3.1 Baseline characteristics of the study population
A total of 495 patients were included in the final analysis. To better explore the relationship between CCL17 and the prognosis of patients receiving coronary angiogram, the studied patients were divided into 3 groups according to CCL17 concentration: CCL17 low (CCL17≤185.960 pg/mL, N=137), CCL17 medium (185.960 pg/mL<CCL17≤268.173 pg/mL, N=136), and CCL17 high (CCL17>268.173pg/mL, N=222). The setting of cutoff concentrations was displayed in Supplementary Figure 1.
As shown in Table 1, the patients in higher CCL17 groups showed significantly worse syndrome. With the elevation of CCL17, a higher Gensini score (p=0.019) and more lesion vessels (p<0.001) were recorded, indicating that a high CCL17 level was correlated with worse atherosclerotic lesions. Meanwhile, compared to CCL17 low groups, more patients in the other two groups were diagnosed with NSTEMI and STEMI, demonstrating a more serious cardiac injury.
There was no statistical correlation between CCL17 and other traditional risk factors, such as age, blood pressure, serum cholesterol, PCI history, except hsCRP. CCL17 high group was featured with high hsCRP compared to CCL17 low group, pointing out the hyperinflammatory state of patients with high CCL17.
In summary, a high CCL17 level demonstrated a hyperinflammatory state in patients at risk for CHD, which might aggravate the lesions and induce more severe symptoms. Meanwhile, CCL17 is also a risk factor independent from most other traditional cardiovascular risk factors.
Table 1. Baseline characteristics of the study population
|
All patients
(N=495)
|
CCL17 low
(N=137)
|
CCL17 medium
(N=136)
|
CCL17 high
(N=222)
|
Statistic Value
|
P-value
|
Age, years *
|
61.38±10.37
|
61.18±10.47
|
61.82±10.44
|
61.23±10.30
|
0.169
|
0.844
|
Sex, male §
|
347 (70.1)
|
93 (67.8)
|
95 (69.9)
|
159 (71.6)
|
0.570
|
0.752
|
BMI, kg/m2 *
|
25.79±3.48
|
25.75±3.19
|
25.45±3.68
|
26.02±3.51
|
1.076
|
0.342
|
SBP, mmHg *
|
128.5±17.1
|
128.9±17.5
|
129.7±18.4
|
127.6±16.0
|
0.658
|
0.518
|
DBP, mmHg *
|
74.3±12.2
|
75.6±12.5
|
75.1±11.9
|
73.1±12.1
|
2.218
|
0.110
|
Medical History
|
|
|
|
|
|
|
Smoke §
|
382 (77.2)
|
110 (80.3)
|
106 (77.9)
|
165 (74.3)
|
1.802
|
0.406
|
Drinking §
|
154 (31.7)
|
39 (29.1)
|
39 (28.9)
|
76 (35.0)
|
2.017
|
0.365
|
Hypertension §
|
339 (68.5)
|
87 (63.5)
|
97 (71.3)
|
159 (71.6)
|
2.266
|
0.322
|
Dyslipidemia §
|
338 (68.3)
|
89 (65.0)
|
90 (66.2)
|
159 (71.6)
|
2.118
|
0.347
|
Diabetes §
|
181 (36.6)
|
48 (35.0)
|
44 (32.4)
|
89 (40.1)
|
2.368
|
0.306
|
Angiographic Data
|
|
|
|
|
|
|
Gensini score †
|
32.0 (15.0, 54.0)
|
20.0 (5.0, 43.0)
|
34.0 (18.0, 67.3)
|
