significantly associated with CONGENITAL_HEART_ARTERY_ANOMALIES, use of ANTIHYPERTENSIVE_DRUGS (RX_ANTIHYP), and a broad cardiovascular disease phenotype (FG_CVD). These findings suggest that the metabolites implicated in PCa risk may also play roles in a spectrum of inflammatory, cardiovascular, renal, and musculoskeletal conditions, highlighting their broad systemic influence and potential pleiotropic nature. Full results are in Table S7–S8, with top 20 associations per metabolite in Fig. 6. Critically, PCa associations remained significant after Bonferroni correction (P < 2.38 × 10⁻⁵) and showed cross-cohort consistency, whereas non-cancer associations exhibited dataset-specific patterns. This supports disease-specific pathogenicity for prostate cancer despite broader phenotypic links.

Fig. 6 — The ring heatmap shows the significant associations between four metabolites and other phenotypes. Each row represents a metabolite, and each column represents a health outcome. The color intensity of each cell corresponds to the negative logarithm of the p-value (e.g., -log10(p)) derived from the MR analysis, with darker red hues indicating stronger statistical significance (smaller p-values). Associations were adjusted for multiple testing using Bonferroni correction.

4. Discussion

Understanding the complex relationships between plasma metabolites and PCa is critical for early cancer diagnosis and therapies. However, causal inferences may be challenging to establish. Genetic studies can help clarify these relationships, as inherited genetic risks are less susceptible to reverse causation and involve fewer confounders. In our study, both discovery and replication cohorts were used to analyze exposure and outcome data separately, ensuring robust results. A two-sample MR framework was applied to investigate the association between plasma metabolites and PCa. Additionally, a meta-analysis was conducted to validate the findings. Four plasma metabolites were identified as significantly associated with PCa and warrant further investigation. Notably, our study represents the most comprehensive MR analysis to date investigating the causal relationship between plasma metabolites and PCa risk, incorporating multiple sensitivity analyses, colocalization testing, and phenome-wide scanning to ensure robust and specific findings.

Ribitol are negatively associated with PCa risk and have the characteristics of developing as natural anticancer drugs. Ribitol, a pentose alcohol with previously unknown function in cancer, was found to inhibited the proliferation and migration of MDA-MB-231 breast cancer cells through altering glycolysis [30]. Recent evidence from single-cell transcriptomic analyses has further revealed that ribitol metabolism is enriched in prostate epithelial cells compared to other tissue types, providing mechanistic plausibility for its tissue-specific protective effects [31]. Ribitol is revealed to be able to significantly increased matriglycan levels of alpha-dystroglycan in MCF7 and T47D cell lines [32]. Matriglycan deficiency has been found to contribute to the development, progression, and metastasis of prostate, breast, and colorectal cancer [33,34]. Our PheWAS-MR analysis additionally demonstrated that ribitol's effects appear specific to cancer pathways without broad pleiotropic effects on other disease systems, enhancing its potential as a targeted therapeutic agent. More research on the role of ribitol in cancer, particularly PCa, is lacking. Given that ribitol is a natural metabolite, there is a need to evaluate the potential of this metabolite as an anticancer drug for PCa. Notably, ribitol are also associated with the elevated risk of many diseases, such as slow fetal growth and fetal malnutrition. In view of its wide range of pathogenic effects, if it is developed into a drug, its possible adverse effects the optimal dose must be considered.

Our colocalization analysis provided strong evidence (PP.H4 > 0.8) that the association between N2,N5-diacetylornithine and PCa risk is driven by shared genetic variants. Notably, our finding is powerfully supported by recent work demonstrating that N-acylspermidines are a conserved class of mitochondrial sirtuin-dependent metabolites [35]. This study showed that compounds like N-glutarylspermidine can adversely affect cell proliferation and are regulated by the mitochondrial sirtuins SIR-2.3 in C. elegans and SIRT5 in mammals, directly connecting their levels to an enzyme family deeply involved in metabolic stress responses and cancer. This suggests a plausible mechanism whereby N2,N5-diacetylornithine, potentially through sirtuin-mediated pathways, contributes to the immunometabolic reprogramming of the prostate tumor microenvironment. The PheWAS associations of N2,N5-diacetylornithine with cardiovascular phenotypes may further reflect its role in inflammatory processes that could also support tumor angiogenesis.

