Abstract
Objective
To evaluate the associations between the serum anion gap (AG) with the severity and prognosis of coronary artery disease (CAD).
Methods
We measured serum electrolytes in 18,115 CAD patients indicated by coronary angiography. The serum AG was calculated according to the equation: AG = Na+[(mmol/L) + K+ (mmol/L)] − [Cl− (mmol/L) + HCO3− (mmol/L)].
Results
A total of 4510 (24.9%) participants had their AG levels greater than 16 mmol/L. The serum AG was independently associated with measures of CAD severity, including more severe clinical types of CAD (P < 0.001) and worse cardiac function (P = 0.004). Patients in the 4th quartile of serum AG (≥ 15.92 mmol/L) had a 5.171-fold increased risk of 30 days all-cause death (P < 0.001). This association was robust, even after adjustment for age, sex, evaluated glomerular filtration rate [hazard ratio (HR): 4.861, 95% confidence interval (CI): 2.150–10.993, P < 0.001], clinical diagnosis, severity of coronary artery stenosis, cardiac function grades, and other confounders (HR: 3.318, 95% CI: 1.76–2.27, P = 0.009).
Conclusion
In this large population-based study, our findings reveal a high percentage of increased serum AG in CAD. Higher AG is associated with more severe clinical types of CAD and worse cardiac function. Furthermore, the increased serum AG is an independent, significant, and strong predictor of all-cause mortality. These findings support a role for the serum AG in the risk-stratification of CAD.
Keywords: All-cause death, Anion gap, Coronary artery disease
1. Introduction
Although considerable progress has been made in basic and clinical research in atherosclerosis over the past decades, coronary artery disease (CAD) remains one of the leading causes of deaths worldwide.[1] New prognostic features in CAD patients are always welcomed by clinicians, especially when so easy to obtain and understand! The serum anion gap (AG) is such a mathematically derivated parameter that has been used for more than 50 years.[2] Although it has its widest application in the diagnosis of various forms of metabolic acidosis, it may sometimes provide an important clue to the diagnosis or prognosis of disorders such as advanced kidney disease (AKD).[2] Furthermore, in the general population largely free of AKD the increase in AG may be of prognostic significance as higher levels of AG have been associated with hypertension,[3] insulin resistance,[4] low cardiorespiratory fitness,[5] all-cause[6] and cardiac deaths.[7] Novel risk factors can improve the Framingham risk score. However, it is largely unknown whether such changes of AG occur in the course of CAD might exist a risk difference for either CAD severity or mortality. To date, there have been no population-based studies of acid-base status and CAD. The current study aimed to evaluate the associations between the serum AG with the severity and prognosis of CAD.
2. Methods
2.1. Study population
The study protocol was approved by the Institutional Review Boards of all participating hospitals and informed consent have been obtained. All methods were performed in accordance with the relevant guidelines and regulations by including a statement in the methods section to this effect. From April 2004 to October 2010, a total of 21,620 consecutive patients with complete measurements of serum electrolytes and creatinine were recruited from five centers. All participants aged ≥ 18 years and underwent clinically indicated coronary angiography and percutaneous coronary intervention (PCI). We excluded participants who were missing covariate or mortality data (n = 3465), or had a diagnosed terminal illness (n = 40). Thus there were 18,115 participants remaining in the study cohort.
2.2. Calculation of the serum AG and evaluated glomerular filtration rate (eGFR)
Although methods used to calculate AG may be susceptible to some parameters (including haemoconcentration, albumin concentration, Ca2+ concentration, some medications, and renal function, etc.) and some authors advise to correct AG value by such parameters, the equation used in the present study, [AG = Na+ (mmol/L) + K+ (mmol/L)] − [Cl− (mmol/L) + HCO3− (mmol/L)], was generally acknowledged.[8] Furthermore, eGFR was calculated according to the simplified Modification of Diet in Renal Disease Study prediction equation.[9]
2.3. Assessment of severity of CAD
The severity of CAD was comprehensively evaluated through three ordinal variables: clinical diagnosis, severity of coronary artery stenosis, and cardiac function grades based on left ventricular ejection fraction (LVEF). Although not exactly, all of the variables reflected the severity of CAD to some extent. The levels of clinical diagnosis included stable coronary atherosclerotic disease (SCAD), unstable angina pectoris (UAP), or acute myocardial infarction (AMI).[10]–[12] Significant coronary artery stenosis was defined as ≥ 75% narrowing of the diameter of at least one major epicardial vessel.[13],[14] Severity of coronary artery stenosis was defined according to the number of significantly diseased vessels, namely 1-vessel, 2-vessel, and 3-vesse and/or left main (LM). Simultaneously, the SYNTAX score was calculated retrospectively by reviewing the original diagnostic angiograms.[15] Grades of cardiac function comprised normal (defined as ≥ 50%), preserved (40%–49%), and reduced (< 40%) LVEF.[16]
2.4. Assessment of patient characteristics
Demographic characteristics, medical history, risk factors and medication usage were obtained from the electronic medical records. Baseline fasting venous blood samples were drawn and tested for hemoglobin, leukocytes, platelet counts, serum lipids, alanine aminotransferase, electrolytes, albumin, and glucose, etc.
2.5. Outcome
Thirty days all-cause mortality was collected for 12,946 (71.5%) patients from the electronic medical record system, 4237 (23.4%) from telephone contact, and 932 (5.1%) from household registration system.
2.6. Statistical analysis
Non-normally distributed data were presented as median [interquartile (25th–75th percentiles) range], and normally distributed variables as mean ± SD. Where indicated, one-way analysis of variance and Kruskal–Wallis test or chi-square test were applied to evaluate statistical differences among AG quartile groups. The serum AG was modeled as continuous variable and according to quartiles in multivariate analyses (P for trend was calculated). Ordinal logistic regressions were used to evaluate associations between AG with the severity of CAD. The Kaplan–Meier estimates were used to describe the event-free survival on follow-up. To further evaluate the prognostic value of AG, a Cox proportional hazard analysis was performed. Adjusted models included covariates on the basis of statistical evidence for confounding and clinical judgment. To determine whether our results were driven by participants with AKD, we re-evaluated associations of the serum AG with the severity and prognosis of CAD after excluding those with eGFR < 60 mL/min per 1.73m2.[17] All statistical tests were two-sided and P-values of < 0.05 was considered to be statistically significant. SPSS 22.0 (IBM Corporation) was used to conduct statistical analysis.
3. Results
3.1. Patient characteristics
Across the cohort, the serum AG followed an approximately normal distribution. It ranged from 0.20 to 53.30 mmol/L, with a mean ± SD of 13.73 ± 3.59 mmol/L. The median level was 13.52 (interquartile range: 11.40–15.92) mmol/L. A total of 4510 (24.9%) participants had their AG levels greater than 16 mmol/L, which was usually suggested the upper limit of a normal AG. AG exceeding 24 mmol/L was rare (0.6%). The serum AG was significantly higher in patients with AKD, AMI, and reduced LVEF (< 40%) (Figure 1). Overall, patients with higher levels of AG were younger and taking more medications, more likely to have lower levels of eGFR, higher levels of clinical diagnosis and cardiac function grades (Table 1). It should be noted that patients with higher AG levels had lower low-density lipoprotein cholesterol (LDL-C) and higher high-density lipoprotein cholesterol (HDL-C) concentrations probably secondary to better control of risk factors and higher intake of statins in these groups.
Figure 1. Comparisons of the serum anion gap among different groups.