33.0 (19.0, 53.0)
|
7.967
|
0.019b
|
No. of lesion vessels §
|
|
|
|
|
27.956
|
<0.001a,b
|
0
|
53 (10.7)
|
25 (18.2)
|
18 (13.3)
|
10 (4.5)
|
|
|
1
|
115 (23.2)
|
38 (27.7)
|
24 (17.6)
|
53b (23.9)
|
|
|
2
|
120 (24.2)
|
24 (17.5)
|
28 (20.6)
|
68 (30.6)
|
|
|
3
|
205 (41.4)
|
50 (36.5)
|
64 (47.1)
|
91 (40.9)
|
|
|
Diagnosis
|
|
|
|
|
21.293
|
0.006a,b
|
CHR §
|
57 (11.5)
|
25 (18.2)
|
18 (13.2)
|
14 (6.3)
|
|
|
SAP §
|
97 (19.6)
|
31 (22.6)
|
24 (17.6)
|
42 (18.9)
|
|
|
UA §
|
211 (42.6)
|
57 (41.6)
|
52 (38.2)
|
102 (45.9)
|
|
|
NSTEMI §
|
50 (10.1)
|
11 (8.0)
|
19 (14.0)
|
20 (9.0)
|
|
|
STEMI §
|
79 (16.0)
|
13 (9.5)
|
23(16.9)
|
43 (19.4)
|
|
|
Laboratory Results
|
|
|
|
|
|
|
WBC, 10^9/L *
|
6.79±1.75
|
6.61±1.72
|
6.63±1.55
|
6.99±1.87
|
2.579
|
0.077
|
PLT, 10^9/L †
|
214 (176, 255)
|
196 (155, 235)
|
220 (179. 272)
|
183 (226, 258)
|
2.157
|
0.340
|
TC, mmol/L †
|
4.04 (3.52, 4.55)
|
4.05 (3.46, 4.55)
|
3.86 (3.47, 4.79)
|
4.09 (3.52, 4.54)
|
0.220
|
0.896
|
LDLc, mmol/L *
|
2.27±0.92
|
2.36±0.90
|
2.29±0.92
|
2.20±0.93
|
1.361
|
0.257
|
HbA1c, % †
|
6.15 (5.70, 7.10)
|
6.70 (5.80, 8.00)
|
6.00 (5.65, 6.90)
|
6.00 (5.70, 7.00)
|
0.796
|
0.672
|
hsCRP, mg/L †
|
1.47 (0.68, 4.30)
|
0.94 (0.53, 2.98)
|
1.72 (0.73, 8.40)
|
2.23 (0.86, 4.92)
|
8.708
|
0.013b
|
eGFR, mL/(min*1.73m2) †
|
87.5 (70.4, 98.7)
|
84.1 (65.9, 97.3)
|
94.6 (79.3, 103)
|
85.6 (70.1, 98.7)
|
1.791
|
0.408
|
ALT, U/L †
|
25.0 (19.0, 41.0)
|
26.0 (22.5, 38.5)
|
23.0 (18.0, 43.0)
|
26.0 (19.0, 45.5)
|
1.043
|
0.594
|
Post-discharge medication
|
|
|
|
|
|
|
Statin §
|
399 (91.1)
|
103(83.1)
|
107 (92.2)
|
189 (95.5)
|
14.686
|
0.001a,b
|
ACEI/ARB §
|
266 (60.7)
|
69 (55.6)
|
74 (63.8)
|
123 (62.1)
|
1.961
|
0.375
|
β-blocker §
|
345 (78.8)
|
94 (75.8)
|
89 (76.7)
|
162 (81.8)
|
2.041
|
0.360
|
Aspirin §
|
411 (93.8)
|
114 (91.9)
|
106 (91.4)
|
191 (96.5)
|
4.350
|
0.114
|
Warfarin §
|
3 (0.7)
|
0 (0)
|
3 (2.6)
|
0 (0)
|
8.385
|
0.015
|
Operation history
|
|
|
|
|
|
|
PCI §
|
229 (46.3)
|
57 (41.6)
|
67 (49.3)
|
105 (47.3)
|
1.784
|
0.410
|
CABG §
|
25 (5.1)
|
7 (5.1)
|
5 (3.7)
|
13 (5.9)
|
0.837
|
0.658
|
Notes, * was the label for the continuous variables with normal distribution and were presented as mean±SD, analyzed via ANOVA with the F value. The other continuous variables were labeled with †, presented as median (IQR), analyzed via the Kruskal-Wallis test, with the H value. Categorical variables were labeled with §, presented as n (%), analyzed via Chi-squared test, with value,
For comparison among groups, a: p < 0.05 for equality between CCL17 low vs. medium; b: p < 0.05 for equality between CCL17 low vs. high; c: p < 0.05 for equality between CCL17 medium vs. high.
