The Genetic Risk Score with Variants in PDGFs and PDGFRB for the Risk of Major Cardiovascular Adverse Events in Patients with Coronary Artery Disease

Abstract

Aims: Previous studies have linked platelet-derived growth factors (PDGFs) and their receptor beta (PDGFRB) genetic variants to coronary artery disease (CAD), but their impact on major adverse cardiovascular events (MACEs) remains unclear.

Methods: A cohort study of 3139 patients with CAD followed up until December 1, 2022 (median 5.42 years), genotyped 13 tagSNPs in PDGFs/PDGFRB pathway genes to establish weighted genetic risk scores (wGRS). Multiple Cox regression models analyzed the association of SNPs and wGRS with MACE outcomes using hazard ratios (HRs) and 95% confidence intervals (CIs). The wGRS improvement on traditional risk factors (TRFs) and the Global Registry of Acute Coronary Events (GRACE) score for MACEs were assessed using the C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: Compared to low MACE-GRS (Q1 of quintile), high MACE-GRS (Q5 of quintile) had an increased risk of MACEs, with an adjusted HRs of 1.441 (P = 0.006). Compared to the TRF prediction model, the addition of MACE-GRS showed an improved discrimination with an NRI of 5.1% (95% CI, 0.7%-9.5%, P＜0.001) and IDI of 0.3% (95% CI, 0.0%-0.6%, P＜0.001). In addition, compared to the TRFs and GRACE score model, the addition of MACE-GRS showed an improved discrimination with an NRI of 5.1% (95% CI, 0.7%-9.6%, P＜0.001) and IDI of 0.3% (95% CI, 0.0%-0.5%, P＜0.001).

Conclusions: Variants in the PDGF-PDGFRB pathway genes contribute to the risk of MACEs after CAD, and the wGRS might be able to serve as a risk predictor of MACEs in addition to TRFs.

Xiaojuan Xu and Wen Li contributed equally to this work.

Chong Shen and Song Yang are joint senior authors.

See editorial vol. 32: 924-925

Background

Coronary artery disease (CAD), a common disease influenced by both genetic and environmental factors, continues to be the primary cause of hospitalization and mortality globally^{1, 2)}. The global trend in deaths from CAD has risen steadily since 1990, reaching 9.14 million deaths in 2019 ³⁾. Major adverse cardiovascular events (MACEs) after CAD are strongly associated with a higher risk of mortality⁴⁾. Within 1 year, the rate of MACEs and mortality in the population with CAD may increase to 27.6% and 16.7%, respectively⁵⁾. It is therefore crucial to enhance the identification of CAD patients at high risk of MACEs and improve the decision-making process for treatment.

The controllable risk factors of CAD include being overweight, diabetes, hypertension, dyslipidemia, and smoking, while sex, race, aging, genetic determinants, and one’s family history are non-controllable⁶⁾. Previous studies have suggested that genetic variations that occur naturally contribute to a certain risk of CAD⁷⁾, and genome-wide association studies (GWASs) have identified over 200 major genetic loci associated with CAD⁸⁾. However, for polygenic disorders such as CAD, a single variation is restricted in its actual significance for determining disease risk. Instead, the weighted genetic risk score (wGRS) or polygenic risk score (PRS), a weighted average quantity of risk genetic variants, is the most commonly used approach for assessing the genetic risk of chronic complex diseases⁹⁾, including CAD¹⁰⁾.

Recent studies have indicated that the inclusion of PRS for CAD alongside the Framingham risk score and ACC/AHA pooled risk equations led to enhanced predictive capability¹¹⁾. Previous studies have demonstrated that, in Chinese acute coronary syndrome (ACS) patient cohorts, there was a positive correlation between wGRS and the occurrence of MACEs¹²⁾. In a twin study, the heritability estimates of CAD mortality was found to be 0.57 in men and 0.38 in women, with genetic factors playing a role throughout the entire lifespan¹³⁾. A wGRS incorporated into risk prediction models for CAD can enhance the risk assessment in addition to traditional models¹⁴⁾, and the genetic variants may be useful for tailoring therapy¹⁵⁾. Further exploration for GRS in predicting the CAD prognosis would be warranted to enhance the predictive capacity of the traditional risk factors (TRFs)^{16, 17)}.

Platelet-derived growth factors (PDGFs) were initially detected in platelets and serum as major mitogens for connective tissue cells, fibroblasts, vascular smooth muscle cells (VSMCs), and other cellular types^{18, 19)}. PDGFs and PDGFR participate in a wide array of biological processes, such as fibrosis, wound healing, inflammation, and vascular regeneration, through their influence on various cell types²⁰⁾, which may be involved in the atherosclerosis pathophysiology of CAD. The relationship between PDGFs and atherosclerosis has been observed both in vivo and in vitro^{21, 22)} and has received extensive attention in the field of CAD^{23, 24)}. Studies have also reported associations between activated PDGFs and CAD development^{18, 25)}. A study revealed an altered gene expression in the perivascular adipose tissue (PCAT) of CAD patients, suggesting that activation of the PDGF pathway may contribute to CAD progression²⁶⁾. Another observational study found that serum PDGFs may predict vulnerable plaques in patients with intermediate-to-low-risk non-ST-segment elevation acute coronary syndrome (NSTE-ACS)²⁷⁾.

The genetic effect of PDGF/PDGFRB variants on the long-term prognosis of CAD and MACEs merits further investigation. In this study, we assessed the genetic effect of the PDGF/PDGFRB signaling pathway on MACEs after CAD and estimated the predictive value of wGRS in addition to TRFs.

Methods

Data Source and Study Population

From May 2009 to October 2018, 3538 patients diagnosed with CAD were recruited in a hospital-based retrospective cohort study conducted at Yixing City People’s Hospital. After excluding 399 participants who did not meet the inclusion criteria, 3139 CAD patients were included in this study. Among them, 1589 had acute myocardial infarction (AMI), 1075 had angina pectoris (AP), 57 had heart failure (HF), 90 had arrhythmia, and 328 had occult CAD. The patients were followed up until December 1, 2022, with a median follow-up period of 5.42 years. Long-term rehospitalization and death outcomes were collected using annual data from the Yixing Center for Disease Control and Prevention.

The disease code for stroke events was I60-I69 based on the International Classification of Diseases, 10th version (ICD-10), which consists of ischemic stroke, hemorrhagic stroke, and unspecified stroke. The disease code for CAD events was I20-I25 based on the ICD-10, which consisted of angina pectoris, acute myocardial infarction, subsequent myocardial infarction, certain current complications following acute myocardial infarction, other acute ischemic heart diseases, and chronic ischemic heart disease. In this study, the primary outcome was defined as MACEs, a composite endpoint of cardiovascular incidence (stroke incidence and/or CAD recurrence) and all-cause death, and separate cardiovascular events or combined events and all-cause death were defined as secondary endpoints. The flowchart of the cohort study is shown in Fig.1.

Fig.1. The flow chart of the cohort study

CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; AMI, acute myocardial infarction; AP, angina pectoris; HF, heart failure; CHD, coronary heart disease of unspecified etiology.

Before inclusion in the study, all participants or their legal guardians provided their informed consent. The research protocol was approved by the ethics committee of Nanjing Medical University (2018675) and complied with the principles of the Declaration of Helsinki.

Coronary Angiography and Classification of CAD

Coronary angiography was used as a diagnostic technique to detect coronary artery stenosis. CAD was defined as ≥ 50% stenosis in any section of the left main coronary artery (LCM), left anterior descending artery (LAD), left circumflex artery (LCX), or right coronary artery (RCA)²⁸⁾. Based on the results of coronary angiography, patients with vessel lesions were categorized into three subtypes: single-, dual-, and triple-vessel lesions. The single-vessel lesion type was defined as the presence of one stenosis in the LAD, LCX, or RCA. Dual-vessel lesions were defined by the presence of two stenoses, regardless of whether the LAD or LCX had stenosis. Triple-vessel lesions were defined as the presence of three stenoses. In cases where there was stenosis in the LCM, regardless of whether the LAD or LCX also had stenosis, the patient was classified into the dual-vessel lesion group. An RCA lesion was defined as a triple-vessel lesion²⁹⁾.

Demographic and Clinical Information Collection

All participants were interviewed using a standard questionnaire, including demographic characteristics, smoking and drinking habits, and medical history. They underwent physical examinations, including systolic blood pressure (SBP; mmHg) and diastolic blood pressure (DBP; mmHg). Clinical measurements, including systolic blood fasting blood glucose (FBG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C), were recorded. Platelet parameters, such as platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and platelet count (PCT), were measured using routine blood tests. Smokers were defined as individuals who smoked at least 20 cigarettes per week and had a minimum duration of 3 months within a year³⁰⁾. Drinkers were defined as individuals who currently or previously consumed alcohol at least twice per week, with a minimum duration of six months within a year.

Participants who self-reported a previous diagnosis of hypertension or were currently using anti-hypertensive medications were categorized as having hypertension. A history of diabetes was defined as self-reported diabetes or current use of hypoglycemic agents. Dyslipidemia was defined as the presence of TC ≥ 240 mg/dL, TG ≥ 200 mg/dL, LDL-C ≥ 160 mg/dL, HDL-C ＜40 mg/dL, a self-reported history of dyslipidemia, or current use of lipid-lowering medications³¹⁾. In this study, comorbidities were defined as the coexistence of hypertension, diabetes, and dyslipidemia in patients with CAD.

The Global Registry of Acute Coronary Events (GRACE) score was evaluated for patients with CAD by gathering data on eight variables: the Killip class, age, blood pressure, resuscitated cardiac arrest, positive cardiac marker findings, creatinine level, ST-segment shift, and heart rate³²⁾.

Tagging SNP Selection and Genotyping

According to the Chinese Han population in Beijing (CHB) gene database (GRCh37, http://phase3browser.1000genomes.org/index.html), we searched the tagging SNPs (tagSNPs) from SNPs with a minor allele frequency (MAF) over 0.05 and linkage disequilibrium (LD) with r² ≥ 0.8 within the upstream 2 kb to the downstream 1 kb.

To prioritize the inclusion of predicted functional sites, we performed a bioinformatics analysis using SNPinfo (http://snpinfo.niehs.nih.gov/snpinfo/snpfunc.htm) and RegulomeDB (http://www.regulomedb.org/) tools. In addition, since the Genome Variation Server software program did not provide SNP data for the PDGFA gene in the Chinese population, we selected tagSNPs from the Chinese population database of the International 1000 Genomes Project (http://www.1000genomes.org/). Considering the challenges in primer probe design and preliminary experimental findings, we ultimately selected 13 tagSNPs within the PDGF-PDGFRB pathway genes. Specifically, rs28472363 was selected for PDGFA; rs5757573 and rs13053714 were selected for PDGFB; rs1834389, rs342309, and rs6845322 were selected for PDGFC; rs1053861, rs11226185, and rs4755010 were selected for PDGFD; and rs6579775, rs3828610, rs246390, and rs9324641 were selected for PDGFRB. The biological information of all tagSNPs is listed in Supplementary Table 1.

Supplementary Table 1.Biological information and function prediction for selected tagSNPs

SNP	Allele	Chr:Position	Enhancer	TFBS	eQTL	Nearby Gene	MAF
rs28472363	G/A	7:517648	-	-	-	PDGFA	0.315
rs5757573	T/C	22:39237617	-	-	-	PDGFB	0.108
rs13053714	G/A	22:39230567	-	-	-	PDGFB	0.142
rs1834389	A/C	4:156797460	Y	-	Y	PDGFC	0.170
rs342309	G/A	4:156890289	Y	-	Y	PDGFC	0.225
rs6845322	A/G	4:156962953	Y	-	Y	PDGFC	0.450
rs1053861	C/T	11:103283364	-	-	-	PDGFD	0.471
rs11226185	T/C	11:104120913	Y	-	Y	PDGFD	0.316
rs4755010	G/C	11:104163420	Y	Y	Y	PDGFD	0.232
rs6579775	C/T	5:150154285	Y	-	Y	PDGFRB	0.275
rs3828610	C/A	5:150156062	Y	Y	Y	PDGFRB	0.446
rs246390	A/G	5:150116758	-	Y	-	PDGFRB	0.328
rs9324641	C/T	5:150148281	Y	-	Y	PDGFRB	0.397

SNP, single nucleotide polymorphism; TFBS, transcription factor binding site; eQTL, expression quantitative trait loci; MAF, minor allele frequency

Peripheral blood leukocyte DNA was isolated using a protein precipitation method (Eaglink EGEN2024, Nanjing, China). Subsequently, a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) was used to measure the concentrations of all DNA samples. Genotyping of all SNPs was conducted using a TaqMan-based allelic discrimination assay on the ABI 9700 polymerase chain reaction system (Applied Biosystems, Foster City, CA, USA), and the results were analyzed using the 7900HT real-time PCR system equipped with Sequence Detection System 2.4 (Life Technologies).

GRS Calculation and Categorization

The wGRSs were constructed for MACEs, CVD, stroke, CAD recurrence, CVD death, and all-cause death, resulting in six wGRSs: MACE-GRS, CVD-GRS, Stroke-GRS, CAD-GRS, CVDdeath-GRS, and GRS for all-cause death (ACD-GRS). The wGRS combined all 13 SNPs and was calculated by summing the number of risk alleles (0/1/2) for each SNP, weighted by the corresponding effect size (β estimation) obtained from a Cox regression analysis between a single SNP and each outcome. The wGRSs were further divided into three groups: low-risk (Q1), mid-risk (Q2–Q4), and high-risk (Q5).

Statistical Analyses

Clinical information for each subject was entered using duplicate entries in EpiData 3.1 software (The EpiData Association, Odense, Denmark). For normally distributed quantitative variables, mean±standard deviation was used to represent the data. The median (interquartile range [IQR]) was used for skewed distribution data. Categorical variables are presented as frequencies and percentages.