(A): Comparison of the serum AG between patients with eGFR ≥ and < 60 mL/min per 1.73m2; (B): comparison of the serum AG among patients with SCAD, UAP, and AMI; (C): comparison of the serum AG among patients with 1-vessel, 2-vessel, and 3-vesse and/or LM disease; (D): comparison of the serum AG among patients with LVEF ≥ 50%, 40%–50%, and < 40%. AG: anion gap; AMI: acute myocardial infarction; eGFR: evaluated glomerular filtration rate; LM: left main; LVEF: left ventricular ejection fraction; SCAD: stable coronary atherosclerotic disease; UAP: unstable angina pectoris.
Table 1. Baseline clinical and biochemical characteristics by AG quartiles.
| Characteristics | Cohort (n = 18115) | Quartiles of AG (mmol/L) |
P value | P value for trend | ||||||||||||
| Q1 (n = 4537)< 11.40 | Q2 (n = 4558)11.40–13.52 | Q3 (n = 4508)13.52–15.92 | Q4 (n = 4512)≥ 15.92 | |||||||||||||
| Demographic characteristics | ||||||||||||||||
| Age, yrs | 60 ± 11 | 61 ± 11 | 60 ± 11 | 59 ± 11 | 59 ± 11 | < 0.001 | < 0.001 | |||||||||
| Male | 13,455 (74.3%) | 3440 (75.8%) | 3367 (73.9%) | 3283 (72.8%) | 3365 (74.6%) | 0.011 | 0.100 | |||||||||
| Medical history and coronary risk factors | ||||||||||||||||
| Hypertension | 11,040 (60.9%) | 2734 (60.3%) | 2779 (61.0%) | 2725 (60.4%) | 2802 (62.1%) | 0.273 | 0.123 | |||||||||
| Diabetes | 5220 (28.8%) | 1242 (27.4%) | 1260 (27.6%) | 1336 (29.6%) | 1382 (30.6%) | 0.001 | < 0.001 | |||||||||
| Hypercholesterolemia | 2741 (15.1%) | 619 (13.6%) | 678 (14.9%) | 719 (15.9%) | 725 (16.1%) | 0.004 | < 0.001 | |||||||||
| Smoking | 5733 (31.6%) | 1433 (31.6%) | 1453 (31.9%) | 1418 (31.5%) | 1429 (31.7%) | 0.602 | 0.787 | |||||||||
| Body mass index, kg/m2 | 28 ± 6 | 28 ± 6 | 28 ± 6 | 28 ± 7 | 28 ± 9 | 0.116 | 0.098 | |||||||||
| Prior MI | 2825 (15.6%) | 724 (16.0%) | 746 (16.4%) | 709 (15.7%) | 646 (14.3%) | 0.043 | 0.021 | |||||||||
| Prior stroke | 770 (4.3%) | 186 (4.1%) | 197 (4.3%) | 199 (4.4%) | 188 (4.2%) | 0.876 | 0.826 | |||||||||
| Diagnosis | ||||||||||||||||
| SCAD | 4127 (22.8%) | 1110 (24.5%) | 1060 (23.3%) | 1056 (23.4%) | 901 (20.0%) | < 0.001 | < 0.001 | |||||||||
| UAP | 8818 (48.7%) | 2316 (51.0%) | 2324 (51.0%) | 2240 (49.7%) | 1938 (43.0%) | |||||||||||
| AMI | 5170 (28.5%) | 1111 (24.5%) | 1174 (25.8%) | 1212 (26.9%) | 1673 (37.1%) | |||||||||||
| Medication use | ||||||||||||||||
| Aspirin | 17,206 (95.0%) | 4291 (94.6%) | 4316 (94.7%) | 4264 (94.6%) | 4335 (96.1%) | 0.002 | 0.002 | |||||||||
| Thienopyridines | 17,934 (99.0%) | 4485 (98.9%) | 4517 (99.1%) | 4455 (98.8%) | 4477 (99.2%) | 0.161 | 0.207 | |||||||||
| Beta-blockers | 12,608 (69.6%) | 3099 (68.3%) | 3153 (69.2%) | 3148 (69.8%) | 3208 (71.1%) | 0.031 | 0.003 | |||||||||
| ACEIs | 9226 (50.9%) | 2329 (51.3%) | 2295 (50.4%) | 2219 (49.2%) | 2383 (52.8%) | 0.006 | 0.320 | |||||||||
| ARBs | 3192 (17.6%) | 723 (15.9%) | 807 (17.7%) | 810 (18.0%) | 852 (18.9%) | 0.003 | < 0.001 | |||||||||
| Statins | 16,588 (91.6%) | 4115 (90.7%) | 4158 (91.2%) | 4134 (91.7%) | 4181 (92.7%) | 0.007 | 0.001 | |||||||||
| Laboratory variables | ||||||||||||||||
| Hemoglobin, g/L | 109 ± 56 | 104 ± 57 | 108 ± 56 | 111 ± 55 | 115 ± 54 | < 0.001 | < 0.001 | |||||||||
| Leukocyte, ×109/L | 7.53 ± 2.62 | 7.10 ± 2.29 | 7.23 ± 2.24 | 7.52 ± 2.53 | 8.28 ± 3.17 | < 0.001 | < 0.001 | |||||||||
| Neutrophil, ×109/L | 4.70 ± 2.29 | 4.33 ± 1.93 | 4.42 ± 1.90 | 4.64 ± 2.14 | 5.39 ± 2.89 | < 0.001 | < 0.001 | |||||||||
| Lymphocyte, ×109/L | 2.01 ± 0.70 | 1.96 ± 0.66 | 2.00 ± 0.68 | 2.05 ± 0.71 | 2.02 ± 0.75 | < 0.001 | < 0.001 | |||||||||
| Platelet, ×109/L | 206 ± 60 | 197 ± 56 | 206 ± 60 | 209 ± 61 | 213 ± 61 | < 0.001 | < 0.001 | |||||||||
| eGFR, mL/min per 1.73m2 | 89 (74–105) | 89 (75–106) | 89 (75–106) | 89 (74–105) | 87 (73–103) | < 0.001 | < 0.001 | |||||||||
| Alanine aminotransferase, U/L | 30 (19–47) | 26 (17–41) | 28 (18–44) | 31 (19–47) | 35 (22–55) | < 0.001 | < 0.001 | |||||||||
| Glucose, mmol/L | 6.75 ± 2.57 | 6.64 ± 2.55 | 6.54 ± 2.38 | 6.73 ± 2.54 | 7.09 ± 2.78 | < 0.001 | < 0.001 | |||||||||
| LDL-C, mmol/L | 2.91 ± 0.94 | 2.86 ± 0.93 | 2.19 ± 0.94 | 2.93 ± 0.95 | 2.93 ± 0.93 | 0.001 | < 0.001 | |||||||||
| HDL-C, mmol/L | 0.97 ± 0.23 | 0.95 ± 0.22 | 0.97 ± 0.23 | 0.98 ± 0.24 | 0.99 ± 0.24 | < 0.001 | < 0.001 | |||||||||
| Albumin, g/L | 42.86 ± 4.87 | 42.31 ± 4.56 | 42.86 ± 4.69 | 43.12 ± 4.81 | 43.15 ± 5.36 | < 0.001 | < 0.001 | |||||||||
| Potassium, mmol/L | 4.12 ± 0.45 | 4.13 ± 0.41 | 4.12 ± 0.43 | 4.12 ± 0.43 | 4.10 ± 0.51 | 0.016 | 0.002 | |||||||||
| Anion gap, mmol/L | 13.73 ± 3.59 | 9.41 ± 1.65 | 12.48 ± 0.62 | 14.70 ± 0.69 | 18.37 ± 2.35 | |||||||||||
| Cardiac functional grades | ||||||||||||||||
| LVEF, % | 61.02 ± 10.23 | 61.70 ± 10.12 | 61.59 ± 10.11 | 60.97 ± 10.20 | 59.80 ± 10.39 | < 0.001 | < 0.001 | |||||||||
| < 40% | 652 (3.6%) | 136(3.0%) | 164 (3.6%) | 167 (3.7%) | 185 (4.1%) | |||||||||||
| 40%–50% | 1489 (8.2%) | 349 (7.7%) | 319 (7.0%) | 374 (8.3%) | 447 (9.9%) | < 0.001 | < 0.001 | |||||||||
| ≧ 50% | 15,974 (88.2%) | 4052 (89.3%) | 4075 (89.4%) | 3967 (88.0%) | 3880 (86.0%) | |||||||||||
| Number of significantly diseased vessels | ||||||||||||||||
| 1-vessel | 7367 (40.7%) | 1832 (40.4%) | 1858 (40.8%) | 1837 (40.7%) | 1840 (40.8%) | |||||||||||
| 2-vessel | 5557 (30.7%) | 1402 (30.9%) | 1327 (29.1%) | 1388 (30.8%) | 1440 (31.9%) | 0.052 | 0.195 | |||||||||
| 3-vessel and/or LM | 5191 (28.7%) | 1303 (28.7%) | 1373 (30.1%) | 1283 (28.5%) | 1232 (27.3%) | |||||||||||
| Syntax score | 19.56 ± 3.44 | 19.17 ± 3.49 | 20.50 ± 3.58 | 19.38 ± 3.32 | 20.21 ± 3.55 | 0.372 | 0.525 | |||||||||
Data were presented as n (%), mean ± SD or mean (interquartile range). ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ARBs: angiotensin receptor blockers; eGFR: evaluated glomerular filtration rate; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; MI: myocardial infarction; SCAD: stable coronary atherosclerotic disease; UAP: unstable angina pectoris.
