Methods and Applications: Genetic Insights into Glycine’s Protective Role Against CAD — European and East Asia, 2015 and 2020

In the study, we analyzed the relationship between specific genetic instruments and glycine levels in the UK Biobank (UKB), which included 114,972 individuals of European descent (Nightingale Health Plc; Biomarker Quantification Version 2020) and the study conducted by Wittemans et al. (4), which included 30,118 individuals of European ancestry.

All details regarding the GWAS summary-level data are presented in Supplementary Table S1. In order to address potential weak instrumental bias, instrumental variables (IVs) should significantly associate with the exposure (P<5×10^-8) and exhibit minimal linkage- disequilibrium (LD) with other single nucleotide polymorphisms (SNPs) (R²<0.001) within a clump distance of 1,000 kb (Supplementary Table S2). By utilizing the PhenoScanner database, we identified and subsequently excluded pleiotropic SNPs that are correlated with confounding factors (Supplementary Table S2).

Our study utilized a two-step MR analysis design (Figure 1). The primary analysis was conducted using the inverse variance-weighted (IVW) method. In instances where heterogeneity was detected, we employed the IVW method with random effects. To further explore the robustness of our findings, we conducted sensitivity analyses using alternative approaches, including MR-Egger regression, the weighted median method, and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analysis, accounting for multiple genetic variants (8–9). To assess pleiotropy, we utilized the MR-Egger intercept test and the MR-PRESSO global test. Additionally, we evaluated heterogeneity of the MR findings using Cochran’s Q-statistic and the I² index (10). The MR analyses were conducted using the ‘dplyr’ and ‘TwoSampleMR’ packages in R (version 4.0.5, R foundation for statistical computing, Vienna, Austria) The threshold of statistical significance was P<0.05.

Figure 1. 

Schematic diagram of a two-step Mendelian randomization.

The 19 identified SNPs collectively accounted for approximately 6.5% of the variation in circulating glycine levels (R²=6.5%). Furthermore, the F-statistic, which exceeds 18.66, indicates a low probability of weak instrument bias occurring in this study.

We utilized a panel of 18 SNPs, excluding 1 palindromic SNP with intermediate allele frequencies, to assess the correlation between genetically predicted higher circulating glycine levels and decreased risk of CAD in Europeans. The analysis revealed a significant correlation [odds ratio (OR)=0.84, 95% confidence interval (CI): (0.72, 0.98), P=0.029, P_{Cochran’s Q}=0.018, P_{MR-PRESSO global test}=0.03, P_{MR-Egger intercept test}=0.069]. However, when using instruments consisting of 4, 3, and 1 SNPs, no significant associations were observed, despite consistently indicating the same direction of association (Figure 2A).

Figure 2. 

Forest plots showing effect sizes ± 95% confidence intervals for the association between genetically predicted circulating glycine levels and CAD. (A) European; (B) East Asian.

Note: Four sets of IVs were used to estimate the association between circulating glycine levels and CAD risk: 1) significant glycine-related SNPs (19 SNPs) identified in the GWAS of circulating glycine; 2) loci near genes encoding enzymes-related to glycine metabolism (GLDC, PHGDH, PSPH, ALDH1L1, and CPS1, 4 SNPs); 3) loci near genes encoding enzymes related to glycine metabolism except for the pleiotropic CPS1 locus (which showed significant associations with multiple metabolites, 3 SNPs); and 4) the loci at GCSH and GLDC encoding enzymes of the glycine cleavage system (1 SNP).

Abbreviation: SNP=single nucleotide polymorphism; MR-PRESSO=Mendelian Randomization Pleiotropy RESidual Sum and Outlier; GWAS=genome-wideassociation studies; ALDH1L1=aldehyde dehydrogenase 1 family member L1; CAD=coronary artery disease; CPS1=carbamoyl-phosphate synthase; GLDC=glycine decarboxylase; GCSH=glycine cleavage system protein H; PHGDH=phosphoglycerate dehydrogenase; PSPH=phosphoserine phosphatase; SBP=systolic blood pressure.

Out of the initial 19 SNPs, 14 were included in our East Asian-focused MR analysis using data from Biobank Japan. Five SNPs were excluded due to missing data or palindromic status. Our analysis showed a consistent protective relationship between genetically predicted glycine levels and the risk of CAD (OR=0.76, 95% CI: 0.66, 0.89; P=3.57×10^-4, P_{Cochran’s Q}=0.186, P_{MR-PRESSO global test}=0.199, P_{MR-Egger intercept test}=0.038), However, other IVs sets did not show a significant association, likely due to limited statistical power (Figure 2B).