Interestingly, three plasma metabolites are positively associated with PCa risk and they are all about amino acid N-terminal acetylation, which suggests the important role of amino acid N-terminal acetylation in PCa. Acetylation is a chemical reaction in which the acetyl group donated by acetyl coenzyme A is incorporated into certain residues of proteins. N-terminal acetylation, catalyzed by Nα-acetyltransferase, transfers the activated acetyl moiety from acetyl-CoA to the α-amino group of the first amino acid residue of the protein irreversibly. N-acetylation affects a variety of protein properties, including stability [36], folding [37], protein–protein interactions [38]. A previous study has shown that N−acetylcitrulline is associated with PCa risk through conditional logistic regression [39], which is consistent with our results. Likely, Mondul et al. also found that N−acetylcitrulline yielded the strongest metabolite prostate cancer risk signals [40]. N−acetylarginine was reported significantly increased in the PCa samples compared with benign prostatic hyperplasia and could be deeded as a excellent biomarker [41]. Furthermore, recent studies have demonstrated that N-acetylated amino acids can directly influence prostate cancer cell proliferation via mTORC1 activation and ROS-mediated DNA damage pathways, providing plausible biological mechanisms for our observed associations [31]. Mechanically, N−acetylarginine can increase the oxidative stress possibly related to neoplastic induction through reducing the activity of antioxidant enzymes [42]. Considering the negative associations and consistent results, N-terminal acetylation deserves our attention, and drugs that target N-terminal acetylation can be designed for PCa. Further research is needed as to in which cell and how N-acetylation is responsible for the increased risk of PCa. More importantly, while our PheWAS-MR analysis identified associations between these metabolites and non-cancer phenotypes such as cardiovascular and renal diseases, the associations with PCa remained the most statistically significant after multiple testing correction, suggesting a degree of specificity despite some pleiotropic effects. Given their specificity in PCa susceptibility, they are suitable as disease-causing metabolite markers or as disease targets.

This study has several strengths. First, we utilized the two largest European male metabQTL datasets as exposures and incorporated two large GWAS cohorts to enhance reproducibility, ensuring robust and reliable findings. Second, we applied stringent criteria, including rigorous instrumental variable selection, LD removal in MR analyses, and the most conservative p-value correction methods, to maximize result accuracy. Third, we performed meta-analyses to obtain precise estimates of genetic susceptibility effects through cross-validation. Additionally, the integration of colocalization analyses provided important evidence supporting shared genetic mechanisms for our top association, reducing the likelihood that our findings reflect LD bias.

Several limitations should be acknowledged. While our findings demonstrate statistical genetic associations, functional validation through in vitro/vivo experiments remains essential to establish biological mechanisms. The unavailability of individual-level data precluded stratified analyses by potential effect modifiers like age, smoking status, and hormone levels. Furthermore, our exclusive focus on European populations necessitates future investigations in diverse ancestries to assess result generalizability. The significant heterogeneity observed for ribitol (I² = 74 %) may reflect methodological differences between metabolite profiling platforms (LC-MS in METSIM vs. NMR in CLSA) or population-specific genetic architectures, which should be considered when interpreting this particular association. Additionally, while we employed rigorous approaches to assess horizontal pleiotropy, residual confounding from unknown sources cannot be entirely ruled out.

Overall, our comprehensive analytical strategy addresses a key knowledge gap by integrating both metabolomic and genetic information. This integration demonstrates the potential to advance the understanding of PCa metabolic characteristics through combined metabQTL and GWAS analyses. Disease-predisposing metabolites were identified in PCa, providing insights into pathological pathways and potential clinical diagnostic biomarkers or therapeutic targets. The identified metabolites, particularly those involved in N-terminal acetylation pathways, represent promising targets for further mechanistic studies and potential development as biomarkers for PCa risk stratification. Additionally, further investigation is required to elucidate the mechanisms underlying the genetically predicted effects identified here. Future research directions should include experimental validation of the functional effects of these metabolites on PCa cells, investigation of their potential as early detection biomarkers, and exploration of their interactions with established PCa risk factors in diverse populations.