3.2. Independent determinants of baseline serum AG levels
As shown in Supplemental Table 1, clinical diagnosis of AMI, angiotensin receptor blockers (ARBs) use, higher hemoglobin, leukocyte, platelet, ALT, fasting plasma plasma glucose, HDL-C, and albumin were each independently associated with higher levels of serum AG. In contrast, male sex, a history of prior MI, higher eGFR, LDL-C, and LVEF were each independently associated with lower levels of serum AG.
Supplemental Table 1. Independent determinants of baseline AG levels.
| Difference in AG | 95% CI | P value | |
| Demographic characteristics | |||
| Male (vs. female) | −0.325 | (−0.465 to −0.185) | < 0.001 |
| Medical history and coronary risk factors | |||
| Prior MI (vs. none) | −0.180 | (−0.342 to −0.018) | 0.029 |
| Diagnosis | |||
| UAP (vs. SCAD) | 0.047 | (−0.099 to 0.192) | 0.529 |
| AMI (vs. SCAD) | 0.431 | (0.259 to 0.603) | < 0.001 |
| Medication use | |||
| ARBs (vs. none) | 0.323 | (0.172 to 0.474) | < 0.001 |
| Laboratory variables | |||
| Hemoglobin (1 g/L difference) | 0.005 | (0.004 to 0.006) | < 0.001 |
| Leukocyte (1 × 109/L difference) | 0.188 | (0.164 to 0.213) | < 0.001 |
| Platelet (1 × 109/L difference) | 0.004 | (0.003 to 0.004) | < 0.001 |
| eGFR (10 ml/min per 1.73 m2 difference) | −0.015 | (−0.027 to −0.004) | 0.010 |
| ALT (10 U/L difference) | 0.066 | (0.054 to 0.078) | < 0.001 |
| Glucose (1 mmol/L difference) | 0.055 | (0.032 to 0.078) | < 0.001 |
| LDL-C (1 mmol/L difference) | −0.063 | (−0.125 to 0.000) | 0.048 |
| HDL-C (1 mmol/L difference) | 1.114 | (0.851–1.377) | < 0.001 |
| ALB (1 g/L difference) | 0.056 | (0.044–0.069) | < 0.001 |
| Cardiac functional characteristics | |||
| LVEF (10% difference) | −0.071 | (−0.101 to −0.042) | < 0.001 |
AG: anion gap; ALB: albumin; ALT, alanine aminotransferase; ARBs: angiotensin receptor blockers; CI: confidence interval; eGFR: evaluated glomerular filtration rate; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; MI: myocardial infarction; SCAD: stable coronary atherosclerotic disease; UAP: unstable angina pectoris.
3.3. Association of AG with clinical diagnosis of CAD
The risk of having higher levels of clinical diagnosis increased by 5.7%, 5.5%, and 1.8% for each millimole-per-liter increment in the serum AG in unadjusted, age/sex adjusted, and fully adjusted models, respectively (Table 2). In unadjusted ordinal Logistic models comparing the 4th versus 1st quartile, those patients with AG ≥ 15.92 mmol/L had a 1.595-fold increased risk of higher levels of clinical diagnosis (P < 0.001). After adjustment for all confounders, this association was attenuated in magnitude but remained statistically significant (OR: 1.170, 95% CI: 1.066 to 1.283, P = 0.001).
Table 2. Association of AG with an increase in the levels of clinical diagnosis.
| AG |
||||
| OR (95% CI) | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.067 (0.987–1.153) | 0.101 | ||
| Quartile 3 | 1.094 (1.013–1.183) | 0.022 | ||
| Quartile 4 | 1.595 (1.476–1.723) | < 0.001 | ||
| Continuous | 1.057 (1.048–1.065) | < 0.001 | ||
| Model 2 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.064 (0.985–1.149) | 0.117 | ||
| Quartile 3 | 1.089 (1.007–1.177) | 0.032 | ||
| Quartile 4 | 1.570 (1.452–1.697) | < 0.001 | ||
| Continuous | 1.055 (1.047–1.063) | < 0.001 | ||
| Model 3 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.064 (0.984–1.149) | 0.118 | ||
| Quartile 3 | 1.084 (1.003–1.172) | 0.041 | ||
| Quartile 4 | 1.553 (1.436–1.680) | < 0.001 | ||
| Continuous | 1.053 (1.046–1.062) | < 0.001 | ||
| Model 4 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.035 (0.945–1.132) | 0.464 | ||
| Quartile 3 | 1.050 (0.960–1.148) | 0.286 | ||
| Quartile 4 | 1.170 (1.066–1.283) | 0.001 | ||
| Continuous | 1.018 (1.009–1.027) | < 0.001 | ||
Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, prior MI, prior stroke, aspirin use, thienopyridines use, beta-blockers use, ACEIs use, ARBs use, statins use, hemoglobin, leukocyte, platelet, eGFR, ALT, glucose, LDL-C, HDL-C, anion gap, phosphorus, potassium, calcium, LVEF, and number of significantly diseased vessels. ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ALT: alanine aminotransferase; ARBs: angiotensin receptor blockers; BMI: body mass index; CI: confidence interval; eGFR: evaluated glomerular filtration rate; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; MI: myocardial infarction.
3.4. Association of AG with severity of coronary artery stenosis
Either in unadjusted or fully adjusted models, the findings did not support a role for the serum AG levels as an independent biomarker for severity of coronary artery stenosis (Table 3).