The Cox regression model was used to estimate the association between wGRS groups and MACEs with hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for age, sex, smoking, drinking, hypertension, diabetes, and dyslipidemia. In addition, we conducted a multiple Cox regression analysis to assess MACEs and the occurrence of CVD in different GRS groups based on different coronary artery lesion counts and number of comorbidities. The pROC package in R was used to calculate the area under the curve (AUC), perform DeLong’s test, and output AUC CIs to compare the performances of different prediction models. The code function was then used to compute the sensitivity and specificity of each model at their optimal thresholds. Furthermore, Harrel’s C-statistic was used to estimate the discrimination of the wGRS, GRACE score, TRF model (including age, sex, smoking, drinking, hypertension, diabetes, and dyslipidemia), and the combined model for MACEs, with confidence intervals calculated using the bootstrap method. The net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated using the survIDINRI package in R to measure the improvement in reclassification and discrimination after adding wGRS to the TRFs model, as well as the GRACE score. In addition, Pearson’s correlation analysis was used to examine the correlations between the wGRS and glucose levels, blood pressure, lipids, bilirubin, platelet parameters, and GRACE score.

All data analyses were performed using the SAS software program (version 9.4; SAS Inc., Cary, NC, USA) and R 4.1.2 version. Statistical significance was set at a two-tailed P-value of ＜0.05. The multiplicity of hypothesis testing was controlled using the False Discovery Rate (FDR) method.

Results

Demographic and Clinical Characteristics of the Study Population

Among the 3,139 patients included in this study, there were 2,334 men (74.4%) and 805 women (25.6%). When CAD was initially diagnosed, the median patient age was 64 (IQR: 56–71) years old. During the follow-up period, 574 MACEs occurred, including 396 cardiovascular, 173 stroke, and 263 CAD events. A total of 234 patients died during follow-up, 119 of whom died of cardiovascular diseases. Table 1 displays detailed demographic and clinical characteristics of the study participants.

Table 1.Demographic and clinical characteristics of the study population in the cohort study

Characteristics	Group	CAD cases＊ (n = 3139)	CVD incidence (n = 396)	Stroke incidence (n = 173)	CAD recurrence (n = 263)	CVD death (n = 119)	All cause death (n = 234)	MACEs incidence (n = 574)
Age (year)		64.17 (55.50, 71.01)	66.15 (58.12, 72.74)	67.32 (59.85, 73.41)	65.65 (57.52, 72.49)	71.65 (66.09, 76.69)	71.06 (65.15, 75.92)	67.71 (60.64, 73.82)
Sex [n (%)]	Men	2334 (74.4)	292 (73.7)	120 (69.4)	201 (76.4)	79 (66.4)	162 (69.2)	413 (72.0)
	Women	805 (25.6)	104 (26.3)	53 (30.6)	62 (23.6)	40 (33.6)	72 (30.8)	161 (28.0)
SBP (mmHg)		130 (120, 146)	132 (120, 147)	136 (122, 150)	130 (120, 144)	126 (109, 138)	130 (117, 144)	131 (120, 146)
DBP (mmHg)		80 (71, 87)	80 (71, 86)	80 (71, 87)	79 (70, 84)	75 (68, 80)	76 (69, 84)	78 (70, 84)
GLU (mmol/L)		5.43 (4.71, 7.03)	5.46 (4.71, 7.20)	5.38 (4.66, 6.78)	5.51 (4.76, 7.32)	5.53 (4.69, 7.98)	5.53 (4.68, 7.72)	5.52 (4.72, 7.56)
TC (mmol/L)		4.29 (3.58, 5.02)	4.22 (3.46, 5.03)	4.26 (3.43, 5.08)	4.23 (3.48, 4.96)	4.15 (3.63, 5.36)	4.21 (3.64, 5.06)	4.24 (3.53, 5.04)
TG (mmol/L)		1.43 (1.01, 2.1)	1.48 (1.01, 2.09)	1.51 (1.01, 2.09)	1.48 (1.01, 2.12)	1.31 (0.94, 1.8)	1.3 (0.93, 1.79)	1.4 (0.99, 1.98)
HDL-C (mmol/L)		1.05 (0.92, 1.22)	1.04 (0.92, 1.19)	1.03 (0.90, 1.21)	1.05 (0.92, 1.17)	1.09 (0.97, 1.31)	1.09 (0.96, 1.27)	1.06 (0.93, 1.21)
LDL-C (mmol/L)		2.51 (1.97, 3.07)	2.41 (1.91, 3.02)	2.48 (1.87, 3.12)	2.38 (1.91, 2.97)	2.44 (1.98, 3.14)	2.45 (1.98, 3.00)	2.45 (1.92, 3.04)
Smoking [n (%)]	No	1819 (57.9)	245 (61.9)	112 (64.7)	158 (60.1)	79 (66.4)	157 (67.1)	366 (63.8)
	Yes	1320 (42.1)	151 (38.1)	61 (35.3)	105 (39.9)	40 (33.6)	77 (32.9)	208 (36.2)
Drinking [n (%)]	No	2644 (84.2)	351 (88.6)	155 (89.6)	233 (88.6)	107 (89.9)	203 (86.8)	506 (88.2)
	Yes	495 (15.8)	45 (11.4)	18 (10.4)	30 (11.4)	12 (10.1)	31 (13.2)	68 (11.8)
Hypertension [n (%)]	No	1050 (33.5)	108 (27.3)	43 (24.9)	72 (27.4)	39 (32.8)	72 (30.8)	163 (28.4)
	Yes	2089 (66.5)	288 (72.7)	130 (75.1)	191 (72.6)	80 (67.2)	162 (69.2)	411 (71.6)
Diabetes [n (%)]	No	2156 (68.7)	252 (63.6)	103 (59.5)	172 (65.4)	82 (68.9)	161 (68.8)	372 (64.8)
	Yes	983 (31.3)	144 (36.4)	70 (40.5)	91 (34.6)	37 (31.1)	73 (31.2)	202 (35.2)
Dyslipidemia [n (%)]	No	1515 (48.3)	191 (48.2)	84 (48.6)	129 (49.0)	67 (56.3)	135 (57.7)	296 (51.6)
	Yes	1624 (51.7)	205 (51.8)	89 (51.4)	134 (51.0)	52 (43.7)	99 (42.3)	278 (48.4)

^＊CAD cases aged between 35 and 80 years were selected.

Presented as Median and Interquartile range (IQR), total numbers or percentages in brackets.

MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; CAD, coronary artery disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; GLU, glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein-cholesterol; LDL-C, low-density lipoprotein-cholesterol.

Association between wGRS Stratification and MACEs

After FDR correction, the genotype frequency distribution of all 13 SNPs conformed to the Hardy–Weinberg equilibrium (P_FDR ＞0.05). The genotype and allele frequencies of all SNPs in the patients are reported in Supplementary Table 1, and important genetic parameters related to these SNPs are shown in Supplementary Table 2. The associations between the single loci and outcome events are listed in Supplementary Tables 3 and 4.

Supplementary Table 2.tagSNPs genetic information

SNP ID	Genotype frequency	Allele frequency	Ne	PIC	χ2	P a	P b
rs28472363	0.114 (AA), 0.430 (GA), 0.456 (GG)	0.329 (A), 0.671 (G)	1.791	0.344	1.976	0.160	0.416
rs5757573	0.005 (CC), 0.117 (TC), 0.878 (TT)	0.063 (C), 0.937 (T)	1.134	0.111	0.544	0.461	0.608
rs13053714	0.014 (AA), 0.251 (GA), 0.735 (GG)	0.139 (A), 0.861 (G)	1.315	0.211	6.381	0.012	0.100
rs1834389	0.028 (CC), 0.268 (AC), 0.704 (AA)	0.162 (C), 0.838 (A)	1.373	0.235	0.547	0.459	0.608
rs342309	0.080 (AA), 0.410 (GA), 0.510 (GG)	0.285 (A), 0.715 (G)	1.688	0.325	0.106	0.744	0.777
rs6845322	0.177 (GG), 0.512 (AG), 0.312 (AA)	0.433 (G), 0.567 (A)	1.965	0.370	5.585	0.018	0.100
rs1053861	0.203 (TT), 0.502 (CT), 0.295 (CC)	0.454 (T), 0.546 (C)	1.983	0.373	0.527	0.468	0.608
rs11226185	0.086 (CC), 0.431 (TC), 0.482 (TT)	0.302 (C), 0.698 (T)	1.729	0.333	1.634	0.201	0.436
rs4755010	0.075 (CC), 0.412 (GC), 0.514 (GG)	0.281 (C), 0.719 (G)	1.678	0.322	1.294	0.255	0.474
rs6579775	0.026 (TT), 0.296 (CT), 0.678 (CC)	0.174 (T), 0.826 (C)	1.403	0.246	2.589	0.108	0.351
rs3828610	0.187 (AA), 0.486 (CA), 0.327 (CC)	0.430 (A), 0.570 (C)	1.962	0.370	0.244	0.621	0.734
rs246390	0.123 (GG), 0.429 (AG), 0.449 (AA)	0.337 (G), 0.663 (A)	1.808	0.347	5.147	0.023	0.100
rs9324641	0.169 (TT), 0.487 (CT), 0.344 (CC)	0.412 (T), 0.588 (C)	1.940	0.367	0.080	0.777	0.777

^a P value for Hardy-Weinberg equilibrium test.

^b P value for False Discovery Rate test.

Ne, effective number of alleles；PIC, polymorphism information content.

Supplementary Table 3.Association of single SNP in PDGFs/PDGFRB pathway with MACEs and its individual components after CAD (unadjusted)