BMI: body mass index. SBP: systolic blood pressure. DBP: diastolic blood pressure. CHR: cardiovascular high-risk patients; SAP: stable angina pectoris. UA: Unstable angina. NSTEMI: non-ST segment elevation myocardial infarction. STEMI: ST-segment elevation myocardial infarction. WBC: white blood cell. PLT: platelet. TC: total cholesterol. LDLc: low-density lipoprotein cholesterol. HbA1c: glycosylated hemoglobin A1c. hsCRP: high sensitivity C-reactive protein. eGFR: estimated glomerular filtration rate. ALT: alanine aminotransferase. ACEI: angiotensin-converting enzyme inhibitor. ARB: angiotensin receptor blocker. PCI: percutaneous transluminal coronary intervention. CABG: coronary artery bypass grafting.
3.2 Clinical outcomes and Kaplan-Meier analysis based on CCL17 groups
The prognostic data of the patients were collected through annual follow-up till December 2020 and the median follow-up time is 6.72 (IQR: 6.45, 6.97) years. The incidence of MACE was compared among groups of different CCL17 levels via chi-square test and summarized in Table 2. 116 (23.4%) MACE were recorded, including 42 all-cause death (8.5%), 15 recurrent myocardial infarction (Re-MI, 3.0%), 21 angina (4.2%), 5 heart failure (1.0%), 7 ischemic cerebrovascular events (1.4%), 4 hemorrhagic cerebrovascular events (0.8%), 9 in-stent stenosis (1.8%), 29 Re-PCI (5.9%), 4 Re-CABG (0.8%).
The incidence of all MACE, all-cause death, angina, in-stent stenosis, and Re-PCI increased significantly in patients in CCL17 medium and high groups compared with those in CCL17 low group (all chi-square P < 0.05). However, the Re-MI (chi-square P = 0.079) and Re-CABG (chi-square P = 0.979) were similar among the three groups, which may be caused by the relatively limited patient number. Other events, including chronic heart failure and cerebrovascular diseases, also had no significant differences among the three groups. Therefore, for patients at risk for CHD, an elevated CCL17 may indicate a poorer prognosis and higher risk of acute cardiac syndromes in the future. It is noteworthy that there is no significant difference in MACE incidences between CCL17 medium and high groups (Table 2).
Table 2. Incidence of MACE according to CCL17 groups
|
All patients,
n (%)
|
CCL17 low,
n (%)
|
CCL17 medium,
n (%)
|
CCL17 high,
n (%)
|
P-value
|
MACE
|
116 (23.4)
|
8 (5.8)
|
33 (24.3)
|
75 (33.8)
|
<0.001 a,b
|
Death
|
42 (8.5)
|
3 (2.2)
|
16 (11.8)
|
23 (10.4)
|
0.007 a,b
|
Re-MI
|
15 (3.0)
|
2 (1.5)
|
2 (1.5)
|
11 (5.0)
|
0.079 b,c
|
Angina
|
21 (4.2)
|
0 (0)
|
3 (2.2)
|
18 (8.1)
|
<0.001a,b
|
Heart Failure
|
5 (1.0)
|
0 (0)
|
3 (2.2)
|
2 (0.9)
|
0.186
|
Ischemic Cerebrovascular event
|
7 (1.4)
|
1 (0.7)
|
1 (0.7)
|
5 (2.3)
|
0.363
|
Hemorrhagic Cerebrovascular event
|
4 (0.8)
|
1 (0.7)
|
1 (0.7)
|
2 (0.9)
|
0.979
|
In-stent stenosis
|
9 (1.8)
|
0 (0)
|
1 (0.7)
|
8 (3.6)
|
0.025 a,b
|
Re-PCI
|
29 (5.9)
|
2 (1.5)
|
7 (5.1)
|
20 (9.0)
|
0.012b
|
Re-CABG
|
4 (0.8)
|
1 (0.7)
|
1 (0.7)
|
2 (0.9)
|
0.979
|
For comparison among groups, a: p < 0.05 for equality between CCL17 low vs. medium; b: p < 0.05 for equality between CCL17 low vs. high; c: p < 0.05 for equality between CCL17 medium vs. high.