Table 3. Association of AG with an increase in the number of significantly diseased vessels.
| AG |
||||
| OR (95% CI) | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | 0.229 | ||
| Quartile 2 | 1.019 (0.946–1.100) | 0.617 | ||
| Quartile 3 | 0.986 (0.914–1.064) | 0.714 | ||
| Quartile 4 | 0.963 (0.892–1.039) | 0.327 | ||
| Continuous | 0.995 (0.988–1.003) | 0.194 | ||
| Model 2 | ||||
| Quartile 1 | Referent | 0.948 | ||
| Quartile 2 | 1.042 (0.966–1.125) | 0.286 | ||
| Quartile 3 | 1.022 (0.946–1.103) | 0.581 | ||
| Quartile 4 | 1.009 (0.935–1.090) | 0.811 | ||
| Continuous | 1.000 (0.992–1.007) | 0.939 | ||
| Model 3 | ||||
| Quartile 1 | Referent | 0.938 | ||
| Quartile 2 | 1.042 (0.966–1.125) | 0.287 | ||
| Quartile 3 | 1.019 (0.945–1.101) | 0.622 | ||
| Quartile 4 | 1.004 (0.930–1.084) | 0.916 | ||
| Continuous | 0.999 (0.991–1.007) | 0.802 | ||
| Model 4 | ||||
| Quartile 1 | Referent | 0.548 | ||
| Quartile 2 | 1.042 (0.965–1.124) | 0.297 | ||
| Quartile 3 | 1.010 (0.936–1.091) | 0.790 | ||
| Quartile 4 | 0.985 (0.912–1.064) | 0.705 | ||
| Continuous | 0.997 (0.990–1.005) | 0.494 | ||
Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, and eGFR. AG: anion gap; BMI: body mass index; CI: confidence interval; eGFR: evaluated glomerular filtration rate.
3.5. Association of AG with cardiac function grades
The risk of having higher grades of cardiac function increased by 4.3%, 4.6%, and 2.1% for each millimole-per-liter increment in the serum AG in unadjusted, age/sex adjusted, and fully adjusted models, respectively (Table 4). In unadjusted ordinal Logistic models comparing the 4th versus 1st quartile, those patients with AG ≥ 15.92 mmol/L had a 1.355-fold increased risk of higher grades of cardiac function (P < 0.001). After adjustment for all confounders, this association remained statistically significant (OR: 1.158, 95% CI: 1.001 to 1.340, P = 0.049).
Table 4. Association of AG with cardiac function grades.
| AG |
||||
| OR (95% CI) | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 0.991 (0.855–1.149) | 0.902 | ||
| Quartile 3 | 1.150 (0.996–1.328) | 0.057 | ||
| Quartile 4 | 1.355 (1.179–1.559) | < 0.001 | ||
| Continuous | 1.043 (1.029–1.058) | < 0.001 | ||
| Model 2 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.010 (0.871–1.171) | 0.897 | ||
| Quartile 3 | 1.183 (1.024–1.368) | 0.022 | ||
| Quartile 4 | 1.394 (1.212–1.605) | < 0.001 | ||
| Continuous | 1.046 (1.031–1.061) | < 0.001 | ||
| Model 3 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.008 (0.869–1.169) | 0.917 | ||
| Quartile 3 | 1.172 (1.014–1.355) | 0.031 | ||
| Quartile 4 | 1.365 (1.186–1.571) | < 0.001 | ||
| Continuous | 1.043 (1.029–1.058) | < 0.001 | ||
| Model 4 | ||||
| Quartile 1 | Referent | 0.015 | ||
| Quartile 2 | 0.983 (0.844–1.146) | 0.828 | ||
| Quartile 3 | 1.125 (0.969–1.306) | 0.123 | ||
| Quartile 4 | 1.158 (1.001–1.340) | 0.049 | ||
| Continuous | 1.021 (1.007–1.035) | 0.004 | ||
Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, prior MI, prior stroke, diagnosis, aspirin use, thienopyridines use, beta-blockers use, ACEIs use, ARBs use, statins use, eGFR, anion gap, phosphorus, potassium, calcium, and numbers of diseased vessels. ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ARBs: angiotensin receptor blockers; BMI: body mass index; CI: confidence interval; eGFR: evaluated glomerular filtration rate.
3.6. Association of AG with all-cause mortality
During the 30 days follow-up time, 73 (0.40%) patients died. Kaplan-Meier survival curve demonstrated significantly lower cumulative survivals for patients with higher AG quartiles: 7 deaths (0.15%) in the 1st quartile, 11 deaths (0.24%) in the 2nd quartile, 19 deaths (0.42%) in the 3rd quartile and 36 deaths (0.80%) in the 4th quartile (Log rank P < 0.001, Figure 2). In unadjusted Cox models comparing the 4th vs. 1st quartile, those patients with AG ≥ 15.92 mmol/L had a 5.171-fold increased risk of all-cause death (P < 0.001, Table 5). After adjustment for age, sex, eGFR, risk factors and comorbidities, clinical diagnosis, LVEF, Syntax score, and all other confounders, there remained a significant association of higher AG with all-cause mortality [hazard ratio (HR) for the 4th vs. 1st quartile: 3.318, 95% CI: 1.342 to 8.205]. When examined as continuous variables, each millimole-per-liter higher AG was associated with increased risk of all-cause mortality in unadjusted (HR: 1.244, 95% CI: 1.203 to 1.286, P < 0.001), age/sex adjusted (HR: 1.135, 95% CI: 1.098 to 1.174, P < 0.001), and fully adjusted models (HR: 1.069, 95% CI: 1.020 to 1.121, P = 0.005).
Figure 2. Kaplan–Meier survival curve among quartile groups of the serum AG.
AG: anion gap.
Table 5. Association of AG with all-cause deaths.
| AG |
||||
| HR (95% CI) | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.564 (0.746–4.687) | 0.182 | ||
| Quartile 3 | 2.732 (1.137–6.432) | 0.024 | ||
| Quartile 4 | 5.171 (2.172–11.053) | < 0.001 | ||
| Continuous | 1.244 (1.203–1.286) | < 0.001 | ||
| Model 2 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.996 (0.796–5.006) | 0.141 | ||
| Quartile 3 | 2.794 (1.174–6.647) | 0.020 | ||
| Quartile 4 | 5.407 (2.395–12.208) | < 0.001 | ||
| Continuous | 1.135 (1.098–1.174) | < 0.001 | ||
| Model 3 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.897 (0.756–4.759) | 0.172 | ||
| Quartile 3 | 2.776 (1.167–6.603) | 0.021 | ||
| Quartile 4 | 4.861 (2.150–10.993) | < 0.001 | ||
| Continuous | 1.128 (1.089–1.168) | < 0.001 | ||
| Model 4 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.660 (0.602–4.579) | 0.328 | ||
| Quartile 3 | 2.932 (1.163–7.392) | 0.023 | ||
| Quartile 4 | 3.318 (1.342–8.205) | 0.009 | ||
| Continuous | 1.069 (1.020–1.121) | 0.005 | ||
Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, prior MI, prior stroke, diagnosis, aspirin use, thienopyridines use, beta-blockers use, ACEIs use, ARBs use, statins use, hemoglobin, leukocyte, platelet, eGFR, ALT, glucose, LDL-C, HDL-C, ALB, anion gap, phosphorus, potassium, calcium, LVEF, and Syntax score. ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ARBs: angiotensin receptor blockers; ALB: albumin; ALT: alanine aminotransferase; BMI: body mass index; CI: confidence interval; eGFR: evaluated glomerular filtration rate; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; MI: myocardial infarction.
3.7. Sensitivity analyses
After excluding participants with eGFR < 60 mL/min per 1.73m2, increased AG was still associated with higher levels of clinical diagnosis (Supplemental Table 2), cardiac function grades (Supplemental Table 3), and all-cause mortality (Supplemental Table 4). Furthermore, a similar, non-significant association with severity of coronary artery stenosis remained (Supplemental Table 5).
