3.1 Clinical description of chest pain individuals by PCA and OPLS-DA plots
Tables 1 and 2 show the history examinations and basic laboratory tests of the volunteers. Generally, higher glucose, AST, LDH, HBDH, and CK levels and lower ALB and Ca2+ concentrations were detected in the chest pain patients than the control group. Among the 146 chest pain inpatients, approximately 65% had taken aspirin and statin treatments before blood collection. As a result, TC, TG, LDL-c and HDL-c levels were all lower in chest pain inpatients than the controls.
Table 1
Sample Characteristics: Controls vs all the chest pain cases
Clinical concerns | Variables | The controls | The chest pain cases | Statistics |
| | n = 84 | n = 146 | p Values |
Demographics | Male | 56 | 99 | 0.26 |
Age(years) | 50.25 ± 1.76 | 59.28 ± 1.83 | 0.00 |
Cardiac risk factors | Hypertension | 32 | 83 | 0.00 |
Diabetes | 8 | 36 | 0.00 |
TC(mmol/L) | 4.93 ± 0.15 | 4.08 ± 0.11 | 0.00 |
TG(mmol/L) | 1.43 ± 0.12 | 1.51 ± 0.08 | 0.17 |
LDL-C(mmol/L) | 3.15 ± 0.11 | 2.70 ± 0.08 | 0.00 |
HDL-C(mmol/L) | 1.31 ± 0.04 | 0.95 ± 0.02 | 0.00 |
LPa(mg/L) | 349.35 ± 51.45 | 339.39 ± 27.47 | 0.65 |
Tobacco using | 8 | 43 | 0.00 |
Drinking history | 0 | 16 | 0.00 |
Cardiovascular medications | Aspirin | 1 | 88 | 0.00 |
Statin therapy | 6 | 94 | 0.00 |
β-blockers | 0 | 7 | 0.00 |
Prior cardiovascular disease | 2 | 22 | 0.01 |
Biochemical items | ALT(U/L) | 27.15 ± 2.48 | 39.33 ± 2.90 | 0.00 |
AST(U/L) | 26.31 ± 1.45 | 77.35 ± 9.78 | 0.00 |
ALP(U/L) | 78.20 ± 2.19 | 86.16 ± 2.26 | 0.03 |
GGT(U/L) | 30.69 ± 2.75 | 45.18 ± 3.80 | 0.02 |
LDH(U/L) | 169.30 ± 3.85 | 398.72 ± 32.22 | 0.00 |
CK(U/L) | 119.30 ± 11.27 | 480.97 ± 78.58 | 0.94 |
HBDH(U/L) | 107.80 ± 3.87 | 297.38 ± 28.85 | 0.00 |
TBIL(µmol/L) | 14.36 ± 0.56 | 13.74 ± 0.71 | 0.03 |
DBIL(µmol/L) | 4.51 ± 0.28 | 4.96 ± 0.26 | 0.83 |
IBIL(µmol/L) | 9.16 ± 0.42 | 8.77 ± 0.48 | 0.05 |
TP(g/L) | 70.80 ± 0.47 | 61.12 ± 0.48 | 0.00 |
ALB(g/L) | 44.68 ± 0.54 | 35.80 ± 0.36 | 0.00 |
GLB(g/L) | 25.78 ± 0.48 | 25.33 ± 0.39 | 0.14 |
ALB/GLB | 1.79 ± 0.05 | 1.45 ± 0.03 | 0.00 |
GLU(mmol/L) | 5.77 ± 0.13 | 6.01 ± 0.22 | 0.23 |
Urea(mmol/L) | 5.13 ± 0.18 | 7.37 ± 0.52 | 0.00 |
Cr(µmol/L) | 71.14 ± 2.00 | 102.13 ± 11.16 | 0.00 |
UA(µmol/L) | 325.57 ± 9.61 | 378.91 ± 13.24 | 0.04 |
Ca(mmol/L) | 2.36 ± 0.02 | 2.17 ± 0.01 | 0.00 |
Values are presented as Mean ± SE. Mann-Whitney U test was applied to produce P value. |
Table 2
Sample Characteristics: MI vs non-MI chest pain cases
Clinical concerns | Variables | Controls | Chest pain cases | Statistics |
| | (n = 84) | non-MI(n = 61) | MI(n = 85) | p values |
Demographics | Male | 56 | 32 | 67 | 0.00 |
Age(years) | 50.25 ± 1.76 | 60.18 ± 2.42 | 65.08 ± 1.60 | 0.12 |
Cardiac risk factors | Hypertension | 32 | 32 | 51 | 0.28 |
Diabetes | 8 | 8 | 28 | 0.01 |