Outcome	SNP	Genotype	Incidence	Person-years	Incidence density (/104 Person-years)	HR (95% CI) Additive model
MACEs incidence	rs28472363	GG	257	8702.01	295.33	1.031 (0.913, 1.164)
		GA	246	8421.00	292.13	P = 0.625
		AA	71	2187.40	324.59	P b = 0.739
	rs5757573	TT	500	16879.97	296.21	1.089 (0.868, 1.365)
		TC	68	2343.23	290.20	P = 0.463
		CC	6	87.21	687.98	P b = 0.739
	rs13053714	GG	437	14129.23	309.29	1.155 (0.966, 1.381)
		GA	130	4902.49	265.17	P = 0.113
		AA	7	278.69	251.18	P b = 0.490
	rs1834389	AA	400	13654.61	292.94	1.046 (0.896, 1.221)
		AC	157	5144.17	305.20	P = 0.569
		CC	17	511.63	332.27	P b = 0.739
	rs342309	GG	289	9838.74	293.74	1.036 (0.912, 1.177)
		GA	234	7904.70	296.03	P = 0.589
		AA	51	1566.97	325.47	P b = 0.739
	rs6845322	AA	190	6022.64	315.48	1.013 (0.899, 1.143)
		AG	276	9948.04	277.44	P = 0.825
		GG	108	3339.73	323.38	P b = 0.825
	rs1053861	CC	178	5727.84	310.76	1.044 (0.929, 1.174)
		CT	283	9631.27	293.83	P = 0.471
		TT	113	3951.30	285.98	P b = 0.739
	rs11226185	TT	280	9279.03	301.76	1.015 (0.892, 1.154)
		TC	240	8491.95	282.62	P = 0.824
		CC	54	1539.44	350.78	P b = 0.825
	rs4755010	GG	283	10149.21	278.84	1.109 (0.976, 1.261)
		GC	243	7803.73	311.39	P = 0.113
		CC	48	1357.46	353.60	P b = 0.490
	rs6579775	CC	398	12889.89	308.77	1.096 (0.935, 1.285)
		CT	162	5933.15	273.04	P = 0.255
		TT	14	487.37	287.25	P b = 0.552
	rs3828610	CC	179	6432.27	278.28	1.069 (0.953, 1.200)
		CA	278	9222.91	301.42	P = 0.254
		AA	117	3655.23	320.09	P b = 0.552
	rs246390	AA	275	8309.14	330.96	1.174 (1.036, 1.328)
		AG	242	8550.22	283.03	P = 0.011
		GG	57	2451.05	232.55	P b = 0.143
	rs9324641	CC	185	6695.64	276.30	1.085 (0.964, 1.220)
		CT	285	9435.38	302.05	P = 0.175
		TT	104	3179.39	327.11	P b = 0.552
CVD incidence	rs28472363	GG	175	8702.01	201.10	1.026 (0.887, 1.188)
		GA	175	8421.00	207.81	P = 0.726
		AA	46	2187.40	210.30	P b = 0.858
	rs5757573	TT	345	16879.97	204.38	1.066 (0.809, 1.404)
		TC	48	2343.23	204.85	P = 0.650
		CC	3	87.21	343.99	P b = 0.845
	rs13053714	GG	303	14129.23	214.45	1.147 (0.926, 1.420)
		GA	86	4902.49	175.42	P = 0.209
		AA	7	278.69	251.18	P b = 0.518
	rs1834389	AA	281	13654.61	205.79	1.017 (0.841, 1.230)
		AC	105	5144.17	204.11	P = 0.861
		CC	10	511.63	195.45	P b = 0.861
	rs342309	GG	208	9838.74	211.41	1.019 (0.873, 1.190)
		GA	153	7904.70	193.56	P = 0.812
		AA	35	1566.97	223.36	P b = 0.861
	rs6845322	AA	134	6022.64	222.49	1.067 (0.923, 1.233)
		AG	194	9948.04	195.01	P = 0.380
		GG	68	3339.73	203.61	P b = 0.706
	rs1053861	CC	130	5727.84	226.96	1.088 (0.945, 1.253)
		CT	189	9631.27	196.24	P = 0.239
		TT	77	3951.30	194.87	P b = 0.518
	rs11226185	TT	190	9279.03	204.76	1.046 (0.896, 1.220)
		TC	167	8491.95	196.66	P = 0.572
		CC	39	1539.44	253.34	P b = 0.826
	rs4755010	GG	192	10149.21	189.18	1.158 (0.994, 1.350)
		GC	167	7803.73	214.00	P = 0.060
		CC	37	1357.46	272.57	P b = 0.390
	rs6579775	CC	273	12889.89	211.79	1.078 (0.891, 1.304)
		CT	113	5933.15	190.46	P = 0.439
		TT	10	487.37	205.18	P b = 0.713
	rs3828610	CC	120	6432.27	186.56	1.089 (0.948, 1.251)
		CA	195	9222.91	211.43	P = 0.230
		AA	81	3655.23	221.60	P b = 0.518
	rs246390	AA	189	8309.14	227.46	1.178 (1.015, 1.368)
		AG	169	8550.22	197.66	P = 0.031
		GG	38	2451.05	155.04	P b = 0.390
	rs9324641	CC	124	6695.64	185.20	1.113 (0.966, 1.282)
		CT	199	9435.38	210.91	P = 0.138
		TT	73	3179.39	229.60	P b = 0.518
Stroke incidence	rs28472363	GG	80	8941.19	89.47	1.040 (0.831, 1.300)
		GA	74	8655.02	85.50	P = 0.734
		AA	19	2261.06	84.03	P b = 0.903
	rs5757573	TT	150	17357.71	86.42	1.066 (0.705, 1.611)
		TC	22	2408.42	91.35	P = 0.763
		CC	1	91.14	109.72	P b = 0.903
	rs13053714	GG	131	14541.09	90.09	1.142 (0.827, 1.575)
		GA	40	5018.58	79.70	P = 0.421
		AA	2	297.60	67.20	P b = 0.903
	rs1834389	AA	122	14047.86	86.85	1.018 (0.765, 1.353)
		AC	46	5283.24	87.07	P = 0.905
		CC	5	526.18	95.02	P b = 0.950
	rs342309	GG	85	10144.27	83.79	1.048 (0.832, 1.321)
		GA	74	8099.16	91.37	P = 0.689
		AA	14	1613.85	86.75	P b = 0.903
	rs6845322	AA	59	6208.74	95.03	1.078 (0.866, 1.342)
		AG	85	10213.05	83.23	P = 0.502
		GG	29	3435.48	84.41	P b = 0.903
	rs1053861	CC	52	5914.76	87.92	1.033 (0.835, 1.277)
		CT	88	9887.07	89.01	P = 0.764
		TT	33	4055.45	81.37	P b = 0.903
	rs11226185	TT	79	9549.93	82.72	1.047 (0.829, 1.322)
		TC	81	8698.03	93.12	P = 0.700
		CC	13	1609.32	80.78	P b = 0.903
	rs4755010	GG	87	10408.79	83.58	1.052 (0.831, 1.332)
		GC	74	8030.92	92.14	P = 0.675
		CC	12	1417.57	84.65	P b = 0.903
	rs6579775	CC	114	13292.86	85.76	1.009 (0.762, 1.337)
		CT	56	6059.80	92.41	P = 0.950
		TT	3	504.61	59.45	P b = 0.950
	rs3828610	CC	50	6598.02	75.78	1.140 (0.925, 1.404)
		CA	86	9486.54	90.65	P = 0.219
		AA	37	3772.72	98.07	P b = 0.903
	rs246390	AA	77	8603.55	89.50	1.122 (0.898, 1.403)
		AG	81	8743.86	92.64	P = 0.312
		GG	15	2509.86	59.76	P b = 0.903
	rs9324641	CC	56	6856.15	81.68	1.087 (0.878, 1.347)
		CT	85	9706.41	87.57	P = 0.444
		TT	32	3294.71	97.13	P b = 0.903
CHD incidence	rs28472363	GG	113	8820.31	128.11	1.070 (0.895, 1.278)
		GA	118	8537.09	138.22	P = 0.457
		AA	32	2213.66	144.56	P b = 0.495
	rs5757573	TT	229	17110.63	133.83	1.115 (0.798, 1.556)
		TC	31	2373.22	130.62	P = 0.524
		CC	3	87.21	343.99	P b = 0.524
	rs13053714	GG	204	14321.06	142.45	1.172 (0.899, 1.529)
		GA	53	4968.46	106.67	P = 0.240
		AA	6	281.54	213.12	P b = 0.466
	rs1834389	AA	191	13832.25	138.08	1.112 (0.875, 1.414)
		AC	67	5217.80	128.41	P = 0.384
		CC	5	521.00	95.97	P b = 0.466
	rs342309	GG	147	9961.13	147.57	1.136 (0.935, 1.381)
		GA	95	8014.84	118.53	P = 0.198
		AA	21	1595.09	131.65	P b = 0.466
	rs6845322	AA	92	6108.14	150.62	1.104 (0.924, 1.319)
		AG	127	10087.12	125.90	P = 0.276
		GG	44	3375.81	130.34	P b = 0.466
	rs1053861	CC	91	5806.27	156.73	1.094 (0.920, 1.300)
		CT	117	9772.11	119.73	P = 0.310
		TT	55	3992.68	137.75	P b = 0.466
	rs11226185	TT	127	9401.97	135.08	1.097 (0.909, 1.323)
		TC	104	8621.38	120.63	P = 0.336
		CC	32	1547.71	206.76	P b = 0.466
	rs4755010	GG	129	10263.59	125.69	1.155 (0.957, 1.394)
		GC	107	7931.39	134.91	P = 0.132
		CC	27	1376.08	196.21	P b = 0.466
	rs6579775	CC	185	13077.22	141.47	1.127 (0.890, 1.429)
		CT	71	6001.11	118.31	P = 0.322
		TT	7	492.73	142.07	P b = 0.466
	rs3828610	CC	80	6523.80	122.63	1.077 (0.908, 1.277)
		CA	130	9330.07	139.33	P = 0.394
		AA	53	3717.19	142.58	P b = 0.466
	rs246390	AA	125	8435.29	148.19	1.147 (0.956, 1.376)
		AG	111	8659.34	128.19	P = 0.141
		GG	27	2476.43	109.03	P b = 0.466
	rs9324641	CC	79	6792.40	116.31	1.163 (0.978, 1.383)
		CT	133	9552.71	139.23	P = 0.087
		TT	51	3225.95	158.09	P b = 0.466
CVD death	rs28472363	GG	49	9096.33	53.87	1.267 (0.979, 1.641)
		GA	48	8801.94	54.53	P = 0.072
		AA	22	2296.88	95.78	P b = 0.468
	rs5757573	TT	104	17657.14	58.90	1.114 (0.682, 1.822)
		TC	13	2446.26	53.14	P = 0.666
		CC	2	91.75	217.98	P b = 0.881
	rs13053714	GG	86	14793.51	58.13	1.024 (0.703, 1.490)
		GA	33	5098.69	64.72	P = 0.904
		AA	0	302.94	0.00	P b = 0.929
	rs1834389	AA	77	14288.22	53.89	1.356 (0.992, 1.854)
		AC	35	5371.38	65.16	P = 0.056
		CC	7	535.55	130.71	P b = 0.468
	rs342309	GG	57	10316.43	55.25	1.153 (0.876, 1.516)
		GA	49	8236.75	59.49	P = 0.310
		AA	13	1641.97	79.17	P b = 0.881
	rs6845322	AA	35	6325.58	55.33	1.143 (0.880, 1.484)
		AG	58	10386.74	55.84	P = 0.317
		GG	26	3482.83	74.65	P b = 0.881
	rs1053861	CC	28	6018.26	46.53	1.074 (0.832, 1.386)
		CT	70	10054.62	69.62	P = 0.586
		TT	21	4122.26	50.94	P b = 0.881
	rs11226185	TT	66	9704.80	68.01	1.125 (0.842, 1.502)
		TC	40	8863.76	45.13	P = 0.426
		CC	13	1626.59	79.92	P b = 0.881
	rs4755010	GG	65	10572.08	61.48	1.134 (0.844, 1.522)
		GC	48	8183.29	58.66	P = 0.406
		CC	6	1439.78	41.67	P b = 0.881
	rs6579775	CC	79	13530.75	58.39	1.028 (0.734, 1.440)
		CT	37	6154.43	60.12	P = 0.874
		TT	3	509.97	58.83	P b = 0.929
	rs3828610	CC	36	6711.24	53.64	1.012 (0.785, 1.304)
		CA	63	9633.69	65.40	P = 0.929
		AA	20	3850.22	51.95	P b = 0.929
	rs246390	AA	57	8749.24	65.15	1.074 (0.822, 1.403)
		AG	46	8900.28	51.68	P = 0.600
		GG	16	2545.63	62.85	P b = 0.881
	rs9324641	CC	40	6976.59	57.33	1.056 (0.816, 1.367)
		CT	57	9857.08	57.83	P = 0.678
		TT	22	3361.47	65.45	P b = 0.881
All cause death	rs28472363	GG	105	9096.33	115.43	1.077 (0.892, 1.300)
		GA	95	8801.94	107.93	P = 0.443
		AA	34	2296.88	148.03	P b = 0.716
	rs5757573	TT	203	17657.14	114.97	1.128 (0.797, 1.598)
		TC	28	2446.26	114.46	P = 0.496
		CC	3	91.75	326.97	P b = 0.716
	rs13053714	GG	175	14793.51	118.30	1.147 (0.869, 1.515)
		GA	59	5098.69	115.72	P = 0.332
		AA	0	302.94	0.00	P b = 0.716
	rs1834389	AA	159	14288.22	111.28	1.149 (0.908, 1.453)
		AC	66	5371.38	122.87	P = 0.248
		CC	9	535.55	168.05	P b = 0.716
	rs342309	GG	108	10316.43	104.69	1.125 (0.925, 1.369)
		GA	107	8236.75	129.91	P = 0.239
		AA	19	1641.97	115.71	P b = 0.716
	rs6845322	AA	73	6325.58	115.40	1.101 (0.913, 1.327)
		AG	110	10386.74	105.90	P = 0.313
		GG	51	3482.83	146.43	P b = 0.716
	rs1053861	CC	64	6018.26	106.34	1.044 (0.870, 1.253)
		CT	123	10054.62	122.33	P = 0.642
		TT	47	4122.26	114.02	P b = 0.759
	rs11226185	TT	117	9704.80	120.56	0.993 (0.812, 1.215)
		TC	93	8863.76	104.92	P = 0.948
		CC	24	1626.59	147.55	P b = 0.948
	rs4755010	GG	123	10572.08	116.34	1.029 (0.838, 1.264)
		GC	96	8183.29	117.31	P = 0.785
		CC	15	1439.78	104.18	P b = 0.850
	rs6579775	CC	160	13530.75	118.25	1.063 (0.830, 1.361)
		CT	69	6154.43	112.11	P = 0.631
		TT	5	509.97	98.05	P b = 0.759
	rs3828610	CC	70	6711.24	104.30	1.079 (0.901, 1.292)
		CA	118	9633.69	122.49	P = 0.408
		AA	46	3850.22	119.47	P b = 0.716
	rs246390	AA	114	8749.24	130.30	1.168 (0.963, 1.418)
		AG	95	8900.28	106.74	P = 0.114
		GG	25	2545.63	98.21	P b = 0.716
	rs9324641	CC	74	6976.59	106.07	1.089 (0.906, 1.309)
		CT	118	9857.08	119.71	P = 0.363
		TT	42	3361.47	124.95	P b = 0.716

P value of Cox regression. ^b : False Discovery Rate adjusted p value of association.

PDGF, platelet-derived growth factor; PDGFRB, platelet-derived growth factor receptor beta. MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; CAD, coronary artery disease; HR, hazard ratio; CI, confidence interval.

Supplementary Table 4.Association of single SNP in PDGFs/PDGFRB pathway with MACEs and its individual components after CAD (adjusted)