Supplemental Table 2. Sensitivity analysis of association of AG with an increase in the clinical diagnosis.
| AG |
||||
| OR (95% CI) | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.053 (0.972–1.141) | 0.206 | ||
| Quartile 3 | 1.081 (0.997–1.172) | 0.058 | ||
| Quartile 4 | 1.531 (1.411–1.660) | < 0.001 | ||
| Continuous | 1.051 (1.043–1.060) | < 0.001 | ||
| Model 2 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.048 (0.967–1.135) | 0.256 | ||
| Quartile 3 | 1.070 (0.986–1.161) | 0.103 | ||
| Quartile 4 | 1.492 (1.374–1.619) | < 0.001 | ||
| Continuous | 1.049 (1.040–1.058) | < 0.001 | ||
| Model 3 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.047 (0.967–1.135) | 0.259 | ||
| Quartile 3 | 1.069 (0.986–1.160) | 0.107 | ||
| Quartile 4 | 1.489 (1.372–1.616) | < 0.001 | ||
| Continuous | 1.048 (1.040–1.057) | < 0.001 | ||
| Model 4 | ||||
| Quartile 1 | Referent | 0.003 | ||
| Quartile 2 | 1.019 (0.928–1.121) | 0.689 | ||
| Quartile 3 | 1.029 (0.937–1.130) | 0.547 | ||
| Quartile 4 | 1.126 (1.021–1.241) | 0.017 | ||
| Continuous | 1.014 (1.004–1.024) | 0.005 | ||
Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, prior MI, prior stroke, aspirin use, thienopyridines use, beta-blockers use, ACEIs use, ARBs use, statins use, hemoglobin, leukocyte, platelet, eGFR, ALT, glucose, LDL-C, HDL-C, anion gap, phosphorus, potassium, calcium, LVEF, and number of significantly diseased vessels. ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ALT: alanine aminotransferase; ARBs: angiotensin receptor blockers; BMI: body mass index; eGFR: evaluated glomerular filtration rate; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; MI: myocardial infarction.
Supplemental Table 3. Sensitivity analysis of association of AG with cardiac function grades.
| Anion gap |
||||
| OR (95% CI)* | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 0.972 (0.831–1.138) | 0.724 | ||
| Quartile 3 | 1.137 (0.975–1.324) | 0.102 | ||
| Quartile 4 | 1.340 (1.155–1.557) | < 0.001 | ||
| Continuous | 1.042 (1.026–1.058) | < 0.001 | ||
| Model 2 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 0.992 (0.847–1.161) | 0.916 | ||
| Quartile 3 | 1.169 (1.003–1.363) | 0.046 | ||
| Quartile 4 | 1.376 (1.184–1.598) | < 0.001 | ||
| Continuous | 1.045 (1.029–1.060) | < 0.001 | ||
| Model 3 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 0.991 (0.846–1.160) | 0.910 | ||
| Quartile 3 | 1.168 (1.001–1.361) | 0.048 | ||
| Quartile 4 | 1.372 (1.181–1.594) | < 0.001 | ||
| Continuous | 1.044 (1.028–1.060) | < 0.001 | ||
| Model 4 | ||||
| Quartile 1 | Referent | 0.008 | ||
| Quartile 2 | 0.978 (0.832–1.151) | 0.793 | ||
| Quartile 3 | 1.125 (0.960–1.319) | 0.146 | ||
| Quartile 4 | 1.191 (1.019–1.392) | 0.027 | ||
| Continuous | 1.025 (1.010–1.041) | 0.001 | ||
*OR in patients with eGFR ≥ 60 mL/min per 1.73m2 (n = 16,559, 550 patients with LVEF < 40%, 1315 patients with 40% ≤ LVEF < 50%, and 14,694 patients with LVEF ≥ 50%). Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, prior MI, prior stroke, diagnosis, aspirin use, thienopyridines use, beta-blockers use, ACEIs use, ARBs use, statins use, eGFR, anion gap, phosphorus, potassium, calcium, and numbers of diseased vessels. ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ARBs: angiotensin receptor blockers; BMI: body mass index; eGFR: evaluated glomerular filtration rate; MI: myocardial infarction.
Supplemental Table 4. Sensitivity analysis of association of AG with risk of all-cause death.
| AG |
||||
| HR* (95% CI) | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.687 (0.613–4.640) | 0.311 | ||
| Quartile 3 | 2.312 (0.888–6.016) | 0.086 | ||
| Quartile 4 | 3.465 (1.392–8.629) | 0.008 | ||
| Continuous | 1.121 (1.056–1.1.191) | < 0.001 | ||
| Model 2 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.724 (0.626–4.749) | 0.292 | ||
| Quartile 3 | 2.331 (0.896–6.068) | 0.083 | ||
| Quartile 4 | 3.685 (1.477–9.195) | 0.005 | ||
| Continuous | 1.120 (1.059–1.185) | < 0.001 | ||
| Model 3 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.699 (0.617–4.682) | 0.305 | ||
| Quartile 3 | 2.318 (0.891–6.034) | 0.085 | ||
| Quartile 4 | 3.589 (1.438–8.960) | 0.006 | ||
| Continuous | 1.121 (1.058–1.189) | < 0.001 | ||
| Model 4 | ||||
| Quartile 1 | Referent | 0.003 | ||
| Quartile 2 | 1.591 (0.519–4.873) | 0.416 | ||
| Quartile 3 | 2.728 (0.982–7.584) | 0.054 | ||
| Quartile 4 | 2.967 (1.080–8.149) | 0.035 | ||
| Continuous | 1.084 (1.012–1.162) | 0.022 | ||
*HR in patients with eGFR ≥ 60 mL/min per 1.73m2 (n = 16559, 50 deaths). Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, prior MI, prior stroke, diagnosis, aspirin use, thienopyridines use, beta-blockers use, ACEIs use, ARBs use, statins use, hemoglobin, leukocyte, platelet, eGFR, ALT, fasting plasma glucose, LDL-C, HDL-C, ALB, potassium ion, anion gap, phosphorus, potassium, calcium, LVEF, and Syntax score. ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ALT: alanine aminotransferase; ALB: albumin; ARBs: angiotensin receptor blockers; BMI: body mass index; eGFR: evaluated glomerular filtration rate; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; MI: myocardial infarction.
Supplemental Table 5. Sensitivity analysis of association of AG with an increase in the number of significantly diseased vessels.
| AG |
||||||||
| OR (95% CI)* | P value | P value for trend | ||||||
| Model 1 | ||||||||
| Quartile 1 | Referent | 0.091 | ||||||
| Quartile 2 | 0.988 (0.913–1.069) | 0.766 | ||||||
| Quartile 3 | 0.967 (0.892–1.046) | 0.399 | ||||||
| Quartile 4 | 0.937 (0.865–1.015) | 0.109 | ||||||
| Continuous | 0.992 (0.985–1.000) | 0.065 | ||||||
| Model 2 | ||||||||
| Quartile 1 | Referent | 0.728 | ||||||
| Quartile 2 | 1.009 (0.932–1.092) | 0.822 | ||||||
| Quartile 3 | 1.004 (0.927–1.088) | 0.927 | ||||||
| Quartile 4 | 0.987 (0.910–1.069) | 0.743 | ||||||
| Continuous | 0.998 (0.990–1.006) | 0.667 | ||||||
| Model 3 | ||||||||
| Quartile 1 | Referent | 0.713 | ||||||
| Quartile 2 | 1.009 (0.931–1.092) | 0.829 | ||||||
| Quartile 3 | 1.003 (0.927–1.087) | 0.933 | ||||||
| Quartile 4 | 0.986 (0.909–1.068) | 0.727 | ||||||
| Continuous | 0.998 (0.990–1.006) | 0.649 | ||||||
| Model 4 | ||||||||
| Quartile 1 | Referent | 0.371 | ||||||
| Quartile 2 | 1.005 (0.928–1.089) | 0.900 | ||||||
| Quartile 3 | 0.993 (0.917–1.076) | 0.865 | ||||||
| Quartile 4 | 0.966 (0.890–1.047) | 0.395 | ||||||
| Continuous | 0.996 (0.988–1.004) | 0.361 | ||||||
*OR in patients with eGFR ≥ 60 mL/min per 1.73m2 (n = 16559, 6878 patients with 1-vessel CAD, 5069 patients with 2-vessel CAD, and 4612 patients with 3-vessel and/or LM CAD). Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, and eGFR. AG: anion gap; BMI: body mass index; CAD: coronary artery disease; eGFR: evaluated glomerular filtration rate; LM: left main; OR: odds ratio.