Tobacco using | 8 | 8 | 35 | 0.00 |
TC(mmol/L) | 4.93 ± 0.15 | 3.69 ± 0.12 | 4.28 ± 0.15 | 0.01 |
TG(mmol/L) | 1.43 ± 0.12 | 1.26 ± 0.09 | 1.65 ± 0.11 | 0.02 |
LDL-C(mmol/L) | 3.15 ± 0.11 | 2.40 ± 0.09 | 2.86 ± 0.11 | 0.01 |
HDL-C(mmol/L) | 1.31 ± 0.04 | 0.96 ± 0.03 | 0.94 ± 0.03 | 0.70 |
LPa(mg/L) | 349.35 ± 51.45 | 334.55 ± 47.64 | 341.96 ± 33.82 | 0.56 |
Prior cardiovascular disease | 2 | 12 | 10 | 0.24 |
Serum biomarkers | cTNT(ng/ml) | - | 756.13 ± 242.59 | 1559.30 ± 276.64 | 0.56 |
CK-MB(U/L) | - | 24.98 ± 6.08 | 32.10 ± 5.89 | 0.68 |
Mb(ng/ml) | - | 59.30 ± 23.79 | 66.99 ± 15.32 | 0.46 |
Biochemical items | ALT(U/L) | 27.15 ± 2.48 | 32.17 ± 4.59 | 43.14 ± 3.65 | 0.04 |
AST(U/L) | 26.31 ± 1.45 | 29.12 ± 2.91 | 102.99 ± 14.10 | 0.00 |
LDH(U/L) | 169.30 ± 3.85 | 213.05 ± 10.22 | 497.43 ± 45.36 | 0.00 |
CK(U/L) | 119.30 ± 11.27 | 86.83 ± 12.99 | 690.51 ± 113.49 | 0.00 |
HBDH(U/L) | 107.80 ± 3.87 | 132.79 ± 6.71 | 384.89 ± 40.81 | 0.00 |
TP(g/L) | 70.80 ± 0.47 | 61.95 ± 0.71 | 60.68 ± 0.62 | 0.18 |
GLU(mmol/L) | 5.77 ± 0.13 | 5.49 ± 0.22 | 6.28 ± 0.32 | 0.06 |
Ca(mmol/L) | 2.36 ± 0.02 | 2.20 ± 0.02 | 2.16 ± 0.02 | 0.11 |
Values are presented with Mean ± SE. Mann-Whitney U test was applied to produce P value between MI and non-MI chest pain cases. |
Based on clinical parameters (listed in Table 1 “variables”), including “biochemical items”, “demographics” and “cardiac risk factors”, an unsupervised PCA score plot was created. The model indicated a few outliers when the samples were either divided into 3 (controls, MIs, non-MIs) or 4 groups (controls, UA, MIs and other non-MICs), and each of the groups generally overlapped with the others (Fig. 1A1, 1A2). However, a supervised PLS-DA revealed a visible separation of the groups with only a little overlap when the samples were divided into 3 groups, i.e., MIs, non-MIs and controls. When the samples were divided into 4 groups (MIs, UAs, other non-MICs, and controls), the controls, UAs and MIs were fairly well separated, but the other non-MICs primarily showed overlaps with UAs and MIs (Fig. 1A3, 1A4). These findings suggest that the model was not powerful at differentiating non-MICs from MIs and UAs based on basic laboratory tests and history examinations.
3.2 Plasma metabolomic description of chest pain individuals by PCA and OPLS-DA plots
GC/MS and LC/MS analysis of the plasma samples aligned the metabolites in typical chromatograms (Figure S1-2). Deconvolution of the GC/MS chromatograms produced 135 independent peaks from the plasma samples, 83 of which were authentically identified as metabolites; LC/MS produced 279 peaks, and 76 metabolites were identified (Table S1-2). Quantitative data were acquired for each metabolite in the plasma samples of the control, MI, UA and other non-MI cardiac cases.