Outcome	SNP	Genotype	Incidence	Person-years	Incidence density (/104 Person-years)	Additive model
						Effective allele	HR (95% CI) a	β
MACEs incidence	rs28472363	GG	257	8702.01	295.33	A	1.006 (0.890, 1.137)	0.006
		GA	246	8421.00	292.13		P a = 0.922
		AA	71	2187.40	324.59		P b = 0.922
	rs5757573	TT	500	16879.97	296.21	C	1.059 (0.844, 1.328)	0.057
		TC	68	2343.23	290.20		P a = 0.621
		CC	6	87.21	687.98		P b = 0.839
	rs13053714	GG	437	14129.23	309.29	A	1.168 (0.977, 1.397)	0.156
		GA	130	4902.49	265.17		P a = 0.089
		AA	7	278.69	251.18		P b = 0.438
	rs1834389	AA	400	13654.61	292.94	C	1.038 (0.889, 1.212)	0.038
		AC	157	5144.17	305.20		P a = 0.634
		CC	17	511.63	332.27		P b = 0.839
	rs342309	GG	289	9838.74	293.74	A	1.031 (0.906, 1.173)	0.030
		GA	234	7904.70	296.03		P a = 0.645
		AA	51	1566.97	325.47		P b = 0.839
	rs6845322	AA	190	6022.64	315.48	A	1.010 (0.895, 1.139)	0.010
		AG	276	9948.04	277.44		P a = 0.873
		GG	108	3339.73	323.38		P b = 0.922
	rs1053861	CC	178	5727.84	310.76	C	1.052 (0.935, 1.182)	0.051
		CT	283	9631.27	293.83		P a = 0.396
		TT	113	3951.30	285.98		P b = 0.735
	rs11226185	TT	280	9279.03	301.76	C	1.011 (0.890, 1.149)	0.011
		TC	240	8491.95	282.62		P a = 0.865
		CC	54	1539.44	350.78		P b = 0.922
	rs4755010	GG	283	10149.21	278.84	C	1.113 (0.979, 1.264)	0.107
		GC	243	7803.73	311.39		P a = 0.101
		CC	48	1357.46	353.60		P b = 0.438
	rs6579775	CC	398	12889.89	308.77	C	1.083 (0.924, 1.269)	0.080
		CT	162	5933.15	273.04		P a = 0.325
		TT	14	487.37	287.25		P b = 0.704
	rs3828610	CC	179	6432.27	278.28	A	1.078 (0.960, 1.211)	0.075
		CA	278	9222.91	301.42		P a = 0.204
		AA	117	3655.23	320.09		P b = 0.530
	rs246390	AA	275	8309.14	330.96	A	1.171 (1.034, 1.325)	0.157
		AG	242	8550.22	283.03		P a = 0.013
		GG	57	2451.05	232.55		P b = 0.169
	rs9324641	CC	185	6695.64	276.30	T	1.091 (0.969, 1.227)	0.087
		CT	285	9435.38	302.05		P a = 0.15
		TT	104	3179.39	327.11		P b = 0.488
CVD incidence	rs28472363	GG	175	8702.01	201.10	A	1.001 (0.864, 1.160)	0.001
		GA	175	8421.00	207.81		P a = 0.988
		AA	46	2187.40	210.30		P b = 0.988
	rs5757573	TT	345	16879.97	204.38	C	1.048 (0.796, 1.380)	0.047
		TC	48	2343.23	204.85		P a = 0.738
		CC	3	87.21	343.99		P b = 0.857
	rs13053714	GG	303	14129.23	214.45	A	1.161 (0.936, 1.439)	0.149
		GA	86	4902.49	175.42		P a = 0.174
		AA	7	278.69	251.18		P b = 0.473
	rs1834389	AA	281	13654.61	205.79	A	1.026 (0.848, 1.241)	0.026
		AC	105	5144.17	204.11		P a = 0.791
		CC	10	511.63	195.45		P b = 0.857
	rs342309	GG	208	9838.74	211.41	G	1.034 (0.885, 1.209)	0.034
		GA	153	7904.70	193.56		P a = 0.672
		AA	35	1566.97	223.36		P b = 0.857
	rs6845322	AA	134	6022.64	222.49	A	1.073 (0.929, 1.242)	0.071
		AG	194	9948.04	195.01		P a = 0.34
		GG	68	3339.73	203.61		P b = 0.631
	rs1053861	CC	130	5727.84	226.96	C	1.091 (0.947, 1.256)	0.087
		CT	189	9631.27	196.24		P a = 0.228
		TT	77	3951.30	194.87		P b = 0.494
	rs11226185	TT	190	9279.03	204.76	C	1.049 (0.900, 1.224)	0.048
		TC	167	8491.95	196.66		P a = 0.541
		CC	39	1539.44	253.34		P b = 0.781
	rs4755010	GG	192	10149.21	189.18	C	1.156 (0.992, 1.347)	0.145
		GC	167	7803.73	214.00		P a = 0.064
		CC	37	1357.46	272.57		P b = 0.416
	rs6579775	CC	273	12889.89	211.79	C	1.065 (0.880, 1.287)	0.062
		CT	113	5933.15	190.46		P a = 0.519
		TT	10	487.37	205.18		P b = 0.781
	rs3828610	CC	120	6432.27	186.56	A	1.099 (0.956, 1.263)	0.095
		CA	195	9222.91	211.43		P a = 0.182
		AA	81	3655.23	221.60		P b = 0.473
	rs246390	AA	189	8309.14	227.46	A	1.174 (1.011, 1.362)	0.160
		AG	169	8550.22	197.66		P a = 0.036
		GG	38	2451.05	155.04		P b = 0.416
	rs9324641	CC	124	6695.64	185.20	T	1.119 (0.971, 1.289)	0.112
		CT	199	9435.38	210.91		P a = 0.12
		TT	73	3179.39	229.60		P b = 0.473
Stroke incidence	rs28472363	GG	80	8941.19	89.47	G	1.075 (0.857, 1.348)	0.072
		GA	74	8655.02	85.50		P a = 0.532
		AA	19	2261.06	84.03		P b = 0.935
	rs5757573	TT	150	17357.71	86.42	C	1.037 (0.687, 1.565)	0.036
		TC	22	2408.42	91.35		P a = 0.863
		CC	1	91.14	109.72		P b = 0.935
	rs13053714	GG	131	14541.09	90.09	A	1.176 (0.851, 1.629)	0.163
		GA	40	5018.58	79.70		P a = 0.325
		AA	2	297.60	67.20		P b = 0.935
	rs1834389	AA	122	14047.86	86.85	C	1.007 (0.757, 1.340)	0.007
		AC	46	5283.24	87.07		P a = 0.963
		CC	5	526.18	95.02		P b = 0.963
	rs342309	GG	85	10144.27	83.79	A	1.037 (0.820, 1.310)	0.036
		GA	74	8099.16	91.37		P a = 0.763
		AA	14	1613.85	86.75		P b = 0.935
	rs6845322	AA	59	6208.74	95.03	A	1.075 (0.863, 1.342)	0.073
		AG	85	10213.05	83.23		P a = 0.517
		GG	29	3435.48	84.41		P b = 0.935
	rs1053861	CC	52	5914.76	87.92	C	1.041 (0.842, 1.287)	0.040
		CT	88	9887.07	89.01		P a = 0.711
		TT	33	4055.45	81.37		P b = 0.935
	rs11226185	TT	79	9549.93	82.72	C	1.045 (0.828, 1.318)	0.044
		TC	81	8698.03	93.12		P a = 0.711
		CC	13	1609.32	80.78		P b = 0.935
	rs4755010	GG	87	10408.79	83.58	C	1.051 (0.830, 1.330)	0.050
		GC	74	8030.92	92.14		P a = 0.680
		CC	12	1417.57	84.65		P b = 0.935
	rs6579775	CC	114	13292.86	85.76	T	1.030 (0.779, 1.363)	0.030
		CT	56	6059.80	92.41		P a = 0.834
		TT	3	504.61	59.45		P b = 0.935
	rs3828610	CC	50	6598.02	75.78	A	1.148 (0.931, 1.416)	0.138
		CA	86	9486.54	90.65		P a = 0.196
		AA	37	3772.72	98.07		P b = 0.935
	rs246390	AA	77	8603.55	89.50	A	1.114 (0.890, 1.391)	0.107
		AG	81	8743.86	92.64		P a = 0.346
		GG	15	2509.86	59.76		P b = 0.935
	rs9324641	CC	56	6856.15	81.68	T	1.092 (0.880, 1.355)	0.088
		CT	85	9706.41	87.57		P a = 0.423
		TT	32	3294.71	97.13		P b = 0.935
CHD incidence	rs28472363	GG	113	8820.31	128.11	A	1.046 (0.874, 1.251)	0.045
		GA	118	8537.09	138.22		P a = 0.626
		AA	32	2213.66	144.56		P b = 0.626
	rs5757573	TT	229	17110.63	133.83	C	1.099 (0.788, 1.534)	0.095
		TC	31	2373.22	130.62		P a = 0.578
		CC	3	87.21	343.99		P b = 0.626
	rs13053714	GG	204	14321.06	142.45	A	1.175 (0.900, 1.534)	0.161
		GA	53	4968.46	106.67		P a = 0.235
		AA	6	281.54	213.12		P b = 0.432
	rs1834389	AA	191	13832.25	138.08	A	1.126 (0.886, 1.433)	0.119
		AC	67	5217.80	128.41		P a = 0.332
		CC	5	521.00	95.97		P b = 0.432
	rs342309	GG	147	9961.13	147.57	G	1.161 (0.954, 1.414)	0.150
		GA	95	8014.84	118.53		P a = 0.136
		AA	21	1595.09	131.65		P b = 0.432
	rs6845322	AA	92	6108.14	150.62	A	1.115 (0.933, 1.333)	0.109
		AG	127	10087.12	125.90		P a = 0.231
		GG	44	3375.81	130.34		P b = 0.432
	rs1053861	CC	91	5806.27	156.73	C	1.092 (0.917, 1.297)	0.087
		CT	117	9772.11	119.73		P a = 0.323
		TT	55	3992.68	137.75		P b = 0.432
	rs11226185	TT	127	9401.97	135.08	C	1.102 (0.914, 1.329)	0.097
		TC	104	8621.38	120.63		P a = 0.310
		CC	32	1547.71	206.76		P b = 0.432
	rs4755010	GG	129	10263.59	125.69	C	1.149 (0.952, 1.386)	0.139
		GC	107	7931.39	134.91		P a = 0.147
		CC	27	1376.08	196.21		P b = 0.432
	rs6579775	CC	185	13077.22	141.47	C	1.115 (0.880, 1.412)	0.109
		CT	71	6001.11	118.31		P a = 0.366
		TT	7	492.73	142.07		P b = 0.433
	rs3828610	CC	80	6523.80	122.63	A	1.089 (0.918, 1.292)	0.085
		CA	130	9330.07	139.33		P a = 0.329
		AA	53	3717.19	142.58		P b = 0.432
	rs246390	AA	125	8435.29	148.19	A	1.143 (0.952, 1.370)	0.133
		AG	111	8659.34	128.19		P a = 0.151
		GG	27	2476.43	109.03		P b = 0.432
	rs9324641	CC	79	6792.40	116.31	T	1.170 (0.984, 1.391)	0.157
		CT	133	9552.71	139.23		P a = 0.076
		TT	51	3225.95	158.09		P b = 0.432
CVD death	rs28472363	GG	49	9096.33	53.87	A	1.250 (0.963, 1.623)	0.223
		GA	48	8801.94	54.53		P a = 0.094
		AA	22	2296.88	95.78		P b = 0.611
	rs5757573	TT	104	17657.14	58.90	C	1.048 (0.640, 1.716)	0.047
		TC	13	2446.26	53.14		P a = 0.852
		CC	2	91.75	217.98		P b = 0.913
	rs13053714	GG	86	14793.51	58.13	A	1.025 (0.701, 1.497)	0.025
		GA	33	5098.69	64.72		P a = 0.899
		AA	0	302.94	0.00		P b = 0.913
	rs1834389	AA	77	14288.22	53.89	C	1.366 (0.997, 1.870)	0.312
		AC	35	5371.38	65.16		P a = 0.052
		CC	7	535.55	130.71		P b = 0.611
	rs342309	GG	57	10316.43	55.25	A	1.183 (0.894, 1.564)	0.168
		GA	49	8236.75	59.49		P a = 0.239
		AA	13	1641.97	79.17		P b = 0.780
	rs6845322	AA	35	6325.58	55.33	G	1.172 (0.900, 1.526)	0.158
		AG	58	10386.74	55.84		P a = 0.240
		GG	26	3482.83	74.65		P b = 0.780
	rs1053861	CC	28	6018.26	46.53	T	1.054 (0.816, 1.362)	0.053
		CT	70	10054.62	69.62		P a = 0.686
		TT	21	4122.26	50.94		P b = 0.913
	rs11226185	TT	66	9704.80	68.01	T	1.138 (0.855, 1.515)	0.129
		TC	40	8863.76	45.13		P a = 0.377
		CC	13	1626.59	79.92		P b = 0.913
	rs4755010	GG	65	10572.08	61.48	G	1.100 (0.820, 1.473)	0.095
		GC	48	8183.29	58.66		P a = 0.525
		CC	6	1439.78	41.67		P b = 0.913
	rs6579775	CC	79	13530.75	58.39	T	1.045 (0.747, 1.462)	0.044
		CT	37	6154.43	60.12		P a = 0.798
		TT	3	509.97	58.83		P b = 0.913
	rs3828610	CC	36	6711.24	53.64	A	1.014 (0.784, 1.313)	0.014
		CA	63	9633.69	65.40		P a = 0.913
		AA	20	3850.22	51.95		P b = 0.913
	rs246390	AA	57	8749.24	65.15	A	1.066 (0.814, 1.395)	0.064
		AG	46	8900.28	51.68		P a = 0.642
		GG	16	2545.63	62.85		P b = 0.913
	rs9324641	CC	40	6976.59	57.33	T	1.051 (0.810, 1.365)	0.050
		CT	57	9857.08	57.83		P a = 0.706
		TT	22	3361.47	65.45		P b = 0.913
All cause death	rs28472363	GG	105	9096.33	115.43	A	1.060 (0.876, 1.281)	0.058
		GA	95	8801.94	107.93		P a = 0.55
		AA	34	2296.88	148.03		P b = 0.883
	rs5757573	TT	203	17657.14	114.97	C	1.078 (0.760, 1.528)	0.075
		TC	28	2446.26	114.46		P a = 0.674
		CC	3	91.75	326.97		P b = 0.883
	rs13053714	GG	175	14793.51	118.30	A	1.147 (0.867, 1.520)	0.137
		GA	59	5098.69	115.72		P a = 0.337
		AA	0	302.94	0.00		P b = 0.715
	rs1834389	AA	159	14288.22	111.28	C	1.148 (0.907, 1.452)	0.138
		AC	66	5371.38	122.87		P a = 0.251
		CC	9	535.55	168.05		P b = 0.715
	rs342309	GG	108	10316.43	104.69	A	1.148 (0.941, 1.402)	0.138
		GA	107	8236.75	129.91		P a = 0.174
		AA	19	1641.97	115.71		P b = 0.715
	rs6845322	AA	73	6325.58	115.40	G	1.126 (0.932, 1.360)	0.119
		AG	110	10386.74	105.90		P a = 0.218
		GG	51	3482.83	146.43		P b = 0.715
	rs1053861	CC	64	6018.26	106.34	T	1.029 (0.857, 1.235)	0.028
		CT	123	10054.62	122.33		P a = 0.761
		TT	47	4122.26	114.02		P b = 0.899
	rs11226185	TT	117	9704.80	120.56	T	1.009 (0.827, 1.232)	0.009
		TC	93	8863.76	104.92		P a = 0.931
		CC	24	1626.59	147.55		P b = 0.931
	rs4755010	GG	123	10572.08	116.34	G	1.013 (0.826, 1.242)	0.013
		GC	96	8183.29	117.31		P a = 0.898
		CC	15	1439.78	104.18		P b = 0.931
	rs6579775	CC	160	13530.75	118.25	C	1.054 (0.824, 1.348)	0.052
		CT	69	6154.43	112.11		P a = 0.679
		TT	5	509.97	98.05		P b = 0.883
	rs3828610	CC	70	6711.24	104.30	A	1.084 (0.904, 1.301)	0.081
		CA	118	9633.69	122.49		P a = 0.385
		AA	46	3850.22	119.47		P b = 0.715
	rs246390	AA	114	8749.24	130.30	A	1.164 (0.959, 1.414)	0.153
		AG	95	8900.28	106.74		P a = 0.125
		GG	25	2545.63	98.21		P b = 0.715
	rs9324641	CC	74	6976.59	106.07	T	1.090 (0.905, 1.312)	0.086
		CT	118	9857.08	119.71		P a = 0.363
		TT	42	3361.47	124.95		P b = 0.715

^a : P value of multiple Cox regression adjusted for age, sex, smoking, drinking, hypertension, diabetes and dyslipidemia. ^b : False Discovery Rate adjusted p value of association.

PDGF, platelet-derived growth factor; PDGFRB, platelet-derived growth factor receptor beta. MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; CAD, coronary artery disease; HR, hazard ratio; CI, confidence interval.

Individuals with high MACE-GRSs had a higher risk of MACEs than those with low MACE-GRSs, with an adjusted HR (95% CI) of 1.441 (1.108-1.875) (P = 0.006). In addition, for the incidence of CVD, the adjusted HRs (95% CIs) of high CVD-GRS and medium CVD-GRS were 1.755 (1.270-2.426) (P = 0.001) and 1.386 (1.044-1.840) (P = 0.024), respectively, compared to those with low GRS (Fig.2).