5. Conclusion

This study provides robust genetic evidence supporting the causal role of specific plasma metabolites in PCa development. By employing a TSMR design integrated with Bayesian colocalization analysis, we identified four metabolites—ribitol (inversely associated) and N2,N5-diacetylornithine, N-acetylarginine, and N-acetylcitrulline (positively associated)—with consistent effects across two independent metabolomic cohorts (METSIM and CLSA) and two PCa GWAS datasets. The robustness of these findings was reinforced by comprehensive sensitivity analyses, which showed no significant pleiotropy or heterogeneity, and notably, colocalization evidence for a shared causal variant (rs10201159) for N2,N5-diacetylornithine. This multi-faceted methodological approach enhances the credibility of our causal inferences, suggesting that these metabolites are not merely biomarkers but potentially involved in PCa etiology, possibly through pathways related to polyamine metabolism and immune regulation.

Despite the strengths, several limitations should be acknowledged. The primary limitation is the focus on European-ancestry populations, which may restrict the generalizability of our findings to other ethnic groups. Future studies should prioritize inclusion of multi-ethnic cohorts to validate the transferability of these associations. Additionally, while MR minimizes confounding, the precise biological mechanisms underlying the observed associations remain to be fully elucidated. Further experimental research is needed to validate the functional roles of these metabolites in PCa pathogenesis. Future investigations incorporating multi-omics data (e.g., proteomics, transcriptomics) could help delineate the precise pathways and explore their potential as biomarkers for risk stratification or therapeutic targets.

In summary, our study underscores the value of integrating genetic epidemiology with metabolomics to uncover novel insights into PCa biology. The findings pave the way for further mechanistic and translational research aimed at understanding and potentially intervening in the metabolic basis of PCa.

CRediT authorship contribution statement

Hanghang Chen: Writing – original draft, Visualization, Software, Methodology, Funding acquisition, Formal analysis, Conceptualization. Huiduo Zhao: Validation, Resources, Data curation. Bingxin Meng: Resources, Data curation. Qi Liu: Writing – review & editing, Methodology, Conceptualization.

Ethics approval and consent to participate

This study used publicly available GWAS summary statistics data without individual information, and thus no ethical approval was required.

Clinical trial number

Not applicable.

Consent for publication

This study has been approved by all authors for publication.

Clinical trial number

Not applicable.

Funding

Doctoral Research Foundation of the First Affiliated Hospital of Henan University of Chinese Medicine (Grant No. 2024BSJJ044).

Acknowledgments

The authors sincerely thank all the participants and investigators of the METSIM, CLSA, PRACTICAL consortium, and FinnGen study for making the summary statistics data publicly available, which made this study possible.

We are particularly grateful to the METSIM study for providing the male-specific metabolomic dataset, which was crucial for investigating prostate cancer etiology. We also extend our thanks to the investigators of the CLSA for sharing their metabolomic data, which allowed for the replication of our findings.

Footnotes

This article is part of a special issue entitled: Cancer, Inflammation and Metabolism published in Metabolism Open.

^{Appendix A}

Supplementary data to this article can be found online at https://doi.org/10.1016/j.metop.2025.100421.

Contributor Information

Hanghang Chen, Email: 461652608@qq.com, hanghang461@hactcm.edu.cn.

Qi Liu, Email: liuqi117@hactcm.edu.cn.