Based on the metabolomic data derived from GC/MS and LC/MS analysis, the PCA score plot again showed a few outliers when the samples were divided into 3 or 4 groups, as indicated above. Unlike with the clinical data, unsupervised PCA analysis of metabolomic data showed that the majority of MIs deviated from the others, regardless of whether the 3 or 4 groups were defined, although the control, non-MICs and UAs overlapped with each other to some extent (Fig. 1B1, 1B2). The supervised PLS-DA model revealed that samples from each group clustered closely and anchored away from the other groups when the samples were divided into 3 groups (Fig. 1B3). When the samples were divided into 4 groups, the majority of MIs and controls clustered separately, while the UAs and non-MICs primarily overlapped with each other, with a minority overlapping with MIs and controls (Fig. 1B4). The distant separation of MIs from the other groups suggested distinctly different metabolic patterns between MIs and the groups of UA and non-MICs, while the overlapping of the groups suggested similar plasma metabolic patterns between UA and the other non-MICs. In general, metabolomic data better characterized MIs than history examinations and laboratory tests, and the score plot of non-MI chest pain cases (including UA and non-MI cardiac cases) indicated that they had moderate metabolic perturbation relative to the MI cases because they anchored between MI and the controls (Fig. 1B3). The above data suggest that subgroups of MI can be recognized by multivariate analysis of identified plasma metabolites more effectively than by routine clinical parameters.
3.3 Pathway analysis of differential metabolites
OPLS-DA analysis showed different metabolomic patterns of the non-MIs from the controls (Fig. 2A1). Statistical analysis suggested 50 discriminant metabolites (p < 0.05) that differentiated non-MI chest pain inpatients from the controls (Table 3). Similarly, MI cases primarily showed different metabolomic patterns from non-MIs (Fig. 2B1). According to the statistical analysis and the VIP values, 54 discriminant metabolites were identified between MIs and non-MIs (Table 3).
Table 3
Discriminant metabolites list: non-MIs vs controls and MIs vs non-MIs
Differential metabolites | Controls (n = 84) | MI cases (n = 85) | non-MI cases (n = 61) | MI vs non-MI | non-MI vs Con |
| Mean | SE | Mean | SE | Mean | SE | FC | T-test | FC | T-test |
Deoxyuridine | 26212 | 9533 | 1025278 | 150576 | 16840 | 555 | 60.885 | *** | 0.642 | / |
Adenosine phosphosulfate | 51723 | 4141 | 99104 | 8542 | 42213 | 3793 | 2.348 | *** | 0.816 | / |
Deoxyadenosine monophosphate | 310296 | 7783 | 102462 | 13401 | 311056 | 7624 | 0.329 | *** | 1.002 | / |
Guanosine diphosphate | 7379 | 469 | 27614 | 4153 | 6648 | 530 | 4.154 | *** | 0.901 | / |
Inosine 2'-phosphate | 14875 | 683 | 37785 | 4687 | 14988 | 809 | 2.521 | *** | 1.008 | / |
Adenosine monophosphate | 20124 | 1044 | 25648 | 1689 | 19902 | 1016 | 1.289 | *** | 0.989 | / |
Hypoxanthine | 705034 | 24192 | 373454 | 29693 | 734901 | 52065 | 0.508 | *** | 1.042 | / |
Glycolate | 8319 | 247 | 9121 | 331 | 7620 | 306 | 1.197 | ** | 0.916 | / |
Methionine | 113239 | 2830 | 335994 | 27792 | 109192 | 2631 | 3.077 | *** | 0.964 | / |