Fig.2. Association analyses of the GRS with MACEs and individual components

A total of 3139 CAD cases were selected for this study. The wGRS was categorized into three groups: low-risk (lowest quintile of GRS, n = 628), intermediate-risk (2nd to 4th quintiles of GRS, n = 1883), and high-risk (highest quartile of GRS, n = 628). GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular event incidence; CVD-GRS, GRS for cardiovascular disease incidence; Stroke-GRS, GRS for stroke incidence; CAD-GRS, GRS for coronary artery disease recurrence; CVDdeath-GRS, GRS for cardiovascular disease death; ACD-GRS, GRS for all-cause death; HR, hazard ratio; CI, confidence interval; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; CAD, coronary artery disease; ACD, all-cause death.

Similarly, compared to those with low CAD-GRSs, individuals with high and medium CAD-GRSs exhibited an increased risk of CAD recurrence, with adjusted HRs (95% CIs) of 1.990 (1.325-2.988) (P = 0.001) and 1.523 (1.061-2.187) (P = 0.023), respectively. In addition, individuals with high wGRSs had higher risks of CVD-related and all-cause death than those with low wGRSs, and the adjusted HRs (95% CIs) were 1.868 (1.039-3.358) and 1.604 (1.053-2.443), with P values of 0.028 and 0.037, respectively (Fig.2).

Enhancing the Predictive Model Performance with wGRS

AUCs (95% CIs) of TRFs, GRS, TRFs+GRACE, GRS+TRFs and GRS+TRFs+GRACE were 0.626 (0.601-0.650), 0.538 (0.512-0.563), 0.628 (0.603-0.653), 0.630 (0.605-0.655) and 0.632 (0.608-0.657) respectively. The AUC chances of GRS+TRFs over TRFs and GRS+TRFs+GRACE over TRFs+GRACE were not significant for the overall prediction of MACEs, CVD incidence, CAD recurrence, or all-cause death, but they were significant for the prediction of CVD death (Supplementary Table 5). Similarly, a negative association was observed for the C-index’s chance and outcome events (Table 2).

Supplementary Table 5.Comparison of AUC Values of GRS, TRFs, GRACE Score, and the combined models in predicting MACEs and its individual components

Outcome	Models	AUC				sensitivity	specificity
		AUC (95% CI)	Change (%)	P value	FDR adjusted P value
MACEs incidence	GRS+TRFs+GRACE	0.632 (0.608, 0.657)	0.637% a	0.308 a	0.406	0.611	0.589
	GRS+TRFs	0.630 (0.605, 0.655)	0.639% b	0.338 b	0.406	0.570	0.635
	TRFs+GRACE	0.628 (0.603, 0.653)				0.622	0.572
	TRFs	0.626 (0.601, 0.650)				0.639	0.562
	GRS	0.538 (0.512, 0.563)				0.599	0.471
CVD incidence	GRS+TRFs+GRACE	0.599 (0.570, 0.628)	1.525% a	0.260 a	0.406	0.434	0.719
	GRS+TRFs	0.594 (0.564, 0.623)	1.712% b	0.218 b	0.406	0.523	0.626
	TRFs+GRACE	0.590 (0.561, 0.619)				0.619	0.546
	TRFs	0.584 (0.555, 0.613)				0.735	0.414
	GRS	0.543 (0.513, 0.573)				0.770	0.311
Stroke incidence	GRS+TRFs+GRACE	0.637 (0.597, 0.678)	0.791% a	0.443 a	0.483	0.572	0.642
	GRS+TRFs	0.623 (0.584, 0.663)	0.646% b	0.545 b	0.545	0.809	0.382
	TRFs+GRACE	0.632 (0.592, 0.672)				0.838	0.377
	TRFs	0.619 (0.580, 0.658)				0.757	0.445
	GRS	0.533 (0.490, 0.576)				0.699	0.391
CAD recurrence	GRS+TRFs+GRACE	0.594 (0.559, 0.629)	3.304% a	0.095 a	0.372	0.479	0.656
	GRS+TRFs	0.594 (0.559, 0.629)	3.125% b	0.102 b	0.372	0.441	0.699
	TRFs+GRACE	0.575 (0.540, 0.610)				0.563	0.570
	TRFs	0.576 (0.541, 0.611)				0.551	0.587
	GRS	0.554 (0.519, 0.590)				0.582	0.529
CVD death	GRS+TRFs+GRACE	0.751 (0.705, 0.797)	2.038% a	0.032 a	0.372	0.630	0.802
	GRS+TRFs	0.725 (0.679, 0.772)	1.683% b	0.124 b	0.372	0.630	0.728
	TRFs+GRACE	0.736 (0.688, 0.783)				0.622	0.776
	TRFs	0.713 (0.665, 0.760)				0.681	0.656
	GRS	0.572 (0.519, 0.625)				0.605	0.525
All cause death	GRS+TRFs+GRACE	0.728 (0.695, 0.762)	0.692% a	0.162 a	0.389	0.662	0.702
	GRS+TRFs	0.709 (0.676, 0.742)	0.567% b	0.324 b	0.406	0.607	0.726
	TRFs+GRACE	0.723 (0.690, 0.757)				0.637	0.724
	TRFs	0.705 (0.672, 0.738)				0.637	0.685
	GRS	0.545 (0.507, 0.582)				0.645	0.442

^a comparison between GRS+TRFs+GRACE and TRFs+GRACE models; ^b comparison between GRS+TRFs and TRFs models.The DeLong test is used to compare the AUC of different models.MACEs, major adverse cardiovascular events; ; GRACE, global registry of acute coronary events; CVD, cardiovascular disease; CAD, coronary artery disease;TRFs, traditional risk factors; GRS, genetic risk score; CI, Confidence interval.

Table 2.MACEs and its individual components discrimination using Harrel’s C-statistic, NRI and IDI

Outcome	Models	C			NRI		IDI
		C (95% CI)	Change (%)	FDR-P	NRI (95% CI)	FDR-P	IDI (95% CI)	FDR-P
MACEs incidence
	GRS+TRFs+GRACE	0.624 (0.604, 0.652)	0.425%a	0.440a	5.1% (0.7%, 9.6%)	＜0.001	0.3% (0.0%, 0.5%)	＜0.001
	GRS+TRFs	0.617 (0.597, 0.645)	0.415%b	0.440b	5.1% (0.7%, 9.5%)	＜0.001	0.3% (0.0%, 0.6%)	＜0.001
	TRFs+GRACE	0.619 (0.600, 0.647)
	TRFs	0.613 (0.583, 0.640)
	GRS	0.535 (0.511, 0.560)
CVD incidence	GRS+TRFs+GRACE	0.597 (0.578, 0.634)	0.863%a	0.440a	6.5% (0.1%, 10.7%)	＜0.001	0.4% (0.0%, 0.7%)	＜0.001
	GRS+TRFs	0.597 (0.576, 0.632)	0.931%b	0.440b	6.3% (0.5%, 11.4%)	＜0.001	0.4% (0.0%, 0.5%)	＜0.001
	TRFs+GRACE	0.589 (0.569, 0.624)
	TRFs	0.587 (0.567, 0.622)
	GRS	0.546 (0.516, 0.576)
Stroke incidence	GRS+TRFs+GRACE	0.637 (0.608, 0.692)	0.545%a	0.440a	1.9% (-1.5%, 10.3%)	0.273	0.1% (-0.1%, 0.4%)	0.437
	GRS+TRFs	0.630 (0.599, 0.683)	0.400%b	0.440b	3.1% (-4.9%, 8.8%)	0.727	0.1% (0.0%, 0.3%)	＜0.001
	TRFs+GRACE	0.632 (0.602, 0.685)
	TRFs	0.626 (0.595, 0.678)
	GRS	0.535 (0.495, 0.581)
CAD recurrence	GRS+TRFs+GRACE	0.600 (0.580, 0.645)	1.713%a	0.440a	12.8% (0.4%, 18.5%)	＜0.001	0.4% (0.1%, 0.9%)	＜0.001
	GRS+TRFs	0.600 (0.579, 0.644)	1.701%b	0.440b	12.0% (2.5%, 18.3%)	＜0.001	0.4% (0.0%, 0.9%)	＜0.001
	TRFs+GRACE	0.583 (0.565, 0.628)
	TRFs	0.584 (0.562, 0.626)
	GRS	0.556 (0.522, 0.592)
CVD death	GRS+TRFs+GRACE	0.750 (0.710, 0.800)	1.586%a	0.440a	4.1% (-1.1%, 13.3%)	0.273	0.2% (0.0%, 0.8%)	＜0.001
	GRS+TRFs	0.716 (0.674, 0.770)	1.284%b	0.440b	6.6% (-2.3%, 12.4%)	0.485	0.2% (-0.1%, 0.5%)	0.243
	TRFs+GRACE	0.735 (0.693, 0.787)
	TRFs	0.703 (0.661, 0.757)
	GRS	0.574 (0.519, 0.628)
All cause death	GRS+TRFs+GRACE	0.728 (0.698, 0.765)	0.501%a	0.440a	2.2% (-6.7%, 8.3%)	0.595	0.0% (-0.2%, 0.2%)	0.909
	GRS+TRFs	0.697 (0.668, 0.736)	0.341%b	0.531b	2.6% (-6.6%, 9.0%)	0.595	0.1% (-0.1%, 0.3%)	0.909
	TRFs+GRACE	0.723 (0.692, 0.759)
	TRFs	0.693 (0.663, 0.730)
	GRS	0.543 (0.505, 0.582)

^a comparison between GRS+TRFs+GRACE and TRFs+GRACE models; ^b comparison between GRS+TRFs and TRFs models.

MACEs, major adverse cardiovascular events; GRACE, global registry of acute coronary events; CVD, cardiovascular disease; CAD, coronary artery disease;TRFs, traditional risk factors; GRS, genetic risk score; CI, Confidence interval; NRI, net reclassification index; IDI, integrated discrimination index.

FDR-P, FDR adjusted P value

Of note, the addition of MACE-GRS contributed to an improvement in the NRI (95% CI) of 5.1% (0.7%-9.5%) and IDIs (95% CI) of 0.3% (0.0%-0.6%) compared to the TRFs or TRFs+GRACE prediction models. In particular, the addition of MACE-GRS contributed to significant improvements in predicting CVD incidence and CAD recurrence, with NRI (95% CI) of 6.3% (0.5%-11.4%) and 12.0% (2.5%-18.3%). Furthermore, adding the wGRS improved the IDIs over the TRFs and GRACE model for predicting CVD events and death (Table 2).

Association Analyses of Coronary Artery Lesion Counts with MACE and CVD in Different GRS Stratification

Among the 3139 patients, 2431 underwent coronary angiography, with 689 showing involvement of to 1-3 coronary arteries. In the low-MACE-GRS group, CAD patients with single-vessel lesions exhibited a significantly increased risk of MACEs in comparison to those without vessel lesions, and the adjusted HR (95% CI) was 2.406 (1.102-5.251) (P = 0.028). Furthermore, patients diagnosed with dual-, triple-, or single- to triple-vessel lesions demonstrated a significantly higher risk of MACEs in the mid-MACE-GRS group than those with no vessel lesions and a low MACE-GRS, and with adjusted HRs (95% CIs) of 2.844 (1.855-4.361) (P＜0.001), 2.553 (1.720-3.790) (P＜0.001), and 2.123 (1.502-3.000) (P＜0.001), respectively. Similarly, patients with a high MACE-GRS and no, dual-, triple-, or single- to triple-vessel lesions had a significantly increased risk of MACEs in comparison to those with no lesions and a low MACE-GRS, with adjusted HRs (95% CIs) of 1.499 (1.119-2.009) (P = 0.007), 2.390 (1.148-4.976) (P = 0.020), 2.448 (1.351-4.436) (P = 0.003), and 1.882 (1.140-3.106) (P = 0.013), respectively.

In the low-CVD-GRS group, CAD patients with single-vessel lesions exhibited a significantly increased risk of CVD incidence in comparison to those without vessel lesions, with an adjusted HR (95% CI) of 3.000 (1.181-7.619) (P = 0.021). As for the mid-CVD-GRS group, patients with single-, dual-, triple-, and single- to triple-vessel lesions had a significantly increased incidence of CVD in comparison to patients with no lesions in the low-CVD-GRS group, and the adjusted HRs (95% CIs) were 2.452 (1.316-4.568) (P = 0.005), 3.248 (1.904-5.538) (P＜0.001), 2.820 (1.708-4.655) (P＜0.001) and 2.427 (1.567-3.760) (P＜0.001), respectively. Furthermore, compared with the lowest CVD-GRS group without vessel lesions, there was a significant increase in the risk of post-CAD CVD incidence in the high-CVD-GRS group with no, dual-, triple-, or single- to triple-vessel lesions, with adjusted HRs (95% CIs) of 3.480 (1.634-7.413) (P = 0.001), 3.700 (1.904-7.188) (P＜0.001), and 2.724 (1.552-4.781) (P＜0.001), respectively (Fig.3 and Supplementary Table 6).

Fig.3. Comparisons of the GRS among single-, dual-, and triple-vessel disease groups

A total of 3139 CAD cases were selected. GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular event incidence; CVD-GRS, GRS for cardiovascular disease incidence; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval.

