The biomarker and causal roles of homoarginine in the development of cardiometabolic diseases: an observational and Mendelian randomization analysis

High L-homoarginine (hArg) levels are directly associated with several risk factors for cardiometabolic diseases whereas low levels predict increased mortality in prospective studies. The biomarker role of hArg in young adults remains unknown. To study the predictive value of hArg in the development of cardiometabolic risk factors and diseases, we utilized data on high-pressure liquid chromatography-measured hArg, cardiovascular risk factors, ultrasound markers of preclinical atherosclerosis and type 2 diabetes from the population-based Young Finns Study involving 2,106 young adults (54.6% females, aged 24–39). We used a Mendelian randomization approach involving tens to hundreds of thousands of individuals to test causal associations. In our 10-year follow-up analysis, hArg served as an independent predictor for future hyperglycaemia (OR 1.31, 95% CI 1.06–1.63) and abdominal obesity (OR 1.60, 95% 1.14–2.30) in men and type 2 diabetes in women (OR 1.55, 95% CI 1.02–2.41). The MR analysis revealed no evidence of causal associations between serum hArg and any of the studied cardiometabolic outcomes. In conclusion, lifetime exposure to higher levels of circulating hArg does not seem to alter cardiometabolic disease risk. Whether hArg could be used as a biomarker for identification of individuals at risk developing cardiometabolic abnormalities merits further investigation.


The cardiovascular risk in the Young Finns Study (YFS)
Supplementary Tables   Table S1. Association of hArg related genetic variants with metabolites in the MAGNETIC NMR GWAS. Table S2. Association of hArg related genetic variants with serum hArg in a GWAS (n=5143). Table S3. Association of hArg related genetic variants with BMI in GIANT. Table S4. Association of hArg related genetic variants with waist circumference adjusted for BMI in GIANT. Table S5. Association of hArg related genetic variants with glycemic traits in MAGIC. Table S6. Association of hArg related genetic variants with blood lipids in GLGC. Table S7. Association of hArg related genetic variants with T2DM and CAD in DIAGRAM and CARDIoGRAMplusC4D.
Venous blood samples were drawn after a 12 h fast. Serum triglycerides, total cholesterol, high density lipoprotein (HDL)-cholesterol, were measured as described previously [2]. Low density lipoprotein (LDL)-cholesterol was calculated using the Friedewald formula for participants with triglycerides <4 mmol/l. Glucose concentrations were analyzed enzymatically with a clinical chemistry analyzer (Olympus, AU400), and serum insulin concentrations were measured by microparticle enzyme immunoassay kit (Abbott Laboratories, Diagnostic Division, Dainabot). Serum C-reactive protein (CRP) was analyzed by an automated analyzer (Olympus AU400) with a latex turbidimetric immunoassay kit (CRP-UL assay, Wako Chemicals, Neuss, Germany). The detection limit reported by the manufacturer for the assay was 0.06 mg/l. Sex hormone-binding globulin (SHBG) was measured by Spectria SHBG IRMA.

Clinical measurements and questionnaires
Height, weight and waist circumference were measured. BMI was calculated using the formula: weight [kg]/(height [m]) 2 . Blood pressure was measured using a random zero sphygmomanometer with the average of three measurements used in the analyses. Participants were also asked to complete questionnaires that included questions on smoking habits and family history of premature CAD.  c. Effect allele frequency in individuals of European decent of the 1000 Genomes project. d. Combined effect estimates and standard errors in the units of 1-µmol/L increment in hArg per effect allele were calculated by fixed-effects meta-analysis from the study-specific summary statistics taken from Kleber et al. [3] e. The proportion of variance in hArg explained by the SNP (the R 2 statistic) is approximately equal to 2 × × (1 -), where SNP-hArg β is given in standard deviation units and MAF is minor allele frequency. [4] To convert the βs into standard deviation units, we assume that 1-SD equals approximately 0.65 µmol/L hArg as we observe in YFS ( Table 1):