Arginine | 810028 | 22688 | 169566 | 34842 | 731551 | 31282 | 0.232 | *** | 0.903 | / |
Valine | 2334562 | 53682 | 3710196 | 209295 | 2425637 | 63465 | 1.530 | *** | 1.039 | / |
Citrulline | 422244 | 11079 | 296302 | 13267 | 469763 | 20870 | 0.631 | *** | 1.113 | / |
Shikimate | 14871 | 796 | 8837 | 491 | 15072 | 782 | 0.586 | *** | 1.014 | / |
Ornithine | 747344 | 19685 | 522218 | 23919 | 805398 | 34957 | 0.648 | *** | 1.078 | / |
Alanine | 402892 | 10006 | 663687 | 37026 | 446350 | 16079 | 1.487 | *** | 1.108 | / |
Glycine | 145111 | 2764 | 256201 | 15108 | 150041 | 3152 | 1.708 | *** | 1.034 | / |
Homocysteine | 7517 | 382 | 20169 | 2293 | 7693 | 504 | 2.622 | *** | 1.023 | / |
Aspartate | 155030 | 10889 | 194713 | 12674 | 123523 | 10287 | 1.576 | *** | 0.797 | / |
Ribose | 223498 | 12602 | 340299 | 15678 | 254617 | 23072 | 1.337 | ** | 1.139 | / |
1-Monopalmitin | 30533 | 1705 | 33208 | 1616 | 25699 | 1904 | 1.292 | ** | 0.842 | / |
Glycerate | 125449 | 4077 | 54424 | 3823 | 127894 | 10153 | 0.426 | *** | 1.019 | / |
Citrate | 6687162 | 203004 | 1395841 | 280056 | 6186805 | 304233 | 0.226 | *** | 0.925 | / |
NAD+ | 11766 | 301 | 25536 | 3262 | 11479 | 330 | 2.225 | *** | 0.976 | / |
NADPH | 5344 | 269 | 35996 | 7862 | 4157 | 192 | 8.659 | *** | 0.778 | ### |
Uracil | 146594 | 7319 | 60619 | 9826 | 319956 | 18480 | 0.189 | *** | 2.183 | ### |
Xanthine | 161018 | 5234 | 99621 | 11423 | 234923 | 11566 | 0.424 | *** | 1.459 | ### |
Adenosine | 33452 | 2133 | 31326 | 3112 | 18676 | 1948 | 1.677 | *** | 0.558 | ### |
IDP | 3112579 | 73422 | 1512965 | 153666 | 3597655 | 92953 | 0.421 | *** | 1.156 | ### |
Adenine | 15026 | 2519 | 13092 | 2292 | 5543 | 393 | 2.362 | ** | 0.369 | ### |
Succinate | 88550 | 1816 | 36686 | 3233 | 79439 | 2388 | 0.462 | *** | 0.897 | ## |
Malate | 61898 | 2263 | 35918 | 4299 | 78778 | 4163 | 0.456 | *** | 1.273 | ### |
2-Ketoglutarate | 33628 | 1314 | 21806 | 2264 | 78634 | 4864 | 0.277 | *** | 2.338 | ### |
Acetoacetate | 132245 | 2671 | 235115 | 13087 | 160911 | 7415 | 1.461 | *** | 1.217 | ### |
Carbamoylphosphate | 29690 | 1819 | 23063 | 2179 | 36316 | 1532 | 0.635 | *** | 1.223 | ## |
Dihydroorotate | 33819 | 1003 | 31876 | 2619 | 22310 | 579 | 1.429 | *** | 0.660 | ### |
Pantothenate | 54720 | 3022 | 38266 | 4405 | 83814 | 4827 | 0.457 | *** | 1.532 | ### |
Phenylpyruvate | 51177 | 955 | 63363 | 2279 | 56537 | 1514 | 1.121 | * | 1.105 | ## |
Cysteine | 106041 | 3333 | 68660 | 3639 | 150632 | 7856 | 0.456 | *** | 1.421 | ### |
Isoleucine | 1694252 | 47827 | 4794207 | 400231 | 2264556 | 105757 | 2.117 | *** | 1.337 | ### |
Serine | 187997 | 4449 | 345414 | 14192 | 211966 | 5374 | 1.630 | *** | 1.127 | ### |
Proline | 805696 | 27760 | 1281948 | 56175 | 912165 | 3390 | 1.405 | *** | 1.132 | # |
Threonine | 661526 | 23707 | 1415808 | 70560 | 574241 | 19440 | 2.466 | *** | 0.868 | ## |
Phenylalanine | 1051097 | 31497 | 1757945 | 133629 | 2349797 | 58409 | 0.748 | *** | 2.236 | ### |
Glutamine | 6299813 | 30803 | 6265669 | 37518 | 6115270 | 38706 | 1.025 | ** | 0.971 | ### |
Histidine | 3756407 | 54216 | 2876183 | 73183 | 3370631 | 66123 | 0.853 | *** | 0.897 | ### |
Taurine | 851621 | 35791 | 1382464 | 89177 | 727822 | 36862 | 1.899 | *** | 0.855 | # |