We found a significant relation between higher genetically predicted circulating glycine levels and lower genetically predicted SBP (β=−0.74, 95% CI: −1.28, −0.20; P=0.007, P _{Cochran’s Q} =0.196, P_{MR-PRESSO global test}=0.287], P_{MR-Egger intercept test}=0.759). Using the CPS1 and GLDC instruments, we observed consistent effects of glycine on SBP (β=−0.62, 95% CI: −1.19, −0.06; P=0.03, P_{Cochran’s Q}=0.551, P_{MR-Egger intercept test}=0.786] (Supplementary Figure S1).

Furthermore, our analysis revealed that there was a negative relationship between genetically predicted circulating glycine levels and genetically predicted DBP (β=−0.30, 95% CI: −0.56, −0.05; P=0.02, P_{Cochran’s Q}=0.356, P_{MR-PRESSO global test}=0.455, P_{MR-Egger intercept test}=0.527). This association remained consistent when using alternative IV sets (Supplementary Figure S1). However, we did not find any association between predicted glycine levels and anthropometric, glycemic, inflammatory, or blood lipid traits (Supplementary Figures S2–S5).

Out of the 461 SNPs related to SBP, we included 412 in our MR analysis. Four SNPs were excluded due to insufficient data, and 45 SNPs were removed after an outlier test, using MR-PRESSO. We detected significant heterogeneity (P_{Cochran’s Q}=2.17×10^-8), and the presence of horizontal pleiotropy was confirmed by using the MR-PRESSO global test (P<1×10^-4). Our results demonstrated a positive association between SBP and the risk of CAD, with each unit increase in SBP associated with a 3% increase in CAD risk (OR=1.03, 95% CI: 1.02, 1.03; P_WM=2.33×10^-19) (Table 1).

The potential mediation of systolic blood pressure (SBP) on the association between circulating glycine and CAD risk was investigated. The mediation effect involving SBP was found that 6.06% of the effect of circulating glycine, which was genetically predicted using 19 SNPs, was mediated through the genetically predicted SBP (Supplementary Table S3).

The two-step MR analysis demonstrated a significant causal association between decreased levels of genetically predicted circulating glycine and CAD. Our estimation revealed that approximately 6.06% of this potential causal effect is mediated through genetically predicted SBP, suggesting that the protective influence of circulating glycine may be attributed to its effect on reducing SBP. However, our findings suggest that other risk factors associated with CAD, such as glycemic characteristics, lipid profiles, and inflammatory markers, may not play a considerable role as mediators in this relationship.

Previous studies conducted on European white populations have produced inconsistent results regarding the association between glycine levels and the risk of developing CAD. Specifically, one study on European whites did not provide strong evidence for a causal relationship between glycine and CAD risk (6). Additionally, the relationship between circulating glycine levels and the CAD risk in different racial groups remains uncertain. There is only one previous study that investigated this relationship in Singaporean Chinese individuals, and reported a similar protective effect (5). However, this study had limitations such as a relatively small sample size and the inclusion of only two SNPs, one of which was not validated. Consequently, the study design may have compromised the validity of their findings. In our research, we have cross-validated IVs using two independent GWAS datasets, which strengthens the reliability and robustness of our conclusions.

Previous epidemiological studies have suggested that dietary glycine may have a protective effect on blood pressure regulation (11). Our MR analysis provides further support for this association. We found an inverse genetic correlation between circulating glycine levels and SBP, which accounted for approximately nearly 6.06% of the genetic association between glycine and CAD. In rat models of metabolic syndrome, diets rich in glycine have been shown to reduce hypertension by reducing free radical production and enhancing nitric oxide utilization (12). However, our study did not uncover any significant correlations between glycine and lipid traits or inflammatory markers (13). Further comprehensive investigations are needed to explore other potential mechanisms that may explain the genetic link between glycine and CAD.

Our study had several strengths. First, we implemented a thorough process to select valid genetic instruments for MR analysis. This procedure reduced the potential bias caused by weak instruments and improved the statistical power of our study. We used different MR methodologies to ensure the reliability of our estimates regarding the causal relationship between circulating glycine and the risk of CAD. Additionally, we conducted MR analyses on two separate populations, obtaining consistent results.

This study was subject to some limitations. First, the genetic instruments used in our analysis were derived from datasets consisting only of individuals of European descent. To date, there have been no large-scale GWAS studies investigating the relationship between circulating glycine and genetic instruments in East Asian populations. Additionally, our mediation analysis is subject to potential bias, as accurately establishing causal relationships can be challenging and distinguishing between mediation and confounding can be statistically complex.

The present study utilized MR analysis to investigate the possible causal link between serum glycine levels and CAD. These findings contribute to our understanding of the role of glycine in the development of CAD and provide methodological insights for the prevention and treatment of the disease.

No conflicts of interest.

The study was based on the data provided by the Medical Research Council Integrative Epidemiology Unit. We thank the investigators of the previous studies.