4. Discussion
Gamble is one of the first individuals to emphasize the importance of charge balance in the ionic environment of the blood and other body fluids, which means the sum of serum cations must equal that of serum anions.[2] Because normally the total unmeasured anions exceed the total unmeasured cations, there is an AG. By contrast to a low serum AG, an elevated serum AG is a common occurrence. Examination of 6868 sets of serum electrolyte among miscellaneous hospitalized patients revealed an elevated serum AG in 37.6%.[18] There have been no population-based studies of the incidence of elevated serum AG in CAD. Our findings reveal a relatively high percentage (24.9%) of increased serum AG (greater than 16 mmol/L) in CAD.
Elevation in the serum AG generally is caused by overproduction of organic acid anions and/or the concomitant and proportionate reduction in the excretion of anions, while changes in the equivalents of total proteins, phosphorus, potassium, and calcium are unusual causes.[2] It has been reported that lactate and ketoanions accounted for 62% of the increments in AG.[19] In animals and patients with heart failure (HF) or acute coronary syndrome (ACS), marked increments in metabolic rate, sympathetic nervous system activation, accelerated glycolysis and a modified bioenergetic supply associated with increased lactate levels were described.[20] Lommi, et al.[21] found that patients with HF had elevated blood ketone bodies compared with control subjects. Furthermore, blood ketone bodies were related to LVEF and LVEF was an independent predictor of ketonemia. Most recently, Bedi, et al.[22] observed an increased abundance of ketogenic β-hydroxybutyryl-CoA in HF. Similar results were also found in patients with ACS.[23]–[25] All these data strongly suggest that organic acid anions accumulation might be one of the potential mechanisms, by which higher AG is associated with more severe clinical types of CAD and worse cardiac function. Another potential mechanism lies in the impairment of glomerular filtration accounting for the retention of non-chloride anions, as acid retention has been demonstrated in subjects with only mild reductions in eGFR.[17]
In many cases, the identity of anions that contribute to the elevated AG can be determined, especially when the serum AG > 30 mmol/L.[26] A lesser increase in the serum AG (≤ 24 mmol/L) can be present without an identifiable, accumulating acid in > 30% of cases.[26] In the current study, AG exceeding 24 mmol/L was rare (0.6%). Therefore, it is unclear to what degree these prior results can be extrapolated to our findings.
It has been shown that increased serum AG may be of prognostic significance as higher levels of AG were associated with hypertension,[3] insulin resistance,[4] and low cardiorespiratory fitness.[5] In a large study that included 31590 subjects who underwent a health screening, a trend for increased mortality risk with higher levels of serum AG was present.[6] In another community-based cohort study, higher levels of serum AG was associated with an increased risk of all-cause and cardiac deaths.[7] Our results indicate that the serum AG is strongly associated with all-cause mortality in CAD. Although several observational studies suggested that elevated levels of lactate and ketone bodies were associated with worse outcomes, none of the previous studies provided direct evidence.[27]–[29]
4.1. Limitations
First of all, in light of its observational nature, we cannot conclude the increase in the serum AG is a cause or consequence of more severe clinical types of CAD and worse cardiac function. Secondly, SCAD and UAP diagnoses might be broadened excessively because of some patients with vague symptoms, atypical electrocardiograms, or incomplete myocardial injury markers tests. Thirdly, although we adjusted for eGFR in the multivariate analyses and performed sensitivity analyses excluding participants with AKD, we could not completely rule out the impact of mild renal dysfunction. Additionally, we did not have measurements of lactate, β-hydroxybutyrate and acetoacetate. Thus, we speculate without direct evidence the potential mechanisms by which higher AG is associated with the severity and outcome of CAD.
4.2. Conclusions
In this large population-based study, our findings reveal a high percentage of increased serum AG in CAD. And higher AG is associated with more severe clinical types of CAD and worse cardiac function. Furthermore, the increased serum AG is an independent, significant, and strong predictor of all-cause mortality.
Acknowledgments
This work was supported by the Beijing Nova Program (No. Z121107002512053), the Beijing Health System High Level Health Technology Talent Cultivation Plan (No. 2013-3-013), the Beijing Outstanding Talent Training Program (No. 2014000021223ZK32), and the National Natural Science Foundation of China (No. 81100143) to S.W.Y., and the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (No. ZYLX201303) to Y.J.Z.
References
- 1.Mozaffarian D, Benjamin EJ, Go AS, et al. Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation. 2016;133:e38–e60. doi: 10.1161/CIR.0000000000000350. [DOI] [PubMed] [Google Scholar]
- 2.Kraut JA, Madias NE. Serum anion gap: its uses and limitations in clinical medicine. Clin J Am Soc Nephrol. 2007;2:162–174. doi: 10.2215/CJN.03020906. [DOI] [PubMed] [Google Scholar]
- 3.Taylor EN, Forman JP, Farwell WR. Serum anion gap and blood pressure in the national health and nutrition examination survey. Hypertension. 2007;50:320–324. doi: 10.1161/HYPERTENSIONAHA.107.092643. [DOI] [PubMed] [Google Scholar]
- 4.Farwell WR, Taylor EN. Serum bicarbonate, anion gap and insulin resistance in the National Health and Nutrition Examination Survey. Diabet Med. 2008;25:798–804. doi: 10.1111/j.1464-5491.2008.02471.x. [DOI] [PubMed] [Google Scholar]
- 5.Abramowitz MK, Hostetter TH, Melamed ML. Lower serum bicarbonate and a higher anion gap are associated with lower cardiorespiratory fitness in young adults. Kidney int. 2012;81:1033–1042. doi: 10.1038/ki.2011.479. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Park M, Jung SJ, Yoon S, et al. Association between the markers of metabolic acid load and higher all-cause and cardiovascular mortality in a general population with preserved renal function. Hypertension res. 2015;38:433–438. doi: 10.1038/hr.2015.23. [DOI] [PubMed] [Google Scholar]
- 7.Ahn SY, Ryu J, Baek SH, et al. Serum anion gap is predictive of mortality in an elderly population. Exp Gerontol. 2014;50:122–127. doi: 10.1016/j.exger.2013.12.002. [DOI] [PubMed] [Google Scholar]
- 8.Mehta AN, Emmett JB, Emmett M. GOLD MARK: an anion gap mnemonic for the 21st century. Lancet. 2008;372:892. doi: 10.1016/S0140-6736(08)61398-7. [DOI] [PubMed] [Google Scholar]
- 9.Valente MA, Hillege HL, Navis G, et al. The chronic kidney disease epidemiology collaboration equation outperforms the modification of diet in renal disease equation for estimating glomerular filtration rate in chronic systolic heart failure. Eur J Heart Fail. 2014;16:86–94. doi: 10.1093/eurjhf/hft128. [DOI] [PubMed] [Google Scholar]
- 10.Montalescot G, Sechtem U, Achenbach S, et al. 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J. 2013;34:2949–3003. doi: 10.1093/eurheartj/eht296. [DOI] [PubMed] [Google Scholar]
- 11.Amsterdam EA, Wenger NK, Brindis RG, et al. 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation Acute Coronary Syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;64:e139–e228. doi: 10.1016/j.jacc.2014.09.017. [DOI] [PubMed] [Google Scholar]