Lysine | 1733874 | 38073 | 505537 | 102029 | 2002743 | 55294 | 0.252 | *** | 1.155 | ### |
N-Acetylornithine | 329453 | 18159 | 68946 | 14885 | 265119 | 11352 | 0.260 | *** | 0.805 | ## |
Cytosine | 7058 | 102 | 5624 | 153 | 6609 | 135 | 0.851 | *** | 0.936 | ## |
3-Phospho-Serine | 11045 | 654 | 17870 | 1469 | 8893 | 284 | 2.009 | *** | 0.805 | ## |
Homoserine | 661526 | 23707 | 1415808 | 70560 | 574241 | 19440 | 2.466 | *** | 0.868 | ## |
1-Monostearin | 17548 | 911 | 19128 | 896 | 13626 | 841 | 1.404 | *** | 0.777 | ## |
2-Dehydro-D-Gluconate | 47777 | 2930 | 21494 | 3999 | 90849 | 4764 | 0.237 | *** | 1.902 | ### |
Oxalate | 44511 | 2459 | 48639 | 3025 | 27020 | 2378 | 1.800 | *** | 0.607 | ### |
Tryptophan | 647430 | 23608 | 1366119 | 100129 | 1584205 | 58633 | 0.862 | / | 2.447 | ### |
Glutamate | 253094 | 9911 | 384958 | 35729 | 384612 | 16588 | 1.001 | / | 1.520 | ### |
Hydroxyproline | 26782 | 1974 | 17148 | 1951 | 17583 | 2030 | 0.975 | / | 0.657 | ## |
Salicylic Acid | 4870 | 1788 | 72167 | 12248 | 57320 | 7503 | 1.259 | / | 11.771 | ### |
Pyruvate | 121954 | 5245 | 183553 | 22838 | 184240 | 7253 | 0.996 | / | 1.511 | ### |
Homocysterate | 11932 | 344 | 14822 | 1109 | 13267 | 310 | 1.117 | / | 1.112 | ## |
Cystathionine | 10857 | 625 | 13618 | 1322 | 17869 | 1568 | 0.762 | / | 1.646 | ### |
2-Hydroxybutyrate | 279219 | 12481 | 421962 | 23064 | 405608 | 28986 | 1.040 | / | 1.453 | ### |
3-Hydroxybutyrate | 52649 | 3298 | 79377 | 10282 | 85759 | 13390 | 0.926 | / | 1.629 | # |
Gluconic Acid | 105169 | 4261 | 289949 | 39667 | 203681 | 34215 | 1.424 | / | 1.937 | ## |
Indole-3-Propanate | 55659 | 16898 | 8531 | 1748 | 5464 | 801 | 1.561 | / | 0.098 | ## |
Glycerol | 400975 | 13803 | 290401 | 11659 | 303966 | 15733 | 0.955 | / | 0.758 | ### |
Cholesterol | 822261 | 17789 | 690461 | 14851 | 684060 | 16272 | 1.009 | / | 0.832 | ### |
Alpha-Tocopherol | 116918 | 2949 | 93627 | 2218 | 94284 | 2954 | 0.993 | / | 0.806 | ### |
Deoxyadenosine | 23123 | 1541 | 17568 | 2255 | 13041 | 1288 | 1.347 | / | 0.564 | ### |
Thymine | 544496 | 17903 | 518803 | 20345 | 457002 | 25215 | 1.135 | / | 0.839 | ## |
Inosine | 65657 | 2868 | 28268 | 3939 | 35665 | 2829 | 0.793 | / | 0.543 | ### |
NADP+ | 24365 | 1068 | 23264 | 1803 | 19968 | 712 | 1.165 | / | 0.820 | ### |
The data was not logarized, and expressed as Mean ± SE. Fold change (FC) were calculated by the original, non-logarized data directly. Statistical significance was evaluated by using T-test with equal-variance of two tails, after ANOVA assessment of the variance, ***, **, *: p < 0.001, 0.01, 0.05, respectively, between MI and non-MI chest pain cases; ###, ##, #:: p < 0.001, 0.01, 0.05, respectively, between non-MI chest pain cases and the controls. ‘/’ represents the statistical significance of p values more than 0.05. |
Of the 50 metabolites differentiating non-MIs from the controls, levels of gluconic acid and isoleucine were higher in non-MIs, while succinate, inosine, and arginine were lower, and all the above metabolites deviated further in MIs as the cardiac damage became more severe (Fig. 3C, 3D). These findings indicate that the above metabolites are involved in the development of cardiac damage.