Kaplan-Meier curves for the incidence of all MACE and all-cause death according to different CCL17 levels were shown in Figure 2. Significant differences were observed in Kaplan-Meier curves for both incidence of all MACE (Fig 2A, Log-rank p<0.001) and deaths (Fig 2B, Log-rank p=0.046) between the CCL17 low group and other two groups.
A. Kaplan-Meier curves for the happen of all MACE. B. Kaplan-Meier curves for all-cause death.
The predictive value of CCL17 level on MACE and all-cause death was evaluated by univariate Cox proportional hazard analysis. The results showed that compared to the CCL17 low group, the risk of MACE increases 4.51 (95% CI 2.08-9.77) and 6.61 (95% CI 3.20-13.70) times respectively in CCL17 medium and high groups (Table 3).
Since cardiovascular diseases are complex and multiple risk factors have been well summarized, we also analyzed the conventional risk factors in our cohort (Supplementary Table 1). Some conventional risk factors including age, sex, smoking, dyslipidemia history, and hypertension history did not demonstrate much correlation with the occurrence of MACE, which may be due to a good drug intervention and medication compliance. The number of lesion vessels, disease history of diabetes, and abnormal laboratory test indexes such as platelet count, HbA1c, hsCRP concentration showed a positive correlation with MACE, while eGFR showed a negative correlation with MACE (Supplementary Table 1).
Table 3. Incidence of MACE according to the optimal cutoff point of CCL17 level
Models
|
CCL17 low
|
CCL17 medium
|
CCL17 high
|
HR (95% CI)
|
P-value
|
HR (95% CI)
|
P-value
|
Crude Model
|
1 (Referent)
|
4.51 (2.08-9.77)
|
0.00013
|
6.61 (3.20-13.70)
|
P<0.00001
|
Model 1
|
1 (Referent)
|
4.42 (2.04-9.57)
|
0.00017
|
6.65 (3.2-13.77)
|
P<0.00001
|
Model 2
|
1 (Referent)
|
4.22 (1.94-9.18)
|
0.00028
|
6.24 (3.01-12.98)
|
P<0.00001
|
Model 3
|
1 (Referent)
|
4.53 (2.08-9.88)
|
0.00015
|
6.38 (3.07-13.28)
|
P<0.00001
|
The prediction ability of CCL17 onto the incidence of MACE was analyzed via univariate or multivariate Cox proportional hazard analysis.
Crude model: univariate Cox proportional hazard analysis.
Model 1: adjusted for Age + Sex + BMI.
Model 2: adjusted for Age + Sex + BMI + Diabetes + Hypertension + Number of lesion vessels.
Model 3: adjusted for Age + Sex + BMI + Diabetes + Hypertension + Number of lesion vessels + PLT + hsCRP +eGFR + ALT.
HR: hazard ratio. CI: confidence interval. BMI: body mass index. PLT: platelet count. hsCRP: high sensitivity C-reactive protein. eGFR: estimated glomerular filtration rate. ALT: alanine aminotransferase.
3.3 Cox proportional hazard analysis to evaluate the prognostic implication of CCL17
In multivariate Cox proportional hazard analysis, three models (Model 1, 2, 3) adjusted for different covariates were constructed to evaluate the prediction ability of CCL17 for all MACE. The variables included in the 3 models were selected based on clinical experience and statistical significance from results in Supplementary Table 1. In all 3 models, a higher CCL17 level remained as an independent risk predictor of all MACE (Table 4, all p<0.001). And model 3, the full-adjusted model, described that in CCL17 medium and high groups, the risk of MACE increased to 4.53 (95% CI 2.08-9.88) and 6.38 (95% CI 3.07-13.28) times respectively, compared to CCL17 low group (Table 3).