Supplementary Table 6.Comparison of GRS among patients with single vessel lesion, dual vessel lesion, and triple vessel lesion

GRS	Groups	Subgroups	HR (95% CI)	P	FDR adjusted P value	HR (95% CI)a	Pa	FDR adjusted P value
MACE-GRS	Low (n = 628)	No vessel lesion (n = 488)	1			1
		1 vessel lesion (n = 45)	1.769 (0.815, 3.844)	0.149	0.309	2.406 (1.102, 5.251)	0.028	0.062
		2 vessel lesion (n = 54)	1.255 (0.546, 2.887)	0.593	0.652	1.458 (0.632, 3.364)	0.376	0.414
		3 vessel lesion (n = 41)	1.580 (0.688, 3.630)	0.281	0.386	1.558 (0.677, 3.590)	0.297	0.363
		1-3 vessel lesion (n = 140)	1.439 (0.857, 2.419)	0.169	0.309	1.547 (0.907, 2.637)	0.109	0.199
	Medium (n = 1883)	No vessel lesion (n = 1486)	1.162 (0.903, 1.495)	0.244	0.383	1.153 (0.895, 1.484)	0.271	0.363
		1 vessel lesion (n = 97)	1.285 (0.698, 2.364)	0.421	0.514	1.523 (0.825, 2.810)	0.179	0.281
		2 vessel lesion (n = 132)	2.554 (1.669, 3.906)	＜0.001	＜0.001	2.844 (1.855, 4.361)	＜0.001	＜0.001
		3 vessel lesion (n = 168)	2.582 (1.744, 3.823)	＜0.001	＜0.001	2.553 (1.720, 3.790)	＜0.001	＜0.001
		1-3 vessel lesion (n = 397)	2.169 (1.552, 3.031)	＜0.001	＜0.001	2.123 (1.502, 3.000)	＜0.001	＜0.001
	High (n = 628)	No vessel lesion (n = 476)	1.477 (1.103, 1.979)	0.009	0.049	1.499 (1.119, 2.009)	0.007	0.038
		1 vessel lesion (n = 44)	0.836 (0.263, 2.657)	0.762	0.762	1.075 (0.337, 3.422)	0.903	0.903
		2 vessel lesion (n = 46)	2.231 (1.074, 4.636)	0.032	0.088	2.390 (1.148, 4.976)	0.020	0.055
		3 vessel lesion (n = 62)	2.415 (1.338, 4.359)	0.003	0.033	2.448 (1.351, 4.436)	0.003	0.033
		1-3 vessel lesion (n = 152)	1.746 (1.078, 2.829)	0.024	0.088	1.882 (1.140, 3.106)	0.013	0.047
CVD-GRS	Low (n = 628)	No vessel lesion (n = 490)	1			1
		1 vessel lesion (n = 37)	2.467 (0.979, 6.217)	0.056	0.130	3.000 (1.181, 7.619)	0.021	0.047
		2 vessel lesion (n = 54)	1.113 (0.346, 3.588)	0.857	0.857	1.166 (0.361, 3.767)	0.798	0.798
		3 vessel lesion (n = 47)	1.916 (0.760, 4.828)	0.168	0.210	1.916 (0.757, 4.849)	0.170	0.255
		1-3 vessel lesion (n = 138)	1.734 (0.914, 3.289)	0.092	0.131	1.489 (0.758, 2.925)	0.248	0.302
	Medium (n = 1883)	No vessel lesion (n = 1481)	1.346 (0.977, 1.854)	0.069	0.13	1.363 (0.989, 1.878)	0.059	0.106
		1 vessel lesion (n = 109)	2.186 (1.178, 4.056)	0.013	0.043	2.452 (1.316, 4.568)	0.005	0.015
		2 vessel lesion (n = 128)	2.986 (1.761, 5.064)	＜0.001	＜0.001	3.248 (1.904, 5.538)	＜0.001	＜0.001
		3 vessel lesion (n = 165)	2.858 (1.737, 4.702)	＜0.001	＜0.001	2.820 (1.708, 4.655)	＜0.001	＜0.001
		1-3 vessel lesion (n = 402)	2.607 (1.715, 3.962)	＜0.001	＜0.001	2.427 (1.567, 3.760)	＜0.001	＜0.001
	High (n = 628)	No vessel lesion (n = 479)	1.719 (1.192, 2.481)	0.004	0.020	1.764 (1.222, 2.546)	0.002	0.009
		1 vessel lesion (n = 40)	1.550 (0.481, 4.999)	0.463	0.514	1.942 (0.600, 6.290)	0.268	0.302
		2 vessel lesion (n = 50)	3.316 (1.560, 7.050)	0.002	0.020	3.480 (1.634, 7.413)	0.001	0.009
		3 vessel lesion (n = 59)	3.656 (1.889, 7.075)	＜0.001	＜0.001	3.700 (1.904, 7.188)	＜0.001	＜0.001
		1-3 vessel lesion (n = 149)	2.796 (1.626, 4.806)	0.078	0.130	2.724 (1.552, 4.781)	＜0.001	＜0.001

^a P value of multiple Cox regression adjusted for age, sex, smoking, drinking, hypertension, diabetes and dyslipidemia. GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular incidence; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease.

Association Analyses of Comorbidity Numbers with MACEs and CVD in Different GRS Stratifications

In the high-MACE-GRS group, patients with three comorbidities had a significantly increased risk of MACEs compared to those without comorbidities in the low-MACE-GRS group, with an adjusted HR (95% CI) of 2.393 (1.198-4.781) (P = 0.013).

In the mid-CVD-GRS group, patients with three comorbidities had a significantly increased risk of CVD compared to those without comorbidities in the low-CVD-GRS group, with an adjusted HR (95% CI) of 2.988 (1.184-7.541) (P = 0.020). In the high-CVD-GRS group, although the risk of CVD occurrence significantly increased when patients had no comorbidities, with an adjusted HR (95% CI) of 2.818 (1.003-7.919) (P = 0.049), the risk of CVD occurrence further increased when patients had 1, 2, 3 or any comorbidities, with adjusted HRs (95% CIs) of 2.564 (1.002-6.558), 2.660 (1.034-6.844), 4.044 (1.492-10.963), and 2.985 (1.206-7.384) and adjusted P-values of 0.049, 0.043, 0.006, and 0.018, respectively (Fig.4 and Supplementary Table 7).

Fig.4. Comparisons of the GRS among different comorbidity groups

A total of 3139 CAD cases were selected for this study. Comorbidities included hypertension, diabetes, and dyslipidemia. Adjusted for age, sex, smoking, and drinking. GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular event incidence; CVD-GRS, GRS for cardiovascular disease incidence; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; HR, hazard ratio; CI, confidence interval.

Supplementary Table 7.Comparison of GRS among patients with single comorbidity, dual comorbidities and triple comorbidities

GRS	Groups	Subgroups	HR (95% CI)	P	FDR adjusted P value	HR (95% CI)a	Pa	FDR adjusted P value
MACE-GRS	Low (n = 628)	No comorbidity (n = 90)	1			1
		1 comorbidity (n = 235)	1.192 (0.635, 2.237)	0.586	0.754	1.236 (0.658, 2.321)	0.51	0.712
		2 comorbidities (n = 210)	1.163 (0.611, 2.216)	0.646	0.754	1.192 (0.626, 2.272)	0.593	0.712
		3 comorbidities (n = 93)	1.076 (0.506, 2.288)	0.85	0.85	1.093 (0.514, 2.326)	0.818	0.818
		1-3 comorbidities (n = 538)	1.163 (0.648, 2.086)	0.613	0.754	1.165 (0.649, 2.091)	0.61	0.712
	Medium (n = 1883)	No comorbidity (n = 224)	1.110 (0.584, 2.110)	0.749	0.807	1.137 (0.598, 2.161)	0.695	0.748
		1 comorbidity (n = 739)	1.400 (0.793, 2.473)	0.246	0.492	1.388 (0.786, 2.453)	0.258	0.426
		2 comorbidities (n = 652)	1.353 (0.763, 2.397)	0.301	0.527	1.398 (0.788, 2.478)	0.252	0.426
		3 comorbidities (n = 268)	1.645 (0.901, 3.006)	0.105	0.438	1.629 (0.891, 2.976)	0.113	0.396
		1-3 comorbidities (n = 1659)	1.413 (0.812, 2.462)	0.222	0.492	1.424 (0.818, 2.481)	0.212	0.426
	High (n = 628)	No comorbidity (n = 83)	1.391 (0.662, 2.924)	0.384	0.597	1.514 (0.720, 3.188)	0.274	0.426
		1 comorbidity (n = 253)	1.609 (0.876, 2.956)	0.125	0.438	1.636 (0.890, 3.008)	0.113	0.396
		2 comorbidities (n = 214)	1.509 (0.810, 2.811)	0.195	0.492	1.561 (0.838, 2.909)	0.161	0.426
		3 comorbidities (n = 78)	2.359 (1.181, 4.711)	0.015	0.210	2.393 (1.198, 4.781)	0.013	0.182
		1-3 comorbidities (n = 545)	1.696 (0.955, 3.011)	0.071	0.438	1.759 (0.988, 3.132)	0.055	0.385
CVD-GRS	Low (n = 628)	No comorbidity (n = 81)	1			1
		1 comorbidity (n = 235)	1.757 (0.675, 4.575)	0.249	0.317	1.826 (0.701, 4.755)	0.218	0.290
		2 comorbidities (n = 214)	1.406 (0.522, 3.787)	0.5	0.538	1.462 (0.543, 3.940)	0.452	0.487
		3 comorbidities (n = 98)	1.867 (0.649, 5.373)	0.247	0.317	1.917 (0.666, 5.520)	0.228	0.290
		1-3 comorbidities (n = 530)	1.642 (0.657, 4.103)	0.288	0.336	1.609 (0.643, 4.023)	0.309	0.361
	Medium (n = 1883)	No comorbidity (n = 226)	1.308 (0.486, 3.524)	0.595	0.595	1.380 (0.512, 3.718)	0.524	0.524
		1 comorbidity (n = 745)	2.179 (0.887, 5.357)	0.09	0.158	2.248 (0.914, 5.528)	0.078	0.126
		2 comorbidities (n = 651)	2.119 (0.859, 5.223)	0.103	0.160	2.232 (0.905, 5.506)	0.081	0.126
		3 comorbidities (n = 261)	2.922 (1.159, 7.37)	0.023	0.149	2.988 (1.184, 7.541)	0.02	0.093
		1-3 comorbidities (n = 1657)	2.269 (0.935, 5.505)	0.07	0.158	2.335 (0.962, 5.669)	0.061	0.122
	High (n = 628)	No comorbidity (n = 90)	2.524 (0.9, 7.0810)	0.079	0.158	2.818 (1.003, 7.919)	0.049	0.114
		1 comorbidity (n = 247)	2.433 (0.951, 6.220)	0.063	0.158	2.564 (1.002, 6.558)	0.049	0.114
		2 comorbidities (n = 211)	2.582 (1.004, 6.641)	0.049	0.158	2.660 (1.034, 6.844)	0.043	0.114
		3 comorbidities (n = 80)	3.914 (1.444, 10.610)	0.007	0.098	4.044 (1.492, 10.963)	0.006	0.084
		1-3 comorbidities (n = 538)	2.691 (1.091, 6.640)	0.032	0.149	2.985 (1.206, 7.384)	0.018	0.093

^a P value of multiple Cox regression adjusted for age, sex, smoking, drinking, hypertension, diabetes and dyslipidemia. GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular incidence; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease.

Correlation between wGRS and GRACE Scores and Clinical Indices

MPV had weak positive correlations with Stroke-GRS and CAD-GRS and a weak negative correlation with CVDdeath-GRS after CAD (all | r | ＜0.1, P＜0.05). In addition, PDW had weak negative correlations with the MACE-GRS and CVD-GRS (| r| ＜0.1, P＜0.05). Furthermore, the epidermal growth factor receptor (EGFR) was weakly negatively correlated with CVDdeath-GRS and ACD-GRS (| r | ＜0.1, P＜0.05) after CAD. No statistical correlation was found between wGRS and metabolic indices, including glucose, blood pressure, lipids, and other platelet parameters (P＞0.05), among CAD patients (Supplementary Fig.1).

Supplementary Fig.1. Relationship between GRS and clinical measurements

The p-values and correlation coefficients (r) were obtained from Pearson correlation analysis. GRS, genetic risk score; GRS_MACE, GRS for major adverse cardiovascular incidence; GRS_CVD, GRS for cardiovascular disease incidence; GRS_stroke, GRS for stroke incidence; GRS_CAD, GRS for coronary artery disease recurrence; GRS_{CVD_death}, GRS for cardiovascular disease death; GRS_ACD, GRS for all cause death; SBP, systolic blood pressure; DBP, diastolic blood pressure; GLU, glucose; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; APOA1, apolipoprotein A1; APOB, apolipoprotein B; LPa, Lipoprotein(a); MPV, mean platelet volume; PCT, platelet crit; PLT, platelet parameters including platelet counts; PDW, platelet distribution width; eGFR, epidermal growth factor receptor; TBIL, total bilirubin; DBIL, direct bilirubin

In patients with CAD, weak positive correlations were observed between GRACE score and MACE-GRS and CVD-GRS (| r | ＜0.1, P＜0.05), as shown in Supplementary Table 8. In addition, among AMI patients, the GRACE score was positive correlated with MACE-GRS_, CVD-GRS_, stroke-GRS, and CAD-GRS (all | r | ＜0.1, P＜0.05) (Supplementary Table 9). Furthermore, for ST-segment elevation myocardial infarction (NSTEMI) patients, the GRACE score was positive correlated with the MACE-GRS (r = 0.154, P = 0.005), CVD-GRS (r = 0.153, P = 0.005), stroke-GRS (r = 0.139, P = 0.011), CAD-GRS (r = 0.141, P = 0.010), and ACD-GRS (r = 0.119, P = 0.030) (Supplementary Table 8). However, further subgroup analyses of AP patients into stable angina (SA), unstable angina (UA), and angina pectoris-unspecified etiology (AP_UE) groups did not reveal any statistically significant correlations (Supplementary Table 10).

Supplementary Table 8.Correlation analysis of GRS and GRACE score for all patients

GRS	CAD patients (n = 3139)		AMI patients (n = 1376)		AP patients (n = 1058)		HF patients (n = 56)		Arrhythmia patients (n = 88)		CAD_UE patients (n = 324)
	r	P a	r	P a	r	P a	r	P a	r	P a	r	P a
MACE-GRS	0.037	0.046	0.064	0.018	-0.036	0.239	-0.007	0.961	0.158	0.143	0.049	0.382
CVD-GRS	0.038	0.043	0.068	0.011	-0.044	0.151	-0.010	0.943	0.149	0.167	0.063	0.261
Stroke-GRS	0.031	0.093	0.060	0.027	-0.042	0.167	0.062	0.652	0.085	0.431	0.056	0.314
CAD-GRS	0.035	0.058	0.065	0.015	-0.043	0.163	-0.044	0.748	0.129	0.230	0.062	0.266
CVDdeath-GRS	-0.008	0.678	-0.018	0.512	0.024	0.431	-0.055	0.687	0.028	0.798	-0.062	0.267
ACD-GRS	0.018	0.331	0.029	0.281	-0.011	0.716	-0.032	0.816	0.121	0.261	-0.012	0.827

^a :P value of Pearson correlation test.

GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular incidence; CVD-GRS, GRS for cardiovascular disease incidence; Stroke-GRS, GRS for stroke incidence; CAD-GRS, GRS for coronary artery disease recurrence; CVDdeath-GRS, GRS for cardiovascular disease death; ACD-GRS, GRS for all cause death; GRACE, global registry of acute coronary events; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; CAD, coronary artery disease; GRS, genetic risk score; AMI, acute myocardial infarction; AP, angina pectoris; HF, heart failure; CAD-UE , coronary artery disease of unspecified etiology.

Supplementary Table 9.Correlation analysis of GRS and GRACE score for AMI patients

GRS	AMI patients (n = 1376)		STEMI patients (n = 105)		NSTEMI patients (n = 332)		AMI_UE patients (n = 939)
	r	P a	r	P a	r	P a	r	P a
MACE-GRS	0.064	0.018	0.020	0.839	0.154	0.005	0.032	0.333
CVD-GRS	0.068	0.011	0.028	0.774	0.153	0.005	0.038	0.245
Stroke-GRS	0.060	0.027	0.061	0.535	0.139	0.011	0.027	0.408
CAD-GRS	0.065	0.015	0.077	0.432	0.141	0.010	0.035	0.291
CVDdeath-GRS	-0.018	0.512	-0.142	0.150	0.028	0.611	-0.022	0.504
ACD-GRS	0.029	0.281	-0.002	0.987	0.119	0.030	-0.002	0.948

^a P value of Pearson correlation test.

GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular incidence; CVD-GRS, GRS for cardiovascular disease incidence; Stroke-GRS, GRS for stroke incidence; CAD-GRS, GRS for coronary artery disease recurrence; CVDdeath-GRS, GRS for cardiovascular disease death; ACD-GRS, GRS for all cause death; GRACE, global registry of acute coronary events; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; CAD, coronary artery disease; GRS, genetic risk score; AMI, acute myocardial infarction; STEMI, ST-segment elevation myocardial infarction; NSTEMI, Non-ST-segment elevation myocardial infarction; AMI_UE, acute myocardial infarction of unspecified etiology.

Supplementary Table 10.Correlation analysis of GRS and GRACE score for AP patients

GRS	AP patients (n = 1058)		SA patients (n = 128)		UA patients (n = 407)		AP_UE patients (n = 523)
	r	P a	r	P a	r	P a	r	P a
MACE-GRS	-0.036	0.239	0.073	0.410	-0.019	0.702	-0.081	0.064
CVD-GRS	-0.044	0.151	0.064	0.472	-0.038	0.444	-0.080	0.066
Stroke-GRS	-0.042	0.167	0.094	0.291	-0.016	0.744	-0.103	0.018
CAD-GRS	-0.043	0.163	0.032	0.717	-0.033	0.512	-0.072	0.099
CVDdeath-GRS	0.024	0.431	0.042	0.638	0.035	0.486	0.011	0.798
ACD-GRS	-0.011	0.716	0.084	0.346	0.022	0.652	-0.063	0.150

^a :P value of Pearson correlation test.

GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular incidence; CVD-GRS, GRS for cardiovascular disease incidence; Stroke-GRS, GRS for stroke incidence; CAD-GRS, GRS for coronary artery disease recurrence; CVDdeath-GRS, GRS for cardiovascular disease death; ACD-GRS, GRS for all cause death; GRACE, global registry of acute coronary events; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; CAD, coronary artery disease; GRS, genetic risk score; AP, angina pectoris; SA, stable angina; UA, Unstable Angina; AP_UE, angina pectoris of unspecified etiology.

Sensitivity Analyses

To minimize the effect of population heterogeneity and treatment factors on the association between the GRS and MACEs, we selected a total of 2,664 patients with AMI and AP and 2,613 patients who had not received medication or interventional therapies for a sensitivity analysis. As shown in Supplementary Fig.2 and Supplementary Fig.3, the GRS-MACE association was unchanged in these populations.

Supplementary Fig.2. Association analyses of GRS with the MACEs and its individual components under the patients with acute myocardial infarction and angina pectoris

2664 CAD cases were selected. The wGRS was categorized into three groups: low risk (the lowest quintile of GRS), intermediate risk (the 2nd to 4th quintiles of GRS), and high risk (the highest quartile of GRS).

GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular incidence; CVD-GRS, GRS for cardiovascular disease incidence; Stroke-GRS, GRS for stroke incidence; CAD-GRS, GRS for coronary artery disease recurrence; CVDdeath-GRS, GRS for cardiovascular disease death; ACD-GRS, GRS for all cause death; HR, hazard ratio; CI, confidence interval; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; CAD, coronary artery disease; ACD, all cause death.

Supplementary Fig.3. Association analyses of GRS with the MACEs and its individual components after excluding patients who had taken medication and interventional therapy

2613 CAD cases were selected. The wGRS was categorized into three groups: low risk (the lowest quintile of GRS), intermediate risk (the 2nd to 4th quintiles of GRS), and high risk (the highest quartile of GRS).

GRS, genetic risk score; MACE-GRS, GRS for major adverse cardiovascular incidence; CVD-GRS, GRS for cardiovascular disease incidence; Stroke-GRS, GRS for stroke incidence; CAD-GRS, GRS for coronary artery disease recurrence; CVDdeath-GRS, GRS for cardiovascular disease death; ACD-GRS, GRS for all cause death; HR, hazard ratio; CI, confidence interval; MACEs, major adverse cardiovascular events; CVD, cardiovascular disease; CAD, coronary artery disease; ACD, all cause death.

Discussion

In the present study, we evaluated the associations between genetic variants in PDGFs/PDGFRB signaling pathway genes and the CAD prognosis. In addition to a single locus of rs246390 in PDGFRB associated with an increased risk of MACEs and CVD, we constructed a wGRS for MACEs and validated the effect of medium and high wGRS stratification on the increased risk of MACEs, CVD mortality, and all-cause mortality. In addition, combining the wGRS with TRFs and/or GRACE scores improved the discrimination and reclassification of predictive models for MACEs, CVD, and CAD occurrence. Furthermore, in the medium and high-wGRS groups, the incidences of MACEs and CVD were significantly higher in patients with vessel lesions or other comorbidities than in those without vessel lesions in the low-wGRS group. In patients with NSTEMI, the wGRS showed a positive correlation with the GRACE score. These findings support the predictive value of the wGRS from PDGF/PDGFRB signaling pathway genes for the risk stratification of MCAEs in CAD patients.

Owing to the influence of genetic complexity, the effect of a single locus on the prognosis of CAD within the PDGF signaling pathway may be weak or interfered with by other factors. Therefore, in the present study, a genetic-based risk prediction model was created by incorporating the genetic variation data of PDGF-related SNPs into the gene scores. In line with our findings, several previous studies have reported significant associations between the wGRS and susceptibility to MACEs or recurrent CAD in patients^33-36), highlighting the importance of genetic risk assessment in identifying high-risk individuals and developing personalized treatment strategies for CAD. However, other studies suggested no significant correlation between the wGRS and short-term cardiovascular events³⁷⁾, and the set wGRS did not enhance the accuracy of predicting the 10-year risk of cardiovascular events compared to clinical factors alone³⁸⁾. Comparing the effect sizes of wGRS directly is challenging because of variations in the number of SNPs, wGRS categorizations, and endpoints used across different studies. Based on our results and those of previous studies, we speculate that the inclusion of a single outcome event, such as myocardial infarction, stroke, and all-cause mortality, but not a composite event of MACEs, may dilute the power of the wGRS predictive ability.

Regardless of the predictive accuracy, genetic prediction has several advantages over traditional methods. For instance, wGRS prediction remains highly consistent over time because an individual’s genetic makeup remains essentially unchanged throughout their lifespan³⁹⁾. Second, the wGRS is relatively unaffected by TRFs⁴⁰⁾. According to our research, the wGRS provides better discriminatory ability for MACEs in patients with CAD than risk stratification based solely on TRFs and/or GRSCE scores. Third, determining the wGRS is relatively cost-effective and can be achieved using blood samples. However, the issue that still needs to be addressed is that the construction of the wGRS typically involves the reporting or identification of reliable susceptible SNPs through a GWAS, which may contribute to the potential overestimation of risk prediction for wGRS⁴¹⁾. Furthermore, if the wGRS includes SNPs associated with different pathophysiological axes relevant to MACEs, adding GRSs may not significantly strengthen the ability of TRF model to predict outcome events³⁸⁾.

The PDGF signaling pathway may influence the occurrence, development, and prognosis of CAD through mechanisms involving inflammation, thrombosis, and platelet-endothelial interactions⁴²⁾. The present study is the first to reveal that the G to A variation in PDGFRB rs246390 was significantly associated with an increased risk of MACEs and CVD incidence. In vitro, PDGFRB is essential for the development of mural cells, VSMCs, and pericytes in mice during epithelial-mesenchymal transition (EMT)⁴³⁾. It was also demonstrated that PDGFRB is essential for vascular stability. In addition to regulating vascular perfusion, PDGFRB signaling provides new blood vessels with pericytes and assists in remodeling, stabilizing, and maturing them, thereby contributing to physiological and pathological processes of cardiovascular disease^{44, 45)}. A previous study revealed that PDGFRB exhibited reduced relative expression in samples obtained from individuals with CAD, but no significant correlation was observed between its methylation and corresponding expression⁴⁶⁾. These results suggest that genetic variants of PDGFRB are closely related to the occurrence, progression, and event risk of CAD. In addition, it should be noted that rs246390 is located within introns, which are non-coding sequences typically removed by splicing in eukaryotic genes. Although previously believed not to affect protein expression, non-coding sequences (introns) can lead to undesired gene transcript variants⁴⁷⁾ or alterations in gene expression⁴⁸⁾, which can have detrimental effects on health or increase the disease risk. To date, no association has been reported between PDGFRB rs246390 and any other health risk. As the association was not statistically significant after FDR correction, further validation using a larger sample size is required.

In the present study, patients with vessel lesions or other comorbidities demonstrated a significantly higher risk of CVD occurrence in the mid- and high-CVD-GRS groups than in the low-CVD-GRS group without vessel lesions or comorbidities. Furthermore, as the stratified risk levels and number of lesions and comorbidities increased, this effect became more pronounced. All patients with CAD received basic medication upon discharge. In addition, personalized recommendations for antihypertensive, antidiabetic, and anti-ischemic medications can be made based on associated comorbidities and angina symptoms⁴⁹⁾. Therefore, the results of the stratified analysis partially reflect the potential impact of specific comorbidity medications on the predictive capability of GRS. These findings emphasize the importance of monitoring and managing the CAD progression and prognosis as well as conducting comprehensive cardiovascular risk assessments, particularly for patients with high genetic risk, in preventing recurrent CVD and extending the lifespan.

As expected, pathophysiological alterations in intermediary linkages, such as the platelet function, diabetes, and inflammatory status, may be more closely linked to the incidence of MACEs in patients with CAD than the TRFs such as living style and environmental exposure⁵⁰⁾. In this study, we observed a weak correlation between the MPV, PDW, eGFR, and wGRS, as well as a positive correlation between the GRACE score and wGRS, particularly among patients with NSTEMI. Consistent with our study findings, an observational cohort study of 242 NSTEMI patients followed for 5 years observed a positive correlation between serum levels of PDGF, MPV, and the GRACE score⁵¹⁾. Coupled with the previously mentioned observations, this indicates a potential association between PDGF-related genetic effects, platelet attributes, and severity of acute myocardial infarction gauged by the GRACE score. More comprehensive research is needed to delve deeper into the putative mechanisms and clinical implications of this relationship.

Nevertheless, our study has several limitations. First, the observed associations lack the support of sufficient mechanistic studies and experimental validation to elucidate clinically relevant genetic effects. Second, our study only focused on investigating 13 tagSNPs within the PDGF-PDGFRB pathway and did not cover all possible genes and variations that may be relevant to the risk of MACEs. Third, since this study only involved one center, it should not be surprising that any potential selection bias was unavoidable. Fourth, we did not consider surgical factors, which are important determinants of the prognosis for CAD patients⁵²⁾, such as patients who only require medical treatment without the need for a stent or balloon angioplasty despite having 50%-70% stenosis. Fifth, the potential impact of medication factors on the predictive value of the GRS was not adequately considered. Sixth, the lack of an independent validation dataset or cross-validation in this study led to unavoidable overfitting. Finally, further research is needed to accurately apply the current wGRS to clinical practice. Relevant clinical practice and validation studies are crucial for determining the accuracy, predictive value, and applicability of the wGRS in different populations.

Conclusions

In summary, genetic variations in the PDGF-PDGFRB pathway contribute to the risk of MACEs in patients with CAD, and the wGRS of PDGF-PDGFRB pathway genes may be able to offer incremental value in predicting the risk of MACEs, particularly in patients with coronary artery vessel lesions. These findings provide a new perspective on the molecular causes of MACEs after CAD and emphasize the importance of developing wGRS tools to predict and manage the risk of MACEs in patients with CAD, which might aid in the development of personalized treatment plans and offer important guidance and reference for clinical practice.

Acknowledgements

We would like to extend our sincere appreciation to all the individuals and organizations that played a crucial role in the successful completion of this study. Their valuable support and assistance were indispensable for conducting the research and preparing the manuscript.

Funding

This work was supported by grants from the National Natural Science Foundation of China [82173611 and 81872686].

Ethics Approval and Consent to Participate

Participant consent and ethical approval for all data were obtained in the original studies.

Consent for Publication

Not applicable.

Competing Interests

The authors declare that they have no conflicts of interest to disclose.

Author Contributions

Chong Shen and Song Yang conceived of and designed the experiments. Xiaojuan Xu and Wen Li wrote the manuscript. Fangyuan Liu, Changying Chen, and Feifan Wang performed experiments. Xu Han, Hankun Xie and Qian Zhuang analyzed the data. Xianghai Zhao, Junxiang Sun, and Yunjie Yin contributed reagents, materials, and analytical tools. Pengfei Wei and Yanchun Chen were responsible for investigation and software development.