Although glycerol, salicylic acid, and deoxyadenosine showed significant differences between the non-MI and control groups, they had no significant difference between the MI and non-MI cardiac groups. These are thus suggested as markers of non-MI chest pain. In addition to endogenous metabolites, we found that salicylic acid, an exogenous metabolite, also characterized the group of chest pain cases. Salicylic acid is the primary metabolite of aspirin, and a review of inpatient information and clinical data revealed that a large portion of chest pain patients had taken aspirin for the management of CAD.
Moreover, deviated levels of dU, methionine, homoserine, etc. were only observed in MI cases, but not between the controls and non-MIs, indicating their association with the development of MI (Fig. 3B).
A Venn diagram was created to show the discriminant metabolites between MI and non-MIs and those between non-MIs and controls. The overlapping region (B) in the Venn diagram (Fig. 3E) lists the metabolites screened out in both of the two comparison groups, suggesting that they were mostly likely risk factors or markers of the occurrence and development of MI, reflecting homeostatic disturbance induced by myocardia hypoxia. Figure 3f shows the pathway analysis of the metabolites in the Venn A + B region (control vs non-MI), and Fig. 3G shows the pathway analysis of discriminant metabolites in the Venn B + C region (MI vs non-MI). Generally, arginine biosynthesis and pyrimidine metabolism were the most significantly altered metabolic pathways in non-MI chest pain patients’ plasma compared to healthy individuals’. Enrichment and pathway analysis for the metabolites of the Venn C area by MetaboAnalyst showed that arginine biosynthesis (p < 0.01, FDR < 1%) was the most altered metabolic pathway (Fig. 3H). Alanine, aspartate and glutamate metabolism (p < 0.01, FDR < 1%) deserve attention in MI as well.
3.4 Methionine, dU and homoserine are candidate biomarkers for MI occurrence
A combined biosignature of homoserine, IDP and 2-ketoglutarate discriminated MI from non-MI chest pain inpatients with high accuracy (Fig. 3L,AUC = 0.98, sensitivity: 94.1%, specificity: 100%).
The potential capacity of each discriminant metabolite to diagnose MI was assessed by ROC analysis (Table 4). Notably, although pathway analysis did not draw our attention to the methionine-related metabolic module, methionine and homoserine showed their potential in distinguishing MI from non-MI cardiac cases. Homoserine (AUC = 0.94; specificity = 100%, sensitivity = 81%) was more specific for MI diagnosis but less sensitive than methionine (AUC = 0.96; specificity = 94.6%; sensitivity = 89.4%) (Fig. 3J). dU also scored highly, with an AUC over 90% (Fig. 3F). Some other metabolites that showed good diagnostic potential were cysteine, 2-ketoglutarate, IDP, and uracil (Table 4).