Abbreviations

CI: confidence interval
CLSA: Canadian Longitudinal Study on Aging
FDR: false discovery rate
GWAS: Genome wide association study
IV: Instrumental variable
IVW: Inverse variance weighted
LD: Linkage disequilibrium
MAF: Minor allele frequency
METSIM: The Metabolic Syndrome in Men
MR: Mendelian randomization
OR: Odds ratio
PCa: Prostate cancer
PRACTICAL:: The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome
QTL:: Quantitative trait loci
SNP: Single nucleotide polymorphism

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1

mmc1.xlsx^{(4MB, xlsx)}

Multimedia component 2

Regional association plot of the colocalization analysis for N²,N⁵-diacetylornithine and PCa. This figure presents a ±500 kb regional plot surrounding the rs10201159, which showed strong evidence of colocalization (PP.H4 > 0.8) between N²,N⁵-diacetylornithine and PCa risk in both FinnGen and PRACTICAL consortium datasets. Each point represents a genetic variant, colored by its linkage disequilibrium (R²) with rs10201159 based on 1000 Genomes European reference data. Genes within the locus (including ALMS1 and NAT8) are annotated below the plot.

mmc2.pdf^{(2.8MB, pdf)}

Data availability

The GWAS summary statistics data is available at https://www.finngen.fi/en/access_results and https://www.icr.ac.uk/research-and-discoveries/icr-divisions/genetics-and-epidemiology/oncogenetics/practical, respectively. The metabQTL data of METSIM is available at https://pheweb.org/metsim-metab/. The metabQTL data of CLSA is available at GWAS catalog (https://www.ebi.ac.uk/gwas/labs/downloads/summary-statistics). Accession numbers for European GWAS: GCST90199621-90201020. LD reference data for the European super population can be downloaded directly through this link (https://github.com/MRCIEU/gwasglue).

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1

mmc1.xlsx^{(4MB, xlsx)}

Multimedia component 2

mmc2.pdf^{(2.8MB, pdf)}

Data Availability Statement

[bib1] 1.Gandaglia G., Leni R., Bray F., Fleshner N., Freedland S.J., Kibel A., Stattin P., Van Poppel H., La Vecchia C. Epidemiology and prevention of prostate cancer. Eur Urol Oncol. 2021;4(6):877–892. doi: 10.1016/j.euo.2021.09.006. [DOI] [PubMed] [Google Scholar]

[bib2] 2.Xiang Y-z, Xiong H., Cui Z-l, Jiang S-b, Xia Q-h, Zhao Y., Li G-b, Jin X-b. The association between metabolic syndrome and the risk of prostate cancer, high-grade prostate cancer, advanced prostate cancer, prostate cancer-specific mortality and biochemical recurrence. J Exp Clin Cancer Res. 2013;32(1):9. doi: 10.1186/1756-9966-32-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib3] 3.Fiehn O. Metabolomics--the link between genotypes and phenotypes. Plant Mol Biol. 2002;48(1–2):155–171. [PubMed] [Google Scholar]

[bib4] 4.Veenhuizen J.J., Drackley J.K., Richard M.J., Sanderson T.P., Miller L.D., Young J.W. Metabolic changes in blood and liver during development and early treatment of experimental fatty liver and ketosis in Cows1. J Dairy Sci. 1991;74(12):4238–4253. doi: 10.3168/jds.S0022-0302(91)78619-0. [DOI] [PubMed] [Google Scholar]

[bib5] 5.Cunnane S., Nugent S., Roy M., Courchesne-Loyer A., Croteau E., Tremblay S., Castellano A., Pifferi F., Bocti C., Paquet N., et al. Brain fuel metabolism, aging, and Alzheimer's disease. Nutrition. 2011;27(1):3–20. doi: 10.1016/j.nut.2010.07.021. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib6] 6.Gao X., Ke C., Liu H., Liu W., Li K., Yu B., Sun M. Large-scale metabolomic analysis reveals potential biomarkers for early stage coronary atherosclerosis. Sci Rep. 2017;7(1) doi: 10.1038/s41598-017-12254-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[bib8] 8.Zhang A., Sun H., Yan G., Wang P., Han Y., Wang X. Metabolomics in diagnosis and biomarker discovery of colorectal cancer. Cancer Lett. 2014;345(1):17–20. doi: 10.1016/j.canlet.2013.11.011. [DOI] [PubMed] [Google Scholar]