- 12.Thygesen K, Alpert JS, Jaffe AS, et al. Third universal definition of myocardial infarction. Eur Heart J. 2012;33:2551–267. [Google Scholar]
- 13.Kim M, Park JH, Lee JH, et al. Comparison of long-term clinical outcomes of percutaneous coronary intervention in vasospastic angina patients associated with significant coronary artery stenosis. Int J of cardiol. 2016;218:75–78. doi: 10.1016/j.ijcard.2016.05.056. [DOI] [PubMed] [Google Scholar]
- 14.Mordini FE, Haddad T, Hsu LY, et al. Diagnostic accuracy of stress perfusion CMR in comparison with quantitative coronary angiography: fully quantitative, semiquantitative, and qualitative assessment. JACC Cardiovasc Imaging. 2014;7:14–22. doi: 10.1016/j.jcmg.2013.08.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Windecker S, Kolh P, Alfonso F, et al. 2014 ESC/EACTS Guidelines on myocardial revascularization: the Task Force on Myocardial Revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS)Developed with the special contribution of the European Association of Percutaneous Cardiovascular Interventions (EAPCI) Eur Heart J. 2014;35:2541–2619. doi: 10.1093/eurheartj/ehu278. [DOI] [PubMed] [Google Scholar]
- 16.Ponikowski P, Voors AA, Anker SD, et al. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2016;18:891–975. doi: 10.1002/ejhf.592. [DOI] [PubMed] [Google Scholar]
- 17.Abramowitz MK, Hostetter TH, Melamed ML. The serum anion gap is altered in early kidney disease and associates with mortality. Kidney Int. 2012;82:701–709. doi: 10.1038/ki.2012.196. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Lolekha PH, Vanavanan S, Lolekha S. Update on value of the anion gap in clinical diagnosis and laboratory evaluation. Clin Chim Acta. 2001;307:33–36. doi: 10.1016/s0009-8981(01)00459-4. [DOI] [PubMed] [Google Scholar]
- 19.Gabow PA, Kaehny WD, Fennessey PV, et al. Diagnostic importance of an increased serum anion gap. N Engl J Med. 1980;303:854–858. doi: 10.1056/NEJM198010093031505. [DOI] [PubMed] [Google Scholar]
- 20.Lazzeri C, Valente S, Chiostri M, et al. Clinical significance of lactate in acute cardiac patients. World J Cardiol. 2015;7:483–489. doi: 10.4330/wjc.v7.i8.483. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Lommi J, Kupari M, Koskinen P, et al. Blood ketone bodies in congestive heart failure. J Am Coll Cardiol. 1996;28:665–672. doi: 10.1016/0735-1097(96)00214-8. [DOI] [PubMed] [Google Scholar]
- 22.Bedi KC, Snyder NW, Brandimarto J, et al. Evidence for intramyocardial disruption of lipid metabolism and increased myocardial ketone utilization in advanced human heart failure. Circulation. 2016;133:706–716. doi: 10.1161/CIRCULATIONAHA.115.017545. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jiang M, Kang L, Wang Y, et al. A metabonomic study of cardioprotection of ginsenosides, schizandrin, and ophiopogonin D against acute myocardial infarction in rats. BMC Complement Altern Med. 2014;14:350. doi: 10.1186/1472-6882-14-350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Brunner MP, Shah SH, Craig DM, et al. Effect of heparin administration on metabolomic profiles in samples obtained during cardiac catheterization. Circ Cardiovasc Genet. 2011;4:695–700. doi: 10.1161/CIRCGENETICS.111.960575. [DOI] [PubMed] [Google Scholar]
- 25.Al-Obaidi MK, Stubbs PJ, Collinson P, et al. Elevated homocysteine levels are associated with increased ischemic myocardial injury in acute coronary syndromes. J Am Coll Cardiol. 2000;36:1217–1222. doi: 10.1016/s0735-1097(00)00820-2. [DOI] [PubMed] [Google Scholar]
- 26.Gabow PA. Disorders associated with an altered anion gap. Kidney Int. 1985;27:472–483. doi: 10.1038/ki.1985.34. [DOI] [PubMed] [Google Scholar]
- 27.Kraut JA, Madias NE. Lactic acidosis: current treatments and future directions. Am J Kidney Dis. 2016;68:473–482. doi: 10.1053/j.ajkd.2016.04.020. [DOI] [PubMed] [Google Scholar]
- 28.Harjola VP, Lassus J, Sionis A, et al. Clinical picture and risk prediction of short-term mortality in cardiogenic shock. Eur J Heart Fail. 2015;17:501–509. doi: 10.1002/ejhf.260. [DOI] [PubMed] [Google Scholar]
- 29.Stanley WC. Rationale for a metabolic approach in diabetic coronary patients. Coron Artery Dis. 2005;16(Suppl 1):S11–S15. doi: 10.1097/00019501-200511001-00003. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Table 1. Independent determinants of baseline AG levels.
| Difference in AG | 95% CI | P value | |
| Demographic characteristics | |||
| Male (vs. female) | −0.325 | (−0.465 to −0.185) | < 0.001 |
| Medical history and coronary risk factors | |||
| Prior MI (vs. none) | −0.180 | (−0.342 to −0.018) | 0.029 |
| Diagnosis | |||
| UAP (vs. SCAD) | 0.047 | (−0.099 to 0.192) | 0.529 |
| AMI (vs. SCAD) | 0.431 | (0.259 to 0.603) | < 0.001 |
| Medication use | |||
| ARBs (vs. none) | 0.323 | (0.172 to 0.474) | < 0.001 |
| Laboratory variables | |||
| Hemoglobin (1 g/L difference) | 0.005 | (0.004 to 0.006) | < 0.001 |
| Leukocyte (1 × 109/L difference) | 0.188 | (0.164 to 0.213) | < 0.001 |
| Platelet (1 × 109/L difference) | 0.004 | (0.003 to 0.004) | < 0.001 |
| eGFR (10 ml/min per 1.73 m2 difference) | −0.015 | (−0.027 to −0.004) | 0.010 |
| ALT (10 U/L difference) | 0.066 | (0.054 to 0.078) | < 0.001 |
| Glucose (1 mmol/L difference) | 0.055 | (0.032 to 0.078) | < 0.001 |
| LDL-C (1 mmol/L difference) | −0.063 | (−0.125 to 0.000) | 0.048 |
| HDL-C (1 mmol/L difference) | 1.114 | (0.851–1.377) | < 0.001 |
| ALB (1 g/L difference) | 0.056 | (0.044–0.069) | < 0.001 |
| Cardiac functional characteristics | |||
| LVEF (10% difference) | −0.071 | (−0.101 to −0.042) | < 0.001 |
AG: anion gap; ALB: albumin; ALT, alanine aminotransferase; ARBs: angiotensin receptor blockers; CI: confidence interval; eGFR: evaluated glomerular filtration rate; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; MI: myocardial infarction; SCAD: stable coronary atherosclerotic disease; UAP: unstable angina pectoris.
Supplemental Table 2. Sensitivity analysis of association of AG with an increase in the clinical diagnosis.
| AG |
||||
| OR (95% CI) | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.053 (0.972–1.141) | 0.206 | ||
| Quartile 3 | 1.081 (0.997–1.172) | 0.058 | ||
| Quartile 4 | 1.531 (1.411–1.660) | < 0.001 | ||
| Continuous | 1.051 (1.043–1.060) | < 0.001 | ||
| Model 2 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.048 (0.967–1.135) | 0.256 | ||
| Quartile 3 | 1.070 (0.986–1.161) | 0.103 | ||
| Quartile 4 | 1.492 (1.374–1.619) | < 0.001 | ||
| Continuous | 1.049 (1.040–1.058) | < 0.001 | ||
| Model 3 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.047 (0.967–1.135) | 0.259 | ||
| Quartile 3 | 1.069 (0.986–1.160) | 0.107 | ||
| Quartile 4 | 1.489 (1.372–1.616) | < 0.001 | ||
| Continuous | 1.048 (1.040–1.057) | < 0.001 | ||
| Model 4 | ||||
| Quartile 1 | Referent | 0.003 | ||
| Quartile 2 | 1.019 (0.928–1.121) | 0.689 | ||
| Quartile 3 | 1.029 (0.937–1.130) | 0.547 | ||
| Quartile 4 | 1.126 (1.021–1.241) | 0.017 | ||
| Continuous | 1.014 (1.004–1.024) | 0.005 | ||
Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, prior MI, prior stroke, aspirin use, thienopyridines use, beta-blockers use, ACEIs use, ARBs use, statins use, hemoglobin, leukocyte, platelet, eGFR, ALT, glucose, LDL-C, HDL-C, anion gap, phosphorus, potassium, calcium, LVEF, and number of significantly diseased vessels. ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ALT: alanine aminotransferase; ARBs: angiotensin receptor blockers; BMI: body mass index; eGFR: evaluated glomerular filtration rate; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; MI: myocardial infarction.