Table 4
Differential metabolites and the diagnostic potential between MI and non-MI chest pain cases
Differential metabolites | AUROC | 95%C.I. | Sensitivity | Specificity | log(OR) | 95%C.I. |
Homoserine&IDP&α-Ketoglutarate | 0.9810 | 0.9614-1.0000 | 94.10% | 100% | 3.02 | 2.12–3.93 |
Methionine | 0.9643 | 0.9309–0.9978 | 89.40% | 94.60% | 3.48 | 2.01–4.94 |
Homoserine | 0.9431 | 0.893–0.9932 | 80.90% | 100% | 1.61 | 1.04–2.18 |
α-Ketoglutarate | 0.9390 | 0.8876–0.9905 | 100% | 0.00% | -0.11 | -0.28 - -0.03 |
Uracil | 0.9166 | 0.8585–0.9747 | 100% | 0.00% | -2.38 | -3.45 - -1.32 |
Deoxyuridine | 0.9166 | 0.8462–0.987 | 80.90% | 100% | 3.01 | 1.71–4.30 |
2-Dehydro-D-gluconate | 0.9040 | 0.8362–0.9717 | 2.10% | 100% | -1.69 | -2.53 - -0.84 |
Cysteine | 0.8976 | 0.8302–0.9651 | 100% | 0.00% | -0.41 | -0.61 - -0.20 |
Deoxyadenosine monophosphate | 0.8976 | 0.8335–0.9618 | 100% | 0.00% | -5.57 | -8.61 - -2.53 |
IDP | 0.8838 | 0.8124–0.9553 | 100% | 0.00% | -5.15 | -7.86 - -2.44 |
Glyceric acid | 0.8758 | 0.8022–0.9493 | 100% | 0.00% | -1.53 | -2.08 - -0.98 |
Citrate | 0.8568 | 0.7774–0.9362 | 100% | 0.00% | -1.69 | -2.50 - -0.88 |
Succinate | 0.8562 | 0.7768–0.9357 | 100% | 0.00% | -1.48 | -2.14 - -0.83 |
Pantothenate | 0.8332 | 0.7452–0.9213 | 100% | 0.00% | -0.98 | -1.36 - -0.59 |
Xanthine | 0.8240 | 0.7312–0.9168 | 2.10% | 100% | -1.06 | -1.46 - -0.65 |
Arginine | 0.8235 | 0.7319–0.9151 | 2.10% | 100% | -0.82 | -1.10 - -0.53 |
Logarization transformation of the data was applied to the data before the calculation. |
Moreover, to assess the role of these metabolites as risk factors for the prediction of MI occurrence, OR values were calculated between the MI and non-MI groups. Homoserine, dU and methionine had high scores (Table 4). After adjusting for age, sex, LDL-c, HDL-c, smoking/diabetic/hypertensive history, LogOR of methionine, homoserine and dU, all had ORs greater than 1 (MIs vs non-MIs. Figure 3I). Additionally, the ORs of succinate, IDP and glyceric acid were much less than 1, indicating they are potential protective factors for MI (Table 4).
3.5 Traditional CAD risk factors and cardiac function influence the metabolic pattern
Correlation analysis showed that methionine, homoserine, homocysteine and dU were all affected by smoking history, but none was obviously perturbed by hypertension (Table S3). However, in the subgroup analyses of smoking/nonsmoking, hypertensive/normotensive, diabetic/nondiabetic, aged 45–54/55–65 and male/female, means of homoserine, methionine and dU were higher in MI cases (Table 5).
Table 5
Mean of deoxyuridine, methionine and homoserine in certain subgroups of the controls, non-MIs and MI cases
Risk factors | Sub-groups | Deoxyurdine | Methionine | Homoserine | non-Risk factors | Sub-groups | Deoxyurdine | Methionine | Homoserine | Smokers | The controls | 4.22 | 5.04 | 5.82 | non Smokers | The controls | 4.24 | 5.05 | 5.8 | non-MI cases | 4.22 | 5.03 | 5.76 | non-MI cases | 4.22 | 5.03 | 5.75 | MI cases | 5.48 *,# | 5.44 *,# | 6.13 *,# | MI cases | 5.45 *,# | 5.43 *,# | 6.11 *,# | Diabetic | The controls | 4.22 | 5.04 | 5.8 | non-diabetic or insulin resistance | The controls | 4.24 | 5.05 | 5.8 | non-MI cases | 4.2 | 5.03 | 5.75 | non-MI cases | 4.21 | 5.04 | 5.73 | MI cases | 5.44 *,# | 5.43 *,# | 6.12 *,# | MI cases | 5.34 *,# | 5.40 *,# | 6.09 *,# | Hypertensive | The controls | 4.24 | 5.05 | 5.8 | Normotensive | The controls | 4.22 | 5.04 | 5.8 | non-MI cases | 4.22 | 5.03 | 5.75 | non-MI cases | 4.22 | 5.04 | 5.73 | MI cases | 5.40 *,# | 5.42 *,# | 6.11 *,# | MI cases | 5.37 *,# | 5.42 *,# | 6.10 *,# | Age of 45–54 | The controls | 4.29 | 5.07 | 5.83 | Age: 55–65 | The controls | 4.23 | 5.03 | 5.83 | non-MI cases | 4.21 | 5.05 | 5.72 | non-MI cases | 4.18 | 5.04 | 5.75 | MI cases | 5.30 *,# | 5.37 *,# | 6.13*,# | MI cases | 5.60 *,# | 5.45 *,# | 6.11 *,# | Male | The controls | 4.22 | 5.05 | 5.8 | Female | The controls | 4.28 | 5.05 | 5.82 | non-MI cases | 4.22 | 5.05 | 5.77 | non-MI cases | 4.23 | 5 | 5.72 | MI cases | 5.43 *,# | 5.44 *,# | 6.11 * | MI cases | 5.32 *,# | 5.35 *,# | 6.10 *,# | * MI vs controls, p < 0.05; #, MI vs non-MI p < 0.05. All data were logarized before calculating, the result showed means of each subgroup. |