Table 4. AUC and C index value of different models
|
AUC
|
C index
|
|
Without CCL17
|
With CCL17
|
P-value
|
Without CCL17
|
With CCL17
|
P-value
|
Model 1
|
|
|
|
|
|
|
Cox
|
0.572±0.026
|
0.655±0.022
|
<0.001
|
0.569±0.016
|
0.654±0.016
|
<0.001
|
Coxboost
|
0.557±0.017
|
0.605±0.011
|
<0.001
|
0.537±0.011
|
0.569±0.007
|
<0.001
|
RF2000
|
0.702±0.022
|
0.734±0.021
|
<0.001
|
0.652±0.015
|
0.672±0.014
|
<0.001
|
Model 2
|
|
|
|
|
|
|
Cox
|
0.631±0.025
|
0.667±0.025
|
<0.001
|
0.618±0.018
|
0.685±0.018
|
<0.001
|
Coxboost
|
0.582±0.015
|
0.626±0.009
|
<0.001
|
0.552±0.01
|
0.58±0.007
|
<0.001
|
RF2000
|
0.773±0.021
|
0.819±0.02
|
<0.001
|
0.697±0.014
|
0.724±0.013
|
<0.001
|
Model 3
|
|
|
|
|
|
|
Cox
|
0.627±0.024
|
0.678±0.023
|
<0.001
|
0.631±0.018
|
0.694±0.019
|
<0.001
|
Coxboost
|
0.591±0.014
|
0.621±0.011
|
<0.001
|
0.559±0.01
|
0.577±0.008
|
<0.001
|
RF2000
|
0.803±0.021
|
0.836±0.021
|
<0.001
|
0.718±0.013
|
0.737±0.013
|
<0.001
|
3.4 Incremental effect of CCL17 on MACE prediction
To better evaluate the effectiveness and accuracy of different models, their statistical significance and clinical importance were assessed by area under curve (AUC) and C index (Table 4, Figure 3). Considering the existence of data imbalanced problem (patients with MACE vs patients without MACE: 116 vs 379), which limited the performance of models, CoxBoost and RF with 2000 trees(RF2000) were applied to optimize models as mentioned in the method part. RF2000 was shown to effectively enhance the performance of the models (Figure 3, blue lines.)
For baseline risk models (model 1, 2, 3) with RF2000 optimization, the number of covariates has increased from 3 to a maximum of 10, which increased the AUC (from 0.702±0.026 to 0.803±0.021).
Notably, the addition of CCL17 significantly increased AUC value in all models (Figure 3, models without CCL17 vs models with CCL17: full line vs dotted line, p<0.001), which enhances the prediction ability of all three kinds of baseline risk models. Meanwhile, the addition of CCL17 increases the C-index from 0.718±0.013 to 0.737±0.013 (Table 4), suggesting a significantly increased model accuracy.
The statistical calculation results exhibited a strong prediction potential to MACE of CCL17. The model with the best prediction ability to MACE was also constructed with RF2000 optimization, 10 conventional risk factors, and CCL17 (AUC 0.836±0.021)
ROC curve of Cox is shown in the red line, CoxBoost in the yellow line, and RF2000 in the blue line. Models without CCL17 were drawn in the full line while models with CCL17 drawn in the dotted line.
In different models, the number of covariates was increased in sequence model 1 (A), model 2 (B), and model 3 (C). The pattern is similar. The raw model calculated via Cox proportional hazard analysis was shown in a red full line, with Cox Boost optimization (yellow full line) decreasing AUC while RF2000 optimization (blue full line) increasing AUC. For each model and each optimization, the addition of CCL17 (dotted lines) can further increase AUC.