Abbreviations

PDGFs Platelet-derived growth factors

PDGFRB Platelet-derived growth factor receptor beta

CAD Coronary artery disease

MACEs Major adverse cardiovascular events

wGRS Weighted genetic risk scores

TRFs Traditional risk factors

GRACE Global Registry of Acute Coronary Events

NRI Net reclassification improvement

IDI Integrated discrimination improvement

ACS Acute coronary syndrome

VSMCs Vascular smooth muscle cells

NSTE-ACS Non-ST-segment elevation acute coronary syndrome

AMI Acute myocardial infarction

AP Angina pectoris

HF Heart failure

LCM Left main coronary artery

LAD Left anterior descending artery

LCX Left circumflex artery

RCA Right coronary artery

SBP Systolic blood pressure

DBP Diastolic blood pressure

FBG Fasting blood glucose

TC Total cholesterol

HDL-C High-density lipoprotein cholesterol

TG Triglycerides

LDL-C Low-density lipoprotein cholesterol

PLT Platelet counts

MPV Mean platelet volume

PDW Platelet distribution width

PCT Platelet crit

CHB Chinese Han population in Beijing

MAF Minor allele frequency

References

• 1)  Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet, 2016; 388: 1459-1544
• 2)  Mellgren AM, Smith CL, Olsen GS, Eskiocak B, Zhou B, Kazi MN, Ruiz FR, Pu WT, Tallquist MD: Platelet-derived growth factor receptor beta signaling is required for efficient epicardial cell migration and development of two distinct coronary vascular smooth muscle cell populations. Circ Res, 2008; 103: 1393-1401
• 3)  Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, et al: Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol, 2020; 76: 2982-3021
• 4)  Liu R, Gao X, Liang S, Zhao H: Five-years’ prognostic analysis for coronary artery ectasia patients with coronary atherosclerosis: A retrospective cohort study. Front Cardiovasc Med, 2022; 9: 950291
• 5)  Kosmas N, Manolis AS, Dagres N, Iliodromitis EK: Myocardial infarction or acute coronary syndrome with non-obstructive coronary arteries and sudden cardiac death: a missing connection. Europace, 2020; 22: 1303-1310
• 6)  Ullah M, Wahab A, Khan SU, Zaman U, Rehman KU, Hamayun S, Naeem M, Ali H, Riaz T: Stent as a Novel Technology for Coronary Artery Disease and their Clinical Manifestation. Curr Probl Cardiol, 2022; 101415
• 7)  Klarin D, Natarajan P: Clinical utility of polygenic risk scores for coronary artery disease. Nat Rev Cardiol, 2022; 19: 291-301
• 8)  Aragam KG, Jiang T, Goel A, Kanoni S, Wolford BN, Atri DS, Weeks EM, Wang M, Hindy G, Zhou W, et al: Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat Genet, 2022; 54: 1803-1815
• 9)  Sun L, Pennells L, Kaptoge S, Nelson CP, Ritchie SC, Abraham G, Arnold M, Bell S, Bolton T, Burgess S, et al: Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses. PLoS Med, 2021; 18: e1003498
• 10)  Marston NA, Pirruccello JP, Melloni GEM, Koyama S, Kamanu FK, Weng LC, Roselli C, Kamatani Y, Komuro I, Aragam KG, et al: Predictive Utility of a Coronary Artery Disease Polygenic Risk Score in Primary Prevention. JAMA Cardiol, 2023; 8: 130-137
• 11)  Lambert SA, Abraham G, Inouye M: Towards clinical utility of polygenic risk scores. Hum Mol Genet, 2019; 28: R133-r142
• 12)  Jiang J, Zheng Q, Han Y, Qiao S, Chen J, Yuan Z, Yu B, Ge L, Jia J, Gong Y, et al: Genetic predisposition to coronary artery disease is predictive of recurrent events: a Chinese prospective cohort study. Hum Mol Genet, 2020; 29: 1044-1053
• 13)  Zdravkovic S, Wienke A, Pedersen NL, Marenberg ME, Yashin AI, De Faire U: Heritability of death from coronary heart disease: a 36-year follow-up of 20 966 Swedish twins. J Intern Med, 2002; 252: 247-254
• 14)  Mendonça MI, Henriques E, Borges S, Sousa AC, Pereira A, Santos M, Temtem M, Freitas S, Monteiro J, Sousa JA, et al: Genetic information improves the prediction of major adverse cardiovascular events in the GENEMACOR population. Genet Mol Biol, 2021; 44: e20200448
• 15)  Zhao C, Zhu P, Shen Q, Jin L: Prospective association of a genetic risk score with major adverse cardiovascular events in patients with coronary artery disease. Medicine (Baltimore), 2017; 96: e9473
• 16)  Patel RS, Sun YV, Hartiala J, Veledar E, Su S, Sher S, Liu YX, Rahman A, Patel R, Rab ST, et al: Association of a genetic risk score with prevalent and incident myocardial infarction in subjects undergoing coronary angiography. Circ Cardiovasc Genet, 2012; 5: 441-449
• 17)  Dogan MV, Grumbach IM, Michaelson JJ, Philibert RA: Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study. PLoS One, 2018; 13: e0190549
• 18)  Heldin CH, Westermark B: Mechanism of action and in vivo role of platelet-derived growth factor. Physiol Rev, 1999; 79: 1283-1316
• 19)  Kohler N, Lipton A: Platelets as a source of fibroblast growth-promoting activity. Exp Cell Res, 1974; 87: 297-301
• 20)  Zarei F, Soleimaninejad M: Role of growth factors and biomaterials in wound healing. Artif Cells Nanomed Biotechnol, 2018; 46: 906-911
• 21)  Rutherford C, Martin W, Carrier M, Anggård EE, Ferns GA: Endogenously elicited antibodies to platelet derived growth factor-BB and platelet cytosolic protein inhibit aortic lesion development in the cholesterol-fed rabbit. Int J Exp Pathol, 1997; 78: 21-32
• 22)  Rutherford C, Martin W, Salame M, Carrier M, Anggård E, Ferns G: Substantial inhibition of neo-intimal response to balloon injury in the rat carotid artery using a combination of antibodies to platelet-derived growth factor-BB and basic fibroblast growth factor. Atherosclerosis, 1997; 130: 45-51
• 23)  Zhou J, Huang Y, Huang RS, Wang F, Xu L, Le Y, Yang X, Xu W, Huang X, Lian J, Duan S: A case-control study provides evidence of association for a common SNP rs974819 in PDGFD to coronary heart disease and suggests a sex-dependent effect. Thromb Res, 2012; 130: 602-606
• 24)  Lu Y, Liu H, Dong B, Yang J, Kou L, Qin Q: Correlation between platelet-derived growth factor-B gene polymorphism and coronary heart disease. J Clin Lab Anal, 2022; 36: e24683
• 25)  Koizumi T, Kaneda H, Yamasaki T, Tamaki T, Kikutani T, Muramatsu T, Sano K, Komiyama N, Nishimura S: Impact of platelet-derived growth factor-BB on ischemic myocardial injury after thrombus aspiration for ST-segment elevation myocardial infarction. Int J Cardiol, 2015; 182: 109-111
• 26)  Konwerski M, Gromadka A, Arendarczyk A, Koblowska M, Iwanicka-Nowicka R, Wilimski R, Czub P, Filipiak KJ, Hendzel P, Zielenkiewicz P, et al: Atherosclerosis Pathways are Activated in Pericoronary Adipose Tissue of Patients with Coronary Artery Disease. J Inflamm Res, 2021; 14: 5419-5431
• 27)  Zhang H, Zhang Y, Liu Y, Ma K, Zhou J, Guan J: Platelet-Derived Growth Factor Predicts Vulnerable Plaque in Patients with Non-ST Elevation Acute Coronary Syndrome. Am J Med Sci, 2021; 361: 759-764
• 28)  Alai MS, Beig JR, Kumar S, Yaqoob I, Hafeez I, Lone AA, Dar MI, Rather HA: Prevalence and characterization of coronary artery disease in patients with symptomatic bradyarrhythmias requiring pacemaker implantation. Indian Heart J, 2016; 68 Suppl 3: S21-s25
• 29)  Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, Prescott E, Storey RF, Deaton C, Cuisset T, et al: 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J, 2020; 41: 407-477
• 30)  Dong J, Yang S, Zhuang Q, Sun J, Wei P, Zhao X, Chen Y, Chen X, Li M, Wei L, et al: The Associations of Lipid Profiles With Cardiovascular Diseases and Death in a 10-Year Prospective Cohort Study. Front Cardiovasc Med, 2021; 8: 745539
• 31)  Joint committee for guideline r: 2016 Chinese guidelines for the management of dyslipidemia in adults. Journal of geriatric cardiology: JGC, 2018; 15: 1-29
• 32)  Yan AT, Yan RT, Tan M, Casanova A, Labinaz M, Sridhar K, Fitchett DH, Langer A, Goodman SG: Risk scores for risk stratification in acute coronary syndromes: useful but simpler is not necessarily better. Eur Heart J, 2007; 28: 1072-1078
• 33)  Christiansen MK, Nyegaard M, Larsen SB, Grove EL, Würtz M, Neergaard-Petersen S, Hvas AM, Jensen HK, Kristensen SD: A genetic risk score predicts cardiovascular events in patients with stable coronary artery disease. Int J Cardiol, 2017; 241: 411-416
• 34)  Rincón LM, Subirana I, Pérez Del Villar C, Sánchez PL, Zamorano JL, Marrugat J, Elosua R: Predictive capacity of a genetic risk score for coronary artery disease in assessing recurrences and cardiovascular mortality among patients with myocardial infarction. Front Cardiovasc Med, 2023; 10: 1254066
• 35)  Vaara S, Tikkanen E, Parkkonen O, Lokki ML, Ripatti S, Perola M, Nieminen MS, Sinisalo J: Genetic Risk Scores Predict Recurrence of Acute Coronary Syndrome. Circ Cardiovasc Genet, 2016; 9: 172-178
• 36)  Mega JL, Stitziel NO, Smith JG, Chasman DI, Caulfield M, Devlin JJ, Nordio F, Hyde C, Cannon CP, Sacks F, et al: Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet, 2015; 385: 2264-2271
• 37)  Labos C, Martinez SC, Leo Wang RH, Lenzini PA, Pilote L, Bogaty P, Brophy JM, Engert JC, Cresci S, Thanassoulis G: Utility of a genetic risk score to predict recurrent cardiovascular events 1 year after an acute coronary syndrome: A pooled analysis of the RISCA, PRAXY, and TRIUMPH cohorts. Atherosclerosis, 2015; 242: 261-267
• 38)  Weijmans M, de Bakker PI, van der Graaf Y, Asselbergs FW, Algra A, Jan de Borst G, Spiering W, Visseren FL: Incremental value of a genetic risk score for the prediction of new vascular events in patients with clinically manifest vascular disease. Atherosclerosis, 2015; 239: 451-458
• 39)  Wray NR, Goddard ME, Visscher PM: Prediction of individual genetic risk of complex disease. Curr Opin Genet Dev, 2008; 18: 257-263
• 40)  Roberts R, Campillo A, Schmitt M: Prediction and management of CAD risk based on genetic stratification. Trends Cardiovasc Med, 2020; 30: 328-334
• 41)  Wang XB, Han YD, Cui NH, Gao JJ, Yang J, Huang ZL, Zhu Q, Zheng F: Associations of lipid levels susceptibility loci with coronary artery disease in Chinese population. Lipids Health Dis, 2015; 14: 80
• 42)  Zhao W, Zhao T, Huang V, Chen Y, Ahokas RA, Sun Y: Platelet-derived growth factor involvement in myocardial remodeling following infarction. J Mol Cell Cardiol, 2011; 51: 830-838
• 43)  Smith CL, Baek ST, Sung CY, Tallquist MD: Epicardial-derived cell epithelial-to-mesenchymal transition and fate specification require PDGF receptor signaling. Circ Res, 2011; 108: e15-26
• 44)  Zhang J, Cao R, Zhang Y, Jia T, Cao Y, Wahlberg E: Differential roles of PDGFR-alpha and PDGFR-beta in angiogenesis and vessel stability. Faseb j, 2009; 23: 153-163
• 45)  Dabravolski SA, Markin AM, Andreeva ER, Eremin, II, Orekhov AN, Melnichenko AA: Emerging role of pericytes in therapy of cardiovascular diseases. Biomed Pharmacother, 2022; 156: 113928
• 46)  Miao L, Yin RX, Zhang QH, Hu XJ, Huang F, Chen WX, Cao XL, Wu JZ: Integrated DNA methylation and gene expression analysis in the pathogenesis of coronary artery disease. Aging (Albany NY), 2019; 11: 1486-1500
• 47)  Pagani F, Baralle FE: Genomic variants in exons and introns: identifying the splicing spoilers. Nat Rev Genet, 2004; 5: 389-396
• 48)  Choi JW, Park CS, Hwang M, Nam HY, Chang HS, Park SG, Han BG, Kimm K, Kim HL, Oh B, Kim Y: A common intronic variant of CXCR3 is functionally associated with gene expression levels and the polymorphic immune cell responses to stimuli. J Allergy Clin Immunol, 2008; 122: 1119-1126.e1117
• 49)  Hanlon JT, Schmader KE, Ruby CM, Weinberger M: Suboptimal prescribing in older inpatients and outpatients. J Am Geriatr Soc, 2001; 49: 200-209
• 50)  Mangiacapra F, Bressi E, Colaiori I, Ricottini E, Cavallari I, Capuano M, Viscusi MM, Spoto S, Barbato E, Di Sciascio G: Interaction Between Diabetes Mellitus and Platelet Reactivity in Determining Long-Term Outcomes Following Percutaneous Coronary Intervention. J Cardiovasc Transl Res, 2020; 13: 668-675
• 51)  Liang Y, Wang JX, Wu XY, Cui Y, Zou ZH, Li WQ, Liu Y, Gao J: The prediction value of platelet-derived growth factor for major adverse cardiovascular events in patients with acute non-ST-segment elevation myocardial infarction. Ann Med, 2023; 55: 1047-1057
• 52)  Barssoum K, Kumar A, Rai D, Kharsa A, Chowdhury M, Thakkar S, Patel HP, Amin A, Tan BE, Ibrahim F, et al: Meta-analysis Comparing Percutaneous Coronary Intervention With Coronary Artery Bypass Grafting for Non-ST Elevation Acute Coronary Syndrome in Patients With Multivessel or Left Main Disease. Curr Probl Cardiol, 2022; 47: 101306