As a clinical indicator of cardiac function in MI, positive NT-proBNP represents cardiac dysfunction. Methionine, homoserine and deoxyuridine were further elevated in NT-proBNP-positive cases. Pathway analysis of the discriminant metabolites (Table S4) between the NT-proBNP positive and negative groups suggested that only arginine biosynthesis was severely impaired (P < 0.001, FDR < 1%), indicating that arginine biosynthesis is closely associated with cardiac function. (Figure S3A)
cTnt, CK-MB, AST, LDH and HBDH are well-recognized indicators involved in myocardial damage and infarction. Six metabolites, 2-hydroxybutyrate, 3-hydroxybutyrate, homocysteine, palmitic acid, stearic acid and 1-monooleoylglycerol, were positively and significantly correlated with both cTNT and CK-MB (Figure S3C). As candidate predictors of MI, methionine, dU and homoserine were significantly and positively correlated with LDH, HBDH and AST (Figure S3B).
3.6 Altered plasma pyrimidine and methionine metabolism in MI cases
The pathway of pyrimidine metabolism was deranged in the MI cases, as shown by the dramatic changes in dU and uracil. Figure 4D shows metabolites and metabolic enzymes in the dU-related pathway. According to a circulating transcriptomics dataset (GEO accession: GSE48060), the dU-related enzymes cytidine deaminase (CDA) and uridine phosphorylase 1 (UPP1) are upregulated in MI patient plasma. According to The Human Protein Atlas, CDA and UPP1 are highly enriched in immune cells (mainly in neutrophils and monocytes). Figure 4G shows white blood cell (WBC) counts, neutrophil (NE) counts, monocyte (MO) and lymphocyte (LY) counts and dU abundance in MI and non-MI cases. Immune cells generally increased in MI plasma. However, statistically, the correlation analysis did not detect a relationship between dU abundance and the number of any type of immune cell (including total WBCs).
Our study on transverse aortic constriction (TAC) mouse models showed that cardiac CDA mRNA expression increased as BNP and ANF mRNA levels increased (Figure S3 D-F,H). dU level is significantly higher in the proBNP-positive group than in the proBNP-negative chest pain group (Fig. 4H). We hypothesize that dU is also a potential cardiac function marker.
Figure 4A shows methionine, homocysteine and cystathionine in a panel. Inconsistent with the observed elevation of methionine, circulating transcriptomics of MI patients (GEO accession: GSE48060) showed that among the methionine abundance-related genes, 5-Methyltetrahydrofolate-Homocysteine Methyltransferase (MTR) expression decreased and Cystathionine β-synthase (CBS) increased (p < 0.05, Fig. 4B). The other genes involved in methionine turnover, including methionine-tRNA ligase (MetRS), MrsB, MrsA (MARS), Mat2a and Mat1a, remained statistically unchanged in plasma. The above analysis from circulating transcriptomics suggested that cells in plasma are not likely to be responsible for the marked elevation of methionine in MI patients’ blood. Instead, it is more likely that damaged cardiac tissues (or other tissues) release more or utilize less free methionine. This hypothesis is supported by the fact that a transcriptomics study of a MI mouse model (LAD) revealed that in ischemic cardiac tissue, MetRS decreased significantly in MI mouse cardiac samples. (Fig. 4C; GEO accession: GSM12346)