Supplemental Table 3. Sensitivity analysis of association of AG with cardiac function grades.
| Anion gap |
||||
| OR (95% CI)* | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 0.972 (0.831–1.138) | 0.724 | ||
| Quartile 3 | 1.137 (0.975–1.324) | 0.102 | ||
| Quartile 4 | 1.340 (1.155–1.557) | < 0.001 | ||
| Continuous | 1.042 (1.026–1.058) | < 0.001 | ||
| Model 2 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 0.992 (0.847–1.161) | 0.916 | ||
| Quartile 3 | 1.169 (1.003–1.363) | 0.046 | ||
| Quartile 4 | 1.376 (1.184–1.598) | < 0.001 | ||
| Continuous | 1.045 (1.029–1.060) | < 0.001 | ||
| Model 3 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 0.991 (0.846–1.160) | 0.910 | ||
| Quartile 3 | 1.168 (1.001–1.361) | 0.048 | ||
| Quartile 4 | 1.372 (1.181–1.594) | < 0.001 | ||
| Continuous | 1.044 (1.028–1.060) | < 0.001 | ||
| Model 4 | ||||
| Quartile 1 | Referent | 0.008 | ||
| Quartile 2 | 0.978 (0.832–1.151) | 0.793 | ||
| Quartile 3 | 1.125 (0.960–1.319) | 0.146 | ||
| Quartile 4 | 1.191 (1.019–1.392) | 0.027 | ||
| Continuous | 1.025 (1.010–1.041) | 0.001 | ||
*OR in patients with eGFR ≥ 60 mL/min per 1.73m2 (n = 16,559, 550 patients with LVEF < 40%, 1315 patients with 40% ≤ LVEF < 50%, and 14,694 patients with LVEF ≥ 50%). Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, prior MI, prior stroke, diagnosis, aspirin use, thienopyridines use, beta-blockers use, ACEIs use, ARBs use, statins use, eGFR, anion gap, phosphorus, potassium, calcium, and numbers of diseased vessels. ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ARBs: angiotensin receptor blockers; BMI: body mass index; eGFR: evaluated glomerular filtration rate; MI: myocardial infarction.
Supplemental Table 4. Sensitivity analysis of association of AG with risk of all-cause death.
| AG |
||||
| HR* (95% CI) | P value | P value for trend | ||
| Model 1 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.687 (0.613–4.640) | 0.311 | ||
| Quartile 3 | 2.312 (0.888–6.016) | 0.086 | ||
| Quartile 4 | 3.465 (1.392–8.629) | 0.008 | ||
| Continuous | 1.121 (1.056–1.1.191) | < 0.001 | ||
| Model 2 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.724 (0.626–4.749) | 0.292 | ||
| Quartile 3 | 2.331 (0.896–6.068) | 0.083 | ||
| Quartile 4 | 3.685 (1.477–9.195) | 0.005 | ||
| Continuous | 1.120 (1.059–1.185) | < 0.001 | ||
| Model 3 | ||||
| Quartile 1 | Referent | < 0.001 | ||
| Quartile 2 | 1.699 (0.617–4.682) | 0.305 | ||
| Quartile 3 | 2.318 (0.891–6.034) | 0.085 | ||
| Quartile 4 | 3.589 (1.438–8.960) | 0.006 | ||
| Continuous | 1.121 (1.058–1.189) | < 0.001 | ||
| Model 4 | ||||
| Quartile 1 | Referent | 0.003 | ||
| Quartile 2 | 1.591 (0.519–4.873) | 0.416 | ||
| Quartile 3 | 2.728 (0.982–7.584) | 0.054 | ||
| Quartile 4 | 2.967 (1.080–8.149) | 0.035 | ||
| Continuous | 1.084 (1.012–1.162) | 0.022 | ||
*HR in patients with eGFR ≥ 60 mL/min per 1.73m2 (n = 16559, 50 deaths). Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, prior MI, prior stroke, diagnosis, aspirin use, thienopyridines use, beta-blockers use, ACEIs use, ARBs use, statins use, hemoglobin, leukocyte, platelet, eGFR, ALT, fasting plasma glucose, LDL-C, HDL-C, ALB, potassium ion, anion gap, phosphorus, potassium, calcium, LVEF, and Syntax score. ACEIs: angiotensin-converting enzyme inhibitors; AG: anion gap; ALT: alanine aminotransferase; ALB: albumin; ARBs: angiotensin receptor blockers; BMI: body mass index; eGFR: evaluated glomerular filtration rate; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; LVEF: left ventricular ejection fraction; MI: myocardial infarction.
Supplemental Table 5. Sensitivity analysis of association of AG with an increase in the number of significantly diseased vessels.
| AG |
||||||||
| OR (95% CI)* | P value | P value for trend | ||||||
| Model 1 | ||||||||
| Quartile 1 | Referent | 0.091 | ||||||
| Quartile 2 | 0.988 (0.913–1.069) | 0.766 | ||||||
| Quartile 3 | 0.967 (0.892–1.046) | 0.399 | ||||||
| Quartile 4 | 0.937 (0.865–1.015) | 0.109 | ||||||
| Continuous | 0.992 (0.985–1.000) | 0.065 | ||||||
| Model 2 | ||||||||
| Quartile 1 | Referent | 0.728 | ||||||
| Quartile 2 | 1.009 (0.932–1.092) | 0.822 | ||||||
| Quartile 3 | 1.004 (0.927–1.088) | 0.927 | ||||||
| Quartile 4 | 0.987 (0.910–1.069) | 0.743 | ||||||
| Continuous | 0.998 (0.990–1.006) | 0.667 | ||||||
| Model 3 | ||||||||
| Quartile 1 | Referent | 0.713 | ||||||
| Quartile 2 | 1.009 (0.931–1.092) | 0.829 | ||||||
| Quartile 3 | 1.003 (0.927–1.087) | 0.933 | ||||||
| Quartile 4 | 0.986 (0.909–1.068) | 0.727 | ||||||
| Continuous | 0.998 (0.990–1.006) | 0.649 | ||||||
| Model 4 | ||||||||
| Quartile 1 | Referent | 0.371 | ||||||
| Quartile 2 | 1.005 (0.928–1.089) | 0.900 | ||||||
| Quartile 3 | 0.993 (0.917–1.076) | 0.865 | ||||||
| Quartile 4 | 0.966 (0.890–1.047) | 0.395 | ||||||
| Continuous | 0.996 (0.988–1.004) | 0.361 | ||||||
*OR in patients with eGFR ≥ 60 mL/min per 1.73m2 (n = 16559, 6878 patients with 1-vessel CAD, 5069 patients with 2-vessel CAD, and 4612 patients with 3-vessel and/or LM CAD). Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and eGFR; Model 4: adjusted for age, sex, hypertension, diabetes, hypercholesterolemia, smoking, BMI, and eGFR. AG: anion gap; BMI: body mass index; CAD: coronary artery disease; eGFR: evaluated glomerular filtration rate; LM: left main; OR: odds ratio.