Supplementary Tables
f. The F statistic can then be calculated from the R 2 statistic as = − −1      Figure S5. Tissue-specific GATM mRNA expression and rs1153858. The first boxplot (A) illustrates tissue-specific GATM mRNA expression values by sex (red, women; blue, men). Expression values are shown in log10-transformed RPKM (Reads Per Kilobase of transcript per Million mapped reads), calculated from a gene model with isoforms collapsed to a single gene. The higher the log10(RPKM) the higher the mRNA expression. Box plots are shown as median and 25th and 75th percentiles; points are displayed as outliers if they are above or below 1.5 times the interquartile range. The second boxplot (B) shows GATM mRNA expression values by the GATM rs1153858 genotype groups in three selected tissues (skeletal muscle, thyroid and whole blood). All illustrations are from the Genotype-Tissue Expression (GTEx) project website: http://www.gtexportal.org/.
A B Figure S5. Continues from the last page. The first panel (C) shows the tissue-specific associations of GATM rs1153858 with GATM mRNA expression in all tissues in the GTEx project. The second panel (D) shows the linkage disequilibrium (LD) structure of the GATM locus. The GATM rs1153858 variant is marked with bold font. All illustrations are from the Genotype-Tissue Expression (GTEx) project website: http://www.gtexportal.org/. C D Figure S6. A hypothetical model of hArg metabolism in humans. A model of hArg and creatine synthesis via the AGAT enzyme (encoded by the GATM gene) and its intra mitochondrial substrate bioavailability based on GWAS identified single-nucleotide polymorphisms associated with circulating hArg levels [3].
Although several organs and tissues are capable of the hArg and creatine biosynthesis by AGAT, the kidneys play a pivotal role in the formation and release of hArg and the creatine precursor guanidinoacetate (GAA) into the systemic circulation. Amino acids are readily filtrated into the filtrate in the kidneys and reabsorbed in the renal cortical proximal tubules for metabolism. AGAT and AGXT2 proteins are strongly expressed in human renal tubular cells (www.proteinatlas.org) and upregulated in the porcine isolated renal cortical mitochondria compared to those isolated from the medulla [16]. AGAT catalyses the formation of GAA and ornithine from arginine and glycine as well as the formation of hArg and ornithine from arginine and lysine [17,18]. The decreased bioavailability of glycine for the GAA synthesis due to the decreased and increased activities of AGXT2 and CPS1, respectively, may shift the production of GAA by AGAT towards hArg explaining the associations of the CPS1 and AGXT2 missense variants with circulating hArg levels. In addition, hArg is directly metabolized to 6-guanidino-2-oxocaproic acid (GOCA) by AGXT2 [19]. The direction of association of GATM rs1153858 with GATM mRNA expression is tissue-specific as illustrated in Figure S5. Directions of the effects of the CPS1, AGXT2 and GATM variants on the enzyme activities or mRNA expression are presented by arrows. Directions and p-values of associations of the hArg associated genetic variants (marked by different colours) with blood metabolites are presented by arrows and the reference additionally marked: ↑ or ↓ = P<5×10 -8 ; ↑ or ↓ = P<0.001; ↗ or ↘ = P<0.05; ↔ = P>0.05; 1 = Shin et al. [20], 2 = Kleber et al. [3], 3 = Kettunen et al. [5], 4 = Pattaro et al. [21], 5 = Seppälä et al. [22]. GATM or AGAT, glycine amidinotransferase or Larginine:glycine amidinotransferase; AGXT2, alanine-glyoxylate aminotransferase 2; CPS1, carbamoyl-phosphate synthase 1; GCS, glycine cleavage system; GAMT, guanidinoacetate N-methyltransferase; ADMA, asymmetric dimethylarginine; SDMA, symmetric dimethylarginine; DMGV, α-keto-δ-(N,Ndimethylguanidino)valeric acid; DM'GV, α-keto-δ-(N,N'-dimethylguanidino)valeric acid.