[bib9] 9.Skrivankova V.W., Richmond R.C., Woolf B.A.R., Davies N.M., Swanson S.A., VanderWeele T.J., Timpson N.J., Higgins J.P.T., Dimou N., Langenberg C., et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ (Clinical research ed) 2021;375 doi: 10.1136/bmj.n2233. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[bib11] 11.Yin X., Chan L.S., Bose D., Jackson A.U., VandeHaar P., Locke A.E., Fuchsberger C., Stringham H.M., Welch R., Yu K., et al. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat Commun. 2022;13(1):1644. doi: 10.1038/s41467-022-29143-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[bib13] 13.Schumacher F.R., Al Olama A.A., Berndt S.I., Benlloch S., Ahmed M., Saunders E.J., Dadaev T., Leongamornlert D., Anokian E., Cieza-Borrella C., et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat Genet. 2018;50(7):928–936. doi: 10.1038/s41588-018-0142-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[bib17] 17.Pierce B.L., Burgess S. Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol. 2013;178(7):1177–1184. doi: 10.1093/aje/kwt084. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[bib20] 20.Kamat M.A., Blackshaw J.A., Young R., Surendran P., Burgess S., Danesh J., Butterworth A.S., Staley J.R. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics (Oxford, England) 2019;35(22):4851–4853. doi: 10.1093/bioinformatics/btz469. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib21] 21.Liu M., Jiang Y., Wedow R., Li Y., Brazel D.M., Chen F., Datta G., Davila-Velderrain J., McGuire D., Tian C., et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51(2):237–244. doi: 10.1038/s41588-018-0307-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib22] 22.Verbanck M., Chen C.Y., Neale B., Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–698. doi: 10.1038/s41588-018-0099-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib23] 23.Hemani G., Tilling K., Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13(11) doi: 10.1371/journal.pgen.1007081. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib24] 24.Schwarzer G. Meta: an R package for meta-analysis. R News. 2007;7(3):40–45. [Google Scholar]

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[bib26] 26.Melsen W.G., Bootsma M.C.J., Rovers M.M., Bonten M.J.M. The effects of clinical and statistical heterogeneity on the predictive values of results from meta-analyses. Clin Microbiol Infection. 2014;20(2):123–129. doi: 10.1111/1469-0691.12494. [DOI] [PubMed] [Google Scholar]

[bib27] 27.Wang G., Sarkar A., Carbonetto P., Stephens M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J Roy Stat Soc B Stat Methodol. 2020;82(5):1273–1300. doi: 10.1111/rssb.12388. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib28] 28.Pan R., Liu J., Xiao M., Sun C., Zhu J., Wan L., Xue B. Causal links of human serum metabolites on the risk of prostate cancer: insights from genome-wide Mendelian randomization, single-cell RNA sequencing, and metabolic pathway analysis. Front Endocrinol. 2024;15 doi: 10.3389/fendo.2024.1443330. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[bib30] 30.Doddapaneni R., Tucker J.D., Lu P.J., Lu Q.L. Metabolic reprogramming by ribitol expands the therapeutic window of BETi JQ1 against breast cancer. Cancers. 2023;15(17):4356. doi: 10.3390/cancers15174356. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Novel causal associations between plasma metabolites and prostate cancer risk revealed by mendelian randomization

Hanghang Chen

Huiduo Zhao

Bingxin Meng

Qi Liu

Abstract

Background

Methods

Results

Conclusion

1. Introduction

2. Materials and methods

2.1. Exposure data

2.2. Outcome cohorts

Table 1.

2.3. TSMR

2.4. Sensitivity test

2.5. Meta analysis

2.6. Colocalization analysis

2.7. PheWAS-MR analysis

2.8. Statistical methods

3. Results

Fig. 1.

Fig. 2.

Fig. 3.

Table 2.

Fig. 4.

Fig. 5.

Fig. 6.

4. Discussion

5. Conclusion

CRediT authorship contribution statement

Ethics approval and consent to participate

Clinical trial number

Consent for publication

Clinical trial number

Funding

Acknowledgments

Footnotes

Contributor Information

Abbreviations

Appendix A. Supplementary data

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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